From 23972e706386ed53da1488216ebe866dd1423b28 Mon Sep 17 00:00:00 2001 From: Simon Date: Sun, 20 Sep 2026 07:31:04 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E8=82=A1=E6=81=AF=E7=8E=87=E6=A1=88?= =?UTF-8?q?=E4=BE=8B=E5=8F=A3=E5=BE=84=20+=20=E7=AD=96=E7=95=A5=E5=BA=93?= =?UTF-8?q?=E4=B8=8E=E5=9B=BE=E8=A1=A8=E7=BB=9F=E4=B8=80=20+=20=E5=9B=9E?= =?UTF-8?q?=E6=B5=8B=E5=AD=98=E6=A1=A3=E5=AE=8C=E6=95=B4=E5=8C=96?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 汇总三轮未提交的开发（每轮均在本机 MariaDB + 真实浏览器上验证）： 1) 股息率案例（全市场股息率最高 n 只，默认 20，每 m 月择股） - 新增日频估值表 daily_basic + 迁移；股息率因子（dv_ratio / dividend_yield / TTM） - 名称历史表 stock_name_history：剔除 ST 按**择股日当时名称**判定，消除 「曾高股息后 ST」的股息陷阱（实测 3.70pp 偏差） - 区间择股/调仓双周期（m 择股 / y 调仓）、指数成分与白名单、停牌近似剔除 - 复权因子口径核对（4,164,742 行、缺失 0.0%）、收盘价成交与涨跌停拦单 - 案例实测：2020-01-01~2026-09-04 总收益 +24.86%（年化 3.52%、回撤 -28.58%） 2) 策略库与前端统一 - strategy 表 + CRUD/PUT 原地更新 + `describe_strategy` 按 spec 真实推导 「一句话说明 + 计算公式 + 执行步骤 + 注意事项」（与引擎实执行规则同源） - 任何出现股票代码处都成对显示名称且可点击进个股页 - 全站图表基座统一 TradingView Lightweight Charts（ECharts 依赖、 锁文件、组件与文档标注一并清除），买卖点标记只落在真实交易日上 3) 回测存档完整化（可往复查看） - 同步端点（POST /api/backtests、/api/factor-tests）此前完全不落库 → 现在同样归档， 归档 id 经响应头 X-Experiment-Id 返回（不破坏 response_model） - data_version 首次真实写入（数据快照指纹：最新交易日 + 各表规模） - 个股收益曲线默认**全量保存**（此前硬截断 60 只）；超出体积预算才裁剪， 并写 archive_meta（机器可读）+ unimplemented（人可读）如实标注 - 列表 kind/q 过滤 + X-Total-Count（此前 limit=50 静默截断）、DELETE 归档 - 只读归档页 /experiments/{id}（Server Component，SSR 直出**选股条件**与 **交易执行依据**）；结果视图按 kind 分发（backtest/factor_test/selection）， 非回测归档不套用回测口径 - 新增 CLI：prune_experiments（保留策略，默认 dry-run）、 restore_experiment_from_job（从 Job 副本按原 id 重建被删的历史归档，默认 dry-run） 门禁：pytest 388 passed、ruff All checks passed、tsc 0 错误、图表单测 7 passed、 next build 成功、契约脚本 verify_strategy_workspace 59/59（含按 kind 逐类验证归档页）。 --- .env.example | 2 +- backend/app/agent/tools_impl.py | 36 +- backend/app/api/deps.py | 45 +- backend/app/api/experiments.py | 73 +- backend/app/api/factors.py | 18 +- backend/app/api/jobs.py | 40 +- backend/app/api/research.py | 75 +- backend/app/api/stocks.py | 11 + backend/app/api/strategies.py | 99 +- backend/app/application/services/data_sync.py | 200 + .../services/experiment_archive.py | 288 + .../app/application/services/job_executor.py | 143 +- .../application/services/replay_service.py | 10 +- .../application/services/selection_service.py | 80 +- .../application/services/signal_service.py | 10 +- backend/app/cli/prune_experiments.py | 125 + .../app/cli/restore_experiment_from_job.py | 172 + backend/app/cli/run_job.py | 23 +- backend/app/cli/sync.py | 196 +- backend/app/core/config.py | 84 + backend/app/domain/entities/market.py | 90 + backend/app/domain/entities/research.py | 217 +- backend/app/domain/entities/selection.py | 45 +- backend/app/domain/entities/strategy.py | 15 +- backend/app/domain/providers.py | 30 +- backend/app/domain/repositories/jobs.py | 31 +- backend/app/domain/repositories/market.py | 65 + .../infrastructure/data_sources/failover.py | 29 +- .../app/infrastructure/data_sources/sina.py | 13 +- .../infrastructure/data_sources/tushare.py | 200 +- .../22d7380706f7_add_daily_basic_table.py | 67 + .../7b1c4e9a52d8_widen_result_json.py | 47 + .../a3f8c21d9b47_add_stock_name_history.py | 64 + .../persistence/sqlalchemy/data_version.py | 129 + .../persistence/sqlalchemy/models/__init__.py | 5 + .../persistence/sqlalchemy/models/jobs.py | 17 +- .../persistence/sqlalchemy/models/market.py | 54 + .../sqlalchemy/repositories/jobs_impl.py | 111 +- .../sqlalchemy/repositories/market_impl.py | 400 +- .../sqlalchemy/repositories/strategy_impl.py | 7 +- backend/app/main.py | 26 + backend/app/quant/engine.py | 23 +- backend/app/quant/factors.py | 63 +- backend/app/quant/local_engine.py | 546 +- backend/app/quant/qlib_adapter/engine.py | 11 +- backend/app/quant/selection.py | 165 +- backend/app/quant/service.py | 405 +- backend/app/quant/strategy_doc.py | 583 ++ backend/app/quant/universe.py | 51 +- backend/tests/test_adjust_factor_prices.py | 174 + backend/tests/test_config.py | 111 +- backend/tests/test_dividend_case_e2e.py | 244 + backend/tests/test_experiment_archive.py | 653 ++ backend/tests/test_factor_catalog.py | 47 + backend/tests/test_index_universe.py | 43 + backend/tests/test_interval_selection.py | 328 + backend/tests/test_jobs_experiments.py | 4 +- backend/tests/test_name_history.py | 427 ++ backend/tests/test_prune_experiments.py | 69 + backend/tests/test_restore_experiment.py | 222 + backend/tests/test_run_job.py | 86 + backend/tests/test_selection_job.py | 6 +- backend/tests/test_strategy_doc.py | 544 ++ backend/tests/test_symbol_names.py | 239 + backend/tests/test_tushare_provider.py | 69 + backend/uv.lock | 5600 ++++++++--------- config.yaml | 19 +- frontend/web/app/backtest/page.tsx | 664 +- frontend/web/app/experiments/[id]/page.tsx | 610 ++ frontend/web/app/experiments/page.tsx | 613 +- frontend/web/app/factors/compose/page.tsx | 41 +- frontend/web/app/globals.css | 515 ++ frontend/web/app/layout.tsx | 6 +- frontend/web/app/page.tsx | 53 +- frontend/web/app/selection/page.tsx | 58 +- frontend/web/app/signals/page.tsx | 20 +- frontend/web/app/stocks/[symbol]/page.tsx | 23 +- frontend/web/app/stocks/page.tsx | 9 +- frontend/web/app/strategies/page.tsx | 518 ++ frontend/web/components/ArchiveActions.tsx | 99 + frontend/web/components/ArchiveResultView.tsx | 222 + .../web/components/BacktestResultView.tsx | 656 ++ frontend/web/components/LineChart.tsx | 117 - frontend/web/components/RichText.tsx | 36 + .../web/components/StockChart/CandleChart.tsx | 200 - .../web/components/StockChart/StockChart.tsx | 16 +- frontend/web/components/StrategyDocCard.tsx | 97 + .../web/components/StrategyParamsForm.tsx | 717 +++ frontend/web/components/app-shell.tsx | 25 +- frontend/web/components/charts/LwChart.tsx | 382 ++ .../charts/__tests__/markers.test.ts | 100 + frontend/web/components/charts/markers.ts | 55 + frontend/web/components/charts/theme.ts | 81 + frontend/web/components/icons.tsx | 34 +- frontend/web/components/ui.tsx | 10 +- frontend/web/lib/api.ts | 49 + frontend/web/lib/archive.ts | 62 + frontend/web/lib/jobs.ts | 37 +- frontend/web/lib/labels.ts | 38 + frontend/web/lib/strategy.ts | 94 + frontend/web/lib/symbols.tsx | 204 + frontend/web/lib/types.ts | 169 +- frontend/web/next.config.ts | 4 +- frontend/web/package.json | 4 +- frontend/web/pnpm-lock.yaml | 23 - frontend/web/tsconfig.json | 30 +- scripts/dev.sh | 39 +- scripts/grant_local_mysql_user.sql | 38 + scripts/mysql_recover_and_grant.sh | 218 + scripts/run_dividend_case.py | 133 + scripts/verify_backtest_page_contract.py | 153 + scripts/verify_strategy_workspace.py | 405 ++ 112 files changed, 17908 insertions(+), 3893 deletions(-) create mode 100644 backend/app/application/services/experiment_archive.py create mode 100644 backend/app/cli/prune_experiments.py create mode 100644 backend/app/cli/restore_experiment_from_job.py create mode 100644 backend/app/infrastructure/persistence/migrations/versions/22d7380706f7_add_daily_basic_table.py create mode 100644 backend/app/infrastructure/persistence/migrations/versions/7b1c4e9a52d8_widen_result_json.py create mode 100644 backend/app/infrastructure/persistence/migrations/versions/a3f8c21d9b47_add_stock_name_history.py create mode 100644 backend/app/infrastructure/persistence/sqlalchemy/data_version.py create mode 100644 backend/app/quant/strategy_doc.py create mode 100644 backend/tests/test_adjust_factor_prices.py create mode 100644 backend/tests/test_dividend_case_e2e.py create mode 100644 backend/tests/test_experiment_archive.py create mode 100644 backend/tests/test_interval_selection.py create mode 100644 backend/tests/test_name_history.py create mode 100644 backend/tests/test_prune_experiments.py create mode 100644 backend/tests/test_restore_experiment.py create mode 100644 backend/tests/test_run_job.py create mode 100644 backend/tests/test_strategy_doc.py create mode 100644 backend/tests/test_symbol_names.py create mode 100644 frontend/web/app/experiments/[id]/page.tsx create mode 100644 frontend/web/app/strategies/page.tsx create mode 100644 frontend/web/components/ArchiveActions.tsx create mode 100644 frontend/web/components/ArchiveResultView.tsx create mode 100644 frontend/web/components/BacktestResultView.tsx delete mode 100644 frontend/web/components/LineChart.tsx create mode 100644 frontend/web/components/RichText.tsx delete mode 100644 frontend/web/components/StockChart/CandleChart.tsx create mode 100644 frontend/web/components/StrategyDocCard.tsx create mode 100644 frontend/web/components/StrategyParamsForm.tsx create mode 100644 frontend/web/components/charts/LwChart.tsx create mode 100644 frontend/web/components/charts/__tests__/markers.test.ts create mode 100644 frontend/web/components/charts/markers.ts create mode 100644 frontend/web/components/charts/theme.ts create mode 100644 frontend/web/lib/archive.ts create mode 100644 frontend/web/lib/labels.ts create mode 100644 frontend/web/lib/strategy.ts create mode 100644 frontend/web/lib/symbols.tsx create mode 100644 scripts/grant_local_mysql_user.sql create mode 100644 scripts/mysql_recover_and_grant.sh create mode 100644 scripts/run_dividend_case.py create mode 100644 scripts/verify_backtest_page_contract.py create mode 100644 scripts/verify_strategy_workspace.py diff --git a/.env.example b/.env.example index 3401faa..40291ea 100644 --- a/.env.example +++ b/.env.example @@ -9,7 +9,7 @@ TUSHARE_TOKEN= # ---- 数据库连接 ---- -# 默认库由 config.yaml database.mysql 决定（MySQL 192.168.1.10/qlib）。 +# 默认库由 config.yaml database.mysql 决定（本机 MariaDB 127.0.0.1/qlib）。 # 连接优先级：本文件 DATABASE_URL > config.yaml database.mysql > SQLite 兜底。 DATABASE_URL= # SQLite 示例（显式指定）： diff --git a/backend/app/agent/tools_impl.py b/backend/app/agent/tools_impl.py index a447a23..40ca9fc 100644 --- a/backend/app/agent/tools_impl.py +++ b/backend/app/agent/tools_impl.py @@ -52,6 +52,23 @@ def _pick(mapping: dict, key: str, default=None): return val +def _job_result_json(job, *, session_factory, experiment_repo_factory) -> str | None: + """取 Job 的完整结果 JSON。 + + 2026-09 起完整结果只在 experiment 存一份（`job.result_json` 为 None），故先按 + `job.experiment_id` 回读归档；归档不存在 / 老记录再回退 `job.result_json`。 + """ + if job.experiment_id: + try: + with session_factory() as session: + exp = experiment_repo_factory(session).get(job.experiment_id) + if exp is not None: + return exp.result_json + except Exception: # noqa: BLE001 —— 回读失败则回退 job 副本，不阻断工具 + pass + return job.result_json + + def build_tools(factories: dict | None = None) -> list[Tool]: facts = factories or default_factories() session_factory = facts["session_factory"] @@ -93,8 +110,11 @@ def build_tools(factories: dict | None = None) -> list[Tool]: job = submit_and_run(spec, factories=facts) if job.status != "success": return f"{desc} 执行失败：{job.error}" + result_json = _job_result_json( + job, session_factory=session_factory, experiment_repo_factory=exp_repo_f + ) if spec.type == "backtest": - result = BacktestResult.model_validate_json(job.result_json or "{}") + result = BacktestResult.model_validate_json(result_json or "{}") s = result.summary return ( f"回测完成（Experiment {job.experiment_id}，代码版本 {_code_version(job, exp_repo_f)}）。" @@ -103,7 +123,7 @@ def build_tools(factories: dict | None = None) -> list[Tool]: f"交易 {s.total_trades} 笔，平均换手 {s.avg_turnover_pct:.1f}%。" f"未建模约束 {len(result.unimplemented)} 项（成本/涨跌停近似见实验详情）。" ) - report = FactorTestReport.model_validate_json(job.result_json or "{}") + report = FactorTestReport.model_validate_json(result_json or "{}") qs = ", ".join(f"Q{q.quantile + 1}: {q.return_pct:.2f}%" for q in report.quantile_returns) return ( f"因子测试完成（Experiment {job.experiment_id}）。IC {report.ic_mean:.4f}，" @@ -378,15 +398,19 @@ def build_tools(factories: dict | None = None) -> list[Tool]: job = SqlAlchemyJobRepository(session).get(job_id) if job is None: return f"Job {job_id} 不存在" - if job.status != "success" or not job.result_json: - return f"Job {job_id} 未成功（无结果可归档）" + # 顺序要紧：新形态记录结果只在 experiment 侧（job.result_json 为 None）， + # 先判「已归档」，否则成功 Job 会被误判为「无结果可归档」 if job.experiment_id: return f"Job {job_id} 已归档为 {job.experiment_id}" + if job.status != "success" or not job.result_json: + return f"Job {job_id} 未成功（无结果可归档）" + # 走到这里必然是老形态记录（结果仍在 job 侧，无 experiment 关联） + result_json = job.result_json exp_repo = exp_repo_f(session) summary = None try: if job.kind == "backtest": - r = BacktestResult.model_validate_json(job.result_json) + r = BacktestResult.model_validate_json(result_json) summary = ( f"总收益 {r.summary.total_return_pct:.2f}% · 年化 " f"{r.summary.annual_return_pct:.2f}% · 回撤 {r.summary.max_drawdown_pct:.2f}%" @@ -397,7 +421,7 @@ def build_tools(factories: dict | None = None) -> list[Tool]: id=new_id("EXP"), kind=job.kind, spec_json=job.spec_json, - result_json=job.result_json, + result_json=result_json, summary_text=summary, job_id=job.id, created_at=job.created_at, diff --git a/backend/app/api/deps.py b/backend/app/api/deps.py index e5ab7e0..b4aa107 100644 --- a/backend/app/api/deps.py +++ b/backend/app/api/deps.py @@ -21,7 +21,9 @@ from app.domain.repositories.jobs import ExperimentRepository, JobRepository from app.domain.repositories.market import ( AdjustFactorRepository, DailyBarRepository, + DailyBasicRepository, FinancialRepository, + StockNameHistoryRepository, StockRepository, ) from app.domain.repositories.selection import SelectionRepository @@ -39,7 +41,9 @@ from app.infrastructure.persistence.sqlalchemy.repositories.index_impl import ( from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import ( SqlAlchemyAdjustFactorRepository, SqlAlchemyDailyBarRepository, + SqlAlchemyDailyBasicRepository, SqlAlchemyFinancialRepository, + SqlAlchemyStockNameHistoryRepository, SqlAlchemyStockRepository, ) from app.infrastructure.persistence.sqlalchemy.repositories.selection_impl import ( @@ -74,6 +78,10 @@ def _adjust_repo_factory(session: DbSession) -> AdjustFactorRepository: return SqlAlchemyAdjustFactorRepository(session) +def _daily_basic_repo_factory(session: DbSession) -> DailyBasicRepository: + return SqlAlchemyDailyBasicRepository(session) + + def _chart_service_factory( stock_repo: Annotated[StockRepository, Depends(_stock_repo_factory)], daily_repo: Annotated[DailyBarRepository, Depends(_daily_repo_factory)], @@ -82,6 +90,11 @@ def _chart_service_factory( return ChartService(stock_repo, daily_repo, adj_repo) +def _name_repo_factory(session: DbSession) -> StockNameHistoryRepository: + """名称变更历史仓储（StockNameHistoryRepository 实现）。""" + return SqlAlchemyStockNameHistoryRepository(session) + + def _index_repo_factory(session: DbSession) -> IndexConstituentRepository: return SqlAlchemyIndexConstituentRepository(session) @@ -95,24 +108,39 @@ def _service_factory( daily_repo: Annotated[DailyBarRepository, Depends(_daily_repo_factory)], engine: Annotated[QuantEngine, Depends(_engine_factory)], index_repo: Annotated[IndexConstituentRepository, Depends(_index_repo_factory)], + basic_repo: Annotated[DailyBasicRepository, Depends(_daily_basic_repo_factory)], + financial_repo: Annotated[FinancialRepository, Depends(_financial_repo_factory)], + name_repo: Annotated[ + StockNameHistoryRepository, Depends(_name_repo_factory) + ] = None, ) -> ResearchService: - return ResearchService(stock_repo, daily_repo, engine, index_repo) + return ResearchService( + stock_repo, + daily_repo, + engine, + index_repo, + basic_repo=basic_repo, + financial_repo=financial_repo, + name_repo=name_repo, + ) def _replay_service_factory( stock_repo: Annotated[StockRepository, Depends(_stock_repo_factory)], daily_repo: Annotated[DailyBarRepository, Depends(_daily_repo_factory)], index_repo: Annotated[IndexConstituentRepository, Depends(_index_repo_factory)], + name_repo: Annotated[StockNameHistoryRepository, Depends(_name_repo_factory)], ) -> ReplayService: - return ReplayService(stock_repo, daily_repo, index_repo) + return ReplayService(stock_repo, daily_repo, index_repo, name_repo=name_repo) def _signal_service_factory( stock_repo: Annotated[StockRepository, Depends(_stock_repo_factory)], daily_repo: Annotated[DailyBarRepository, Depends(_daily_repo_factory)], index_repo: Annotated[IndexConstituentRepository, Depends(_index_repo_factory)], + name_repo: Annotated[StockNameHistoryRepository, Depends(_name_repo_factory)], ) -> SignalService: - return SignalService(stock_repo, daily_repo, index_repo) + return SignalService(stock_repo, daily_repo, index_repo, name_repo=name_repo) def _selection_service_factory( @@ -120,8 +148,17 @@ def _selection_service_factory( daily_repo: Annotated[DailyBarRepository, Depends(_daily_repo_factory)], financial_repo: Annotated[FinancialRepository, Depends(_financial_repo_factory)], index_repo: Annotated[IndexConstituentRepository, Depends(_index_repo_factory)], + basic_repo: Annotated[DailyBasicRepository, Depends(_daily_basic_repo_factory)], + name_repo: Annotated[StockNameHistoryRepository, Depends(_name_repo_factory)], ) -> SelectionService: - return SelectionService(stock_repo, daily_repo, financial_repo, index_repo) + return SelectionService( + stock_repo, + daily_repo, + financial_repo, + index_repo, + basic_repo=basic_repo, + name_repo=name_repo, + ) def _selection_repo_factory(session: DbSession) -> SelectionRepository: diff --git a/backend/app/api/experiments.py b/backend/app/api/experiments.py index 2ded92b..3d41e69 100644 --- a/backend/app/api/experiments.py +++ b/backend/app/api/experiments.py @@ -1,25 +1,41 @@ -"""Experiment API（Phase 4）：列表 / 详情 / 一键复跑。""" +"""Experiment API（Phase 4）：列表 / 详情 / 删除 / 一键复跑。""" from __future__ import annotations import json from datetime import datetime +from typing import Annotated -from fastapi import APIRouter, BackgroundTasks, HTTPException +from fastapi import APIRouter, BackgroundTasks, HTTPException, Query, Response from app.api.deps import DbSession, ExperimentRepoDep, JobRepoDep from app.application.services.job_executor import new_id, run_job_background from app.domain.entities.research import ( ExperimentRecord, + ExperimentSummary, JobRecord, JobStatus, ) router = APIRouter(prefix="/experiments", tags=["experiments"]) +# 列表默认返回条数与上限：原实现硬编码 limit=50 且不暴露总数，>50 条时更老的 +# 归档静默不可见（AGENT.md §7）；现在默认 200、上限 1000，并用 X-Total-Count +# 暴露过滤后的真实总数，客户端可据此翻页（offset）。 +LIST_DEFAULT_LIMIT = 200 +LIST_MAX_LIMIT = 1000 -def _experiment_meta(exp: ExperimentRecord) -> dict: + +def _experiment_meta(exp: ExperimentRecord | ExperimentSummary) -> dict: + """列表项视图（body 形状与旧版一致，仅**新增** data_version / job_id / result_bytes）。 + + `exp` 可以是完整实体（详情路径）或 `ExperimentSummary`（列表路径，不含 + result_json）：两条路径都只读元数据字段，无需把大字段拉回来算体积。 + """ spec = json.loads(exp.spec_json) + result_bytes = getattr(exp, "result_bytes", None) + if result_bytes is None: # 完整实体：result_json 已在内存，len() 零成本 + result_bytes = len(exp.result_json or "") return { "id": exp.id, "kind": exp.kind, @@ -29,6 +45,9 @@ def _experiment_meta(exp: ExperimentRecord) -> dict: "top_n": spec.get("selection", {}).get("top_n"), "summary_text": exp.summary_text, "code_version": exp.code_version, + "data_version": exp.data_version, + "job_id": exp.job_id, + "result_bytes": int(result_bytes), "created_at": exp.created_at, } @@ -43,9 +62,27 @@ def _experiment_full(exp: ExperimentRecord) -> dict: } -@router.get("", summary="Experiment 列表") -def list_experiments(experiment_repo: ExperimentRepoDep) -> list[dict]: - return [_experiment_meta(e) for e in experiment_repo.list_recent(limit=50)] +@router.get("", summary="Experiment 列表（过滤 + 分页，X-Total-Count 给总数）") +def list_experiments( + experiment_repo: ExperimentRepoDep, + response: Response, + kind: Annotated[str | None, Query(description="按类型精确过滤（backtest/factor_test/selection）")] = None, + q: Annotated[ + str | None, Query(description="大小写不敏感模糊匹配 id / 因子名 / summary_text") + ] = None, + limit: Annotated[int, Query(ge=1, le=LIST_MAX_LIMIT)] = LIST_DEFAULT_LIMIT, + offset: Annotated[int, Query(ge=0)] = 0, +) -> list[dict]: + """归档列表。 + + - 过滤与分页在 SQL 层完成（仓储 `list_filtered`），不把全表拉回内存； + - 响应头 `X-Total-Count` = **过滤后**的归档总数（不受 limit/offset 影响）， + 客户端据此判断是否被截断并翻页（AGENT.md §7：不静默截断）。 + """ + rows = experiment_repo.list_filtered(kind=kind, q=q, limit=limit, offset=offset) + total = experiment_repo.count_filtered(kind=kind, q=q) + response.headers["X-Total-Count"] = str(total) + return [_experiment_meta(e) for e in rows] @router.get("/{experiment_id}", summary="Experiment 详情（含完整结果）") @@ -56,6 +93,28 @@ def get_experiment(experiment_id: str, experiment_repo: ExperimentRepoDep) -> di return _experiment_full(exp) +@router.delete("/{experiment_id}", summary="删除 Experiment 归档（不影响关联 Job 记录）") +def delete_experiment( + experiment_id: str, + session: DbSession, + experiment_repo: ExperimentRepoDep, +) -> dict: + """删除归档本身，返回 `{"deleted": ""}`。 + + 语义（写清楚避免误解）： + - **只删 experiment 行**。关联的 job 记录是「执行历史」，一律保留， + `GET /api/jobs/{id}` 仍可查到该 Job 的状态、阶段与错误信息。 + - 注意：完整结果现在只存归档一份（见 experiment_archive / job_executor）， + 因此删除归档后 `GET /api/jobs/{id}` 的 `result` 会是 null，并附带 + `result_unavailable_reason` 说明归档已被删除（如实暴露，不静默给空结果）。 + - 删除不可恢复；如需长期保留结果，请勿删除对应归档。 + """ + if not experiment_repo.delete(experiment_id): + raise HTTPException(status_code=404, detail=f"Experiment {experiment_id} 不存在") + session.commit() + return {"deleted": experiment_id} + + @router.post("/{experiment_id}/rerun", summary="一键复跑（AGENT §21：历史实验可重放）") def rerun_experiment( experiment_id: str, @@ -77,4 +136,4 @@ def rerun_experiment( job_repo.create(job) session.commit() background.add_task(run_job_background, job.id) - return {"job_id": job.id, "status": job.status, "origin_experiment": exp.id} + return {"job_id": job.id, "status": job.status, "origin_experiment": exp.id} \ No newline at end of file diff --git a/backend/app/api/factors.py b/backend/app/api/factors.py index a653667..a9c01e6 100644 --- a/backend/app/api/factors.py +++ b/backend/app/api/factors.py @@ -11,12 +11,24 @@ from fastapi import APIRouter from app.api.deps import DbSession, FactorRepoDep from app.application.services.factor_catalog import seed_registry_factors from app.domain.entities.factor import FactorDefinition +from app.quant.factors import list_factors router = APIRouter(prefix="/factors", tags=["factors"]) @router.get("", summary="因子目录（含元数据，来自 factor_definition 表）") def list_factor_catalog(factor_repo: FactorRepoDep, session: DbSession) -> list[FactorDefinition]: - if not factor_repo.list(): - seed_registry_factors(factor_repo, session) # 首次：从代码注册表 seed - return factor_repo.list() + """读目录；**缺失的注册表因子当场补齐**（幂等），稳态下零写入。 + + 历史 bug：原先只在「表为空」时 seed，于是表非空后**代码里新增的因子永远进不了目录**—— + 实测表内 9 条、注册表 11 条，`dividend_yield` / `dividend_yield_ttm` 长期缺失， + 前端因子下拉选不到「股息率」、归档页也查不到它的方向与含义（违反 §7 不静默）。 + 现在按「注册表有、库里没有」的差集触发 upsert：既保证目录与可计算因子一致， + 又不会覆盖用户登记的自定义因子元数据（只补不删）。 + """ + existing = factor_repo.list() + missing = {d.name for d in list_factors()} - {f.name for f in existing} + if not existing or missing: + seed_registry_factors(factor_repo, session) + existing = factor_repo.list() + return existing diff --git a/backend/app/api/jobs.py b/backend/app/api/jobs.py index fb02b20..fe8632c 100644 --- a/backend/app/api/jobs.py +++ b/backend/app/api/jobs.py @@ -18,7 +18,7 @@ from typing import Annotated from fastapi import APIRouter, BackgroundTasks, HTTPException, Query from fastapi.responses import StreamingResponse -from app.api.deps import DbSession, JobRepoDep +from app.api.deps import DbSession, ExperimentRepoDep, JobRepoDep from app.application.services.job_executor import new_id, run_job_background, terminate_active from app.domain.entities.research import ( BacktestResult, @@ -46,11 +46,36 @@ def _decode_result(kind: str, result_json: str | None): return model.model_validate_json(result_json) -def _job_view(job: JobRecord) -> dict: +def _job_view(job: JobRecord, experiment_repo=None) -> dict: + """Job 视图：`result` 契约不变（成功时内嵌**完整**结果）。 + + 结果来源（2026-09 起完整结果只在 experiment 存一份，job.result_json 不再重复写）： + 1. `job.experiment_id` 有值且能读到归档 → 解码 experiment.result_json； + 2. 否则回退解码 `job.result_json`（老记录 / 归档被删除前的历史数据）； + 3. 归档被删除且 job 侧无副本 → `result=None`，并给出 + `result_unavailable_reason` 如实说明原因（AGENT §7：不静默给空结果）。 + """ view = job.model_dump() - view["result"] = _decode_result(job.kind, job.result_json) view.pop("result_json", None) view["spec"] = json.loads(job.spec_json) + + result = None + source = None + if job.experiment_id and experiment_repo is not None: + exp = experiment_repo.get(job.experiment_id) + if exp is not None: + result = _decode_result(job.kind, exp.result_json) + source = "experiment" + else: + view["result_unavailable_reason"] = ( + f"归档 {job.experiment_id} 已不存在（可能已被删除）；" + "完整结果仅存于归档，Job 记录本身不再保存结果副本" + ) + if result is None and job.result_json: + result = _decode_result(job.kind, job.result_json) + source = "job" + view["result"] = result + view["result_source"] = source return view @@ -77,10 +102,13 @@ def create_job( @router.get("", summary="Job 列表") def list_jobs( job_repo: JobRepoDep, + experiment_repo: ExperimentRepoDep, kind: Annotated[str | None, Query(description="按类型过滤（backtest/factor_test）")] = None, limit: Annotated[int, Query(ge=1, le=200)] = 20, ) -> list[dict]: - return [_job_view(j) for j in job_repo.list_recent(kind=kind, limit=limit)] + return [ + _job_view(j, experiment_repo) for j in job_repo.list_recent(kind=kind, limit=limit) + ] @router.post("/{job_id}/cancel", summary="取消 Job（queued/running）") @@ -99,11 +127,11 @@ def cancel_job(job_id: str, session: DbSession, job_repo: JobRepoDep) -> dict: @router.get("/{job_id}", summary="查询 Job 状态与结果") -def get_job(job_id: str, job_repo: JobRepoDep) -> dict: +def get_job(job_id: str, job_repo: JobRepoDep, experiment_repo: ExperimentRepoDep) -> dict: job = job_repo.get(job_id) if job is None: raise HTTPException(status_code=404, detail=f"Job {job_id} 不存在") - return _job_view(job) + return _job_view(job, experiment_repo) @router.get("/{job_id}/events", summary="Job 进度 SSE") diff --git a/backend/app/api/research.py b/backend/app/api/research.py index 3bc6a76..adf4bcb 100644 --- a/backend/app/api/research.py +++ b/backend/app/api/research.py @@ -1,17 +1,23 @@ """研究执行 API：/api/factor-tests 与 /api/backtests。 Phase 3 为同步执行（样本有限）；Phase 4 将改为 Job + SSE 异步（接口契约不变）。 -最近一次结果在内存中可读，便于前端展示；持久化实验归档在 Phase 4。 + +**归档（2026-09 补齐）**：同步端点此前只把结果塞进进程内存 `_LAST_*`，重启即丢， +完全不落库 —— 与「研究可复现」（AGENT.md §21）矛盾。现改为：返回结果前调用 +`archive_experiment` 落库（复用与异步 Job 完全相同的归档实现），并通过响应头 +`X-Experiment-Id` 暴露归档 id。body 形状保持不变（前端与既有测试依赖它）。 """ from __future__ import annotations +import logging from datetime import date -from fastapi import APIRouter, HTTPException +from fastapi import APIRouter, HTTPException, Response from pydantic import BaseModel, Field -from app.api.deps import ResearchServiceDep +from app.api.deps import DbSession, ExperimentRepoDep, ResearchServiceDep +from app.application.services.experiment_archive import archive_experiment from app.domain.entities.research import ( BacktestResult, FactorCorrelationReport, @@ -23,23 +29,73 @@ from app.domain.entities.research import ( ) from app.quant.factors import FactorError +logger = logging.getLogger(__name__) + router = APIRouter(tags=["research"]) -# 内存中的最近结果（Phase 4 迁移到 Experiment 表） +# 内存中的最近结果（仅作「最近一次」便捷读取；持久化归档见 archive_experiment） _LAST_BACKTEST: dict[str, BacktestResult] = {} _LAST_FACTOR_TEST: dict[str, FactorTestReport] = {} +def _archive_or_expose( + response: Response, + *, + session, + kind: str, + spec_json: str, + result, + experiment_repo, +) -> None: + """归档同步端点的计算结果，并把归档结果如实反映到响应头。 + + 取舍（AGENT.md §7「不静默」+ §24）：研究结果是用户真实等待数十秒得到的产出， + 归档是副产物。若数据库故障导致归档失败，直接抛 500 会把**已经算出来的可用结果** + 一并丢掉；故此处捕获异常、接口仍然 200 返回完整结果，同时： + - `logger.warning` 落盘（服务端可观测）； + - 响应头 `X-Archive-Error: : ` 如实暴露失败原因（客户端可判读）。 + 即「结果不丢 + 失败不静默」两者兼顾；成功时给 `X-Experiment-Id`。 + """ + try: + experiment = archive_experiment( + session=session, + kind=kind, + spec_json=spec_json, + result=result, + experiment_repo=experiment_repo, + ) + except Exception as exc: # noqa: BLE001 —— 归档失败不得吞掉计算结果 + logger.warning("同步端点归档失败（kind=%s）：%s: %s", kind, type(exc).__name__, exc) + # HTTP 头只能承载 latin-1：中文错误信息降级为 ASCII 转义，避免编码异常掩盖原因 + reason = f"{type(exc).__name__}: {exc}"[:180] + response.headers["X-Archive-Error"] = reason.encode("ascii", "backslashreplace").decode( + "ascii" + ) + return + response.headers["X-Experiment-Id"] = experiment.id + + @router.post("/factor-tests", response_model=FactorTestReport, summary="运行单因子测试（同步）") def run_factor_test( spec: ResearchSpec, service: ResearchServiceDep, + session: DbSession, + experiment_repo: ExperimentRepoDep, + response: Response, ) -> FactorTestReport: try: report = service.run_factor_test(spec) except (ValueError, FactorError) as exc: raise HTTPException(status_code=400, detail=str(exc)) from exc _LAST_FACTOR_TEST["default"] = report + _archive_or_expose( + response, + session=session, + kind="factor_test", + spec_json=spec.model_dump_json(), + result=report, + experiment_repo=experiment_repo, + ) return report @@ -47,12 +103,23 @@ def run_factor_test( def run_backtest( spec: ResearchSpec, service: ResearchServiceDep, + session: DbSession, + experiment_repo: ExperimentRepoDep, + response: Response, ) -> BacktestResult: try: result = service.run_backtest(spec) except (ValueError, FactorError) as exc: raise HTTPException(status_code=400, detail=str(exc)) from exc _LAST_BACKTEST["default"] = result + _archive_or_expose( + response, + session=session, + kind="backtest", + spec_json=spec.model_dump_json(), + result=result, + experiment_repo=experiment_repo, + ) return result diff --git a/backend/app/api/stocks.py b/backend/app/api/stocks.py index edd5dbc..4fec3f9 100644 --- a/backend/app/api/stocks.py +++ b/backend/app/api/stocks.py @@ -26,6 +26,17 @@ def list_stocks( return stocks[offset : offset + limit] +@router.get("/names", response_model=dict[str, str], summary="全市场股票名称映射") +def list_stock_names(repo: StockRepoDep) -> dict[str, str]: + """`{symbol: name}` 全市场名称缓存（前端启动时一次性拉取，避免表格 N+1 查询）。 + + 契约固定为 dict（不是数组）：前端已有调用方按 dict 形状消费。 + 路由**必须**声明在 `/{symbol}` 之前，否则会被路径参数吞掉（"names" 被当作代码）。 + 名称缺失（空字符串）的标的直接略过 —— 前端按「名称未知」渲染，不塞空串假装有名称。 + """ + return {s.symbol: s.name for s in repo.list() if s.name} + + @router.get("/{symbol}", response_model=Stock, summary="按代码查询") def get_stock(symbol: str, repo: StockRepoDep) -> Stock: stock = repo.get_by_symbol(symbol) diff --git a/backend/app/api/strategies.py b/backend/app/api/strategies.py index b6b8310..c6a2d9a 100644 --- a/backend/app/api/strategies.py +++ b/backend/app/api/strategies.py @@ -1,10 +1,16 @@ -"""策略 API（M8.3）：/api/strategies CRUD + 展开为 ResearchSpec。 +"""策略 API（M8.3）：/api/strategies CRUD + 展开为 ResearchSpec + 说明/公式生成。 -POST /api/strategies 保存策略（name 唯一） -GET /api/strategies 列表 +POST /api/strategies 保存策略（name 唯一；description 为空时自动补全） +POST /api/strategies/describe body: ResearchSpec → StrategyDoc（未保存的策略也能预览） +GET /api/strategies 列表 GET /api/strategies/{id} +PUT /api/strategies/{id} 原地更新（不新建、不刷新 created_at） DELETE /api/strategies/{id} -POST /api/strategies/{id}/expand body: {period:[start,end], initial_capital?} → ResearchSpec +POST /api/strategies/{id}/expand body: {period:[start,end], initial_capital?} → ResearchSpec +GET /api/strategies/{id}/describe → StrategyDoc + +路由顺序注意：`/describe` 这类**字面量路径**一律声明在 `/{strategy_id}` 之前 —— +否则会被路径参数吞掉（AGENT.md §17 的既有教训，/api/stocks/names 同源问题）。 """ from __future__ import annotations @@ -18,6 +24,7 @@ from app.api.deps import DbSession, StrategyRepoDep from app.application.services.job_executor import new_id from app.domain.entities.research import ResearchSpec from app.domain.entities.strategy import StrategyDefinition +from app.quant.strategy_doc import StrategyDoc, describe_strategy router = APIRouter(prefix="/strategies", tags=["strategies"]) @@ -27,6 +34,28 @@ class ExpandRequest(BaseModel): initial_capital: float = Field(default=1_000_000.0, gt=0) +# strategy.description 列宽（StrategyModel.description = String(300)）。 +# 自动补全的说明必须落在列宽内，否则 MySQL 严格模式会直接报 Data too long（SQLite 不拦， +# 所以只在测试库上跑是发现不了的）。超长时按字符截断并加省略号 —— 显式标记有截断， +# 不做「悄悄改短」；完整说明始终可由 POST /describe 重新生成。 +_DESCRIPTION_MAX_CHARS = 300 + + +def _ensure_description(definition: StrategyDefinition) -> StrategyDefinition: + """说明为空/纯空白时，用 `describe_strategy(...).summary` 补全（需求：策略必须有说明）。 + + 说明由 spec **真实推导**（AGENT.md §24：不许编造），只在空值时补、不覆盖显式说明。 + 放在 API 层是因为这是「保存契约」的准入补全；Agent 的 create_strategy 工具走仓储 + 直写（description 非必填），因此不受影响（AGENT.md §28 工具链路保持可用）。 + """ + if definition.description.strip(): + return definition + summary = describe_strategy(definition).summary + if len(summary) > _DESCRIPTION_MAX_CHARS: + summary = summary[: _DESCRIPTION_MAX_CHARS - 1] + "…" + return definition.model_copy(update={"description": summary}) + + @router.post("", response_model=StrategyDefinition, summary="保存策略") def create_strategy( definition: StrategyDefinition, @@ -34,11 +63,25 @@ def create_strategy( session: DbSession, ) -> StrategyDefinition: try: - saved = strategy_repo.save(definition.model_copy(update={"id": new_id("STG")})) + saved = strategy_repo.save( + _ensure_description(definition).model_copy(update={"id": new_id("STG")}) + ) session.commit() - return saved except ValueError as exc: raise HTTPException(status_code=400, detail=str(exc)) from exc + # 回读持久化后的实体：仓储 save() 返回的是入参（created_at 为空）， + # 直接返回会让 POST 响应缺创建时间、与 GET/列表不一致（前端展示依赖该字段）。 + return strategy_repo.get(saved.id) or saved + + +@router.post("/describe", response_model=StrategyDoc, summary="按 ResearchSpec 生成策略说明与公式") +def describe_research_spec(spec: ResearchSpec) -> StrategyDoc: + """回测页参数即时预览用：**未保存的策略**（只有 spec）也能生成说明/公式。 + + 纯函数实现（app.quant.strategy_doc），无 IO/DB，因此不会因保存状态而失败。 + 路径与 `POST /api/strategies` 不冲突（字面量 /describe 优先于路径参数声明）。 + """ + return describe_strategy(spec) @router.get("", response_model=list[StrategyDefinition], summary="策略列表") @@ -54,6 +97,50 @@ def get_strategy(strategy_id: str, strategy_repo: StrategyRepoDep) -> StrategyDe return row +@router.get("/{strategy_id}/describe", response_model=StrategyDoc, summary="生成策略说明与公式") +def describe_saved_strategy( + strategy_id: str, strategy_repo: StrategyRepoDep +) -> StrategyDoc: + """已保存策略的说明/公式（404 语义与 GET /{strategy_id} 一致）。 + + 策略定义不含回测区间，说明里的区间为占位文本（`warnings` 中已如实标注）， + 展开回测后（/expand）再用该 ResearchSpec 调 `POST /describe` 即为确定区间的版本。 + """ + row = strategy_repo.get(strategy_id) + if row is None: + raise HTTPException(status_code=404, detail=f"策略 {strategy_id} 不存在") + return describe_strategy(row) + + +@router.put("/{strategy_id}", response_model=StrategyDefinition, summary="原地更新策略") +def update_strategy( + strategy_id: str, + definition: StrategyDefinition, + strategy_repo: StrategyRepoDep, + session: DbSession, +) -> StrategyDefinition: + """原地更新（策略库「编辑」用）：id 以**路径**为准，created_at 沿用库中已有值。 + + 为什么必须显式带上 created_at：仓储 `save()` 只在 created_at 为空时才写 now() + （既有行不会覆盖该列），但返回值是**传入的实体**；若这里不带，响应里的创建时间 + 就会变成 None，而策略库按创建时间展示 —— 一改就丢时间同样会误导前端。 + 改名撞车由仓储 `save()` 抛 ValueError（策略名已存在：X），这里转 400。 + """ + existing = strategy_repo.get(strategy_id) + if existing is None: + raise HTTPException(status_code=404, detail=f"策略 {strategy_id} 不存在") + payload = definition.model_copy( + update={"id": strategy_id, "created_at": existing.created_at} + ) + try: + saved = strategy_repo.save(_ensure_description(payload)) + session.commit() + except ValueError as exc: + raise HTTPException(status_code=400, detail=str(exc)) from exc + # 与 create 一致：回读持久化实体，保证响应 == GET 读回（含 description/created_at） + return strategy_repo.get(saved.id) or saved + + @router.delete("/{strategy_id}", summary="删除策略") def delete_strategy( strategy_id: str, diff --git a/backend/app/application/services/data_sync.py b/backend/app/application/services/data_sync.py index 50fa67b..6e61698 100644 --- a/backend/app/application/services/data_sync.py +++ b/backend/app/application/services/data_sync.py @@ -241,6 +241,31 @@ class DailySymbolResult: notes: list[str] = field(default_factory=list) +@dataclass +class DailyBasicDayResult: + """单个交易日的每日指标同步结果（status: skip|ok|failed）。""" + + trade_date: date + status: str + source: str | None = None # tushare（新浪不支持本接口） + rows_fetched: int = 0 + rows_written: int = 0 + notes: list[str] = field(default_factory=list) + + +@dataclass +class NameHistoryChunkResult: + """单个时间分片的名称变更同步结果（status: ok|failed）。""" + + start: date + end: date + status: str + source: str | None = None + rows_fetched: int = 0 + rows_written: int = 0 + notes: list[str] = field(default_factory=list) + + # ---------------------------------------------------------------- 财务同步服务 class VerifiedFinancialSyncer: @@ -577,6 +602,181 @@ class VerifiedDailySyncer: ) +# ---------------------------------------------------------------- 每日指标同步服务 + +class DailyBasicSyncer: + """每日指标（估值 / 股息率 / 市值）同步：按交易日整表拉取。 + + 设计（与财务/日线的「逐股校验兜底」不同）： + - daily_basic 是**横截面整表**接口，Tushare 一次返回当日全市场，无法逐股兜底； + - 新浪不提供本接口 → 主源失败时**如实失败并写 sync_log**，禁止静默留缺口； + - 幂等键 (symbol, trade_date)：重跑同日无副作用（upsert）。 + - 交易日集合由日历仓储给出：只同步 is_open 且本地缺失的日期 + （`missing_dates`），因此增量与断点续跑天然安全。 + """ + + def __init__( + self, + *, + primary: MarketDataProvider, + repo, + audit: Callable[[SyncLog], None], + batch_flush: int = 5, + ) -> None: + self.primary = primary + self.repo = repo + self.audit = audit + self.batch_flush = max(batch_flush, 1) + + def sync_day(self, trade_date: date) -> DailyBasicDayResult: + try: + rows = self.primary.get_daily_basic(trade_date) + except DataSourceAuthenticationError: + raise # 凭证/权限故障 → 快速失败（不要逐日重试把配额烧光） + except Exception as exc: # noqa: BLE001 —— 逐日失败不中断整段 + _audit_sync( + self.audit, + source=self.primary.name, + api="get_daily_basic", + success=False, + reason=str(exc), + start=trade_date, + end=trade_date, + ) + return DailyBasicDayResult( + trade_date=trade_date, + status="failed", + source=self.primary.name, + notes=[f"拉取失败: {exc}"], + ) + # 过滤掉非法行（ts_code 缺失）—— 避免脏键污染幂等 + rows = [r for r in rows if r.symbol and r.symbol != "None"] + written = self.repo.upsert_many(rows) + _audit_sync( + self.audit, + source=self.primary.name, + api="get_daily_basic", + success=True, + row_count=len(rows), + start=trade_date, + end=trade_date, + ) + return DailyBasicDayResult( + trade_date=trade_date, + status="ok", + source=self.primary.name, + rows_fetched=len(rows), + rows_written=written, + ) + + def sync_range( + self, + start: date, + end: date, + *, + on_progress: Callable[[int, int, date], None] | None = None, + should_stop: Callable[[], bool] | None = None, + ) -> list[DailyBasicDayResult]: + """补齐 [start, end] 内开市但本地缺失的交易日。 + + on_progress(done, total, trade_date)：进度回调（CLI 打印 / 日志）。 + should_stop()：返回 True 时提前收尾（优雅中断，已落库的行保持有效）。 + """ + days = self.repo.missing_dates(start, end) + results: list[DailyBasicDayResult] = [] + total = len(days) + for idx, day in enumerate(days, start=1): + if should_stop is not None and should_stop(): + break + results.append(self.sync_day(day)) + if on_progress is not None: + on_progress(idx, total, day) + return results + + +class NameHistorySyncer: + """股票名称变更历史同步（Tushare namechange）—— 时点 ST 判定的数据基础。 + + 设计要点： + - **按年分片**：namechange 支持区间批量查询（2020+ 仅 4031 行），但全历史 + （1990 起）会触及单次 6000 行上限被**静默截断**，因此按自然年分片调用， + 每片独立审计与计数，超限时 Provider 会告警。 + - **幂等键 (symbol, start_date)**：重跑无副作用（upsert），可安全续跑。 + - 新浪不提供本接口 → 主源失败如实写 sync_log（禁止静默留缺口，AGENT.md §24）。 + - 为什么值得同步：`stock.name` 只是最新名称快照，用最新名称做 `exclude_st` + 会把「曾为高股息、后变 ST/退市」的股息陷阱样本整段排除 —— + 实测影响约 3.70pp 收益（见 docs/DEV_PLAN_DIVIDEND_BACKTEST.md §10.5）。 + """ + + def __init__( + self, + *, + primary: MarketDataProvider, + repo, + audit: Callable[[SyncLog], None], + ) -> None: + self.primary = primary + self.repo = repo + self.audit = audit + + def sync_chunk(self, start: date, end: date) -> NameHistoryChunkResult: + try: + rows = self.primary.get_name_changes(start, end) + except DataSourceAuthenticationError: + raise # 凭证/权限故障 → 快速失败，不要逐片重试烧配额 + except Exception as exc: # noqa: BLE001 —— 单片失败不中断整段 + _audit_sync( + self.audit, + source=self.primary.name, + api="get_namechange", + success=False, + reason=str(exc), + start=start, + end=end, + ) + return NameHistoryChunkResult( + start=start, end=end, status="failed", + source=self.primary.name, notes=[f"拉取失败: {exc}"], + ) + rows = [r for r in rows if r.symbol and r.symbol != "None"] + written = self.repo.upsert_many(rows) + _audit_sync( + self.audit, + source=self.primary.name, + api="get_namechange", + success=True, + row_count=len(rows), + start=start, + end=end, + ) + return NameHistoryChunkResult( + start=start, end=end, status="ok", source=self.primary.name, + rows_fetched=len(rows), rows_written=written, + ) + + def sync_range( + self, + start: date, + end: date, + *, + on_progress: Callable[[int, int, date], None] | None = None, + ) -> list[NameHistoryChunkResult]: + """按自然年分片同步 [start, end]（每片 1 次 API 调用）。""" + chunks: list[tuple[date, date]] = [] + cursor = date(start.year, 1, 1) + while cursor <= end: + chunk_end = min(date(cursor.year, 12, 31), end) + chunks.append((max(cursor, start), chunk_end)) + cursor = date(cursor.year + 1, 1, 1) + results: list[NameHistoryChunkResult] = [] + total = len(chunks) + for idx, (cs, ce) in enumerate(chunks, start=1): + results.append(self.sync_chunk(cs, ce)) + if on_progress is not None: + on_progress(idx, total, cs) + return results + + # ---------------------------------------------------------------- 小工具 def _fin_key(row: FinancialIndicator) -> tuple: diff --git a/backend/app/application/services/experiment_archive.py b/backend/app/application/services/experiment_archive.py new file mode 100644 index 0000000..c83d6f8 --- /dev/null +++ b/backend/app/application/services/experiment_archive.py @@ -0,0 +1,288 @@ +"""Experiment 归档服务（AGENT.md §21「研究可复现」）：同步与异步两条路径共用。 + +谁在用： +- 异步 Job：`job_executor._execute_inner` 执行成功后归档； +- 同步研究端点：`api/research.py` 的 `POST /backtests`、`POST /factor-tests` + （Phase 3 遗留的同步路径，此前只塞进程内存 `_LAST_*`，重启即丢，完全不落库）。 + +为什么单独成模块：`job_executor.py` 顶部依赖较重（研究服务、子进程调度）， +把归档抽到此处既避免 `api/research.py` → `job_executor` 的循环导入风险，也让 +「一次研究结果如何落库」只有一份实现（AGENT.md §40 简单可替换优先）。 + +归档内容：spec_json（复现依据）、完整结果 JSON、summary_text（人读摘要）、 +code_version（git 短 rev）、**data_version（真实数据快照指纹，见 data_version 模块）**、 +job_id（可追溯执行记录）、created_at。同一份结果**只在 experiment 存一份**： + +- 异步 Job 路径下 `job.result_json` 不再重复写入（见 `job_executor`）， + `GET /api/jobs/{id}` 经 `job.experiment_id` 回读 experiment； +- 同步端点路径下不存在 Job 记录，`job_id=None`。 + +大小护栏（诚实 + 不炸库）：`experiment.result_json` 是 MEDIUMTEXT，**上限 +16,777,215 字节**（MySQL 的 MEDIUMTEXT 按字节计，utf8mb4 下中文占 3 字节）， +故预算按 UTF-8 字节执行，默认 12,000,000 字节（约上限的 71.5%，给行内其它 +列、SQL 协议与未预料字段留余量）。超预算时按 |期末收益| 降序裁剪 +`symbol_curves`，并把证据写进结果的 `archive_meta`（机器可读）与 +`unimplemented`（人可读）——绝不静默丢弃。 +""" + +from __future__ import annotations + +import json +import logging +import subprocess +import uuid +from datetime import datetime +from pathlib import Path + +from app.domain.entities.research import ExperimentRecord + +logger = logging.getLogger(__name__) + +# backend/app/application/services/experiment_archive.py → parents[4] = 项目根 +PROJECT_ROOT = Path(__file__).resolve().parents[4] + +# 归档结果 JSON 的字节预算（可被 config research.archive_max_chars 覆盖） +DEFAULT_ARCHIVE_MAX_CHARS = 12_000_000 + +# MEDIUMTEXT 硬上限（字节），仅用于计算/说明默认预算的余量 +MEDIUMTEXT_MAX_BYTES = 16_777_215 + + +def new_id(prefix: str) -> str: + """生成 `PREFIX-XXXXXXXX` 形式的业务主键（Job/Experiment/Selection 等共用）。""" + return f"{prefix}-{uuid.uuid4().hex[:8].upper()}" + + +def _git_short_rev() -> str | None: + """当前代码版本（git 短 rev）；无 git / 超时则如实返回 None。""" + try: + out = subprocess.run( + ["git", "rev-parse", "--short", "HEAD"], + cwd=PROJECT_ROOT, + capture_output=True, + text=True, + timeout=5, + check=False, + ) + return out.stdout.strip() or None + except Exception: # noqa: BLE001 + return None + + +def _summary_text(kind: str, result) -> str | None: + """人读摘要（列表页展示）；未知 kind 返回 None 而不是编造。""" + from app.domain.entities.research import BacktestResult, FactorTestReport + from app.domain.entities.selection import SelectionResult + + if kind == "backtest" and isinstance(result, BacktestResult): + s = result.summary + return ( + f"总收益 {s.total_return_pct:.2f}% · 年化 {s.annual_return_pct:.2f}% · " + f"回撤 {s.max_drawdown_pct:.2f}%" + ) + if isinstance(result, FactorTestReport): + return ( + f"IC {result.ic_mean:.4f} · RankIC {result.rank_ic_mean:.4f} · " + f"样本 {result.sample_days} 日" + ) + if kind == "selection" and isinstance(result, SelectionResult): + return ( + f"as_of {result.as_of_date} · 选出 {result.statistics.selected} / " + f"评估 {result.statistics.evaluated}" + ) + return None + + +def _archive_budget_bytes() -> int: + """归档字节预算：config `research.archive_max_chars`（名义字符数）> 代码默认。""" + from app.core.config import get_settings + + try: + configured = get_settings().research_archive_max_chars + except Exception as exc: # noqa: BLE001 —— 配置读不到不能阻断归档 + logger.warning("归档预算配置读取失败，用默认值：%s: %s", type(exc).__name__, exc) + configured = None + value = int(configured) if configured else DEFAULT_ARCHIVE_MAX_CHARS + return value if value > 0 else DEFAULT_ARCHIVE_MAX_CHARS + + +def _curve_sort_key(curve: dict): + """个股曲线排序键：|期末收益| 降序（与引擎默认输出顺序一致，保证可预期）。""" + try: + return abs(float(curve.get("final_return_pct") or 0.0)) + except (TypeError, ValueError): + return 0.0 + + +def _truncation_note(stored: int, total: int, budget_bytes: int) -> str: + """裁剪说明（人可读）：必须写清「实际存了多少 / 共多少 / 为什么」。""" + return ( + f"归档体积超过预算（{budget_bytes:,} 字节，MEDIUMTEXT 上限 16MB）：" + f"个股收益曲线按 |期末收益| 降序仅存 {stored} / 共 {total} 只" + "（完整明细见 trades / signal_history；archive_meta.truncated=true）" + ) + + +def _over_budget_note(budget_bytes: int, curves_total: int) -> str: + """「曲线全裁掉仍超预算」的说明：主体（trades/positions/signal_history 等） + 无法裁剪，如实标注而不是假装达标。""" + return ( + f"归档主体（trades / positions / signal_history 等，期内持有 {curves_total} 只）" + f"已超过配置的体积预算（{budget_bytes:,} 字节）：个股收益曲线裁剪至 0 只仍无法达标，" + "本次仍尝试完整落库；若 MEDIUMTEXT 写入失败会如实报错（archive_meta.over_budget=true）" + ) + + +def _fit_payload(payload: dict, *, budget_bytes: int) -> tuple[dict, str]: + """给结果 payload 写入 `archive_meta`，必要时按预算裁剪 symbol_curves。 + + 返回 (payload, 已序列化的 result_json)。裁剪判定全部作用于「最终字符串的 + UTF-8 字节数」，不是估算：每次候选都真实序列化一次（超预算只发生在 + 异常巨大的归档上，此时多几次序列化的代价可接受）。 + """ + has_curves = isinstance(payload.get("symbol_curves"), list) + curves: list[dict] = list(payload.get("symbol_curves") or []) + total = len(curves) + ordered = sorted(curves, key=_curve_sort_key, reverse=True) + notes = list(payload.get("unimplemented") or []) + + def build(kept: list[dict], *, over_budget: bool = False) -> tuple[dict, str, dict]: + """按 kept 组装候选 payload 并序列化（含 archive_meta 自收敛）。""" + truncated = has_curves and len(kept) < total + candidate = dict(payload) + extra_notes: list[str] = [] + if has_curves: + candidate["symbol_curves"] = kept + if truncated: + extra_notes.append(_truncation_note(len(kept), total, budget_bytes)) + if over_budget: + extra_notes.append(_over_budget_note(budget_bytes, total)) + if extra_notes: + candidate["unimplemented"] = [*notes, *extra_notes] + meta = { + "budget_chars": budget_bytes, + "budget_bytes": budget_bytes, + "over_budget": over_budget, + "result_chars": 0, + "result_bytes": 0, + } + if has_curves: + meta = { + "curves_stored": len(kept), + "curves_total": total, + "truncated": truncated, + **meta, + } + text = "" + # result_chars/result_bytes 会改变自身长度，迭代至收敛（通常 2 轮内） + for _ in range(8): + meta["result_chars"] = len(text) + meta["result_bytes"] = len(text.encode("utf-8")) + candidate["archive_meta"] = meta + text = json.dumps(candidate, ensure_ascii=False) + if meta["result_chars"] == len(text) and meta["result_bytes"] == len( + text.encode("utf-8") + ): + break + return candidate, text, meta + + if not has_curves: # 因子测试 / 选股等无曲线结果：不裁剪，只记录体积 + fitted, text, meta = build([]) + if meta["result_bytes"] > budget_bytes: + # 无曲线可裁：如实标注超预算，仍尝试落库（写入失败会如实报错） + logger.warning( + "归档超过预算且无曲线可裁剪：%d 字节 > %d 字节", meta["result_bytes"], budget_bytes + ) + fitted, text, meta = build([], over_budget=True) + return fitted, text + + fitted, text, meta = build(ordered) + if meta["result_bytes"] <= budget_bytes: + return fitted, text + + # 超预算：二分找「能放进预算的最大曲线数」（降序保留收益绝对值最大的那些） + lo, hi, best = 0, total, 0 + while lo <= hi: + mid = (lo + hi) // 2 + _, candidate_text, _ = build(ordered[:mid]) + if len(candidate_text.encode("utf-8")) <= budget_bytes: + best = mid + lo = mid + 1 + else: + hi = mid - 1 + fitted, text, meta = build(ordered[:best]) + if meta["result_bytes"] > budget_bytes: + # 连 0 条曲线都放不下：主体（trades/positions/…）超预算，如实标注 + fitted, text, meta = build(ordered[:best], over_budget=True) + logger.warning( + "归档超过预算：symbol_curves 裁剪为 %d/%d（预算 %d 字节，实际 %d 字节）", + meta.get("curves_stored"), + total, + budget_bytes, + meta.get("result_bytes"), + ) + return fitted, text + + +def archive_experiment( + *, + session, + kind: str, + spec_json: str, + result, + job_id: str | None = None, + experiment_repo=None, +) -> ExperimentRecord: + """把一次研究结果落为 Experiment 归档，返回归档记录（已 save + commit）。 + + 参数： + - `session`：SQLAlchemy Session（由调用方持有生命周期；本函数负责 commit）； + - `kind`：`backtest` / `factor_test` / `selection`； + - `spec_json`：完整研究 spec（复现依据）； + - `result`：pydantic 结果对象（`model_dump(mode="json")` 序列化）； + - `job_id`：来源 Job（同步端点没有 Job，留 None）； + - `experiment_repo`：ExperimentRepository（不传则用 SqlAlchemy 实现；测试可注入假仓储）。 + + `data_version` 由 `compute_data_version(session)` 计算（真实数据指纹，取不到则 + 如实降级为 `unavailable`，见该模块 docstring）。 + """ + from app.infrastructure.persistence.sqlalchemy.data_version import compute_data_version + + payload = result.model_dump(mode="json") + budget_bytes = _archive_budget_bytes() + payload, result_json = _fit_payload(payload, budget_bytes=budget_bytes) + meta = payload.get("archive_meta") or {} + + # 把归档元数据同步写回结果对象：同步端点的响应体因此也如实标注裁剪/超预算 + #（AGENT.md §24：不假装支持 / 不静默降级）。没有该字段的结果类型跳过。 + if hasattr(result, "archive_meta"): + result.archive_meta = meta + archived_notes = payload.get("unimplemented") + if isinstance(archived_notes, list) and hasattr(result, "unimplemented"): + # 归档 JSON 里的 unimplemented 才是权威（含归档侧追加的裁剪/超预算说明）， + # 让同步响应体与归档内容保持一字不差 + result.unimplemented = list(archived_notes) + + experiment = ExperimentRecord( + id=new_id("EXP"), + kind=kind, + spec_json=spec_json, + result_json=result_json, + summary_text=_summary_text(kind, result), + code_version=_git_short_rev(), + data_version=compute_data_version(session), + job_id=job_id, + created_at=datetime.now(), + ) + repo = experiment_repo if experiment_repo is not None else _default_experiment_repo(session) + repo.save(experiment) + session.commit() + return experiment + + +def _default_experiment_repo(session): + from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import ( + SqlAlchemyExperimentRepository, + ) + + return SqlAlchemyExperimentRepository(session) \ No newline at end of file diff --git a/backend/app/application/services/job_executor.py b/backend/app/application/services/job_executor.py index 21db646..8be010e 100644 --- a/backend/app/application/services/job_executor.py +++ b/backend/app/application/services/job_executor.py @@ -18,15 +18,12 @@ import os import subprocess import sys import threading -import uuid from collections.abc import Callable from datetime import datetime from pathlib import Path +from app.application.services.experiment_archive import archive_experiment, new_id from app.domain.entities.research import ( - BacktestResult, - ExperimentRecord, - FactorTestReport, JobRecord, JobStatus, ResearchSpec, @@ -42,38 +39,6 @@ _active_jobs: dict[str, subprocess.Popen | None] = {} _active_lock = threading.Lock() -def new_id(prefix: str) -> str: - return f"{prefix}-{uuid.uuid4().hex[:8].upper()}" - - -def _git_short_rev() -> str | None: - try: - out = subprocess.run( - ["git", "rev-parse", "--short", "HEAD"], - cwd=PROJECT_ROOT, - capture_output=True, - text=True, - timeout=5, - check=False, - ) - return out.stdout.strip() or None - except Exception: # noqa: BLE001 - return None - - -def _summary_text(kind: str, result) -> str | None: - from app.domain.entities.selection import SelectionResult - - if kind == "backtest" and isinstance(result, BacktestResult): - s = result.summary - return f"总收益 {s.total_return_pct:.2f}% · 年化 {s.annual_return_pct:.2f}% · 回撤 {s.max_drawdown_pct:.2f}%" - if isinstance(result, FactorTestReport): - return f"IC {result.ic_mean:.4f} · RankIC {result.rank_ic_mean:.4f} · 样本 {result.sample_days} 日" - if kind == "selection" and isinstance(result, SelectionResult): - return f"as_of {result.as_of_date} · 选出 {result.statistics.selected} / 评估 {result.statistics.evaluated}" - return None - - def _execute_inner( job_id: str, *, @@ -83,7 +48,22 @@ def _execute_inner( stock_repo_factory: Callable, daily_repo_factory: Callable, engine, + basic_repo_factory: Callable | None = None, + financial_repo_factory: Callable | None = None, + index_repo_factory: Callable | None = None, + name_repo_factory: Callable | None = None, ) -> None: + """执行 Job。 + + 可选工厂的语义（AGENT.md §24：不做静默降级）： + - `basic_repo_factory`：daily_basic（dv_ratio 等每日指标）—— 因子/条件引用时必需 + - 复权（qfq/hfq）折算在行情仓储 SQL 内完成，无需 adjust_factor 工厂 + - `financial_repo_factory`：财务表 —— 条件引用 fundamental.* 时必需 + - `index_repo_factory`：指数成分 —— universe.index_code 时必需 + - `name_repo_factory`：名称变更历史 —— universe.exclude_st 时点口径；未注入则 + 回退最新名称（旧行为），结果如实标注残余偏差 + 未注入且 spec 需要时，由 Service 抛出明确错误（而非返回空结果）。 + """ with session_factory() as session: job_repo = job_repo_factory(session) experiment_repo = experiment_repo_factory(session) @@ -96,18 +76,34 @@ def _execute_inner( session.commit() try: is_selection = job.kind == "selection" + basic_repo = basic_repo_factory(session) if basic_repo_factory else None + index_repo = index_repo_factory(session) if index_repo_factory else None + name_repo = name_repo_factory(session) if name_repo_factory else None if is_selection: from app.application.services.selection_service import SelectionService from app.domain.entities.selection import SelectionQuery spec = SelectionQuery.model_validate_json(job.spec_json) service = SelectionService( - stock_repo_factory(session), daily_repo_factory(session) + stock_repo_factory(session), + daily_repo_factory(session), + financial_repo_factory(session) if financial_repo_factory else None, + index_repo=index_repo, + basic_repo=basic_repo, + name_repo=name_repo, ) else: spec = ResearchSpec.model_validate_json(job.spec_json) service = ResearchService( - stock_repo_factory(session), daily_repo_factory(session), engine + stock_repo_factory(session), + daily_repo_factory(session), + engine, + index_repo=index_repo, + basic_repo=basic_repo, + financial_repo=( + financial_repo_factory(session) if financial_repo_factory else None + ), + name_repo=name_repo, ) def _set_stage(name: str) -> None: @@ -130,20 +126,20 @@ def _execute_inner( else: result = service.run_factor_test(spec, on_stage=_set_stage) - result_json = json.dumps(result.model_dump(mode="json"), ensure_ascii=False) - experiment = ExperimentRecord( - id=new_id("EXP"), + experiment = archive_experiment( + session=session, kind=job.kind, spec_json=job.spec_json, - result_json=result_json, - summary_text=_summary_text(job.kind, result), - code_version=_git_short_rev(), + result=result, job_id=job.id, - created_at=datetime.now(), + experiment_repo=experiment_repo, ) - experiment_repo.save(experiment) - job.result_json = result_json + # 完整结果**只在 experiment 存一份**（此前 job.result_json 与 + # experiment.result_json 各存一份相同内容，完整存档后 2× 浪费）。 + # GET /api/jobs/{id} 经 job.experiment_id 回读 experiment； + # 老记录（result_json 有值、experiment_id 为空）仍走 job 回退解码。 + job.result_json = None job.experiment_id = experiment.id job.status = JobStatus.SUCCESS job.error = None @@ -173,6 +169,10 @@ def execute_job( stock_repo_factory: Callable, daily_repo_factory: Callable, engine, + basic_repo_factory: Callable | None = None, + financial_repo_factory: Callable | None = None, + index_repo_factory: Callable | None = None, + name_repo_factory: Callable | None = None, ) -> None: """入口包装：任何未预期异常都将 Job 标记 failed（防止卡在 queued/running）。""" try: @@ -184,6 +184,10 @@ def execute_job( stock_repo_factory=stock_repo_factory, daily_repo_factory=daily_repo_factory, engine=engine, + basic_repo_factory=basic_repo_factory, + financial_repo_factory=financial_repo_factory, + index_repo_factory=index_repo_factory, + name_repo_factory=name_repo_factory, ) except Exception as exc: # noqa: BLE001 try: @@ -202,12 +206,18 @@ def execute_job( def default_factories() -> dict: """后台执行所需的独立 Session / Repository / 引擎装配（跨请求生命周期）。""" + from app.infrastructure.persistence.sqlalchemy.repositories.index_impl import ( + SqlAlchemyIndexConstituentRepository, + ) from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import ( SqlAlchemyExperimentRepository, SqlAlchemyJobRepository, ) from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import ( SqlAlchemyDailyBarRepository, + SqlAlchemyDailyBasicRepository, + SqlAlchemyFinancialRepository, + SqlAlchemyStockNameHistoryRepository, SqlAlchemyStockRepository, ) from app.infrastructure.persistence.sqlalchemy.session import SessionLocal @@ -219,6 +229,13 @@ def default_factories() -> dict: "experiment_repo_factory": lambda s: SqlAlchemyExperimentRepository(s), "stock_repo_factory": lambda s: SqlAlchemyStockRepository(s), "daily_repo_factory": lambda s: SqlAlchemyDailyBarRepository(s), + # 新增装配（daily_basic / adjust_factor / 财务 / 指数成分）： + # 缺失时对应能力（dv_ratio 因子、qfq/hfq 复权、fundamental 条件、指数成份池） + # 会由 Service 明确报错，绝不静默降级 + "basic_repo_factory": lambda s: SqlAlchemyDailyBasicRepository(s), + "financial_repo_factory": lambda s: SqlAlchemyFinancialRepository(s), + "index_repo_factory": lambda s: SqlAlchemyIndexConstituentRepository(s), + "name_repo_factory": lambda s: SqlAlchemyStockNameHistoryRepository(s), "engine": LocalEngine(), } @@ -252,9 +269,21 @@ def submit_and_run(spec: ResearchSpec, *, factories: dict | None = None) -> JobR stock_repo_factory=facts["stock_repo_factory"], daily_repo_factory=facts["daily_repo_factory"], engine=facts["engine"], + basic_repo_factory=facts.get("basic_repo_factory"), + financial_repo_factory=facts.get("financial_repo_factory"), + index_repo_factory=facts.get("index_repo_factory"), + name_repo_factory=facts.get("name_repo_factory"), ) with session_factory() as session: done = job_repo(session).get(job.id) + # **仅内存**读透（不落库）：完整结果只存 experiment 一份，但 submit_and_run 的 + # 既有调用方（scripts/run_dividend_case.py、agent 工具）习惯从 job.result_json + # 取结果，这里按 experiment_id 回读一次填进返回对象，避免调用方静默拿到空结果。 + # 数据库中的 job.result_json 仍然保持 NULL（P1：不重复存第二份）。 + if done is not None and done.experiment_id and done.result_json is None: + exp = facts["experiment_repo_factory"](session).get(done.experiment_id) + if exp is not None: + done.result_json = exp.result_json assert done is not None return done @@ -341,6 +370,21 @@ def _mark_failed( pass +def _subprocess_log_target(): + """研究子进程 stderr 的落盘目标。 + + 原先父进程用 DEVNULL，会吞掉子进程全部诊断（含 run_job 打印的内存上限设置失败 + 警告），使「如实提示未实现项」落空。改为追加写入 <项目根>/logs/job-subprocess.log。 + 记日志失败（无权限/磁盘满）绝不能导致 Job 失败 —— 此时退回 DEVNULL。 + """ + try: + log_dir = BACKEND_ROOT.parent / "logs" + log_dir.mkdir(parents=True, exist_ok=True) + return (log_dir / "job-subprocess.log").open("a", encoding="utf-8") + except OSError: + return subprocess.DEVNULL + + def _run_in_subprocess( job_id: str, *, @@ -361,13 +405,14 @@ def _run_in_subprocess( env["QLIB_JOB_MEM_LIMIT_GB"] = str(_job_memory_limit_gb()) rc = -1 already_marked = False + errlog = _subprocess_log_target() try: proc = subprocess.Popen( _job_worker_cmd(job_id), cwd=BACKEND_ROOT, env=env, stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, + stderr=errlog, ) with _active_lock: _active_jobs[job_id] = proc @@ -381,6 +426,8 @@ def _run_in_subprocess( ) already_marked = True finally: + if errlog is not subprocess.DEVNULL: + errlog.close() _release_slot(job_id) if rc != 0 and not already_marked: _mark_failed( diff --git a/backend/app/application/services/replay_service.py b/backend/app/application/services/replay_service.py index fdc7f80..63e991a 100644 --- a/backend/app/application/services/replay_service.py +++ b/backend/app/application/services/replay_service.py @@ -18,7 +18,7 @@ from app.domain.repositories.market import DailyBarRepository, StockRepository from app.quant.selection import factor_columns from app.quant.service import load_daily_df from app.quant.signal import generate_signals -from app.quant.universe import filter_stocks, resolve_members +from app.quant.universe import filter_stocks, names_as_of, resolve_members MAX_SYMBOLS = 40 MAX_DAYS = 90 @@ -30,10 +30,13 @@ class ReplayService: stock_repo: StockRepository, daily_repo: DailyBarRepository, index_repo=None, + name_repo=None, ) -> None: self._stock_repo = stock_repo self._daily_repo = daily_repo self._index_repo = index_repo + # 名称变更历史仓储：exclude_st 的时点口径（与选股/回测一致，v2 §25） + self._name_repo = name_repo def replay( self, @@ -48,9 +51,12 @@ class ReplayService: raise ValueError("Bar Replay 需要 universe.symbols 白名单（≤40 只），避免全市场长任务") if len(symbols) > MAX_SYMBOLS: raise ValueError(f"Bar Replay 白名单最多 {MAX_SYMBOLS} 只，当前 {len(symbols)}") + all_stocks = self._stock_repo.list() + name_at, _applied = names_as_of(all_stocks, start, self._name_repo) stocks = filter_stocks( - self._stock_repo.list(), query.universe, as_of=start, + all_stocks, query.universe, as_of=start, members=resolve_members(self._index_repo, query.universe, start), + name_at=name_at, ) if not stocks: return ReplayResult(start=start, end=end, top_n=top_n) diff --git a/backend/app/application/services/selection_service.py b/backend/app/application/services/selection_service.py index 915f6c5..4211d78 100644 --- a/backend/app/application/services/selection_service.py +++ b/backend/app/application/services/selection_service.py @@ -28,12 +28,36 @@ from app.quant.selection import ( run_condition_selection, run_score_selection, ) -from app.quant.service import load_daily_df -from app.quant.universe import filter_stocks, resolve_members +from app.quant.service import ( + load_basic_df, + load_daily_df, + merge_basic_into_daily, + split_factor_columns, +) +from app.quant.universe import filter_stocks, names_as_of, resolve_members _FUNDAMENTAL_PREFIX = "fundamental." +def fill_candidate_names(result: SelectionResult, stocks: list) -> SelectionResult: + """把股票池的 `symbol → name` 回填进候选股（展示增强，未命中保持 None）。 + + 为什么在业务层做：名称是展示数据而非选股语义，引擎（quant/selection.py） + 只做纯数值计算，不应感知名称；而本用例的 `stocks` 已是 universe 过滤后的 + 股票实体列表（天然带 name），在这里一次性建立映射即可，不必在各出口各自查库。 + 查不到名称的候选保持 None —— 前端按「名称未知」渲染，不伪造也不报错。 + """ + if not result.candidates or not stocks: + return result + name_map = {s.symbol: s.name for s in stocks if getattr(s, "name", None)} + if not name_map: + return result + for cand in result.candidates: + if cand.name is None: + cand.name = name_map.get(cand.symbol) + return result + + class SelectionService: """选股用例入口：select(query) → SelectionResult（当前或历史 as_of）。""" @@ -43,17 +67,26 @@ class SelectionService: daily_repo: DailyBarRepository, financial_repo: FinancialRepository | None = None, index_repo=None, + basic_repo=None, + name_repo=None, ) -> None: self._stock_repo = stock_repo self._daily_repo = daily_repo self._financial_repo = financial_repo self._index_repo = index_repo + # 每日指标仓储（daily_basic）：score 因子/条件引用 dv_ratio 等列时使用 + self._basic_repo = basic_repo + # 名称变更历史仓储：exclude_st 的时点口径（与回测口径一致，v2 §25） + self._name_repo = name_repo def select(self, query: SelectionQuery) -> SelectionResult: as_of = query.as_of or date.today() + all_stocks = self._stock_repo.list() + name_at, _applied = names_as_of(all_stocks, as_of, self._name_repo) stocks = filter_stocks( - self._stock_repo.list(), query.universe, as_of=as_of, + all_stocks, query.universe, as_of=as_of, members=resolve_members(self._index_repo, query.universe, as_of), + name_at=name_at, ) if not stocks: return self._run(query, pd.DataFrame(), stocks, as_of, financial={}) @@ -62,14 +95,19 @@ class SelectionService: columns = sorted(factor_columns(query)) else: columns = sorted(condition_needed_columns(query)) + bar_cols, basic_cols = split_factor_columns(columns) + data_start = as_of - timedelta(days=query.warmup_days) daily = load_daily_df( self._daily_repo, symbols, - as_of - timedelta(days=query.warmup_days), + data_start, as_of, - columns, - adjust=query.price_adjustment, + sorted(bar_cols), + adjust="none", + price_adjust=query.price_adjustment, ) + if basic_cols: + daily = self._attach_basic(daily, symbols, data_start, as_of, sorted(basic_cols)) financial: dict[str, FinancialIndicator] = {} if query.method == "condition" and self._uses_fundamental(query): financial = self._load_financial(symbols, as_of) @@ -77,6 +115,28 @@ class SelectionService: # ---- 内部 ---- + def _attach_basic( + self, + daily: pd.DataFrame, + symbols: list[str], + start: date, + end: date, + columns: list[str], + ) -> pd.DataFrame: + """并入 daily_basic 列（与 ResearchService 同一装配逻辑，保证 v2 §25 一致性）。""" + if self._basic_repo is None: + raise ValueError( + f"选股条件/因子需要每日指标列 {columns}（daily_basic），" + "但未注入 DailyBasicRepository。请检查 API 的依赖装配。" + ) + basic = load_basic_df(self._basic_repo, symbols, start, end, columns) + if basic.empty: + raise ValueError( + f"daily_basic 表在 {start}~{end} 无数据，无法计算需要 {columns} 的因子/条件。" + "请先运行：python -m app.cli.sync daily_basic --start 20200101" + ) + return merge_basic_into_daily(daily, basic) + def _run( self, query: SelectionQuery, @@ -86,8 +146,12 @@ class SelectionService: financial: dict[str, FinancialIndicator], ) -> SelectionResult: if query.method == "score": - return run_score_selection(daily, query, as_of) - return run_condition_selection(daily, stocks, query, as_of, financial) + result = run_score_selection(daily, query, as_of) + else: + result = run_condition_selection(daily, stocks, query, as_of, financial) + # 名称只在业务层回填：引擎（quant/selection.py）保持纯符号计算， + # 而 `stocks` 是本用例已经装配好的股票池，天然带 name，无需再查库/join。 + return fill_candidate_names(result, stocks) @staticmethod def _uses_fundamental(query: SelectionQuery) -> bool: diff --git a/backend/app/application/services/signal_service.py b/backend/app/application/services/signal_service.py index 27045ed..3bac2cf 100644 --- a/backend/app/application/services/signal_service.py +++ b/backend/app/application/services/signal_service.py @@ -15,7 +15,7 @@ from app.domain.repositories.market import DailyBarRepository, StockRepository from app.quant.selection import factor_columns from app.quant.service import load_daily_df from app.quant.signal import generate_signals -from app.quant.universe import filter_stocks, resolve_members +from app.quant.universe import filter_stocks, names_as_of, resolve_members class SignalService: @@ -24,16 +24,22 @@ class SignalService: stock_repo: StockRepository, daily_repo: DailyBarRepository, index_repo=None, + name_repo=None, ) -> None: self._stock_repo = stock_repo self._daily_repo = daily_repo self._index_repo = index_repo + # 名称变更历史仓储：exclude_st 的时点口径（与选股/回测一致，v2 §25） + self._name_repo = name_repo def signal(self, query: SelectionQuery, rules: SignalRules) -> SignalResult: as_of = query.as_of or date.today() + all_stocks = self._stock_repo.list() + name_at, _applied = names_as_of(all_stocks, as_of, self._name_repo) stocks = filter_stocks( - self._stock_repo.list(), query.universe, as_of=as_of, + all_stocks, query.universe, as_of=as_of, members=resolve_members(self._index_repo, query.universe, as_of), + name_at=name_at, ) if not stocks: return generate_signals(pd.DataFrame(), query, rules, as_of) diff --git a/backend/app/cli/prune_experiments.py b/backend/app/cli/prune_experiments.py new file mode 100644 index 0000000..2af6b23 --- /dev/null +++ b/backend/app/cli/prune_experiments.py @@ -0,0 +1,125 @@ +"""实验归档保留策略 CLI（归档体积治理）。 + +用法（cd backend）： + # 只看会删什么（**默认就是 dry-run**，不写库） + uv run python -m app.cli.prune_experiments --keep 30 + uv run python -m app.cli.prune_experiments --keep 20 --kind backtest + uv run python -m app.cli.prune_experiments --older-than 180 # 180 天前的归档 + # 真正删除（必须显式加 --apply） + uv run python -m app.cli.prune_experiments --keep 30 --apply + +为什么默认 dry-run：归档是**研究结论的唯一凭据**（结果 + spec + 代码/数据版本）， +误删不可恢复。因此： +- 默认只打印候选清单（含 id / 类型 / 时间 / 体积 / 摘要）与合计释放空间，不写库； +- 只有显式 `--apply` 才执行删除，且删除前**再打印一次**清单； +- 按 `created_at` **由新到旧保留**：先按过滤条件选出候选集合，再保留最新的 N 条， + 其余删除（`--keep` 与 `--older-than` 可同时给，取交集）； +- 只删 `experiment` 表（归档本身），**不动 `job_record`**（执行历史仍可追溯）； + 但要注意：完整结果只存归档一份（`job.result_json` 对新记录为 NULL）， + 因此**删除归档 = 该次回测结果不可再查看**。要留底先导出（归档页「导出完整 JSON」）； +- `--kind` 可只清理某一类（如只清 `factor_test`，保留回测结论）。 + +本模块是组装层（composition root）：装配 Session 与 Repository，删除逻辑走仓储。 +""" + +from __future__ import annotations + +import argparse +import sys +from datetime import datetime, timedelta + +from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import ( + SqlAlchemyExperimentRepository, +) +from app.infrastructure.persistence.sqlalchemy.session import SessionLocal + + +def select_prune_candidates( + rows: list, + *, + keep: int | None, + older_than_days: int | None, + now: datetime | None = None, +) -> list: + """从归档列表里挑出**该删除**的那些（纯函数，便于单测）。 + + `rows`：任意具备 `id` / `created_at` 的归档摘要（不依赖 ORM 与数据库）。 + 规则（两个条件同时给出时取交集）： + - `keep=N`：按 `created_at` 由新到旧排序后，**保留最新 N 条**，其余为候选； + - `older_than_days=D`：创建时间早于 `now - D 天` 的才是候选； + - `created_at` 为空的归档视为**最旧**（排序末位）——缺失时间时宁可被列为候选， + 也不静默把它当作「最新」而永久留在库里。 + """ + ordered = sorted(rows, key=lambda e: (e.created_at or datetime.min), reverse=True) + candidates = list(ordered) + if older_than_days is not None: + cutoff = (now or datetime.now()) - timedelta(days=older_than_days) + candidates = [e for e in candidates if (e.created_at or datetime.min) < cutoff] + if keep is not None: + keep_ids = {e.id for e in ordered[: max(keep, 0)]} + candidates = [e for e in candidates if e.id not in keep_ids] + # 输出仍按新 → 旧，便于人核对 + candidates.sort(key=lambda e: (e.created_at or datetime.min), reverse=True) + return candidates + + +def _parse_args(argv: list[str]) -> argparse.Namespace: + p = argparse.ArgumentParser( + prog="python -m app.cli.prune_experiments", + description="实验归档保留策略（默认 dry-run，需 --apply 才真正删除）", + ) + p.add_argument("--keep", type=int, default=None, help="按时间由新到旧保留的最新条数") + p.add_argument("--kind", default=None, help="只处理某类归档（backtest/factor_test/selection）") + p.add_argument( + "--older-than", + type=int, + default=None, + metavar="DAYS", + help="只处理创建时间早于 N 天的归档", + ) + p.add_argument("--apply", action="store_true", help="真正执行删除（否则仅预览）") + args = p.parse_args(argv) + if args.keep is None and args.older_than is None: + p.error("至少给出 --keep 或 --older-than 之一，避免误删全部归档") + if args.keep is not None and args.keep < 0: + p.error("--keep 不能为负数") + return args + + +def main(argv: list[str] | None = None) -> int: + args = _parse_args(sys.argv[1:] if argv is None else argv) + with SessionLocal() as session: + repo = SqlAlchemyExperimentRepository(session) + # 列表查询刻意不取 result_json，体积用库侧算出的 result_bytes（单位：字符） + rows = repo.list_filtered(kind=args.kind, limit=1_000_000, offset=0) + candidates = select_prune_candidates( + rows, keep=args.keep, older_than_days=args.older_than + ) + + total_bytes = sum(e.result_bytes or 0 for e in candidates) + print(f"归档总数 {len(rows)} 条，命中删除条件 {len(candidates)} 条，" + f"预计释放约 {total_bytes / 1024 / 1024:.1f} MB") + for e in candidates: + created = e.created_at.strftime("%Y-%m-%d %H:%M") if e.created_at else "未知时间" + print( + f" - {e.id} {e.kind:<12} {created} " + f"{((e.result_bytes or 0) / 1024):>8.0f} KB {(e.summary_text or '')[:60]}" + ) + if not candidates: + print("没有需要删除的归档。") + return 0 + if not args.apply: + print("\n[dry-run] 未删除任何记录；确认无误后加 --apply 执行。") + return 0 + + deleted = 0 + for e in candidates: + if repo.delete(e.id): + deleted += 1 + session.commit() + print(f"\n已删除 {deleted} 条归档（job_record 未改动）。") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/backend/app/cli/restore_experiment_from_job.py b/backend/app/cli/restore_experiment_from_job.py new file mode 100644 index 0000000..164b2b0 --- /dev/null +++ b/backend/app/cli/restore_experiment_from_job.py @@ -0,0 +1,172 @@ +"""从 Job 结果副本重建 Experiment 归档（删除后的可选恢复路径）。 + +**为什么会有这个工具**：完整存档上线前，历史归档在 `job.result_json` 里另存了一份完整结果 +（双写遗留）。删除归档只删 `experiment` 行、不动 `job` 行，因此这些历史记录 +**可以从 Job 副本原样重建** —— 删除不等于数据永久消失。 +新归档（`job.result_json IS NULL`）没有副本，删除即不可恢复，本工具会明确拒绝而不是假装能救。 + +注意：本工具是**恢复手段**，不是"删除的撤销键"。删除本身是正常功能： +已有历史归档可重建、新归档不可；是否恢复由人决定，工具默认 dry-run、不做任何自动动作。 + +用法（**默认 dry-run**，只打印将要写入的内容；`--apply` 才写库）： + + cd backend + PYTHONPATH=. .venv/bin/python -m app.cli.restore_experiment_from_job --job-id JOB-XXXX + PYTHONPATH=. .venv/bin/python -m app.cli.restore_experiment_from_job --job-id JOB-XXXX \ + --code-version 92627f5 --apply + +诚实性要求（AGENT.md §7/§24）： +- **归档 id 用回原 id**（`job.experiment_id`），移动端/书签里的旧链接继续有效； +- `spec_json` / `result_json` 逐字复制 Job 副本，不重新计算、不"顺手修正"； +- `summary_text` 由副本 JSON 反序列化后按归档同一函数重新生成（口径一致）； +- `code_version` **必须显式提供**，不做猜测：显式传 `--code-version ""` 表示"未知则留空"； +- `data_version` 留空 —— 历史归档当年没有数据指纹，补一个今天的指纹是伪造复现依据； +- 重建前会检查该 job 的归档是否已存在，已存在则拒绝（除非 `--force`）。 +""" + +from __future__ import annotations + +import argparse +import json +import sys +from datetime import datetime + +from app.application.services.experiment_archive import _summary_text +from app.domain.entities.research import ( + BacktestResult, + ExperimentRecord, + FactorTestReport, +) +from app.domain.entities.selection import SelectionResult +from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import ( + SqlAlchemyExperimentRepository, +) +from app.infrastructure.persistence.sqlalchemy.session import SessionLocal + + +def _parse_result(kind: str, raw: str): + """按 kind 把 Job 里的结果 JSON 反序列化成领域对象（用于生成同一个摘要口径）。""" + payload = json.loads(raw) + if kind == "backtest": + return BacktestResult.model_validate(payload) + if kind == "factor_test": + return FactorTestReport.model_validate(payload) + if kind == "selection": + return SelectionResult.model_validate(payload) + return None + + +def _load_job(session, job_id: str): + """按 ORM 读 Job（**不要用裸 SQL**）。 + + 裸 `text()` 查询在 SQLite 下把 DateTime 列原样返回成字符串，写回 ORM 的 DateTime + 字段会抛 `TypeError: SQLite DateTime type only accepts Python datetime...` + （MySQL+pymysql 返回 datetime 所以当时看不出问题）。ORM 读法跨方言类型一致， + 这个坑由 `tests/test_restore_experiment.py::test_restores_archive_from_job_copy` 钉住。 + """ + from app.infrastructure.persistence.sqlalchemy.models.jobs import JobModel + + return session.get(JobModel, job_id) + + +def _experiment_exists(session, exp_id: str) -> bool: + from app.infrastructure.persistence.sqlalchemy.models import ExperimentModel + + return session.get(ExperimentModel, exp_id) is not None + + +def main(argv: list[str] | None = None) -> int: + ap = argparse.ArgumentParser(description="从 Job 结果副本重建 Experiment 归档（删除后的可选恢复路径）") + ap.add_argument("--job-id", required=True, help="来源 Job id（如 JOB-D3C120DC）") + ap.add_argument( + "--code-version", + default=None, + help="重建记录的 code_version（必填；传空串表示未知留空）。不猜版本。", + ) + ap.add_argument("--force", action="store_true", help="归档已存在时也覆盖（默认拒绝）") + ap.add_argument("--apply", action="store_true", help="真正写库（默认 dry-run）") + args = ap.parse_args(argv) + + if args.code_version is None: + print( + "❌ 必须显式给出 --code-version（不猜版本）；确定未知请传 --code-version \"\"。", + file=sys.stderr, + ) + return 2 + + session = SessionLocal() + try: + row = _load_job(session, args.job_id) + if row is None: + print(f"❌ Job {args.job_id} 不存在", file=sys.stderr) + return 1 + job_id = row.id + kind = row.kind + status = row.status + spec_json = row.spec_json + result_json = row.result_json + exp_id = row.experiment_id + finished_at = row.finished_at + created_at = row.created_at + if not exp_id: + print(f"❌ Job {job_id} 没有 experiment_id，无法确定重建为哪个归档", file=sys.stderr) + return 1 + if result_json is None: + print( + f"❌ Job {job_id} 的 result_json 为空（完整存档上线后结果只存归档一份），" + "本工具无法重建 —— 这类归档删除后不可恢复。", + file=sys.stderr, + ) + return 1 + + exists = _experiment_exists(session, exp_id) + result = _parse_result(kind, result_json) + summary = _summary_text(kind, result) if result is not None else None + restored_at = finished_at or created_at or datetime.now() + + print(f"来源 Job : {job_id}（status={status}，finished_at={finished_at}）") + print(f"归档 id : {exp_id}（已存在: {exists}）") + print(f"kind : {kind}") + print(f"spec 长度 : {len(spec_json)} 字符") + print(f"result 长度 : {len(result_json)} 字符") + print(f"summary_text : {summary!r}") + print(f"code_version : {args.code_version!r}") + print("data_version : None（历史归档无指纹，不伪造）") + print(f"created_at : {restored_at}") + + if exists and not args.force: + print("❌ 该归档已存在；如确要覆盖请加 --force", file=sys.stderr) + return 1 + if not args.apply: + print("\n(dry-run) 未写库。确认无误后加 --apply。") + return 0 + + # 走仓储而不是直接操作 ORM 模型（AGENT §10：持久化只经 Repository）。 + # 这样 experiment 表 ↔ 领域实体的字段映射只有仓储一份：将来加列（尤其 NOT NULL） + # 不会在这里静默漏写。覆盖语义由 `upsert`（merge）承担，归档 id 保持不变。 + record = ExperimentRecord( + id=exp_id, + kind=kind, + spec_json=spec_json, + result_json=result_json, + summary_text=summary, + code_version=args.code_version or None, + data_version=None, + job_id=job_id, + created_at=restored_at, + ) + repo = SqlAlchemyExperimentRepository(session) + repo.upsert(record) + session.commit() + got = repo.get(exp_id) + print( + f"\n✅ 已重建：{got.id} / {got.kind} / 体积 {len(got.result_json)} 字符 / " + f"code_version={got.code_version}" + ) + return 0 + finally: + session.close() + + +if __name__ == "__main__": + raise SystemExit(main()) \ No newline at end of file diff --git a/backend/app/cli/run_job.py b/backend/app/cli/run_job.py index f00deaf..51bd9d8 100644 --- a/backend/app/cli/run_job.py +++ b/backend/app/cli/run_job.py @@ -16,8 +16,17 @@ import sys def _apply_memory_limit() -> None: - """设置 RLIMIT_AS（Linux）。达到上限时分配抛 MemoryError → Job 归档 failed， - 而不是让内核 OOM 杀掉整机（8G Pi5 上保护同机其它服务）。""" + """尝试施加 RLIMIT_AS：达到上限时分配抛 MemoryError → Job 归档 failed， + 而不是让内核 OOM 杀掉整机（8G Pi5 上保护同机其它服务）。 + + 不按平台硬门禁：RLIMIT_AS 在 Linux / macOS 上都能设置，但**限额必须高于进程 + 当前的虚拟地址空间基线**，否则内核返回 EINVAL（CPython 表现为 + `ValueError: current limit exceeds maximum limit`）。macOS 上 Python 进程的 + 地址空间基线可达数十 GB（共享缓存 + malloc 区预留），故 `job.max_memory_gb: 6` + 在这类机器上会失败 —— 这是**配置阈值问题，不是平台不支持**，不应掩盖。 + + 设置失败不阻断 Job，但如实打印原因与后果（AGENT.md §24「未实现项须如实标注」）。 + """ try: gb = int(os.environ.get("QLIB_JOB_MEM_LIMIT_GB") or "") except (TypeError, ValueError): @@ -27,7 +36,15 @@ def _apply_memory_limit() -> None: limit = gb * 1024**3 import resource - resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) + try: + resource.setrlimit(resource.RLIMIT_AS, (limit, limit)) + except (ValueError, OSError) as exc: + print( + f"[run_job] 内存上限 {gb}GB 设置失败（{type(exc).__name__}: {exc}）；" + "本 Job 无内存隔离保护。若限额低于进程虚拟地址空间基线（macOS 常见），" + "可调大 config.yaml job.max_memory_gb 后重试。", + file=sys.stderr, + ) def main(argv: list[str] | None = None) -> int: diff --git a/backend/app/cli/sync.py b/backend/app/cli/sync.py index 3407e16..5f4fb98 100644 --- a/backend/app/cli/sync.py +++ b/backend/app/cli/sync.py @@ -8,6 +8,7 @@ uv run python -m app.cli.sync financial --all # 财务指标（增量） uv run python -m app.cli.sync financial --all --full # 财务指标（强制全量重拉） uv run python -m app.cli.sync verify --symbol 600519.SH # 新浪交叉验证 + uv run python -m app.cli.sync daily_basic --start 20200101 # 每日指标（股息率等） 增量与兜底： - daily --resume：从本地最新交易日续传（已有）；Tushare 失败时走新浪校验兜底， @@ -31,8 +32,10 @@ from datetime import date, datetime, timedelta from sqlalchemy import select from app.application.services.data_sync import ( + DailyBasicSyncer, DailySymbolResult, FinancialSymbolResult, + NameHistorySyncer, VerifiedDailySyncer, VerifiedFinancialSyncer, ) @@ -47,7 +50,9 @@ from app.infrastructure.persistence.sqlalchemy.repositories.index_impl import ( from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import ( SqlAlchemyAdjustFactorRepository, SqlAlchemyDailyBarRepository, + SqlAlchemyDailyBasicRepository, SqlAlchemyFinancialRepository, + SqlAlchemyStockNameHistoryRepository, SqlAlchemyStockRepository, SqlAlchemySyncLogRepository, SqlAlchemyTradingCalendarRepository, @@ -80,13 +85,44 @@ def _session_ctx(): def cmd_basic(args) -> int: + """股票基础信息。--include-delisted 同时拉取已退市/暂停上市（幸存者偏差修正）。""" + statuses = ["L", "P", "D"] if getattr(args, "include_delisted", False) else ["L"] with _session_ctx() as session: provider = _failover_provider(session) - stocks = provider.get_stock_basic() repo = SqlAlchemyStockRepository(session) - touched = repo.upsert_many(stocks) - session.commit() - print(f"[basic] 拉取 {len(stocks)} 只，落库 {touched} 条") + total = 0 + failed: list[str] = [] + for st in statuses: + try: + stocks = provider.get_stock_basic(st) + except DataSourceError as exc: + # failover 会把备用源（新浪）的 NotSupported 包装成 DataSourceError， + # 因此这里必须捕获 DataSourceError 而不是 DataSourceNotSupported， + # 否则 --include-delisted 在主源抖动时会整体抛栈退出（L 已写、P/D 静默缺失）。 + print(f"[error] list_status={st} 拉取失败：{exc}", file=sys.stderr) + failed.append(st) + continue + touched = repo.upsert_many(stocks) + session.commit() + total += touched + n_delisted = sum(1 for s in stocks if s.delist_date is not None) + print( + f"[basic] list_status={st} 拉取 {len(stocks)} 只（含 delist_date {n_delisted} 只），" + f"落库 {touched} 条" + ) + if len(statuses) > 1: + print( + "[basic] 已退市股票已入库；其历史行情需另行同步（否则回测仍无法使用）：\n" + " 请执行 sync daily --symbols <退市代码逗号分隔> --start 20200101" + ) + print(f"[basic] 合计落库 {total} 条") + if failed: + print( + f"[error] 以下 list_status 拉取失败：{','.join(failed)}；" + "退市股缺失会让回测重新出现幸存者偏差，请重跑", + file=sys.stderr, + ) + return 1 return 0 @@ -104,6 +140,48 @@ def cmd_calendar(args) -> int: return 0 +def cmd_namechange(args) -> int: + """股票名称变更历史同步（时点 ST 判定依据；按自然年分片）。""" + start = _parse_day(args.start) if args.start else date(1990, 1, 1) + end = _parse_day(args.end) if args.end else date.today() + if start > end: + print("[error] --start 不能晚于 --end", file=sys.stderr) + return 2 + + with _session_ctx() as session: + audit_repo = SqlAlchemySyncLogRepository(session) + primary = TushareProvider(token=get_settings().tushare_token) + repo = SqlAlchemyStockNameHistoryRepository(session) + syncer = NameHistorySyncer(primary=primary, repo=repo, audit=audit_repo.add) + + total_span = (end.year - start.year) + 1 + print(f"[namechange] {start}~{end} 共 {total_span} 个年度分片") + ok = failed = written = fetched = 0 + t0 = time.time() + + def _on(idx: int, total: int, chunk_start: date) -> None: + _progress(idx, total, t0, f"{chunk_start.year} 年") + + results = syncer.sync_range(start, end, on_progress=_on) + for res in results: + if res.status == "ok": + ok += 1 + written += res.rows_written + fetched += res.rows_fetched + else: + failed += 1 + for note in res.notes: + print(f"\n[warn] {res.start}~{res.end}: {note}", file=sys.stderr) + session.commit() + lo, hi = repo.namechange_dates() + print( + f"\n[namechange] 分片成功 {ok} / 失败 {failed}；" + f"拉取 {fetched} 行、写入 {written} 行；" + f"本地生效起点 {lo} ~ {hi}" + ) + return 1 if failed else 0 + + def _symbols_of(args) -> list[str]: if getattr(args, "all", False): with _session_ctx() as session: @@ -382,11 +460,102 @@ def cmd_export(args) -> int: return 0 +_DAILY_BASIC_DEFAULT_START = date(2020, 1, 1) + + +def cmd_daily_basic(args) -> int: + """每日指标同步（按交易日整表；新浪不支持 → 失败如实记录，不留静默缺口）。""" + start = _parse_day(args.start) if args.start else _DAILY_BASIC_DEFAULT_START + end = _parse_day(args.end) if args.end else date.today() + if start > end: + print("[error] --start 不能晚于 --end", file=sys.stderr) + return 2 + + with _session_ctx() as session: + audit_repo = SqlAlchemySyncLogRepository(session) + primary = TushareProvider(token=get_settings().tushare_token) + repo = SqlAlchemyDailyBasicRepository(session) + syncer = DailyBasicSyncer(primary=primary, repo=repo, audit=audit_repo.add) + + if args.full: + cal = SqlAlchemyTradingCalendarRepository(session) + days = [d.calendar_date for d in cal.list_range(start, end) if d.is_open] + else: + days = repo.missing_dates(start, end) + total = len(days) + if total == 0: + print(f"[daily_basic] {start}~{end} 无待补交易日（本地已完整）") + return 0 + print(f"[daily_basic] {start}~{end} 待同步 {total} 个交易日") + + ok = failed = written = 0 + failures: list[date] = [] + t0 = time.time() + for idx, day in enumerate(days, start=1): + try: + res = syncer.sync_day(day) + except DataSourceError as exc: + print(f"\n[error] 第 {idx}/{total} 日 {day} 权限/凭证故障，中止：{exc}", file=sys.stderr) + session.commit() + return 1 + if res.status == "ok": + ok += 1 + written += res.rows_written + else: + failed += 1 + failures.append(day) + _progress(idx, total, t0, f"{day} 行数={res.rows_fetched}") + if idx % 20 == 0 or idx == total: + session.commit() # 分批提交：中断时已完成的部分保持有效 + if args.sleep: + time.sleep(args.sleep) + session.commit() + + span = time.time() - t0 + print( + f"\n[daily_basic] 完成：成功 {ok} 日 / 失败 {failed} 日，累计写入 {written} 行，" + f"耗时 {span:.1f}s" + ) + if failures: + print( + "[daily_basic] 失败交易日（可重跑本命令补齐）：" + + ", ".join(d.isoformat() for d in failures[:20]) + + (" …" if len(failures) > 20 else ""), + file=sys.stderr, + ) + return 1 + return 0 + + +def _progress(idx: int, total: int, t0: float, extra: str = "") -> None: + """单行进度条（同步长任务可读性）。""" + elapsed = time.time() - t0 + rate = idx / elapsed if elapsed > 0 else 0.0 + eta = (total - idx) / rate if rate > 0 else 0.0 + pct = idx / total * 100 if total else 100.0 + end = "\n" if idx >= total else "\r" + print( + f" 进度 {idx}/{total} ({pct:5.1f}%) 已用 {elapsed:6.1f}s 预计剩余 {eta:6.1f}s {extra}", + end=end, + flush=True, + ) + + def build_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser(prog="app.cli.sync", description="Tushare 数据同步 CLI") sub = parser.add_subparsers(dest="command", required=True) + p_nc = sub.add_parser("namechange", help="同步股票名称变更历史（时点 ST 判定）") + p_nc.add_argument("--start", help="起始日 YYYYMMDD（默认 19900101，覆盖全历史）") + p_nc.add_argument("--end", help="结束日 YYYYMMDD（默认今天）") + p_nc.set_defaults(func=cmd_namechange) + p_basic = sub.add_parser("basic", help="同步股票基础信息") + p_basic.add_argument( + "--include-delisted", + action="store_true", + help="同时拉取已退市(D)与暂停上市(P)，填充 delist_date（幸存者偏差修正）", + ) p_basic.set_defaults(func=cmd_basic) p_cal = sub.add_parser("calendar", help="同步交易日历") @@ -394,6 +563,25 @@ def build_parser() -> argparse.ArgumentParser: p_cal.add_argument("--end", required=True, help="YYYYMMDD") p_cal.set_defaults(func=cmd_calendar) + p_db = sub.add_parser( + "daily_basic", + help="同步每日指标（估值/股息率/市值，按交易日整表；新浪不支持本接口）", + ) + p_db.add_argument("--start", default="20200101", help="YYYYMMDD（默认 20200101）") + p_db.add_argument("--end", default="", help="YYYYMMDD（默认今天）") + p_db.add_argument( + "--full", + action="store_true", + help="忽略本地已有日期，重拉区间内全部开市日（默认只补缺失日）", + ) + p_db.add_argument( + "--sleep", + type=float, + default=0, + help="每个交易日请求间隔秒数（限速时加大，如 0.2 或 1）", + ) + p_db.set_defaults(func=cmd_daily_basic) + p_daily = sub.add_parser("daily", help="同步日线与复权因子（Tushare 失败 → 新浪校验兜底补缺）") p_daily.add_argument("--symbols", default="", help="600519.SH,000001.SZ") p_daily.add_argument("--all", action="store_true", help="遍历 stock 表全部股票") diff --git a/backend/app/core/config.py b/backend/app/core/config.py index 5b9dea1..3da7f2e 100644 --- a/backend/app/core/config.py +++ b/backend/app/core/config.py @@ -75,11 +75,36 @@ class Settings: job_mode: str job_memory_limit_gb: int job_max_concurrent: int + # research.archive_curve_limit：归档个股收益曲线数量上限；None = 完整存档（默认） + research_archive_curve_limit: int | None + # research.archive_max_chars：归档结果 JSON 的字节预算（MEDIUMTEXT 上限 16MB 的余量） + research_archive_max_chars: int | None config_path: Path = CONFIG_PATH env_path: Path = ENV_PATH project_root: Path = PROJECT_ROOT +def _env_or(value_env: str, configured: object) -> object: + """环境变量优先于 config.yaml（空串视为未设置）。""" + env = os.environ.get(value_env) + if env not in (None, ""): + return env + return configured + + +def _optional_int(value: object) -> int | None: + """可选整数配置：None / 空串 / 非法值都视为「未配置」（返回 None）。 + + 用于 `research.archive_curve_limit`：None 表示不截断（完整存档）。 + """ + if value is None or value == "": + return None + try: + return int(value) # type: ignore[arg-type] + except (TypeError, ValueError): + return None + + def _resolve_storage_dirs(cfg: dict) -> dict[str, Path]: storage_cfg = _deep(cfg, "storage") or {} return { @@ -143,6 +168,50 @@ def _build_mysql_url(mysql: dict | None) -> str | None: return f"mysql+pymysql://{auth}@{host}:{port}/{db}?charset={charset}" +# 被禁止的数据库目标主机（用户明确要求：禁止使用 192.168.1.10 作为 DB 目标）。 +# 可用环境变量 QLIB_FORBIDDEN_DB_HOSTS 覆盖（逗号分隔；设为空串表示不做限制）。 +_FORBIDDEN_DB_HOSTS_ENV = "QLIB_FORBIDDEN_DB_HOSTS" +_FORBIDDEN_DB_HOSTS_DEFAULT = "192.168.1.10" + + +def _forbidden_db_hosts() -> set[str]: + raw = os.environ.get(_FORBIDDEN_DB_HOSTS_ENV) + if raw is None: + raw = _FORBIDDEN_DB_HOSTS_DEFAULT + return {h.strip().lower() for h in raw.split(",") if h.strip()} + + +def assert_db_target_allowed(database_url: str) -> None: + """拒绝把数据库指向被禁主机；命中即**抛错**，不允许「起得来但连错库」。 + + 为什么必须硬失败（AGENT.md §7 不静默）：库目标错了不会有任何报错或界面异常—— + 回测结果、实验归档、策略、信号会**安静地读写另一台机器上的数据**， + 而用户以为看的是本机库；这类错误事后极难发现（数据看似正常，只是「不对」）。 + 因此在这里直接失败并说明原因与修改方法。 + + 只拦主机名/IP 精确匹配（不做网段推断）：`192.168.1.10` 与写进 userinfo 的 + 同名字符串、SQLite 路径都不会误判；IPv6/带方括号的地址会去掉括号后比较。 + """ + forbidden = _forbidden_db_hosts() + if not forbidden or not database_url: + return + try: + from sqlalchemy.engine import make_url + + url = make_url(database_url) + except Exception: # noqa: BLE001 —— 解析失败交给 SQLAlchemy 自己报错，这里不抢 + return + host = (url.host or "").lower().strip("[]") + if host and host in forbidden: + raise RuntimeError( + f"数据库目标 {host} 已被禁止（{_FORBIDDEN_DB_HOSTS_ENV}=" + f"{','.join(sorted(forbidden))}）。" + "本项目只允许使用**本机 MariaDB**：把 config.yaml 的 database.mysql.host " + "设为 127.0.0.1，或清空 .env 的 DATABASE_URL 让它走本机配置。" + "确需临时放开请显式设置 QLIB_FORBIDDEN_DB_HOSTS（设为空串表示不限制）。" + ) + + @lru_cache def get_settings() -> Settings: """加载并缓存 Settings。config / env 路径可通过参数覆盖以便测试。""" @@ -164,6 +233,9 @@ def get_settings() -> Settings: _deep(cfg, "database.mysql") or {} ) or default_url + # 用户约束：禁止把数据写向 192.168.1.10（远端库）——命中直接抛错，不静默降级 + assert_db_target_allowed(database_url) + migrations_rel = _deep(cfg, "database.migrations_dir") or ( "app/infrastructure/persistence/migrations" ) @@ -183,6 +255,16 @@ def get_settings() -> Settings: except (TypeError, ValueError): job_max_concurrent = 2 + # 归档（research 段）：曲线数量上限（None = 完整存档）+ 结果 JSON 字节预算 + research_cfg = _deep(cfg, "research") + research_cfg = research_cfg if isinstance(research_cfg, dict) else {} + archive_curve_limit = _optional_int( + _env_or("QLIB_ARCHIVE_CURVE_LIMIT", research_cfg.get("archive_curve_limit")) + ) + archive_max_chars = _optional_int( + _env_or("QLIB_ARCHIVE_MAX_CHARS", research_cfg.get("archive_max_chars")) + ) + return Settings( app_name=str(_deep(cfg, "app.name") or "qlib-platform"), app_version=str(_deep(cfg, "app.version") or "0.1.0"), @@ -202,4 +284,6 @@ def get_settings() -> Settings: job_mode=os.environ.get("JOB_MODE") or job_cfg.get("mode") or "subprocess", job_memory_limit_gb=job_memory_limit_gb, job_max_concurrent=job_max_concurrent, + research_archive_curve_limit=archive_curve_limit, + research_archive_max_chars=archive_max_chars, ) diff --git a/backend/app/domain/entities/market.py b/backend/app/domain/entities/market.py index 082c9d6..e50eae9 100644 --- a/backend/app/domain/entities/market.py +++ b/backend/app/domain/entities/market.py @@ -17,6 +17,29 @@ from pydantic import BaseModel, ConfigDict, Field PRICE_PLACES = Decimal("0.0001") AMOUNT_PLACES = Decimal("0.01") +# ---- 研究面板数值列白名单（单一事实来源） ---- +# 说明：研究装配（quant 层）与持久化列裁剪（infrastructure 层）必须用同一套列名， +# 否则会出现「因子需要某列、仓储却拒绝」的隐蔽不一致。故在此统一定义，两处引用。 +# 只影响数值列；symbol / trade_date 恒返回。 +DAILY_BAR_NUMERIC_FIELDS: tuple[str, ...] = ("open", "high", "low", "close", "volume", "amount") +DAILY_BASIC_NUMERIC_FIELDS: tuple[str, ...] = ( + "close", + "turnover_rate", + "volume_ratio", + "pe", + "pe_ttm", + "pb", + "ps", + "ps_ttm", + "dv_ratio", + "dv_ttm", + "total_share", + "float_share", + "free_share", + "total_mv", + "circ_mv", +) + class Stock(BaseModel): """A 股基础信息。symbol 统一为 Tushare 风格，如 600519.SH。""" @@ -78,6 +101,73 @@ class AdjustFactor(BaseModel): factor: Decimal +class DailyBasic(BaseModel): + """每日指标快照（Tushare daily_basic）—— 估值 / 股息率 / 市值。 + + 时点性说明（防未来函数，AGENT.md §9）： + - 本表每一行都是**该交易日收盘后**即可得的横截面指标（dv_ratio 由 + 「过去 12 个月现金分红 / 当日总市值」逐日重算），属时点值； + - 研究侧一律按 trade_date <= as_of_date 取值，不存在未来信息。 + + 列语义： + - dv_ratio 股息率(%)：近 12 个月现金分红 / 总市值 × 100 + - dv_ttm 股息率(TTM,%)：滚动 12 个月口径 + - 两者均可能因**特别分红**出现畸高值（实测 600738 在 2020-01-02 为 37.2%）， + 使用时建议配合上限过滤。 + """ + + symbol: str + trade_date: date + close: Decimal | None = Field(default=None, description="当日收盘价（不复权，与 stock_daily 一致）") + turnover_rate: Decimal | None = Field(default=None, description="换手率(%)") + volume_ratio: Decimal | None = Field(default=None, description="量比") + pe: Decimal | None = None + pe_ttm: Decimal | None = None + pb: Decimal | None = None + ps: Decimal | None = None + ps_ttm: Decimal | None = None + dv_ratio: Decimal | None = Field(default=None, description="股息率(%)，近 12 个月现金分红/总市值") + dv_ttm: Decimal | None = Field(default=None, description="股息率 TTM(%)") + total_share: Decimal | None = Field(default=None, description="总股本（万股）") + float_share: Decimal | None = Field(default=None, description="流通股本（万股）") + free_share: Decimal | None = Field(default=None, description="自由流通股本（万股）") + total_mv: Decimal | None = Field(default=None, description="总市值（万元）") + circ_mv: Decimal | None = Field(default=None, description="流通市值（万元）") + source: str = Field(default="tushare", description="tushare | sina（新浪不提供本接口）") + + +class StockNameHistory(BaseModel): + """股票名称变更历史（Tushare namechange）—— 时点 ST / 风险警示判定的依据。 + + 为什么需要它（实测背景）：`stock.name` 只是**最新名称快照**，用它做 + `universe.exclude_st` 会把「曾为高股息、后来才变 ST/退市」的标的在**整段历史**里 + 都排除掉 —— 而那正是「股息陷阱」样本。实测 `600565.SH` 2020 年叫「迪马股份」 + （dv_ratio 7.9%，当年高股息候选），2024-05-06 才变「ST迪马」，用最新名称判定 + 会在 2020 年就把它排除，导致高股息回测收益被高估（对照组实测约 3.70pp）。 + + 一行 = 一个「名称生效区间」： + - `name` 在 `[start_date, end_date]` 内有效（`end_date` 为空表示至今有效） + - `change_reason` 为 tushare 口径：ST / *ST / 撤销ST / 撤销*ST / 从ST变为*ST / 其他 + - 时点取值按**生效区间**：`start_date <= as_of <= end_date`（实现口径，无前视： + 名称自 `start_date` 起即对市场可见）。`ann_date` 为公告日，仅作留痕/审计， + **不参与**判定 —— 实测数据中 `ann_date` 恒早于或等于 `start_date`， + 若改用「公告即改名」会让 `ann_date` 为空的记录整体丢失。 + """ + + symbol: str + name: str + start_date: date + end_date: date | None = None + ann_date: date | None = None + change_reason: str | None = None + source: str = "tushare" + + @property + def is_risk_warned(self) -> bool: + """该区间名称是否含风险警示（ST / *ST）。""" + return "ST" in self.name.upper() + + class FinancialIndicator(BaseModel): """核心财务指标（快照）。 diff --git a/backend/app/domain/entities/research.py b/backend/app/domain/entities/research.py index 47dc3fa..dcea6ae 100644 --- a/backend/app/domain/entities/research.py +++ b/backend/app/domain/entities/research.py @@ -42,10 +42,90 @@ class FactorSpec(BaseModel): weight: float = Field(default=1.0, gt=0) -class SelectionSpec(BaseModel): - """选股方式。MVP：按加权因子得分取 Top N 等权。""" +class ConditionSpec(BaseModel): + """结构化选股条件（回测与选股共用）。 - top_n: int = Field(default=30, ge=1, le=1000) + 字段域： + - static.*：股票基础字段（industry / market / area / exchange / status…） + - 行情/技术字段：close / ma20 / ma60 / volume 及全部已注册因子名（momentum_60 等）， + 以及每日指标列（dv_ratio / dv_ttm / pe / pb / total_mv …） + - fundamental.*：财务字段（eps / roe / total_revenue / net_profit / gross_margin）， + 仅取 announce_date <= as_of 的最新已公告值（防未来函数） + + 右操作数取 value（字面量）或 ref（另一字段名），二者二选一。 + + 定义位置说明：本模型被 ResearchSpec（回测）与 SelectionQuery（选股）共用， + 故落在 research.py（被 selection.py 依赖的低层模块），selection.py 再 re-export， + 避免循环导入。 + """ + + field: str + op: str = Field(pattern="^(gt|gte|lt|lte|eq|ne|in|not_in)$") + value: float | int | str | list | None = None + ref: str | None = None # 与另一字段比较（如 close vs ma60） + + @model_validator(mode="after") + def _require_operand(self) -> ConditionSpec: + if self.value is None and self.ref is None: + raise ValueError("value 与 ref 必须提供一个") + if self.value is not None and self.ref is not None: + raise ValueError("value 与 ref 只能提供一个") + if self.op in ("in", "not_in") and not isinstance(self.value, list): + raise ValueError("in/not_in 的 value 必须是列表") + return self + + +class SelectionSpec(BaseModel): + """选股方式（两级截断）。 + + 口径（用户案例「选 n 只 → 持仓前 x 只」）： + - `top_n` = n：**候选池**大小。universe ∩ conditions 过滤后，按复合因子分降序取前 n + 只 → 这就是「择股条件选出来的股数」（写入 selection_history）。 + - `hold_top_x` = x：**实际持仓数**，取候选池前 x 只等权。x 必须 ≤ n； + 另受「池内实际可买股票数」约束（过滤/缺数据会让实际池子小于 n）。 + None → 等于 top_n（此时与旧行为一致：选出多少就持多少）。 + + `allow_substitute` 与 `defer_buy` 决定「买不进」时的处理（两者互斥，只能选一个）： + - `allow_substitute=True`（**默认**，保持历史语义不变）：从 n 名**之外**继续往下找 + 可买标的补足 x 只 —— 引擎既有行为，见 v3 §20.3 的 Signal↔Fill 测试。 + - `defer_buy=True`（本项目「只买选出来的前 x 只」口径，推荐显式开启）： + **不替补**，把这只股票的买单**顺延到之后第一个可成交的交易日**（涨停/停牌解除后 + 按当日收盘价买入）；到下一次调仓仍未成交则作废，未投入资金留作现金。 + - 两者都 False：意图被拒后直接放弃，资金留现金（不替补也不顺延）。 + + 默认值刻意保持「向后兼容」：既有 Strategy / Experiment 的语义不因本次扩展而静默改变 + （AGENT.md §35）。高股息案例在前端与 spec 中显式设置 defer_buy=True。 + """ + + top_n: int = Field(default=30, ge=1, le=1000, description="n：候选池大小") + hold_top_x: int | None = Field( + default=None, ge=1, le=1000, description="x：实际持仓数；None → = top_n" + ) + allow_substitute: bool = Field( + default=True, + description="True（默认，历史语义）：从 n 名之外替补补足；False：不引入计划外标的", + ) + defer_buy: bool = Field( + default=False, + description="True：买不进（涨停/停牌）时顺延到之后首个可成交日的收盘买入", + ) + + @model_validator(mode="after") + def _check_x_le_n(self) -> SelectionSpec: + if self.hold_top_x is not None and self.hold_top_x > self.top_n: + raise ValueError( + f"hold_top_x（持仓 x={self.hold_top_x}）不能大于 top_n（候选池 n={self.top_n}）" + ) + if self.allow_substitute and self.defer_buy: + raise ValueError( + "allow_substitute=True（往下替补）与 defer_buy=True（顺延买入）语义互斥，只能选一个" + ) + return self + + @property + def x(self) -> int: + """实际持仓目标数（未显式给 x 时等于 n）。""" + return self.hold_top_x if self.hold_top_x is not None else self.top_n class PortfolioSpec(BaseModel): @@ -63,14 +143,22 @@ class PortfolioSpec(BaseModel): class CostSpec(BaseModel): - """交易成本模型（单边比例）。 + """交易成本模型。 - buy = commission + slippage；sell = commission + stamp_tax + slippage。 + 买：commission（≥ min_commission）+ slippage + 卖：commission（≥ min_commission）+ stamp_tax + slippage + + `min_commission` 为**单笔最低佣金**（A 股常见 5 元）。默认 0.0 = 不启用， + 以保持既有回测数值不变（AGENT.md §35）；高股息等实盘贴近场景建议显式设 5.0。 + 注意：最低佣金对**小额单**影响显著，x 越多、单笔越小，成本占比越高。 """ commission_rate: float = Field(default=0.0003, ge=0, le=0.01) stamp_tax_rate: float = Field(default=0.0005, ge=0, le=0.01) slippage_rate: float = Field(default=0.001, ge=0, le=0.05) + min_commission: float = Field( + default=0.0, ge=0, le=100.0, description="单笔最低佣金（元）；0 = 不启用" + ) benchmark: str = Field(default="000300.SH", description="对照基准指数代码") @@ -80,12 +168,37 @@ class ResearchSpec(BaseModel): type: str = Field(default="backtest", pattern="^(factor_test|backtest)$") universe: UniverseSpec = UniverseSpec() price_adjustment: str = Field( - default="none", pattern="^(none|qfq)$", - description="研究行情口径：none 不复权（默认）/ qfq 前复权（result 与 config_snapshot 中显式）", + default="none", pattern="^(none|qfq|hfq)$", + description=( + "研究行情口径：none 不复权（默认）/ qfq 前复权 / hfq 后复权。" + "qfq/hfq 基于 adjust_factor 折算（v3 §20.5）；结果与 config_snapshot 中显式记录。" + ), ) factors: list[FactorSpec] = Field(min_length=1) + conditions: list[ConditionSpec] = Field( + default_factory=list, + description=( + "选股过滤条件（AND，可选）：universe 之后、因子排序之前执行。" + "字段域同 SelectionQuery.conditions（static.* / 行情列 / 已注册因子 / " + "fundamental.*），回测与 /api/selections 共用同一求值器（v2 §25 一致性）。" + ), + ) selection: SelectionSpec = SelectionSpec() rebalance: str = Field(default="monthly", pattern="^(weekly|monthly)$") + selection_interval_months: int | None = Field( + default=None, ge=1, le=60, + description=( + "m：择股间隔（月）。None → 每次调仓都重新择股（等价于 rebalance 频率）。" + "择股日 = 起始月锚定，月序号 % m == 0 的月份的首个交易日。" + ), + ) + rebalance_interval_months: int | None = Field( + default=None, ge=1, le=60, + description=( + "y：调仓间隔（月）。None → 等于 selection_interval_months（未给则按 rebalance 频率）。" + "y < m 时池子在下一次择股前保持不变（结果中会标注池子陈旧）。" + ), + ) period: tuple[date, date] costs: CostSpec = CostSpec() portfolio: PortfolioSpec = PortfolioSpec() @@ -105,6 +218,33 @@ class ResearchSpec(BaseModel): raise ValueError("factors 存在重复因子名") return self + @model_validator(mode="after") + def _check_intervals(self) -> ResearchSpec: + m = self.selection_interval_months + y = self.rebalance_interval_months + # 未给 m 却给了 y：语义不完整（y 无锚点可依）→ 明确拒绝而非猜 + if m is None and y is not None and y != 1: + raise ValueError( + "只给了 rebalance_interval_months(y) 而没给 selection_interval_months(m)：" + "请同时给出 m，否则无法确定择股日集合" + ) + if m is not None and y is not None and y < m and y != 1: + # 允许但不静默：池子会在多个调仓日复用（陈旧），交由结果 unimplemented 标注 + return self + return self + + @property + def effective_selection_months(self) -> int | None: + """实际择股间隔（月）；None 表示「每次调仓都择股」。""" + return self.selection_interval_months + + @property + def effective_rebalance_months(self) -> int | None: + """实际调仓间隔（月）；None 表示按 rebalance 频率（周/月）。""" + if self.rebalance_interval_months is not None: + return self.rebalance_interval_months + return self.selection_interval_months + # ---------- 回测结果 ---------- @@ -145,6 +285,7 @@ class Trade(BaseModel): entry_date: date exit_date: date symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") entry_price: float exit_price: float return_pct: float @@ -153,6 +294,7 @@ class Trade(BaseModel): class Position(BaseModel): date: date symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") weight: float @@ -161,6 +303,7 @@ class RankedPick(BaseModel): date: date symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") rank: int score: float @@ -170,16 +313,41 @@ class ActionRecord(BaseModel): signal=BUY/SELL（策略意图）；filled=是否实际成交；reject_reason 给出未成交原因 （涨停/跌停/无价/现金不足等）。fills = [a for a in signal_history if a.filled]。 + + `name` 为展示增强字段：由服务层按股票池统一回填（未命中则为 None）， + 引擎自身不感知名称 —— 引擎只处理 symbol，保持纯行情计算职责。 """ date: date symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") signal: str = Field(pattern="^(BUY|SELL)$") filled: bool reject_reason: str | None = None price: float | None = Field(default=None, description="成交价（fill）或意图参考价") +class SymbolCurve(BaseModel): + """个股收益率趋势曲线 + 该股买卖点标注（回测结果可视化用）。 + + `points[].value` 语义：该股**持仓期间**的累计收益率（%，以建仓日收盘为 0% 基准， + 按日复利）。只在该股被持有的交易日落点（未持有期间不落点，以压缩结果体积）； + 建仓当日会补一个基准点，保证买卖点标注总能在曲线上取到数值。多段持仓以累计值 + 连乘衔接，读图时以 marks 中的 BUY/SELL 区分各段持仓区间。 + + `marks` 为该股实际成交（BUY/SELL fill）的日期与价格，与 `signal_history` + 中 filled=True 的记录一致（v3 §20.3 的成交口径）。 + """ + + symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") + points: list[CurvePoint] = Field(default_factory=list) + marks: list[ActionRecord] = Field(default_factory=list) + final_return_pct: float = Field( + default=0.0, description="该股持仓期累计收益率（%，多段持仓连乘）" + ) + + class BacktestResult(BaseModel): """标准化回测结果（ARCHITECTURE §14）。前端只依赖该结构。""" @@ -199,12 +367,27 @@ class BacktestResult(BaseModel): fills: list[ActionRecord] = Field( default_factory=list, description="实际成交（signal_history 中 filled=True 的子集）" ) + symbol_curves: list[SymbolCurve] = Field( + default_factory=list, + description="个股收益率曲线 + 买卖点标注（按期末收益绝对值降序，体积可控）", + ) turnover_pct: float unimplemented: list[str] = Field( default_factory=list, description="本结果中未建模的约束（AGENT §24：必须显式标注，禁止假装支持）", ) config_snapshot: dict = Field(default_factory=dict, description="复现用完整配置快照") + archive_meta: dict = Field( + default_factory=dict, + description=( + "归档元数据（由 experiment_archive 在落库时写入）：curves_stored / " + "curves_total / truncated / budget_chars / budget_bytes / result_chars / " + "result_bytes。用于说明归档是否因体积预算被裁剪（AGENT §24 不静默）" + ), + ) + + +SymbolCurve.model_rebuild() # ---------- 因子测试结果 ---------- @@ -289,3 +472,23 @@ class ExperimentRecord(BaseModel): data_version: str | None = None job_id: str | None = None created_at: datetime | None = None + + +class ExperimentSummary(BaseModel): + """归档列表项（**不含 result_json**）。 + + 列表接口一次可能返回上百条归档，而 `result_json` 是 MEDIUMTEXT（完整存档后 + 单条可达数 MB）：为避免把上百 MB 拉进内存，仓储的列表查询只取元数据列， + `result_bytes` 由 SQL 的字符长度函数（MySQL CHAR_LENGTH / SQLite length） + 在库侧算出，不取回大字段本身。 + """ + + id: str + kind: str + spec_json: str + summary_text: str | None = None + code_version: str | None = None + data_version: str | None = None + job_id: str | None = None + created_at: datetime | None = None + result_bytes: int = 0 # 归档 JSON 的字符数（SQL 侧计算，不拉大字段） diff --git a/backend/app/domain/entities/selection.py b/backend/app/domain/entities/selection.py index 8bf6d46..5a1c3fe 100644 --- a/backend/app/domain/entities/selection.py +++ b/backend/app/domain/entities/selection.py @@ -18,7 +18,7 @@ from datetime import date, datetime from pydantic import BaseModel, Field, field_validator, model_validator -from app.domain.entities.research import FactorSpec, UniverseSpec +from app.domain.entities.research import ConditionSpec, FactorSpec, UniverseSpec # noqa: F401 class SelectionQuery(BaseModel): @@ -26,8 +26,11 @@ class SelectionQuery(BaseModel): universe: UniverseSpec = UniverseSpec() price_adjustment: str = Field( - default="none", pattern="^(none|qfq)$", - description="行情口径：none 不复权（默认）/ qfq 前复权（结果 config_snapshot 中显式）", + default="none", pattern="^(none|qfq|hfq)$", + description=( + "行情口径：none 不复权（默认）/ qfq 前复权 / hfq 后复权（按 adjust_factor 折算）。" + "与回测（ResearchSpec.price_adjustment）同一口径域，结果 config_snapshot 中显式记录" + ), ) # 研究时点：None → 引擎用 <= 今天最近可用交易日；显式给历史日期即做历史选股 as_of: date | None = Field( @@ -64,37 +67,21 @@ class SelectionQuery(BaseModel): return self -class ConditionSpec(BaseModel): - """结构化选股条件（M6.2 使用）。 - - field 域： - - static.*：股票基础字段（industry / market / area / exchange / status…） - - 行情/技术字段：close / ma20 / ma60 / volume 及全部已注册因子名（momentum_60 等） - - fundamental.*：财务字段（eps / roe / total_revenue / net_profit / gross_margin）， - 仅取 announce_date <= as_of 的最新已公告值（防未来函数） - 右操作数取 value（字面量）或 ref（另一字段名），二者二选一。 - """ - - field: str - op: str = Field(pattern="^(gt|gte|lt|lte|eq|ne|in|not_in)$") - value: float | int | str | list | None = None - ref: str | None = None # 与另一字段比较（如 close vs ma60） - - @model_validator(mode="after") - def _require_operand(self) -> ConditionSpec: - if self.value is None and self.ref is None: - raise ValueError("value 与 ref 必须提供一个") - if self.value is not None and self.ref is not None: - raise ValueError("value 与 ref 只能提供一个") - if self.op in ("in", "not_in") and not isinstance(self.value, list): - raise ValueError("in/not_in 的 value 必须是列表") - return self +# ConditionSpec 的实体定义已上移到 research.py（ResearchSpec 回测条件与 +# SelectionQuery 选股条件共用同一模型）。此处 re-export：`selection.ConditionSpec` +# 就是 research.ConditionSpec 这**同一个类对象**，因此两侧 isinstance 判断一致。 +# （见文件顶部 import 处的引用） class SelectionCandidate(BaseModel): - """单只候选股（v2 §14.3/§21.1）。""" + """单只候选股（v2 §14.3/§21.1）。 + + `name` 为展示增强字段（可选默认 None）：由 SelectionService 用已装配的股票池 + 统一回填，引擎/选股算法本身不感知名称 —— 避免在每个出口零散 join。 + """ symbol: str + name: str | None = Field(default=None, description="股票名称（展示用）") rank: int score: float factor_values: dict[str, float] = Field(default_factory=dict) diff --git a/backend/app/domain/entities/strategy.py b/backend/app/domain/entities/strategy.py index 728ea69..7cba792 100644 --- a/backend/app/domain/entities/strategy.py +++ b/backend/app/domain/entities/strategy.py @@ -12,6 +12,7 @@ from datetime import date, datetime from pydantic import BaseModel, Field from app.domain.entities.research import ( + ConditionSpec, CostSpec, FactorSpec, PortfolioSpec, @@ -26,10 +27,19 @@ class StrategyDefinition(BaseModel): description: str = "" spec_type: str = Field(default="backtest", pattern="^(backtest|factor_test)$") universe: UniverseSpec = UniverseSpec() - price_adjustment: str = Field(default="none", pattern="^(none|qfq)$") + price_adjustment: str = Field(default="none", pattern="^(none|qfq|hfq)$") factors: list[FactorSpec] = Field(min_length=1) selection: SelectionSpec = SelectionSpec() rebalance: str = Field(default="monthly", pattern="^(weekly|monthly)$") + conditions: list[ConditionSpec] = Field( + default_factory=list, description="选股过滤条件（与 ResearchSpec.conditions 一致）" + ) + selection_interval_months: int | None = Field( + default=None, ge=1, le=60, description="m：择股间隔（月）" + ) + rebalance_interval_months: int | None = Field( + default=None, ge=1, le=60, description="y：调仓间隔（月）；缺省 = m" + ) costs: CostSpec = CostSpec() portfolio: PortfolioSpec = PortfolioSpec() version: str = "1" @@ -44,8 +54,11 @@ class StrategyDefinition(BaseModel): universe=self.universe, price_adjustment=self.price_adjustment, factors=self.factors, + conditions=self.conditions, selection=self.selection, rebalance=self.rebalance, + selection_interval_months=self.selection_interval_months, + rebalance_interval_months=self.rebalance_interval_months, period=period, costs=self.costs, portfolio=self.portfolio, diff --git a/backend/app/domain/providers.py b/backend/app/domain/providers.py index 6031c9d..f9e0bea 100644 --- a/backend/app/domain/providers.py +++ b/backend/app/domain/providers.py @@ -13,8 +13,10 @@ from app.domain.entities.index import IndexWeight from app.domain.entities.market import ( AdjustFactor, DailyBar, + DailyBasic, FinancialIndicator, Stock, + StockNameHistory, TradingCalendar, ) @@ -30,7 +32,13 @@ class MarketDataProvider(Protocol): name: str - def get_stock_basic(self) -> list[Stock]: ... + def get_stock_basic(self, list_status: str = "L") -> list[Stock]: + """股票基础信息。list_status: L=上市 / D=退市 / P=暂停上市（tushare 口径）。 + + 默认 "L" 保持既有行为不变；研究侧的**幸存者偏差**修正依赖 "D"（已退市） + ——退市股的历史行情与 delist_date 缺失会让回测系统性高估收益。 + """ + ... def get_trade_cal(self, start: date, end: date) -> list[TradingCalendar]: ... @@ -56,3 +64,23 @@ class MarketDataProvider(Protocol): 供 index_weight 同步与历史成分 Universe（v3 §9）。""" + def get_name_changes(self, start: date, end: date) -> list[StockNameHistory]: + """区间内全市场股票名称变更（时点 ST / 风险警示判定依据）。 + + 仅 Tushare 提供；备用源应抛 `DataSourceNotSupported`（不得静默返回空列表， + 否则名称历史会「看起来同步成功但一条没有」，导致时点 ST 静默降级）。 + """ + ... + + def get_daily_basic(self, trade_date: date) -> list[DailyBasic]: + """单交易日全市场每日指标（估值 / 股息率 / 市值）。 + + 按**交易日**整表拉取（Tushare daily_basic 支持 trade_date 参数一次返回全市场）， + 幂等键 (symbol, trade_date)。 + + 实现约定： + - 只能返回该 trade_date 当天已可得的值（dv_ratio 为时点值，天然无未来函数）； + - 不支持本接口的数据源（如新浪）必须抛 DataSourceNotSupported， + 禁止返回空列表冒充成功（AGENT.md §7：禁止静默切换）。 + """ + diff --git a/backend/app/domain/repositories/jobs.py b/backend/app/domain/repositories/jobs.py index 3f1f87b..99f8371 100644 --- a/backend/app/domain/repositories/jobs.py +++ b/backend/app/domain/repositories/jobs.py @@ -4,7 +4,7 @@ from __future__ import annotations from typing import Protocol -from app.domain.entities.research import ExperimentRecord, JobRecord +from app.domain.entities.research import ExperimentRecord, ExperimentSummary, JobRecord class JobRepository(Protocol): @@ -23,6 +23,35 @@ class JobRepository(Protocol): class ExperimentRepository(Protocol): def save(self, experiment: ExperimentRecord) -> ExperimentRecord: ... + def upsert(self, experiment: ExperimentRecord) -> ExperimentRecord: + """按 id 插入或**覆盖**（重建归档用：同一 id 已存在时替换整行）。 + + 与 `save` 的区别：`save` 只插入（同 id 会主键冲突），用于新归档； + `upsert` 用于灾备/重建场景 —— 例如从 Job 副本重建一条被删除的历史归档， + 此时归档 id 必须保持不变（外部链接、对比记录仍指向它）。 + """ + ... + def get(self, experiment_id: str) -> ExperimentRecord | None: ... def list_recent(self, limit: int = 50) -> list[ExperimentRecord]: ... + + def list_filtered( + self, + *, + kind: str | None = None, + q: str | None = None, + limit: int = 200, + offset: int = 0, + ) -> list[ExperimentSummary]: + """按 kind 精确过滤 + q 模糊过滤（id / 因子名 / summary_text，大小写不敏感）。 + + 只返回元数据（ExperimentSummary，**不含 result_json**），过滤与分页在 + SQL 层完成；排序为 created_at 倒序 + id 倒序（同秒创建时保证分页稳定）。 + """ + + def count_filtered(self, *, kind: str | None = None, q: str | None = None) -> int: + """与 `list_filtered` 同口径的过滤总数（列表接口 X-Total-Count 用）。""" + + def delete(self, experiment_id: str) -> bool: + """删除归档本身，返回是否存在。**不触碰**关联的 Job 记录。""" diff --git a/backend/app/domain/repositories/market.py b/backend/app/domain/repositories/market.py index a2340fe..5a8b59a 100644 --- a/backend/app/domain/repositories/market.py +++ b/backend/app/domain/repositories/market.py @@ -13,8 +13,10 @@ from typing import Protocol from app.domain.entities.market import ( AdjustFactor, DailyBar, + DailyBasic, FinancialIndicator, Stock, + StockNameHistory, SyncLog, TradingCalendar, ) @@ -76,6 +78,69 @@ class AdjustFactorRepository(Protocol): def get_range(self, symbol: str, start: date, end: date) -> list[AdjustFactor]: ... +class DailyBasicRepository(Protocol): + """每日指标（估值 / 股息率 / 市值）仓储。 + + 幂等键 (symbol, trade_date)。研究侧一律按 trade_date <= as_of 取用， + 禁止用未来时点的指标回填历史（AGENT.md §9）。 + """ + + def upsert_many(self, rows: Sequence[DailyBasic]) -> int: ... + + def get_range(self, symbol: str, start: date, end: date) -> list[DailyBasic]: ... + + def get_range_many( + self, + symbols: Sequence[str], + start: date, + end: date, + ) -> list[DailyBasic]: + """批量区间查询（接口对齐 DailyBarRepository）。""" + + def stream_range_many_columns( + self, + symbols: Sequence[str], + start: date, + end: date, + columns: Sequence[str], + ) -> Iterator[tuple]: + """流式返回 symbol, trade_date(iso str), *数值列(float)。 + + 研究装配面板专用（避免 Decimal / ORM 对象全量物化）。 + """ + + def latest_date(self) -> date | None: + """本表全局最新交易日（增量同步断点）。""" + + def missing_dates(self, start: date, end: date) -> list[date]: + """区间内「交易日历为开市、但本表无任何行」的交易日（补齐用）。""" + + +class StockNameHistoryRepository(Protocol): + """股票名称变更历史仓储（时点 ST / 风险警示判定）。 + + 用于把 `universe.exclude_st` 从「最新名称快照」升级为**时点名称**： + 研究侧必须按 as_of 取当时生效的名称，否则「曾为高股息、后来才 ST/退市」的 + 股息陷阱样本会被整段排除（实测影响约 3.70pp 收益）。 + """ + + def upsert_many(self, rows: Sequence[StockNameHistory]) -> int: ... + + def names_as_of( + self, symbols: Sequence[str], as_of: date + ) -> dict[str, str]: + """返回 as_of 时点生效的名称（缺该股记录则不返回该键，由调用方回退最新名称）。""" + + def name_spans(self, symbols: Sequence[str]) -> dict[str, list[tuple[date, date | None, str]]]: + """返回各股票的名称生效区间列表 [(start, end, name)]（批量时点查询复用）。""" + + def count_rows(self) -> int: + """本表总行数（未同步时用于降级回退最新名称）。""" + + def namechange_dates(self) -> tuple[date | None, date | None]: + """已同步的 (最早 start_date, 最晚 start_date)（增量断点）。""" + + class FinancialRepository(Protocol): def upsert_many(self, rows: Sequence[FinancialIndicator]) -> int: ... diff --git a/backend/app/infrastructure/data_sources/failover.py b/backend/app/infrastructure/data_sources/failover.py index acfdda9..63f144d 100644 --- a/backend/app/infrastructure/data_sources/failover.py +++ b/backend/app/infrastructure/data_sources/failover.py @@ -35,11 +35,20 @@ class FailoverProvider: # ---- 各 API 代理 ---- - def get_stock_basic(self) -> list: + def get_stock_basic(self, list_status: str = "L") -> list: return self._with_failover( "get_stock_basic", - primary_call=lambda: self.primary.get_stock_basic(), - fallback_call=lambda: self.fallback.get_stock_basic(), + primary_call=lambda: self.primary.get_stock_basic(list_status), + fallback_call=lambda: self.fallback.get_stock_basic(list_status), + ) + + def get_name_changes(self, start: date, end: date) -> list: + return self._with_failover( + "get_name_changes", + start=start, + end=end, + primary_call=lambda: self.primary.get_name_changes(start, end), + fallback_call=lambda: self.fallback.get_name_changes(start, end), ) def get_trade_cal(self, start: date, end: date) -> list: @@ -87,6 +96,20 @@ class FailoverProvider: fallback_call=lambda: self.fallback.get_financial(symbol, start, end), ) + def get_daily_basic(self, trade_date: date) -> list: + """每日指标：新浪不支持本接口 → 主源失败时如实报错，不做假兜底。""" + return self._with_failover( + "get_daily_basic", + start=trade_date, + end=trade_date, + primary_call=lambda: self.primary.get_daily_basic(trade_date), + fallback_call=( + (lambda: self.fallback.get_daily_basic(trade_date)) + if self.fallback is not None + else None + ), + ) + # ---- 内部 ---- def _with_failover( diff --git a/backend/app/infrastructure/data_sources/sina.py b/backend/app/infrastructure/data_sources/sina.py index 943b005..5c173b2 100644 --- a/backend/app/infrastructure/data_sources/sina.py +++ b/backend/app/infrastructure/data_sources/sina.py @@ -222,9 +222,12 @@ class SinaProvider: # ---- 不支持 ---- - def get_stock_basic(self): + def get_stock_basic(self, list_status: str = "L"): raise DataSourceNotSupported("新浪不提供股票基础信息列表") + def get_name_changes(self, start, end): + raise DataSourceNotSupported("新浪不提供股票名称变更历史（namechange）") + def get_index_weight(self, index_code): raise DataSourceNotSupported("新浪不提供指数成分接口") @@ -233,3 +236,11 @@ class SinaProvider: def get_adjust_factor(self, symbol, start, end): raise DataSourceNotSupported("新浪不提供复权因子（日线接口为前复权口径）") + + def get_daily_basic(self, trade_date): + """新浪无估值/股息率接口。 + + 禁止返回空列表冒充成功 —— 否则 daily_basic 同步会把「接口不支持」 + 误记成「当日无数据」，导致缺口被静默固化（AGENT.md §7）。 + """ + raise DataSourceNotSupported("新浪不提供每日指标（估值/股息率/市值）接口") diff --git a/backend/app/infrastructure/data_sources/tushare.py b/backend/app/infrastructure/data_sources/tushare.py index 59250dc..5757863 100644 --- a/backend/app/infrastructure/data_sources/tushare.py +++ b/backend/app/infrastructure/data_sources/tushare.py @@ -9,6 +9,7 @@ from __future__ import annotations import importlib import logging +import re import time from datetime import date, datetime, timedelta from decimal import Decimal @@ -18,8 +19,10 @@ from app.domain.entities.index import IndexWeight from app.domain.entities.market import ( AdjustFactor, DailyBar, + DailyBasic, FinancialIndicator, Stock, + StockNameHistory, TradingCalendar, ) from app.infrastructure.data_sources.errors import ( @@ -32,9 +35,40 @@ _TS_DATE = "%Y%m%d" def _to_date(value: str | None) -> date | None: + """日期归一：None / NaN / 空串 → None。 + + pandas 读到的缺失日期是 float NaN（namechange 的 end_date、部分财务字段）， + 若不拦住会抛 `time data 'nan' does not match format '%Y%m%d'` 并**中断整批拉取** + ——实测 namechange 按年分片时 32/37 片因此失败。 + """ if value is None or value == "": return None - return datetime.strptime(str(value)[:10], _TS_DATE).date() + if isinstance(value, float) and value != value: # NaN + return None + text = str(value).strip() + if text == "" or text.lower() in {"nan", "none", "null", "nat"}: + return None + return datetime.strptime(text[:10], _TS_DATE).date() + + +# 本地 symbol 规范：6 位数字 + 交易所后缀（与 Stock 实体的 pattern 校验一致） +_SYMBOL_RE = re.compile(r"^\d{6}\.(SH|SZ|BJ)$") + + +def _to_opt_str(value) -> str | None: + """可选字符串字段归一：None/NaN/空串 → None。 + + pandas 读到的缺失值是 float NaN（如退市股的 industry/area），直接塞进 + `str | None` 字段会被 pydantic 拒绝（string_type）——实测退市股拉取时命中。 + """ + if value is None: + return None + if isinstance(value, float) and value != value: # NaN + return None + text = str(value).strip() + if text == "" or text.lower() in {"nan", "none", "null"}: + return None + return text def _to_decimal(value) -> Decimal | None: @@ -69,20 +103,28 @@ class TushareProvider: # ---- 归一化（纯函数，输入 list[dict]，可单测） ---- @staticmethod - def normalize_stock(records: list[dict[str, Any]]) -> list[Stock]: + def normalize_stock( + records: list[dict[str, Any]], default_status: str = "L" + ) -> list[Stock]: + """归一化为 Stock。 + + `default_status`：tushare `stock_basic(list_status='D')` 返回的 status 字段 + 为空（实测 None），若一律兜底成 "L" 会把退市股标成在市 → 调用方按查询的 + list_status 传入，保证 status 与 delist_date 语义一致。 + """ stocks: list[Stock] = [] for rec in records: stocks.append( Stock( symbol=str(rec.get("ts_code") or rec.get("symbol") or ""), - name=str(rec.get("name") or ""), - industry=rec.get("industry"), - area=rec.get("area"), - market=rec.get("market"), - exchange=rec.get("exchange"), + name=str(rec.get("name") or "").strip(), + industry=_to_opt_str(rec.get("industry")), + area=_to_opt_str(rec.get("area")), + market=_to_opt_str(rec.get("market")), + exchange=_to_opt_str(rec.get("exchange")), list_date=_to_date(rec.get("list_date")) or date.min, delist_date=_to_date(rec.get("delist_date")), - status=str(rec.get("status") or "L"), + status=_to_opt_str(rec.get("status")) or default_status, ) ) return stocks @@ -152,6 +194,68 @@ class TushareProvider: for rec in records ] + @staticmethod + def normalize_daily_basic(records: list[dict[str, Any]]) -> list[DailyBasic]: + """daily_basic → DailyBasic。 + + 单位保持 Tushare 原样（不做隐式换算，避免口径漂移）： + - dv_ratio / dv_ttm / turnover_rate / volume_ratio / pe / pb / ps … 为百分数或倍数 + - total_share / float_share / free_share 单位万股；total_mv / circ_mv 单位万元 + - close 为**不复权**收盘价，与 stock_daily(adjust=none) 同口径 + """ + rows: list[DailyBasic] = [] + for rec in records: + rows.append( + DailyBasic( + symbol=str(rec.get("ts_code") or ""), + trade_date=_to_date(rec.get("trade_date")) or date.min, + source="tushare", + close=_to_decimal(rec.get("close")), + turnover_rate=_to_decimal(rec.get("turnover_rate")), + volume_ratio=_to_decimal(rec.get("volume_ratio")), + pe=_to_decimal(rec.get("pe")), + pe_ttm=_to_decimal(rec.get("pe_ttm")), + pb=_to_decimal(rec.get("pb")), + ps=_to_decimal(rec.get("ps")), + ps_ttm=_to_decimal(rec.get("ps_ttm")), + dv_ratio=_to_decimal(rec.get("dv_ratio")), + dv_ttm=_to_decimal(rec.get("dv_ttm")), + total_share=_to_decimal(rec.get("total_share")), + float_share=_to_decimal(rec.get("float_share")), + free_share=_to_decimal(rec.get("free_share")), + total_mv=_to_decimal(rec.get("total_mv")), + circ_mv=_to_decimal(rec.get("circ_mv")), + ) + ) + return rows + + @staticmethod + def normalize_name_history(records: list[dict[str, Any]]) -> list[StockNameHistory]: + """namechange → StockNameHistory（名称生效区间）。 + + 注意：`namechange` 的区间是**完整历史**（一行一个名称生效段）， + `end_date` 为 NaN 表示「至今有效」；`change_reason` 为 ST/*ST/撤销ST 等。 + """ + rows: list[StockNameHistory] = [] + for rec in records: + symbol = _to_opt_str(rec.get("ts_code")) + start = _to_date(rec.get("start_date")) + name = _to_opt_str(rec.get("name")) + if not symbol or not start or not name or not _SYMBOL_RE.match(symbol): + continue + rows.append( + StockNameHistory( + symbol=symbol, + name=name, + start_date=start, + end_date=_to_date(rec.get("end_date")), + ann_date=_to_date(rec.get("ann_date")), + change_reason=_to_opt_str(rec.get("change_reason")), + source="tushare", + ) + ) + return rows + @staticmethod def normalize_financial(records: list[dict[str, Any]]) -> list[FinancialIndicator]: rows: list[FinancialIndicator] = [] @@ -171,12 +275,34 @@ class TushareProvider: # ---- 接口调用 ---- - def get_stock_basic(self) -> list[Stock]: + def get_stock_basic(self, list_status: str = "L") -> list[Stock]: + """股票基础信息（list_status: L=上市 / D=退市 / P=暂停上市）。 + + tushare `stock_basic` 不带 list_status 时**只返回在市股票**，因此 + `delist_date` 恒为空、退市股整体缺失 → 回测存在幸存者偏差。 + 需要退市股时必须显式传 "D"（实测 2019-12 之后退市 230 只）。 + """ records = self._call( "stock_basic", + list_status=list_status, fields="ts_code,symbol,name,area,industry,market,exchange,list_date,delist_date,status", ) - return self.normalize_stock(records) + # 代码规范过滤：tushare 退市表含极少数非本地代码规范的记录 + # （实测 'T600018.SH' = 上港集箱(退)，2006 年退市，T 前缀表示转入三板）， + # 直接归一化会因 symbol 正则校验失败而**中断整个列表** —— 跳过并如实告警， + # 不做静默丢弃（AGENT.md §24）。 + kept, skipped = [], [] + for rec in records: + code = str(rec.get("ts_code") or rec.get("symbol") or "") + (kept if _SYMBOL_RE.match(code) else skipped).append(rec) + if skipped: + logger.warning( + "tushare.stock_basic(list_status=%s) 跳过 %d 条不符合本地代码规范的记录：%s", + list_status, + len(skipped), + [r.get("ts_code") for r in skipped[:5]], + ) + return self.normalize_stock(kept, default_status=list_status) def get_trade_cal(self, start: date, end: date) -> list[TradingCalendar]: records = self._call( @@ -252,6 +378,60 @@ class TushareProvider: records = self._call("index_weight", ts_code=index_code) return self.normalize_index_weight(records, index_code_fallback=index_code) + # Tushare 单次接口返回上限（实测 daily_basic 全市场单日 3700~5600 行、 + # namechange 2020+ 区间 4031 行）：取满即告警，避免静默截断。 + MAX_ROWS_PER_CALL = 6000 + + # namechange 单次请求上限同样约 6000 行；实测 2020+ 区间仅 4031 行， + # 但全历史（1990 起）会超限 —— 由 Syncer 按年分片调用，避免静默截断。 + _NAMECHANGE_FIELDS = "ts_code,name,start_date,end_date,ann_date,change_reason" + + def get_name_changes(self, start: date, end: date) -> list[StockNameHistory]: + """区间内全市场名称变更（Tushare namechange，按公告/生效区间批量取）。""" + records = self._call( + "namechange", + start_date=start.strftime(_TS_DATE), + end_date=end.strftime(_TS_DATE), + fields=self._NAMECHANGE_FIELDS, + ) + if len(records) >= self.MAX_ROWS_PER_CALL: + logger.warning( + "tushare.namechange(%s~%s) 返回 %d 行，可能触及单次上限被截断，" + "请缩小区间后重跑", + start, + end, + len(records), + ) + return self.normalize_name_history(records) + + # daily_basic 单次请求上限 6000 行（全市场一日约 3700~5600 行），按交易日调用即可 + _DAILY_BASIC_FIELDS = ( + "ts_code,trade_date,close,turnover_rate,volume_ratio,pe,pe_ttm,pb,ps,ps_ttm," + "dv_ratio,dv_ttm,total_share,float_share,free_share,total_mv,circ_mv" + ) + + def get_daily_basic(self, trade_date: date) -> list[DailyBasic]: + """单交易日全市场每日指标（daily_basic）。 + + 注意：Tushare 单次 6000 行上限 —— 全市场单日实测 3700~5600 行 + （2020 年约 3700，2026 年约 5560），当前安全；但若未来上市公司数 + 逼近 6000，需要按 ts_code 分片。此处对「恰好取满 6000 行」做告警， + 避免静默截断（AGENTS §7 数据可追溯）。 + """ + records = self._call( + "daily_basic", + trade_date=trade_date.strftime(_TS_DATE), + fields=self._DAILY_BASIC_FIELDS, + ) + if len(records) >= self.MAX_ROWS_PER_CALL: + logger.warning( + "daily_basic %s 返回 %d 行（达到 %d 行上限），可能被截断，需按 ts_code 分片", + trade_date, + len(records), + self.MAX_ROWS_PER_CALL, + ) + return self.normalize_daily_basic(records) + def _call(self, api: str, **kwargs) -> list[dict[str, Any]]: """带限速退避的调用：频率超限按指数退避（最长 _rate_limit_wait）等待后重试。""" last_error: Exception | None = None diff --git a/backend/app/infrastructure/persistence/migrations/versions/22d7380706f7_add_daily_basic_table.py b/backend/app/infrastructure/persistence/migrations/versions/22d7380706f7_add_daily_basic_table.py new file mode 100644 index 0000000..a9514dd --- /dev/null +++ b/backend/app/infrastructure/persistence/migrations/versions/22d7380706f7_add_daily_basic_table.py @@ -0,0 +1,67 @@ +"""add daily_basic table + +Revision ID: 22d7380706f7 +Revises: f5e0d1c2b3a4 +Create Date: 2026-09-19 15:53:37.029097 + +每日指标表（Tushare daily_basic）：估值 / 股息率 / 市值，幂等键 (symbol, trade_date)。 +供高股息等横截面选股因子使用（研究侧按 trade_date <= as_of 取用，无未来函数）。 + +说明：本文件由 `alembic revision --autogenerate` 生成后**手工裁剪**。 +自动生成时同时检出了 `index_weight` 的 drop —— 那是 `IndexWeightModel` +未在 `models/__init__.py` 注册导致的假差异（表实际存在）， +已在同一提交中补上注册，此处只保留 daily_basic 的建表语句。 +""" +from __future__ import annotations + +from collections.abc import Sequence + +import sqlalchemy as sa +from alembic import op + +revision: str = '22d7380706f7' +down_revision: str | None = 'f5e0d1c2b3a4' +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + + +def upgrade() -> None: + op.create_table( + 'daily_basic', + sa.Column( + 'id', + sa.BigInteger().with_variant(sa.Integer(), 'sqlite'), + autoincrement=True, + nullable=False, + ), + sa.Column('symbol', sa.String(length=12), nullable=False), + sa.Column('trade_date', sa.Date(), nullable=False), + sa.Column('source', sa.String(length=16), server_default='tushare', nullable=False), + sa.Column('close', sa.Numeric(precision=12, scale=4), nullable=True), + sa.Column('turnover_rate', sa.Numeric(precision=12, scale=4), nullable=True), + sa.Column('volume_ratio', sa.Numeric(precision=12, scale=4), nullable=True), + sa.Column('pe', sa.Numeric(precision=16, scale=4), nullable=True), + sa.Column('pe_ttm', sa.Numeric(precision=16, scale=4), nullable=True), + sa.Column('pb', sa.Numeric(precision=16, scale=4), nullable=True), + sa.Column('ps', sa.Numeric(precision=16, scale=4), nullable=True), + sa.Column('ps_ttm', sa.Numeric(precision=16, scale=4), nullable=True), + sa.Column('dv_ratio', sa.Numeric(precision=12, scale=4), nullable=True), + sa.Column('dv_ttm', sa.Numeric(precision=12, scale=4), nullable=True), + sa.Column('total_share', sa.Numeric(precision=24, scale=4), nullable=True), + sa.Column('float_share', sa.Numeric(precision=24, scale=4), nullable=True), + sa.Column('free_share', sa.Numeric(precision=24, scale=4), nullable=True), + sa.Column('total_mv', sa.Numeric(precision=24, scale=4), nullable=True), + sa.Column('circ_mv', sa.Numeric(precision=24, scale=4), nullable=True), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('symbol', 'trade_date', name='uq_basic_symbol_date'), + ) + with op.batch_alter_table('daily_basic', schema=None) as batch_op: + batch_op.create_index(batch_op.f('ix_daily_basic_symbol'), ['symbol'], unique=False) + batch_op.create_index(batch_op.f('ix_daily_basic_trade_date'), ['trade_date'], unique=False) + + +def downgrade() -> None: + with op.batch_alter_table('daily_basic', schema=None) as batch_op: + batch_op.drop_index(batch_op.f('ix_daily_basic_trade_date')) + batch_op.drop_index(batch_op.f('ix_daily_basic_symbol')) + op.drop_table('daily_basic') \ No newline at end of file diff --git a/backend/app/infrastructure/persistence/migrations/versions/7b1c4e9a52d8_widen_result_json.py b/backend/app/infrastructure/persistence/migrations/versions/7b1c4e9a52d8_widen_result_json.py new file mode 100644 index 0000000..9148294 --- /dev/null +++ b/backend/app/infrastructure/persistence/migrations/versions/7b1c4e9a52d8_widen_result_json.py @@ -0,0 +1,47 @@ +"""research result_json → MEDIUMTEXT（长回测结果落库） + +Revision ID: 7b1c4e9a52d8 +Revises: 22d7380706f7 +Create Date: 2026-09-19 + +背景：全市场多年回测结果（净值/回撤曲线 + 逐笔成交 + Signal↔Fill + 个股收益曲线） +实测约 1.2MB，MySQL `TEXT`（64KB）会报 1406 Data too long → Job 归档失败 +（2026-09 高股息案例实测：回测本身成功，落库失败）。 + +本迁移把 job.result_json / experiment.result_json 放宽到 MEDIUMTEXT（16MB）。 +SQLite 等方言不区分 TEXT 长度，迁移里做方言判断，非 MySQL 直接跳过。 +""" + +from __future__ import annotations + +from alembic import op +from sqlalchemy import text + +revision = "7b1c4e9a52d8" +down_revision = "22d7380706f7" +branch_labels = None +depends_on = None + +_COLUMNS = (("job", "result_json", True), ("experiment", "result_json", False)) + + +def _dialect() -> str: + return op.get_bind().dialect.name + + +def upgrade() -> None: + if _dialect() != "mysql": + return # SQLite/TEXT 无长度限制，无需变更 + for table, column, nullable in _COLUMNS: + null_clause = "NULL" if nullable else "NOT NULL" + op.execute( + text(f"ALTER TABLE {table} MODIFY COLUMN {column} MEDIUMTEXT {null_clause}") + ) + + +def downgrade() -> None: + if _dialect() != "mysql": + return + for table, column, nullable in _COLUMNS: + null_clause = "NULL" if nullable else "NOT NULL" + op.execute(text(f"ALTER TABLE {table} MODIFY COLUMN {column} TEXT {null_clause}")) \ No newline at end of file diff --git a/backend/app/infrastructure/persistence/migrations/versions/a3f8c21d9b47_add_stock_name_history.py b/backend/app/infrastructure/persistence/migrations/versions/a3f8c21d9b47_add_stock_name_history.py new file mode 100644 index 0000000..71b4017 --- /dev/null +++ b/backend/app/infrastructure/persistence/migrations/versions/a3f8c21d9b47_add_stock_name_history.py @@ -0,0 +1,64 @@ +"""add stock_name_history table + +Revision ID: a3f8c21d9b47 +Revises: 7b1c4e9a52d8 +Create Date: 2026-09-19 21:40:12.000000 + +股票名称变更历史（Tushare namechange）：时点 ST / 风险警示判定的依据。 + +**为什么需要**：`stock.name` 是最新名称快照，用它做 `exclude_st` 会把 +「曾为高股息、后来才变 ST/退市」的标的在整段历史里都排除 —— 而那正是 +「股息陷阱」样本。实测对照：同一 spec 仅改 exclude_st，收益 +35.71% → +32.01%， +即约 3.70pp 的收益被名称快照口径隐藏。 + +幂等键 (symbol, start_date)。时点查询： +`start_date <= as_of AND (end_date IS NULL OR end_date >= as_of)`。 +""" +from __future__ import annotations + +from collections.abc import Sequence + +import sqlalchemy as sa +from alembic import op + +revision: str = 'a3f8c21d9b47' +down_revision: str | None = '7b1c4e9a52d8' +branch_labels: str | Sequence[str] | None = None +depends_on: str | Sequence[str] | None = None + + +def upgrade() -> None: + op.create_table( + 'stock_name_history', + sa.Column( + 'id', + sa.BigInteger().with_variant(sa.Integer(), 'sqlite'), + autoincrement=True, + nullable=False, + ), + sa.Column('symbol', sa.String(length=12), nullable=False), + sa.Column('name', sa.String(length=64), nullable=False), + sa.Column('start_date', sa.Date(), nullable=False), + sa.Column('end_date', sa.Date(), nullable=True), + sa.Column('ann_date', sa.Date(), nullable=True), + sa.Column('change_reason', sa.String(length=32), nullable=True), + sa.Column('source', sa.String(length=16), server_default='tushare', nullable=False), + sa.PrimaryKeyConstraint('id'), + sa.UniqueConstraint('symbol', 'start_date', name='uq_name_symbol_start'), + ) + with op.batch_alter_table('stock_name_history', schema=None) as batch_op: + batch_op.create_index(batch_op.f('ix_stock_name_history_symbol'), ['symbol'], unique=False) + batch_op.create_index( + batch_op.f('ix_stock_name_history_start_date'), ['start_date'], unique=False + ) + batch_op.create_index( + batch_op.f('ix_stock_name_history_end_date'), ['end_date'], unique=False + ) + + +def downgrade() -> None: + with op.batch_alter_table('stock_name_history', schema=None) as batch_op: + batch_op.drop_index(batch_op.f('ix_stock_name_history_end_date')) + batch_op.drop_index(batch_op.f('ix_stock_name_history_start_date')) + batch_op.drop_index(batch_op.f('ix_stock_name_history_symbol')) + op.drop_table('stock_name_history') diff --git a/backend/app/infrastructure/persistence/sqlalchemy/data_version.py b/backend/app/infrastructure/persistence/sqlalchemy/data_version.py new file mode 100644 index 0000000..6de7783 --- /dev/null +++ b/backend/app/infrastructure/persistence/sqlalchemy/data_version.py @@ -0,0 +1,129 @@ +"""数据集快照指纹：`experiment.data_version` 的真实取值来源。 + +归档的用途是「往复查看 / 复现」，因此除了代码版本（code_version）还必须记录 +**当时的数据口径**：数据同步到哪一天、各表规模多大。此前该字段从未被写入 +（永远 NULL），本模块补上。 + +诚实性与成本（AGENT.md §7 不静默 / §24 不假装支持）： +- 交易日 `MAX(trade_date)` 走索引，实测 ~0.2ms，**真实值**； +- 大表（stock_daily / adjust_factor / daily_basic，各 800 万行量级）的全表 + `COUNT(*)` 实测单次 ~1.2s，三次合计 ~3.6s —— 不允许出现在请求路径上； + 故 MySQL 下改用 `information_schema.TABLES.TABLE_ROWS`（单次查询实测 ~0.5ms）， + 它是 InnoDB 统计缓存的**近似值**（实测 stock_daily 7688126 vs 真实 COUNT(*) + 8052698，偏差 ~4.6%），因此字符串里用 `≈` 明确标注为近似，绝不冒充精确计数。 +- 非 MySQL 方言（测试用的 SQLite 等）数据量小，直接 `COUNT(*)` 得到精确值， + 不带 `≈` 标记； +- 任何一段取不到（连接失败 / 表不存在 / 权限不足）都**降级**：要么丢弃该段， + 要么整体返回 `unavailable`，绝不编造数字。 + +字符串格式（≤40 字符 —— experiment.data_version 列是 varchar(40)，本文件不新增 +迁移，故必须塞得下；段超长时从右往左丢弃低优先级段，再退化到 `d`）： + + d stock_daily 的最大交易日（`d-` 表示该表无数据 / 取不到交易日） + n stock_daily 行数 + a adjust_factor 行数 + b daily_basic 行数 + +行数段写法：`≈k` = MySQL 近似值（k = 千行，四舍五入）；`` = 精确值。 +示例：`d20260904;n≈8053k;a≈8189k;b≈7718k` +""" + +from __future__ import annotations + +import logging + +from sqlalchemy import func, select, text +from sqlalchemy.orm import Session + +from app.infrastructure.persistence.sqlalchemy.models.market import ( + AdjustFactorModel, + DailyBasicModel, + StockDailyModel, +) + +logger = logging.getLogger(__name__) + +# experiment.data_version 列宽（varchar(40)，见 models/jobs.py + 既有迁移） +DATA_VERSION_MAX_LEN = 40 + +# 段定义：(段名, model)，顺序即优先级（越靠前越重要） +_TABLES: tuple[tuple[str, type], ...] = ( + ("n", StockDailyModel), + ("a", AdjustFactorModel), + ("b", DailyBasicModel), +) + +_FALLBACK = "unavailable" + + +def _fmt_rows(rows: int | None, *, approx: bool) -> str | None: + """行数段：近似值用「≈Nk」（千行），精确值用原样数字。""" + if rows is None: + return None + if not approx: + return str(rows) + return f"≈{round(rows / 1000)}k" + + +def _latest_trade_date(session: Session) -> str: + """stock_daily 最大交易日 → `YYYYMMDD`；无数据 / 取不到 → `-`。""" + try: + day = session.execute(select(func.max(StockDailyModel.trade_date))).scalar() + except Exception as exc: # noqa: BLE001 —— 指纹取不到必须降级，不影响归档主体 + logger.warning("data_version: MAX(trade_date) 取不到：%s: %s", type(exc).__name__, exc) + return "-" + return day.strftime("%Y%m%d") if day is not None else "-" + + +def _approx_rows_mysql(session: Session) -> dict[str, int]: + """MySQL：一次 information_schema 查询拿全部表的近似行数（实测 ~0.5ms）。""" + try: + rows = session.execute( + text( + "SELECT TABLE_NAME, TABLE_ROWS FROM information_schema.TABLES " + "WHERE TABLE_SCHEMA = DATABASE() AND TABLE_NAME IN :names" + ).bindparams(names=tuple(m.__tablename__ for _, m in _TABLES)) + ).all() + except Exception as exc: # noqa: BLE001 + logger.warning("data_version: information_schema 查询失败：%s: %s", type(exc).__name__, exc) + return {} + return {str(name): int(n) for name, n in rows if n is not None} + + +def _exact_rows(session: Session, model: type) -> int | None: + """其它方言（SQLite 等，数据量小）：精确 COUNT(*)。""" + try: + return int(session.execute(select(func.count()).select_from(model)).scalar() or 0) + except Exception as exc: # noqa: BLE001 + logger.warning("data_version: COUNT(*) 失败：%s: %s", type(exc).__name__, exc) + return None + + +def compute_data_version(session: Session) -> str: + """计算数据集快照指纹（格式与取舍见模块 docstring）。失败降级为 `unavailable`。""" + try: + dialect = session.get_bind().dialect.name + except Exception: # noqa: BLE001 + dialect = "" + + approx = dialect == "mysql" + approx_rows = _approx_rows_mysql(session) if approx else {} + + # 首段恒为交易日段：`d-` 本身也是真实信息（表为空 / 取不到交易日），保留 + segments: list[str] = [f"d{_latest_trade_date(session)}"] + for name, model in _TABLES: + rows = ( + approx_rows.get(model.__tablename__) if approx else _exact_rows(session, model) + ) + seg = _fmt_rows(rows, approx=approx) + if seg is not None: + segments.append(f"{name}{seg}") + + # 列宽护栏：超 40 字符时从右往左丢弃低优先级段（保留的段仍是真实值，绝不截断数字） + while len(segments) > 1 and len(";".join(segments)) > DATA_VERSION_MAX_LEN: + segments.pop() + + if len(segments) == 1 and segments[0] == "d-": + # 三张表连行数都读不到、交易日也没有：如实标记「不可用」，不编造 + return _FALLBACK + return ";".join(segments) \ No newline at end of file diff --git a/backend/app/infrastructure/persistence/sqlalchemy/models/__init__.py b/backend/app/infrastructure/persistence/sqlalchemy/models/__init__.py index 914cb34..cb2254a 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/models/__init__.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/models/__init__.py @@ -10,15 +10,20 @@ from app.infrastructure.persistence.sqlalchemy.models.composite import ( # noqa from app.infrastructure.persistence.sqlalchemy.models.factor import ( # noqa: F401 FactorDefinitionModel, ) +from app.infrastructure.persistence.sqlalchemy.models.index import ( # noqa: F401 + IndexWeightModel, +) from app.infrastructure.persistence.sqlalchemy.models.jobs import ( # noqa: F401 ExperimentModel, JobModel, ) from app.infrastructure.persistence.sqlalchemy.models.market import ( # noqa: F401 AdjustFactorModel, + DailyBasicModel, FinancialIndicatorModel, StockDailyModel, StockModel, + StockNameHistoryModel, SyncLogModel, TradingCalendarModel, ) diff --git a/backend/app/infrastructure/persistence/sqlalchemy/models/jobs.py b/backend/app/infrastructure/persistence/sqlalchemy/models/jobs.py index 35314b6..93a1097 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/models/jobs.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/models/jobs.py @@ -1,14 +1,25 @@ -"""Phase 4：Job（异步任务）与 Experiment（实验归档）表。""" +"""Phase 4：Job（异步任务）与 Experiment（实验归档）表。 + +`result_json` 使用 MEDIUMTEXT（MySQL 上限 16MB）：全市场多年回测的结果含 +净值/回撤曲线、逐笔成交、Signal↔Fill 记录与个股收益曲线，实测可达数 MB， +MySQL `TEXT`（64KB）会直接报 1406 Data too long 导致 Job 归档失败 +（2026-09 高股息案例实测：约 1.2MB → 落库失败）。 +SQLite 不区分 TEXT 长度，故模型层统一用 MEDIUMTEXT.with_variant 保持跨库可用。 +""" from __future__ import annotations from datetime import datetime from sqlalchemy import DateTime, String, Text +from sqlalchemy.dialects.mysql import MEDIUMTEXT from sqlalchemy.orm import Mapped, mapped_column from app.infrastructure.persistence.sqlalchemy.base import Base +# 长 JSON 列类型：MySQL 用 MEDIUMTEXT（16MB），其它方言退化为 TEXT（SQLite 无长度限制） +_LONG_JSON = Text().with_variant(MEDIUMTEXT(), "mysql") + class JobModel(Base): __tablename__ = "job" @@ -19,7 +30,7 @@ class JobModel(Base): stage: Mapped[str | None] = mapped_column(String(24), nullable=True) spec_json: Mapped[str] = mapped_column(Text) error: Mapped[str | None] = mapped_column(Text, nullable=True) - result_json: Mapped[str | None] = mapped_column(Text, nullable=True) + result_json: Mapped[str | None] = mapped_column(_LONG_JSON, nullable=True) experiment_id: Mapped[str | None] = mapped_column(String(32), nullable=True) created_at: Mapped[datetime] = mapped_column(DateTime) started_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True) @@ -32,7 +43,7 @@ class ExperimentModel(Base): id: Mapped[str] = mapped_column(String(32), primary_key=True) kind: Mapped[str] = mapped_column(String(16)) spec_json: Mapped[str] = mapped_column(Text) - result_json: Mapped[str] = mapped_column(Text) + result_json: Mapped[str] = mapped_column(_LONG_JSON) summary_text: Mapped[str | None] = mapped_column(String(200), nullable=True) code_version: Mapped[str | None] = mapped_column(String(40), nullable=True) data_version: Mapped[str | None] = mapped_column(String(40), nullable=True) diff --git a/backend/app/infrastructure/persistence/sqlalchemy/models/market.py b/backend/app/infrastructure/persistence/sqlalchemy/models/market.py index ef976d1..a5d1aab 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/models/market.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/models/market.py @@ -81,6 +81,37 @@ class AdjustFactorModel(Base): factor: Mapped[Decimal] = mapped_column(Numeric(20, 6)) +class DailyBasicModel(Base): + """每日指标快照（Tushare daily_basic）—— 估值 / 股息率 / 市值。 + + 幂等键 (symbol, trade_date)：同一交易日同一股票唯一一行。 + dv_ratio/dv_ttm 为时点值，研究侧按 trade_date <= as_of 取用（无未来函数）。 + """ + + __tablename__ = "daily_basic" + __table_args__ = (UniqueConstraint("symbol", "trade_date", name="uq_basic_symbol_date"),) + + id: Mapped[int] = mapped_column(PK_INT, primary_key=True, autoincrement=True) + symbol: Mapped[str] = mapped_column(String(SYMBOL_LEN), index=True) + trade_date: Mapped[date] = mapped_column(Date, index=True) + source: Mapped[str] = mapped_column(String(16), default="tushare", server_default="tushare") + close: Mapped[Decimal | None] = mapped_column(Numeric(12, 4), nullable=True) + turnover_rate: Mapped[Decimal | None] = mapped_column(Numeric(12, 4), nullable=True) + volume_ratio: Mapped[Decimal | None] = mapped_column(Numeric(12, 4), nullable=True) + pe: Mapped[Decimal | None] = mapped_column(Numeric(16, 4), nullable=True) + pe_ttm: Mapped[Decimal | None] = mapped_column(Numeric(16, 4), nullable=True) + pb: Mapped[Decimal | None] = mapped_column(Numeric(16, 4), nullable=True) + ps: Mapped[Decimal | None] = mapped_column(Numeric(16, 4), nullable=True) + ps_ttm: Mapped[Decimal | None] = mapped_column(Numeric(16, 4), nullable=True) + dv_ratio: Mapped[Decimal | None] = mapped_column(Numeric(12, 4), nullable=True) + dv_ttm: Mapped[Decimal | None] = mapped_column(Numeric(12, 4), nullable=True) + total_share: Mapped[Decimal | None] = mapped_column(Numeric(24, 4), nullable=True) + float_share: Mapped[Decimal | None] = mapped_column(Numeric(24, 4), nullable=True) + free_share: Mapped[Decimal | None] = mapped_column(Numeric(24, 4), nullable=True) + total_mv: Mapped[Decimal | None] = mapped_column(Numeric(24, 4), nullable=True) + circ_mv: Mapped[Decimal | None] = mapped_column(Numeric(24, 4), nullable=True) + + class FinancialIndicatorModel(Base): """财务指标快照 —— report_date(报告期) 与 announce_date(公告日) 并存。""" @@ -115,3 +146,26 @@ class SyncLogModel(Base): row_count: Mapped[int] = mapped_column(default=0) data_start: Mapped[date | None] = mapped_column(Date, nullable=True) data_end: Mapped[date | None] = mapped_column(Date, nullable=True) + + +class StockNameHistoryModel(Base): + """股票名称变更历史（Tushare namechange）—— 时点 ST / 风险警示判定的依据。 + + 幂等键 (symbol, start_date)：同一股票同一名称生效起点唯一一行。 + 查询语义：`name` 在 [start_date, end_date] 内有效；`end_date` 为空表示至今有效。 + 时点取值：`start_date <= as_of AND (end_date IS NULL OR end_date >= as_of)`。 + """ + + __tablename__ = "stock_name_history" + __table_args__ = ( + UniqueConstraint("symbol", "start_date", name="uq_name_symbol_start"), + ) + + id: Mapped[int] = mapped_column(PK_INT, primary_key=True, autoincrement=True) + symbol: Mapped[str] = mapped_column(String(SYMBOL_LEN), index=True) + name: Mapped[str] = mapped_column(String(64)) + start_date: Mapped[date] = mapped_column(Date, index=True) + end_date: Mapped[date | None] = mapped_column(Date, nullable=True, index=True) + ann_date: Mapped[date | None] = mapped_column(Date, nullable=True) + change_reason: Mapped[str | None] = mapped_column(String(32), nullable=True) + source: Mapped[str] = mapped_column(String(16), default="tushare", server_default="tushare") diff --git a/backend/app/infrastructure/persistence/sqlalchemy/repositories/jobs_impl.py b/backend/app/infrastructure/persistence/sqlalchemy/repositories/jobs_impl.py index 3a9287e..519b6d3 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/repositories/jobs_impl.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/repositories/jobs_impl.py @@ -2,10 +2,10 @@ from __future__ import annotations -from sqlalchemy import select +from sqlalchemy import Select, func, select from sqlalchemy.orm import Session -from app.domain.entities.research import ExperimentRecord, JobRecord +from app.domain.entities.research import ExperimentRecord, ExperimentSummary, JobRecord from app.infrastructure.persistence.sqlalchemy.models.jobs import ExperimentModel, JobModel @@ -53,6 +53,12 @@ class SqlAlchemyJobRepository: class SqlAlchemyExperimentRepository: + """Experiment 归档仓储。 + + `list_filtered` 刻意**不加载 `result_json`**（MEDIUMTEXT，完整存档后单条数 MB）， + 只 SELECT 元数据列 + SQL 侧算出的字符长度，避免列表接口把上百 MB 拉进内存。 + """ + def __init__(self, session: Session) -> None: self._session = session @@ -61,6 +67,12 @@ class SqlAlchemyExperimentRepository: self._session.flush() return experiment + def upsert(self, experiment: ExperimentRecord) -> ExperimentRecord: + """按 id 插入或覆盖（`merge` 语义）：重建归档时保持原 id 不变。""" + self._session.merge(ExperimentModel(**experiment.model_dump())) + self._session.flush() + return experiment + def get(self, experiment_id: str) -> ExperimentRecord | None: row = self._session.get(ExperimentModel, experiment_id) return ExperimentRecord.model_validate(row, from_attributes=True) if row else None @@ -70,3 +82,98 @@ class SqlAlchemyExperimentRepository: select(ExperimentModel).order_by(ExperimentModel.created_at.desc()).limit(limit) ).all() return [ExperimentRecord.model_validate(r, from_attributes=True) for r in rows] + + # -- 列表 / 过滤（SQL 层完成过滤+分页，参数绑定，不做字符串拼接）------------- + + @staticmethod + def _escape_like(value: str) -> str: + """转义 LIKE 元字符：用户输入里的 % / _ / \\ 不应被当成通配符。""" + return value.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_") + + def _filter_conditions(self, *, kind: str | None, q: str | None): + """kind 精确匹配 + q 大小写不敏感模糊匹配（id / spec_json=因子名 / summary_text）。""" + conditions = [] + if kind: + conditions.append(ExperimentModel.kind == kind) + if q: + pattern = f"%{self._escape_like(q.lower())}%" + conditions.append( + func.lower(ExperimentModel.id).like(pattern, escape="\\") + | func.lower(ExperimentModel.spec_json).like(pattern, escape="\\") + | func.lower(func.coalesce(ExperimentModel.summary_text, "")).like( + pattern, escape="\\" + ) + ) + return conditions + + def _summary_stmt(self, conditions) -> Select: + return select( + ExperimentModel.id, + ExperimentModel.kind, + ExperimentModel.spec_json, + ExperimentModel.summary_text, + ExperimentModel.code_version, + ExperimentModel.data_version, + ExperimentModel.job_id, + ExperimentModel.created_at, + self._char_length().label("result_bytes"), + ).where(*conditions) + + def _char_length(self): + """归档 JSON 的**字符数**（不取大字段本身）。 + + MySQL 的 `LENGTH()` 返回字节数，字符数要用 `CHAR_LENGTH()`；SQLite 只有 + `length()`（对 TEXT 返回字符数）。按方言选择，避免中文归档在两个库上口径 + 不一致。 + """ + if self._session.get_bind().dialect.name == "sqlite": + return func.length(ExperimentModel.result_json) + return func.char_length(ExperimentModel.result_json) + + def list_filtered( + self, + *, + kind: str | None = None, + q: str | None = None, + limit: int = 200, + offset: int = 0, + ) -> list[ExperimentSummary]: + stmt = ( + self._summary_stmt(self._filter_conditions(kind=kind, q=q)) + # created_at 在 MySQL datetime(0) 下只有秒精度，同秒创建的归档必须有 + # 稳定的次级排序键，否则分页会重复/漏项 + .order_by(ExperimentModel.created_at.desc(), ExperimentModel.id.desc()) + .limit(max(limit, 0)) + .offset(max(offset, 0)) + ) + return [ + ExperimentSummary( + id=row.id, + kind=row.kind, + spec_json=row.spec_json, + summary_text=row.summary_text, + code_version=row.code_version, + data_version=row.data_version, + job_id=row.job_id, + created_at=row.created_at, + result_bytes=int(row.result_bytes or 0), + ) + for row in self._session.execute(stmt).all() + ] + + def count_filtered(self, *, kind: str | None = None, q: str | None = None) -> int: + stmt = ( + select(func.count()) + .select_from(ExperimentModel) + .where(*self._filter_conditions(kind=kind, q=q)) + ) + return int(self._session.execute(stmt).scalar() or 0) + + def delete(self, experiment_id: str) -> bool: + """删除归档本身；返回 False 表示不存在。不触碰 job 表。""" + row = self._session.get(ExperimentModel, experiment_id) + if row is None: + return False + self._session.delete(row) + self._session.flush() + return True diff --git a/backend/app/infrastructure/persistence/sqlalchemy/repositories/market_impl.py b/backend/app/infrastructure/persistence/sqlalchemy/repositories/market_impl.py index 156b8d2..9f44457 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/repositories/market_impl.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/repositories/market_impl.py @@ -12,28 +12,39 @@ from collections.abc import Iterator, Sequence from datetime import date from typing import Any -from sqlalchemy import Float, String, cast, select -from sqlalchemy.orm import Session +from sqlalchemy import Float, String, and_, cast, func, insert, or_, select +from sqlalchemy.orm import Session, aliased from app.domain.entities.market import ( + DAILY_BAR_NUMERIC_FIELDS, + DAILY_BASIC_NUMERIC_FIELDS, AdjustFactor, DailyBar, + DailyBasic, FinancialIndicator, Stock, + StockNameHistory, SyncLog, TradingCalendar, ) from app.infrastructure.persistence.sqlalchemy.models.market import ( AdjustFactorModel, + DailyBasicModel, FinancialIndicatorModel, StockDailyModel, StockModel, + StockNameHistoryModel, SyncLogModel, TradingCalendarModel, ) -# 日线数值列白名单（研究面板只需这些；symbol/trade_date 恒返回） -BAR_FLOAT_COLUMNS = ("open", "high", "low", "close", "volume", "amount") +# 数值列白名单（研究面板只需这些；symbol/trade_date 恒返回） +# 单一事实来源在 domain.entities.market —— quant 层装配与本次列裁剪共用同一套列名。 +BAR_FLOAT_COLUMNS = DAILY_BAR_NUMERIC_FIELDS +BASIC_FLOAT_COLUMNS = DAILY_BASIC_NUMERIC_FIELDS + +# 复权折算只作用于价格列；volume/amount 保持原始口径（股数/金额不因除权而缩放） +_ADJUSTED_PRICE_COLUMNS = frozenset({"open", "high", "low", "close"}) # 实体类型 → (ORM Model, 幂等键列) _TABLE = { @@ -41,7 +52,9 @@ _TABLE = { TradingCalendar: (TradingCalendarModel, ["calendar_date"]), DailyBar: (StockDailyModel, ["symbol", "trade_date"]), AdjustFactor: (AdjustFactorModel, ["symbol", "trade_date"]), + DailyBasic: (DailyBasicModel, ["symbol", "trade_date"]), FinancialIndicator: (FinancialIndicatorModel, ["symbol", "report_date", "announce_date"]), + StockNameHistory: (StockNameHistoryModel, ["symbol", "start_date"]), SyncLog: (SyncLogModel, ["id"]), } @@ -60,9 +73,14 @@ def _upsert_by_business_key( ) -> int: """按业务幂等键**批量**查重后 insert/update（AGENT.md §1 性能友好：不做逐行 select）。 - - 一次查询取出本批已有的键 → 新行批量 add，已有行就地更新 + - 先按业务键分批查出**已存在的键**（只取键列，不物化 ORM 实体） + - 新行走 Core 批量 insert（SQLAlchemy 2.0 insertmanyvalues：多行单语句）， + 避免「逐行 ORM add + flush」在整表同步下产生数千次往返 + - 已存在行仍走 ORM 就地更新（量小，且保留属性级语义） - 同一批内重复键（数据源偶发）：以「后出现者」为准覆盖（避免 UNIQUE 冲突） - 返回处理实体总数（含新增与更新），与旧逐行实现语义一致 + + 方言无关：只用 select/insert/update，无 SQLite / MySQL 专有语法。 """ if not entities: return 0 @@ -74,30 +92,72 @@ def _upsert_by_business_key( values = _fields_of(ent) keyed.append((tuple(values[k] for k in key_cols), values)) - existing_rows: dict[tuple, Any] = {} - keys = [k for k, _v in keyed] - if keys: - from sqlalchemy import tuple_ + existing_rows = _load_existing(session, model_cls, key_cols, key_col_attrs, keyed) - stmt = select(model_cls).where(tuple_(*key_col_attrs).in_(keys)) - for row in session.scalars(stmt): - key = tuple(getattr(row, k) for k in key_cols) - existing_rows[key] = row - - pending: dict[tuple, Any] = {} + to_insert: list[dict] = [] + to_update: list[Any] = [] + seen: dict[tuple, Any] = {} for key, values in keyed: row = existing_rows.get(key) - if row is None and key in pending: - row = pending[key] # 批内已待插入的同键 → 覆盖为新值 - if row is None: - pending[key] = model_cls(**values) - else: + if row is not None: for col, val in values.items(): setattr(row, col, val) - session.add_all(pending.values()) + to_update.append(row) + seen[key] = row + continue + if key in seen: + # 批内重复键：先插入的那条就地改名（同一 session 内 pending 对象可直接改属性） + holder = seen[key] + if isinstance(holder, dict): + holder.clear() + holder.update(values) + else: + for col, val in values.items(): + setattr(holder, col, val) + continue + payload = dict(values) + seen[key] = payload + to_insert.append(payload) + + if to_insert: + # insertmanyvalues：分批以控制单条 SQL 参数规模（避免超出驱动的占位符上限） + chunk = 500 + for i in range(0, len(to_insert), chunk): + session.execute(insert(model_cls), to_insert[i : i + chunk]) + if to_update: + session.flush() # ORM 更新落库 return len(keyed) +def _load_existing( + session: Session, + model_cls, + key_cols: Sequence[str], + key_col_attrs: Sequence, + keyed: Sequence[tuple[tuple, dict]], +) -> dict[tuple, Any]: + """查出已存在的业务键 → ORM 行（键列分批查询，规避超长 IN 子句）。""" + from sqlalchemy import tuple_ + + keys = [k for k, _v in keyed] + if not keys: + return {} + unique_keys = list(dict.fromkeys(keys)) + out: dict[tuple, Any] = {} + chunk = 500 + for i in range(0, len(unique_keys), chunk): + part = unique_keys[i : i + chunk] + # 单键（如 symbol）直接用 IN，快且可走索引；复合键用 tuple IN + cond = ( + key_col_attrs[0].in_([k[0] for k in part]) + if len(key_col_attrs) == 1 + else tuple_(*key_col_attrs).in_(part) + ) + for row in session.scalars(select(model_cls).where(cond)): + out[tuple(getattr(row, k) for k in key_cols)] = row + return out + + class SqlAlchemyStockRepository: def __init__(self, session: Session) -> None: self._session = session @@ -185,28 +245,90 @@ class SqlAlchemyDailyBarRepository: end: date, columns: Sequence[str], adjust: str = "none", + price_adjust: str = "none", ) -> Iterator[tuple]: """流式返回 (symbol, trade_date_iso, *float_cols) 元组，分批拉取。 内存优化：与 get_range_many 不同，不实例化 ORM 对象 / Decimal， 只 SELECT 所需列并在 SQL 侧 CAST 为 REAL，适合一次装配几十万~几百万行面板。 + + 两个 adjust 参数**语义不同，不可混用**（v3 §20.5）： + - `adjust`：行集口径过滤（stock_daily.adjust = none/qfq 兜底行），默认 none 主口径。 + - `price_adjust`：复权折算口径（none/qfq/hfq），在 SQL 侧 JOIN adjust_factor 折算 + **价格列**（open/high/low/close；volume/amount 不折算，保持原口径）： + hfq → price × factor；qfq → price × factor / 该股最新 factor。 + 因子缺失时按 1.0 兜底（不折算），缺口由 count_price_adjust_gaps 统计上报。 """ cols = list(columns) unknown = [c for c in cols if c not in BAR_FLOAT_COLUMNS] if unknown: raise ValueError(f"不支持的行情列: {unknown}（可用: {BAR_FLOAT_COLUMNS}）") - numeric_expr = [cast(getattr(StockDailyModel, c), Float) for c in cols] - stmt = ( - select(StockDailyModel.symbol, cast(StockDailyModel.trade_date, String), *numeric_expr) - .where( - StockDailyModel.symbol.in_(list(symbols)), - StockDailyModel.trade_date >= start, - StockDailyModel.trade_date <= end, - StockDailyModel.adjust == adjust, + if price_adjust not in ("none", "qfq", "hfq"): + raise ValueError(f"不支持的复权口径: {price_adjust}（可用: none/qfq/hfq）") + + day_col = cast(StockDailyModel.trade_date, String) + if price_adjust == "none": + numeric_expr = [cast(getattr(StockDailyModel, c), Float) for c in cols] + stmt = ( + select(StockDailyModel.symbol, day_col, *numeric_expr) + .where( + StockDailyModel.symbol.in_(list(symbols)), + StockDailyModel.trade_date >= start, + StockDailyModel.trade_date <= end, + StockDailyModel.adjust == adjust, + ) + .order_by(StockDailyModel.symbol, StockDailyModel.trade_date) + .execution_options(yield_per=20000) ) - .order_by(StockDailyModel.symbol, StockDailyModel.trade_date) - .execution_options(yield_per=20000) - ) + else: + af = aliased(AdjustFactorModel) + mult = func.coalesce(af.factor, 1.0) + from_clause = None + if price_adjust == "qfq": + # 以该股**最新**因子归一（表内最大值，与 chart_service 的显示口径一致）。 + # ⚠️ COALESCE 必须在最外层：若写成 coalesce(factor,1)/max，缺失因子的行会被 + # 缩放到 1/max（例：max=5 → 假跌 80%），与「缺失因子按 1.0 兜底不折算」的 + # 口径相矛盾，并污染 qfq 的收益/回撤/个股曲线。 + latest = ( + select( + AdjustFactorModel.symbol.label("symbol"), + func.max(AdjustFactorModel.factor).label("mx"), + ) + .group_by(AdjustFactorModel.symbol) + .subquery() + ) + mult = func.coalesce(af.factor / func.coalesce(latest.c.mx, 1.0), 1.0) + from_clause = latest + numeric_expr = [ + cast(getattr(StockDailyModel, c), Float) * mult + if c in _ADJUSTED_PRICE_COLUMNS + else cast(getattr(StockDailyModel, c), Float) + for c in cols + ] + stmt = ( + select(StockDailyModel.symbol, day_col, *numeric_expr) + .select_from(StockDailyModel) + .outerjoin( + af, + and_( + af.symbol == StockDailyModel.symbol, + af.trade_date == StockDailyModel.trade_date, + ), + ) + ) + if from_clause is not None: + stmt = stmt.outerjoin(from_clause, from_clause.c.symbol == StockDailyModel.symbol) + stmt = ( + stmt.where( + StockDailyModel.symbol.in_(list(symbols)), + StockDailyModel.trade_date >= start, + StockDailyModel.trade_date <= end, + StockDailyModel.adjust == adjust, + ) + .order_by(StockDailyModel.symbol, StockDailyModel.trade_date) + .execution_options(yield_per=20000) + ) + result = self._session.execute(stmt) while True: chunk = result.fetchmany(20000) @@ -215,6 +337,47 @@ class SqlAlchemyDailyBarRepository: for row in chunk: yield tuple(row) + def count_price_adjust_gaps( + self, symbols: Sequence[str], start: date, end: date + ) -> tuple[int, int]: + """统计复权因子覆盖缺口：返回 (行情行数, 无对应 adjust_factor 的行数)。 + + 用途：复权回测必须如实告知「哪些行是按 1.0 兜底未折算的」 + （AGENT.md §24 禁止假装支持）。单条聚合查询，代价可忽略。 + """ + syms = list(symbols) + if not syms: + return 0, 0 + total = self._session.scalar( + select(func.count()) + .select_from(StockDailyModel) + .where( + StockDailyModel.symbol.in_(syms), + StockDailyModel.trade_date >= start, + StockDailyModel.trade_date <= end, + StockDailyModel.adjust == "none", + ) + ) + missing = self._session.scalar( + select(func.count()) + .select_from(StockDailyModel) + .outerjoin( + AdjustFactorModel, + and_( + AdjustFactorModel.symbol == StockDailyModel.symbol, + AdjustFactorModel.trade_date == StockDailyModel.trade_date, + ), + ) + .where( + StockDailyModel.symbol.in_(syms), + StockDailyModel.trade_date >= start, + StockDailyModel.trade_date <= end, + StockDailyModel.adjust == "none", + AdjustFactorModel.id.is_(None), + ) + ) + return int(total or 0), int(missing or 0) + def latest_date(self, symbol: str) -> date | None: return self._session.scalar( select(StockDailyModel.trade_date) @@ -244,6 +407,179 @@ class SqlAlchemyAdjustFactorRepository: return [AdjustFactor.model_validate(r, from_attributes=True) for r in rows] +class SqlAlchemyDailyBasicRepository: + """每日指标（估值 / 股息率 / 市值）仓储实现。""" + + def __init__(self, session: Session) -> None: + self._session = session + + def upsert_many(self, rows: Sequence[DailyBasic]) -> int: + return _upsert_by_business_key(self._session, DailyBasic, rows) + + def get_range(self, symbol: str, start: date, end: date) -> list[DailyBasic]: + rows = self._session.scalars( + select(DailyBasicModel) + .where( + DailyBasicModel.symbol == symbol, + DailyBasicModel.trade_date >= start, + DailyBasicModel.trade_date <= end, + ) + .order_by(DailyBasicModel.trade_date) + ).all() + return [DailyBasic.model_validate(r, from_attributes=True) for r in rows] + + def get_range_many( + self, + symbols: Sequence[str], + start: date, + end: date, + ) -> list[DailyBasic]: + if not symbols: + return [] + rows = self._session.scalars( + select(DailyBasicModel) + .where( + DailyBasicModel.symbol.in_(list(symbols)), + DailyBasicModel.trade_date >= start, + DailyBasicModel.trade_date <= end, + ) + .order_by(DailyBasicModel.symbol, DailyBasicModel.trade_date) + ).all() + return [DailyBasic.model_validate(r, from_attributes=True) for r in rows] + + def stream_range_many_columns( + self, + symbols: Sequence[str], + start: date, + end: date, + columns: Sequence[str], + ) -> Iterator[tuple]: + """流式 (symbol, trade_date_iso, *float_cols)；SQL 侧 CAST 为 REAL。""" + cols = list(columns) + unknown = [c for c in cols if c not in BASIC_FLOAT_COLUMNS] + if unknown: + raise ValueError(f"不支持的指标列: {unknown}（可用: {BASIC_FLOAT_COLUMNS}）") + if not symbols: + return + numeric_expr = [cast(getattr(DailyBasicModel, c), Float) for c in cols] + stmt = ( + select(DailyBasicModel.symbol, cast(DailyBasicModel.trade_date, String), *numeric_expr) + .where( + DailyBasicModel.symbol.in_(list(symbols)), + DailyBasicModel.trade_date >= start, + DailyBasicModel.trade_date <= end, + ) + .order_by(DailyBasicModel.symbol, DailyBasicModel.trade_date) + .execution_options(yield_per=20000) + ) + result = self._session.execute(stmt) + while True: + chunk = result.fetchmany(20000) + if not chunk: + break + for row in chunk: + yield tuple(row) + + def latest_date(self) -> date | None: + return self._session.scalar( + select(DailyBasicModel.trade_date) + .order_by(DailyBasicModel.trade_date.desc()) + .limit(1) + ) + + def missing_dates(self, start: date, end: date) -> list[date]: + """开市但表内无任何行的交易日（按交易日历判定）。""" + have = select(DailyBasicModel.trade_date).where( + DailyBasicModel.trade_date >= start, + DailyBasicModel.trade_date <= end, + ) + rows = self._session.scalars( + select(TradingCalendarModel.calendar_date) + .where( + TradingCalendarModel.calendar_date >= start, + TradingCalendarModel.calendar_date <= end, + TradingCalendarModel.is_open.is_(True), + TradingCalendarModel.calendar_date.not_in(have), + ) + .order_by(TradingCalendarModel.calendar_date) + ).all() + return list(rows) + + def has_date(self, day: date) -> bool: + exists = self._session.scalar( + select(DailyBasicModel.id).where(DailyBasicModel.trade_date == day).limit(1) + ) + return exists is not None + + +class SqlAlchemyStockNameHistoryRepository: + """名称变更历史仓储：时点名称查询（exclude_st 的点时口径依据）。""" + + def __init__(self, session: Session) -> None: + self._session = session + + def upsert_many(self, rows: Sequence[StockNameHistory]) -> int: + return _upsert_by_business_key(self._session, StockNameHistory, rows) + + def names_as_of(self, symbols: Sequence[str], as_of: date) -> dict[str, str]: + if not symbols: + return {} + stmt = ( + select( + StockNameHistoryModel.symbol, + StockNameHistoryModel.name, + StockNameHistoryModel.start_date, + ) + .where( + StockNameHistoryModel.symbol.in_(list(symbols)), + StockNameHistoryModel.start_date <= as_of, + or_( + StockNameHistoryModel.end_date.is_(None), + StockNameHistoryModel.end_date >= as_of, + ), + ) + .order_by(StockNameHistoryModel.symbol, StockNameHistoryModel.start_date) + ) + out: dict[str, str] = {} + for symbol, name, _start in self._session.execute(stmt): + out[symbol] = name # 升序遍历 → 最后写入的是该时点生效的那个区间 + return out + + def name_spans( + self, symbols: Sequence[str] + ) -> dict[str, list[tuple[date, date | None, str]]]: + if not symbols: + return {} + stmt = ( + select( + StockNameHistoryModel.symbol, + StockNameHistoryModel.start_date, + StockNameHistoryModel.end_date, + StockNameHistoryModel.name, + ) + .where(StockNameHistoryModel.symbol.in_(list(symbols))) + .order_by(StockNameHistoryModel.symbol, StockNameHistoryModel.start_date) + ) + spans: dict[str, list[tuple[date, date | None, str]]] = {} + for symbol, start, end, name in self._session.execute(stmt): + spans.setdefault(symbol, []).append((start, end, name)) + return spans + + def count_rows(self) -> int: + return int(self._session.scalar(select(func.count()).select_from(StockNameHistoryModel)) or 0) + + def namechange_dates(self) -> tuple[date | None, date | None]: + row = self._session.execute( + select( + func.min(StockNameHistoryModel.start_date), + func.max(StockNameHistoryModel.start_date), + ) + ).first() + if not row: + return None, None + return row[0], row[1] + + class SqlAlchemyFinancialRepository: def __init__(self, session: Session) -> None: self._session = session diff --git a/backend/app/infrastructure/persistence/sqlalchemy/repositories/strategy_impl.py b/backend/app/infrastructure/persistence/sqlalchemy/repositories/strategy_impl.py index f0a98a4..b832762 100644 --- a/backend/app/infrastructure/persistence/sqlalchemy/repositories/strategy_impl.py +++ b/backend/app/infrastructure/persistence/sqlalchemy/repositories/strategy_impl.py @@ -77,10 +77,13 @@ class SqlAlchemyStrategyRepository: def _to_entity(row: StrategyModel) -> StrategyDefinition: data = json.loads(row.config_json) - # 列字段由 DB 行回填，避免与 config_json 重复 + # 列字段由 DB 行回填，避免与 config_json 重复。 + # description 必须一并回填：它是列字段（String(300)），save() 会写入， + # 但这里若只从 config_json 里 pop 掉却不回填，读回的策略说明会恒为空串 + # （读写不对称：保存的说明看不到，策略库/编辑页都拿不到）。 for key in ("name", "version", "description", "spec_type"): data.pop(key, None) return StrategyDefinition( - id=row.id, name=row.name, version=row.version, + id=row.id, name=row.name, version=row.version, description=row.description, created_at=row.created_at, **data, ) diff --git a/backend/app/main.py b/backend/app/main.py index ddb87a6..caae71b 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -38,8 +38,34 @@ def _cleanup_stale_jobs() -> None: logger.exception("启动清理残留 Job 失败") +def _log_db_target() -> None: + """启动时把**数据库目标**打印出来（只打主机/库名，绝不打密码）。 + + 为什么值得一行日志：连错库不会有任何报错（回测/归档安静写到另一台机器）， + 而「我到底连的哪个库」是排查这类问题的第一信息。同时把禁用主机的拦截结果显性化。 + """ + try: + from sqlalchemy.engine import make_url + + url = make_url(settings.database_url) + if url.get_backend_name().startswith("sqlite"): + logger.info("数据库目标：SQLite %s", url.database) + else: + logger.info( + "数据库目标：%s %s@%s:%s/%s", + url.get_backend_name(), + url.username or "-", + url.host or "-", + url.port or "-", + url.database or "-", + ) + except Exception: # noqa: BLE001 —— 仅日志，失败不影响启动 + logger.warning("数据库目标解析失败（仅日志）") + + @asynccontextmanager async def lifespan(_app: FastAPI): + _log_db_target() _cleanup_stale_jobs() yield diff --git a/backend/app/quant/engine.py b/backend/app/quant/engine.py index b634047..4c16c08 100644 --- a/backend/app/quant/engine.py +++ b/backend/app/quant/engine.py @@ -12,14 +12,14 @@ import pandas as pd from app.domain.entities.research import BacktestResult, FactorTestReport, ResearchSpec from app.quant.factors import FactorError, get_factor from app.quant.local_engine import TopKBacktestRunner, run_spec_factor_test -from app.quant.selection import score_panel_for_factors +from app.quant.selection import condition_needed_columns, score_panel_for_factors # LocalEngine 路径恒需 close（TopK 收盘撮合 / 前瞻收益） _CLOSE = {"close"} def factor_required_columns(spec: ResearchSpec) -> set[str]: - """spec 因子计算 + 回测撮合所需的行情数值列（含 close）。""" + """spec 因子计算 + 选股条件 + 回测撮合所需的行情数值列（含 close）。""" needed = set(_CLOSE) for fs in spec.factors: try: @@ -27,6 +27,10 @@ def factor_required_columns(spec: ResearchSpec) -> set[str]: except FactorError: continue # 未知因子由执行期统一报错 needed.update(defn.requires) + if spec.conditions: + # 条件字段同样决定装配列（dv_ratio / volume / ma60 依赖列等）； + # 与选股路径共用 condition_needed_columns（v2 §25 一致性） + needed.update(condition_needed_columns(spec)) return needed @@ -42,7 +46,14 @@ class QuantEngine(Protocol): self, daily: pd.DataFrame, spec: ResearchSpec, horizon_days: int = 21 ) -> FactorTestReport: ... - def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult: ... + def run_backtest( + self, daily: pd.DataFrame, spec: ResearchSpec, eligibility_fn=None + ) -> BacktestResult: + """eligibility_fn(as_of: date) -> set[symbol] | None：选股条件过滤（可选）。 + + None 表示不过滤；由业务层注入（复用 selection.eligible_symbols）， + 保证「历史某日 Selection == 回测当日 Selection」（v3 §28）。 + """ class LocalEngine: @@ -59,8 +70,10 @@ class LocalEngine: report, _panels = run_spec_factor_test(daily, spec, horizon_days) return report - def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult: + def run_backtest( + self, daily: pd.DataFrame, spec: ResearchSpec, eligibility_fn=None + ) -> BacktestResult: # 评分面板与选股共用同一构建（v2 §25：回测与当前选股同引擎） score = score_panel_for_factors(daily, spec.factors) close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index() - return TopKBacktestRunner(spec, score, close).run() + return TopKBacktestRunner(spec, score, close, eligibility_fn=eligibility_fn).run() diff --git a/backend/app/quant/factors.py b/backend/app/quant/factors.py index 53a2ee0..0c6c502 100644 --- a/backend/app/quant/factors.py +++ b/backend/app/quant/factors.py @@ -1,9 +1,11 @@ """因子引擎：因子注册表、元数据与计算（Phase 2，低频选股因子）。 -数据形态：行情长表 DataFrame（列 symbol/trade_date/close/high/low/volume/amount）， +数据形态：行情长表 DataFrame（列 symbol/trade_date/close/high/low/volume/amount， +以及经 ResearchService 并入的每日指标列如 dv_ratio/dv_ttm）， 因子计算返回 面板 DataFrame（index=trade_date，columns=symbol）。 -所有内置因子只用行情字段（无财务），天然规避未来函数；财务因子接入时必须以 -announce_date 控制可见性（见 domain.entities.market.FinancialIndicator）。 +行情内置因子只用行情字段（无财务），天然规避未来函数；每日指标（daily_basic） +为逐日时点值，按 trade_date <= as_of 取值同样无未来函数； +财务因子接入时必须以 announce_date 控制可见性（见 domain.entities.market.FinancialIndicator）。 """ from __future__ import annotations @@ -205,3 +207,58 @@ def _ma_bias_20(fields: dict[str, pd.DataFrame]) -> pd.DataFrame: ) def _reversal_5(fields: dict[str, pd.DataFrame]) -> pd.DataFrame: return -1.0 * _rolling_return(fields["close"], 5) + + +# ---------- 每日指标（daily_basic）因子 ---------- +# 数据来源：daily_basic 表（Tushare daily_basic 接口），由 ResearchService / SelectionService +# 装配后并入 daily 长表（见 quant/service.load_basic_df）。requires 里的列名即 +# domain.entities.market.DAILY_BASIC_NUMERIC_FIELDS 中的列。 + +# 特别分红导致的股息率畸高阈值（%）：dv_ratio 会因一次性特别分红冲到 30%+， +# 直接用「最高股息率」排序会被这类非经常性事件占满头部（实测 600738 在 2020-01-02 +# 为 37.2%）。本因子不隐式截断（截断属选股条件，应由用户在 conditions 里显式配置）， +# 但把阈值作为常量暴露，供前端/条件模板引用。 +DIVIDEND_YIELD_SPECIAL_CAP_PCT = 30.0 + + +@register( + FactorDef( + "dividend_yield", + "股息率（近 12 个月现金分红 / 总市值 × 100，%）", + "dv_ratio（Tushare daily_basic，逐日时点值）", + brief=( + "高股息：熊市/震荡市防御性较强，分红提供现金回报底；" + "需警惕「高股息陷阱」——股息率高常因股价下跌或一次性特别分红，" + "建议配合 dv_ratio 上限过滤与盈利质量条件使用。" + ), + frequency="daily", + lookback=0, # 时点截面值，无滚动窗口 + direction="higher_is_better", + requires=("dv_ratio",), + ) +) +def _dividend_yield(fields: dict[str, pd.DataFrame]) -> pd.DataFrame: + """股息率面板（index=trade_date, columns=symbol）。 + + 直接取当日 dv_ratio 时点值：该值由数据源按「过去 12 个月现金分红 / 当日总市值」 + 逐日重算，只含已发生事件，按 trade_date <= as_of 取值即无未来函数。 + 缺失值保持 NaN（由复合分/排序统一 dropna 处理），不做 0 填充 —— 0 会被误读成 + 「股息率为 0 的合格标的」，从而污染横截面排序。 + """ + return fields["dv_ratio"] + + +@register( + FactorDef( + "dividend_yield_ttm", + "股息率 TTM（近 12 个月滚动现金分红 / 总市值 × 100，%）", + "dv_ttm（Tushare daily_basic，逐日时点值）", + brief="同 dividend_yield，但口径为 TTM；与 dv_ratio 多数日期取值一致，可作交叉验证。", + frequency="daily", + lookback=0, + direction="higher_is_better", + requires=("dv_ttm",), + ) +) +def _dividend_yield_ttm(fields: dict[str, pd.DataFrame]) -> pd.DataFrame: + return fields["dv_ttm"] diff --git a/backend/app/quant/local_engine.py b/backend/app/quant/local_engine.py index 35df1d6..f740911 100644 --- a/backend/app/quant/local_engine.py +++ b/backend/app/quant/local_engine.py @@ -1,10 +1,17 @@ """LocalEngine —— 默认研究引擎（纯 pandas，AGENT.md §40 简单可替换优先）。 无未来函数纪律： -- 调仓日 t 的选股只使用 <=t 的因子值与收盘价 -- 成交发生在 t 收盘（价格 = close[t] ± 滑点）；t 当日组合收益用 t-1 收盘持仓结算， +- 择股日 s 的选股只使用 <= s 的因子值、条件字段与收盘价 +- 成交发生在调仓日 t 收盘（价格 = close[t] ± 滑点）；t 当日组合收益用 t-1 收盘持仓结算， 调仓在 t 收盘生效、自 t+1 起计收益 —— 不存在「当日买入当日计收益」的未来函数 +- 顺延买入（defer_buy）只在**之后的交易日**补成交，绝不回溯到择股日之前 - 涨跌停 / 停牌约束按可达信息近似建模，未建模部分显式写入结果 unimplemented + +周期模型（本次扩展，见 ResearchSpec）： +- 择股日集合 S：每 m 个月（selection_interval_months），锚定回测起始月 +- 调仓日集合 R：每 y 个月（rebalance_interval_months，缺省 = m） +- m 未给 → S = R（每次调仓都重新择股，与历史行为一致） +- 候选池 = S 日按因子分排序的 Top n（top_n）；实际持仓 = 池内前 x（hold_top_x） """ from __future__ import annotations @@ -25,6 +32,7 @@ from app.domain.entities.research import ( Position, RankedPick, ResearchSpec, + SymbolCurve, Trade, YearlyReturn, ) @@ -41,9 +49,41 @@ from app.quant.portfolio import ( ) TRADING_DAYS = 252 + +# 个股收益曲线数量上限： +# None（默认）= 不截断，期内持有的每只都输出（「完整存档」；体积由归档侧的 +# 字节预算兜底，见 app/application/services/experiment_archive.py）； +# 数字 = 按 |期末收益| 降序截断，且如实写入 unimplemented 说明。 +# 取值优先级：本模块变量（测试 monkeypatch 用）> config research.archive_curve_limit。 +_MAX_SYMBOL_CURVES: int | None = None + + +def _resolved_curve_limit() -> int | None: + """当前生效的曲线数量上限（None = 完整输出）。""" + if _MAX_SYMBOL_CURVES is not None: + return _MAX_SYMBOL_CURVES + from app.core.config import get_settings + + return get_settings().research_archive_curve_limit + +# 恒定的未建模说明（AGENT.md §24：未实现项必须在结果中显式标注） _DEFAULT_UNIMPLEMENTED = [ "涨跌停按收盘价相对上一有效收盘近似判定（未建模开盘一字 / 集合竞价路径）", "成交假设发生在调仓日收盘（未建模盘中价格路径与流动性冲击）", + ( + "调仓为「全部卖出 → 按目标等权重新买入」，未做权重漂移微调：" + "保留在目标名单中的股票也会产生一次完整买卖往返，交易成本估计偏保守" + ), + ( + "股票池来自本地行情表（已含退市股：stock.status='D' 且带 delist_date，" + "退市日之后自动退出池子）。残余偏差：库里仅有 2019-12 之后退市的标的，" + "更早退市者无行情数据" + ), + ( + "exclude_st 的名称口径见 config_snapshot.price_basis.name_basis：时点口径依赖 " + "stock_name_history（sync namechange），未同步时回退最新名称快照，" + "会漏掉「曾是高股息、后来才变 ST」的股息陷阱样本" + ), ] @@ -57,16 +97,92 @@ def _limit_up_ratio(symbol: str) -> float: return 1.099 -def rebalance_dates(index: pd.Index, rebalance: str, start: date) -> list[pd.Timestamp]: - """按频率取首个交易日（>= start）。""" - periods = index.to_period("M" if rebalance == "monthly" else "W") +def _month_firsts(index: pd.Index) -> list[pd.Timestamp]: + """每个自然月的首个交易日（按 index 顺序）。""" + periods = index.to_period("M") seen: dict = {} order: list[pd.Timestamp] = [] for ts, per in zip(index, periods, strict=True): if per not in seen: seen[per] = ts order.append(ts) - return [ts for ts in order if ts.date() >= start] + return order + + +def _week_firsts(index: pd.Index) -> list[pd.Timestamp]: + """每个自然周的首个交易日。""" + periods = index.to_period("W") + seen: dict = {} + order: list[pd.Timestamp] = [] + for ts, per in zip(index, periods, strict=True): + if per not in seen: + seen[per] = ts + order.append(ts) + return order + + +def _month_seq(ts: pd.Timestamp) -> int: + """月序号（year*12+month），用于「每 m 个月」的锚定计算。""" + return int(ts.year) * 12 + int(ts.month) + + +def rebalance_dates( + index: pd.Index, + rebalance: str, + start: date, + end: date | None = None, + every_months: int | None = None, +) -> list[pd.Timestamp]: + """调仓/择股日集合（按频率取首个交易日，>= start）。 + + every_months=n（n>0）：忽略 rebalance 频率，改用「每 n 个月」—— + 锚定 **首个 >= start 的交易日所在月**（锚点月 t0），取月序号满足 + `(t - t0) % n == 0` 的月份的首个交易日。 + + 这样 2020-01-01 起、n=6 → 2020-01、2020-07、2021-01 …； + 起始日改为 2020-03-15（该月首个交易日 03-02 早于 start）→ **2020-03-16** + （03 月内首个 >= start 的交易日）、2020-09、2021-03 …。 + ⚠️ 刻意**不丢弃锚点月**：若把锚点月整体过滤掉，m=y=6 且起始日非月初时 + 会白等 6 个月才首次建仓（净值在前期恒等于初始资金，指标明显失真）。 + + every_months=None：沿用 weekly / monthly 频率（原行为，保持向后兼容）。 + """ + firsts = _week_firsts(index) if rebalance == "weekly" else _month_firsts(index) + if every_months and every_months > 0: + # 锚点 = start 所在月内首个 >= start 的交易日（可能不是该月首个交易日） + days = pd.DatetimeIndex(index) + after_start = days[days >= pd.Timestamp(start)] + if len(after_start) == 0: + return [] + anchor_ts = after_start[0] + anchor = _month_seq(anchor_ts) + # 后续月份：月序号与锚点月相差整数倍 m，取该月首个交易日 + out = [anchor_ts] + [ + ts + for ts in firsts + if _month_seq(ts) > anchor + and (_month_seq(ts) - anchor) % every_months == 0 + and ts.date() >= start + and ts != anchor_ts + ] + else: + out = [ts for ts in firsts if ts.date() >= start] + if end is not None: + out = [ts for ts in out if ts.date() <= end] + return out + + +@dataclass +class PendingBuy: + """顺延买单：调仓日买不进（涨停/停牌）时挂起，之后逐日重试。 + + 仅当 SelectionSpec.defer_buy=True 时产生；到下一次调仓日仍未成交则作废。 + `budget` 是调仓日按等权/上限为该标的预留的资金，成交时按 min(budget, 可用现金) 执行。 + """ + + symbol: str + budget: float + since: date @dataclass @@ -78,9 +194,15 @@ class EngineResult: class TopKBacktestRunner: - """TopK 等权、固定调仓频率的低频回测。""" + """TopK 等权、固定调仓频率的低频回测（支持择股/调仓双周期与顺延买入）。""" - def __init__(self, spec: ResearchSpec, score: pd.DataFrame, close: pd.DataFrame) -> None: + def __init__( + self, + spec: ResearchSpec, + score: pd.DataFrame, + close: pd.DataFrame, + eligibility_fn=None, + ) -> None: self.spec = spec close = close.copy() close.index = pd.to_datetime(close.index) @@ -89,18 +211,49 @@ class TopKBacktestRunner: self.costs = spec.costs # 上一有效收盘（用于涨跌停与收益结算，处理停牌日） self.prev_close = self.close.ffill().shift(1) + # 条件过滤（可选）：(as_of: date) -> set[symbol] | None + # 由 Service 注入（复用 selection.eligible_symbols），保证回测与选股同一套求值逻辑 + self.eligibility_fn = eligibility_fn # M9-2：调仓意图与信号/成交记录（v3 §20.3/§22.3） self.selection_history: list[RankedPick] = [] self.signal_history: list[ActionRecord] = [] + # 当前候选池（择股日刷新）：current_ranked 为全市场可评分排序，current_pool = 前 n + self.current_ranked: list[str] = [] + self.current_pool: list[str] = [] + # 本次回测期内被持有过的股票（用于个股收益曲线） + self.traded_symbols: list[str] = [] + self._traded: set[str] = set() + # 无前收导致涨停无法判定、按可买处理并**实际成交**的标的集合（结果中如实标注） + self._no_prev_close_symbols: set[str] = set() + + # ---- 主流程 ---- def run(self) -> BacktestResult: end_date = self.spec.period[1] dates = [d for d in self.close.index if self.spec.period[0] <= d.date() <= end_date] - rebal = { - d - for d in rebalance_dates(self.close.index, self.spec.rebalance, self.spec.period[0]) - if d.date() <= end_date - } + if not dates: + raise ValueError( + f"回测区间 {self.spec.period[0]}~{end_date} 内没有任何行情数据，无法回测" + ) + + m = self.spec.effective_selection_months + y = self.spec.effective_rebalance_months + rebal = set( + rebalance_dates( + self.close.index, self.spec.rebalance, self.spec.period[0], end_date, every_months=y + ) + ) + if m is None: + # 未给 m：每次调仓都重新择股（与历史行为一致） + select = set(rebal) + else: + select = set( + rebalance_dates( + self.close.index, self.spec.rebalance, self.spec.period[0], end_date, + every_months=m, + ) + ) + cash = float(self.spec.initial_capital) shares: dict[str, float] = {} entry_date: dict[str, date] = {} @@ -109,6 +262,10 @@ class TopKBacktestRunner: trades: list[Trade] = [] positions: list[Position] = [] notional: list[float] = [] + pending: list[PendingBuy] = [] + # 个股收益曲线：cum = 该股「持仓期间」的累计净值（1.0 = 未涨未跌） + cum: dict[str, float] = {} + curve_rows: dict[str, list[CurvePoint]] = {} def _value(d: pd.Timestamp) -> float: total = cash @@ -122,21 +279,56 @@ class TopKBacktestRunner: return total for d in dates: + # 1) 先用「上一交易日收盘持仓」结算当日个股收益（与组合净值同一时序口径： + # 当日收益来自昨日持仓）→ 建仓当日不计收益、卖出当日仍有收益 + self._accrue_symbol_returns(d, shares, cum, curve_rows) + # 2) 择股 / 调仓（成交发生在当日收盘） + if d in select: + self.current_ranked, self.current_pool = self._select(d) if d in rebal: + # 上一次调仓挂起的顺延单作废（只在两次调仓之间有效） + pending = [] cash = self._rebalance( - d, cash, shares, entry_date, entry_price, trades, positions, notional + d, cash, shares, entry_date, entry_price, trades, positions, notional, + pending, ) + elif pending: + cash = self._fill_pending(d, cash, shares, entry_date, entry_price, pending, notional) equity_rows[d] = _value(d) + # 3) 建仓当日补「基准点」：成交在当日收盘、收益自次日起计；该点使 BUY 标注 + # 能精确落在曲线上，也让多段持仓的分段起点可见（见 _mark_curve_dates） + self._mark_curve_dates(d, shares, cum, curve_rows) equity = pd.Series(equity_rows).sort_index() - return self._to_result(equity, trades, positions, notional) + return self._to_result(equity, trades, positions, notional, cum, curve_rows) + + # ---- 择股（择股日 s：只用 <= s 的数据） ---- + + def _select(self, d: pd.Timestamp) -> tuple[list[str], list[str]]: + """返回 (全市场可评分排序, 候选池 top n)，并记录 selection_history。""" + score_d = self.score.loc[d].dropna() + eligible = None + if self.eligibility_fn is not None: + eligible = self.eligibility_fn(d.date()) + if eligible is not None: + score_d = score_d[score_d.index.isin(eligible)] + ranked = score_d.sort_values(ascending=False).index.tolist() + n = self.spec.selection.top_n + pool = ranked[:n] + day = d.date() + for rank, sym in enumerate(pool, start=1): + self.selection_history.append( + RankedPick(date=day, symbol=sym, rank=rank, score=round(float(score_d[sym]), 6)) + ) + return ranked, pool # ---- 调仓（t 收盘执行，自 t+1 生效） ---- - def _rebalance(self, d, cash, shares, entry_date, entry_price, trades, positions, notional): + def _rebalance( + self, d, cash, shares, entry_date, entry_price, trades, positions, notional, pending + ): close_d = self.close.loc[d] prev_d = self.prev_close.loc[d] - sold_notional = 0.0 day = d.date() # 1) 卖出：逐持仓记录 SELL 意图与实际成交（跌停/无价则保留并说明） @@ -156,12 +348,11 @@ class TopKBacktestRunner: continue # 跌停无法卖出：保留到下一调仓 qty = shares[s] proceeds = qty * float(c) * (1 - self.costs.slippage_rate) - fee = proceeds * (self.costs.commission_rate + self.costs.stamp_tax_rate) + commission = max(proceeds * self.costs.commission_rate, self.costs.min_commission) + fee = commission + proceeds * self.costs.stamp_tax_rate cash += proceeds - fee - sold_notional += proceeds self.signal_history.append( - ActionRecord(date=day, symbol=s, signal="SELL", filled=True, - price=float(c)) + ActionRecord(date=day, symbol=s, signal="SELL", filled=True, price=float(c)) ) trades.append( Trade( @@ -177,43 +368,57 @@ class TopKBacktestRunner: entry_date.pop(s, None) entry_price.pop(s, None) - # 2) 买入：先记录「选股意图」（= select(as_of) 前 top_n，v3 §22.3） - score_d = self.score.loc[d].dropna() - top = score_d.sort_values(ascending=False).index.tolist() - top_n = self.spec.selection.top_n - picks = top[:top_n] - for rank, sym in enumerate(picks, start=1): - self.selection_history.append( - RankedPick(date=day, symbol=sym, rank=rank, - score=round(float(score_d[sym]), 6)) + # 2) 买入意图：候选池（= selection_history 记录的那批） + picks = list(self.current_pool) + x = min(self.spec.selection.x, len(picks)) + sel = self.spec.selection + if x < self.spec.selection.x: + self.signal_history.append( + ActionRecord( + date=day, + symbol="", + signal="BUY", + filled=False, + reject_reason=( + f"候选池仅 {len(picks)} 只（< 目标持仓 x={self.spec.selection.x}），" + "按池内数量持仓" + ), + ) ) - # 执行：顺序寻找可买（涨停/无价剔除；替补仅在意图被拒时进入） def _buyable(sym) -> tuple[bool, str | None]: c, p = close_d[sym], prev_d[sym] - if _nan(c) or _nan(p) or p <= 0: + if _nan(c): return False, "无行情（停牌），无法买入" + if _nan(p) or p <= 0: + # 无有效前收（数据窗口起点 / 长期停牌后复牌）：无法判定涨停 → 按可买处理。 + # 这里不计数：_buyable 是纯探测函数（替补扫描会重复调用同一标的）， + # 计数放在真实成交路径 `_execute_buy`，避免把探测次数报成买入次数。 + return True, None if c / p >= _limit_up_ratio(sym): return False, "涨停，无法追买" return True, None + # 目标名单：默认 = 池内前 x；allow_substitute=True 时从全市场排序继续往下找 targets: list[str] = [] - for sym in top: - if len(targets) >= top_n: - break - ok, _ = _buyable(sym) - if ok: - targets.append(sym) - target_set = set(targets) + if sel.allow_substitute: + for sym in self.current_ranked: + if len(targets) >= self.spec.selection.x: + break + ok, _ = _buyable(sym) + if ok: + targets.append(sym) + else: + targets = picks[:x] - # BUY 信号/成交记录：意图入选（filled）或意图被拒（原因）；替补成交同样如实记录 + pending_specs: list[tuple[str, str | None]] = [] + spends: dict[str, float] = {} if targets: cap = self.spec.portfolio.max_position_pct if cap is None: - # 默认等权（与原实现位级一致，保持回归数值不变） + # 默认等权：按「目标持仓数」均分可用现金（顺延未成交的部分留作现金） budget = equal_weight_budget(cash, len(targets)) spends = {s: budget for s in targets} - total_spend = budget * len(targets) else: # Portfolio v1.1：按单股上限（相对当日组合市值）分配，超出部分留现金 equity_now = cash + sum( @@ -222,31 +427,61 @@ class TopKBacktestRunner: if qty > 0 and not _nan(self.close.at[d, s]) ) spends = allocate_with_max_position(cash, targets, equity_now, cap) - total_spend = sum(spends.values()) + for s in targets: budget = spends[s] if budget <= 1e-9: + # 分配额过小（可用现金≈0 或上限约束）：不成交且无额度可顺延，如实留痕 + self.signal_history.append( + ActionRecord( + date=day, symbol=s, signal="BUY", filled=False, + reject_reason="分配额不足（可用现金≈0），未成交", + ) + ) continue - c = float(close_d[s]) - price_in = c * (1 + self.costs.slippage_rate) - invest = budget * (1 - self.costs.commission_rate) - shares[s] = invest / price_in - entry_date[s] = day - entry_price[s] = price_in - notional.append(budget) + ok, reason = _buyable(s) + if not ok: + if sel.defer_buy: + # 顺延：挂单到之后首个可成交交易日（本次不成交，资金留现金） + pending_specs.append((s, reason)) + self.signal_history.append( + ActionRecord( + date=day, symbol=s, signal="BUY", filled=False, + reject_reason=f"{reason}，顺延到之后首个可成交日买入", + ) + ) + else: + self.signal_history.append( + ActionRecord( + date=day, symbol=s, signal="BUY", filled=False, + reject_reason=reason or "不可买入", + ) + ) + continue + if not self._execute_buy( + s, budget, d, close_d[s], shares, entry_date, entry_price, notional + ): + self.signal_history.append( + ActionRecord( + date=day, symbol=s, signal="BUY", filled=False, + reject_reason="预算不足以覆盖最低佣金，未成交", + ) + ) + continue + cash -= budget + + # 替补模式下目标名单取自 n 名之外，池内被跳过的标的也要记录意图， + # 否则「信号有了却没买」无法解释（v3 §20.3 Signal↔Fill 透明化）。 + # 非替补模式下 targets == picks[:x]，池内标的都已在上面留痕，无需再遍历。 + if sel.allow_substitute: + for sym in picks: + if sym in set(targets): + continue + _ok, reason = _buyable(sym) self.signal_history.append( - ActionRecord(date=day, symbol=s, signal="BUY", filled=True, - price=round(price_in, 4)) + ActionRecord(date=day, symbol=sym, signal="BUY", filled=False, + reject_reason=reason or "资金不足（未成交）") ) - cash -= total_spend - for sym in picks: - if sym in target_set: - continue - _ok, reason = _buyable(sym) - self.signal_history.append( - ActionRecord(date=day, symbol=sym, signal="BUY", filled=False, - reject_reason=reason or "资金不足（未成交）") - ) # 3) 记录调仓后仓位 total = cash + sum( @@ -262,11 +497,120 @@ class TopKBacktestRunner: date=day, symbol=s, weight=float(qty * self.close.at[d, s] / total) ) ) + # 顺延单登记：预留额度 = 调仓日的等权/上限分配额（不因后续价格变化而变） + for sym, _reason in pending_specs: + pending.append(PendingBuy(symbol=sym, budget=spends.get(sym, 0.0), since=day)) return cash + def _execute_buy( + self, s, budget, d, close_value, shares, entry_date, entry_price, notional + ) -> bool: + """按收盘价 + 滑点买入；佣金（含最低佣金）从投入资金中扣除。 + + 现金支出恒为 budget：shares = (budget - 佣金) / (收盘价 × (1 + 滑点))。 + 返回是否成交（预算不足以覆盖最低佣金时不成交，调用方不得扣减现金）。 + """ + c = float(close_value) + price_in = c * (1 + self.costs.slippage_rate) + commission = max(budget * self.costs.commission_rate, self.costs.min_commission) + invest = budget - commission + if invest <= 0: + return False + # 累加而非覆盖：避免「跌停/停牌未卖出而保留的旧仓位」被静默清零 + shares[s] = shares.get(s, 0.0) + invest / price_in + entry_date[s] = d.date() + entry_price[s] = price_in + prev = self.prev_close.at[d, s] if d in self.prev_close.index else float("nan") + if _nan(prev) or prev <= 0: + self._no_prev_close_symbols.add(s) # 无前收→涨停不可判定，如实记入标注 + notional.append(budget) + self.signal_history.append( + ActionRecord(date=d.date(), symbol=s, signal="BUY", filled=True, + price=round(price_in, 4)) + ) + if s not in self._traded: + self._traded.add(s) + self.traded_symbols.append(s) + return True + + # ---- 顺延买入（defer_buy）：之后逐日重试 ---- + + def _fill_pending(self, d, cash, shares, entry_date, entry_price, pending, notional): + close_d = self.close.loc[d] + prev_d = self.prev_close.loc[d] + remaining: list[PendingBuy] = [] + for order in pending: + if order.symbol in shares and shares[order.symbol] > 0: + continue # 期间已通过其他路径持有 → 撤销该顺延单 + c, p = close_d.get(order.symbol), prev_d.get(order.symbol) + if _nan(c) or _nan(p) or p <= 0: + remaining.append(order) + continue + if c / p >= _limit_up_ratio(order.symbol): + remaining.append(order) # 仍涨停 → 继续顺延 + continue + budget = min(order.budget, cash) + if budget <= 1e-9: + remaining.append(order) # 无可用现金（理论上不会发生） + continue + if not self._execute_buy( + order.symbol, budget, d, c, shares, entry_date, entry_price, notional + ): + remaining.append(order) # 预算不足：保留挂单（下日现金可能已变化） + continue + cash -= budget + pending[:] = remaining + return cash + + # ---- 个股收益曲线 ---- + + def _accrue_symbol_returns(self, d, shares, cum, curve_rows) -> None: + """逐日累计各持仓股的「持仓期收益」（以建仓日收盘为 0% 基准）。 + + 口径：cum 以 1.0 起算，仅在该股**持有期间**按日复利（close/prev_close）。 + 本方法在当日调仓**之前**调用，因此： + - 建仓当日不计收益（成交发生在当日收盘）→ 不存在当日买入当日计收益的未来函数 + - 卖出当日仍计收益（当日收益来自昨日持仓） + 未持有期间不产生数据点（曲线不落点），多段持仓则以 cum 连乘衔接； + 前端以买卖点标注区分各段持仓区间。 + """ + prev_d = self.prev_close.loc[d] + close_d = self.close.loc[d] + for s, qty in shares.items(): + if qty <= 0: + continue + c, p = close_d.get(s), prev_d.get(s) + if _nan(c) or _nan(p) or p <= 0: + continue # 停牌/无前收：无有效收益 + cum[s] = cum.get(s, 1.0) * (float(c) / float(p)) + # 只为「当日持有」的股票落点（未持有期间不落点，显著压缩结果体积） + for s, qty in shares.items(): + if qty <= 0: + continue + curve_rows.setdefault(s, []).append( + CurvePoint(date=d.date(), value=round((cum.get(s, 1.0) - 1.0) * 100, 4)) + ) + + def _mark_curve_dates(self, d, shares, cum, curve_rows) -> None: + """为当日持有但尚未落点的股票补一个基准点（建仓当日 / 顺延成交当日）。 + + 值为该股当前的 `cum`（新标的为 1.0 → 0%，复买标的延续上一段的累计值）， + 因此曲线总能在买卖点当日取到数值，前端标注不会因缺数据点而被丢弃。 + """ + day = d.date() + for s, qty in shares.items(): + if qty <= 0: + continue + points = curve_rows.setdefault(s, []) + if points and points[-1].date == day: + continue + points.append( + CurvePoint(date=day, value=round((cum.get(s, 1.0) - 1.0) * 100, 4)) + ) + # ---- 指标 ---- - def _to_result(self, equity, trades, positions, notional) -> BacktestResult: + def _to_result(self, equity, trades, positions, notional, cum, curve_rows) -> BacktestResult: start, end = equity.index[0].date(), equity.index[-1].date() init = float(self.spec.initial_capital) final = float(equity.iloc[-1]) @@ -322,6 +666,7 @@ class TopKBacktestRunner: total_trades=len(trades), avg_turnover_pct=round(avg_turn, 2), ) + curves, curve_note = self._symbol_curves(curve_rows, cum) return BacktestResult( summary=summary, equity_curve=eq_pts, @@ -333,11 +678,78 @@ class TopKBacktestRunner: selection_history=self.selection_history, signal_history=self.signal_history, fills=[a for a in self.signal_history if a.filled], + symbol_curves=curves, turnover_pct=round(sum(notional) / max(init, 1) * 100, 2), - unimplemented=list(_DEFAULT_UNIMPLEMENTED) + unimplemented_notes(self.spec.portfolio), + unimplemented=self._unimplemented(curve_note), config_snapshot=self.spec.model_dump(mode="json"), ) + def _symbol_curves(self, curve_rows, cum) -> tuple[list[SymbolCurve], str | None]: + """按「期末收益绝对值」降序输出个股曲线（前端默认展示前若干只）。 + + 返回 (曲线列表, 截断说明)。默认**不截断**（`config research.archive_curve_limit` + 为 null）：期内持有的每只都输出，保证归档完整；体积由归档侧的字节预算兜底 + （见 experiment_archive）。仅当配置了数字上限时才截断，并如实标注哪一部分 + 被丢弃、为什么（AGENT §24：不静默降级，绝不假装完整）。 + """ + marks: dict[str, list[ActionRecord]] = {} + for a in self.signal_history: + if a.filled and a.symbol: + marks.setdefault(a.symbol, []).append(a) + out: list[SymbolCurve] = [] + for s, points in curve_rows.items(): + if not points: + continue + out.append( + SymbolCurve( + symbol=s, + points=points, + marks=marks.get(s, []), + final_return_pct=round((cum.get(s, 1.0) - 1.0) * 100, 4), + ) + ) + out.sort(key=lambda c: abs(c.final_return_pct), reverse=True) + note = None + limit = _resolved_curve_limit() + if limit is not None and len(out) > limit: + note = ( + f"个股收益曲线仅输出收益绝对值最大的 {limit} 只" + f"（期内共持有 {len(out)} 只）：完整明细见 trades / signal_history" + ) + out = out[:limit] + return out, note + + def _unimplemented(self, curve_note: str | None = None) -> list[str]: + notes = list(_DEFAULT_UNIMPLEMENTED) + unimplemented_notes(self.spec.portfolio) + if self._no_prev_close_symbols: + notes.append( + f"有 {len(self._no_prev_close_symbols)} 只标的成交时缺少上一有效收盘价，" + "无法判定涨停（数据窗口起点或长期停牌后复牌），按可买处理" + ) + if curve_note: + notes.append(curve_note) + sel = self.spec.selection + m = self.spec.effective_selection_months + y = self.spec.effective_rebalance_months + if m is not None and y is not None and y < m: + notes.append( + f"调仓间隔 y={y} 个月 < 择股间隔 m={m} 个月：两次择股之间会复用同一候选池" + "（池子陈旧），并非每次调仓都重新择股" + ) + if sel.defer_buy: + notes.append( + "顺延买入：调仓日涨停/停牌无法买入的标的挂单至之后首个可成交交易日，" + "按该日收盘价成交；到下一次调仓仍未成交则作废并留作现金" + ) + if self.spec.price_adjustment == "none": + notes.append( + "行情口径为不复权：现金分红未计入收益，除权日的价格下移会被计为亏损。" + "股息类策略建议使用 price_adjustment=hfq（后复权）" + ) + if not self.spec.conditions: + notes.append("未配置选股过滤条件（conditions），候选池仅由 universe + 因子排序决定") + return notes + def run_spec_factor_test( daily: pd.DataFrame, @@ -357,7 +769,13 @@ def run_spec_factor_test( def _nan(v) -> bool: + """缺失判定：None / NaN / 不可转 float 一律视为「无有效值」。 + + 注意 Series.get(key) 对不存在的键返回 None（而非 NaN），故必须把 None 判为缺失。 + """ + if v is None: + return True try: return bool(math.isnan(float(v))) except (TypeError, ValueError): - return False + return True \ No newline at end of file diff --git a/backend/app/quant/qlib_adapter/engine.py b/backend/app/quant/qlib_adapter/engine.py index 45c34da..305766d 100644 --- a/backend/app/quant/qlib_adapter/engine.py +++ b/backend/app/quant/qlib_adapter/engine.py @@ -61,7 +61,14 @@ class QlibEngine(QuantEngine): report, _panels = run_spec_factor_test(daily, spec, horizon_days) return report - def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult: + def run_backtest( + self, daily: pd.DataFrame, spec: ResearchSpec, eligibility_fn=None + ) -> BacktestResult: + """回测（与 LocalEngine 共用 TopKBacktestRunner → 口径一致）。 + + `eligibility_fn`（选股条件/时点 ST 过滤）必须透传，否则条件与 + `exclude_st` 在 Qlib 引擎下会被**静默忽略**（AGENT.md §24 禁止假装支持）。 + """ panels = build_factor_panels(daily, spec.factors) score = composite_score(panels) @@ -78,7 +85,7 @@ class QlibEngine(QuantEngine): ) close = close.sort_index() - result = TopKBacktestRunner(spec, score, close).run() + result = TopKBacktestRunner(spec, score, close, eligibility_fn=eligibility_fn).run() result.config_snapshot = spec.model_dump(mode="json") note = _ENGINE_NOTE result.unimplemented = [note, *result.unimplemented] diff --git a/backend/app/quant/selection.py b/backend/app/quant/selection.py index d26b702..8cb801c 100644 --- a/backend/app/quant/selection.py +++ b/backend/app/quant/selection.py @@ -14,7 +14,7 @@ from datetime import date import pandas as pd -from app.domain.entities.market import FinancialIndicator +from app.domain.entities.market import DAILY_BASIC_NUMERIC_FIELDS, FinancialIndicator from app.domain.entities.selection import ( SelectionCandidate, SelectionQuery, @@ -182,8 +182,12 @@ _STATIC_PREFIX = "static." _FUNDAMENTAL_PREFIX = "fundamental." -def condition_needed_columns(query: SelectionQuery) -> set[str]: - """条件引用的行情列（fundamental/static 走元数据与财务表，不需要行情列）。""" +def condition_needed_columns(query) -> set[str]: + """条件引用的行情/指标列（fundamental/static 走元数据与财务表，不需要面板列）。 + + query 可以是 SelectionQuery，也可以是任何带 `conditions` 的对象 + （ResearchSpec 亦然）—— 回测与选股共用本函数，保证列裁剪一致。 + """ needed = {"close"} names = [c.field for c in query.conditions] + [ c.ref for c in query.conditions if c.ref and not c.ref.startswith(_FUNDAMENTAL_PREFIX) @@ -195,16 +199,110 @@ def condition_needed_columns(query: SelectionQuery) -> set[str]: if f not in _TECH_DERIVED: needed.add(f) continue + if f in DAILY_BASIC_NUMERIC_FIELDS: + needed.add(f) # 每日指标列（dv_ratio / pe / pb / total_mv …） + continue try: # 其余按已注册因子处理 defn, _fn = get_factor(f) except FactorError: raise ValueError( - f"条件字段未知：{f}（可用: 行情列/ma20/ma60/已注册因子/static.*/fundamental.*）" + f"条件字段未知：{f}（可用: 行情列/ma20/ma60/已注册因子/" + "每日指标列(dv_ratio 等)/static.*/fundamental.*）" ) from None needed.update(defn.requires) return needed +def build_condition_fields( + daily: pd.DataFrame, + conditions, + obs: pd.Timestamp, +) -> dict[str, pd.Series]: + """条件各字段在 obs（<= as_of 的最近交易日）的截面值。 + + 返回 {字段名: Series(index=symbol)}，覆盖： + - 行情原列：close / open / high / low / volume / amount + - 技术派生：ma20 / ma60 + - 每日指标列：dv_ratio / dv_ttm / pe / pb / total_mv …（由 Service 并入 daily） + - 已注册因子：momentum_60 等（在 <=obs 的截断数据上计算，无未来函数） + + 回测与选股共用本函数（v2 §25 一致性）。 + """ + view = daily[pd.to_datetime(daily["trade_date"]) <= obs] + if view.empty: + return {} + close = view.pivot(index="trade_date", columns="symbol", values="close").sort_index() + close.index = pd.to_datetime(close.index) + + fields: dict[str, pd.Series] = {} + for col in ("open", "high", "low", "volume", "amount"): + if col in view.columns: + panel = view.pivot(index="trade_date", columns="symbol", values=col).sort_index() + panel.index = pd.to_datetime(panel.index) + if obs in panel.index: + fields[col] = panel.loc[obs] + if obs in close.index: + fields["close"] = close.loc[obs] + ma20 = close.rolling(20).mean() + ma60 = close.rolling(60).mean() + if obs in ma20.index: + fields["ma20"] = ma20.loc[obs] + if obs in ma60.index: + fields["ma60"] = ma60.loc[obs] + + wanted: set[str] = set() + for cond in conditions: + for f in (cond.field, cond.ref): + if not f or f.startswith((_STATIC_PREFIX, _FUNDAMENTAL_PREFIX)): + continue + if f in _TECH_DERIVED or f in fields: + continue + wanted.add(f) + for f in sorted(wanted): + if f in DAILY_BASIC_NUMERIC_FIELDS: + if f in view.columns: + panel = view.pivot(index="trade_date", columns="symbol", values=f).sort_index() + panel.index = pd.to_datetime(panel.index) + if obs in panel.index: + fields[f] = panel.loc[obs] + continue + try: + _defn, panel = compute_factor(f, view) + except FactorError: + continue # 已在 condition_needed_columns 报错；此处防御 + if obs in panel.index: + fields[f] = panel.loc[obs] + return fields + + +def eligible_symbols( + candidates, + conditions, + statics: dict[str, dict], + fields: dict[str, pd.Series], + financial: dict[str, FinancialIndicator], +) -> dict[str, list[str]]: + """逐股求值全部条件（AND），返回 {symbol: [各条件通过情况文案]}（仅通过者）。 + + `candidates` 限定参与求值的股票（通常 = universe 过滤后的 symbol 列表）。 + 回测（ResearchSpec.conditions）与选股（SelectionQuery.conditions）共用， + 确保「历史某日 Selection == 回测当日 Selection」（v2 §25 / v3 §28）。 + """ + passed: dict[str, list[str]] = {} + for sym in candidates: + statuses: list[str] = [] + all_ok = True + for cond in conditions: + ok = _eval_condition(cond, sym, statics, fields, financial) + statuses.append( + f"{cond.field} {cond.op} {cond.ref or cond.value}: {'通过' if ok else '未通过'}" + ) + all_ok = all_ok and ok + if all_ok: + passed[sym] = statuses + return passed + + def run_condition_selection( daily: pd.DataFrame, stocks: list, @@ -229,57 +327,22 @@ def run_condition_selection( config_snapshot=query.model_dump(mode="json"), ) - view = daily[pd.to_datetime(daily["trade_date"]) <= obs] - close = view.pivot(index="trade_date", columns="symbol", values="close").sort_index() - close.index = pd.to_datetime(close.index) - - # 技术字段面板（obs 行） - tech: dict[str, pd.Series] = {} - for col in ("close", "open", "high", "low", "volume", "amount"): - if col in view.columns and col != "close": - panel = view.pivot(index="trade_date", columns="symbol", values=col).sort_index() - panel.index = pd.to_datetime(panel.index) - tech[col] = panel.loc[obs] - tech["close"] = close.loc[obs] - tech["ma20"] = close.rolling(20).mean().loc[obs] - tech["ma60"] = close.rolling(60).mean().loc[obs] - # 因子字段按需计算 - for cond in query.conditions: - for f in (cond.field, cond.ref): - if f is None or f.startswith((_STATIC_PREFIX, _FUNDAMENTAL_PREFIX)) or f in tech: - continue - if f in _TECH_DERIVED or f in ("close", "open", "high", "low", "volume", "amount"): - continue - try: - _defn, panel = compute_factor(f, view) - except FactorError: - continue # 已在 condition_needed_columns 报错；此处防御 - if obs in panel.index: - tech[f] = panel.loc[obs] + # 共享字段面板 + 求值器（回测与选股同一实现，v2 §25 一致性） + tech = build_condition_fields(daily, query.conditions, obs) statics = {s.symbol: s.model_dump() for s in stocks} + passed = eligible_symbols(sorted(statics), query.conditions, statics, tech, financial or {}) candidates: list[SelectionCandidate] = [] - passed_symbols: list[str] = [] - for sym in sorted(statics): - statuses: list[str] = [] - all_ok = True - for cond in query.conditions: - ok = _eval_condition(cond, sym, statics, tech, financial or {}) - statuses.append(f"{cond.field} {cond.op} {cond.ref or cond.value}: {'通过' if ok else '未通过'}") - all_ok = all_ok and ok - if all_ok: - passed_symbols.append(sym) - candidates.append( - SelectionCandidate( - symbol=sym, - rank=0, # 占位，末尾统一编号 - score=1.0, - filter_status=statuses, - selection_reason=[f"通过全部 {len(query.conditions)} 条条件"], - ) + for rank, sym in enumerate(sorted(passed), start=1): + candidates.append( + SelectionCandidate( + symbol=sym, + rank=rank, + score=1.0, + filter_status=passed[sym], + selection_reason=[f"通过全部 {len(query.conditions)} 条条件"], ) - for rank, c in enumerate(candidates, start=1): - c.rank = rank + ) return SelectionResult( as_of_date=resolved, diff --git a/backend/app/quant/service.py b/backend/app/quant/service.py index c9fcea4..a1c8c58 100644 --- a/backend/app/quant/service.py +++ b/backend/app/quant/service.py @@ -12,9 +12,15 @@ from __future__ import annotations from collections.abc import Iterable from datetime import date, timedelta +from typing import Any import pandas as pd +from app.domain.entities.market import ( + DAILY_BAR_NUMERIC_FIELDS, + DAILY_BASIC_NUMERIC_FIELDS, + FinancialIndicator, +) from app.domain.entities.research import ( BacktestResult, FactorCorrelationReport, @@ -28,7 +34,12 @@ from app.domain.repositories.market import ( from app.quant.composite import build_factor_panels from app.quant.engine import QuantEngine from app.quant.evaluation import factor_correlation_report -from app.quant.universe import filter_stocks, resolve_members # noqa: F401 —— 范围过滤 +from app.quant.selection import build_condition_fields, eligible_symbols +from app.quant.universe import ( # noqa: F401 —— 范围过滤 + filter_stocks, + names_as_of, + resolve_members, +) # 流式路径每攒多少行落一个 DataFrame 分片（控制 concat 峰值） _FRAME_CHUNK_ROWS = 50_000 @@ -79,22 +90,44 @@ def load_daily_df( end: date, columns: list[str], adjust: str = "none", + price_adjust: str = "none", ) -> pd.DataFrame: """从 Repository 装配行情长表（供研究/选股共用）。 + 两个 adjust 语义不同（v3 §20.5）： + - `adjust`：行集过滤（stock_daily.adjust 主口径 none / 新浪兜底 qfq 行） + - `price_adjust`：复权折算（none/qfq/hfq，按 adjust_factor 在 SQL 侧折算价格列） + 优先走流式列裁剪（stream_range_many_columns，SQL 侧转 REAL、分批）， - 失败或实现缺失时回退 get_range_many / 逐只 get_range。 + 失败或实现缺失时回退 get_range_many / 逐只 get_range（回退路径不支持 price_adjust， + 此时由调用方保证 price_adjust=none，避免静默混入未折算价格）。 """ if not symbols: return pd.DataFrame() streamer = getattr(daily_repo, "stream_range_many_columns", None) if streamer is not None: try: + return _frame_from_stream( + streamer( + symbols, start, end, sorted(columns), + adjust=adjust, price_adjust=price_adjust, + ), + sorted(columns), + ) + except TypeError: # 实现未升级（无 price_adjust 形参）→ 回退 + if price_adjust != "none": + raise return _frame_from_stream( streamer(symbols, start, end, sorted(columns), adjust=adjust), sorted(columns) ) except Exception: # noqa: BLE001 —— 流式路径失败回退旧路径（兼容非 SQL 实现） + if price_adjust != "none": + raise # 复权路径失败必须显式报错，禁止静默退回不复权 pass + elif price_adjust != "none": + raise ValueError( + f"行情仓储不支持流式列裁剪，无法按 {price_adjust} 复权装配（拒绝静默退回不复权）" + ) get_many = getattr(daily_repo, "get_range_many", None) if get_many is not None: bars = list(get_many(symbols, start, end, adjust=adjust)) @@ -105,6 +138,127 @@ def load_daily_df( return bars_to_daily_df(bars) +def split_factor_columns(columns: Iterable[str]) -> tuple[set[str], set[str]]: + """把因子所需列拆成 (行情列, 每日指标列)。 + + 行情列来自 stock_daily（含 close 等）；每日指标列来自 daily_basic（dv_ratio 等）。 + 未识别的列按行情列处理（由仓储的列白名单兜底报错，错误信息更贴近调用点）。 + """ + bars: set[str] = set() + basics: set[str] = set() + for col in columns: + if col in DAILY_BASIC_NUMERIC_FIELDS and col not in DAILY_BAR_NUMERIC_FIELDS: + basics.add(col) + else: + bars.add(col) + return bars, basics + + +def load_basic_df( + basic_repo, + symbols: list[str], + start: date, + end: date, + columns: list[str], +) -> pd.DataFrame: + """装配每日指标长表（daily_basic）—— 仅取所需列。 + + 与 load_daily_df 同构（流式优先、回退批量/逐只）。 + """ + if not symbols or not columns: + return pd.DataFrame() + streamer = getattr(basic_repo, "stream_range_many_columns", None) + if streamer is not None: + try: + return _frame_from_stream( + streamer(symbols, start, end, sorted(columns)), sorted(columns) + ) + except Exception: # noqa: BLE001 —— 回退旧路径 + pass + get_many = getattr(basic_repo, "get_range_many", None) + if get_many is not None: + rows = [ + {"symbol": r.symbol, "trade_date": r.trade_date, **{c: getattr(r, c) for c in columns}} + for r in get_many(symbols, start, end) + ] + else: # 兜底：逐只查询 + rows = [] + for sym in symbols: + for r in basic_repo.get_range(sym, start, end): + rows.append( + { + "symbol": r.symbol, + "trade_date": r.trade_date, + **{c: getattr(r, c) for c in columns}, + } + ) + df = pd.DataFrame(rows) + if df.empty: + return df + df["trade_date"] = pd.to_datetime(df["trade_date"]) + for col in columns: + df[col] = pd.to_numeric(df[col], errors="coerce") + return df + + +def merge_basic_into_daily(daily: pd.DataFrame, basic: pd.DataFrame) -> pd.DataFrame: + """把每日指标列并入行情长表（按 symbol + trade_date 左连接）。 + + 命名冲突保护：daily_basic 也有 close 列，若与行情 close 冲突则**丢弃指标侧**列 + （成交/估值口径以 stock_daily 不复权 close 为准，避免静默改口径，AGENT.md §8）。 + """ + if basic is None or basic.empty: + return daily + if daily is None or daily.empty: + return daily + add_cols = [ + c for c in basic.columns if c not in ("symbol", "trade_date") and c not in daily.columns + ] + if not add_cols: + return daily + left = daily.copy() + left["trade_date"] = pd.to_datetime(left["trade_date"]) + right = basic[["symbol", "trade_date", *add_cols]].copy() + right["trade_date"] = pd.to_datetime(right["trade_date"]) + merged = left.merge(right, on=["symbol", "trade_date"], how="left") + return merged + + +def _fill_names(result: BacktestResult, stocks) -> BacktestResult: + """把股票池的 `symbol → name` 回填进回测结果的各展示结构（就地修改并返回）。 + + 为什么放在服务层而不是引擎里：名称只是展示增强，两个引擎（LocalEngine / QlibEngine） + 都经本方法返回，改一处即同时覆盖；引擎保持「只认 symbol」的纯计算职责，不引入 + 名称查询/IO，也就不必在引擎内部为零散字段各写一次映射。 + + `stocks` 为空（universe 过滤后无标的、或调用方未装配）时**静默跳过**，不抛错： + 名称缺失只影响展示，不应让一个已经算完的回测失败（AGENT.md §24 的诚实标注 + 由引擎的 unimplemented 承担，不由名称缺失承担）。 + """ + if not stocks: + return result + name_map = {s.symbol: s.name for s in stocks if getattr(s, "name", None)} + if not name_map: + return result + + def _fill(rows) -> None: + for row in rows: + if row.name is None: + row.name = name_map.get(row.symbol) + + _fill(result.symbol_curves) + # marks 与 signal_history 在 LocalEngine 中共享同一批 ActionRecord 对象， + # 这里仍显式回填一次：对独立实现（如 Qlib 适配器）也成立，幂等无副作用。 + for curve in result.symbol_curves: + _fill(curve.marks) + _fill(result.positions) + _fill(result.trades) + _fill(result.signal_history) + _fill(result.fills) + _fill(result.selection_history) + return result + + class ResearchService: """研究用例入口（因子测试 / 回测）。依赖注入 Repository 与引擎。""" @@ -114,11 +268,31 @@ class ResearchService: daily_repo: DailyBarRepository, engine: QuantEngine, index_repo=None, + basic_repo=None, + financial_repo=None, + name_repo=None, ) -> None: self._stock_repo = stock_repo self._daily_repo = daily_repo self._engine = engine self._index_repo = index_repo + # 名称变更历史仓储：universe.exclude_st 的**时点**口径依据。 + # 未注入 → 回退 stock.name 最新快照（旧行为，结果页会如实标注残余偏差）。 + self._name_repo = name_repo + # 每日指标仓储（daily_basic）：仅当 spec 因子引用 dv_ratio 等列时使用； + # 未注入而在 spec 中引用 → 明确报错，不做静默降级（否则因子会全是 NaN）。 + self._basic_repo = basic_repo + # 注：复权（qfq/hfq）折算在行情仓储 `stream_range_many_columns` 的 SQL 内完成 + # （与成交价同源，v3 §20.5），业务层不需要 adjust_factor 仓储；缺口统计走 + # `self._daily_repo.count_price_adjust_gaps`。 + # 财务仓储：spec.conditions 引用 fundamental.* 时使用 + self._financial_repo = financial_repo + # 最近一次装配的复权因子缺口报告（未复权时为 None），用于结果如实标注 + self.last_adjust_gaps: dict | None = None + # 最近一次装配的 exclude_st 名称口径（None = 未启用 ST 过滤） + self.last_name_basis: dict | None = None + # 最近一次装配的 universe 股票列表（条件求值需要 static.* 元数据） + self._last_stocks: list = [] def run_factor_test( self, spec: ResearchSpec, horizon_days: int = 21, on_stage=None @@ -140,9 +314,12 @@ class ResearchService: _stage(on_stage, "data_loading") daily = self._load_daily(spec) _stage(on_stage, "backtesting") - result = self._engine.run_backtest(daily, spec) + eligibility = self._build_eligibility(spec, daily) + result = self._engine.run_backtest(daily, spec, eligibility_fn=eligibility) _stage(on_stage, "analysis") - return result + self._annotate_price_basis(result, spec) + # 两个引擎都在此汇合：名称回填只做一次（`_last_stocks` 在 _load_daily 中装配） + return _fill_names(result, self._last_stocks) def run_factor_correlation(self, spec: ResearchSpec) -> FactorCorrelationReport: """多因子两两相关（v3 §12）：同 universe/period 装配 → 横截面相关矩阵。""" @@ -156,20 +333,230 @@ class ResearchService: start, end = spec.period # 回测前预留因子 warmup（lookback≤120 交易日，取 300 自然日余量） data_start = start - timedelta(days=300) + all_stocks = self._stock_repo.list() + name_at, applied = names_as_of(all_stocks, start, self._name_repo) stocks = filter_stocks( - self._stock_repo.list(), spec.universe, as_of=start, + all_stocks, spec.universe, as_of=start, members=resolve_members(self._index_repo, spec.universe, start), + name_at=name_at, ) + # 供结果如实标注：exclude_st 是否用了时点名称、覆盖了多少只 + self.last_name_basis = ( + {"point_in_time": applied[0], "covered": applied[1], "total": len(all_stocks)} + if spec.universe.exclude_st + else None + ) + self._last_stocks = stocks # 引擎所需列裁剪（LocalEngine 只取 close + 因子字段；Qlib 回测取全 OHLCV） required = self._engine.required_columns(spec) - return load_daily_df( + bar_cols, basic_cols = split_factor_columns(required) + symbols = [s.symbol for s in stocks] + # 行集口径恒为 none（主口径）；复权折算由 price_adjust 承担（v3 §20.5 严格分离） + daily = load_daily_df( self._daily_repo, - [s.symbol for s in stocks], + symbols, data_start, end, - sorted(required), - adjust=spec.price_adjustment, + sorted(bar_cols), + adjust="none", + price_adjust=spec.price_adjustment, ) + if spec.price_adjustment != "none": + self.last_adjust_gaps = self._adjust_gap_report(symbols, data_start, end) + if basic_cols: + daily = self._attach_basic(daily, symbols, data_start, end, sorted(basic_cols)) + return daily + + def _adjust_gap_report(self, symbols: list[str], start: date, end: date) -> dict: + """复权因子缺口统计（如实上报「按 1.0 兜底未折算」的行占比）。""" + counter = getattr(self._daily_repo, "count_price_adjust_gaps", None) + if counter is None: + return {"checked": False, "reason": "仓储未实现 count_price_adjust_gaps"} + total, missing = counter(symbols, start, end) + return { + "checked": True, + "rows": total, + "rows_without_factor": missing, + "missing_pct": round(missing / total * 100, 4) if total else 0.0, + } + + def _build_eligibility(self, spec: ResearchSpec, daily: pd.DataFrame): + """按 spec.conditions 构造「择股日 → 合格股票集合」的求值闭包（可选）。 + + 与选股路径共用 `build_condition_fields` / `eligible_symbols`，因此 + 「历史某日 /api/selections 的候选」与「回测在该日的候选池」由同一实现产出 + （v2 §25 / v3 §28 一致性要求）。逐择股日结果缓存，避免重复计算。 + + 未来函数红线：条件字段只取 <= as_of 的截面；fundamental.* 只取 + announce_date <= as_of 的已公告值（由仓储 list_announced_many 保证）。 + """ + if not self._last_stocks: + if not spec.conditions: + return None + raise ValueError("universe 过滤结果为空，无法构造选股条件求值器") + statics = {s.symbol: s.model_dump() for s in self._last_stocks} + candidates = sorted(statics) + # 时点 ST 过滤：exclude_st 必须在**每个择股日**按当时名称重判，否则 + # 「2020 年入池、2022 年才变 ST」的标的会一直被当作合格候选（与 + # /api/selections 的单时点语义不一致，v2 §25）。名称历史缺失时降级为 + # 池子基准日口径（旧行为），由结果 name_basis 如实标注。 + st_fn = self._build_st_filter(spec, candidates) + if not spec.conditions: + if st_fn is None: + return None + # 只有 ST 约束时，eligible 必须是「候选 − 该日 ST」，不能直接返回 st_fn + # （st_fn 返回的是**不合格**集合，直接返回会把语义取反） + allowed_cache: dict[date, set[str]] = {} + + def _st_only(as_of: date) -> set[str]: + if as_of not in allowed_cache: + allowed_cache[as_of] = set(candidates) - st_fn(as_of) + return allowed_cache[as_of] + + return _st_only + uses_fundamental = any( + f.startswith("fundamental.") + for c in spec.conditions + for f in (c.field, c.ref or "") + ) + cache: dict[date, set[str]] = {} + + def _fn(as_of: date) -> set[str]: + if as_of in cache: + return cache[as_of] + financial = ( + self._load_financial(candidates, as_of) if uses_fundamental else {} + ) + fields = build_condition_fields(daily, spec.conditions, pd.Timestamp(as_of)) + if not fields: + cache[as_of] = set() + return cache[as_of] + passed = set( + eligible_symbols(candidates, spec.conditions, statics, fields, financial) + ) + if st_fn is not None: + passed -= st_fn(as_of) # 该日名称含 ST → 不合格 + cache[as_of] = passed + return cache[as_of] + + return _fn + + def _build_st_filter(self, spec: ResearchSpec, candidates: list[str]): + """「择股日 → 该日名称含 ST 的股票集合」；不可用（未启用/未注入）时返回 None。 + + 与 `filter_stocks` 共用 `names_as_of`，因此回测在任一择股日的 + `exclude_st` 结果与该日 `/api/selections` 的候选池同口径（v2 §25）。 + 逐日缓存；每次查询只取该日生效的名称区间。 + """ + if not spec.universe.exclude_st or self._name_repo is None: + return None + cache: dict[date, set[str]] = {} + + def _fn(as_of: date) -> set[str]: + if as_of not in cache: + name_at, applied = names_as_of(self._last_stocks, as_of, self._name_repo) + if not applied[0]: + # 无时点名称可用（表为空/查询失败）：整段时间都退回池子基准日口径， + # 不逐日过滤 —— 与 filter_stocks 的回退一致，且 name_basis 会标注 false + cache[as_of] = set() + else: + # 逐股优先时点名称、缺失回退最新名称快照 —— 必须与 + # filter_stocks 的 `(name_at or {}).get(sym) or s.name` 完全同口径， + # 否则同一择股日「回测入选」与「/api/selections 排除」会打架（v2 §25） + st_syms: set[str] = set() + for st in self._last_stocks: + nm = (name_at or {}).get(st.symbol) or st.name + if nm and "ST" in nm.upper(): + st_syms.add(st.symbol) + cache[as_of] = st_syms + return cache[as_of] + + return _fn + + def _load_financial(self, symbols: list[str], as_of: date) -> dict[str, Any]: + """按 announce_date <= as_of 批量取财务，每 symbol 保留最新一版。""" + if self._financial_repo is None: + raise ValueError( + "回测条件引用了 fundamental.* 字段，但未注入 FinancialRepository。" + "请检查 API/CLI 的依赖装配。" + ) + getter = getattr(self._financial_repo, "list_announced_many", None) + if getter is not None: + rows = list(getter(symbols, as_of)) + else: # 兜底：逐只取最新已公告 + rows = [] + for sym in symbols: + latest = self._financial_repo.latest_announced(sym, as_of) + if latest is not None: + rows.append(latest) + out: dict[str, FinancialIndicator] = {} + for r in rows: # 约定升序返回 → 后者覆盖前者即「最新一版」 + out[r.symbol] = r + return out + + def _annotate_price_basis(self, result, spec: ResearchSpec) -> None: + """把价格口径写入结果（v3 §20.5：adjust_mode / price_basis / execution_price_basis）。 + + 同时如实标注复权因子缺口（AGENT.md §24：未覆盖部分必须显式说明，禁止假装支持）。 + """ + mode = spec.price_adjustment + result.config_snapshot["price_basis"] = { + "adjust_mode": mode, + "price_basis": "adjust_factor" if mode != "none" else "raw_close", + "execution_price_basis": "close_adj" if mode != "none" else "close_raw", + "adjust_gaps": self.last_adjust_gaps, + # exclude_st 的名称口径：时点（stock_name_history）还是最新快照 + "name_basis": self.last_name_basis, + } + nb = self.last_name_basis + if nb is not None: + if nb["point_in_time"]: + result.unimplemented.append( + f"exclude_st 采用**时点名称**（stock_name_history，覆盖 {nb['covered']} 只生效名称）：" + "「曾为高股息、后变 ST」的标的在其非 ST 期间会被正常纳入（股息陷阱可见）" + ) + else: + result.unimplemented.append( + "exclude_st 回退**最新名称快照**（名称变更历史表未同步/未注入）：" + "「曾为高股息、后变 ST/退市」的标的会被整段排除，收益可能被高估；" + "修复：python -m app.cli.sync namechange" + ) + if mode == "none": + return + label = "后复权（hfq）" if mode == "hfq" else "前复权（qfq）" + result.unimplemented.append( + f"价格口径为{label}：价格列 = 原始价 × adjust_factor" + + ("；现金分红按复权因子隐含再投资处理" if mode == "hfq" else "（以前复权归一）") + + "；volume/amount 不做复权折算" + ) + gaps = self.last_adjust_gaps or {} + if gaps.get("checked") and gaps.get("rows_without_factor", 0) > 0: + result.unimplemented.append( + f"复权因子覆盖不全：{gaps['rows_without_factor']} / {gaps['rows']} 行" + f"（{gaps['missing_pct']}%）无对应 adjust_factor，已按系数 1.0 兜底（未折算）" + ) + + def _attach_basic( + self, + daily: pd.DataFrame, + symbols: list[str], + start: date, + end: date, + columns: list[str], + ) -> pd.DataFrame: + """把 daily_basic 列并入行情长表（dv_ratio 等因子依赖）。""" + if self._basic_repo is None: + raise ValueError( + f"因子需要每日指标列 {columns}（daily_basic），但未注入 DailyBasicRepository。" + "请检查 API/CLI 的依赖装配。" + ) + basic = load_basic_df(self._basic_repo, symbols, start, end, columns) + if basic.empty: + raise ValueError( + f"daily_basic 表在 {start}~{end} 无数据，无法计算需要 {columns} 的因子。" + "请先运行：python -m app.cli.sync daily_basic --start 20200101" + ) + return merge_basic_into_daily(daily, basic) def _stage(cb, name: str) -> None: diff --git a/backend/app/quant/strategy_doc.py b/backend/app/quant/strategy_doc.py new file mode 100644 index 0000000..32a9b55 --- /dev/null +++ b/backend/app/quant/strategy_doc.py @@ -0,0 +1,583 @@ +"""策略说明书生成器：ResearchSpec / StrategyDefinition → 一句话说明 + 计算公式 + 步骤 + 注意事项。 + +纯函数模块：无 IO、无 DB、不调用引擎，因而可被 API 复用（含**未保存**的策略即时预览） +并被单测直接覆盖。 + +为什么要有它（需求背景）： +- 策略必须「能被解释」：前端回测页要在一句话里说清「用什么因子/条件、取多少只、 + 多久择股一次、多久调仓一次、什么口径」（AGENT.md §26 不暴露引擎内部配置）； +- 保存策略时说明不得为空：说明由 spec **真实推导**，而非让调用方手写或让 Agent 编造 + （AGENT.md §24 禁止假装支持、§28 Agent 只走 Tool）。 + +两条纪律： +1. 所有文本只能来自 spec 与因子注册表元数据（`FactorDef.description/formula/brief/direction`， + AGENT.md §22 因子元数据要求）；元数据缺失、字段未知、约束未建模一律进 `warnings`， + **绝不臆造公式**，也绝不把未建模的东西写成已支持。 +2. 条件渲染必须与 `app.quant.selection` 的真实求值语义一致（`_eval_condition` 的字段域与 + 运算符分支），步骤必须与 `app.quant.local_engine` 的真实行为一致。两处都以代码为准。 +""" + +from __future__ import annotations + +from collections.abc import Mapping, Sequence +from datetime import date + +from pydantic import BaseModel, Field + +from app.domain.entities.market import ( + DAILY_BAR_NUMERIC_FIELDS, + DAILY_BASIC_NUMERIC_FIELDS, +) +from app.domain.entities.research import ResearchSpec +from app.domain.entities.strategy import StrategyDefinition +from app.quant.factors import FactorDef, FactorError, get_factor + +# StrategyDefinition 没有回测区间字段（区间在回测时补全），但 to_research_spec 的 +# period 是必填的 —— 用一个不可能被误读为真实区间的占位区间满足校验， +# 真正展示时以 `period_known=False` 走占位文案（不抛错、也不假装知道区间）。 +_PLACEHOLDER_PERIOD: tuple[date, date] = (date(1900, 1, 1), date(1900, 1, 2)) +_PERIOD_UNKNOWN_TEXT = "（回测区间未指定：策略定义本身不含 period，展开回测后才确定）" + +# 运算符的展示文本（键集合由 ConditionSpec 的 pattern 保证） +_OP_TEXT: dict[str, str] = { + "gt": ">", + "gte": ">=", + "lt": "<", + "lte": "<=", + "eq": "==", + "ne": "!=", + "in": "∈", + "not_in": "∉", +} + +# 与 selection.build_condition_fields 一致的技术派生字段（非行情原列，需滚动窗口） +_TECH_DERIVED: tuple[str, ...] = ("ma20", "ma60") +_STATIC_PREFIX = "static." +_FUNDAMENTAL_PREFIX = "fundamental." + +_ADJUST_TEXT: dict[str, str] = { + "none": "不复权（原始收盘价；现金分红未计入收益，除权日价格下移会被计为亏损）", + "qfq": "前复权（价格 = 原始价 × adjust_factor，以前复权归一）", + "hfq": "后复权（价格 = 原始价 × adjust_factor，现金分红按因子隐含再投资）", +} + + +class StrategyDoc(BaseModel): + """策略说明书（前端 `
` 直接展示 formula；summary 兼作策略默认 description）。"""
+
+    summary: str = Field(description="一句话功能说明（由 spec 真实推导）")
+    formula: str = Field(description="计算公式（多行文本：打分/过滤/截断/成本与成交口径）")
+    steps: list[str] = Field(default_factory=list, description="执行步骤（按回测引擎真实行为）")
+    warnings: list[str] = Field(
+        default_factory=list,
+        description="需要注意的点（未建模约束 / 未知因子 / 口径近似，诚实标注）",
+    )
+
+
+def describe_strategy(
+    spec: ResearchSpec | StrategyDefinition,
+    *,
+    factor_meta: Mapping[str, FactorDef] | None = None,
+) -> StrategyDoc:
+    """生成策略说明书。
+
+    `spec`：ResearchSpec（回测页参数，含 period）或 StrategyDefinition（策略库资产，
+    无 period）。后者经 `to_research_spec(period=占位)` 展开 —— 区间缺失只影响展示文案
+    （走 `_PERIOD_UNKNOWN_TEXT`），不影响其余推导，更不抛错。
+
+    `factor_meta`：可选的因子元数据覆盖/补充（如内置注册表之外的实验因子）。
+    查找顺序为 `factor_meta` → `app.quant.factors.get_factor`；两处都没有则记入
+    warnings（说明该因子的含义/公式/方向未知，执行期会直接报错）。
+    """
+    rspec, period_text, period_known = _coerce_spec(spec)
+    warnings: list[str] = []
+    factors = _describe_factors(rspec, factor_meta, warnings)
+    conditions = _describe_conditions(rspec, warnings)
+
+    summary = _build_summary(rspec, factors)
+    formula = _build_formula(rspec, factors, conditions, period_text)
+    steps = _build_steps(rspec, period_text)
+    warnings.extend(_collect_warnings(rspec, period_known))
+    return StrategyDoc(summary=summary, formula=formula, steps=steps, warnings=warnings)
+
+
+# ---------- 入参归一化 ----------
+
+
+def _coerce_spec(spec) -> tuple[ResearchSpec, str, bool]:
+    """归一化为 (ResearchSpec, 区间展示文本, 区间是否已知)。
+
+    StrategyDefinition 无 period：按需求用占位区间展开并如实标记「区间未知」，
+    而不是抛错（策略库列表/详情页也要能看说明）。
+    """
+    if isinstance(spec, StrategyDefinition):
+        return spec.to_research_spec(period=_PLACEHOLDER_PERIOD), _PERIOD_UNKNOWN_TEXT, False
+    if isinstance(spec, ResearchSpec):
+        start, end = spec.period
+        return spec, f"{start.isoformat()} ~ {end.isoformat()}", True
+    raise TypeError(
+        "describe_strategy 只接受 ResearchSpec 或 StrategyDefinition，"
+        f"收到 {type(spec).__name__}"
+    )
+
+
+# ---------- 因子元数据 ----------
+
+
+def _lookup_factor(
+    name: str, factor_meta: Mapping[str, FactorDef] | None
+) -> FactorDef | None:
+    """按 name 取因子元数据：先查传入覆盖，再查内置注册表；都取不到返回 None。"""
+    if factor_meta is not None and name in factor_meta:
+        return factor_meta[name]
+    try:
+        defn, _fn = get_factor(name)
+    except FactorError:
+        return None
+    return defn
+
+
+def _describe_factors(
+    spec: ResearchSpec,
+    factor_meta: Mapping[str, FactorDef] | None,
+    warnings: list[str],
+) -> list[tuple[str, float, FactorDef | None]]:
+    """返回 [(因子名, 权重, 元数据|None)]；元数据缺失的因子记入 warnings。"""
+    out: list[tuple[str, float, FactorDef | None]] = []
+    for fs in spec.factors:
+        defn = _lookup_factor(fs.name, factor_meta)
+        if defn is None:
+            warnings.append(
+                f"未知因子 {fs.name}（权重 {fs.weight:g}）：未在因子注册表中登记，"
+                "无法给出含义/公式/方向；回测执行时会直接报错（不会静默忽略该因子）"
+            )
+        out.append((fs.name, float(fs.weight), defn))
+    return out
+
+
+def _direction_text(defn: FactorDef | None) -> str:
+    if defn is None:
+        return "方向未知"
+    return "越高越好" if defn.direction == "higher_is_better" else "越低越好"
+
+
+# ---------- summary ----------
+
+
+def _build_summary(
+    spec: ResearchSpec, factors: list[tuple[str, float, FactorDef | None]]
+) -> str:
+    """一句话说明：股票池 + 排序依据 + 两级截断 + 择股/调仓周期 + 口径与成本。"""
+    sel = spec.selection
+    x = sel.x
+    pool = _universe_text(spec.universe)
+    cond_text = f"，并先通过 {len(spec.conditions)} 条过滤条件" if spec.conditions else ""
+    rank_text = _rank_text(factors)
+    cycle_select, cycle_rebal = _cycle_text(spec)
+    hold_text = (
+        f"先筛出前 {sel.top_n} 只候选池、再持有其中前 {x} 只等权"
+        if x != sel.top_n
+        else f"按复合分取出前 {sel.top_n} 只等权持有"
+    )
+    # 买不进的处置直接决定「实际持有什么」，必须进一句话说明（不能只写在公式里）
+    hold_text += f"（{_buy_mode_short(sel)}）"
+    adjust = spec.price_adjustment
+    adjust_text = {"none": "不复权", "qfq": "前复权", "hfq": "后复权"}[adjust]
+    type_prefix = "" if spec.type == "backtest" else "单因子测试（非组合回测）："
+    return (
+        f"{type_prefix}{pool}{cond_text}，{rank_text}，{hold_text}，"
+        f"{cycle_select}、{cycle_rebal}，{adjust_text}口径、按调仓日收盘价成交"
+        f"（含佣金 {_pct(spec.costs.commission_rate)}／印花税 {_pct(spec.costs.stamp_tax_rate)}"
+        f"／滑点 {_pct(spec.costs.slippage_rate)}）。"
+    )
+
+
+def _universe_text(universe) -> str:
+    """股票池口径（只写**实际生效**的过滤；未建模项只进 warnings，不在这里冒充生效）。"""
+    if universe.index_code:
+        base = f"{universe.index_code} 指数成分（按择股日历史成分）"
+    else:
+        base = "全市场"
+    filters: list[str] = []
+    if universe.symbols:
+        filters.append(f"限定白名单 {len(universe.symbols)} 只")
+    if universe.exclude_st:
+        filters.append("剔除名称含 ST 的股票")
+    if universe.min_listing_days:
+        filters.append(f"上市满 {universe.min_listing_days} 个自然日")
+    # 括号内只放结束句号外的短过滤项，避免出现「（…（…）…）」的嵌套括号
+    return f"{base}（{'，'.join(filters)}）" if filters else base
+
+
+def _rank_text(factors: list[tuple[str, float, FactorDef | None]]) -> str:
+    """排序依据文案：单因子按该因子方向，多因子按复合分（截面 z-score 加权和）。"""
+    names = "、".join(name for name, _w, _defn in factors)
+    if len(factors) == 1:
+        name, _w, defn = factors[0]
+        label = defn.description if defn is not None else name
+        # 方向未知时不猜方向：按「复合分从高到低」描述（复合分本身就是降序取头部）
+        lower = defn is not None and defn.direction == "lower_is_better"
+        direction = "从低到高" if lower else "从高到低"
+        return f"按 {label}{direction}排序（因子 {name}）"
+    return f"按多因子复合分（截面 z-score 加权和）从高到低排序（因子 {names}）"
+
+
+def _cycle_text(spec: ResearchSpec) -> tuple[str, str]:
+    """(择股周期文案, 调仓周期文案)，与 ResearchSpec.effective_* / local_engine 一致。"""
+    m = spec.effective_selection_months
+    y = spec.effective_rebalance_months
+    select_text = f"每 {m} 个月重新择股" if m is not None else "每次调仓都重新择股"
+    if y is not None:
+        rebal_text = f"每 {y} 个月调仓"
+    else:
+        rebal_text = "按月度调仓" if spec.rebalance == "monthly" else "按周度调仓"
+    return select_text, rebal_text
+
+
+def _months_text(months: int | None, fallback: str) -> str:
+    """「每 n 个月」/ 回退文案（月数为 None 时用 fallback），避免散落的 % 格式化。"""
+    return f"每 {months} 个月" if months is not None else fallback
+
+
+# ---------- formula ----------
+
+
+def _build_formula(
+    spec: ResearchSpec,
+    factors: list[tuple[str, float, FactorDef | None]],
+    conditions: list[str],
+    period_text: str,
+) -> str:
+    lines: list[str] = [f"# 策略计算公式（由 spec 真实推导；回测区间：{period_text}）", ""]
+    lines += _formula_scoring(spec, factors)
+    lines += _formula_conditions(conditions)
+    lines += _formula_truncation(spec)
+    lines += _formula_costs(spec)
+    return "\n".join(lines)
+
+
+def _formula_scoring(
+    spec: ResearchSpec, factors: list[tuple[str, float, FactorDef | None]]
+) -> list[str]:
+    lines = [
+        "【1】候选池打分（每个择股日在股票池内做横截面标准化，加权求和）",
+        "  z_f,i = (x_f,i - mean_i(x_f)) / std_i(x_f)        # 因子 f 在当日截面上的 z-score",
+        "  d_f    = -1（方向 lower_is_better）/ +1（higher_is_better）",
+        "  score_i = Σ_f  w_f × d_f × z_f,i                  # 按 score 降序取候选池",
+        "  权重与因子明细：",
+    ]
+    for name, weight, defn in factors:
+        if defn is None:
+            lines.append(f"  - {name}  w = {weight:g}  方向：未知（元数据缺失，见 warnings）")
+            continue
+        lines += [
+            f"  - {name}  w = {weight:g}  方向：{_direction_text(defn)}"
+            f"（{defn.direction}）  频率：{defn.frequency}  回看：{defn.lookback} 个交易日"
+            f"  输入列：{', '.join(defn.requires)}",
+            f"      含义：{defn.description}",
+            f"      公式：{defn.formula}",
+        ]
+        if defn.brief:
+            lines.append(f"      用法：{defn.brief}")
+    lines += [
+        "  说明：因子值缺失（NaN）的标的当日不可评分，不进入候选池；",
+        "        单只股票池（截面不足 2 只）时 z-score 退化为 0，仍保留为可候选值。",
+        "",
+    ]
+    return lines
+
+
+def _formula_conditions(conditions: list[str]) -> list[str]:
+    lines = ["【2】过滤条件（全部 AND；universe 之后、因子排序之前逐股求值）"]
+    if not conditions:
+        lines += ["  （无）候选池仅由 universe + 因子排序决定。"]
+    else:
+        lines += [f"  {text}" for text in conditions]
+    lines += [
+        "  字段域（与选股 /api/selections 使用同一求值器，逐字段语义如下）：",
+        "    · static.<字段>：股票基础信息（industry / market / area / exchange / status …）",
+        "    · fundamental.<字段>：announce_date <= 择股日 的最新**已公告**值（防未来函数）",
+        "    · 其它：当日行情列（close/open/high/low/volume/amount）、ma20 / ma60、",
+        "            每日指标列（dv_ratio / pe / pb / total_mv …）或已注册因子",
+        "  缺失值语义：字段取不到（None/NaN）时，除 != 外一律判为「未通过」；",
+        "              != 在缺失时按「不等于字面量」通过（真实求值器行为）。",
+        "",
+    ]
+    return lines
+
+
+def _formula_truncation(spec: ResearchSpec) -> list[str]:
+    sel = spec.selection
+    x = sel.x
+    m = spec.effective_selection_months
+    y = spec.effective_rebalance_months
+    lines = [
+        "【3】两级截断与择股 / 调仓周期",
+        f"  候选池：n = {sel.top_n}（按 score 降序取前 n；池内可评分股票不足 n 时以实际数量为准）",
+        f"  实际持仓：x = {x}"
+        + ("（未显式给出 hold_top_x，取 x = n）" if sel.hold_top_x is None else ""),
+        f"  择股日：{_months_text(m, '每次调仓日')}（月序号锚定回测起始月）",
+        f"  调仓日：{_months_text(y, '每月' if spec.rebalance == 'monthly' else '每周')}的首个交易日",
+        "  资金分配：" + (
+            f"等权（可用现金 / 目标持仓数）；设置了单股上限 {spec.portfolio.max_position_pct:.0%}，"
+            "超出部分按上限分配并留作现金"
+            if spec.portfolio.max_position_pct is not None
+            else "等权（可用现金 / 目标持仓数）"
+        ),
+        "  买不进的处理：" + _buy_fallback_text(sel),
+        "",
+    ]
+    return lines
+
+
+def _buy_fallback_text(sel) -> str:
+    """与 local_engine._rebalance/_fill_pending 的真实分支一一对应。"""
+    if sel.defer_buy:
+        return (
+            "不替补（顺延买入）：涨停/停牌买不进的标的把买单顺延到之后首个可成交交易日、"
+            "按当日收盘价买入；到下一次调仓仍未成交则作废，资金留作现金"
+        )
+    if sel.allow_substitute:
+        return (
+            "替补买入：从候选池之外继续按复合分往下找可买标的，补足 x 只"
+            "（池内被跳过的标的也会在 signal_history 中留下未成交原因）"
+        )
+    return "不替补也不顺延：买不进则放弃该标的，资金留作现金"
+
+
+def _buy_mode_short(sel) -> str:
+    """一句话说明里的买不进处置短语（与 _buy_fallback_text 同源、同分支）。"""
+    if sel.defer_buy:
+        return "买不进则顺延买入"
+    if sel.allow_substitute:
+        return "买不进时从候选池之外替补"
+    return "买不进则放弃"
+
+
+def _target_text(sel) -> str:
+    """调仓日「目标名单」如何产生 —— 与 local_engine._rebalance 的分支一一对应。"""
+    if sel.allow_substitute:
+        # 引擎从 current_ranked（全市场排序）往下扫描，而不是 picks[:x]
+        return (
+            f"替补买入：按复合分排序从头扫描（含候选池之外），"
+            f"买得进的依次入选直到凑满 {sel.x} 只"
+        )
+    return f"取候选池前 {sel.x} 只为目标名单（{_buy_mode_short(sel)}）"
+
+
+def _universe_step(spec: ResearchSpec) -> str:
+    """股票池装配步骤（与 service._load_daily / universe.filter_stocks 的真实口径一致）。
+
+    时点必须写准：universe 过滤只在**回测起始日**执行一次（filter_stocks(as_of=start)），
+    之后每个择股日仅按**当日名称**重判 exclude_st；不能写成「每日对全池重新过滤」。
+    """
+    u = spec.universe
+    filters = ["已退市标的排除"]
+    if u.exclude_st:
+        filters.append("名称含 ST 的标的排除")
+    if u.min_listing_days:
+        filters.append(f"上市不足 {u.min_listing_days} 个自然日的排除")
+    if u.symbols:
+        filters.append(f"限定为白名单 {len(u.symbols)} 只")
+    if u.index_code:
+        filters.append(f"限定为 {u.index_code} 的当日历史成分")
+    text = "股票池：在回测起始日按 universe 过滤一次（" + "，".join(filters) + "）。"
+    if u.exclude_st:
+        text += (
+            "此后每个择股日再按**当日名称**判定 exclude_st"
+            "（名称变更历史未同步时回退最新名称快照）。"
+        )
+    return text
+
+
+def _formula_costs(spec: ResearchSpec) -> list[str]:
+    c = spec.costs
+    min_comm = (
+        f"最低佣金 {c.min_commission:g} 元/笔"
+        if c.min_commission > 0
+        else "最低佣金 0（未启用）"
+    )
+    return [
+        "【4】成本与成交价口径",
+        f"  行情口径：{_ADJUST_TEXT[spec.price_adjustment]}",
+        "  成交时点：调仓日收盘（调仓在当日收盘生效，自次日起计收益）",
+        f"  买入：成交价 = 收盘价 × (1 + 滑点 {_pct(c.slippage_rate)})；"
+        f"佣金 = max(投入资金 × {_pct(c.commission_rate)}, {min_comm})，从投入资金中扣除",
+        f"  卖出：成交金额 = 数量 × 收盘价 × (1 - 滑点 {_pct(c.slippage_rate)})；"
+        f"费用 = max(成交金额 × {_pct(c.commission_rate)}, {min_comm})"
+        f" + 成交金额 × 印花税 {_pct(c.stamp_tax_rate)}（仅卖出）",
+        f"  初始资金：{spec.initial_capital:,.0f} 元；对照基准：{c.benchmark}（仅展示，不参与交易）",
+    ]
+
+
+# ---------- steps ----------
+
+
+def _build_steps(spec: ResearchSpec, period_text: str) -> list[str]:
+    """按 local_engine.TopKBacktestRunner.run 的真实流程写（顺序、时序口径都以代码为准）。"""
+    sel = spec.selection
+    m = spec.effective_selection_months
+    y = spec.effective_rebalance_months
+    warmup = (
+        "数据装配：自回测起始日往前 300 个自然日起装配行情/指标（覆盖因子预热窗口）；"
+        f"回测区间 {period_text}。所有截面只使用 <= 当日的数据（无未来函数）。"
+    )
+    cond_step = (
+        f"先跑 {len(spec.conditions)} 条过滤条件（AND）"
+        if spec.conditions
+        else "无过滤条件，直接按复合分排序"
+    )
+    steps = [
+        warmup,
+        _universe_step(spec),
+        (
+            f"择股日（每 {m} 个月的首个交易日，锚定起始月）" if m is not None
+            else "择股日（未配置 m：每次调仓日都重新择股）"
+        )
+        + f"：{cond_step}，再按复合分降序取前 "
+        + f"{sel.top_n} 只作为候选池，逐条写入 selection_history（含 rank 与 score）。",
+        (
+            f"调仓日（每 {y} 个月的首个交易日）" if y is not None
+            else ("调仓日（每月首个交易日）" if spec.rebalance == "monthly" else "调仓日（每周首个交易日）")
+        )
+        + "：在**当日收盘**执行调仓。",
+        "调仓第一步：卖出全部当前持仓（跌停、无行情/停牌时不成交并保留该持仓到下一次调仓），"
+        "逐笔记入 signal_history。",
+        f"调仓第二步：{_target_text(sel)}；"
+        "把可用现金等权分配到目标名单，按收盘价 + 滑点买入，佣金（含最低佣金）从投入资金中扣除。",
+        "买入约束：涨停（收盘价/上一有效收盘 >= 板块涨停系数）或当日无行情（停牌）时不可买入。",
+        "收益结算：每日先用**上一交易日收盘持仓**结算个股收益"
+        "（建仓当日不计收益、卖出当日仍有收益），因此调仓日收盘生效、次日起计收益。",
+        "期末：不强制平仓 —— 最后一次调仓之后的持仓一直保留，按最后一个交易日收盘估值；"
+        "Trade 只记录「已卖出」的完整买卖往返。",
+    ]
+    if spec.type != "backtest":
+        steps.insert(0, "本 spec 的 type 为 factor_test：只计算因子 IC/分层收益，"
+                        "不执行选股、调仓与成本（下列步骤仅在转为 backtest 后生效）。")
+    return steps
+
+
+# ---------- conditions ----------
+
+
+def _describe_conditions(spec: ResearchSpec, warnings: list[str]) -> list[str]:
+    """渲染过滤条件为可读表达式，并对未识别字段给出 warnings（不静默）。"""
+    out: list[str] = []
+    for cond in spec.conditions:
+        out.append(_render_condition(cond))
+        for field in (cond.field, cond.ref):
+            if field and not _is_known_field(field):
+                warnings.append(
+                    f"条件字段 {field} 未识别（不在行情列 / ma20 / ma60 / 每日指标列 / "
+                    "已注册因子 / static.* / fundamental.* 之内）：回测装配列时会直接报错，"
+                    "该条件不会生效"
+                )
+    return out
+
+
+def _render_condition(cond) -> str:
+    """单条条件 → 可读表达式（语义与 selection._eval_condition 一一对应）。
+
+    `ref` 非空时右操作数是**另一个字段**（同一股票同一日的取值），否则是字面量；
+    in/not_in 的 value 是列表。
+    """
+    op = _OP_TEXT.get(cond.op, cond.op)
+    if cond.ref is not None:
+        right = cond.ref
+    elif cond.op in ("in", "not_in"):
+        items = cond.value if isinstance(cond.value, Sequence) else [cond.value]
+        right = "[" + ", ".join(_fmt_value(v) for v in items) + "]"
+    else:
+        right = _fmt_value(cond.value)
+    return f"{cond.field} {op} {right}"
+
+
+def _is_known_field(field: str) -> bool:
+    """字段是否属于已知域（与 selection.condition_needed_columns 的识别范围一致）。"""
+    if field.startswith((_STATIC_PREFIX, _FUNDAMENTAL_PREFIX)):
+        return True
+    if field in DAILY_BAR_NUMERIC_FIELDS or field in DAILY_BASIC_NUMERIC_FIELDS:
+        return True
+    if field in _TECH_DERIVED:
+        return True
+    try:
+        get_factor(field)
+    except FactorError:
+        return False
+    return True
+
+
+def _fmt_value(value) -> str:
+    if isinstance(value, bool):  # bool 是 int 子类，先判
+        return "true" if value else "false"
+    if isinstance(value, (int, float)):
+        return f"{value:g}"
+    if isinstance(value, str):
+        return f'"{value}"'
+    return str(value)
+
+
+def _pct(rate: float) -> str:
+    return f"{rate * 100:g}%"
+
+
+# ---------- warnings ----------
+
+
+def _collect_warnings(spec: ResearchSpec, period_known: bool) -> list[str]:
+    """诚实标注「未建模 / 近似 / 口径依赖」项（AGENT.md §24：禁止假装支持）。"""
+    warnings: list[str] = []
+    if not period_known:
+        warnings.append(
+            "入参为策略定义（无回测区间）：说明中的区间为占位文本，"
+            "实际回测区间在展开为 ResearchSpec 时确定"
+        )
+    if spec.type != "backtest":
+        warnings.append(
+            f"spec.type={spec.type}：这是因子测试而非组合回测，"
+            "择股/调仓/成本/成交价等说明不适用于本次执行"
+        )
+    if spec.universe.exclude_st:
+        warnings.append(
+            "exclude_st 依据股票名称判定：若未同步名称变更历史（stock_name_history，"
+            "python -m app.cli.sync namechange），则回退**最新名称快照**，"
+            "「曾为高股息、后来才变 ST」的标的会被整段排除（收益可能被高估）；"
+            "实际口径见回测结果 config_snapshot.price_basis.name_basis"
+        )
+    if spec.universe.exclude_suspended:
+        warnings.append(
+            "exclude_suspended=True 但**停牌数据未建模**：universe 过滤不会剔除停牌股"
+            "（仅有「当日无行情时不成交」的撮合近似）"
+        )
+    if spec.universe.index_code:
+        warnings.append(
+            f"指数成分过滤（{spec.universe.index_code}）依赖本地指数成分表："
+            "该指数历史成分未同步时，候选池可能为空或偏小"
+        )
+    if spec.portfolio.max_industry_weight_pct is not None:
+        warnings.append(
+            f"最大行业权重 {spec.portfolio.max_industry_weight_pct:.0%} **未建模**"
+            "（需行业元数据注入），结果中会列在 unimplemented"
+        )
+    if spec.price_adjustment == "none":
+        warnings.append(
+            "行情口径为不复权：现金分红未计入收益，除权日价格下移会被计为亏损；"
+            "股息类策略建议 price_adjustment=hfq"
+        )
+    if not spec.conditions:
+        warnings.append("未配置过滤条件（conditions）：候选池仅由 universe + 因子排序决定")
+    m = spec.effective_selection_months
+    y = spec.effective_rebalance_months
+    if m is not None and y is not None and y < m:
+        warnings.append(
+            f"调仓间隔 y={y} 个月 < 择股间隔 m={m} 个月：两次择股之间复用同一候选池"
+            "（池子陈旧），并非每次调仓都重新择股"
+        )
+    warnings += [
+        "涨跌停按「收盘价 / 上一有效收盘」近似判定，未建模开盘一字板、集合竞价与盘中价格路径",
+        "成交假设发生在调仓日收盘，未建模流动性冲击与冲击成本",
+        "调仓为「全部卖出 → 按目标等权重新买入」：保留在目标名单中的股票也会产生一次完整买卖往返，成本估计偏保守",
+        "股票池来自本地行情表（含 2019-12 之后退市的标的）：更早退市者无行情数据，存在残余幸存者偏差",
+        "期末不强制平仓：最后持仓按最后一个交易日收盘估值，浮盈浮亏计入净值但不产生 Trade 记录",
+    ]
+    return warnings
diff --git a/backend/app/quant/universe.py b/backend/app/quant/universe.py
index 5bbf18e..ebf2a33 100644
--- a/backend/app/quant/universe.py
+++ b/backend/app/quant/universe.py
@@ -2,20 +2,23 @@
 
 把 ResearchService.filter_stocks 的语义规则化并集中于此：
 - 当前日与历史日（as_of）都必须正确：退市股（delist < as_of）、上市时间（list_date）
-- exclude_st 按**当前名称快照**含 ST 判定（历史可追溯数据；历史改名无法回溯，属近似，
-  见结果 unimplemented 说明）
+- exclude_st 优先用**时点名称**（`name_at` 映射，来自 stock_name_history）判定；
+  未提供或该股无记录时回退最新名称快照（历史改名无法回溯，属近似，见 unimplemented 说明）
 - exclude_suspended 依赖停牌数据表（尚未建模），此处不做剔除，由上层显式标注
 - symbols 白名单：非空时仅这些 symbol 参与（自选池 / 测试用）
 """
 
 from __future__ import annotations
 
-from collections.abc import Sequence
+import logging
+from collections.abc import Mapping, Sequence
 from datetime import date
 
 from app.domain.entities.market import Stock
 from app.domain.entities.research import UniverseSpec
 
+logger = logging.getLogger(__name__)
+
 
 def resolve_members(index_repo, universe: UniverseSpec, as_of: date) -> set[str] | None:
     """若 universe 指定指数成分 → 取 as_of 当日历史成分；否则 None（不过滤）。"""
@@ -29,10 +32,15 @@ def filter_stocks(
     universe: UniverseSpec,
     as_of: date,
     members: set[str] | None = None,
+    name_at: Mapping[str, str] | None = None,
 ) -> list[Stock]:
     """按股票池口径过滤，返回 as_of 时点应纳入的股票列表。
 
     members：指数历史成分集合（resolve_members 结果）；提供时取交集。
+    name_at：各股票在 as_of **时点生效的名称**（stock_name_history 查询结果）。
+      提供时 `exclude_st` 用它判定，缺失的股票回退 `stock.name`（最新快照）。
+      这是「股息陷阱」能否被正确纳入的关键：用最新名称会把「后来才 ST」的标的
+      在整段历史上提前排除（实测影响约 3.70pp 收益，见 DEV_PLAN §10.5）。
     """
     symbols = set(universe.symbols) if universe.symbols else None
     out: list[Stock] = []
@@ -43,8 +51,10 @@ def filter_stocks(
             continue
         if s.delist_date is not None and s.delist_date < as_of:
             continue
-        if universe.exclude_st and s.name and "ST" in s.name.upper():
-            continue
+        if universe.exclude_st:
+            name = (name_at or {}).get(s.symbol) or s.name
+            if name and "ST" in name.upper():
+                continue
         if (
             universe.min_listing_days
             and s.list_date
@@ -53,3 +63,34 @@ def filter_stocks(
             continue
         out.append(s)
     return out
+
+
+def names_as_of(
+    stocks: Sequence[Stock],
+    as_of: date,
+    name_repo,
+) -> tuple[dict[str, str] | None, tuple[bool, int]]:
+    """装配 as_of 时点的名称映射，供 `filter_stocks(exclude_st=True)` 使用。
+
+    返回 `(name_at, (是否时点口径, 覆盖只数))`：
+    - `name_repo` 为 None（未注入 / 表为空）→ `(None, (False, 0))`，
+      调用方回退 `stock.name`（最新快照，旧行为），结果页据此标注残余偏差；
+    - 否则返回时点名称映射，`filter_stocks` 逐股优先取时点名称、缺失回退最新名称。
+
+    时点语义（无未来函数）：取 `start_date <= as_of <= end_date` 的生效名称区间。
+    名称自 `start_date` 起即对市场可见，故不构成前视；`ann_date` 仅作留痕。
+    """
+    if name_repo is None:
+        return None, (False, 0)
+    try:
+        name_at = name_repo.names_as_of([s.symbol for s in stocks], as_of)
+    except Exception:  # noqa: BLE001 —— 名称历史缺失不应让选股/回测整体失败
+        logger.warning("名称变更历史查询失败，回退最新名称快照判定 exclude_st", exc_info=True)
+        return None, (False, 0)
+    if not name_at:
+        # 表存在但**无任何生效区间**（未执行 sync namechange / 表被清空）：
+        # `filter_stocks` 会逐股回退最新名称，因此口径仍是快照，**不得**声称时点，
+        # 否则结果页会把「未被修正的股息陷阱偏差」当成已修正上报（AGENT.md §24）。
+        logger.warning("名称变更历史为空（as_of=%s），exclude_st 回退最新名称快照", as_of)
+        return None, (False, 0)
+    return name_at, (True, len(name_at))
diff --git a/backend/tests/test_adjust_factor_prices.py b/backend/tests/test_adjust_factor_prices.py
new file mode 100644
index 0000000..8929100
--- /dev/null
+++ b/backend/tests/test_adjust_factor_prices.py
@@ -0,0 +1,174 @@
+"""复权折算（v3 §20.5）测试：SQL 侧按 adjust_factor 折算价格列。
+
+覆盖：
+- hfq：price × factor（后复权）
+- qfq：price × factor / 该股最新 factor（前复权，归一）
+- volume/amount 不折算
+- 因子缺失按 1.0 兜底，并由 count_price_adjust_gaps 如实统计
+- hfq 与 qfq 的**收益率序列完全一致**（仅差一个常数倍），这是复权口径自洽性的关键断言
+"""
+
+from __future__ import annotations
+
+from datetime import date
+from decimal import Decimal
+
+import pytest
+from app.domain.entities.market import AdjustFactor, DailyBar
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+    SqlAlchemyDailyBarRepository,
+)
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+_D = [date(2024, 6, 3), date(2024, 6, 4), date(2024, 6, 5)]
+
+
+def _bar(day: date, close: str, symbol: str = "600519.SH") -> DailyBar:
+    return DailyBar(
+        symbol=symbol,
+        trade_date=day,
+        source="tushare",
+        adjust="none",
+        open=Decimal(close),
+        high=Decimal(close),
+        low=Decimal(close),
+        close=Decimal(close),
+        volume=Decimal("1000"),
+        amount=Decimal("100000"),
+    )
+
+
+def _factor(day: date, factor: str, symbol: str = "600519.SH") -> AdjustFactor:
+    return AdjustFactor(symbol=symbol, trade_date=day, factor=Decimal(factor), source="tushare")
+
+
+@pytest.fixture
+def session(tmp_path) -> Session:
+    engine = create_engine(f"sqlite:///{tmp_path / 'adjprice.db'}", future=True)
+    Base.metadata.create_all(engine)
+    with Session(engine) as s:
+        repo = SqlAlchemyDailyBarRepository(s)
+        # 100 → 除权前 1.0；110 → 之后因子 1.1（模拟一次分红/送股）
+        repo.upsert_many([_bar(_D[0], "100"), _bar(_D[1], "110"), _bar(_D[2], "121")])
+        s.add_all(
+            [
+                _factor_model(_D[0], "1.0"),
+                _factor_model(_D[1], "1.1"),
+                _factor_model(_D[2], "1.1"),
+            ]
+        )
+        s.commit()
+        yield s
+
+
+def _factor_model(day: date, factor: str, symbol: str = "600519.SH"):
+    from app.infrastructure.persistence.sqlalchemy.models.market import AdjustFactorModel
+
+    return AdjustFactorModel(symbol=symbol, trade_date=day, factor=Decimal(factor))
+
+
+def _stream(session, price_adjust: str, columns=("close",)) -> dict[date, tuple]:
+    repo = SqlAlchemyDailyBarRepository(session)
+    rows = list(
+        repo.stream_range_many_columns(
+            ["600519.SH"], _D[0], _D[2], list(columns),
+            adjust="none", price_adjust=price_adjust,
+        )
+    )
+    return {date.fromisoformat(r[1]): r[2:] for r in rows}
+
+
+class TestAdjustedPrices:
+    def test_hfq_multiplies_by_factor(self, session) -> None:
+        out = _stream(session, "hfq")
+        assert out[_D[0]] == (100.0,)  # 100 × 1.0
+        assert out[_D[1]] == pytest.approx((121.0,))  # 110 × 1.1
+        assert out[_D[2]] == pytest.approx((133.1,))  # 121 × 1.1
+
+    def test_qfq_normalizes_by_latest_factor(self, session) -> None:
+        out = _stream(session, "qfq")
+        assert out[_D[0]] == pytest.approx((100 / 1.1,))  # 100 × 1.0 / 1.1
+        assert out[_D[1]] == pytest.approx((110.0,))  # 110 × 1.1 / 1.1
+        assert out[_D[2]] == pytest.approx((121.0,))
+
+    def test_volume_and_amount_not_adjusted(self, session) -> None:
+        out = _stream(session, "hfq", columns=("close", "volume", "amount"))
+        close, volume, amount = out[_D[2]]
+        assert close == pytest.approx(133.1)
+        assert volume == 1000.0 and amount == 100000.0  # 不随复权缩放
+
+    def test_none_is_raw(self, session) -> None:
+        out = _stream(session, "none")
+        assert out[_D[0]] == (100.0,) and out[_D[2]] == (121.0,)
+
+    def test_hfq_and_qfq_give_identical_returns(self, session) -> None:
+        hfq, qfq = _stream(session, "hfq"), _stream(session, "qfq")
+        for a, b in zip(_D, _D[1:], strict=False):
+            r_hfq = hfq[b][0] / hfq[a][0]
+            r_qfq = qfq[b][0] / qfq[a][0]
+            assert r_hfq == pytest.approx(r_qfq, rel=1e-12)
+
+    def test_invalid_price_adjust_rejected(self, session) -> None:
+        repo = SqlAlchemyDailyBarRepository(session)
+        with pytest.raises(ValueError, match="复权"):
+            list(
+                repo.stream_range_many_columns(
+                    ["600519.SH"], _D[0], _D[2], ["close"], price_adjust="bad"
+                )
+            )
+
+
+class TestAdjustGapReporting:
+    def test_missing_factor_falls_back_and_is_reported(self, session) -> None:
+        repo = SqlAlchemyDailyBarRepository(session)
+        # 追加一行无因子的行情（模拟 25 只蓝筹 2020-2022 缺因子的情形）
+        repo.upsert_many([_bar(date(2024, 6, 6), "200")])
+        session.commit()
+        out = dict(
+            (date.fromisoformat(r[1]), r[2])
+            for r in repo.stream_range_many_columns(
+                ["600519.SH"], _D[0], date(2024, 6, 6), ["close"], price_adjust="hfq"
+            )
+        )
+        assert out[date(2024, 6, 6)] == 200.0  # 缺因子 → 系数 1.0（未折算）
+        total, missing = repo.count_price_adjust_gaps(
+            ["600519.SH"], _D[0], date(2024, 6, 6)
+        )
+        assert total == 4 and missing == 1
+
+    def test_no_gap_when_all_covered(self, session) -> None:
+        repo = SqlAlchemyDailyBarRepository(session)
+        total, missing = repo.count_price_adjust_gaps(["600519.SH"], _D[0], _D[2])
+        assert total == 3 and missing == 0
+
+    def test_empty_symbols(self, session) -> None:
+        repo = SqlAlchemyDailyBarRepository(session)
+        assert repo.count_price_adjust_gaps([], _D[0], _D[2]) == (0, 0)
+
+class TestQfqMissingFactor:
+    """qfq 缺因子行不得被错误缩放。
+
+    回归用例：`coalesce(factor,1)/latest` 会把缺口行缩放到 `1/latest`
+    （latest=5 → 121/5=24.2，凭空 −80% 单日跌幅），正确写法必须把 COALESCE
+    放在最外层，使缺口行保持原始价（口径：缺失因子按 1.0 兜底、不折算）。
+    """
+
+    def test_qfq_gap_row_is_not_rescaled(self, session) -> None:
+        from app.infrastructure.persistence.sqlalchemy.models.market import AdjustFactorModel
+
+        session.query(AdjustFactorModel).delete()  # 清掉 fixture 的 1.0/1.1/1.1
+        session.add_all([_factor_model(_D[0], "5.0"), _factor_model(_D[1], "5.0")])
+        session.commit()  # 第 3 天无因子 → 缺口
+
+        out = _stream(session, "qfq")
+        # 有因子的行：price × factor / max(factor=5.0) → 等于原始价
+        assert out[_D[0]] == pytest.approx((100.0,))
+        assert out[_D[1]] == pytest.approx((110.0,))
+        # 缺口行：保持原始价（若被错误归一则为 121/5 = 24.2）
+        assert out[_D[2]] == pytest.approx((121.0,))
+
+        repo = SqlAlchemyDailyBarRepository(session)
+        total, missing = repo.count_price_adjust_gaps(["600519.SH"], _D[0], _D[2])
+        assert (total, missing) == (3, 1)
diff --git a/backend/tests/test_config.py b/backend/tests/test_config.py
index f56d83a..4cfab13 100644
--- a/backend/tests/test_config.py
+++ b/backend/tests/test_config.py
@@ -6,7 +6,9 @@ from __future__ import annotations
 
 import os
 
-from app.core.config import PROJECT_ROOT, _load_env_file, get_settings
+import pytest
+import yaml
+from app.core.config import CONFIG_PATH, PROJECT_ROOT, _load_env_file, get_settings
 
 
 def test_project_root_points_to_repo_root() -> None:
@@ -22,15 +24,24 @@ def test_test_runner_isolation_uses_tmp_sqlite() -> None:
 
 
 def test_mysql_default_from_config_yaml(monkeypatch) -> None:
-    """未设 DATABASE_URL 时，config.yaml database.mysql 段组装 MySQL URL（不连接）。"""
+    """未设 DATABASE_URL 时，config.yaml database.mysql 段组装 MySQL URL（不连接）。
+
+    期望值直接取自 config.yaml，而不硬编码部署地址：切库（192.168.1.10 → 127.0.0.1）
+    属于配置变更，不应让本用例失效。
+    """
+    mysql = yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8"))["database"]["mysql"]
     monkeypatch.delenv("DATABASE_URL", raising=False)
     get_settings.cache_clear()
     try:
         url = get_settings().database_url
     finally:
         get_settings.cache_clear()
-    assert url.startswith("mysql+pymysql://qlib:")
-    assert "192.168.1.10:3306/qlib?charset=utf8mb4" in url
+    assert url.startswith(f"mysql+pymysql://{mysql['user']}:")
+    expected = (
+        f"{mysql['host']}:{mysql['port']}/{mysql['db']}?charset={mysql['charset']}"
+    )
+    # 断言只比较 @ 之后的部分：失败时 pytest 不会把含密码的 userinfo 打进日志
+    assert expected in url.split("@")[-1]
 
 
 def test_build_mysql_url(monkeypatch) -> None:
@@ -117,3 +128,95 @@ def test_llm_env_overrides_yaml(monkeypatch) -> None:
     assert s.llm_model == "env-model"
     assert s.llm_base_url == "https://example.com/v1"
     assert s.llm_api_key == "sk-test"
+
+
+class TestForbiddenDbTarget:
+    """用户约束：**禁止使用 192.168.1.10 作为 DB 目标**（只允许本机 MariaDB）。
+
+    连错库是「最危险的静默错误」：不报错、界面正常，只是数据在另一台机器上读写。
+    因此这里是硬失败（抛错），不是警告；本类把守卫生效与放行口子都钉住。
+    """
+
+    def _clear(self):
+        get_settings.cache_clear()
+
+    def test_forbidden_host_rejected_from_env_url(self, monkeypatch) -> None:
+        monkeypatch.setenv(
+            "DATABASE_URL", "mysql+pymysql://qlib:pw@192.168.1.10:3306/qlib"
+        )
+        monkeypatch.delenv("QLIB_FORBIDDEN_DB_HOSTS", raising=False)
+        self._clear()
+        try:
+            with pytest.raises(RuntimeError, match="192.168.1.10"):
+                get_settings()
+        finally:
+            self._clear()
+
+    def test_forbidden_host_rejected_from_config_yaml(self, monkeypatch, tmp_path) -> None:
+        """即使走 config.yaml 的 mysql 段（而非 env）也必须拦住。"""
+        import app.core.config as config_mod
+
+        cfg = {
+            "database": {
+                "mysql": {
+                    "enabled": True,
+                    "host": "192.168.1.10",
+                    "port": 3306,
+                    "db": "qlib",
+                    "user": "qlib",
+                    "charset": "utf8mb4",
+                }
+            }
+        }
+        path = tmp_path / "config.yaml"
+        path.write_text(yaml.safe_dump(cfg, allow_unicode=True), encoding="utf-8")
+        monkeypatch.delenv("DATABASE_URL", raising=False)
+        monkeypatch.delenv("QLIB_FORBIDDEN_DB_HOSTS", raising=False)
+        monkeypatch.setattr(config_mod, "CONFIG_PATH", path)
+        self._clear()
+        try:
+            with pytest.raises(RuntimeError, match="已被禁止"):
+                get_settings()
+        finally:
+            self._clear()
+
+    def test_local_host_allowed(self, monkeypatch) -> None:
+        monkeypatch.setenv(
+            "DATABASE_URL", "mysql+pymysql://qlib:pw@127.0.0.1:3306/qlib"
+        )
+        self._clear()
+        try:
+            assert get_settings().database_url.startswith("mysql+pymysql://qlib")
+        finally:
+            self._clear()
+
+    def test_forbidden_list_is_env_overridable(self, monkeypatch) -> None:
+        """显式放开的口子必须存在（空串＝不限制），否则紧急情况下无法操作。"""
+        monkeypatch.setenv(
+            "DATABASE_URL", "mysql+pymysql://qlib:pw@192.168.1.10:3306/qlib"
+        )
+        monkeypatch.setenv("QLIB_FORBIDDEN_DB_HOSTS", "")
+        self._clear()
+        try:
+            assert "192.168.1.10" in get_settings().database_url
+        finally:
+            self._clear()
+
+    def test_sqlite_and_other_hosts_unaffected(self, monkeypatch) -> None:
+        """SQLite（无 host）与其它主机不受影响 —— 守卫只拦精确匹配的禁用主机。"""
+        from app.core.config import assert_db_target_allowed
+
+        monkeypatch.delenv("QLIB_FORBIDDEN_DB_HOSTS", raising=False)
+        assert_db_target_allowed("sqlite:////tmp/x.db")
+        assert_db_target_allowed("mysql+pymysql://u:p@127.0.0.1:3306/qlib")
+        assert_db_target_allowed("mysql+pymysql://u:p@db.internal:3306/qlib")
+        assert_db_target_allowed("")
+        with pytest.raises(RuntimeError):
+            assert_db_target_allowed("mysql+pymysql://u:p@192.168.1.10:3306/qlib")
+
+    def test_guard_only_matches_host_not_userinfo(self, monkeypatch) -> None:
+        """只比 host：把禁用 IP 写进用户名/密码不应被误判（避免误报挡住正常连接）。"""
+        from app.core.config import assert_db_target_allowed
+
+        monkeypatch.delenv("QLIB_FORBIDDEN_DB_HOSTS", raising=False)
+        assert_db_target_allowed("mysql+pymysql://192.168.1.10:pw@127.0.0.1:3306/qlib")
diff --git a/backend/tests/test_dividend_case_e2e.py b/backend/tests/test_dividend_case_e2e.py
new file mode 100644
index 0000000..416bdd8
--- /dev/null
+++ b/backend/tests/test_dividend_case_e2e.py
@@ -0,0 +1,244 @@
+"""用户案例端到端集成测试（合成数据）：高股息 Top n → 持仓前 x，m/y 双周期。
+
+不连真库，用确定性合成行情 + 内存 SQLite（覆盖 daily_basic 与 adjust_factor
+两条新链路），验证：
+- dv_ratio 因子参与评分（dividend_yield）
+- conditions（dv_ratio <= 30）真正过滤掉特殊分红股
+- hfq 复权生效（除权日不再被计为亏损）
+- m/y 双周期、x<=n、顺延买入的端到端组合行为
+- 最低佣金 min_commission
+"""
+
+from __future__ import annotations
+
+from datetime import date
+from decimal import Decimal
+
+import pandas as pd
+import pytest
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.infrastructure.persistence.sqlalchemy.models.market import (
+    AdjustFactorModel,
+    DailyBasicModel,
+    StockDailyModel,
+    StockModel,
+)
+from app.quant.engine import LocalEngine
+from app.quant.service import ResearchService
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+_SYMS = ["600000.SH", "600001.SH", "600002.SH", "600003.SH"]
+_START = date(2024, 1, 1)
+
+
+def _seed(session: Session, *, spike_date: date | None = None) -> None:
+    """4 只股票：A/B 高股息，C 中股息，D 低股息；除权日因子在 2024-06-03 跳到 1.1。
+
+    `spike_date`：把 A 股在该日的 dv_ratio 抬到 45%（特殊分红尖峰，
+    用于验证 `dv_ratio <= 30` 条件确实把它剔除）。
+    """
+    session.add_all(
+        [
+            StockModel(
+                symbol=s, name=f"股票{s[:6]}", industry="银行" if i < 2 else "制造",
+                market="主板", area="深圳", list_date=date(2000, 1, 1), status="L",
+            )
+            for i, s in enumerate(_SYMS)
+        ]
+    )
+    # 股息率：A=8% B=6% C=4% D=2%；special 时 A 在首个交易日出现 45% 的异常高值
+    dv = {"600000.SH": 8.0, "600001.SH": 6.0, "600002.SH": 4.0, "600003.SH": 2.0}
+    dates = pd.bdate_range(_START, periods=130)
+    bars, basics, factors = [], [], []
+    for i, sym in enumerate(_SYMS):
+        price = 10.0 + i
+        for d in dates:
+            price = price * (1 + 0.0008 + 0.0003 * i)
+            bars.append(
+                StockDailyModel(
+                    symbol=sym, trade_date=d.date(), source="tushare", adjust="none",
+                    open=Decimal(str(price)), high=Decimal(str(price)),
+                    low=Decimal(str(price)), close=Decimal(str(price)),
+                    volume=Decimal("1000000"), amount=Decimal(str(price * 1e6)),
+                )
+            )
+            rate = dv[sym]
+            if spike_date is not None and sym == "600000.SH" and d.date() == spike_date:
+                rate = 45.0  # 特殊分红尖峰（应按条件在该择股日被剔除）
+            basics.append(
+                DailyBasicModel(
+                    symbol=sym, trade_date=d.date(), source="tushare",
+                    close=Decimal(str(price)), dv_ratio=Decimal(str(rate)),
+                    dv_ttm=Decimal(str(rate)), pe=Decimal("8"), pb=Decimal("1"),
+                    total_mv=Decimal("1e11"),
+                )
+            )
+            # 2024-06-03 起因子 1.1（模拟一次除权）：hfq 价格应整体上移 10%
+            f = 1.1 if d.date() >= date(2024, 6, 3) else 1.0
+            factors.append(
+                AdjustFactorModel(symbol=sym, trade_date=d.date(), factor=Decimal(str(f)))
+            )
+    session.add_all(bars + basics + factors)
+    session.commit()
+
+
+@pytest.fixture
+def service(tmp_path) -> ResearchService:
+    engine = create_engine(f"sqlite:///{tmp_path / 'case.db'}", future=True)
+    Base.metadata.create_all(engine)
+    session = Session(engine)
+    _seed(session, spike_date=date(2024, 3, 1))
+    from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+        SqlAlchemyDailyBarRepository,
+        SqlAlchemyDailyBasicRepository,
+        SqlAlchemyStockRepository,
+    )
+
+    # 复权折算发生在 SqlAlchemyDailyBarRepository 的 SQL 内（price_adjust=...），
+    # 业务层无需 adjust_factor 仓储
+    yield ResearchService(
+        SqlAlchemyStockRepository(session),
+        SqlAlchemyDailyBarRepository(session),
+        LocalEngine(),
+        basic_repo=SqlAlchemyDailyBasicRepository(session),
+    )
+    session.close()
+
+
+def _spec(**kw):
+    from app.domain.entities.research import (
+        ConditionSpec,
+        CostSpec,
+        FactorSpec,
+        ResearchSpec,
+        SelectionSpec,
+        UniverseSpec,
+    )
+
+    base = dict(
+        type="backtest",
+        universe=UniverseSpec(exclude_st=False, min_listing_days=0),
+        price_adjustment="hfq",
+        factors=[FactorSpec(name="dividend_yield")],
+        conditions=[ConditionSpec(field="dv_ratio", op="lte", value=30)],
+        selection=SelectionSpec(
+            top_n=3, hold_top_x=2, allow_substitute=False, defer_buy=True
+        ),
+        rebalance="monthly",
+        selection_interval_months=3,
+        rebalance_interval_months=3,
+        period=(date(2024, 3, 1), date(2024, 6, 28)),
+        costs=CostSpec(
+            commission_rate=0.0003, stamp_tax_rate=0.0005, slippage_rate=0.001,
+            min_commission=5.0,
+        ),
+    )
+    base.update(kw)
+    return ResearchSpec(**base)
+
+
+class TestUserCaseEndToEnd:
+    def test_backtest_runs_with_all_new_knobs(self, service) -> None:
+        result = service.run_backtest(_spec())
+        assert result.summary.total_trades >= 1
+        # 配置可溯源（v3 §20.5）
+        snap = result.config_snapshot
+        assert snap["price_adjustment"] == "hfq"
+        assert snap["price_basis"]["execution_price_basis"] == "close_adj"
+        assert snap["selection_interval_months"] == 3
+        assert snap["selection"]["hold_top_x"] == 2
+        assert result.symbol_curves, "应输出个股收益曲线"
+
+    def test_condition_excludes_special_dividend_spike(self, service) -> None:
+        """A 股在 2024-03-01 的 dv_ratio=45% > 30% → 该日不得进入候选池；
+        其它择股日 A 股息率仍最高（8%）→ 应正常入选（证明过滤是按日求值而非一刀切）。"""
+        result = service.run_backtest(
+            _spec(period=(date(2024, 3, 1), date(2024, 6, 28)),
+                  selection_interval_months=3, rebalance_interval_months=3)
+        )
+        by_day: dict = {}
+        for p in result.selection_history:
+            by_day.setdefault(p.date, []).append(p.symbol)
+        assert date(2024, 3, 1) in by_day
+        assert "600000.SH" not in by_day[date(2024, 3, 1)], "尖峰日应按条件剔除"
+        later = [d for d in by_day if d > date(2024, 3, 1)]
+        assert later, "应存在后续择股日"
+        assert all("600000.SH" in by_day[d] for d in later), "尖峰解除后应恢复入选"
+
+    def test_top_x_cap_and_pool_recorded(self, service) -> None:
+        result = service.run_backtest(_spec())
+        # 候选池 = n = 3（4 只中剔除 1 只）
+        per_day: dict = {}
+        for p in result.selection_history:
+            per_day.setdefault(p.date, []).append(p.symbol)
+        assert per_day and all(len(v) <= 3 for v in per_day.values())
+        # 持仓 ≤ x = 2
+        held: dict = {}
+        for pos in result.positions:
+            held.setdefault(pos.date, []).append(pos.symbol)
+        assert held and all(len(v) <= 2 for v in held.values())
+
+    def test_hfq_reflects_dividend_jump(self, service) -> None:
+        """hfq 下 2024-06-03 不复权价与复权价之比应为 1.1（无横截面跳变计入收益）。"""
+        from app.quant.service import load_daily_df
+
+        daily = load_daily_df(
+            service._daily_repo, ["600000.SH"], date(2024, 5, 31), date(2024, 6, 4),
+            ["close"], price_adjust="hfq",
+        )
+        raw = load_daily_df(
+            service._daily_repo, ["600000.SH"], date(2024, 5, 31), date(2024, 6, 4),
+            ["close"], price_adjust="none",
+        )
+        adj = daily.sort_values("trade_date")["close"].tolist()
+        rawp = raw.sort_values("trade_date")["close"].tolist()
+        # 6/3 之前因子 1.0；之后 1.1 → 复权价整体上移
+        assert adj[0] == pytest.approx(rawp[0])
+        assert adj[-1] == pytest.approx(rawp[-1] * 1.1, rel=1e-9)
+
+    def test_min_commission_increases_cost(self, service) -> None:
+        """小资金 + x=2 → 单笔约 1 万元，佣金 3 元低于最低 5 元 → 成本上升。"""
+        free = service.run_backtest(
+            _spec(
+                initial_capital=20_000.0,
+                costs=_spec().costs.model_copy(update={"min_commission": 0.0}),
+            )
+        )
+        costly = service.run_backtest(_spec(initial_capital=20_000.0))  # min_commission=5.0
+        assert costly.summary.final_equity < free.summary.final_equity
+
+    def test_unimplemented_notes_surfaced(self, service) -> None:
+        result = service.run_backtest(_spec())
+        joined = " | ".join(result.unimplemented)
+        assert "后复权" in joined
+        assert "顺延买入" in joined
+        assert "退市" in joined  # 幸存者偏差显式标注
+
+
+class TestSelectionBacktestConsistencyWithConditions:
+    """v3 §28：回测择股日候选池 == 同日 /api/selections（同一求值器）。"""
+
+    def test_pool_matches_selection_service(self, service) -> None:
+        from app.application.services.selection_service import SelectionService
+        from app.domain.entities.selection import SelectionQuery
+
+        result = service.run_backtest(
+            _spec(selection_interval_months=3, rebalance_interval_months=3)
+        )
+        sel_service = SelectionService(
+            service._stock_repo, service._daily_repo, basic_repo=service._basic_repo
+        )
+        first_day = min(p.date for p in result.selection_history)
+        query = SelectionQuery(
+            method="condition",
+            price_adjustment="hfq",
+            conditions=[{"field": "dv_ratio", "op": "lte", "value": 30}],
+            as_of=first_day,
+        )
+        sel = sel_service.select(query)
+        eligible = {c.symbol for c in sel.candidates}
+        # 回测当日候选池（记为条件通过者中的前 n）必是条件合格集合的子集
+        pool = {p.symbol for p in result.selection_history if p.date == first_day}
+        assert pool <= eligible, f"候选池 {pool} 不在条件合格集 {eligible} 内"
+        assert eligible, "条件合格集不应为空（B/C/D 股 dv_ratio ≤ 30）"
\ No newline at end of file
diff --git a/backend/tests/test_experiment_archive.py b/backend/tests/test_experiment_archive.py
new file mode 100644
index 0000000..2ec2d69
--- /dev/null
+++ b/backend/tests/test_experiment_archive.py
@@ -0,0 +1,653 @@
+"""Experiment 归档补齐（2026-09）测试。
+
+覆盖五件事：
+1. **同步端点归档**：`POST /api/backtests` / `POST /api/factor-tests` 落库（此前只写
+   进程内存），并用响应头 `X-Experiment-Id` 返回归档 id；归档失败时仍返回计算结果
+   并用 `X-Archive-Error` 如实暴露（AGENT §7 不静默）。
+2. **data_version**：真实数据指纹（交易日 + 各表行数），格式与列宽（varchar(40)）合规。
+3. **列表过滤/分页**：`kind` / `q` / `limit` / `offset` + `X-Total-Count`（不静默截断）。
+4. **DELETE**：200 → 再 GET 404 → 再 DELETE 404，且不触碰 job 记录。
+5. **归档完整性**：默认不再按 60 只截断个股曲线；超体积预算时裁剪并留下
+   `archive_meta`（机器可读）+ `unimplemented`（人可读）证据；
+   完整结果只存 experiment 一份（`GET /api/jobs/{id}` 契约不变，老记录可回退）。
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from datetime import date, datetime
+from types import SimpleNamespace
+
+import pytest
+from app.api import deps
+from app.application.services import experiment_archive as ea
+from app.domain.entities.market import Stock
+from app.domain.entities.research import (
+    BacktestResult,
+    CostSpec,
+    ExperimentRecord,
+    ExperimentSummary,
+    FactorSpec,
+    JobRecord,
+    JobStatus,
+    ResearchSpec,
+    SelectionSpec,
+    UniverseSpec,
+)
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.infrastructure.persistence.sqlalchemy.models.jobs import ExperimentModel, JobModel
+from app.infrastructure.persistence.sqlalchemy.models.market import StockDailyModel
+from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import (
+    SqlAlchemyExperimentRepository,
+    SqlAlchemyJobRepository,
+)
+from app.main import app
+from app.quant.engine import LocalEngine
+from app.quant.service import ResearchService
+from fastapi.testclient import TestClient
+from sqlalchemy import create_engine, select
+from sqlalchemy.orm import sessionmaker
+
+from conftest_quant import bars_dataframe_to_daily_bars, synthetic_daily
+
+_SYMS = ["60000" + str(i) + ".SH" for i in range(5)]
+
+_BACKTEST_BODY = {
+    "type": "backtest",
+    "universe": {"exclude_st": False, "min_listing_days": 0},
+    "factors": [{"name": "momentum_20", "weight": 1.0}],
+    "selection": {"top_n": 2},
+    "rebalance": "monthly",
+    "period": ["2024-03-01", "2024-10-31"],
+}
+
+# data_version 格式：d，后跟可选的行数段（MySQL 近似值带 ≈ 与 k）
+_DATA_VERSION_RE = re.compile(
+    r"^d(\d{8}|-)(;n(≈\d+k|\d+))?(;a(≈\d+k|\d+))?(;b(≈\d+k|\d+))?$"
+)
+
+
+class _MemStockRepo:
+    def __init__(self, stocks: list[Stock]) -> None:
+        self._stocks = stocks
+
+    def get_by_symbol(self, symbol: str) -> Stock | None:
+        return next((s for s in self._stocks if s.symbol == symbol), None)
+
+    def list(self) -> list[Stock]:
+        return self._stocks
+
+
+class _MemDailyRepo:
+    def __init__(self, bars) -> None:
+        self._bars = bars
+
+    def get_range_many(self, symbols, start, end, adjust="none"):
+        return [b for b in self._bars if b.symbol in symbols and start <= b.trade_date <= end]
+
+    def get_range(self, symbol, start, end):
+        return [b for b in self._bars if b.symbol == symbol and start <= b.trade_date <= end]
+
+
+@pytest.fixture()
+def api(tmp_path):
+    """TestClient + 临时 SQLite（job/experiment/行情表）+ 内存行情仓储。"""
+    drifts = {sym: 0.003 - 0.0015 * i for i, sym in enumerate(_SYMS)}
+    bars = bars_dataframe_to_daily_bars(synthetic_daily(drifts, n=300))
+    stocks = [
+        Stock(symbol=sym, name=f"测试股份{i}", list_date=date(1999, 11, 10))
+        for i, sym in enumerate(_SYMS)
+    ]
+    service = ResearchService(_MemStockRepo(stocks), _MemDailyRepo(bars), LocalEngine())
+
+    engine = create_engine(f"sqlite:///{tmp_path / 'archive.db'}", future=True)
+    Base.metadata.create_all(engine)
+    Session = sessionmaker(bind=engine, expire_on_commit=False)
+
+    def _session_override():
+        with Session() as s:
+            yield s
+
+    app.dependency_overrides[deps.get_session] = _session_override
+    app.dependency_overrides[deps._service_factory] = lambda: service
+    app.dependency_overrides[deps._stock_repo_factory] = lambda: _MemStockRepo(stocks)
+    with TestClient(app) as client:
+        yield client, Session
+    app.dependency_overrides.clear()
+
+
+def _exp_by_id(Session, exp_id: str) -> ExperimentRecord | None:
+    with Session() as session:
+        return SqlAlchemyExperimentRepository(session).get(exp_id)
+
+
+class TestSyncEndpointsArchive:
+    """任务 1：同步端点必须落库，并通过 X-Experiment-Id 暴露归档 id。"""
+
+    def test_backtest_archives_to_db_and_returns_header(self, api) -> None:
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["equity_curve"], "body 形状不变：仍是完整 BacktestResult"
+
+        exp_id = resp.headers.get("X-Experiment-Id")
+        assert exp_id and exp_id.startswith("EXP-"), "必须用响应头返回归档 id"
+        exp = _exp_by_id(Session, exp_id)
+        assert exp is not None, "同步回测必须在 experiment 表落库（不再只写进程内存）"
+        assert exp.kind == "backtest"
+        assert json.loads(exp.spec_json)["factors"][0]["name"] == "momentum_20"
+        assert exp.summary_text and "总收益" in exp.summary_text
+        assert exp.code_version, "归档必须带代码版本"
+        assert exp.job_id is None, "同步端点没有 Job，job_id 应为 None"
+        # 归档结果可解码为完整 BacktestResult
+        archived = BacktestResult.model_validate_json(exp.result_json)
+        assert archived.summary.total_return_pct == body["summary"]["total_return_pct"]
+
+        # 详情接口能读回
+        detail = client.get(f"/api/experiments/{exp_id}")
+        assert detail.status_code == 200
+        assert detail.json()["result"]["summary"]["total_return_pct"] == (
+            body["summary"]["total_return_pct"]
+        )
+
+    def test_factor_test_archives_with_kind(self, api) -> None:
+        client, Session = api
+        payload = dict(_BACKTEST_BODY, type="factor_test")
+        resp = client.post("/api/factor-tests", json=payload)
+        assert resp.status_code == 200
+        assert resp.json()["factor_name"] == "momentum_20"
+
+        exp_id = resp.headers.get("X-Experiment-Id")
+        assert exp_id
+        exp = _exp_by_id(Session, exp_id)
+        assert exp is not None
+        assert exp.kind == "factor_test"
+        assert exp.result_json and json.loads(exp.result_json)["factor_name"] == "momentum_20"
+
+    def test_archive_failure_keeps_result_and_exposes_reason(self, api, monkeypatch) -> None:
+        """归档失败不得吞掉计算结果，且失败必须如实暴露（AGENT §7 / §24）。"""
+        client, _Session = api
+
+        def _boom(self, experiment):  # noqa: ANN001
+            raise RuntimeError("db down")
+
+        monkeypatch.setattr(SqlAlchemyExperimentRepository, "save", _boom)
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        assert resp.status_code == 200, "归档失败也必须返回已算出的结果"
+        assert resp.json()["equity_curve"], "结果本身必须完整返回"
+        assert "X-Experiment-Id" not in resp.headers
+        err = resp.headers.get("X-Archive-Error")
+        assert err and "RuntimeError" in err and "db down" in err, (
+            f"归档失败原因必须如实暴露，实际：{err!r}"
+        )
+
+
+class TestDataVersion:
+    """任务 2：data_version 必须是真实、可比较、且塞得进 varchar(40) 的指纹。"""
+
+    def test_format_and_column_width(self, api) -> None:
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp = _exp_by_id(Session, resp.headers["X-Experiment-Id"])
+        assert exp is not None
+        assert exp.data_version, "data_version 不允许为空（此前永远是 NULL）"
+        assert _DATA_VERSION_RE.match(exp.data_version), exp.data_version
+        assert len(exp.data_version) <= 40, "experiment.data_version 是 varchar(40)"
+
+    def test_real_values_from_seeded_bars(self, api) -> None:
+        """种子 3 行日线（SQLite 走精确 COUNT(*)）→ 指纹必须是精确的真实值。"""
+        client, Session = api
+        days = [date(2024, 9, 26), date(2024, 9, 27), date(2024, 9, 30)]
+        with Session() as session:
+            session.execute(
+                StockDailyModel.__table__.insert(),
+                [
+                    {
+                        "symbol": f"60010{i}.SH",
+                        "trade_date": d,
+                        "source": "tushare",
+                        "adjust": "none",
+                        "open": 1.0,
+                        "high": 1.0,
+                        "low": 1.0,
+                        "close": 1.0,
+                        "volume": 1.0,
+                        "amount": 1.0,
+                    }
+                    for i, d in enumerate(days)
+                ],
+            )
+            session.commit()
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp = _exp_by_id(Session, resp.headers["X-Experiment-Id"])
+        assert exp is not None
+        assert exp.data_version == "d20240930;n3;a0;b0", exp.data_version
+
+    def test_unavailable_when_tables_missing(self, tmp_path) -> None:
+        """取不到数据口径时如实降级为 unavailable，绝不编造数字。"""
+        from app.infrastructure.persistence.sqlalchemy.data_version import compute_data_version
+
+        engine = create_engine(f"sqlite:///{tmp_path / 'empty.db'}", future=True)
+        with sessionmaker(bind=engine)() as session:
+            assert compute_data_version(session) == "unavailable"
+
+
+class TestListFilters:
+    """任务 3：列表过滤/分页在 SQL 层完成，并用 X-Total-Count 暴露总数。"""
+
+    @staticmethod
+    def _seed(Session) -> list[str]:
+        """直接造 4 条归档：2 个 backtest（其一含 DIVIDEND_YIELD 因子）、1 个
+        factor_test、1 个 selection。"""
+        rows = [
+            ("EXP-A1", "backtest", "DIVIDEND_YIELD", "总收益 12.00% · 年化 8.00% · 回撤 5.00%"),
+            ("EXP-A2", "backtest", "momentum_20", "总收益 3.00% · 年化 2.00% · 回撤 1.00%"),
+            ("EXP-A3", "factor_test", "momentum_20", "IC 0.0100 · RankIC 0.0200 · 样本 100 日"),
+            ("EXP-A4", "selection", "momentum_60", "as_of 2024-10-31 · 选出 3 / 评估 100"),
+        ]
+        with Session() as session:
+            repo = SqlAlchemyExperimentRepository(session)
+            for i, (exp_id, kind, factor, summary) in enumerate(rows):
+                repo.save(
+                    ExperimentRecord(
+                        id=exp_id,
+                        kind=kind,
+                        spec_json=json.dumps(
+                            {
+                                "factors": [{"name": factor, "weight": 1.0}],
+                                "period": ["2024-03-01", "2024-10-31"],
+                                "rebalance": "monthly",
+                                "selection": {"top_n": i + 1},
+                            }
+                        ),
+                        result_json=json.dumps({"kind": kind, "n": i}),
+                        summary_text=summary,
+                        code_version="abc1234",
+                        data_version="d20260904;n1;a2;b3",
+                        created_at=datetime(2026, 9, 4, 10, i, 0),
+                    )
+                )
+            session.commit()
+        return [r[0] for r in rows]
+
+    def test_kind_filter_and_total_count(self, api) -> None:
+        client, Session = api
+        self._seed(Session)
+        resp = client.get("/api/experiments", params={"kind": "backtest"})
+        assert resp.status_code == 200
+        body = resp.json()
+        assert {e["id"] for e in body} == {"EXP-A1", "EXP-A2"}
+        assert resp.headers["X-Total-Count"] == "2"
+
+    def test_q_is_case_insensitive_over_factor_name_and_summary(self, api) -> None:
+        client, Session = api
+        self._seed(Session)
+        # 因子名（在 spec_json 里，大小写不敏感）
+        r1 = client.get("/api/experiments", params={"q": "dividend_yield"})
+        assert [e["id"] for e in r1.json()] == ["EXP-A1"]
+        assert r1.headers["X-Total-Count"] == "1"
+        # id 片段
+        r2 = client.get("/api/experiments", params={"q": "exp-a"})
+        assert r2.headers["X-Total-Count"] == "4"
+        # summary_text（中文）
+        r3 = client.get("/api/experiments", params={"q": "选出 3"})
+        assert [e["id"] for e in r3.json()] == ["EXP-A4"]
+        # kind + q 组合
+        r4 = client.get("/api/experiments", params={"kind": "backtest", "q": "momentum"})
+        assert [e["id"] for e in r4.json()] == ["EXP-A2"]
+
+    def test_like_wildcards_and_injection_are_bound(self, api) -> None:
+        client, Session = api
+        self._seed(Session)
+        # `%` 是 LIKE 通配符，必须被转义成字面量：种子里只有 EXP-A1/A2 的摘要带
+        # 百分号，若未转义会返回全部 4 条
+        r = client.get("/api/experiments", params={"q": "%"})
+        assert {e["id"] for e in r.json()} == {"EXP-A1", "EXP-A2"}
+        assert r.headers["X-Total-Count"] == "2"
+        # 注入尝试必须是普通字符串（参数绑定），不得改变语义
+        inj = client.get("/api/experiments", params={"q": "' OR 1=1 --"})
+        assert inj.json() == [] and inj.headers["X-Total-Count"] == "0"
+
+    def test_limit_offset_and_total_count_not_silently_truncated(self, api) -> None:
+        client, Session = api
+        self._seed(Session)
+        page1 = client.get("/api/experiments", params={"limit": 2, "offset": 0})
+        assert len(page1.json()) == 2
+        assert page1.headers["X-Total-Count"] == "4", "总数必须暴露，客户端才知道被截断"
+        page2 = client.get("/api/experiments", params={"limit": 2, "offset": 2})
+        assert len(page2.json()) == 2
+        ids = {e["id"] for e in page1.json()} | {e["id"] for e in page2.json()}
+        assert len(ids) == 4, "分页不得重复/漏项（created_at 同秒时靠 id 次级排序稳定）"
+        # 默认 limit（200）远大于旧硬编码的 50
+        assert client.get("/api/experiments").status_code == 200
+
+    def test_meta_includes_data_version_job_id_and_result_bytes(self, api) -> None:
+        client, Session = api
+        seeded = self._seed(Session)
+        row = next(e for e in client.get("/api/experiments").json() if e["id"] == "EXP-A1")
+        assert row["data_version"] == "d20260904;n1;a2;b3", "列表必须暴露数据指纹"
+        assert row["job_id"] is None
+        assert row["result_bytes"] == len(json.dumps({"kind": "backtest", "n": 0}))
+
+        # 详情路径（result_json 在内存，len() 零成本）与列表口径必须一致
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp_id = resp.headers["X-Experiment-Id"]
+        exp = _exp_by_id(Session, exp_id)
+        assert exp is not None
+        detail = client.get(f"/api/experiments/{exp_id}").json()
+        assert detail["result_bytes"] == len(exp.result_json)
+        assert detail["data_version"] == exp.data_version
+        assert detail["job_id"] is None
+        listed = next(e for e in client.get("/api/experiments").json() if e["id"] == exp_id)
+        assert listed["result_bytes"] == detail["result_bytes"], "列表与详情体积口径一致"
+        assert set(seeded) == {"EXP-A1", "EXP-A2", "EXP-A3", "EXP-A4"}
+
+    def test_repository_list_filtered_returns_summaries_without_result_json(self, api) -> None:
+        _client, Session = api
+        self._seed(Session)
+        with Session() as session:
+            repo = SqlAlchemyExperimentRepository(session)
+            rows = repo.list_filtered(kind="backtest", limit=10)
+            assert all(isinstance(r, ExperimentSummary) for r in rows)
+            assert not hasattr(rows[0], "result_json"), "列表不得拉取 MEDIUMTEXT 大字段"
+            assert repo.count_filtered() == 4
+            assert repo.count_filtered(kind="selection") == 1
+
+
+class TestDelete:
+    """任务 4：DELETE 只删归档，不删 Job 记录。"""
+
+    def test_delete_then_404(self, api) -> None:
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp_id = resp.headers["X-Experiment-Id"]
+
+        deleted = client.delete(f"/api/experiments/{exp_id}")
+        assert deleted.status_code == 200
+        assert deleted.json() == {"deleted": exp_id}
+        assert _exp_by_id(Session, exp_id) is None
+        assert client.get(f"/api/experiments/{exp_id}").status_code == 404
+        again = client.delete(f"/api/experiments/{exp_id}")
+        assert again.status_code == 404
+        assert "不存在" in again.json()["detail"]
+
+    def test_delete_keeps_job_record_and_reports_missing_result(self, api) -> None:
+        """删除归档不触碰 job 表；job 的结果只剩归档一份，故如实说明原因而非静默 200 空值。"""
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp_id = resp.headers["X-Experiment-Id"]
+        with Session() as session:
+            job = JobRecord(
+                id="JOB-DEL-1",
+                kind="backtest",
+                spec_json=json.dumps(_BACKTEST_BODY),
+                status=JobStatus.SUCCESS,
+                experiment_id=exp_id,
+                created_at=datetime.now(),
+            )
+            SqlAlchemyJobRepository(session).create(job)
+            session.commit()
+
+        assert client.delete(f"/api/experiments/{exp_id}").status_code == 200
+        with Session() as session:
+            kept = SqlAlchemyJobRepository(session).get("JOB-DEL-1")
+            assert kept is not None, "job 记录是执行历史，DELETE 不得连带删除"
+            assert kept.experiment_id == exp_id
+        view = client.get("/api/jobs/JOB-DEL-1")
+        assert view.status_code == 200
+        assert view.json()["result"] is None
+        assert "已不存在" in view.json()["result_unavailable_reason"]
+
+
+class TestCurveCompleteness:
+    """P0：默认完整存档（不再按 60 只截断）；超预算时裁剪并留证据。"""
+
+    @staticmethod
+    def _spec(top_n: int, symbols: list[str]) -> ResearchSpec:
+        return ResearchSpec(
+            type="backtest",
+            universe=UniverseSpec(
+                exclude_st=False, min_listing_days=0, symbols=list(symbols)
+            ),
+            factors=[FactorSpec(name="momentum_20")],
+            selection=SelectionSpec(top_n=top_n, hold_top_x=top_n, allow_substitute=False, defer_buy=True),
+            rebalance="monthly",
+            selection_interval_months=6,
+            rebalance_interval_months=6,
+            period=(date(2024, 3, 1), date(2024, 6, 30)),
+            costs=CostSpec(
+                commission_rate=0.0, stamp_tax_rate=0.0, slippage_rate=0.0
+            ),
+        )
+
+    def test_default_stores_every_held_symbol_beyond_60(self) -> None:
+        syms = [f"60{i:04d}.SH" for i in range(70)]
+        drifts = {s: 0.001 * (i % 4) + 0.0005 for i, s in enumerate(syms)}
+        daily = synthetic_daily(drifts, n=120)
+        res = LocalEngine().run_backtest(daily, self._spec(len(syms), syms))
+
+        held = {a.symbol for a in res.fills if a.symbol}
+        assert len(held) > 60, f"构造的样本应持有 60 只以上，实际 {len(held)}"
+        curve_syms = {c.symbol for c in res.symbol_curves}
+        assert held <= curve_syms, "期内持有的每只都必须有曲线（完整存档，默认不截断）"
+        assert not [n for n in res.unimplemented if "个股收益曲线" in n], "默认不得出现截断说明"
+
+    def test_configured_limit_truncates_and_notes_actual_limit(
+        self, monkeypatch: pytest.MonkeyPatch
+    ) -> None:
+        """配置了上限就截断 —— 且 note 里写清「实际保留几只 / 共几只」。"""
+        from app.core import config as config_mod
+        from app.quant import local_engine as le
+
+        monkeypatch.setattr(
+            config_mod,
+            "get_settings",
+            lambda: SimpleNamespace(research_archive_curve_limit=3),
+        )
+        assert le._MAX_SYMBOL_CURVES is None, "默认代码路径不设上限（配置优先）"
+        syms = [f"60{i:04d}.SH" for i in range(10)]
+        drifts = {s: 0.001 * (i % 3) + 0.0005 for i, s in enumerate(syms)}
+        daily = synthetic_daily(drifts, n=90)
+        res = LocalEngine().run_backtest(daily, self._spec(len(syms), syms))
+
+        assert len(res.symbol_curves) == 3
+        note = [n for n in res.unimplemented if "个股收益曲线" in n]
+        assert note and "3 只" in note[0] and "共持有" in note[0]
+
+    def test_archive_budget_truncates_with_machine_readable_evidence(
+        self, api, monkeypatch
+    ) -> None:
+        """归档字节预算：超预算时裁剪曲线，并把证据同时写进 archive_meta 与 unimplemented。
+
+        预算不靠猜：先按默认预算归档一次拿到「完整归档的字节数」L，再把预算设为
+        L - 2,000 重新归档 —— 必然需要裁掉若干条曲线，且裁剪后必须真的落到预算内。
+        """
+        client, Session = api
+        body = {
+            **_BACKTEST_BODY,
+            "selection": {"top_n": 5},
+            "universe": {**_BACKTEST_BODY["universe"], "symbols": _SYMS},
+        }
+        first = client.post("/api/backtests", json=body)
+        assert first.status_code == 200
+        assert first.json()["archive_meta"]["truncated"] is False, "默认预算下应完整存档"
+        full = _exp_by_id(Session, first.headers["X-Experiment-Id"])
+        assert full is not None
+        full_bytes = len(full.result_json.encode("utf-8"))
+        assert first.json()["archive_meta"]["curves_total"] >= 3, "样本需有足够曲线才能验证裁剪"
+
+        budget = full_bytes - 2_000
+        monkeypatch.setattr(ea, "_archive_budget_bytes", lambda: budget)
+        resp = client.post("/api/backtests", json=body)
+        assert resp.status_code == 200
+        meta = resp.json()["archive_meta"]
+        assert meta["truncated"] is True, f"预算 {budget} < 完整体积 {full_bytes}，必须触发裁剪"
+        assert 0 < meta["curves_stored"] < meta["curves_total"], meta
+        assert meta["budget_bytes"] == budget
+        assert meta["over_budget"] is False
+        assert any("个股收益曲线" in n for n in resp.json()["unimplemented"])
+
+        exp = _exp_by_id(Session, resp.headers["X-Experiment-Id"])
+        assert exp is not None
+        assert len(exp.result_json.encode("utf-8")) <= budget, "裁剪后必须真的放进预算"
+        archived = json.loads(exp.result_json)
+        assert archived["archive_meta"]["truncated"] is True
+        assert archived["archive_meta"]["curves_stored"] == len(archived["symbol_curves"])
+        assert any("个股收益曲线" in n for n in archived["unimplemented"])
+        assert archived["archive_meta"]["result_bytes"] == len(exp.result_json.encode("utf-8"))
+
+    def test_archive_over_budget_is_labelled_not_hidden(self, api, monkeypatch) -> None:
+        """预算小到连归档主体都放不下：必须标 over_budget + 说明，绝不假装达标。"""
+        client, Session = api
+        budget = 5_000
+        monkeypatch.setattr(ea, "_archive_budget_bytes", lambda: budget)
+
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        assert resp.status_code == 200
+        body = resp.json()
+        meta = body["archive_meta"]
+        assert meta["curves_stored"] == 0 and meta["truncated"] is True
+        assert meta["over_budget"] is True, "主体超预算必须显式标注，而不是静默超标"
+        assert any("超过配置的体积预算" in n for n in body["unimplemented"])
+        exp = _exp_by_id(Session, resp.headers["X-Experiment-Id"])
+        assert exp is not None
+        archived = json.loads(exp.result_json)
+        assert archived["archive_meta"]["over_budget"] is True
+        assert any("超过配置的体积预算" in n for n in archived["unimplemented"])
+        # 如实记录「确实超标」这一事实（不掩盖，也不假装裁剪到位）
+        assert archived["archive_meta"]["result_bytes"] > budget
+
+    def test_archive_meta_complete_when_under_budget(self, api) -> None:
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp = _exp_by_id(Session, resp.headers["X-Experiment-Id"])
+        assert exp is not None
+        archived = json.loads(exp.result_json)
+        meta = archived["archive_meta"]
+        assert meta["truncated"] is False
+        assert meta["curves_stored"] == meta["curves_total"] == len(archived["symbol_curves"])
+        assert meta["result_bytes"] == len(exp.result_json.encode("utf-8"))
+        assert meta["result_chars"] == len(exp.result_json)
+
+
+class TestSingleResultCopy:
+    """P1：完整结果只存 experiment 一份；GET /api/jobs/{id} 契约与老记录兼容。"""
+
+    def test_submit_and_run_reads_through_in_memory_only(self, tmp_path) -> None:
+        """`submit_and_run` 的返回对象仍带完整结果（既有调用方如
+        scripts/run_dividend_case.py 依赖它），但**数据库里不存第二份**。"""
+        from app.application.services.job_executor import submit_and_run
+
+        engine = create_engine(f"sqlite:///{tmp_path / 'submit.db'}", future=True)
+        Base.metadata.create_all(engine)
+        Session = sessionmaker(bind=engine, expire_on_commit=False)
+        drifts = {sym: 0.003 - 0.0015 * i for i, sym in enumerate(_SYMS)}
+        bars = bars_dataframe_to_daily_bars(synthetic_daily(drifts, n=300))
+        stocks = [
+            Stock(symbol=sym, name=f"测试股份{i}", list_date=date(1999, 11, 10))
+            for i, sym in enumerate(_SYMS)
+        ]
+        spec = ResearchSpec.model_validate(_BACKTEST_BODY)
+        done = submit_and_run(
+            spec,
+            factories={
+                "session_factory": Session,
+                "job_repo_factory": lambda s: SqlAlchemyJobRepository(s),
+                "experiment_repo_factory": lambda s: SqlAlchemyExperimentRepository(s),
+                "stock_repo_factory": lambda s: _MemStockRepo(stocks),
+                "daily_repo_factory": lambda s: _MemDailyRepo(bars),
+                "engine": LocalEngine(),
+            },
+        )
+        assert done.status == JobStatus.SUCCESS
+        assert done.experiment_id
+        assert done.result_json, "返回对象必须带完整结果（内存读透，供既有调用方使用）"
+        assert BacktestResult.model_validate_json(done.result_json).symbol_curves
+
+        with Session() as session:
+            persisted = SqlAlchemyJobRepository(session).get(done.id)
+            exp = SqlAlchemyExperimentRepository(session).get(done.experiment_id)
+        assert persisted is not None and persisted.result_json is None, "数据库不得存第二份"
+        assert exp is not None and exp.result_json, "完整结果在 experiment 侧"
+
+    def test_job_view_reads_experiment_and_keeps_full_result(self, api) -> None:
+        from app.api.jobs import _job_view
+
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp_id = resp.headers["X-Experiment-Id"]
+        with Session() as session:
+            job = JobRecord(
+                id="JOB-VIEW-1",
+                kind="backtest",
+                spec_json=json.dumps(_BACKTEST_BODY),
+                status=JobStatus.SUCCESS,
+                result_json=None,  # 新形态：job 侧不再保存结果副本
+                experiment_id=exp_id,
+                created_at=datetime.now(),
+            )
+            SqlAlchemyJobRepository(session).create(job)
+            session.commit()
+            view = _job_view(job, SqlAlchemyExperimentRepository(session))
+        assert view["result_source"] == "experiment"
+        assert view["result"].symbol_curves, "契约不变：GET /api/jobs/{id} 仍返回完整结果"
+        assert (
+            view["result"].summary.total_return_pct
+            == resp.json()["summary"]["total_return_pct"]
+        )
+        assert client.get("/api/jobs/JOB-VIEW-1").json()["result"]["symbol_curves"]
+
+    def test_legacy_job_record_still_decodes_from_result_json(self, api) -> None:
+        """老形态（result_json 有值、experiment_id 为空）必须继续可用。"""
+        from app.api.jobs import _job_view
+
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        full = json.dumps(resp.json(), ensure_ascii=False)
+        with Session() as session:
+            job = JobRecord(
+                id="JOB-LEGACY-1",
+                kind="backtest",
+                spec_json=json.dumps(_BACKTEST_BODY),
+                status=JobStatus.SUCCESS,
+                result_json=full,
+                experiment_id=None,
+                created_at=datetime.now(),
+            )
+            SqlAlchemyJobRepository(session).create(job)
+            session.commit()
+            view = _job_view(job, SqlAlchemyExperimentRepository(session))
+        assert view["result_source"] == "job"
+        assert view["result"].symbol_curves
+        assert view["result"].summary.total_return_pct == resp.json()["summary"]["total_return_pct"]
+
+    def test_job_table_no_longer_duplicates_result(self, api) -> None:
+        """json 列确实没有第二份结果（体积审计的机器可读证据）。"""
+        client, Session = api
+        resp = client.post("/api/backtests", json=_BACKTEST_BODY)
+        exp_id = resp.headers["X-Experiment-Id"]
+        with Session() as session:
+            job = JobRecord(
+                id="JOB-SIZE-1",
+                kind="backtest",
+                spec_json=json.dumps(_BACKTEST_BODY),
+                status=JobStatus.SUCCESS,
+                experiment_id=exp_id,
+                created_at=datetime.now(),
+            )
+            SqlAlchemyJobRepository(session).create(job)
+            session.commit()
+        with Session() as session:
+            row = session.execute(
+                select(JobModel.result_json).where(JobModel.id == "JOB-SIZE-1")
+            ).scalar()
+            exp_row = session.execute(
+                select(ExperimentModel.result_json).where(ExperimentModel.id == exp_id)
+            ).scalar()
+        assert row is None, "job 侧不得再存结果副本"
+        assert exp_row, "完整结果必须在 experiment 侧"
+        archived = _exp_by_id(Session, exp_id)
+        assert archived is not None
+        assert len(exp_row) == len(archived.result_json)
\ No newline at end of file
diff --git a/backend/tests/test_factor_catalog.py b/backend/tests/test_factor_catalog.py
index e5e6e39..f36d153 100644
--- a/backend/tests/test_factor_catalog.py
+++ b/backend/tests/test_factor_catalog.py
@@ -95,3 +95,50 @@ class TestFactorsApi:
         resp = client.get("/api/factors")
         names = {r["name"] for r in resp.json()}
         assert names == {d.name for d in list_factors()}
+
+
+class TestRegistrySyncRegression:
+    """回归：表非空时也必须补齐「注册表有、库里没有」的因子。
+
+    历史 bug：seed 只在表为空时触发，导致 `dividend_yield` 等后加的因子永远不进目录
+    （实测真实库表里 9 条、注册表 11 条），前端因子下拉与归档说明都取不到它们。
+    """
+
+    def test_missing_registry_factor_is_seeded_when_table_not_empty(self, client, tmp_path) -> None:
+        from sqlalchemy import text
+
+        # 1) 先正常读一次 → 目录完整（含股息率因子）
+        full = {r["name"] for r in client.get("/api/factors").json()}
+        assert "dividend_yield" in full, "注册表里的股息率因子必须出现在目录中"
+
+        # 2) 人为删除一行，复现「表非空但缺因子」的历史状态
+        engine = create_engine(f"sqlite:///{tmp_path / 'api.db'}", future=True)
+        with engine.begin() as conn:
+            conn.execute(text("DELETE FROM factor_definition WHERE name = 'dividend_yield'"))
+        rows = client.get("/api/factors").json()
+        # 3) 再读 → 缺失因子被当场补齐
+        names = {r["name"] for r in rows}
+        assert "dividend_yield" in names, "表非空时也必须补齐缺失的注册表因子"
+        assert names == {d.name for d in list_factors()}
+        # 4) 用户登记的自定义因子元数据不被覆盖/删除（只补不删）
+        Session = sessionmaker(bind=engine, expire_on_commit=False)
+        with Session() as s2:
+            SqlAlchemyFactorRepository(s2).upsert_many(
+                [
+                    FactorDefinition(
+                        name="my_custom_factor",
+                        description="自定义因子",
+                        formula="x",
+                        brief="b",
+                        frequency="daily",
+                        lookback=5,
+                        direction="higher_is_better",
+                        requires=[],
+                        version="1",
+                    )
+                ]
+            )
+            s2.commit()
+        names2 = {r["name"] for r in client.get("/api/factors").json()}
+        assert "my_custom_factor" in names2, "只补不删：自定义因子必须保留"
+        assert "dividend_yield" in names2
diff --git a/backend/tests/test_index_universe.py b/backend/tests/test_index_universe.py
index 0f2080b..f87703d 100644
--- a/backend/tests/test_index_universe.py
+++ b/backend/tests/test_index_universe.py
@@ -117,3 +117,46 @@ class TestUniverseIndexCodeFilter:
 
     def test_index_code_filter(self, tmp_path) -> None:
         self._build(tmp_path)
+
+
+class TestDelistedUniverse:
+    """退市股的时点股票池语义（幸存者偏差修正的核心断言）。
+
+    退市股必须在**退市日之前**纳入池子、退市之后排除；否则回测只剩「活下来的
+    赢家」，收益被系统性高估（高股息策略尤其容易被股息陷阱的退市股反噬）。
+    """
+
+    def _stock(self, symbol: str, list_date: date, delist_date: date | None):
+        from app.domain.entities.market import Stock
+
+        return Stock(
+            symbol=symbol, name=f"测试{symbol}", list_date=list_date, delist_date=delist_date
+        )
+
+    def test_delisted_included_before_excluded_after(self) -> None:
+        from app.domain.entities.research import UniverseSpec
+        from app.quant.universe import filter_stocks
+
+        stocks = [
+            self._stock("600000.SH", date(1999, 11, 10), None),
+            self._stock("000005.SZ", date(1990, 12, 10), date(2024, 4, 26)),
+        ]
+        u = UniverseSpec(exclude_st=False, min_listing_days=0)
+        before = {s.symbol for s in filter_stocks(stocks, u, as_of=date(2024, 1, 2))}
+        after = {s.symbol for s in filter_stocks(stocks, u, as_of=date(2024, 6, 3))}
+        on_delist_day = {s.symbol for s in filter_stocks(stocks, u, as_of=date(2024, 4, 26))}
+        assert "000005.SZ" in before and "000005.SZ" not in after
+        assert "000005.SZ" in on_delist_day  # 退市日当天仍在（数据截至当日）
+        assert "600000.SH" in before and "600000.SH" in after
+
+    def test_st_name_excludes_regardless_of_period(self) -> None:
+        """已知局限：exclude_st 用**最新名称**判定，会把曾用名非 ST 的标的整段排除。"""
+        from app.domain.entities.research import UniverseSpec
+        from app.quant.universe import filter_stocks
+
+        stocks = [self._stock("000005.SZ", date(1990, 12, 10), date(2024, 4, 26))]
+        stocks[0].name = "ST星源(退)"
+        u = UniverseSpec(exclude_st=True, min_listing_days=0)
+        assert filter_stocks(stocks, u, as_of=date(2024, 1, 2)) == []
+        u2 = UniverseSpec(exclude_st=False, min_listing_days=0)
+        assert len(filter_stocks(stocks, u2, as_of=date(2024, 1, 2))) == 1
diff --git a/backend/tests/test_interval_selection.py b/backend/tests/test_interval_selection.py
new file mode 100644
index 0000000..3a8cbc1
--- /dev/null
+++ b/backend/tests/test_interval_selection.py
@@ -0,0 +1,328 @@
+"""择股/调仓双周期（m/y）、两级截断（n/x）与顺延买入（defer_buy）测试。
+
+对应用户案例：「全市场股息率最高的 n 只 → 持仓前 x 只；每 m 个月择股一次；
+每 y 个月调仓，默认 y=m；买卖点为收盘价；买不进时顺延到之后不涨停的交易日买入」。
+"""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pandas as pd
+import pytest
+from app.domain.entities.research import (
+    CostSpec,
+    FactorSpec,
+    ResearchSpec,
+    SelectionSpec,
+    UniverseSpec,
+)
+from app.quant.engine import LocalEngine
+from app.quant.local_engine import rebalance_dates
+from pydantic import ValidationError
+
+from conftest_quant import synthetic_daily
+
+
+def _spec(
+    top_n: int = 2,
+    hold_top_x: int | None = None,
+    start: date = date(2024, 3, 1),
+    end: date = date(2024, 12, 20),
+    m: int | None = None,
+    y: int | None = None,
+    allow_substitute: bool = False,
+    defer_buy: bool = True,
+) -> ResearchSpec:
+    """合成行情自 2024-01-01 起（因子预热），回测自 2024-03-01 起（首个调仓日即有信号）。"""
+    return ResearchSpec(
+        type="backtest",
+        universe=UniverseSpec(exclude_st=False, min_listing_days=0),
+        factors=[FactorSpec(name="momentum_20")],
+        selection=SelectionSpec(
+            top_n=top_n,
+            hold_top_x=hold_top_x,
+            allow_substitute=allow_substitute,
+            defer_buy=defer_buy,
+        ),
+        rebalance="monthly",
+        selection_interval_months=m,
+        rebalance_interval_months=y,
+        period=(start, end),
+        costs=CostSpec(commission_rate=0.0, stamp_tax_rate=0.0, slippage_rate=0.0),
+    )
+
+
+class TestIntervalSchedule:
+    def test_every_n_months_anchored_at_start_month(self) -> None:
+        idx = pd.bdate_range("2020-01-01", "2021-12-31")
+        out = rebalance_dates(idx, "monthly", date(2020, 1, 1), every_months=6)
+        assert [d.strftime("%Y-%m-%d") for d in out] == [
+            "2020-01-01", "2020-07-01", "2021-01-01", "2021-07-01",
+        ]
+
+    def test_anchor_moves_with_start_month(self) -> None:
+        idx = pd.bdate_range("2020-01-01", "2021-12-31")
+        out = rebalance_dates(idx, "monthly", date(2020, 3, 1), every_months=6)
+        assert [d.strftime("%Y-%m-%d") for d in out] == [
+            "2020-03-02", "2020-09-01", "2021-03-01", "2021-09-01",
+        ]
+
+    def test_start_not_first_trading_day_keeps_anchor_month(self) -> None:
+        """起始日非月初时不得跳过锚点月（否则白等 m 个月才首次建仓）。"""
+        idx = pd.bdate_range("2024-01-01", "2025-12-31")
+        out = rebalance_dates(idx, "monthly", date(2024, 3, 15), every_months=6)
+        # 2024-03-15 本身是交易日 → 锚点即当日；后续按 +6 个月推进
+        assert [d.strftime("%Y-%m-%d") for d in out] == [
+            "2024-03-15", "2024-09-02", "2025-03-03", "2025-09-01",
+        ]
+        # 起始日落在非交易日（2024-03-16/17 为周末）→ 取之后首个交易日，仍属 3 月
+        out2 = rebalance_dates(idx, "monthly", date(2024, 3, 16), every_months=6)
+        assert out2[0].strftime("%Y-%m-%d") == "2024-03-18"
+
+    def test_start_after_last_trading_day_of_month_moves_anchor(self) -> None:
+        """起始日晚于该月最后一个交易日时，锚点自然落到下一个月（不产生空区间）。"""
+        idx = pd.bdate_range("2024-01-01", "2025-12-31")
+        out = rebalance_dates(idx, "monthly", date(2024, 3, 31), every_months=6)
+        assert [d.strftime("%Y-%m-%d") for d in out][:2] == ["2024-04-01", "2024-10-01"]
+
+    def test_non_month_start_does_not_leave_early_cash(self) -> None:
+        """非月初起始 + m=y=6：首个择股/调仓日不应晚于起始月，净值不得长期恒为初始值。"""
+        daily = synthetic_daily({"600000.SH": 0.004, "600001.SH": 0.002}, n=260)
+        res = LocalEngine().run_backtest(
+            daily, _spec(top_n=1, m=6, y=6, start=date(2024, 3, 15), end=date(2024, 12, 20))
+        )
+        first_pos = min(p.date for p in res.positions)
+        assert first_pos == date(2024, 3, 15), first_pos
+        # 起始日之后的前 20 个交易日里不应出现「净值恒为初始资金」
+        head = res.equity_curve[:20]
+        assert len({p.value for p in head}) > 1, "起始月即应建仓，净值不应恒为初始资金"
+
+    def test_selection_and_rebalance_schedules_are_independent(self) -> None:
+        """m=6、y=3：择股 2 次，调仓 4 次。"""
+        daily = synthetic_daily({f"60000{i}.SH": 0.004 - 0.001 * i for i in range(4)}, n=260)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=2, m=6, y=3))
+        sel_dates = sorted({p.date for p in res.selection_history})
+        rebal_dates_ = sorted({p.date for p in res.positions})
+        assert len(sel_dates) == 2, sel_dates  # 2024-03-01 / 2024-09-02
+        assert len(rebal_dates_) == 4, rebal_dates_  # 03/06/09/12 各一次
+        assert set(sel_dates) < set(rebal_dates_)
+
+    def test_default_y_equals_m(self) -> None:
+        spec = _spec(m=6)
+        assert spec.effective_selection_months == 6
+        assert spec.effective_rebalance_months == 6  # y 缺省 → 跟随 m
+
+    def test_y_without_m_rejected(self) -> None:
+        with pytest.raises(ValidationError):
+            _spec(m=None, y=3)
+
+
+class TestTwoLevelTruncation:
+    def test_hold_only_top_x_of_pool(self) -> None:
+        """n=4、x=2：候选池记录 4 只，实际只持仓前 2 只。"""
+        daily = synthetic_daily({f"60000{i}.SH": 0.006 - 0.0015 * i for i in range(6)}, n=260)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=4, hold_top_x=2, m=2, y=2))
+        pool_dates = {p.date for p in res.selection_history}
+        for d in pool_dates:
+            assert len([p for p in res.selection_history if p.date == d]) == 4
+        # 持仓数量不超过 x=2
+        per_date: dict = {}
+        for p in res.positions:
+            per_date.setdefault(p.date, []).append(p.symbol)
+        assert per_date and all(len(v) <= 2 for v in per_date.values())
+
+    def test_x_cannot_exceed_n(self) -> None:
+        with pytest.raises(ValidationError):
+            SelectionSpec(top_n=5, hold_top_x=10)
+
+    def test_substitute_and_defer_are_mutually_exclusive(self) -> None:
+        with pytest.raises(ValidationError):
+            SelectionSpec(top_n=5, allow_substitute=True, defer_buy=True)
+
+    def test_no_substitute_keeps_pool_membership(self) -> None:
+        """defer 模式（不替补）：持仓必属候选池，绝不出现池外标的。"""
+        daily = synthetic_daily({f"60000{i}.SH": 0.006 - 0.0015 * i for i in range(6)}, n=260)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=2, hold_top_x=2, m=6, y=6))
+        for pos in res.positions:
+            same_day_pool = {p.symbol for p in res.selection_history if p.date == pos.date}
+            assert pos.symbol in same_day_pool, f"{pos.symbol} 不在当日候选池 {same_day_pool}"
+
+
+class TestDeferBuy:
+    """顺延买入：涨停当日不成交，之后首个不涨停交易日按收盘价买入。"""
+
+    def _limit_up_frame(self) -> tuple[pd.DataFrame, str, date, date]:
+        """构造：600000.SH 在首个调仓日涨停，之后恢复；动量高于 600001.SH。"""
+        daily = synthetic_daily({"600000.SH": 0.006, "600001.SH": 0.001}, n=260)
+        dates = sorted(pd.to_datetime(daily["trade_date"].unique()))
+        d0 = next(d for d in dates if d.date() >= date(2024, 3, 1))  # 首个调仓日
+        d1 = dates[dates.index(d0) + 1]
+        prev = dates[dates.index(d0) - 1]
+        prev_close = float(
+            daily[(daily["symbol"] == "600000.SH") & (daily["trade_date"] == prev.date())][
+                "close"
+            ].iloc[0]
+        )
+        mask = (daily["symbol"] == "600000.SH") & (daily["trade_date"] == d0.date())
+        daily.loc[mask, "close"] = prev_close * 1.10  # 主板涨停
+        daily.loc[mask, "high"] = prev_close * 1.10
+        return daily, "600000.SH", d0.date(), d1.date()
+
+    def test_buy_deferred_to_next_tradable_day(self) -> None:
+        daily, sym, d0, d1 = self._limit_up_frame()
+        res = LocalEngine().run_backtest(daily, _spec(top_n=2, hold_top_x=2, m=6, y=6))
+        # 调仓日意图登记为未成交，原因含「涨停」与「顺延」
+        rejects = [
+            a for a in res.signal_history
+            if a.date == d0 and a.symbol == sym and a.signal == "BUY" and not a.filled
+        ]
+        assert rejects and "涨停" in (rejects[0].reject_reason or "")
+        assert "顺延" in (rejects[0].reject_reason or "")
+        # 当日无成交
+        assert not [a for a in res.fills if a.date == d0 and a.symbol == sym]
+        # 次一交易日按收盘价成交（价格 = 该日 close，滑点为 0）
+        fills = [a for a in res.fills if a.symbol == sym and a.signal == "BUY"]
+        assert fills, "顺延后应成交"
+        assert fills[0].date == d1
+        px = float(
+            daily[(daily["symbol"] == sym) & (daily["trade_date"] == d1)]["close"].iloc[0]
+        )
+        assert fills[0].price == pytest.approx(px, rel=1e-6)
+
+    def test_defer_disabled_gives_up_immediately(self) -> None:
+        """defer_buy=False：涨停当日被拒后直接放弃，不顺延（区间内无下一次调仓）。"""
+        daily, sym, d0, d1 = self._limit_up_frame()
+        res = LocalEngine().run_backtest(
+            daily, _spec(top_n=2, hold_top_x=2, m=6, y=6, end=date(2024, 6, 28), defer_buy=False)
+        )
+        assert not [a for a in res.fills if a.symbol == sym and a.signal == "BUY"]
+        rejects = [
+            a for a in res.signal_history
+            if a.date == d0 and a.symbol == sym and a.signal == "BUY" and not a.filled
+        ]
+        assert rejects
+        assert "顺延" not in (rejects[0].reject_reason or "")
+
+    def test_pending_order_cleared_at_next_rebalance(self) -> None:
+        """顺延单不跨调仓：3/1 挂的单在 4/1 调仓时作废，4/1 是新的调仓尝试。"""
+        daily = synthetic_daily({"600000.SH": 0.006, "600001.SH": 0.001}, n=260)
+        dates = sorted(pd.to_datetime(daily["trade_date"].unique()))
+        d0 = min(d for d in dates if d.date() >= date(2024, 3, 1))
+        d1 = min(d for d in dates if d.date() >= date(2024, 4, 1))
+        # 2024-03-01 ~ 04-01 连续每日涨停（链式 +10%）
+        seed = dates[dates.index(d0) - 1]
+        prev_close = float(
+            daily[(daily["symbol"] == "600000.SH") & (daily["trade_date"] == seed.date())][
+                "close"
+            ].iloc[0]
+        )
+        for d in dates:
+            if not (d0.date() <= d.date() <= d1.date()):
+                continue
+            prev_close = prev_close * 1.10
+            mask = (daily["symbol"] == "600000.SH") & (daily["trade_date"] == d.date())
+            daily.loc[mask, "close"] = prev_close
+            daily.loc[mask, "high"] = prev_close
+        res = LocalEngine().run_backtest(
+            daily, _spec(top_n=1, hold_top_x=1, end=date(2024, 4, 30), m=12, y=1)
+        )
+        # 3/1 与 4/1 两次调仓尝试均被涨停拒绝
+        reject_dates = {
+            a.date for a in res.signal_history
+            if a.symbol == "600000.SH" and a.signal == "BUY" and not a.filled
+        }
+        assert d0.date() in reject_dates and d1.date() in reject_dates
+        # 3/1 挂出的顺延单在 4/1 之前一次都没成交（期间每日涨停）
+        assert not [a for a in res.fills if a.date < d1.date()]
+        # 4/1 之后涨停解除 → 新一次调仓的顺延单成交
+        fills = [a for a in res.fills if a.symbol == "600000.SH"]
+        assert len(fills) == 1 and fills[0].date > d1.date()
+
+
+class TestSymbolCurves:
+    def test_symbol_curves_and_marks(self) -> None:
+        daily = synthetic_daily({"600000.SH": 0.004, "600001.SH": -0.002}, n=260)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=1, m=6, y=6))
+        assert res.symbol_curves, "应输出个股收益曲线"
+        for curve in res.symbol_curves:
+            assert curve.points, f"{curve.symbol} 曲线无数据点"
+            if curve.marks:
+                assert all(m.symbol == curve.symbol for m in curve.marks)
+                assert all(m.filled for m in curve.marks)
+        # 成交记录中的股票都应有曲线
+        traded = {a.symbol for a in res.fills if a.symbol}
+        assert traded <= {c.symbol for c in res.symbol_curves}
+
+    def test_symbol_curve_pct_matches_holding_gain(self) -> None:
+        """单股全程持有：曲线期末收益 ≈ 期末价/建仓日收盘 - 1（零成本下）。"""
+        daily = synthetic_daily({"600000.SH": 0.004}, n=130)
+        res = LocalEngine().run_backtest(
+            daily, _spec(top_n=1, m=12, y=12, end=date(2024, 6, 28))
+        )
+        curve = res.symbol_curves[0]
+        closes = daily[daily["symbol"] == "600000.SH"].sort_values("trade_date")
+        entry_close = float(closes[closes["trade_date"] == date(2024, 3, 1)]["close"].iloc[0])
+        exit_close = float(closes["close"].iloc[-1])
+        expected = (exit_close / entry_close - 1) * 100
+        assert curve.final_return_pct == pytest.approx(expected, rel=1e-3)
+        # 建仓当日曲线为 0%（当日收盘成交，不计当日涨跌）
+        assert curve.points[0].date == date(2024, 3, 1)
+        assert curve.points[0].value == pytest.approx(0.0, abs=1e-9)
+
+class TestFirstDayWithoutPrevClose:
+    """数据窗口起点无上一有效收盘价时的买入处理（案例 start=2020-01-01 实测命中）。
+
+    `_buyable` 在无前收时无法判定涨停：若按「不可买」处理，回测首个调仓日会被
+    整体放弃（顺延到次日，白付一天空仓）；现按「可买」处理并在 unimplemented
+    中如实标注次数（AGENT.md §24）。
+    """
+
+    @staticmethod
+    def _runner(daily: pd.DataFrame, spec: ResearchSpec):
+        """用常量 score 直接驱动 Runner：绕开因子预热，隔离「首个交易日」这一场景。"""
+        from app.quant.local_engine import TopKBacktestRunner
+
+        close = daily.pivot(index="trade_date", columns="symbol", values="close")
+        close.index = pd.to_datetime(close.index)
+        score = pd.DataFrame(1.0, index=close.index, columns=close.columns)
+        return TopKBacktestRunner(spec, score, close)
+
+    def test_buy_on_first_bar_without_prev_close(self) -> None:
+        daily = synthetic_daily({"600000.SH": 0.004, "600001.SH": 0.002}, n=20)
+        first_day = min(daily["trade_date"])
+        res = self._runner(
+            daily, _spec(top_n=1, m=1, y=1, start=first_day, end=max(daily["trade_date"]))
+        ).run()
+        buy_days = [a.date for a in res.fills if a.signal == "BUY"]
+        assert buy_days and min(buy_days) == first_day, "首个交易日即应成交，不应被整体顺延"
+        assert any("无法判定涨停" in n for n in res.unimplemented), "应如实标注无前收的判定降级"
+
+    def test_no_note_when_prev_close_available(self) -> None:
+        daily = synthetic_daily({"600000.SH": 0.004, "600001.SH": 0.002}, n=60)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=1, m=1, y=1))
+        assert not any("无法判定涨停" in n for n in res.unimplemented)
+
+
+class TestSymbolCurvePayloadBound:
+    def test_curves_capped_with_note(self, monkeypatch: pytest.MonkeyPatch) -> None:
+        """曲线数量上限生效并如实标注（结果体积约束：超限会让落库/传输不可用）。"""
+        from app.quant import local_engine as le
+
+        monkeypatch.setattr(le, "_MAX_SYMBOL_CURVES", 2)
+        drifts = {f"60000{i}.SH": 0.001 * (i % 3) for i in range(10)}
+        daily = synthetic_daily(drifts, n=90)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=10, hold_top_x=10, m=6, y=6))
+        assert len(res.symbol_curves) == 2
+        note = [n for n in res.unimplemented if "个股收益曲线" in n]
+        assert note and "共持有" in note[0]
+
+    def test_no_flat_points_when_not_held(self) -> None:
+        """未持有期间不落点（只落持仓日 + 建仓基准点），显著压缩结果体积。"""
+        daily = synthetic_daily({"600000.SH": 0.004, "600001.SH": 0.002}, n=260)
+        res = LocalEngine().run_backtest(daily, _spec(top_n=1, m=6, y=6))
+        all_days = sorted(daily["trade_date"].unique())
+        for curve in res.symbol_curves:
+            point_days = {p.date for p in curve.points}
+            assert point_days <= set(all_days)
+            assert len(point_days) < len(all_days), f"{curve.symbol} 不应逐日落点"
diff --git a/backend/tests/test_jobs_experiments.py b/backend/tests/test_jobs_experiments.py
index 840a071..d022126 100644
--- a/backend/tests/test_jobs_experiments.py
+++ b/backend/tests/test_jobs_experiments.py
@@ -112,11 +112,13 @@ class TestJobExecutor:
             done = SqlAlchemyJobRepository(session).get("JOB-TEST-1")
             assert done is not None
             assert done.status == JobStatus.SUCCESS
-            assert done.result_json is not None
+            # 完整结果只在 experiment 存一份（job 侧不再重复落库）
+            assert done.result_json is None
             assert done.experiment_id is not None
             exp = SqlAlchemyExperimentRepository(session).get(done.experiment_id)
             assert exp is not None
             assert exp.kind == "backtest"
+            assert exp.result_json  # 归档里必须有完整结果
             assert exp.summary_text is not None
             assert "总收益" in (exp.summary_text or "")
 
diff --git a/backend/tests/test_name_history.py b/backend/tests/test_name_history.py
new file mode 100644
index 0000000..2b4b6e2
--- /dev/null
+++ b/backend/tests/test_name_history.py
@@ -0,0 +1,427 @@
+"""名称变更历史（时点 ST）测试：Repository、Provider 归一、universe 时点口径。
+
+背景（实测）：`stock.name` 只是最新名称快照。用它做 `exclude_st` 会把
+「曾是高股息、后来才变 ST/退市」的标的在**整段历史**里排除 —— 而那正是
+「股息陷阱」样本。实测对照（同一 spec 仅改 exclude_st）：+35.71% → +32.01%，
+即约 3.70pp 收益被名称快照口径隐藏。本模块锁定修复后的时点语义。
+"""
+
+from __future__ import annotations
+
+from datetime import date
+from decimal import Decimal
+
+import pandas as pd
+import pytest
+from app.domain.entities.market import Stock, StockNameHistory
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.infrastructure.persistence.sqlalchemy.models.market import (
+    DailyBasicModel,
+    StockDailyModel,
+    StockModel,
+    StockNameHistoryModel,
+)
+from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+    SqlAlchemyStockNameHistoryRepository,
+)
+from sqlalchemy import create_engine
+from sqlalchemy.orm import Session
+
+
+@pytest.fixture
+def session(tmp_path):
+    engine = create_engine(f"sqlite:///{tmp_path / 'name.db'}", future=True)
+    Base.metadata.create_all(engine)
+    s = Session(engine)
+    yield s
+    s.close()
+
+
+def _seed_dima(session: Session) -> None:
+    """600565.SH 迪马股份：2002-07-23 上市 → 2024-05-06 变 ST迪马（真实数据）。"""
+    session.add_all(
+        [
+            StockModel(
+                symbol="600565.SH", name="ST迪马", list_date=date(2002, 7, 23), status="L"
+            ),
+            StockModel(
+                symbol="600519.SH", name="贵州茅台", list_date=date(2001, 8, 27), status="L"
+            ),
+            StockNameHistoryModel(
+                symbol="600565.SH", name="迪马股份", start_date=date(2002, 7, 23),
+                end_date=date(2024, 5, 5), ann_date=date(2002, 7, 23), change_reason="其他",
+            ),
+            StockNameHistoryModel(
+                symbol="600565.SH", name="ST迪马", start_date=date(2024, 5, 6),
+                end_date=None, ann_date=date(2024, 4, 30), change_reason="ST",
+            ),
+            StockNameHistoryModel(
+                symbol="600519.SH", name="贵州茅台", start_date=date(2001, 8, 27),
+                end_date=None, ann_date=date(2001, 8, 27), change_reason="其他",
+            ),
+        ]
+    )
+    session.commit()
+
+
+class TestRepository:
+    def test_names_as_of_picks_effective_span(self, session) -> None:
+        _seed_dima(session)
+        repo = SqlAlchemyStockNameHistoryRepository(session)
+        symbols = ["600565.SH", "600519.SH"]
+        # 2020 年：迪马股份（不是 ST）
+        assert repo.names_as_of(symbols, date(2020, 1, 2))["600565.SH"] == "迪马股份"
+        # 区间边界：末日仍为旧名，次日起为新名
+        assert repo.names_as_of(["600565.SH"], date(2024, 5, 5))["600565.SH"] == "迪马股份"
+        assert repo.names_as_of(["600565.SH"], date(2024, 5, 6))["600565.SH"] == "ST迪马"
+        # end_date 为空的区间至今有效
+        assert repo.names_as_of(["600565.SH"], date(2026, 9, 4))["600565.SH"] == "ST迪马"
+
+    def test_unknown_symbol_absent_from_map(self, session) -> None:
+        """无记录 → 不返回该键，由调用方回退最新名称（不抛错）。"""
+        _seed_dima(session)
+        repo = SqlAlchemyStockNameHistoryRepository(session)
+        assert repo.names_as_of(["000001.SZ"], date(2020, 1, 2)) == {}
+
+    def test_upsert_is_idempotent(self, session) -> None:
+        repo = SqlAlchemyStockNameHistoryRepository(session)
+        row = StockNameHistory(
+            symbol="600565.SH", name="迪马股份", start_date=date(2002, 7, 23),
+            end_date=date(2024, 5, 5), change_reason="其他",
+        )
+        assert repo.upsert_many([row]) == 1
+        assert repo.upsert_many([row]) == 1  # 重跑无副作用
+        assert repo.count_rows() == 1
+        assert repo.namechange_dates() == (date(2002, 7, 23), date(2002, 7, 23))
+
+    def test_name_spans_grouped_by_symbol(self, session) -> None:
+        _seed_dima(session)
+        repo = SqlAlchemyStockNameHistoryRepository(session)
+        spans = repo.name_spans(["600565.SH"])
+        assert [n for _s, _e, n in spans["600565.SH"]] == ["迪马股份", "ST迪马"]
+
+
+class TestUniversePointInTime:
+    """filter_stocks(name_at=...) 的时点语义（案例收益口径的关键）。"""
+
+    def _stocks(self) -> list[Stock]:
+        return [
+            Stock(symbol="600565.SH", name="ST迪马", list_date=date(2002, 7, 23)),
+            Stock(symbol="600519.SH", name="贵州茅台", list_date=date(2001, 8, 27)),
+        ]
+
+    def test_name_at_overrides_snapshot(self) -> None:
+        from app.domain.entities.research import UniverseSpec
+        from app.quant.universe import filter_stocks
+
+        u = UniverseSpec(exclude_st=True, min_listing_days=0)
+        # 旧口径（无 name_at）：最新名称含 ST → 2020 年就被排除（股息陷阱被隐藏）
+        assert "600565.SH" not in {s.symbol for s in filter_stocks(self._stocks(), u, date(2020, 1, 2))}
+        # 时点口径：2020 年它叫「迪马股份」→ 必须纳入
+        got = {
+            s.symbol
+            for s in filter_stocks(
+                self._stocks(), u, date(2020, 1, 2), name_at={"600565.SH": "迪马股份"}
+            )
+        }
+        assert "600565.SH" in got
+        # 2024-05-06 起为 ST迪马 → 排除
+        got2 = {
+            s.symbol
+            for s in filter_stocks(
+                self._stocks(), u, date(2024, 6, 3), name_at={"600565.SH": "ST迪马"}
+            )
+        }
+        assert "600565.SH" not in got2
+
+    def test_name_at_missing_falls_back_to_snapshot(self) -> None:
+        from app.domain.entities.research import UniverseSpec
+        from app.quant.universe import filter_stocks
+
+        u = UniverseSpec(exclude_st=True, min_listing_days=0)
+        got = filter_stocks(self._stocks(), u, date(2020, 1, 2), name_at={})
+        assert "600565.SH" not in {s.symbol for s in got}  # 回退快照，行为不变
+
+    def test_exclude_st_false_ignores_names(self) -> None:
+        from app.domain.entities.research import UniverseSpec
+        from app.quant.universe import filter_stocks
+
+        u = UniverseSpec(exclude_st=False, min_listing_days=0)
+        got = filter_stocks(self._stocks(), u, date(2020, 1, 2), name_at={"600565.SH": "ST迪马"})
+        assert "600565.SH" in {s.symbol for s in got}
+
+
+class TestNamesAsOfHelper:
+    def test_none_repo_reports_snapshot_basis(self) -> None:
+        from app.quant.universe import names_as_of
+
+        name_at, applied = names_as_of([], date(2020, 1, 2), None)
+        assert name_at is None
+        assert applied == (False, 0)
+
+    def test_repo_failure_degrades_without_raising(self) -> None:
+        """名称历史查询异常不得让选股/回测整体失败（降级为快照口径）。"""
+        from app.quant.universe import names_as_of
+
+        class Boom:
+            def names_as_of(self, symbols, as_of):
+                raise RuntimeError("表不存在")
+
+        name_at, applied = names_as_of([], date(2020, 1, 2), Boom())
+        assert name_at is None and applied == (False, 0)
+
+
+class TestProviderNormalize:
+    def test_normalize_name_history(self) -> None:
+        from app.infrastructure.data_sources.tushare import TushareProvider
+
+        rows = TushareProvider.normalize_name_history(
+            [
+                {
+                    "ts_code": "600565.SH", "name": "ST迪马", "start_date": "20240506",
+                    "end_date": None, "ann_date": "20240430", "change_reason": "ST",
+                },
+                {
+                    "ts_code": "600565.SH", "name": "迪马股份", "start_date": "20020723",
+                    "end_date": "20240505", "ann_date": "20020723", "change_reason": "其他",
+                },
+            ]
+        )
+        assert rows[1].start_date == date(2002, 7, 23)
+        assert rows[0].end_date is None
+        assert rows[0].is_risk_warned is True
+        assert rows[1].is_risk_warned is False
+
+    def test_nan_end_date_and_bad_code_skipped(self) -> None:
+        """实测：namechange 的 end_date 是 float NaN，曾让 32/37 个分片整体失败。"""
+        from app.infrastructure.data_sources.tushare import TushareProvider
+
+        rows = TushareProvider.normalize_name_history(
+            [
+                {
+                    "ts_code": "000001.SZ", "name": "平安银行", "start_date": "19910403",
+                    "end_date": float("nan"), "ann_date": float("nan"), "change_reason": None,
+                },
+                {"ts_code": "T600018.SH", "name": "上港集箱(退)", "start_date": "19960101"},
+            ]
+        )
+        assert len(rows) == 1
+        assert rows[0].end_date is None and rows[0].ann_date is None
+
+    def test_to_date_rejects_nan_variants(self) -> None:
+        from app.infrastructure.data_sources.tushare import _to_date
+
+        assert _to_date(float("nan")) is None
+        assert _to_date("nan") is None
+        assert _to_date("None") is None
+        assert _to_date("") is None
+        assert _to_date(None) is None
+        assert _to_date("20240506") == date(2024, 5, 6)
+
+
+class TestPerDateStFilter:
+    """回测的 exclude_st 必须**逐择股日**重判（与 /api/selections 单时点同口径）。
+
+    否则「入池时非 ST、之后才变 ST」的标的会在之后所有择股日继续被选中 ——
+    正是高股息策略最危险的股息陷阱路径。
+    """
+
+    def _build(self, tmp_path):
+        """A 高股息但 2024-06-01 变 ST；B 低股息且始终非 ST。"""
+        from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
+            SqlAlchemyDailyBarRepository,
+            SqlAlchemyDailyBasicRepository,
+            SqlAlchemyStockNameHistoryRepository,
+            SqlAlchemyStockRepository,
+        )
+
+        engine = create_engine(f"sqlite:///{tmp_path / 'pit.db'}", future=True)
+        Base.metadata.create_all(engine)
+        session = Session(engine)
+        syms = ["600000.SH", "600001.SH"]
+        session.add_all(
+            [
+                StockModel(symbol=syms[0], name="ST阿甲", list_date=date(2000, 1, 1), status="L"),
+                StockModel(symbol=syms[1], name="阿乙", list_date=date(2000, 1, 1), status="L"),
+                # 名称历史：A 2024-06-01 起为 ST阿甲
+                StockNameHistoryModel(
+                    symbol=syms[0], name="阿甲", start_date=date(2000, 1, 1),
+                    end_date=date(2024, 5, 31), change_reason="其他",
+                ),
+                StockNameHistoryModel(
+                    symbol=syms[0], name="ST阿甲", start_date=date(2024, 6, 1),
+                    end_date=None, change_reason="ST",
+                ),
+                StockNameHistoryModel(
+                    symbol=syms[1], name="阿乙", start_date=date(2000, 1, 1),
+                    end_date=None, change_reason="其他",
+                ),
+            ]
+        )
+        days = pd.bdate_range("2023-12-01", "2024-12-31")
+        for sym, dv in ((syms[0], 12.0), (syms[1], 4.0)):
+            session.add_all(
+                [
+                    StockDailyModel(
+                        symbol=sym, trade_date=d.date(), open=Decimal("10"),
+                        high=Decimal("10"), low=Decimal("10"), close=Decimal("10"),
+                        volume=Decimal("1000"), amount=Decimal("10000"), source="tushare",
+                    )
+                    for d in days
+                ]
+            )
+            session.add_all(
+                [
+                    DailyBasicModel(symbol=sym, trade_date=d.date(), dv_ratio=Decimal(str(dv)))
+                    for d in days
+                ]
+            )
+        session.commit()
+        from app.quant.engine import LocalEngine
+        from app.quant.service import ResearchService
+
+        svc = ResearchService(
+            SqlAlchemyStockRepository(session),
+            SqlAlchemyDailyBarRepository(session),
+            LocalEngine(),
+            basic_repo=SqlAlchemyDailyBasicRepository(session),
+            name_repo=SqlAlchemyStockNameHistoryRepository(session),
+        )
+        return svc, session, syms
+
+    def _spec(self):
+        from app.domain.entities.research import (
+            FactorSpec,
+            ResearchSpec,
+            SelectionSpec,
+            UniverseSpec,
+        )
+
+        return ResearchSpec(
+            type="backtest",
+            universe=UniverseSpec(exclude_st=True, min_listing_days=0),
+            factors=[FactorSpec(name="dividend_yield", weight=1.0)],
+            selection=SelectionSpec(top_n=2, hold_top_x=1, allow_substitute=True),
+            period=("2024-01-01", "2024-12-31"),
+            selection_interval_months=6,
+            rebalance_interval_months=6,
+            initial_capital=100000.0,
+        )
+
+    def test_st_stock_dropped_from_later_selection(self, tmp_path) -> None:
+        svc, session, syms = self._build(tmp_path)
+        try:
+            result = svc.run_backtest(self._spec())
+        finally:
+            session.close()
+        picks = {}
+        for p in result.selection_history:
+            picks.setdefault(p.date, []).append(p.symbol)
+        dates = sorted(picks)
+        assert len(dates) >= 2, picks
+        first, second = dates[0], dates[-1]
+        # 首个择股日：A 非 ST（高股息）→ 入选
+        assert syms[0] in picks[first]
+        # 变 ST 之后的择股日：A 必须消失，B 顶上
+        assert syms[0] not in picks[second]
+        assert syms[1] in picks[second]
+        # 口径标注：时点名称（不是快照回退）
+        assert result.config_snapshot["price_basis"]["name_basis"]["point_in_time"] is True
+
+    def test_snapshot_fallback_without_repo(self, tmp_path) -> None:
+        """未注入名称历史 → 回退最新名称（旧行为），并如实标注 point_in_time=False。"""
+        svc, session, syms = self._build(tmp_path)
+        try:
+            svc._name_repo = None
+            result = svc.run_backtest(self._spec())
+        finally:
+            session.close()
+        # 最新名称是 ST阿甲 → 首个择股日就被排除（股息陷阱被隐藏，已标注）
+        first = min(p.date for p in result.selection_history)
+        assert syms[0] not in [p.symbol for p in result.selection_history if p.date == first]
+        assert result.config_snapshot["price_basis"]["name_basis"]["point_in_time"] is False
+
+
+class TestReviewRegressions:
+    """代码审查发现的缺陷回归（P1/P2/P3）：不得复活。"""
+
+    def test_empty_table_does_not_claim_point_in_time(self) -> None:
+        """[P1] 表存在但为空时必须降级为快照口径，不得声称 point_in_time=true。
+
+        否则结果页会把「未被修正的 10.85pp 股息陷阱偏差」当成已修正上报（AGENT.md §24）。
+        """
+        from app.quant.universe import names_as_of
+
+        class EmptyRepo:
+            def names_as_of(self, symbols, as_of):
+                return {}
+
+        stocks = [Stock(symbol="600565.SH", name="ST迪马", list_date=date(2000, 1, 1))]
+        name_at, applied = names_as_of(stocks, date(2020, 1, 2), EmptyRepo())
+        assert name_at is None
+        assert applied == (False, 0)
+
+    def test_empty_table_end_to_end_reports_snapshot_basis(self, tmp_path) -> None:
+        """[P1] 端到端：名称表为空 → name_basis.point_in_time 必须为 False。"""
+        svc, session, _syms = TestPerDateStFilter()._build(tmp_path)
+        try:
+            session.query(StockNameHistoryModel).delete()
+            session.commit()
+            result = svc.run_backtest(TestPerDateStFilter()._spec())
+        finally:
+            session.close()
+        assert result.config_snapshot["price_basis"]["name_basis"]["point_in_time"] is False
+        assert any("最新名称快照" in n for n in result.unimplemented)
+
+    def test_missing_span_falls_back_to_snapshot_in_backtest(self, tmp_path) -> None:
+        """[P2] 某股无生效区间时，逐择股日 ST 必须与 filter_stocks 同口径回退最新名称。
+
+        否则同一择股日会出现「回测入选、/api/selections 排除」的打架（v2 §25）。
+        """
+        svc, session, syms = TestPerDateStFilter()._build(tmp_path)
+        try:
+            # 删掉 A 的**全部**名称区间：只剩最新名称 ST阿甲
+            session.query(StockNameHistoryModel).filter(
+                StockNameHistoryModel.symbol == syms[0]
+            ).delete()
+            session.commit()
+            result = svc.run_backtest(TestPerDateStFilter()._spec())
+            dates = sorted({p.date for p in result.selection_history})
+            first = dates[0]
+            picks_first = [p.symbol for p in result.selection_history if p.date == first]
+        finally:
+            session.close()
+        # 快照口径：A 名称含 ST → 首个择股日即被排除（与 filter_stocks 一致）
+        assert syms[0] not in picks_first
+        assert syms[1] in picks_first
+
+    def test_protocol_declares_name_changes(self) -> None:
+        """[P2] MarketDataProvider 必须声明 get_name_changes（§6 业务层只依赖抽象）。"""
+
+        from app.domain.providers import MarketDataProvider
+
+        assert hasattr(MarketDataProvider, "get_name_changes")
+        assert "get_name_changes" in dir(MarketDataProvider)
+
+    def test_sina_declares_not_supported(self) -> None:
+        """[P2] 备用源必须显式 NotSupported，不得静默返回空列表（否则时点 ST 静默降级）。"""
+        from app.infrastructure.data_sources.errors import DataSourceNotSupported
+        from app.infrastructure.data_sources.sina import SinaProvider
+
+        provider = SinaProvider.__new__(SinaProvider)
+        with pytest.raises(DataSourceNotSupported):
+            provider.get_name_changes(date(2024, 1, 1), date(2024, 12, 31))
+
+    def test_qlib_engine_accepts_eligibility_fn(self) -> None:
+        """[P2] 引擎协议一致性：所有引擎都必须接受 eligibility_fn。
+
+        否则注入 QlibEngine 后任何回测都会 TypeError（且条件/时点 ST 会被静默忽略）。
+        """
+        import inspect
+
+        from app.quant.engine import QuantEngine
+        from app.quant.qlib_adapter.engine import QlibEngine
+
+        for cls in (QuantEngine, QlibEngine):
+            params = inspect.signature(cls.run_backtest).parameters
+            assert "eligibility_fn" in params, cls.__name__
diff --git a/backend/tests/test_prune_experiments.py b/backend/tests/test_prune_experiments.py
new file mode 100644
index 0000000..c5738e7
--- /dev/null
+++ b/backend/tests/test_prune_experiments.py
@@ -0,0 +1,69 @@
+"""归档保留策略（`app.cli.prune_experiments`）的候选选择单测。
+
+删除归档是**不可恢复**操作，因此候选集合的判定必须被测试钉住：多删一条就是丢了
+一份研究结论，少删一条则保留策略失效。这里只测纯函数与参数护栏（不连数据库）。
+"""
+
+from __future__ import annotations
+
+from datetime import datetime, timedelta
+
+import pytest
+from app.cli.prune_experiments import _parse_args, select_prune_candidates
+
+
+class _Row:
+    """最小归档摘要替身（只有 id / created_at 参与判定）。"""
+
+    def __init__(self, id_: str, age_days: float | None) -> None:
+        self.id = id_
+        self.created_at = None if age_days is None else NOW - timedelta(days=age_days)
+
+
+NOW = datetime(2026, 9, 20, 12, 0, 0)
+
+
+def _rows() -> list[_Row]:
+    return [_Row("E1", 1), _Row("E2", 2), _Row("E3", 10), _Row("E4", 30), _Row("E5", None)]
+
+
+class TestSelectPruneCandidates:
+    def test_keep_newest_n(self) -> None:
+        got = [e.id for e in select_prune_candidates(_rows(), keep=2, older_than_days=None, now=NOW)]
+        assert got == ["E3", "E4", "E5"], "只保留最新 2 条（E1/E2），其余都是候选"
+
+    def test_keep_zero_deletes_all(self) -> None:
+        got = [e.id for e in select_prune_candidates(_rows(), keep=0, older_than_days=None, now=NOW)]
+        assert set(got) == {"E1", "E2", "E3", "E4", "E5"}
+
+    def test_older_than_only(self) -> None:
+        got = [e.id for e in select_prune_candidates(_rows(), keep=None, older_than_days=5, now=NOW)]
+        assert got == ["E3", "E4", "E5"], "5 天内创建的 E1/E2 必须保留"
+
+    def test_intersection_of_both_conditions(self) -> None:
+        got = [e.id for e in select_prune_candidates(_rows(), keep=4, older_than_days=5, now=NOW)]
+        # older_than 选出 E3/E4/E5，keep=4 保留 E1..E4 → 交集只剩 E5
+        assert got == ["E5"]
+
+    def test_missing_created_at_treated_as_oldest(self) -> None:
+        """created_at 为空按最旧处理：不能被当作「最新」从而永久留库。"""
+        got = [e.id for e in select_prune_candidates(_rows(), keep=4, older_than_days=None, now=NOW)]
+        assert "E5" in got
+
+    def test_result_is_newest_first(self) -> None:
+        got = [e.id for e in select_prune_candidates(_rows(), keep=0, older_than_days=None, now=NOW)]
+        assert got == ["E1", "E2", "E3", "E4", "E5"], "输出按新→旧，便于人工核对"
+
+
+class TestArgGuard:
+    def test_requires_keep_or_older_than(self) -> None:
+        with pytest.raises(SystemExit):
+            _parse_args([])  # 两个条件都不给 → 拒绝执行（避免误删全部）
+
+    def test_negative_keep_rejected(self) -> None:
+        with pytest.raises(SystemExit):
+            _parse_args(["--keep", "-1"])
+
+    def test_default_is_dry_run(self) -> None:
+        args = _parse_args(["--keep", "10"])
+        assert args.apply is False, "默认必须 dry-run，只有显式 --apply 才写库"
diff --git a/backend/tests/test_restore_experiment.py b/backend/tests/test_restore_experiment.py
new file mode 100644
index 0000000..f3b37ca
--- /dev/null
+++ b/backend/tests/test_restore_experiment.py
@@ -0,0 +1,222 @@
+"""`app.cli.restore_experiment_from_job` 测试：从 Job 结果副本重建归档。
+
+为什么要测：删除归档是正常功能，但**历史归档在 `job.result_json` 里另存了一份完整结果**
+（完整存档上线前的双写遗留），所以「删了能不能救回来」是有确定答案的：
+历史归档能按原 id 重建，新归档（`job.result_json IS NULL`）不能。
+这个工具承载该结论，必须验证真写库路径与全部拒绝路径，而不是只做 dry-run。
+
+测试全部在 conftest 强制的 /tmp sqlite 上跑，不触碰真实 MySQL。
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import date, datetime
+from unittest.mock import patch
+
+from app.cli import restore_experiment_from_job as rj
+from app.domain.entities.research import (
+    BacktestResult,
+    BacktestSummary,
+    CurvePoint,
+)
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.infrastructure.persistence.sqlalchemy.models.jobs import ExperimentModel, JobModel
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+
+def _payload(final_equity: float = 1_100_000.0) -> str:
+    """一份最小可用的回测结果 JSON（与归档里存的结构同源）。"""
+    result = BacktestResult(
+        summary=BacktestSummary(
+            start=date(2024, 1, 2),
+            end=date(2024, 6, 28),
+            initial_capital=1_000_000.0,
+            final_equity=final_equity,
+            total_return_pct=10.0,
+            annual_return_pct=21.5,
+            sharpe=1.2,
+            max_drawdown_pct=-8.5,
+            volatility_pct=18.0,
+            win_rate_pct=55.0,
+            total_trades=12,
+            avg_turnover_pct=30.0,
+        ),
+        equity_curve=[CurvePoint(date=date(2024, 1, 2), value=1_000_000.0)],
+        drawdown=[],
+        monthly_returns=[],
+        yearly_returns=[],
+        positions=[],
+        trades=[],
+        turnover_pct=30.0,
+    )
+    return json.dumps(result.model_dump(mode="json"), ensure_ascii=False)
+
+
+def _make_session(tmp_path):
+    engine = create_engine(f"sqlite:///{tmp_path}/restore.db", future=True)
+    Base.metadata.create_all(engine)
+    return sessionmaker(bind=engine, autoflush=False, expire_on_commit=False)
+
+
+def _seed_job(session, *, job_id="JOB-TEST", exp_id="EXP-TEST", result_json=None):
+    session.add(
+        JobModel(
+            id=job_id,
+            kind="backtest",
+            status="success",
+            spec_json=json.dumps({"type": "backtest", "factors": []}),
+            result_json=result_json,
+            experiment_id=exp_id,
+            created_at=datetime(2026, 1, 5, 9, 0, 0),
+            finished_at=datetime(2026, 1, 5, 9, 0, 1),
+        )
+    )
+    session.commit()
+
+
+def _run(session_factory, argv):
+    with patch.object(rj, "SessionLocal", session_factory):
+        return rj.main(argv)
+
+
+def test_dry_run_writes_nothing(tmp_path, capsys) -> None:
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, result_json=_payload())
+    code = _run(factory, ["--job-id", "JOB-TEST", "--code-version", "abc1234"])
+    out = capsys.readouterr().out
+    assert code == 0
+    assert "(dry-run)" in out
+    with factory() as s:
+        assert s.get(ExperimentModel, "EXP-TEST") is None
+
+
+def test_restores_archive_from_job_copy(tmp_path, capsys) -> None:
+    """真写库路径：原 id、逐字复制、摘要按归档口径重算、data_version 留空。"""
+    factory = _make_session(tmp_path)
+    payload = _payload()
+    with factory() as s:
+        _seed_job(s, result_json=payload)
+    code = _run(
+        factory, ["--job-id", "JOB-TEST", "--code-version", "abc1234", "--apply"]
+    )
+    assert code == 0
+    assert "已重建" in capsys.readouterr().out
+    with factory() as s:
+        row = s.get(ExperimentModel, "EXP-TEST")
+        assert row is not None
+        assert row.kind == "backtest"
+        assert row.result_json == payload  # 逐字复制，不重新计算
+        assert row.job_id == "JOB-TEST"
+        assert row.code_version == "abc1234"
+        # 历史归档当年没有数据指纹：留空而不是补今天的（伪造复现依据）
+        assert row.data_version is None
+        assert row.created_at == datetime(2026, 1, 5, 9, 0, 1)
+        assert row.summary_text == "总收益 10.00% · 年化 21.50% · 回撤 -8.50%"
+
+
+def test_refuses_when_archive_already_exists(tmp_path, capsys) -> None:
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, result_json=_payload())
+        s.add(
+            ExperimentModel(
+                id="EXP-TEST",
+                kind="backtest",
+                spec_json="{}",
+                result_json=_payload(1.0),
+                summary_text="原有",
+                code_version="old",
+                data_version=None,
+                job_id="JOB-TEST",
+                created_at=datetime(2026, 1, 5, 9, 0, 1),
+            )
+        )
+        s.commit()
+    code = _run(
+        factory, ["--job-id", "JOB-TEST", "--code-version", "abc1234", "--apply"]
+    )
+    assert code == 1
+    assert "已存在" in capsys.readouterr().err
+    with factory() as s:
+        assert s.get(ExperimentModel, "EXP-TEST").summary_text == "原有"  # 未被改动
+
+
+def test_force_overwrites_existing_archive(tmp_path) -> None:
+    factory = _make_session(tmp_path)
+    payload = _payload()
+    with factory() as s:
+        _seed_job(s, result_json=payload)
+        s.add(
+            ExperimentModel(
+                id="EXP-TEST",
+                kind="backtest",
+                spec_json="{}",
+                result_json="{}",
+                summary_text="旧的",
+                code_version="old",
+                data_version="d20260101",
+                job_id="JOB-TEST",
+                created_at=datetime(2026, 1, 5, 9, 0, 1),
+            )
+        )
+        s.commit()
+    code = _run(
+        factory,
+        ["--job-id", "JOB-TEST", "--code-version", "abc1234", "--apply", "--force"],
+    )
+    assert code == 0
+    with factory() as s:
+        row = s.get(ExperimentModel, "EXP-TEST")
+        assert row.result_json == payload
+        assert row.code_version == "abc1234"
+        assert row.data_version is None  # 覆盖后也留空，不继承旧指纹
+
+
+def test_refuses_new_archive_without_job_copy(tmp_path, capsys) -> None:
+    """完整存档上线后结果只存归档一份 → 删了不可恢复，工具必须如实拒绝。"""
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, result_json=None)
+    code = _run(
+        factory, ["--job-id", "JOB-TEST", "--code-version", "abc1234", "--apply"]
+    )
+    assert code == 1
+    err = capsys.readouterr().err
+    assert "result_json 为空" in err
+    assert "不可恢复" in err
+
+
+def test_requires_explicit_code_version(tmp_path, capsys) -> None:
+    """不猜版本：缺失时直接拒绝（退出码 2），避免写进一个编造的复现依据。"""
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, result_json=_payload())
+    code = _run(factory, ["--job-id", "JOB-TEST", "--apply"])
+    assert code == 2
+    assert "code-version" in capsys.readouterr().err
+
+
+def test_empty_code_version_is_allowed_as_unknown(tmp_path) -> None:
+    """显式传空串＝如实表示"版本未知"，此时写 NULL 而不是空字符串。"""
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, result_json=_payload())
+    code = _run(
+        factory, ["--job-id", "JOB-TEST", "--code-version", "", "--apply"]
+    )
+    assert code == 0
+    with factory() as s:
+        assert s.get(ExperimentModel, "EXP-TEST").code_version is None
+
+
+def test_rejects_unknown_job_and_job_without_experiment_id(tmp_path, capsys) -> None:
+    factory = _make_session(tmp_path)
+    with factory() as s:
+        _seed_job(s, job_id="JOB-NOEXP", exp_id=None, result_json=_payload())
+    assert _run(factory, ["--job-id", "JOB-MISSING", "--code-version", "x"]) == 1
+    assert "不存在" in capsys.readouterr().err
+    assert _run(factory, ["--job-id", "JOB-NOEXP", "--code-version", "x"]) == 1
+    assert "experiment_id" in capsys.readouterr().err
\ No newline at end of file
diff --git a/backend/tests/test_run_job.py b/backend/tests/test_run_job.py
new file mode 100644
index 0000000..383086e
--- /dev/null
+++ b/backend/tests/test_run_job.py
@@ -0,0 +1,86 @@
+"""run_job 研究子进程入口的平台/配置分支单测。
+
+覆盖点（Review 补测）：
+- `_apply_memory_limit` 成功路径确实施加了请求的上限；
+- 环境变量缺失时回退 `Settings.job_memory_limit_gb`；
+- setrlimit 失败（如 macOS 上限额低于进程虚拟地址空间基线 → EINVAL/ValueError）
+  **不得抛出**，否则研究子进程启动即失败；
+- `_subprocess_log_target` 落盘失败时退回 DEVNULL —— 记日志失败不能拖垮 Job。
+"""
+
+from __future__ import annotations
+
+import resource
+
+import pytest
+from app.application.services import job_executor as je
+from app.cli import run_job
+
+
+def test_apply_memory_limit_sets_requested_value(monkeypatch) -> None:
+    captured: dict = {}
+    monkeypatch.setenv("QLIB_JOB_MEM_LIMIT_GB", "3")
+    monkeypatch.setattr(resource, "setrlimit", lambda which, lim: captured.update(w=which, lim=lim))
+
+    run_job._apply_memory_limit()
+
+    assert captured["w"] == resource.RLIMIT_AS
+    assert captured["lim"] == (3 * 1024**3, 3 * 1024**3)
+
+
+def test_apply_memory_limit_falls_back_to_settings(monkeypatch) -> None:
+    """未设 QLIB_JOB_MEM_LIMIT_GB 时用 Settings 的上限（config.yaml job.max_memory_gb）。"""
+    from app.core.config import get_settings
+
+    captured: dict = {}
+    monkeypatch.delenv("QLIB_JOB_MEM_LIMIT_GB", raising=False)
+    monkeypatch.setattr(resource, "setrlimit", lambda which, lim: captured.update(lim=lim))
+
+    run_job._apply_memory_limit()
+
+    gb = get_settings().job_memory_limit_gb
+    assert captured["lim"] == (gb * 1024**3, gb * 1024**3)
+
+
+def test_apply_memory_limit_failure_does_not_raise(monkeypatch, capsys) -> None:
+    """setrlimit 失败（macOS 限额低于地址空间基线的典型情形）只提示、不抛出。"""
+
+    def _boom(which, lim):  # noqa: ANN001
+        raise ValueError("current limit exceeds maximum limit")
+
+    monkeypatch.setenv("QLIB_JOB_MEM_LIMIT_GB", "6")
+    monkeypatch.setattr(resource, "setrlimit", _boom)
+
+    run_job._apply_memory_limit()  # 不应抛异常
+
+    err = capsys.readouterr().err
+    assert "内存上限 6GB 设置失败" in err
+    assert "无内存隔离保护" in err
+
+
+def test_subprocess_log_target_falls_back_to_devnull(tmp_path, monkeypatch) -> None:
+    """日志目录不可创建时退回 DEVNULL，不影响 Job 执行。"""
+    blocker = tmp_path / "not-a-dir"
+    blocker.write_text("x", encoding="utf-8")
+    monkeypatch.setattr(je, "BACKEND_ROOT", blocker / "sub")
+
+    assert je._subprocess_log_target() is je.subprocess.DEVNULL
+
+
+def test_subprocess_log_target_writes_under_project_logs(tmp_path, monkeypatch) -> None:
+    """正常路径：在 <项目根>/logs/job-subprocess.log 追加写入。"""
+    monkeypatch.setattr(je, "BACKEND_ROOT", tmp_path / "backend")
+
+    target = je._subprocess_log_target()
+    try:
+        target.write("hello-subprocess\n")
+    finally:
+        target.close()
+
+    assert (tmp_path / "logs" / "job-subprocess.log").read_text(encoding="utf-8") == (
+        "hello-subprocess\n"
+    )
+
+
+if __name__ == "__main__":  # pragma: no cover
+    raise SystemExit(pytest.main([__file__, "-q"]))
\ No newline at end of file
diff --git a/backend/tests/test_selection_job.py b/backend/tests/test_selection_job.py
index 0f128a8..da5de29 100644
--- a/backend/tests/test_selection_job.py
+++ b/backend/tests/test_selection_job.py
@@ -76,9 +76,11 @@ class TestSelectionJobExecutor:
             job = SqlAlchemyJobRepository(session).get("JOB-SEL-1")
             exp = SqlAlchemyExperimentRepository(session).get(job.experiment_id or "")
         assert job.status == JobStatus.SUCCESS
-        result = SelectionResult.model_validate_json(job.result_json or "{}")
-        assert len(result.candidates) == 3
+        # 完整结果只在 experiment 存一份：从归档解码验证（job.result_json 为新形态空值）
+        assert job.result_json is None
         assert exp is not None and exp.kind == "selection"
+        result = SelectionResult.model_validate_json(exp.result_json)
+        assert len(result.candidates) == 3
         assert "as_of" in (exp.summary_text or "") and "选出 3" in (exp.summary_text or "")
 
 
diff --git a/backend/tests/test_strategy_doc.py b/backend/tests/test_strategy_doc.py
new file mode 100644
index 0000000..800a1ef
--- /dev/null
+++ b/backend/tests/test_strategy_doc.py
@@ -0,0 +1,544 @@
+"""策略说明书生成器（app.quant.strategy_doc）测试 + 策略说明/公式/编辑接口测试。
+
+目标（AGENT.md §24/§31）：说明书必须由 spec **真实推导**，并且
+- 条件表达式的渲染必须与 `app.quant.selection._eval_condition` 的实际求值一致
+  （含 ref 语义、缺失值语义、in/not_in 的列表语义）；
+- 步骤必须与 `app.quant.local_engine` 的真实行为一致（择股日/调仓日/顺延/期末）；
+- 未知因子、未建模约束一律进 warnings（不编造、不假装支持）。
+
+同一文件还覆盖策略 API 的说明接口与「原地更新（PUT）」，因为三者共用同一契约。
+"""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pandas as pd
+import pytest
+from app.api import deps
+from app.domain.entities.research import (
+    ConditionSpec,
+    CostSpec,
+    FactorSpec,
+    ResearchSpec,
+    SelectionSpec,
+    UniverseSpec,
+)
+from app.domain.entities.strategy import StrategyDefinition
+from app.infrastructure.persistence.sqlalchemy.base import Base
+from app.main import app
+from app.quant.factors import FactorDef
+from app.quant.selection import build_condition_fields, eligible_symbols
+from app.quant.strategy_doc import describe_strategy
+from fastapi.testclient import TestClient
+from sqlalchemy import create_engine
+from sqlalchemy.orm import sessionmaker
+
+from conftest_quant import synthetic_daily
+
+
+def _spec(**kw) -> ResearchSpec:
+    """高股息案例参数（n=30 → x=20、m=y=6、hfq、显式成本与最低佣金）。"""
+    base = dict(
+        type="backtest",
+        universe=UniverseSpec(exclude_st=False, min_listing_days=250),
+        price_adjustment="hfq",
+        factors=[FactorSpec(name="dividend_yield", weight=1.0)],
+        conditions=[ConditionSpec(field="dv_ratio", op="lte", value=30)],
+        selection=SelectionSpec(top_n=30, hold_top_x=20, allow_substitute=False, defer_buy=True),
+        rebalance="monthly",
+        selection_interval_months=6,
+        rebalance_interval_months=6,
+        period=(date(2020, 1, 1), date(2024, 12, 31)),
+        costs=CostSpec(min_commission=5.0),
+    )
+    base.update(kw)
+    return ResearchSpec(**base)
+
+
+class TestSummary:
+    def test_summary_contains_factors_truncation_cycles_and_costs(self) -> None:
+        doc = describe_strategy(_spec())
+        s = doc.summary
+        assert "股息率" in s, "summary 必须由因子元数据推导出因子含义"
+        assert "dividend_yield" in s
+        assert "前 30 只候选池" in s and "前 20 只等权" in s, "必须说清两级截断 n → x"
+        assert "每 6 个月重新择股" in s and "每 6 个月调仓" in s, "必须说清 m / y"
+        assert "后复权" in s and "收盘价成交" in s
+        assert "佣金 0.03%" in s and "印花税 0.05%" in s and "滑点 0.1%" in s
+
+    def test_summary_lower_is_better_factor_方向(self) -> None:
+        doc = describe_strategy(_spec(factors=[FactorSpec(name="volatility_20", weight=1.0)]))
+        assert "从低到高排序" in doc.summary
+
+    def test_summary_without_hold_top_x_uses_n(self) -> None:
+        doc = describe_strategy(
+            _spec(selection=SelectionSpec(top_n=20, allow_substitute=False), conditions=[])
+        )
+        assert "前 20 只等权持有" in doc.summary
+        assert "先通过" not in doc.summary, "无 conditions 时不得声称有过滤条件"
+
+    def test_summary_states_buy_fallback_mode(self) -> None:
+        """买不进的处置决定实际持仓，必须出现在一句话说明里（三种模式各一）。"""
+        defer = describe_strategy(_spec(conditions=[]))
+        assert "买不进则顺延买入" in defer.summary
+        subst = describe_strategy(
+            _spec(selection=SelectionSpec(top_n=5, allow_substitute=True), conditions=[])
+        )
+        assert "从候选池之外替补" in subst.summary
+        none = describe_strategy(
+            _spec(selection=SelectionSpec(top_n=5, allow_substitute=False), conditions=[])
+        )
+        assert "买不进则放弃" in none.summary
+
+
+class TestFormula:
+    def test_formula_has_factor_name_meaning_and_formula(self) -> None:
+        doc = describe_strategy(_spec())
+        f = doc.formula
+        assert "dividend_yield" in f
+        # 因子元数据（AGENT.md §22）逐项落进公式：含义 / 公式 / 方向 / 输入列
+        assert "含义：股息率" in f
+        assert "公式：dv_ratio（Tushare daily_basic，逐日时点值）" in f
+        assert "越高越好" in f and "higher_is_better" in f
+        assert "输入列：dv_ratio" in f
+        assert "score_i = Σ_f  w_f × d_f × z_f,i" in f
+        assert "z_f,i = (x_f,i - mean_i(x_f)) / std_i(x_f)" in f
+
+    def test_formula_has_two_level_truncation_and_cycles(self) -> None:
+        f = describe_strategy(_spec()).formula
+        assert "n = 30" in f and "x = 20" in f
+        assert "每 6 个月" in f
+        assert "择股日" in f and "调仓日" in f
+
+    def test_formula_has_costs_and_execution_price_basis(self) -> None:
+        f = describe_strategy(_spec()).formula
+        assert "佣金 = max(投入资金 × 0.03%, 最低佣金 5 元/笔)" in f
+        assert "印花税 0.05%（仅卖出）" in f, "印花税只对卖出计提（local_engine._rebalance）"
+        assert "滑点 0.1%" in f
+        assert "后复权" in f and "乘以 adjust_factor" not in f  # 口径文案由 _ADJUST_TEXT 给出
+        assert "成交时点：调仓日收盘" in f
+        assert "1,000,000 元" in f
+
+    def test_formula_renders_conditions_readably(self) -> None:
+        spec = _spec(
+            conditions=[
+                ConditionSpec(field="dv_ratio", op="lte", value=30),
+                ConditionSpec(field="close", op="gte", ref="ma60"),
+                ConditionSpec(field="fundamental.roe", op="gte", value=15),
+                ConditionSpec(field="static.industry", op="in", value=["白酒", "银行"]),
+                ConditionSpec(field="static.industry", op="ne", value="白酒"),
+                ConditionSpec(field="pe", op="lt", value=30.5),
+            ]
+        )
+        f = describe_strategy(spec).formula
+        assert "dv_ratio <= 30" in f
+        assert "close >= ma60" in f, "ref 条件必须渲染成「字段 op 字段」（同一股票同一日比较）"
+        assert "fundamental.roe >= 15" in f
+        assert 'static.industry ∈ ["白酒", "银行"]' in f
+        assert 'static.industry != "白酒"' in f
+        assert "pe < 30.5" in f
+        # 说明字段域语义，避免使用者误以为 fundamental.* 是当日值
+        assert "announce_date <= 择股日" in f
+
+    def test_empty_conditions_does_not_crash(self) -> None:
+        doc = describe_strategy(_spec(conditions=[]))
+        assert "（无）" in doc.formula
+        assert any("未配置过滤条件" in w for w in doc.warnings)
+        assert doc.steps
+
+    def test_unknown_factor_goes_to_warnings_not_crash(self) -> None:
+        doc = describe_strategy(_spec(factors=[FactorSpec(name="no_such_factor", weight=2.0)]))
+        assert any("未知因子 no_such_factor" in w for w in doc.warnings)
+        assert "元数据缺失" in doc.formula
+        assert doc.summary  # 仍然可用（说明不因未知因子而失败）
+
+    def test_factor_meta_override_is_used(self) -> None:
+        meta = {
+            "my_signal": FactorDef(
+                name="my_signal", description="自定义信号", formula="close / ma20",
+                brief="实验因子", direction="lower_is_better", requires=("close",),
+            )
+        }
+        doc = describe_strategy(
+            _spec(factors=[FactorSpec(name="my_signal", weight=0.5)], conditions=[]),
+            factor_meta=meta,
+        )
+        assert "自定义信号" in doc.formula and "公式：close / ma20" in doc.formula
+        assert "越低越好" in doc.formula
+        assert not any("未知因子" in w for w in doc.warnings)
+        assert "从低到高排序" in doc.summary
+
+    def test_unknown_condition_field_goes_to_warnings(self) -> None:
+        doc = describe_strategy(
+            _spec(conditions=[ConditionSpec(field="no_such_col", op="gt", value=1)])
+        )
+        assert any("条件字段 no_such_col 未识别" in w for w in doc.warnings)
+        assert "no_such_col > 1" in doc.formula
+
+
+class TestConditionSemanticsMatchSelectionEvaluator:
+    """渲染文本必须与 selection 的真实求值一致（以代码为准，不以注释/直觉为准）。"""
+
+    def test_gte_ref_and_lte_value_match_eligible_symbols(self) -> None:
+        syms = ["600000.SH", "600001.SH", "600002.SH"]
+        daily = synthetic_daily({"600000.SH": 0.006, "600001.SH": -0.006, "600002.SH": 0.0005}, n=300)
+        obs = pd.Timestamp("2024-12-31")
+        conds = [
+            ConditionSpec(field="close", op="gte", ref="ma60"),
+            ConditionSpec(field="momentum_20", op="lte", value=0.05),
+        ]
+        fields = build_condition_fields(daily, conds, obs)
+        passed = eligible_symbols(sorted(syms), conds, {}, fields, {})
+
+        # 期望值直接按「渲染出的表达式」的语义计算，与求值器结果对齐
+        for sym in syms:
+            expected = bool(
+                fields["close"][sym] >= fields["ma60"][sym]
+                and fields["momentum_20"][sym] <= 0.05
+            )
+            assert (sym in passed) == expected, f"{sym} 的渲染语义与求值器不一致"
+
+        # 且文档里出现的就是这两条表达式
+        doc = describe_strategy(_spec(conditions=conds))
+        assert "close >= ma60" in doc.formula
+        assert "momentum_20 <= 0.05" in doc.formula
+
+    def test_in_not_in_and_missing_value_semantics(self) -> None:
+        """in/not_in 的右操作数是列表，语义为 `left in right`；缺失值语义见下。"""
+        conds = [
+            ConditionSpec(field="static.industry", op="in", value=["白酒"]),
+            ConditionSpec(field="static.pe", op="ne", value=10),  # 缺失字段
+        ]
+        statics = {"A": {"industry": "白酒"}, "B": {"industry": "银行"}}
+        passed = eligible_symbols(["A", "B"], conds, statics, {}, {})
+        # 代码事实（selection._compare）：op == "ne" 在 None 判定**之前**返回 left != right，
+        # 因此字段缺失时 `ne` 判定为「不等于」→ 通过；其余运算符在缺失值上不通过。
+        assert set(passed) == {"A"}
+        doc = describe_strategy(_spec(conditions=conds))
+        assert 'static.industry ∈ ["白酒"]' in doc.formula
+        assert "static.pe != 10" in doc.formula
+        assert "除 != 外一律判为「未通过」" in doc.formula
+
+    def test_string_comparison_is_lexicographic_in_real_evaluator(self) -> None:
+        """对照 selection._compare 的实际实现，修正其内联注释的说法。
+
+        `_compare` 的注释写「字符串会 ValueError → False」，但代码里 gt/gte/lt/lte
+        最终走 `_num_cmp(left, right, op)`，而 `_num_cmp` **不做 float 转换**，
+        因此两个字符串是按 Python 原生（字典序）比较的，并不会 ValueError。
+        说明书按代码事实渲染为 `static.industry > "M"`（同一运算符、同一语义）。
+        """
+        conds = [ConditionSpec(field="static.industry", op="gt", value="M")]
+        statics = {"A": {"industry": "白酒"}}  # '白酒' > 'M'（Unicode 码位更大）
+        passed = eligible_symbols(["A"], conds, statics, {}, {})
+        assert set(passed) == {"A"}
+        assert 'static.industry > "M"' in describe_strategy(_spec(conditions=conds)).formula
+
+
+class TestStepsAndWarnings:
+    def test_steps_follow_local_engine_behaviour(self) -> None:
+        doc = describe_strategy(_spec())
+        joined = " | ".join(doc.steps)
+        assert "300 个自然日" in joined, "预热窗口来自 service._load_daily"
+        assert "候选池" in joined and "selection_history" in joined
+        assert "收盘" in joined, "成交发生在调仓日收盘"
+        assert "涨停" in joined and "停牌" in joined
+        assert "顺延" in joined, "defer_buy=True 必须如实说明顺延买入"
+        assert "次日起计收益" in joined, "调仓日收盘生效（无未来函数）"
+        assert "不强制平仓" in joined, "期末不平仓是引擎的真实行为，必须披露"
+
+    def test_steps_describe_substitute_mode(self) -> None:
+        doc = describe_strategy(
+            _spec(selection=SelectionSpec(top_n=5, allow_substitute=True), conditions=[])
+        )
+        assert any("替补" in s for s in doc.steps)
+
+    def test_universe_step_conditional_and_time_accurate(self) -> None:
+        """股票池步骤只写实际生效的过滤，且时点口径必须正确（起始日过滤一次）。
+
+        以 service._load_daily / universe.filter_stocks 的代码为准：universe 过滤在
+        as_of=回测起始日执行一次，之后只有 exclude_st 会按当日名称逐择股日重判。
+        """
+        doc = describe_strategy(_spec(universe=UniverseSpec(min_listing_days=250), conditions=[]))
+        pool_step = doc.steps[1]
+        assert "回测起始日按 universe 过滤一次" in pool_step
+        assert "当日名称" in pool_step
+        assert "指数成分" not in pool_step, "未配置 index_code 时不得声称有指数成分过滤"
+
+        idx = describe_strategy(
+            _spec(
+                universe=UniverseSpec(index_code="000300.SH", exclude_st=False, min_listing_days=0),
+                conditions=[],
+            )
+        )
+        assert "000300.SH 的当日历史成分" in idx.steps[1]
+        assert "名称含 ST" not in idx.steps[1]
+
+    def test_warnings_cover_unmodelled_constraints(self) -> None:
+        # exclude_st 默认 True（UniverseSpec 默认值）时才需要标注时点/快照口径
+        doc = describe_strategy(_spec(universe=UniverseSpec(min_listing_days=250)))
+        joined = " | ".join(doc.warnings)
+        assert "exclude_suspended" in joined, "停牌未建模必须标注"
+        assert "名称变更历史" in joined, "exclude_st 时点/快照口径必须标注"
+        assert "幸存者偏差" in joined
+        assert "涨跌停" in joined
+
+    def test_warnings_flag_none_adjustment_and_industry_cap(self) -> None:
+        from app.domain.entities.research import PortfolioSpec
+
+        doc = describe_strategy(
+            _spec(price_adjustment="none", portfolio=PortfolioSpec(max_industry_weight_pct=0.3))
+        )
+        joined = " | ".join(doc.warnings)
+        assert "不复权" in joined
+        assert "未建模" in joined and "行业权重" in joined
+
+    def test_stale_pool_warning_when_y_lt_m(self) -> None:
+        doc = describe_strategy(
+            _spec(selection_interval_months=12, rebalance_interval_months=3, conditions=[])
+        )
+        assert any("池子陈旧" in w for w in doc.warnings)
+
+
+class TestDefinitionInput:
+    def test_strategy_definition_input_uses_placeholder_period(self) -> None:
+        st = StrategyDefinition(
+            name="演示",
+            factors=[FactorSpec(name="momentum_60", weight=1.0)],
+            selection=SelectionSpec(top_n=10),
+        )
+        doc = describe_strategy(st)
+        assert doc.summary and doc.formula and doc.steps
+        assert "momentum_60" in doc.formula
+        assert "1900" not in doc.formula, "占位区间不得泄漏到展示文本"
+        assert any("无回测区间" in w for w in doc.warnings)
+
+    def test_real_spec_has_no_placeholder_warning(self) -> None:
+        doc = describe_strategy(_spec())
+        assert not any("无回测区间" in w for w in doc.warnings)
+        assert "2020-01-01 ~ 2024-12-31" in doc.formula
+
+    def test_describe_does_not_mutate_input(self) -> None:
+        """纯函数约定：只生成文本，不改写 spec（更不改写研究结果）。"""
+        spec = _spec()
+        before = spec.model_dump()
+        describe_strategy(spec)
+        assert spec.model_dump() == before
+
+        st = StrategyDefinition(name="演示", factors=[FactorSpec(name="momentum_60")])
+        before_st = st.model_dump()
+        describe_strategy(st)
+        assert st.model_dump() == before_st
+
+    def test_unsupported_input_raises_clear_type_error(self) -> None:
+        with pytest.raises(TypeError, match="ResearchSpec 或 StrategyDefinition"):
+            describe_strategy({"factors": []})  # type: ignore[arg-type]
+
+
+# ---------- 策略 API：说明接口 + 原地更新（PUT） ----------
+
+
+@pytest.fixture()
+def client(tmp_path) -> TestClient:
+    engine = create_engine(f"sqlite:///{tmp_path / 'stratdoc.db'}", future=True)
+    Base.metadata.create_all(engine)
+    Session = sessionmaker(bind=engine, expire_on_commit=False)
+
+    def _session_override():
+        with Session() as s:
+            yield s
+
+    app.dependency_overrides[deps.get_session] = _session_override
+    with TestClient(app) as c:
+        yield c
+    app.dependency_overrides.clear()
+
+
+_SPEC_BODY = {
+    "type": "backtest",
+    "universe": {"exclude_st": False, "min_listing_days": 0},
+    "factors": [{"name": "dividend_yield", "weight": 1.0}],
+    "selection": {"top_n": 20, "hold_top_x": 20, "allow_substitute": False, "defer_buy": True},
+    "rebalance": "monthly",
+    "selection_interval_months": 6,
+    "rebalance_interval_months": 6,
+    "period": ["2020-01-01", "2024-12-31"],
+}
+
+
+class TestStrategyDocApi:
+    def test_post_describe_from_research_spec_without_saving(self, client: TestClient) -> None:
+        resp = client.post("/api/strategies/describe", json=_SPEC_BODY)
+        assert resp.status_code == 200
+        doc = resp.json()
+        assert set(doc) == {"summary", "formula", "steps", "warnings"}
+        assert "dividend_yield" in doc["formula"]
+        assert "每 6 个月" in doc["summary"]
+        # 未保存：列表仍为空（POST /describe 与 POST /strategies 不冲突）
+        assert client.get("/api/strategies").json() == []
+
+    def test_post_describe_invalid_spec_422(self, client: TestClient) -> None:
+        bad = dict(_SPEC_BODY, period=["2024-01-01", "2020-01-01"])
+        assert client.post("/api/strategies/describe", json=bad).status_code == 422
+
+    def test_get_describe_saved_strategy_and_404(self, client: TestClient) -> None:
+        created = client.post(
+            "/api/strategies",
+            json={"name": "高股息", "factors": [{"name": "dividend_yield", "weight": 1}]},
+        )
+        sid = created.json()["id"]
+        resp = client.get(f"/api/strategies/{sid}/describe")
+        assert resp.status_code == 200
+        doc = resp.json()
+        assert doc["summary"]
+        assert any("无回测区间" in w for w in doc["warnings"])
+        assert client.get("/api/strategies/STG-NOT-EXIST/describe").status_code == 404
+
+    def test_post_fills_empty_description(self, client: TestClient) -> None:
+        """需求：策略必须有说明 —— description 为空/纯空白时由 summary 自动补全。"""
+        for desc in ("", "   "):
+            resp = client.post(
+                "/api/strategies",
+                json={
+                    "name": f"无说明策略-{len(desc)}",
+                    "description": desc,
+                    "factors": [{"name": "momentum_60", "weight": 1}],
+                },
+            )
+            assert resp.status_code == 200
+            body = resp.json()
+            assert body["description"].strip()
+            assert "momentum_60" in body["description"]
+            # 响应中的说明与 GET 读回一致（真的落库了）
+            assert client.get(f"/api/strategies/{body['id']}").json()["description"] == body[
+                "description"
+            ]
+
+    def test_post_keeps_explicit_description(self, client: TestClient) -> None:
+        resp = client.post(
+            "/api/strategies",
+            json={
+                "name": "有说明策略",
+                "description": "我自己写的说明",
+                "factors": [{"name": "momentum_60", "weight": 1}],
+            },
+        )
+        assert resp.json()["description"] == "我自己写的说明"
+
+    def test_auto_description_fits_column_width(self, client: TestClient) -> None:
+        """自动说明必须落在 `StrategyModel.description = String(300)` 之内。
+
+        超长在 SQLite（测试库）不会报错、到 MySQL 严格模式会 Data too long，
+        因此这里显式断言列宽；截断必须带省略号（显式标记，不静默改短）。
+        """
+        from app.api.strategies import _DESCRIPTION_MAX_CHARS
+        from app.quant.factors import list_factors
+
+        all_factors = [{"name": f.name, "weight": 1} for f in list_factors()]
+        resp = client.post(
+            "/api/strategies", json={"name": "全因子策略", "factors": all_factors}
+        )
+        assert resp.status_code == 200
+        desc = resp.json()["description"]
+        assert len(desc) <= _DESCRIPTION_MAX_CHARS
+        assert desc.endswith("…"), "超长被截断时必须显式带省略号"
+
+        # 常规（单因子）说明远短于列宽：不应被截断
+        normal = client.post(
+            "/api/strategies",
+            json={"name": "单因子策略", "factors": [{"name": "dividend_yield", "weight": 1}]},
+        ).json()["description"]
+        assert len(normal) <= _DESCRIPTION_MAX_CHARS
+        assert not normal.endswith("…")
+
+
+class TestStrategyUpdateApi:
+    def _create(self, client: TestClient, name: str = "策略A") -> dict:
+        resp = client.post(
+            "/api/strategies",
+            json={
+                "name": name,
+                "description": "初始说明",
+                "factors": [{"name": "momentum_60", "weight": 1}],
+                "selection": {"top_n": 10},
+            },
+        )
+        assert resp.status_code == 200
+        return resp.json()
+
+    def test_put_updates_in_place_and_keeps_created_at(self, client: TestClient) -> None:
+        created = self._create(client)
+        sid = created["id"]
+        created_at = created["created_at"]
+        assert created_at is not None
+
+        resp = client.put(
+            f"/api/strategies/{sid}",
+            json={
+                "name": "策略A",
+                "description": "改后的说明",
+                "factors": [{"name": "momentum_20", "weight": 2}],
+                "selection": {"top_n": 5},
+            },
+        )
+        assert resp.status_code == 200
+        body = resp.json()
+        assert body["id"] == sid, "原地更新必须保持 id 不变（不新建）"
+        assert body["created_at"] == created_at, "PUT 不得刷新创建时间"
+        assert body["selection"]["top_n"] == 5
+        assert body["factors"][0]["name"] == "momentum_20"
+        assert body["description"] == "改后的说明"
+
+        # 再读一次：落库后的创建时间同样未变，且列表仍只有一条
+        assert client.get(f"/api/strategies/{sid}").json()["created_at"] == created_at
+        assert len(client.get("/api/strategies").json()) == 1
+
+    def test_put_path_id_wins_over_body_id(self, client: TestClient) -> None:
+        created = self._create(client, "策略B")
+        sid = created["id"]
+        resp = client.put(
+            f"/api/strategies/{sid}",
+            json={
+                "id": "STG-FAKE",
+                "name": "策略B",
+                "factors": [{"name": "momentum_60", "weight": 1}],
+            },
+        )
+        assert resp.status_code == 200
+        assert resp.json()["id"] == sid
+        assert client.get("/api/strategies/STG-FAKE").status_code == 404
+
+    def test_put_missing_strategy_404(self, client: TestClient) -> None:
+        resp = client.put(
+            "/api/strategies/STG-NOT-EXIST",
+            json={"name": "X", "factors": [{"name": "momentum_60", "weight": 1}]},
+        )
+        assert resp.status_code == 404
+        assert "不存在" in resp.json()["detail"]
+
+    def test_put_duplicate_name_400(self, client: TestClient) -> None:
+        first = self._create(client, "策略C")
+        second = self._create(client, "策略D")
+        resp = client.put(
+            f"/api/strategies/{second['id']}",
+            json={"name": "策略C", "factors": [{"name": "momentum_60", "weight": 1}]},
+        )
+        assert resp.status_code == 400
+        assert "策略名已存在" in resp.json()["detail"]
+        # 撞车失败后第二条策略名不变（未被部分写入）
+        assert client.get(f"/api/strategies/{second['id']}").json()["name"] == "策略D"
+        assert client.get(f"/api/strategies/{first['id']}").json()["name"] == "策略C"
+
+    def test_put_fills_empty_description(self, client: TestClient) -> None:
+        created = self._create(client, "策略E")
+        resp = client.put(
+            f"/api/strategies/{created['id']}",
+            json={
+                "name": "策略E",
+                "description": "  ",
+                "factors": [{"name": "dividend_yield", "weight": 1}],
+            },
+        )
+        assert resp.status_code == 200
+        assert resp.json()["description"].strip()
+        assert "dividend_yield" in resp.json()["description"]
diff --git a/backend/tests/test_symbol_names.py b/backend/tests/test_symbol_names.py
new file mode 100644
index 0000000..f3add6f
--- /dev/null
+++ b/backend/tests/test_symbol_names.py
@@ -0,0 +1,239 @@
+"""股票名称（`name`）填充与 `GET /api/stocks/names` 接口测试。
+
+覆盖三件事（需求：前端任何出现代码的地方都要能显示名称）：
+1) 回测结果的展示结构（symbol_curves / positions / trades / fills / signal_history /
+   selection_history）由 `ResearchService.run_backtest` 统一回填名称；
+   股票池为空时**静默跳过**（名称只是展示增强，不得让已算完的回测失败）；
+2) 选股结果 `candidates[].name` 由 `SelectionService` 用已装配股票池回填；
+3) `GET /api/stocks/names` 返回 `{symbol: name}` **dict**（前端契约），
+   且 `GET /api/stocks/{symbol}` 未被 `/names` 破坏（路由顺序）。
+
+用内存 Repository + 合成行情（不连真库、不跑长区间），秒级完成。
+"""
+
+from __future__ import annotations
+
+from datetime import date
+
+import pandas as pd
+import pytest
+from app.api import deps
+from app.application.services.selection_service import SelectionService
+from app.domain.entities.market import Stock
+from app.domain.entities.research import (
+    FactorSpec,
+    ResearchSpec,
+    SelectionSpec,
+    UniverseSpec,
+)
+from app.domain.entities.selection import SelectionQuery
+from app.main import app
+from app.quant.engine import LocalEngine
+from app.quant.service import ResearchService, _fill_names
+from fastapi.testclient import TestClient
+
+from conftest_quant import bars_dataframe_to_daily_bars, synthetic_daily
+
+_SYMS = ["600000.SH", "600001.SH", "600002.SH"]
+_NAMES = {"600000.SH": "浦发银行", "600001.SH": "邯郸钢铁", "600002.SH": "齐鲁石化"}
+
+
+def _stocks(symbols: list[str] | None = None) -> list[Stock]:
+    return [
+        Stock(symbol=s, name=_NAMES[s], list_date=date(1999, 11, 10))
+        for s in (symbols or _SYMS)
+    ]
+
+
+class _MemStockRepo:
+    def __init__(self, stocks: list[Stock]) -> None:
+        self._stocks = stocks
+
+    def list(self) -> list[Stock]:
+        return self._stocks
+
+    def get_by_symbol(self, symbol: str) -> Stock | None:
+        return next((s for s in self._stocks if s.symbol == symbol), None)
+
+
+class _MemDailyRepo:
+    def __init__(self, df: pd.DataFrame) -> None:
+        self._bars = bars_dataframe_to_daily_bars(df)
+
+    def get_range(self, symbol, start, end):
+        return [b for b in self._bars if b.symbol == symbol and start <= b.trade_date <= end]
+
+    def get_range_many(self, symbols, start, end, adjust="none"):
+        syms = set(symbols)
+        return [b for b in self._bars if b.symbol in syms and start <= b.trade_date <= end]
+
+
+def _daily_df() -> pd.DataFrame:
+    return synthetic_daily(
+        {"600000.SH": 0.004, "600001.SH": 0.001, "600002.SH": -0.003}, n=300
+    )
+
+
+def _backtest_spec() -> ResearchSpec:
+    return ResearchSpec(
+        type="backtest",
+        universe=UniverseSpec(exclude_st=False, min_listing_days=0),
+        factors=[FactorSpec(name="momentum_20", weight=1.0)],
+        selection=SelectionSpec(top_n=2),
+        rebalance="monthly",
+        # 半年区间（约 150 个交易日）：够触发月度调仓与卖出，又足够快
+        period=(date(2024, 6, 3), date(2024, 12, 31)),
+    )
+
+
+class TestBacktestNameFill:
+    def test_all_display_structures_carry_name(self) -> None:
+        service = ResearchService(
+            _MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()), LocalEngine()
+        )
+        result = service.run_backtest(_backtest_spec())
+
+        assert result.symbol_curves, "应持有过股票并输出个股曲线"
+        assert result.positions, "应有调仓后仓位记录"
+        assert result.selection_history, "应有择股记录"
+        assert result.signal_history, "应有交易意图记录"
+
+        for curve in result.symbol_curves:
+            assert curve.name == _NAMES[curve.symbol]
+        for pos in result.positions:
+            assert pos.name == _NAMES[pos.symbol]
+        for pick in result.selection_history:
+            assert pick.name == _NAMES[pick.symbol]
+        # 成交/意图记录里存在 symbol="" 的池子不足提示（非股票），其 name 保持 None
+        for action in result.signal_history + result.fills:
+            if action.symbol:
+                assert action.name == _NAMES[action.symbol]
+            else:
+                assert action.name is None
+
+    def test_trades_and_marks_carry_name(self) -> None:
+        service = ResearchService(
+            _MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()), LocalEngine()
+        )
+        result = service.run_backtest(_backtest_spec())
+        assert result.trades, "月度调仓 + 因子换手应产生已卖出往返"
+        for trade in result.trades:
+            assert trade.name == _NAMES[trade.symbol]
+        # marks 与 signal_history 同源，也应带上名称（前端个股曲线标注用）
+        for curve in result.symbol_curves:
+            for mark in curve.marks:
+                assert mark.name == _NAMES[mark.symbol]
+
+    def test_fill_names_skips_silently_when_stock_pool_empty(self) -> None:
+        """空股票池 → 静默跳过（名称缺失只影响展示，不得抛错）。"""
+        service = ResearchService(
+            _MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()), LocalEngine()
+        )
+        result = service.run_backtest(_backtest_spec())
+        before = result.symbol_curves[0].name
+
+        returned = _fill_names(result, [])
+        assert returned is result
+        assert result.symbol_curves[0].name == before  # 未被清空、也未被改写
+
+    def test_fill_names_is_idempotent_and_keeps_name_without_stock_record(self) -> None:
+        """名称回填幂等；股票池里没有该代码时保持 None（不伪造名称）。"""
+        service = ResearchService(
+            _MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()), LocalEngine()
+        )
+        result = service.run_backtest(_backtest_spec())
+        snapshot = [(c.symbol, c.name) for c in result.symbol_curves]
+
+        _fill_names(result, _stocks())
+        assert [(c.symbol, c.name) for c in result.symbol_curves] == snapshot
+
+        only_other = [Stock(symbol="601398.SH", name="工商银行", list_date=date(2006, 1, 1))]
+        _fill_names(result, only_other)
+        assert all(c.name == _NAMES[c.symbol] for c in result.symbol_curves)
+
+
+class TestSelectionCandidateName:
+    def _service(self) -> SelectionService:
+        return SelectionService(_MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()))
+
+    def test_score_mode_candidates_carry_name(self) -> None:
+        result = self._service().select(
+            SelectionQuery(
+                method="score",
+                factors=[{"name": "momentum_20", "weight": 1.0}],
+                top_n=2,
+                as_of=date(2024, 12, 31),
+                universe=UniverseSpec(exclude_st=False, min_listing_days=0),
+            )
+        )
+        assert result.candidates
+        for cand in result.candidates:
+            assert cand.name == _NAMES[cand.symbol]
+
+    def test_condition_mode_candidates_carry_name(self) -> None:
+        result = self._service().select(
+            SelectionQuery(
+                method="condition",
+                conditions=[{"field": "close", "op": "gte", "ref": "ma20"}],
+                as_of=date(2024, 12, 31),
+                universe=UniverseSpec(exclude_st=False, min_listing_days=0),
+            )
+        )
+        assert result.candidates
+        for cand in result.candidates:
+            assert cand.name == _NAMES[cand.symbol]
+
+
+class TestQlibEngineNameFill:
+    def test_qlib_engine_path_also_fills_names(self, tmp_path) -> None:
+        """名称回填写在服务层，因此 LocalEngine / QlibEngine 两条路径都必须生效。
+
+        这条用例走真实的 Qlib 数据管线（落盘 bin → D.features 读回），
+        而不是只测本地引擎后「假设」另一条路径也覆盖到了。
+        """
+        pytest.importorskip("qlib")  # 未安装 qlib 的环境跳过，不假装覆盖
+        from app.quant.qlib_adapter.engine import QlibEngine
+
+        service = ResearchService(
+            _MemStockRepo(_stocks()), _MemDailyRepo(_daily_df()), QlibEngine(qlib_dir=tmp_path)
+        )
+        result = service.run_backtest(_backtest_spec())
+        assert result.symbol_curves, "qlib 路径也应产生个股曲线"
+        for curve in result.symbol_curves:
+            assert curve.name == _NAMES[curve.symbol]
+        for pos in result.positions:
+            assert pos.name == _NAMES[pos.symbol]
+        for pick in result.selection_history:
+            assert pick.name == _NAMES[pick.symbol]
+
+
+@pytest.fixture()
+def client() -> TestClient:
+    """只覆盖 stock 仓储（这三个接口只用 StockRepoDep，不需要 DB session）。"""
+    stocks = [*_stocks(), Stock(symbol="600519.SH", name="贵州茅台", list_date=date(2001, 8, 27))]
+    app.dependency_overrides[deps._stock_repo_factory] = lambda: _MemStockRepo(stocks)  # noqa: SLF001
+    with TestClient(app) as c:
+        yield c
+    app.dependency_overrides.clear()
+
+
+class TestStockNamesApi:
+    def test_names_returns_symbol_to_name_dict(self, client: TestClient) -> None:
+        resp = client.get("/api/stocks/names")
+        assert resp.status_code == 200
+        body = resp.json()
+        # 契约：dict（不是数组），前端按 Record 消费
+        assert isinstance(body, dict)
+        assert body["600519.SH"] == "贵州茅台"
+        assert body["600000.SH"] == _NAMES["600000.SH"]
+        assert set(body) == {"600000.SH", "600001.SH", "600002.SH", "600519.SH"}
+
+    def test_symbol_path_not_swallowed_by_names_route(self, client: TestClient) -> None:
+        """路由顺序回归：/{symbol} 仍按代码查询（证明 /names 未被参数吞掉、也没吞掉它）。"""
+        one = client.get("/api/stocks/600519.SH")
+        assert one.status_code == 200
+        assert one.json()["symbol"] == "600519.SH"
+        assert one.json()["name"] == "贵州茅台"
+
+        assert client.get("/api/stocks/999999.SZ").status_code == 404
+        assert client.get("/api/stocks?q=茅台").json()[0]["symbol"] == "600519.SH"
diff --git a/backend/tests/test_tushare_provider.py b/backend/tests/test_tushare_provider.py
index b6d0f6c..788416a 100644
--- a/backend/tests/test_tushare_provider.py
+++ b/backend/tests/test_tushare_provider.py
@@ -20,10 +20,14 @@ class FakePro:
         self.payload = payload or []
         self.error = error
         self.calls: list[str] = []
+        # 关键：必须记录关键字参数，否则「list_status 是否真的透传给 tushare」无法被断言
+        # （退市股同步完全依赖该参数，见 TestDelistedStocks）
+        self.call_kwargs: list[dict] = []
 
     def __getattr__(self, api: str):
         def _run(**kwargs):
             self.calls.append(api)
+            self.call_kwargs.append(kwargs)
             if self.error is not None:
                 raise self.error
             return self.payload
@@ -257,3 +261,68 @@ class TestIndexWeight:
         rows = p.get_index_weight("000300.SH")
         assert pro.calls == ["index_weight"]
         assert len(rows) == 1 and rows[0].symbol == "600519.SH"
+
+
+class TestDelistedStocks:
+    """退市股（幸存者偏差修正）：list_status 透传、NaN 归一、代码规范过滤。
+
+    实测背景：tushare `stock_basic` 不带 list_status 时**只返回在市股票**，
+    退市股整体缺失（230 只 2019-12 后退市）→ 回测系统性高估收益；
+    而退市股记录的 industry/area 是 NaN、status 为空，且含 T 前缀异常代码。
+    """
+
+    def test_list_status_passed_to_api(self) -> None:
+        pro = FakePro(payload=[])
+        p = TushareProvider(token="t", pro=pro)
+        p.get_stock_basic("D")
+        assert pro.calls == ["stock_basic"]
+        # tushare 不带 list_status 时只返回在市股票 → 退市股同步必须真的传 "D"
+        assert pro.call_kwargs[0]["list_status"] == "D"
+
+    def test_default_list_status_is_listed_only(self) -> None:
+        pro = FakePro(payload=[])
+        p = TushareProvider(token="t", pro=pro)
+        p.get_stock_basic()
+        assert pro.calls == ["stock_basic"]  # 默认 "L"，保持既有行为
+        assert pro.call_kwargs[0]["list_status"] == "L"
+
+    def test_nan_optional_fields_become_none(self) -> None:
+        stocks = TushareProvider.normalize_stock(
+            [
+                {
+                    "ts_code": "000005.SZ",
+                    "name": "ST星源(退)",
+                    "area": float("nan"),
+                    "industry": float("nan"),
+                    "market": float("nan"),
+                    "exchange": "SZSE",
+                    "list_date": "19901210",
+                    "delist_date": "20240426",
+                }
+            ]
+        )
+        s = stocks[0]
+        assert s.industry is None and s.area is None and s.market is None
+        assert s.exchange == "SZSE"
+        assert s.delist_date == date(2024, 4, 26)
+
+    def test_missing_status_falls_back_to_query_status(self) -> None:
+        """退市表 status 为空：必须按查询的 list_status 兜底，不能一律标成 L。"""
+        rec = [{"ts_code": "000005.SZ", "name": "ST星源(退)", "list_date": "19901210",
+                "delist_date": "20240426", "status": None}]
+        assert TushareProvider.normalize_stock(rec)[0].status == "L"  # 既有默认
+        assert TushareProvider.normalize_stock(rec, default_status="D")[0].status == "D"
+
+    def test_abnormal_code_skipped_not_fatal(self) -> None:
+        """'T600018.SH'（上港集箱(退)，2006 退市）不得让整批退市列表拉取失败。"""
+        pro = FakePro(
+            payload=[
+                {"ts_code": "000005.SZ", "name": "ST星源(退)", "list_date": "19901210",
+                 "delist_date": "20240426"},
+                {"ts_code": "T600018.SH", "name": "上港集箱(退)", "list_date": "19960101",
+                 "delist_date": "20061020"},
+            ]
+        )
+        p = TushareProvider(token="t", pro=pro)
+        stocks = p.get_stock_basic("D")
+        assert [s.symbol for s in stocks] == ["000005.SZ"]
diff --git a/backend/uv.lock b/backend/uv.lock
index 88c10b2..f4e5e0d 100644
--- a/backend/uv.lock
+++ b/backend/uv.lock
@@ -13,16 +13,16 @@ resolution-markers = [
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 version = "2.7.1"
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+source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+sdist = { url = "https://mirrors.aliyun.com/pypi/packages/ce/f4/eec0465c2f67b2664688d0240b3212d5196fd89e741df67ddb81f8d35658/aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d" }
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+    { url = "https://mirrors.aliyun.com/pypi/packages/71/43/1947f06babed6b3f1d7f38b0c767f52df66bfb2bc10b468c4a7de9eceff2/aiohappyeyeballs-2.7.1-py3-none-any.whl", hash = "sha256:9243213661e29250eb41368e5daa826fc017156c3b8a11440826b2e3ed376472" },
 ]
 
 [[package]]
 name = "aiohttp"
 version = "3.14.3"
-source = { registry = "https://pypi.org/simple" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
 dependencies = [
     { name = "aiohappyeyeballs" },
     { name = "aiosignal" },
@@ -33,286 +33,286 @@ dependencies = [
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     { name = "yarl" },
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+sdist = { url = "https://mirrors.aliyun.com/pypi/packages/58/d9/22ce5786ac0c1653ae8b6c23bded02c1686d11f0dbb45b31ce128e0df985/aiohttp-3.14.3.tar.gz", hash = "sha256:9491196535a88924a60afd5b5f434b5b203b6cc616250878dbdb223a8f7844bc" }
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+    { url = "https://mirrors.aliyun.com/pypi/packages/45/44/5769b96298c1e195fb412997b6090af2a84105cf59c17613558a2d011d1f/yarl-1.24.5-cp314-cp314t-manylinux_2_31_riscv64.manylinux_2_39_riscv64.whl", hash = "sha256:82f75e05912e84b7a0fe57075d9c59de3cb352b928330f2eb69b2e1f54c3e1f0" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/4c/40/009e8e791fd9762c0e1567e69248acb4f49064597e1680874c16dd8bb798/yarl-1.24.5-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:16a2f5010280020e90f5330257e6944bc33e73593b136cc5a241e6c1dc292498" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/20/c6/b7480578f8a0a80946f36ad6df547ecec704f9ba69d2de60f8aa6f1c1cbf/yarl-1.24.5-cp314-cp314t-musllinux_1_2_armv7l.whl", hash = "sha256:ffcd54362564dc1a30fb74d8b8a6e5a6b11ebd5e27266adc3b7427a21a6c9104" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/d4/27/4476f3360b91a48c5cf125e91f59a3bd35299d84a431a258d57f5977bb11/yarl-1.24.5-cp314-cp314t-musllinux_1_2_ppc64le.whl", hash = "sha256:0465ec8cedc2349b97a6b595ace64084a50c6e839eca40aa0626f38b8350e331" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/4c/4b/5cdd3e5ee944e8af31e52f6cd3d3af5fd7b937e036ccbbba2c9ffebede95/yarl-1.24.5-cp314-cp314t-musllinux_1_2_riscv64.whl", hash = "sha256:4db9aecb141cb7a5447171b57aa1ed3a8fee06af40b992ffc31206c0b0121550" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/18/86/f406b0c2a6f99575de2da671ef47aa06f89a5be83a27a46971c3b86cecdb/yarl-1.24.5-cp314-cp314t-musllinux_1_2_s390x.whl", hash = "sha256:f540c013589084679a6c7fac07096b10159737918174f5dfc5e11bf5bca4dfe6" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/f0/6c/9f3adfbd3b30b4fa0f7ccb3a83eba2c1152d3fff554d535e640ba0f7ba2b/yarl-1.24.5-cp314-cp314t-musllinux_1_2_x86_64.whl", hash = "sha256:a61834fb15d81322d872eaafd333838ae7c9cea84067f232656f75965933d047" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/dd/37/91eb2e5ca883a529c1b390348a74cd9fc0512171727f547ce70bfe02be5c/yarl-1.24.5-cp314-cp314t-win_amd64.whl", hash = "sha256:5c88e5815a49d289e599f3513aa7fde0bc2092ff188f99c940f007f90f53d104" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/bf/f4/ed5c402ac8fde4403ed3366c2716bfddc8a6677ebd59f3d62772cc7fe468/yarl-1.24.5-cp314-cp314t-win_arm64.whl", hash = "sha256:cf139c02f5f23ef6532040a30ff662c00a318c952334f211046b8e60b7f17688" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/61/02/962c1cbfc401a30c1d034dc67ff395f64b52302c6d62de556c1fca99acc0/yarl-1.24.5-py3-none-any.whl", hash = "sha256:a33700d13d9b7d84fd10947b09ff69fb9a792e519c8cb9764a3ca70baa6c23a7" },
 ]
 
 [[package]]
 name = "zipp"
 version = "4.1.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602", size = 26214, upload-time = "2026-05-18T20:08:57.967Z" }
+source = { registry = "https://mirrors.aliyun.com/pypi/simple/" }
+sdist = { url = "https://mirrors.aliyun.com/pypi/packages/b9/d8/eab98a517c14134c0b2eb4e2387bc5f457334293ec5d2dd3857ec2966802/zipp-4.1.0.tar.gz", hash = "sha256:4cb57381f544315db7688e976e922a2b18cdb513d21cc194eb42232ba2a3e602" }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f", size = 10238, upload-time = "2026-05-18T20:08:57.045Z" },
+    { url = "https://mirrors.aliyun.com/pypi/packages/3a/13/547360d81e6d88d58492968ffda9f9542854f11310ee556fef14260cc886/zipp-4.1.0-py3-none-any.whl", hash = "sha256:25ad4e16390cd314347dd8f1de67a2ac538ae658ed4ab9db16029c07c188e97f" },
 ]
diff --git a/config.yaml b/config.yaml
index b4321fb..bd9e5b5 100644
--- a/config.yaml
+++ b/config.yaml
@@ -25,12 +25,15 @@ database:
   echo: false
   # Alembic 迁移脚本目录（相对 backend/）
   migrations_dir: "app/infrastructure/persistence/migrations"
-  # ---- MySQL（当前默认库；2026-09 由 SQLite data/quant.db 全量迁移而来）----
+  # ---- MySQL（当前默认库）----
+  # 2026-09 由 SQLite data/quant.db 全量迁移而来；2026-09 再由远端库
+  # （192.168.1.10/qlib）切换到本机 MariaDB 10.11（127.0.0.1/qlib），
+  # 数据为逐表一致副本（stock_daily 7,792,869 / adjust_factor 7,898,717 等）。
   # host/port/db/user 明文可提交；密码经 password_env 引用 .env 变量
   # （AGENT.md §33：密钥只放根目录 .env，本文件不写明文密码）。
   mysql:
     enabled: true
-    host: "192.168.1.10"
+    host: "127.0.0.1"
     port: 3306
     db: "qlib"
     user: "qlib"
@@ -60,6 +63,18 @@ job:
   # 并发研究子进程上限（超出直接失败并提示稍后再试）
   max_concurrent_jobs: 2
 
+research:
+  # 归档（Experiment.result_json）行为：
+  # archive_curve_limit —— 个股收益曲线（symbol_curves）数量上限：
+  #   null = **完整存档**（默认，期内持有的每只都落库）；填数字 = 按 |期末收益|
+  #   降序截断，且结果里会如实写入截断说明（unimplemented）与 archive_meta。
+  # 可用环境变量 QLIB_ARCHIVE_CURVE_LIMIT 覆盖。
+  archive_curve_limit: null
+  # archive_max_chars —— 归档结果 JSON 的字节预算（超预算时按 |期末收益| 降序裁剪
+  #   曲线并如实标注；MEDIUMTEXT 硬上限 16,777,215 字节，默认留约 28% 余量）。
+  # 可用环境变量 QLIB_ARCHIVE_MAX_CHARS 覆盖。
+  archive_max_chars: 12000000
+
 agent:
   llm:
     # 大模型接入：URL 与模型名在此配置（明文、可提交）；
diff --git a/frontend/web/app/backtest/page.tsx b/frontend/web/app/backtest/page.tsx
index 2893966..80be2f9 100644
--- a/frontend/web/app/backtest/page.tsx
+++ b/frontend/web/app/backtest/page.tsx
@@ -1,88 +1,280 @@
 "use client";
 
-import { useEffect, useState } from "react";
-import { apiGet } from "@/lib/api";
-import { submitJob, waitJob } from "@/lib/jobs";
-import type { BacktestResult, FactorMeta, ResearchSpec } from "@/lib/types";
+/**
+ * 选股回测页 —— 研究闭环里的「定规则 + 验证」环节。
+ *
+ * 本页承担三种进入方式（都可分享 URL）：
+ * - 直接进入：从默认参数开始调；
+ * - `?strategy=STG-xxx`：从策略库载入一个已保存策略（可微调、可另存）；
+ * - `?from_selection=SEL-xxx`：从选股结果直通过来，预填当时的条件/因子/TopN
+ *   （解决「选出来的股票要手工抄到回测页」的断点）；
+ * - `?from_experiment=EXP-xxx`：从实验详情「以此参数再跑一次」进来。
+ *
+ * 交互改进：参数区与说明/公式同屏（说明随参数实时更新，由后端推导）；
+ * 「保存为策略」把当前参数存进策略库；结果区所有股票代码都带名称且可点进个股页。
+ */
+import { Suspense, useEffect, useMemo, useRef, useState } from "react";
+import Link from "next/link";
+import { useSearchParams } from "next/navigation";
+import { apiGet, apiPost } from "@/lib/api";
+import { STAGE_LABEL, submitJob, waitJob } from "@/lib/jobs";
 import { recentRange } from "@/lib/dates";
+import type {
+  ActionRecord,
+  BacktestResult,
+  FactorMeta,
+  ResearchCondition,
+  ResearchSpec,
+  SelectionResult,
+  StrategyDefinition,
+  SymbolCurve,
+} from "@/lib/types";
+import { PageHeader, Card, Pill, Btn, Banner, Empty, Loading } from "@/components/ui";
 import {
-  PageHeader,
-  Card,
-  Pill,
-  Field,
-  Btn,
-  Banner,
-  Progress,
-  Empty,
-  BacktestMetrics,
-  MonthlyReturnsTable,
-  UnimplementedNote,
-} from "@/components/ui";
-import { LineChart } from "@/components/LineChart";
+  StrategyParamsForm,
+  casePreset,
+  emptyParams,
+  paramsFromSpec,
+  paramsFromStrategy,
+  strategyFromParams,
+  validateParams,
+  type StrategyParams,
+} from "@/components/StrategyParamsForm";
+import { StrategyDocCard } from "@/components/StrategyDocCard";
+import { BacktestResultView } from "@/components/BacktestResultView";
+import { adjustLabel } from "@/lib/labels";
+import { useStrategyDoc } from "@/lib/strategy";
+import { SymbolLink, useSymbolNames } from "@/lib/symbols";
 
+type LoadState = { kind: "none" | "strategy" | "selection" | "experiment"; label: string; note?: string };
+
+/**
+ * Next 15 要求使用 `useSearchParams` 的组件处于 Suspense 边界内，
+ * 否则静态渲染阶段会报错（本页用 query 决定「从策略/选股/实验载入」）。
+ */
 export default function BacktestPage() {
+  return (
+    }>
+      
+    
+  );
+}
+
+function BacktestInner() {
+  const search = useSearchParams();
+  const strategyId = search.get("strategy");
+  const selectionId = search.get("from_selection");
+  const experimentId = search.get("from_experiment");
+
+  const range = useMemo(() => recentRange(), []);
+  const [params, setParams] = useState(null);
   const [factors, setFactors] = useState([]);
-  const [factor, setFactor] = useState("momentum_60");
-  const [topN, setTopN] = useState(5);
-  const [rebalance, setRebalance] = useState<"monthly" | "weekly">("monthly");
-  const [start, setStart] = useState("");
-  const [end, setEnd] = useState("");
-  const [excludeSt, setExcludeSt] = useState(true);
-  const [commission, setCommission] = useState(0.03); // %
-  const [stamp, setStamp] = useState(0.05);
-  const [slippage, setSlippage] = useState(0.1);
-  const [capital, setCapital] = useState(1_000_000);
+  const [loaded, setLoaded] = useState({ kind: "none", label: "" });
   const [result, setResult] = useState(null);
   const [running, setRunning] = useState(false);
   const [jobId, setJobId] = useState("");
+  const [stage, setStage] = useState("");
+  const [elapsed, setElapsed] = useState(0);
   const [error, setError] = useState("");
+  const [notice, setNotice] = useState("");
+  const [saving, setSaving] = useState(false);
+  const [lastExp, setLastExp] = useState(null);
+  const [archiveId, setArchiveId] = useState("");
+  const [restoring, setRestoring] = useState(false);
+  const bootstrapped = useRef(false);
 
+  /* ---------- 初始化：因子表 + 默认/来源参数 ---------- */
   useEffect(() => {
     let alive = true;
     apiGet("/factors")
-      .then((list) => {
-        if (!alive) return;
-        setFactors(list);
-        if (list.length > 0 && !list.some((f) => f.name === factor)) {
-          setFactor(list[0].name);
-        }
-      })
+      .then((list) => alive && setFactors(list))
       .catch((e: Error) => alive && setError(e.message));
-    const { start: s, end: e } = recentRange();
-    setStart(s);
-    setEnd(e);
     return () => {
       alive = false;
     };
   }, []);
 
+  useEffect(() => {
+    if (bootstrapped.current) return;
+    bootstrapped.current = true;
+    const base = emptyParams(range);
+    try {
+      const prev = window.localStorage.getItem("qlib:last-backtest-experiment");
+      if (prev) setLastExp(prev);
+    } catch {
+      /* localStorage 不可用（隐私模式）：只是少一个便捷入口，不影响主流程 */
+    }
+
+    (async () => {
+      // 1) 策略库载入
+      if (strategyId) {
+        try {
+          const st = await apiGet(`/strategies/${encodeURIComponent(strategyId)}`);
+          setParams({ ...paramsFromStrategy(st, base), start: base.start, end: base.end });
+          setLoaded({
+            kind: "strategy",
+            label: `来自策略库：${st.name}（${st.id}）`,
+            note: "参数已载入；改完可「另存为新策略」或直接运行回测。",
+          });
+          return;
+        } catch (e) {
+          setError(`策略 ${strategyId} 载入失败：${(e as Error).message}`);
+        }
+      }
+      // 2) 选股结果直通：把当时的口径原样搬过来
+      if (selectionId) {
+        try {
+          const sel = await apiGet(`/selections/${encodeURIComponent(selectionId)}`);
+          // 选股把当时的查询原样存进 config_snapshot（复现用），这里据此预填
+          const snapQ = (sel.config_snapshot ?? {}) as {
+            universe?: { exclude_st?: boolean; min_listing_days?: number };
+            price_adjustment?: "none" | "qfq" | "hfq";
+            factors?: { name: string; weight: number }[];
+            conditions?: ResearchCondition[];
+            top_n?: number | null;
+          };
+          const topNQ = snapQ.top_n ?? base.topN;
+          setParams({
+            ...base,
+            factors: snapQ.factors?.length ? snapQ.factors.map((f) => ({ ...f })) : base.factors,
+            conditions: (snapQ.conditions ?? []).map((c) => ({ ...c })),
+            topN: topNQ,
+            holdX: Math.max(1, Math.min(topNQ, base.holdX)),
+            excludeSt: snapQ.universe?.exclude_st ?? base.excludeSt,
+            minListingDays: snapQ.universe?.min_listing_days ?? base.minListingDays,
+            priceAdjustment: snapQ.price_adjustment ?? base.priceAdjustment,
+            start: base.start,
+            end: base.end,
+          });
+          setLoaded({
+            kind: "selection",
+            label: `来自选股 ${sel.as_of_date}（${sel.method}）`,
+            note:
+              "已把该次选股的**规则**（条件/因子/TopN/口径）预填为回测参数。" +
+              "注意：回测会在每个择股日按同一规则重新选股，而不是固定持有该次选出的这批股票。",
+          });
+          return;
+        } catch (e) {
+          setError(`选股结果 ${selectionId} 载入失败：${(e as Error).message}`);
+        }
+      }
+      // 3) 实验参数复用
+      if (experimentId) {
+        try {
+          const exp = await apiGet<{ result?: BacktestResult & { config_snapshot?: Record } }>(
+            `/experiments/${encodeURIComponent(experimentId)}`
+          );
+          const snap = exp.result?.config_snapshot as Partial | undefined;
+          if (snap) {
+            setParams(paramsFromSpec(snap, base));
+            setLoaded({
+              kind: "experiment",
+              label: `来自实验 ${experimentId}`,
+              note: exp.result?.equity_curve?.length
+                ? `已复刻该实验参数，并直接展示它的归档结果（未重跑）。归档快照请到 /experiments/${experimentId} 查看；改参数后可再跑一次并与它对比。`
+                : "已复刻该实验的参数，可改后重跑并与原实验对比。",
+            });
+            // 实验已归档完整结果：直接展示，省一次几分钟的重跑
+            if (exp.result?.equity_curve?.length) {
+              setResult(exp.result);
+              setLastExp(experimentId);
+              setArchiveId(experimentId); // 提示条里给「打开归档」入口
+            }
+            return;
+          }
+          setError(`实验 ${experimentId} 没有可复用的参数快照`);
+        } catch (e) {
+          const msg = (e as Error).message;
+          setError(`实验 ${experimentId} 载入失败：${msg}`);
+          // 归档可能已被删除：清掉 localStorage 里的指针，避免「载入上次结果」每次都给坏链接。
+          // 只清指针（不影响参数区），并且只有在确实是 404 时才清。
+          if (msg.includes("404")) {
+            try {
+              if (window.localStorage.getItem("qlib:last-backtest-experiment") === experimentId) {
+                window.localStorage.removeItem("qlib:last-backtest-experiment");
+                setLastExp(null);
+              }
+            } catch {
+              /* localStorage 不可用：无需清理 */
+            }
+          }
+        }
+      }
+      setParams(base);
+    })();
+    // 仅首次进入时按 URL 载入
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
+  const errors = params ? validateParams(params, { requireMeta: params.name.trim() !== "" }) : {};
+  const blocking = ["topN", "holdX", "factors", "mMonths", "yMonths", "costs", "capital", "period", "conditions"].filter(
+    (k) => errors[k]
+  );
+  const doc = useStrategyDoc(params);
+
   async function run() {
-    if (!factor) return;
+    if (!params) return;
+    if (blocking.length) {
+      setError(errors[blocking[0]]);
+      return;
+    }
     setRunning(true);
     setError("");
+    setNotice("");
     setJobId("");
+    setArchiveId("");
     setResult(null);
     try {
-      const spec: ResearchSpec = {
-        type: "backtest",
-        universe: { exclude_st: excludeSt, min_listing_days: 0 },
-        factors: [{ name: factor, weight: 1 }],
-        selection: { top_n: topN },
-        rebalance,
-        costs: {
-          commission_rate: commission / 100,
-          stamp_tax_rate: stamp / 100,
-          slippage_rate: slippage / 100,
+      const spec = {
+        type: "backtest" as const,
+        universe: { exclude_st: params.excludeSt, min_listing_days: params.minListingDays },
+        price_adjustment: params.priceAdjustment,
+        factors: params.factors.filter((f) => f.name.trim() !== ""),
+        conditions: params.conditions.filter((c) => c.field.trim() !== ""),
+        selection: {
+          top_n: params.topN,
+          hold_top_x: params.holdX,
+          allow_substitute: params.fillPolicy === "substitute",
+          defer_buy: params.fillPolicy === "defer",
         },
-        initial_capital: capital,
-        period: [start, end],
+        rebalance: params.rebalance,
+        selection_interval_months:
+          params.mMonths > 0 ? params.mMonths : params.yMonths > 0 ? params.yMonths : null,
+        rebalance_interval_months:
+          params.yMonths > 0 ? params.yMonths : params.mMonths > 0 ? params.mMonths : null,
+        costs: {
+          commission_rate: params.commission / 100,
+          stamp_tax_rate: params.stamp / 100,
+          slippage_rate: params.slippage / 100,
+          min_commission: params.minCommission,
+        },
+        initial_capital: params.capital,
+        period: [params.start, params.end] as [string, string],
       };
-      // 异步 Job：后台执行（全市场可能数十秒），轮询到终态
       const { job_id } = await submitJob(spec);
       setJobId(job_id);
-      const out = await waitJob(job_id);
+      setStage("queued");
+      const out = await waitJob(job_id, 900_000, (info) => {
+        setStage(info.stage);
+        setElapsed(info.elapsedMs);
+      });
       if (out.status === "success" && out.result) {
         setResult(out.result);
+        const exp = out.experimentId ?? null;
+        if (exp) {
+          setLastExp(exp);
+          setArchiveId(exp);
+          try {
+            window.localStorage.setItem("qlib:last-backtest-experiment", exp);
+          } catch {
+            /* 忽略：仅影响下次进入的便捷入口 */
+          }
+        }
+        setNotice(
+          exp
+            ? `回测完成，已归档为实验 ${exp}（可在「实验对比」页与其它版本对比）。`
+            : "回测完成，已自动归档到「实验对比」页。"
+        );
       } else {
         setError(`任务${out.status}${out.error ? `：${out.error}` : ""}`);
       }
@@ -94,229 +286,201 @@ export default function BacktestPage() {
     }
   }
 
+  /** 载入上次回测结果：避免刷新页面后必须重跑一个几分钟的作业 */
+  async function loadLast() {
+    if (!lastExp) return;
+    setRestoring(true);
+    setError("");
+    try {
+      const exp = await apiGet<{ result?: BacktestResult }>(`/experiments/${encodeURIComponent(lastExp)}`);
+      if (exp.result?.equity_curve?.length) {
+        setResult(exp.result);
+        setArchiveId(lastExp);
+        setNotice(`已载入上次归档结果（${lastExp}），未重新执行作业。`);
+      } else {
+        setError(`实验 ${lastExp} 没有可载入的回测结果`);
+      }
+    } catch (e) {
+      setError((e as Error).message);
+    } finally {
+      setRestoring(false);
+    }
+  }
+
+  async function saveAsStrategy() {
+    if (!params) return;
+    const need = validateParams(params, { requireMeta: true });
+    if (Object.keys(need).length) {
+      setError(Object.values(need)[0]);
+      return;
+    }
+    setSaving(true);
+    setError("");
+    try {
+      const saved = await apiPost("/strategies", strategyFromParams(params));
+      setNotice(`已保存到策略库：「${saved.name}」（${saved.id}）—— 可在策略库一键回测/编辑。`);
+      setLoaded({ kind: "strategy", label: `已保存为策略 ${saved.name}（${saved.id}）` });
+    } catch (e) {
+      setError((e as Error).message);
+    } finally {
+      setSaving(false);
+    }
+  }
+
   return (
     <>
       
-              最近一次回测已完成
-            
-          ) : null
+          

+            
+              策略库
+            
+            
+              实验对比
+            
+          

         }
       />
 
-      
-        

-          
-            
-          
-          
-             setTopN(Number(e.target.value))}
-            />
-          
-          
-            
-          
-          
-             setCommission(Number(e.target.value))} />
-          
-          
-             setStamp(Number(e.target.value))} />
-          
-          
-             setSlippage(Number(e.target.value))} />
-          
-          
-             setCapital(Number(e.target.value))} />
-          
-          
-             setStart(e.target.value)} />
-          
-          
-             setEnd(e.target.value)} />
-          
-          
-            
-          
-          
-            {running ? "后台运行中…" : "运行回测"}
+      {lastExp && !result && !running ? (
+        

+          
+            上次结果
+          
+          
+            本机上次回测已归档为实验 {lastExp}；
+            直接载入可省去重跑（全市场 8 年区间约 3~5 分钟）。
+          
+          
+            载入上次结果
+          
+           {
+              setLastExp(null);
+              try {
+                window.localStorage.removeItem("qlib:last-backtest-experiment");
+              } catch {
+                /* 忽略 */
+              }
+            }}
+          >
+            忽略
           
         

+      ) : null}
+
+      {loaded.kind !== "none" ? (
+        
+          {loaded.label}
+          {loaded.note ? ` · ${loaded.note}` : ""}
+        
+      ) : null}
+      {error ? {error} : null}
+      {notice ? (
+        
+          {notice}
+          {archiveId ? (
+            <>
+              {" "}
+              
+                打开归档（完整快照）
+              
+            
+          ) : null}
+        
+      ) : null}
+
+      
+             params && setParams({ ...casePreset(range), name: params.name, description: params.description })}>
+              载入高股息案例默认参数
+            
+            
+              保存为策略
+            
+          

+        }
+      >
+        {params ? (
+          <>
+            
+            

+               0} onClick={run}>
+                {running ? "后台运行中…" : "运行回测"}
+              
+              
+                n={params.topN} → x={params.holdX} · m={params.mMonths} / y={params.yMonths} ·{" "}
+                {adjustLabel(params.priceAdjustment)} · {params.start} ~ {params.end}
+              
+              {blocking.length ? (
+                
+                  有 {blocking.length} 处参数需要修正
+                
+              ) : (
+                
+                  参数校验通过
+                
+              )}
+            

+          
+        ) : (
+          
+        )}
 
         {running ? (
-          

-            
-                  任务 {jobId || "排队中…"} 后台执行中，请稍候…
-                
-              }
-            />
+          

+            {["data_loading", "selection", "backtesting", "analysis"].map((st) => {
+              const order = ["queued", "data_loading", "selection", "backtesting", "analysis", "done"];
+              const cur = order.indexOf(stage);
+              const mine = order.indexOf(st);
+              const cls = mine < cur ? "stage is-done" : mine === cur ? "stage is-active" : "stage";
+              return (
+                
+                  {mine < cur ? "✓" : mine === cur ? "●" : "○"} {STAGE_LABEL[st] ?? st}
+                
+              );
+            })}
+            
+              作业 {jobId || "排队中…"} · 已用{" "}
+              {Math.round(elapsed / 1000)}s（全市场 8 年区间约 3~5 分钟，可离开本页，结果会归档）
+            
           

         ) : null}
-        {error ? 
{error}
 : null}
       
 
+      
+
       {!result && !running && !error ? (
         
           
         
       ) : null}
 
-      {result ?  : null}
+      {result ?  : null}
     
   );
 }
-
-function ResultView({ result }: { result: BacktestResult }) {
-  const s = result.summary;
-  const last = result.positions.at(-1);
-  const holdings = result.positions.filter((p) => p.date === last?.date);
-
-  return (
-    <>
-      

-        

-          完成
-          Top N 等权
-          {s.start} ~ {s.end}
-        

-        

-          总收益 
-        

-      

-
-      
-
-      

-        期末 {s.final_equity.toLocaleString()}}>
-           d.slice(5)} yFmt={(v) => v.toFixed(2)} />
-        
-        最大 {s.max_drawdown_pct.toFixed(2)}%}>
-           d.slice(5)} yFmt={(v) => v.toFixed(2) + "%"} />
-        
-      

-
-      
-        
-      
-
-      
-        {holdings.length === 0 ? (
-          
-        ) : (
-          

-            {holdings.map((p) => (
-              
-                {p.symbol}
-                {(p.weight * 100).toFixed(0)}%
-              
-            ))}
-          

-        )}
-      
-
-      {result.yearly_returns.length ? (
-        均值 {(result.yearly_returns.reduce((a, y) => a + y.return_pct, 0) / result.yearly_returns.length).toFixed(2)}%}>
-          

-            {result.yearly_returns.map((y) => (
-              
-                {y.year}
-                = 0 ? "tone-pos" : "tone-neg"}>
-                  {y.return_pct.toFixed(2)}%
-                
-              
-            ))}
-          

-        
-      ) : null}
-
-      {result.trades.length ? (
-        {result.turnover_pct.toFixed(2)}% 累计换手}
-        >
-          

-            
-              
-                
-                  
-                  
-                  
-                  
-                  
-                  
-                
-              
-              
-                {result.trades.map((t) => (
-                  
-                    
-                    
-                    
-                    
-                    
-                    
-                  
-                ))}
-              
-            
买入日卖出日代码买价卖价收益
{t.entry_date}{t.exit_date}{t.symbol}{t.entry_price?.toFixed(2) ?? "-"}{t.exit_price?.toFixed(2) ?? "-"}= 0 ? "tone-pos" : "tone-neg"}>
-                      {t.return_pct.toFixed(2)}%
-                    

-          

-        
-      ) : null}
-
-      
-    
-  );
-}
-
-function SignedText({ value }: { value: number }) {
-  const cls = value >= 0 ? "tone-pos" : "tone-neg";
-  return (
-    
-      {value > 0 ? "+" : ""}
-      {value.toFixed(2)}%
-    
-  );
-}
diff --git a/frontend/web/app/experiments/[id]/page.tsx b/frontend/web/app/experiments/[id]/page.tsx
new file mode 100644
index 0000000..647b526
--- /dev/null
+++ b/frontend/web/app/experiments/[id]/page.tsx
@@ -0,0 +1,610 @@
+/**
+ * 归档详情页（`/experiments/{id}`）—— 「往复查看」的落点。
+ *
+ * 为什么单独一页，而不复用回测页：
+ * 回测页是**可编辑参数**的工作台，从它看归档会被误认为「当前参数就是归档参数」；
+ * 归档需要的是一个**只读、口径固定、可分享**的视图：URL 即快照地址，刷新/换设备都能看，
+ * 且必须明确回答两个问题 ——
+ *   ① **选股条件**：这次回测到底按什么规则选股（因子/权重/条件/股票池/两级截断/周期/补位）
+ *   ② **交易执行依据**：成交时点、价格口径、成本、涨跌停与停牌怎么处理、期末是否平仓
+ * 这两块来自归档里存的 **spec（复现依据）**，不来自当前页面状态，因此永不漂移；
+ * 同时用后端 `describe_strategy`（与引擎实执行规则同源）生成完整说明与计算公式。
+ *
+ * 结果区交给 `ArchiveResultView` 按归档 `kind` 分发（backtest/factor_test/selection 结构不同，
+ * 不能假定回测字段）——回测归档与刚跑完时看到的**完全同一套图表与表格**，
+ * 避免「归档里少一张图」的隐性不一致。
+ *
+ * **这里是 Server Component**：归档是只读内容，选股条件/执行依据/元数据必须在
+ * 服务端直出（否则不带 JS 的抓取与链接预览拿到的只是一个 Loading 骨架——
+ * 实测 SSR HTML 里搜不到「选股条件」）。图表与删除/导出等交互由客户端子组件承担。
+ */
+import { notFound } from "next/navigation";
+import Link from "next/link";
+import { ArchiveActions } from "@/components/ArchiveActions";
+import { StrategyDocCard } from "@/components/StrategyDocCard";
+import { ArchiveResultView } from "@/components/ArchiveResultView";
+import { archivedFieldSummary } from "@/lib/archive";
+import { PageHeader, Card, Pill, Empty, Banner } from "@/components/ui";
+import { adjustLabel, experimentKindLabel, rebalanceLabel } from "@/lib/labels";
+import { RichText } from "@/components/RichText";
+import type { ExperimentDetail, FactorMeta, ResearchCondition, ResearchSpec, StrategyDoc } from "@/lib/types";
+
+export const dynamic = "force-dynamic"; // 归档可能被删除/新增，禁止静态化缓存
+
+/** 服务端访问后端：与 next.config.ts 的代理同一默认地址（服务端没有「同源」可用） */
+const BACKEND = (process.env.BACKEND_API_URL ?? "http://127.0.0.1:8000").replace(/\/$/, "");
+
+async function serverGet(path: string): Promise {
+  try {
+    const r = await fetch(`${BACKEND}/api${path}`, { cache: "no-store" });
+    if (!r.ok) return null;
+    return (await r.json()) as T;
+  } catch {
+    return null;
+  }
+}
+
+async function serverPost(path: string, body: unknown): Promise {
+  try {
+    const r = await fetch(`${BACKEND}/api${path}`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+      cache: "no-store",
+    });
+    if (!r.ok) return null;
+    return (await r.json()) as T;
+  } catch {
+    return null;
+  }
+}
+
+export default async function ExperimentArchivePage({
+  params,
+}: {
+  params: Promise<{ id: string }>;
+}) {
+  const { id: rawId } = await params;
+  const id = decodeURIComponent(rawId ?? "");
+
+  const [detail, factors] = await Promise.all([
+    serverGet(`/experiments/${encodeURIComponent(id)}`),
+    serverGet("/factors"),
+  ]);
+  // 归档不存在 → 交给 Next 的 404（比渲染一个空壳页更诚实）
+  if (!detail) notFound();
+
+  const spec = (detail.spec ?? null) as Partial | null;
+  const result = detail.result;
+  // 关键字段计数按 kind 给不同口径（用回测字段去数因子/选股结果是错的）
+  const fieldSummary = archivedFieldSummary(detail.kind, result);
+  // 与引擎执行规则同源的说明（后端按归档 spec 推导；推导失败时如实显示错误）
+  // 只有回测归档才需要「完整说明与计算公式」：describe 推导的是回测策略口径，
+  // 因子测试/选股用同一 spec 结构但规则并未执行，拉回来展示等于误导（且白花一次请求）。
+  const doc = spec && detail.kind === "backtest"
+    ? await serverPost("/strategies/describe", spec)
+    : null;
+  const docBody = doc ? ((doc as { doc?: StrategyDoc }).doc ?? doc) : null;
+
+  return (
+    <>
+      
+
+      
+
+      
+
+      {detail.kind === "backtest" ? (
+        
+      ) : null}
+
+      
+        

+          
+          
+          
+          
+          
+          
+          
+          
+        

+        

+          
+        

+        

+          {fieldSummary ? {fieldSummary} : null}
+        

+      
+
+      {result ? (
+        
+      ) : (
+        
+          
+        
+      )}
+    
+  );
+}
+
+/* ------------------------------------------------------------------ */
+
+function KV({
+  label,
+  value,
+  mono,
+  warn,
+}: {
+  label: string;
+  value: string;
+  mono?: boolean;
+  warn?: boolean;
+}) {
+  return (
+    

+      {label}
+      {value}
+    

+  );
+}
+
+function fmtTime(v?: string | null): string {
+  if (!v) return "—";
+  const d = new Date(v);
+  if (Number.isNaN(d.getTime())) return v;
+  const p = (n: number) => String(n).padStart(2, "0");
+  return `${d.getFullYear()}-${p(d.getMonth() + 1)}-${p(d.getDate())} ${p(d.getHours())}:${p(d.getMinutes())}`;
+}
+
+function directionText(name: string, factors: FactorMeta[]): string {
+  const f = factors.find((x) => x.name === name);
+  if (!f) return "—";
+  return f.direction === "lower_is_better" ? "越低越好" : "越高越好";
+}
+
+function opText(op?: string): string {
+  switch (op) {
+    case "gt":
+      return ">";
+    case "gte":
+      return "≥";
+    case "lt":
+      return "<";
+    case "lte":
+      return "≤";
+    case "eq":
+      return "=";
+    case "ne":
+      return "≠";
+    case "in":
+      return "∈";
+    case "not_in":
+      return "∉";
+    default:
+      return op ?? "?";
+  }
+}
+
+function condText(c: ResearchCondition): string {
+  const right = c.ref ? `（字段 ${c.ref}）` : `${c.value ?? ""}`;
+  return `${c.field} ${opText(c.op)} ${right}`;
+}
+
+/**
+ * 因子测试归档的 spec 卡：只展示**真正生效**的字段。
+ *
+ * 诚实性要点（AGENT §7/§24）：因子测试按横截面算 IC / RankIC / 分层收益，
+ * **不做两级截断、不下单、不计成本**。但归档 spec 沿用了与回测相同的请求结构
+ * （含 `selection` / `costs` / `initial_capital`）—— 把这些字段按回测口径展示，
+ * 会让人以为这次跑过调仓与撮合。所以这里只列生效项，并显式说明哪些字段未生效。
+ */
+function FactorTestSpecCards({
+  spec,
+  factors,
+}: {
+  spec: Partial;
+  factors: FactorMeta[];
+}) {
+  const uni = (spec.universe ?? {}) as Record;
+  const factorList = (spec.factors ?? []) as { name: string; weight: number }[];
+  const period = spec.period ?? [];
+  return (
+    <>
+      来自归档 spec，非当前页面参数}
+      >
+        

+          
+            {factorList.length === 0 ? (
+              
+            ) : (
+              factorList.map((f) => (
+                
+              ))
+            )}
+          
+          
+            
+            
+            
+            
+          
+          
+            
+          
+        

+        

+          
+        

+      
+    
+  );
+}
+
+/**
+ * 选股归档的 spec 卡：一次性选股的规则（无调仓、无成交）。
+ */
+function SelectionSpecCards({
+  spec,
+  factors,
+}: {
+  spec: Partial;
+  factors: FactorMeta[];
+}) {
+  const uni = (spec.universe ?? {}) as Record;
+  const factorList = (spec.factors ?? []) as { name: string; weight: number }[];
+  const conds = ((spec as Record).conditions ?? []) as ResearchCondition[];
+  const method = String((spec as Record).method ?? "score");
+  const topN = (spec as Record).top_n;
+  const topPct = (spec as Record).top_pct;
+  return (
+    来自归档 spec，非当前页面参数}
+    >
+      

+        
+          ).as_of ?? "最近交易日")}
+            mono
+          />
+          
+          
+        
+        
+          {method === "condition"
+            ? conds.length === 0
+              ? 
+              : conds.map((c) => )
+            : factorList.length === 0
+              ? 
+              : factorList.map((f) => (
+                  
+                ))}
+        
+        
+          
+          
+          
+          
+        
+      

+      

+        
+      

+    
+  );
+}
+
+/** 选股条件 + 交易执行依据：直接用归档 spec 渲染，回答「这次回测到底怎么选的、怎么成交的」 */
+function ArchivedSpecCards({
+  detail,
+  spec,
+  factors,
+}: {
+  detail: ExperimentDetail;
+  spec: Partial | null;
+  factors: FactorMeta[];
+}) {
+  if (!spec) {
+    return (
+      
+        

+          该归档没有存储 spec（早于 spec 归档上线），无法回溯当时的规则；只能看下方结果。
+        

+      
+    );
+  }
+
+  // 按归档类型分发：**只有回测**才有「选股条件 + 交易执行依据」这一对。
+  // 因子测试/选股与回测共用同一请求结构（spec 里也有 selection/costs/initial_capital），
+  // 但那些字段在因子测试/选股里**从未生效** —— 沿用回测口径展示等于谎报执行依据。
+  if (detail.kind === "factor_test") {
+    return ;
+  }
+  if (detail.kind === "selection") {
+    return ;
+  }
+  if (detail.kind !== "backtest") {
+    return (
+      
+        

+          
+        

+      
+    );
+  }
+
+  const uni = (spec.universe ?? {}) as Record;
+  const sel = (spec.selection ?? {}) as Record;
+  const costs = (spec.costs ?? {}) as Record;
+  const conds = (spec.conditions ?? []) as ResearchCondition[];
+  const factorList = (spec.factors ?? []) as { name: string; weight: number }[];
+  const fillPolicy =
+    sel.defer_buy === true
+      ? "顺延买入（等它到之后首个不涨停的交易日按收盘价买）"
+      : sel.allow_substitute === false
+        ? "不补位（买不进就空着，实际持仓可能少于目标数）"
+        : "替补买入（从候选池之外按复合分往下找可买标的）";
+
+  return (
+    <>
+      来自归档 spec，非当前页面参数}
+      >
+        

+          
+            
+            
+            
+            
+            
+            
+          
+
+          
+            {factorList.length === 0 ? (
+              
无（纯条件选股）

+            ) : (
+              factorList.map((f) => (
+                
+              ))
+            )}
+            

+              复合分 = Σ(权重 × 方向 × 当日截面 z-score)，降序取候选池。
+            

+          
+
+          
+            {conds.length === 0 ? (
+              
无附加条件

+            ) : (
+              conds.map((c, i) => )
+            )}
+          
+
+          
+            
+            
+            
+            
+            
+            
+          
+        

+      
+
+      
+        

+          
+            
+            
+            
+            
+            
+            
+          
+
+          
+            
+            
+            
+             0
+                  ? `${costs.min_commission} 元/笔（max(按比例, 最低)）`
+                  : "未启用（0）"
+              }
+            />
+            
+          
+
+          
+            
+            
+            
+            
+            
+          
+
+          
+            

+              ① 同样的 spec + 同样的代码版本 + 同样的数据版本才应复现同样结果；
+              ② 复权口径为 {adjustLabel(spec.price_adjustment as string)}，
+              {spec.price_adjustment === "none"
+                ? "现金分红未计入收益、除权日价格下移会记为亏损 —— 股息类策略建议用 hfq 重跑对比。"
+                : "分红按复权因子隐含再投资处理。"}
+              ③ 未建模项（涨跌停开盘路径、停牌明细、流动性冲击等）写在该归档结果的
+               列表里，下方结果区已如实展示。
+            

+          
+        

+        {detail.kind !== "backtest" ? (
+          

+            该归档类型为「{experimentKindLabel(detail.kind)}」，其 spec 中的择股/调仓/成本字段不参与计算。
+          

+        ) : null}
+      
+    
+  );
+}
+
+function SpecBlock({ title, children }: { title: string; children: React.ReactNode }) {
+  return (
+    

+      
{title}

+      {children}
+    

+  );
+}
+
+function SpecRow({ k, v, mono }: { k: string; v: string; mono?: boolean }) {
+  return (
+    

+      {k}
+      
+        
+      
+    

+  );
+}
+
+function pct(v: unknown): string {
+  const n = Number(v);
+  if (!Number.isFinite(n)) return "未记录";
+  return `${(n * 100).toFixed(3).replace(/0+$/, "").replace(/\.$/, "")}%`;
+}
\ No newline at end of file
diff --git a/frontend/web/app/experiments/page.tsx b/frontend/web/app/experiments/page.tsx
index 6dae9db..f3a7c3f 100644
--- a/frontend/web/app/experiments/page.tsx
+++ b/frontend/web/app/experiments/page.tsx
@@ -1,8 +1,24 @@
 "use client";
 
-import { useCallback, useEffect, useState } from "react";
-import { apiGet, apiPost } from "@/lib/api";
-import type { BacktestResult } from "@/lib/types";
+/**
+ * 实验归档 + **对比**（微调闭环的核心）。
+ *
+ * 过去这一页只能「看详情 / 复跑」，微调时只能翻列表比数字。现在：
+ * - 勾选 2~3 个实验 → 净值曲线归一化为「累计收益率 %」叠加（消除初始资金差异），
+ *   因此可以直接看出「改了哪个参数、曲线变好还是变坏」；
+ * - 指标对比表：每个指标一行、每个实验一列，最后一列是与第一个实验的**差值**；
+ * - 参数 diff 表：把 config_snapshot 扁平化后逐项对比，默认只显示「有差异」的项 ——
+ *   微调时一眼看出「这次到底改了什么」。
+ *
+ * 曲线用 TradingView Lightweight Charts（全站统一图表基座）。
+ */
+import { Suspense, useCallback, useEffect, useMemo, useState } from "react";
+import Link from "next/link";
+import { useSearchParams } from "next/navigation";
+import { apiGet, apiGetWithHeaders, apiPost } from "@/lib/api";
+import { experimentKindLabel } from "@/lib/labels";
+import type { BacktestResult, ExperimentDetail, ExperimentMeta } from "@/lib/types";
+import { isBacktestDetail, type BacktestDetail } from "@/lib/archive";
 import {
   PageHeader,
   Card,
@@ -12,70 +28,98 @@ import {
   Empty,
   Metric,
   SkeletonLines,
+  Loading,
 } from "@/components/ui";
 import { Icon } from "@/components/icons";
+import { LwChart, type LwSeries } from "@/components/charts/LwChart";
+import { CHART, seriesColor } from "@/components/charts/theme";
 
-interface ExperimentMeta {
-  id: string;
-  kind: string;
-  factors: string[];
-  period?: [string, string] | null;
-  rebalance?: string;
-  top_n?: number | null;
-  summary_text?: string | null;
-  code_version?: string | null;
-  created_at?: string;
-}
-
-interface ExperimentDetail extends ExperimentMeta {
-  spec: { universe?: Record };
-  result: BacktestResult | null;
-}
-
-const KIND_MAP: Record = {
-  backtest: { label: "回测", tone: "accent" },
-  factor_test: { label: "因子测试", tone: "violet" },
+/**
+ * 类型徽标的**色调**（措辞统一走 `lib/labels.ts::experimentKindLabel`）。
+ *
+ * 之前这里自己维护了一份 `KIND_MAP` 文案，只覆盖 backtest/factor_test，
+ * 于是 `selection` 在表格里显示成英文原值 `selection`，而同一页的筛选下拉写「选股」、
+ * 归档页写「选股」—— 同一口径三种叫法。现在文案只有一份，这里只管颜色。
+ */
+const KIND_TONE: Record = {
+  backtest: "accent",
+  factor_test: "violet",
+  selection: "default",
 };
 
 function kindView(kind: string) {
-  const k = KIND_MAP[kind] ?? { label: kind, tone: "default" as const };
-  return {k.label};
+  return {experimentKindLabel(kind)};
 }
 
 function statusView(status: string) {
   switch (status) {
-    case "success":
-      return (
-        
-          完成
-        
-      );
-    case "running":
-      return (
-        
-          运行中
-        
-      );
     case "queued":
-      return (
-        
-          排队
-        
-      );
+      return 排队中;
+    case "running":
+      return 执行中;
+    case "success":
+      return 完成;
     case "failed":
-      return (
-        
-          失败
-        
-      );
-    case "cancelled":
-      return 已取消;
+      return 失败;
     default:
-      return {status};
+      return {status || "—"};
   }
 }
 
+/** config_snapshot 扁平化：嵌套对象转成 a.b.c 路径，便于逐项 diff */
+function flatten(obj: unknown, prefix = "", out: Record = {}): Record {
+  if (obj === null || obj === undefined) {
+    out[prefix || "—"] = "—";
+    return out;
+  }
+  if (Array.isArray(obj)) {
+    out[prefix] = obj.length ? JSON.stringify(obj) : "[]";
+    return out;
+  }
+  if (typeof obj === "object") {
+    const entries = Object.entries(obj as Record);
+    if (!entries.length) {
+      out[prefix || "—"] = "{}";
+      return out;
+    }
+    for (const [k, v] of entries) flatten(v, prefix ? `${prefix}.${k}` : k, out);
+    return out;
+  }
+  out[prefix || "—"] = String(obj);
+  return out;
+}
+
+/** 数据指纹在列表里只显示关键前缀（完整值放 title，悬停可见） */
+function shortDataVersion(v: string): string {
+  return v.length > 28 ? `${v.slice(0, 28)}…` : v;
+}
+
+const METRICS: { key: keyof BacktestResult["summary"]; label: string; fmt: (v: number) => string; better?: "high" | "low" }[] = [
+  { key: "total_return_pct", label: "区间收益", fmt: (v) => `${v >= 0 ? "+" : ""}${v.toFixed(2)}%`, better: "high" },
+  { key: "annual_return_pct", label: "年化收益", fmt: (v) => `${v >= 0 ? "+" : ""}${v.toFixed(2)}%`, better: "high" },
+  { key: "sharpe", label: "Sharpe", fmt: (v) => v.toFixed(3), better: "high" },
+  { key: "max_drawdown_pct", label: "最大回撤", fmt: (v) => `${v.toFixed(2)}%`, better: "low" },
+  { key: "volatility_pct", label: "年化波动", fmt: (v) => `${v.toFixed(2)}%`, better: "low" },
+  { key: "win_rate_pct", label: "胜率", fmt: (v) => `${v.toFixed(2)}%`, better: "high" },
+  { key: "total_trades", label: "成交笔数", fmt: (v) => String(v) },
+  { key: "avg_turnover_pct", label: "平均换手", fmt: (v) => `${v.toFixed(2)}%` },
+  { key: "final_equity", label: "期末权益", fmt: (v) => v.toLocaleString("zh-CN", { maximumFractionDigits: 0 }) },
+];
+
+/**
+ * Next 15 要求 `useSearchParams` 处于 Suspense 边界内。
+ * `?exp=EXP-x,EXP-y` 用于从其它页面直接带实验进对比（策略库一键回测后「去对比」）。
+ */
 export default function ExperimentsPage() {
+  return (
+    }>
+      
+    
+  );
+}
+
+function ExperimentsInner() {
+  const search = useSearchParams();
   const [exps, setExps] = useState([]);
   const [loading, setLoading] = useState(true);
   const [detail, setDetail] = useState(null);
@@ -84,22 +128,69 @@ export default function ExperimentsPage() {
   const [jobStatus, setJobStatus] = useState("");
   const [error, setError] = useState("");
 
+  // 对比状态
+  const [picked, setPicked] = useState([]);
+  const [compare, setCompare] = useState(null);
+  const [comparing, setComparing] = useState(false);
+  const [onlyDiff, setOnlyDiff] = useState(true);
+
+  // 列表过滤：后端支持 kind/q（并把过滤后总数放在 X-Total-Count 响应头）。
+  // 初始值取自 URL（`?q=`/`?kind=`），且每次改动**回写 URL**：筛选条件可分享、刷新不丢，
+  // 也让「当前看到的是哪一批」有据可查（而不是只存在于组件状态里）。
+  const [q, setQ] = useState(() => search.get("q") ?? "");
+  const [kind, setKind] = useState(() => search.get("kind") ?? "");
+  const [total, setTotal] = useState(null);
+
+  const syncUrl = useCallback(
+    (nextQ: string, nextKind: string) => {
+      const params = new URLSearchParams();
+      if (nextKind) params.set("kind", nextKind);
+      if (nextQ.trim()) params.set("q", nextQ.trim());
+      // 保留已勾选的对比实验，避免筛一下就丢掉选择
+      const exp = search.get("exp");
+      if (exp) params.set("exp", exp);
+      const qs = params.toString();
+      window.history.replaceState(null, "", qs ? `/experiments?${qs}` : "/experiments");
+    },
+    [search]
+  );
+
   const load = useCallback(() => {
-    apiGet("/experiments")
-      .then(setExps)
+    const params = new URLSearchParams();
+    if (kind) params.set("kind", kind);
+    if (q.trim()) params.set("q", q.trim());
+    const qs = params.toString();
+    apiGetWithHeaders(`/experiments${qs ? `?${qs}` : ""}`)
+      .then(({ data, headers }) => {
+        setExps(data);
+        const t = headers.get("X-Total-Count");
+        setTotal(t ? Number(t) : data.length);
+      })
       .catch((e: Error) => setError(e.message))
       .finally(() => setLoading(false));
-  }, []);
+  }, [q, kind]);
 
   useEffect(load, [load]);
 
+  // 从 URL 预勾选要对比的实验（最多 3 个，与手动勾选同一上限）
+  useEffect(() => {
+    const raw = search.get("exp");
+    if (!raw) return;
+    const ids = raw
+      .split(",")
+      .map((x) => x.trim())
+      .filter(Boolean)
+      .slice(0, 3);
+    if (ids.length) setPicked(ids);
+    // 仅在首次进入时读一次，避免用户手动改选后被 URL 覆盖
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, []);
+
   function open(id: string) {
     setDetailLoading(true);
     setDetail(null);
     apiGet(`/experiments/${id}`)
-      .then((d) => {
-        setDetail(d);
-      })
+      .then((d) => setDetail(d))
       .catch((e: Error) => setError(e.message))
       .finally(() => setDetailLoading(false));
   }
@@ -134,31 +225,135 @@ export default function ExperimentsPage() {
     }, 1200);
   }
 
-  const s = detail?.result?.summary;
+  function togglePick(id: string) {
+    setError("");
+    setPicked((prev) => {
+      if (prev.includes(id)) return prev.filter((x) => x !== id);
+      if (prev.length >= 3) {
+        setError("最多同时对比 3 个实验（再多曲线会互相遮挡，也不利于读图）");
+        return prev;
+      }
+      return [...prev, id];
+    });
+  }
+
+  async function runCompare() {
+    setComparing(true);
+    setError("");
+    try {
+      const rows = await Promise.all(
+        picked.map((id) => apiGet(`/experiments/${id}`))
+      );
+      const usable = rows.filter(isBacktestDetail);
+      if (usable.length < 2) {
+        setError("所选实验里可对比的净值曲线不足 2 条（因子测试没有净值曲线）。");
+        setCompare(null);
+      } else {
+        setCompare(usable);
+      }
+    } catch (e) {
+      setError((e as Error).message);
+    } finally {
+      setComparing(false);
+    }
+  }
+
+  const s = detail && isBacktestDetail(detail) ? detail.result.summary : undefined;
 
   return (
     <>
        0 ? 共 {exps.length} 个实验 : undefined}
+        title="实验对比"
+        sub="每次因子测试与回测自动归档（含 Research Spec 与代码版本），保证可复现；勾选 2~3 个即可叠加净值曲线、对比指标与参数差异。"
+        actions={
+          exps.length > 0 ? (
+            
+              共 {exps.length} 个实验
+            
+          ) : undefined
+        }
       />
 
       {error ? {error} : null}
 
+      {picked.length > 0 ? (
+        

+          
+            已选 {picked.length} 个
+          
+          {picked.join(" · ")}
+          
+            对比选中的实验
+          
+           { setPicked([]); setCompare(null); }}>
+            清空
+          
+          {picked.length < 2 ? 再选 1 个即可对比 : null}
+        

+      ) : null}
+
+      {compare ? (
+         setCompare(null)} />
+      ) : null}
+
       {jobId ? (
         
-          

-            任务状态实时轮询；完成后列表自动刷新，可直接展开查看最新结果。
-          

+          
任务状态实时轮询；完成后列表自动刷新，可直接展开查看最新结果。

         
       ) : null}
 
-      
+       exps.length ? "（后端单页上限内未列全，可用搜索或类型筛选缩小范围）" : ""
+              }`
+            : "勾选左侧方框可选 2~3 个做对比"
+        }
+        tools={
+          

+             {
+                setQ(e.target.value);
+                syncUrl(e.target.value, kind);
+              }}
+              aria-label="搜索实验"
+            />
+            
+            {q || kind ? (
+               { setQ(""); setKind(""); syncUrl("", ""); }}>
+                清除
+              
+            ) : null}
+          

+        }
+        flush
+      >
         {loading ? (
           

             
@@ -167,25 +362,35 @@ export default function ExperimentsPage() {
           
         ) : (
           

             
+                  
-                  
-                  
+                  
+                  
+                  
                 {exps.map((e) => (
                   
+                    
-                    
+                    
+                    

               
                 

                   ID
                   类型
                   因子
                   区间
                   摘要代码版本操作版本（代码 / 数据）体积操作
                 

               
               

+                       togglePick(e.id)}
+                        aria-label={`选择 ${e.id} 用于对比`}
+                      />
+                    
                     {e.id}
                     {kindView(e.kind)}
                     
@@ -199,13 +404,43 @@ export default function ExperimentsPage() {
                     
                     {e.period ? `${e.period[0]}~${e.period[1]}` : "—"}
                     {e.summary_text ?? "—"}{e.code_version ? {e.code_version} : —}
+                      
+                        {e.code_version ? (
+                          {e.code_version}
+                        ) : (
+                          —
+                        )}
+                        {e.data_version ? (
+                          
+                            {shortDataVersion(e.data_version)}
+                          
+                        ) : (
+                          无数据版本
+                        )}
+                      
+                    
+                      {e.result_bytes ? `${(e.result_bytes / 1024).toFixed(0)} KB` : "—"}
+                    
                     
                       
+                        
+                          打开归档
+                        
                          open(e.id)}>
                           详情
                         
-                         rerun(e)} disabled={jobStatus === "running" || jobStatus === "queued" || jobStatus === "submitting"}>
+                        
+                          以此参数再跑
+                        
+                         rerun(e)}
+                          disabled={jobStatus === "running" || jobStatus === "queued" || jobStatus === "submitting"}
+                        >
                           复跑
                         
                       
@@ -224,10 +459,15 @@ export default function ExperimentsPage() {
           title={`${detail.id} · ${detail.factors.join("、")}`}
           sub={detail.period ? `${detail.period[0]} ~ ${detail.period[1]}` : undefined}
           tools={
-             setDetail(null)}>
-              
-              关闭
-            
+            

+              
+                打开归档（完整视图）
+              
+               setDetail(null)}>
+                
+                关闭
+              
+            

           }
         >
           {detailLoading ? (
@@ -252,12 +492,36 @@ export default function ExperimentsPage() {
               ) : (
                 
该实验没有可展示的汇总结果（可能是因子测试或历史版本）。

               )}
-              {detail.spec ? (
-                

-                  spec（可复现输入）：{JSON.stringify(detail.spec).slice(0, 500)}
-                  {JSON.stringify(detail.spec).length > 500 ? "…" : ""}
+              {detail && isBacktestDetail(detail) && detail.result.equity_curve.length ? (
+                

+                   `${v.toFixed(2)}%`}
+                    zeroLine
+                    ariaLabel={`${detail.id} 累计收益率曲线`}
+                  />
                 

               ) : null}
+              {detail.spec ? (
+                

+                  
+                    查看 spec（可复现输入）
+                  
+                  
+                    {JSON.stringify(detail.spec, null, 2)}
+                  

+                

+              ) : null}
             
           )}
         
@@ -265,3 +529,198 @@ export default function ExperimentsPage() {
     
   );
 }
+
+/* ------------------------------------------------------------------ */
+
+/** 净值曲线 → 累计收益率 %（以首个点为基准，消除初始资金差异后可直接叠加） */
+function normalize(r: BacktestResult): { time: string; value: number }[] {
+  const base = r.equity_curve[0]?.value || 1;
+  return r.equity_curve.map((p) => ({ time: p.date, value: (p.value / base - 1) * 100 }));
+}
+
+/** 参数签名：一眼看出这一版的关键旋钮（微调时最重要） */
+function sig(r: BacktestResult): string {
+  const c = r.config_snapshot as {
+    selection?: { top_n?: number; hold_top_x?: number | null };
+    selection_interval_months?: number | null;
+    rebalance_interval_months?: number | null;
+    price_adjustment?: string;
+    factors?: { name: string; weight: number }[];
+    costs?: { commission_rate?: number; min_commission?: number };
+    universe?: { exclude_st?: boolean; min_listing_days?: number };
+  };
+  const parts = [
+    (c.factors ?? []).map((f) => f.name).join("+") || "—",
+    `n=${c.selection?.top_n ?? "—"}/x=${c.selection?.hold_top_x ?? "—"}`,
+    `m=${c.selection_interval_months ?? "—"}/y=${c.rebalance_interval_months ?? "—"}`,
+    c.price_adjustment ?? "—",
+    c.universe?.exclude_st ? "剔ST" : "含ST",
+    `佣金${(((c.costs?.commission_rate ?? 0) * 100) as number).toFixed(3)}%`,
+  ];
+  return parts.join(" · ");
+}
+
+function CompareView({
+  rows,
+  onlyDiff,
+  setOnlyDiff,
+  onClose,
+}: {
+  rows: BacktestDetail[];
+  onlyDiff: boolean;
+  setOnlyDiff: (v: boolean) => void;
+  onClose: () => void;
+}) {
+  const series = useMemo(
+    () =>
+      rows.map((r, i) => ({
+        key: r.id,
+        label: `${r.id}（${sig(r.result)}）`,
+        color: seriesColor(i),
+        data: normalize(r.result),
+        type: "line" as const,
+        lineWidth: 2 as const,
+        lastValueVisible: i === 0,
+      })),
+    [rows]
+  );
+
+  // 参数 diff：扁平化后并集，只显示有差异的项（默认）
+  const diffRows = useMemo(() => {
+    const flat = rows.map((r) => flatten(r.result?.config_snapshot ?? {}));
+    const keys = Array.from(new Set(flat.flatMap((f) => Object.keys(f)))).sort();
+    return keys
+      // price_basis.* 是回测运行元数据（数据快照/复权缺口核对），不是用户可调的旋钮：
+      // 混进「参数差异」表只会制造噪声（复权口径本身在顶层 price_adjustment 里已展示）
+      .filter((k) => !k.startsWith("price_basis."))
+      .map((k) => ({ key: k, vals: flat.map((f) => f[k] ?? "—") }))
+      .filter((row) => (onlyDiff ? new Set(row.vals).size > 1 : true));
+  }, [rows, onlyDiff]);
+
+  const base = rows[0].result.summary;
+
+  return (
+     r.id).join("  vs  ")}
+      tools={
+        

+          
+          
+            
+            关闭对比
+          
+        

+      }
+    >
+       `${v.toFixed(2)}%`}
+        zeroLine
+        ariaLabel="实验净值曲线叠加对比"
+      />
+      

+        曲线已归一化为**累计收益率 %**（各自以首个净值为 0%），因此初始资金不同也能直接比。
+        点击图例可临时隐藏某条曲线（只算收益差的实验建议隐藏基准再比）。
+      

+
+      

+        
+          
+            
+              
+              {rows.map((r) => (
+                
+              ))}
+              
+            
+          
+          
+            {METRICS.map((m) => {
+              const vals = rows.map((r) => Number(r.result!.summary[m.key] ?? 0));
+              const delta = vals[vals.length - 1] - vals[0];
+              const good =
+                m.better === undefined
+                  ? null
+                  : m.better === "high"
+                    ? delta > 0
+                    : delta < 0;
+              return (
+                
+                  
+                  {vals.map((v, i) => (
+                    
+                  ))}
+                  
+                
+              );
+            })}
+          
+        
指标{r.id}相对 {rows[0].id} 的差值
{m.label}
+                      {m.fmt(v)}
+                      {i === 0 ?  （基准） : null}
+                    
+                    {m.better === undefined
+                      ? m.fmt(delta)
+                      : `${delta >= 0 ? "+" : ""}${m.fmt(delta)}`}
+                    {good !== null && delta !== 0 ? (good ? " ✅" : " ⚠️") : ""}
+                  

+      

+      

+        差额列 = 最后一个实验 − 基准（{rows[0].id}）。✅ 表示该指标改善方向，⚠️ 表示变差
+        （收益/夏普/胜率越高越好，回撤/波动越低越好）。基准区间收益{" "}
+        {base.total_return_pct.toFixed(2)}%。
+      

+
+      

+        参数差异（config_snapshot 逐项对比）
+        {diffRows.length === 0 ? (
+          

+            这几个实验的参数快照完全一致（说明结果是**同一配置**下的数据变动，或只是复跑）。
+            已排除 price_basis.* 运行元数据。
+          

+        ) : (
+          

+            
+              
+                
+                  
+                  {rows.map((r) => (
+                    
+                  ))}
+                
+              
+              
+                {diffRows.map((row) => (
+                  
+                    
+                    {row.vals.map((v, i) => (
+                      
+                    ))}
+                  
+                ))}
+              
+            
参数{r.id}
{row.key} 1 && v !== row.vals[0] ? "cmp-diff-add num" : "num"
+                        }
+                      >
+                        {v}
+                      

+          

+        )}
+        {onlyDiff ? (
+          

+            已隐藏所有实验取值相同的参数（取消上方勾选可看全量）。
+          

+        ) : null}
+      

+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/app/factors/compose/page.tsx b/frontend/web/app/factors/compose/page.tsx
index 91cf924..4fcc382 100644
--- a/frontend/web/app/factors/compose/page.tsx
+++ b/frontend/web/app/factors/compose/page.tsx
@@ -18,7 +18,8 @@ import {
   MonthlyReturnsTable,
   UnimplementedNote,
 } from "@/components/ui";
-import { LineChart } from "@/components/LineChart";
+import { LwChart, type LwSeries } from "@/components/charts/LwChart";
+import { CHART, fmtNum, fmtPct } from "@/components/charts/theme";
 import { Icon } from "@/components/icons";
 
 interface Pick {
@@ -291,6 +292,27 @@ function ResultView({
   params: { picks: Pick[]; topN: number; rebalance: string; excludeSt: boolean; start: string; end: string };
 }) {
   const s = result.summary;
+
+  // 曲线数据点：LwChart 的 time 用 "YYYY-MM-DD" 字符串，向后端 CurvePoint 的 date 直接映射
+  const equitySeries: LwSeries[] = [
+    {
+      key: "equity",
+      label: "净值",
+      type: "area",
+      color: CHART.pos,
+      data: result.equity_curve.map((p) => ({ time: p.date, value: p.value })),
+    },
+  ];
+  const drawdownSeries: LwSeries[] = [
+    {
+      key: "drawdown",
+      label: "回撤",
+      type: "line",
+      color: CHART.neg,
+      data: result.drawdown.map((p) => ({ time: p.date, value: p.value })),
+    },
+  ];
+
   return (
     <>
       

@@ -312,10 +334,23 @@ function ResultView({
 
       

         期末 {s.final_equity.toLocaleString()}}>
-           d.slice(5)} yFmt={(v) => v.toFixed(2)} />
+           fmtNum(v, 2)}
+            ariaLabel="净值曲线"
+            emptyHint="该区间没有净值数据"
+          />
         
         最大 {s.max_drawdown_pct.toFixed(2)}%}>
-           d.slice(5)} yFmt={(v) => v.toFixed(2) + "%"} />
+           fmtPct(v, 2)}
+            ariaLabel="回撤曲线"
+            emptyHint="该区间没有回撤数据"
+          />
         
       

 
diff --git a/frontend/web/app/globals.css b/frontend/web/app/globals.css
index e7b9973..6942d6f 100644
--- a/frontend/web/app/globals.css
+++ b/frontend/web/app/globals.css
@@ -1290,3 +1290,518 @@ table.tbl {
     flex-direction: column;
   }
 }
+
+/* ============================================================
+   图表（TradingView Lightweight Charts）—— 图例 / tooltip / 容器
+   ============================================================ */
+
+.lw-wrap {
+  display: flex;
+  flex-direction: column;
+  gap: var(--sp-2);
+  min-width: 0;
+}
+
+.lw-legend {
+  display: flex;
+  flex-wrap: wrap;
+  gap: var(--sp-1) var(--sp-2);
+  align-items: center;
+}
+
+.lw-legend-item {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 3px 8px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: var(--surface-2);
+  color: var(--text-2);
+  font-size: var(--fs-xs);
+  cursor: pointer;
+  transition: opacity 0.12s ease, border-color 0.12s ease, background 0.12s ease;
+}
+
+.lw-legend-item:hover {
+  border-color: var(--line-strong);
+  background: var(--surface-3);
+}
+
+.lw-legend-item:focus-visible {
+  outline: 2px solid var(--focus);
+  outline-offset: 1px;
+}
+
+.lw-legend-item i {
+  width: 9px;
+  height: 9px;
+  border-radius: 2px;
+  flex: 0 0 auto;
+}
+
+.lw-legend-item b {
+  color: var(--text-1);
+  font-weight: 600;
+}
+
+.lw-legend-item.is-off {
+  opacity: 0.42;
+  text-decoration: line-through;
+}
+
+.lw-canvas {
+  position: relative;
+  width: 100%;
+  min-width: 0;
+}
+
+/* tooltip：LW 无内置实现，由 LwChart 订阅十字光标渲染 */
+.lw-tip {
+  position: absolute;
+  z-index: 5;
+  pointer-events: none;
+  min-width: 132px;
+  padding: 7px 9px;
+  border: 1px solid rgba(150, 165, 195, 0.28);
+  border-radius: var(--r-sm);
+  background: rgba(20, 29, 48, 0.96);
+  box-shadow: 0 8px 24px rgba(0, 0, 0, 0.4);
+  font-size: var(--fs-xs);
+}
+
+.lw-tip-time {
+  color: var(--text-3);
+  margin-bottom: 4px;
+}
+
+.lw-tip-row {
+  display: flex;
+  align-items: center;
+  gap: 6px;
+  line-height: 1.6;
+}
+
+.lw-tip-row i {
+  width: 8px;
+  height: 8px;
+  border-radius: 2px;
+  flex: 0 0 auto;
+}
+
+.lw-tip-label {
+  color: var(--text-2);
+  flex: 1 1 auto;
+  white-space: nowrap;
+}
+
+.lw-tip-row b {
+  color: var(--text-1);
+}
+
+/* ============================================================
+   股票代码 + 名称（任何出现代码的地方都必须成对出现且可点击）
+   ============================================================ */
+
+.symlink {
+  display: inline-flex;
+  align-items: baseline;
+  gap: 6px;
+  max-width: 100%;
+  padding: 1px 3px;
+  margin: -1px -3px;
+  border-radius: var(--r-xs);
+  color: inherit;
+  text-decoration: none;
+  transition: background 0.12s ease;
+}
+
+.symlink:hover {
+  background: var(--accent-soft);
+  text-decoration: none;
+}
+
+.symlink:focus-visible {
+  outline: 2px solid var(--focus);
+  outline-offset: 1px;
+}
+
+.symlink .sym-code {
+  font-family: var(--font-mono);
+  font-size: 0.92em;
+  color: var(--text-2);
+  white-space: nowrap;
+}
+
+.symlink:hover .sym-code {
+  color: var(--accent);
+}
+
+.symlink .sym-name {
+  color: var(--text-1);
+  font-weight: 600;
+  white-space: nowrap;
+  overflow: hidden;
+  text-overflow: ellipsis;
+  max-width: 9em;
+}
+
+.symlink .sym-name--missing {
+  color: var(--text-3);
+  font-weight: 400;
+}
+
+/* 名称列优先：窄屏时允许名称换行而不是把代码挤掉 */
+.sym-cell {
+  min-width: 132px;
+}
+
+/* ============================================================
+   策略说明与计算公式
+   ============================================================ */
+
+.doc-block {
+  display: flex;
+  flex-direction: column;
+  gap: var(--sp-2);
+}
+
+.doc-summary {
+  display: flex;
+  align-items: flex-start;
+  gap: var(--sp-2);
+  padding: 10px 12px;
+  border: 1px solid var(--line);
+  border-left: 3px solid var(--accent);
+  border-radius: var(--r-sm);
+  background: linear-gradient(90deg, rgba(63, 182, 255, 0.08), transparent 70%);
+  color: var(--text-1);
+  line-height: 1.65;
+}
+
+.doc-formula {
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+}
+
+.doc-formula pre {
+  margin: 0;
+  padding: 10px 12px;
+  border: 1px solid var(--line);
+  border-radius: var(--r-sm);
+  background: var(--bg-1);
+  color: var(--text-1);
+  font-family: var(--font-mono);
+  font-size: var(--fs-xs);
+  line-height: 1.75;
+  overflow-x: auto;
+  white-space: pre;
+}
+
+.doc-steps {
+  margin: 0;
+  padding-left: 18px;
+  color: var(--text-2);
+  font-size: var(--fs-sm);
+  line-height: 1.8;
+}
+
+.doc-steps li b {
+  color: var(--text-1);
+}
+
+/* 策略卡片 */
+.strategy-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(360px, 1fr));
+  gap: var(--sp-4);
+}
+
+.strategy-desc {
+  color: var(--text-2);
+  line-height: 1.7;
+  font-size: var(--fs-sm);
+}
+
+.strategy-actions {
+  display: flex;
+  flex-wrap: wrap;
+  gap: var(--sp-2);
+}
+
+/* ============================================================
+   实验对比
+   ============================================================ */
+
+.cmp-toolbar {
+  display: flex;
+  flex-wrap: wrap;
+  align-items: center;
+  gap: var(--sp-2);
+}
+
+.cmp-diff-add {
+  color: var(--pos);
+}
+.cmp-diff-del {
+  color: var(--neg);
+}
+.cmp-diff-same {
+  color: var(--text-3);
+}
+
+.cmp-badge {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  font-size: var(--fs-xs);
+  color: var(--text-3);
+}
+
+/* ============================================================
+   易用性：粘性操作条 / 结果锚点 / 键盘提示
+   ============================================================ */
+
+.sticky-bar {
+  position: sticky;
+  top: 0;
+  z-index: 20;
+  display: flex;
+  flex-wrap: wrap;
+  align-items: center;
+  gap: var(--sp-3);
+  padding: 10px 12px;
+  margin-bottom: var(--sp-3);
+  border: 1px solid var(--line);
+  border-radius: var(--r-md);
+  background: rgba(10, 15, 28, 0.92);
+  backdrop-filter: blur(8px);
+}
+
+.kbd {
+  display: inline-block;
+  padding: 1px 5px;
+  border: 1px solid var(--line-strong);
+  border-bottom-width: 2px;
+  border-radius: 4px;
+  background: var(--surface-2);
+  color: var(--text-2);
+  font-family: var(--font-mono);
+  font-size: 11px;
+  line-height: 1.5;
+}
+
+/* 表单校验：错误态 + 行内提示 */
+.field--invalid input,
+.field--invalid select {
+  border-color: var(--neg);
+}
+
+.field-err {
+  color: var(--neg);
+  font-size: var(--fs-xs);
+  margin-top: 4px;
+}
+
+/* 可点击行（表格） */
+.tbl tbody tr.row-clickable {
+  cursor: pointer;
+}
+
+/* 进度阶段列表 */
+.stages {
+  display: flex;
+  flex-wrap: wrap;
+  gap: var(--sp-2);
+  align-items: center;
+}
+
+.stage {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  padding: 3px 9px;
+  border: 1px solid var(--line);
+  border-radius: 999px;
+  background: var(--surface-2);
+  color: var(--text-3);
+  font-size: var(--fs-xs);
+}
+
+.stage.is-done {
+  color: var(--pos);
+  border-color: rgba(61, 220, 151, 0.35);
+}
+
+.stage.is-active {
+  color: var(--accent);
+  border-color: rgba(63, 182, 255, 0.45);
+  background: var(--accent-soft);
+}
+
+/* 面包屑 / 返回 */
+.crumb {
+  display: inline-flex;
+  align-items: center;
+  gap: 6px;
+  color: var(--text-3);
+  font-size: var(--fs-sm);
+}
+
+.crumb a {
+  color: var(--text-2);
+}
+
+.crumb a:hover {
+  color: var(--accent);
+}
+
+/* ---------- 补充：既有页面已在用但此前没有样式规则的类 ---------- */
+
+/* .dim：次要信息（日期/计数），此前无规则导致与正文同色，层级不清 */
+.dim {
+  color: var(--text-3);
+}
+
+/* .input--invalid：单个输入框的错误态（.field--invalid 是整字段态） */
+.input--invalid,
+.input--invalid:focus {
+  border-color: var(--neg);
+  box-shadow: 0 0 0 3px var(--neg-soft);
+}
+
+/* ---------- 单选组（买不进补位策略：三选一） ---------- */
+.radio-col {
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+}
+.radio-row {
+  display: flex;
+  align-items: flex-start;
+  gap: 7px;
+  font-size: 13px;
+  color: var(--text-2);
+  cursor: pointer;
+  line-height: 1.45;
+}
+.radio-row input {
+  margin-top: 2px;
+  flex: none;
+}
+.radio-row:hover {
+  color: var(--text-1);
+}
+
+/* ---------- 归档详情：元数据条 / KV / 规格表 / 代码块 ---------- */
+
+/* 归档元数据与完整度提示条：信息密度高但不抢图表注意力 */
+.archive-bar {
+  display: flex;
+  align-items: center;
+  gap: 10px;
+  flex-wrap: wrap;
+  padding: 9px 12px;
+  margin: 0 0 10px;
+  border: 1px solid var(--line);
+  border-radius: var(--r-sm);
+  background: linear-gradient(180deg, var(--surface-2), var(--surface-1));
+}
+
+.kv-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fill, minmax(260px, 1fr));
+  gap: 8px 18px;
+}
+
+.kv {
+  display: flex;
+  align-items: baseline;
+  gap: 8px;
+  font-size: 13px;
+  min-width: 0;
+}
+
+.kv__k {
+  flex: none;
+  color: var(--text-3);
+}
+
+.kv__v {
+  color: var(--text-1);
+  word-break: break-all;
+}
+
+.kv__v--warn {
+  color: var(--warn);
+}
+
+/* 规格表：选股条件 / 交易执行依据 */
+.spec-grid {
+  display: grid;
+  grid-template-columns: repeat(auto-fit, minmax(320px, 1fr));
+  gap: 14px;
+}
+
+.spec-block {
+  border: 1px solid var(--line);
+  border-radius: var(--r-sm);
+  padding: 10px 12px;
+  background: var(--surface-1);
+}
+
+.spec-block__title {
+  font-size: 12px;
+  font-weight: 600;
+  letter-spacing: 0.04em;
+  color: var(--text-2);
+  margin-bottom: 8px;
+  padding-bottom: 6px;
+  border-bottom: 1px dashed var(--line);
+}
+
+.spec-row {
+  display: flex;
+  gap: 10px;
+  align-items: baseline;
+  font-size: 13px;
+  padding: 3px 0;
+  line-height: 1.5;
+}
+
+.spec-row__k {
+  flex: none;
+  min-width: 96px;
+  color: var(--text-3);
+}
+
+.spec-row__v {
+  color: var(--text-1);
+  min-width: 0;
+  word-break: break-word;
+}
+
+.codeblock {
+  max-height: 420px;
+  overflow: auto;
+  font-size: 12px;
+  line-height: 1.55;
+  padding: 10px 12px;
+  border: 1px solid var(--line);
+  border-radius: var(--r-sm);
+  background: var(--bg-1);
+  white-space: pre-wrap;
+  word-break: break-word;
+}
+
+/* 行内代码：后端说明文本用 `…` 标记字段/命令名 */
+.inline-code {
+  font-family: var(--font-mono, ui-monospace, SFMono-Regular, Menlo, monospace);
+  font-size: 0.92em;
+  padding: 1px 5px;
+  border: 1px solid var(--line);
+  border-radius: var(--r-xs);
+  background: var(--bg-2);
+  color: var(--text-1);
+}
diff --git a/frontend/web/app/layout.tsx b/frontend/web/app/layout.tsx
index d8ce566..8ec4420 100644
--- a/frontend/web/app/layout.tsx
+++ b/frontend/web/app/layout.tsx
@@ -1,5 +1,6 @@
 import type { Metadata, Viewport } from "next";
 import { AppShell } from "@/components/app-shell";
+import { SymbolNamesProvider } from "@/lib/symbols";
 import "./globals.css";
 
 export const metadata: Metadata = {
@@ -15,7 +16,10 @@ export default function RootLayout({ children }: { children: React.ReactNode })
   return (
     
       
-        {children}
+        {/* 全站股票名称缓存：任何页面渲染代码时都能就地拿到名称 */}
+        
+          {children}
+        
       
     
   );
diff --git a/frontend/web/app/page.tsx b/frontend/web/app/page.tsx
index 0004b85..370262b 100644
--- a/frontend/web/app/page.tsx
+++ b/frontend/web/app/page.tsx
@@ -14,7 +14,44 @@ interface LoadState {
   error: string;
 }
 
+/**
+ * 快速入口按**研究闭环的真实顺序**排列：
+ * 出候选（筛选）→ 定规则（策略库）→ 验规则（回测）→ 复盘（实验对比）。
+ * 顺序即引导：新用户按从左到右走一遍就是完整流程。
+ *
+ * ④ 之后是**归档回看**：每次回测（含同步接口调用）都会自动写一份完整归档，
+ * 入口在实验列表的「打开归档」、以及回测跑完后的提示条里；归档页是只读快照，
+ * 明确写出这次的**选股条件**与**交易执行依据**。
+ */
 const LINKS = [
+  {
+    href: "/selection",
+    title: "① 股票筛选",
+    desc: "因子评分 TopN 或条件选股，支持当前/历史时点，结果可解释、可查历史",
+    icon: "target" as const,
+    tone: "quick-card--violet",
+  },
+  {
+    href: "/strategies",
+    title: "② 策略库",
+    desc: "把选股规则存成命名策略：一句话说明 + 计算公式，可一键回测、可复用对比",
+    icon: "book" as const,
+    tone: "quick-card--pos",
+  },
+  {
+    href: "/backtest",
+    title: "③ 选股回测",
+    desc: "两级截断（候选池 n → 持仓 x）+ 择股/调仓双周期（m/y），买卖点标注在曲线上",
+    icon: "gauge" as const,
+    tone: "quick-card--warn",
+  },
+  {
+    href: "/experiments",
+    title: "④ 实验对比与归档",
+    desc: "勾选 2~3 次回测做对比；每次回测都已完整存档，点「打开归档」可随时回看选股条件与交易执行依据",
+    icon: "archive" as const,
+    tone: "quick-card--violet",
+  },
   {
     href: "/factors",
     title: "因子研究",
@@ -29,20 +66,6 @@ const LINKS = [
     icon: "layers" as const,
     tone: "quick-card--pos",
   },
-  {
-    href: "/backtest",
-    title: "选股回测",
-    desc: "单因子完整回测：净值 / 回撤 / 月度收益 / 持仓",
-    icon: "gauge" as const,
-    tone: "quick-card--warn",
-  },
-  {
-    href: "/selection",
-    title: "股票筛选",
-    desc: "因子评分 TopN 或条件选股，支持当前/历史时点，结果可解释",
-    icon: "target" as const,
-    tone: "quick-card--violet",
-  },
   {
     href: "/signals",
     title: "交易信号",
@@ -124,7 +147,7 @@ export default function DashboardPage() {
         

       )}
 
-      
+      
         

           {LINKS.map((l) => (
             
diff --git a/frontend/web/app/selection/page.tsx b/frontend/web/app/selection/page.tsx
index bfd5190..1ea29e4 100644
--- a/frontend/web/app/selection/page.tsx
+++ b/frontend/web/app/selection/page.tsx
@@ -4,8 +4,10 @@
  * 评分模式（多因子加权 TopN）与条件模式（结构化条件 AND）；
  * 支持当前/历史 as_of；结果落库可查（selection_id），右侧展示最近选股历史。
  */
+import Link from "next/link";
 import { useEffect, useState } from "react";
 import { apiGet, apiPost } from "@/lib/api";
+import { SymbolLink } from "@/lib/symbols";
 import { waitJob } from "@/lib/jobs";
 import type {
   FactorMeta,
@@ -39,6 +41,22 @@ const FIELD_OPTIONS: { value: string; label: string; kind: "num" | "str" | "ref"
 ];
 const OPS = ["gt", "gte", "lt", "lte", "eq", "ne", "in"];
 
+/**
+ * 读取后端 payload 里的 name（若存在）。
+ *
+ * 为什么用宽松读取而非直接 `c.name`：`lib/types.ts` 的 SelectionCandidate 目前没有
+ * 声明 name 字段（该文件由其他改动维护），直接访问会类型报错；但后端可能已经返回更权威的
+ * 中文名。这里只做「有且非空则用」的读取，取不到就交给 SymbolLink 渲染占位「—」，
+ * 绝不编造名称（AGENT §24）。
+ */
+function payloadName(row: unknown): string | undefined {
+  if (row && typeof row === "object" && "name" in row) {
+    const n = (row as { name?: unknown }).name;
+    if (typeof n === "string" && n.trim()) return n;
+  }
+  return undefined;
+}
+
 function emptyCondition(): SelectionCondition {
   return { field: "static.industry", op: "eq", value: "白酒" };
 }
@@ -53,6 +71,14 @@ export default function SelectionPage() {
   const [conds, setConds] = useState([emptyCondition()]);
   const [result, setResult] = useState(null);
   const [selectionId, setSelectionId] = useState("");
+  /**
+   * 已落库的 selection_id —— 只有「同步选股(SelectionRun.selection_id)」和「从历史列表打开(id)」两条路径才有。
+   *
+   * 为什么不能直接用 selectionId：异步路径（POST /selections/jobs）拿到的是 JOB id，
+   * 后端不会为它写 selection 记录（backend/app/api/selections.py 只在同步接口里 selection_repo.save），
+   * 拿它拼 /backtest?from_selection= 会让接收端 GET /api/selections/{id} 404 → 坏链接。
+   */
+  const [savedSelectionId, setSavedSelectionId] = useState("");
   const [running, setRunning] = useState(false);
   const [asyncBusy, setAsyncBusy] = useState(false);
   const [error, setError] = useState("");
@@ -101,9 +127,12 @@ export default function SelectionPage() {
     setRunning(true);
     setError("");
     setResult(null);
+    // 新一轮结果尚未落库/尚未返回：先清掉旧的 selection_id，避免「结果 A + 直通到记录 B」的错配链接
+    setSavedSelectionId("");
     try {
       const run = await apiPost("/selections", buildQuery());
       setSelectionId(run.selection_id);
+      setSavedSelectionId(run.selection_id);
       setResult(run.result);
       apiGet("/selections?limit=8").then(setHistory).catch(() => undefined);
     } catch (e) {
@@ -118,6 +147,8 @@ export default function SelectionPage() {
     setAsyncBusy(true);
     setError("");
     setResult(null);
+    // 同上：异步任务不落库选股记录，直通回测必须隐藏按钮（不给坏链接）
+    setSavedSelectionId("");
     try {
       const { job_id } = await apiPost<{ job_id: string }>("/selections/jobs", buildQuery());
       const out = await waitJob(job_id);
@@ -140,6 +171,7 @@ export default function SelectionPage() {
     try {
       const detail = await apiGet(`/selections/${id}`);
       setSelectionId(id);
+      setSavedSelectionId(id);
       setResult(detail);
     } catch (e) {
       setError((e as Error).message);
@@ -319,7 +351,7 @@ export default function SelectionPage() {
 
       {!result && !running && history === null ?  : null}
 
-      {result ?  : null}
+      {result ?  : null}
 
       {history && history.length > 0 ? (
         
@@ -343,7 +375,7 @@ export default function SelectionPage() {
   );
 }
 
-function ResultView({ result }: { result: SelectionResult }) {
+function ResultView({ result, selectionId }: { result: SelectionResult; selectionId: string }) {
   const s = result.statistics;
   if (result.candidates.length === 0) {
     return (
@@ -365,15 +397,30 @@ function ResultView({ result }: { result: SelectionResult }) {
           范围 {s.universe_size}
           评分 {s.evaluated}
           选出 {s.selected}
+          {/* 选股 → 回测直通：只带「已落库」的 selection_id；没有它（如异步任务未落库）就不渲染按钮，避免坏链接 */}
+          {selectionId ? (
+            
+              按此条件回测
+            
+          ) : null}
         
       }
     >
+      {selectionId ? (
+        

+          按此条件回测：把这次的规则（条件 / 因子 / TopN / 口径）预填到回测页；回测会在每个择股日按同一规则重新选股，不是固定持有这批股票。
+        

+      ) : null}
       

         
-              
+              
@@ -383,8 +430,9 @@ function ResultView({ result }: { result: SelectionResult }) {
             {result.candidates.map((c) => (
               
-                
-                
+                
@@ -179,7 +195,7 @@ function ResultView({ result }: { result: SignalResult }) {
                       {e.signal_type}
                     
-                  
+                  

           
             

               #代码代码 / 名称
               得分
               因子值
               入选理由

                 {c.rank}
-                  {c.symbol}
+                
+                  {/* 后端 payload 若已带 name 就用它（更权威），否则回落到全局名称表 */}
+                  
                 
                 {c.score.toFixed(4)}
                 
diff --git a/frontend/web/app/signals/page.tsx b/frontend/web/app/signals/page.tsx
index 960e9ed..630439d 100644
--- a/frontend/web/app/signals/page.tsx
+++ b/frontend/web/app/signals/page.tsx
@@ -5,6 +5,7 @@
  */
 import { useEffect, useState } from "react";
 import { apiGet, apiPost } from "@/lib/api";
+import { SymbolLink } from "@/lib/symbols";
 import type {
   FactorMeta,
   SelectionQuery,
@@ -23,6 +24,21 @@ import {
   SkeletonLines,
 } from "@/components/ui";
 
+/**
+ * 读取后端 payload 里的 name（若存在）。
+ *
+ * 为什么用宽松读取而非直接 `e.name`：`lib/types.ts` 的 SignalEvent 目前没有声明 name
+ * 字段（该文件由其他改动维护）；后端可能已返回更权威的中文名。取不到就交给 SymbolLink
+ * 渲染占位「—」，不编造名称（AGENT §24）。
+ */
+function payloadName(row: unknown): string | undefined {
+  if (row && typeof row === "object" && "name" in row) {
+    const n = (row as { name?: unknown }).name;
+    if (typeof n === "string" && n.trim()) return n;
+  }
+  return undefined;
+}
+
 export default function SignalsPage() {
   const [factors, setFactors] = useState([]);
   const [factor, setFactor] = useState("momentum_60");
@@ -165,7 +181,7 @@ function ResultView({ result }: { result: SignalResult }) {
             

               

                 类型代码代码 / 名称
                 得分
                 价格
                 触发理由
                   {e.symbol}
                   {e.score?.toFixed(4)}
                   {e.price ?? "-"}
                   
diff --git a/frontend/web/app/stocks/[symbol]/page.tsx b/frontend/web/app/stocks/[symbol]/page.tsx
index 4109a61..3444178 100644
--- a/frontend/web/app/stocks/[symbol]/page.tsx
+++ b/frontend/web/app/stocks/[symbol]/page.tsx
@@ -8,6 +8,7 @@
 import { useEffect, useMemo, useState } from "react";
 import { useParams } from "next/navigation";
 import { apiGet } from "@/lib/api";
+import { SymbolLink } from "@/lib/symbols";
 import type { ChartMarker, ChartResult, Stock } from "@/lib/types";
 import {
   PageHeader,
@@ -19,7 +20,7 @@ import {
   SkeletonLines,
 } from "@/components/ui";
 import type { CandleDatum } from "@/components/StockChart/chartTypes";
-import { StockChart, type ChartLibrary } from "@/components/StockChart/StockChart";
+import { StockChart } from "@/components/StockChart/StockChart";
 
 export default function StockResearchPage() {
   const params = useParams<{ symbol: string }>();
@@ -29,7 +30,6 @@ export default function StockResearchPage() {
   const [signalMarkers, setSignalMarkers] = useState([]);
   const [selectionMarkers, setSelectionMarkers] = useState([]);
   const [adjust, setAdjust] = useState<"none" | "qfq" | "hfq">("none");
-  const [chartLib, setChartLib] = useState("lightweight");
   const [loading, setLoading] = useState(true);
   const [error, setError] = useState("");
 
@@ -77,16 +77,11 @@ export default function StockResearchPage() {
         title={stock ? `${stock.name} ${stock.symbol}` : symbol}
         sub={
           stock
-            ? `行业 ${stock.industry ?? "-"} · 市场 ${stock.market ?? "-"} · 上市 ${stock.list_date} · 状态 ${stock.status}`
+            ? `名称 ${stock.name} · 行业 ${stock.industry ?? "-"} · 市场 ${stock.market ?? "-"} · 上市 ${stock.list_date} · 状态 ${stock.status}`
             : "个股研究视图（K 线 / 信号 / 成交）"
         }
         actions={
           

-            {(["echarts", "lightweight"] as const).map((l) => (
-               setChartLib(l)}>
-                {l === "echarts" ? "ECharts" : "Lightweight"}
-              
-            ))}
             {(["none", "qfq", "hfq"] as const).map((m) => (
               
           
                 共 {chart?.metadata.bar_count ?? 0} 根
@@ -121,13 +116,12 @@ export default function StockResearchPage() {
               
             ) : (
               
             )}
           
@@ -135,7 +129,12 @@ export default function StockResearchPage() {
           {chart && (chart.signals.length || chart.fills.length) ? (
             
+                  回测成交与信号 ·{" "}
+                  
+                
+              }
               tools={
                 <>
                   成交 {chart.fills.length}
diff --git a/frontend/web/app/stocks/page.tsx b/frontend/web/app/stocks/page.tsx
index 24ce5f7..b2b0a14 100644
--- a/frontend/web/app/stocks/page.tsx
+++ b/frontend/web/app/stocks/page.tsx
@@ -1,8 +1,8 @@
 "use client";
 
-import Link from "next/link";
 import { useCallback, useEffect, useRef, useState } from "react";
 import { apiGet } from "@/lib/api";
+import { SymbolLink } from "@/lib/symbols";
 import type { Stock } from "@/lib/types";
 import {
   PageHeader,
@@ -118,8 +118,7 @@ export default function StocksPage() {
           
-                
-                
+                
@@ -130,8 +129,8 @@ export default function StocksPage() {
             
               {stocks.map((s) => (
                 
-                  
-                  
+                  {/* 代码列直接合并名称（后端 payload 的 name 更权威），避免与独立名称列重复展示 */}
+                  
diff --git a/frontend/web/app/strategies/page.tsx b/frontend/web/app/strategies/page.tsx
new file mode 100644
index 0000000..f28fd7b
--- /dev/null
+++ b/frontend/web/app/strategies/page.tsx
@@ -0,0 +1,518 @@
+"use client";
+
+/**
+ * 策略库（/strategies）—— 策略的命名资产中心。
+ *
+ * 职责：
+ * - 列表 + 检索：每个策略显示**一句话说明**与**计算公式**（后端 describe_strategy 推导）。
+ * - 新建 / 编辑：复用 StrategyParamsForm（与回测页同一份参数模型与校验）。
+ * - 一键回测：`POST /strategies/{id}/expand`（补 period + 资金）→ `POST /jobs` → 轮询 →
+ *   就地展示核心指标，并提供「查看实验详情 / 回到回测页看曲线」。
+ * - 去回测页：`/backtest?strategy={id}`（回测页载入后可保存为策略、可微调）。
+ *
+ * 设计取舍：编辑不弹窗而是就地展开表单（页面滚动位置不丢，参数多，弹窗太挤）。
+ */
+import Link from "next/link";
+import { useCallback, useEffect, useMemo, useState } from "react";
+import { apiDelete, apiGet, apiPost, apiPut } from "@/lib/api";
+import { STAGE_LABEL, submitJob, waitJob, type JobOutcome } from "@/lib/jobs";
+import { recentRange } from "@/lib/dates";
+import type { BacktestResult, FactorMeta, StrategyDefinition } from "@/lib/types";
+import {
+  Btn,
+  Banner,
+  Card,
+  Empty,
+  Loading,
+  PageHeader,
+  Pill,
+  Progress,
+  BacktestMetrics,
+  Field,
+} from "@/components/ui";
+import {
+  StrategyParamsForm,
+  casePreset,
+  emptyParams,
+  paramsFromStrategy,
+  strategyFromParams,
+  validateParams,
+  type StrategyParams,
+} from "@/components/StrategyParamsForm";
+import { StrategyDocBody, StrategyDocCard } from "@/components/StrategyDocCard";
+import { useStrategyDoc, useStrategyDocById } from "@/lib/strategy";
+
+export default function StrategiesPage() {
+  const [list, setList] = useState(null);
+  const [factors, setFactors] = useState([]);
+  const [query, setQuery] = useState("");
+  const [err, setErr] = useState("");
+  const [msg, setMsg] = useState("");
+  const [editing, setEditing] = useState(null);
+  const [editingId, setEditingId] = useState(null);
+  const [busy, setBusy] = useState(false);
+  const [docId, setDocId] = useState(null);
+
+  const range = useMemo(() => recentRange(), []);
+
+  const load = useCallback(async () => {
+    try {
+      const rows = await apiGet("/strategies");
+      setList(rows);
+      setErr("");
+    } catch (e) {
+      setErr((e as Error).message);
+      setList([]);
+    }
+  }, []);
+
+  useEffect(() => {
+    void load();
+    let alive = true;
+    apiGet("/factors")
+      .then((f) => alive && setFactors(f))
+      .catch(() => alive && setFactors([]));
+    return () => {
+      alive = false;
+    };
+  }, [load]);
+
+  const filtered = useMemo(() => {
+    if (!list) return null;
+    const q = query.trim().toLowerCase();
+    if (!q) return list;
+    return list.filter((s) => {
+      const hay = [
+        s.name,
+        s.description,
+        (s.factors ?? []).map((f) => f.name).join(" "),
+        (s.conditions ?? []).map((c) => c.field).join(" "),
+      ]
+        .join(" ")
+        .toLowerCase();
+      return hay.includes(q);
+    });
+  }, [list, query]);
+
+  /* ---------- 新建 / 编辑 ---------- */
+  function startCreate() {
+    setEditingId(null);
+    setEditing(emptyParams(range));
+    setDocId(null);
+    setMsg("");
+    setErr("");
+  }
+
+  function startEdit(s: StrategyDefinition) {
+    setEditingId(s.id ?? null);
+    setEditing(paramsFromStrategy(s, { ...emptyParams(range) }));
+    setDocId(s.id ?? null);
+    setMsg("");
+    setErr("");
+  }
+
+  async function save() {
+    if (!editing) return;
+    const errors = validateParams(editing, { requireMeta: true });
+    if (Object.keys(errors).length) {
+      setErr(Object.values(errors)[0]);
+      return;
+    }
+    setBusy(true);
+    setErr("");
+    try {
+      const body = strategyFromParams(editing, editingId ?? undefined);
+      if (editingId) {
+        await apiPut(`/strategies/${encodeURIComponent(editingId)}`, body);
+        setMsg(`已更新策略「${body.name}」`);
+      } else {
+        const saved = await apiPost("/strategies", body);
+        setMsg(`已保存策略「${saved.name}」（${saved.id}）`);
+      }
+      setEditing(null);
+      setEditingId(null);
+      setDocId(null);
+      await load();
+    } catch (e) {
+      setErr((e as Error).message);
+    } finally {
+      setBusy(false);
+    }
+  }
+
+  async function remove(s: StrategyDefinition) {
+    if (!s.id) return;
+    if (!window.confirm(`删除策略「${s.name}」？此操作不可撤销（已跑过的实验不受影响）。`)) return;
+    setBusy(true);
+    try {
+      await apiDelete(`/strategies/${encodeURIComponent(s.id)}`);
+      setMsg(`已删除策略「${s.name}」`);
+      await load();
+    } catch (e) {
+      setErr((e as Error).message);
+    } finally {
+      setBusy(false);
+    }
+  }
+
+  return (
+    <>
+      
+            
+              查看实验
+            
+            
+              新建策略
+            
+          
+        }
+      />
+
+      {err ? {err} : null}
+      {msg ? {msg} : null}
+
+      {/* ---------- 编辑器 ---------- */}
+      {editing ? (
+        
+               { setEditing(null); setEditingId(null); setDocId(null); }} disabled={busy}>
+                取消
+              
+              
+                {editingId ? "保存修改" : "保存到策略库"}
+              
+            
+          }
+        >
+          
+          

+            策略只保存「怎么选股/怎么调仓/怎么收费」，**不含回测区间与初始资金** ——
+            这两项在运行回测时才指定，因此同一策略可用于不同区间的复现与对比。
+          

+          

+            
+                setEditing({
+                  ...casePreset(range),
+                  name: editing.name,
+                  description: editing.description,
+                })
+              }
+            >
+              载入高股息案例默认参数
+            
+          

+        
+      ) : null}
+
+      {editing ? (
+        
+      ) : null}
+
+      {docId && !editing ?  setDocId(null)} /> : null}
+
+      {/* ---------- 列表 ---------- */}
+       setQuery(e.target.value)}
+            aria-label="搜索策略"
+          />
+        }
+      >
+        {filtered === null ? (
+          
+        ) : filtered.length === 0 ? (
+          
+        ) : (
+          

+            {filtered.map((s) => (
+               startEdit(s)}
+                onDelete={() => remove(s)}
+                onDoc={() => setDocId(s.id ?? null)}
+              />
+            ))}
+          

+        )}
+      
+    
+  );
+}
+
+/* ------------------------------------------------------------------ */
+
+/** 未保存参数的实时说明预览 */
+function EditingDoc({ params, savedId }: { params: StrategyParams; savedId: string | null }) {
+  const live = useStrategyDoc(params, !savedId);
+  const saved = useStrategyDocById(savedId);
+  const state = savedId ? saved : live;
+  return (
+    
+  );
+}
+
+function SavedDoc({ id, onClose }: { id: string; onClose: () => void }) {
+  const state = useStrategyDocById(id);
+  return (
+    收起}
+    >
+      {state.doc ? (
+        
+      ) : (
+        

+          {state.loading
+            ? "正在生成说明与公式…"
+            : `说明暂不可用${state.error ? `：${state.error}` : "（后端 /api/strategies/{id}/describe 未就绪）"}。`}
+        

+      )}
+    
+  );
+}
+
+/** 单个策略卡片：说明 + 参数摘要 + 一键回测 */
+function StrategyCard({
+  s,
+  range,
+  busy,
+  onEdit,
+  onDelete,
+  onDoc,
+}: {
+  s: StrategyDefinition;
+  range: { start: string; end: string };
+  busy: boolean;
+  onEdit: () => void;
+  onDelete: () => void;
+  onDoc: () => void;
+}) {
+  const [start, setStart] = useState("2020-01-01");
+  const [end, setEnd] = useState(range.end);
+  const [capital, setCapital] = useState(1_000_000);
+  const [running, setRunning] = useState(false);
+  const [jobId, setJobId] = useState("");
+  const [result, setResult] = useState(null);
+  const [stage, setStage] = useState("");
+  const [expId, setExpId] = useState("");
+  const [error, setError] = useState("");
+  const [open, setOpen] = useState(false);
+
+  const sel = s.selection ?? {};
+  const costs = s.costs ?? {};
+
+  async function run() {
+    if (!s.id) return;
+    if (start >= end) {
+      setError("开始日期必须早于结束日期");
+      return;
+    }
+    setRunning(true);
+    setError("");
+    setResult(null);
+    setExpId("");
+    try {
+      const spec = await apiPost>(
+        `/strategies/${encodeURIComponent(s.id)}/expand`,
+        { period: [start, end], initial_capital: capital }
+      );
+      const { job_id } = await submitJob(spec as never);
+      setJobId(job_id);
+      setStage("queued");
+      const out: JobOutcome = await waitJob(job_id, 900_000, (info) =>
+        setStage(info.stage)
+      );
+      if (out.status === "success" && out.result) {
+        setResult(out.result);
+        setExpId(out.experimentId ?? "");
+      } else {
+        setError(`任务${out.status}${out.error ? `：${out.error}` : ""}`);
+      }
+    } catch (e) {
+      setError((e as Error).message);
+    } finally {
+      setRunning(false);
+      setJobId("");
+      setStage("");
+    }
+  }
+
+  return (
+    

+      

+        

+          

+            {s.name}
+          

+          

+            {s.id}
+            {s.created_at ? 创建 {String(s.created_at).slice(0, 10)} : null}
+          

+        

+        
+          {s.factors?.map((f) => f.name).join(" + ") || "—"}
+        
+      

+
+      
{s.description || "（无说明：建议补一句话说明，便于日后识别）"}

+
+      

+        
+          候选池 {sel.top_n ?? "—"} → 持仓 {sel.hold_top_x ?? sel.top_n ?? "—"}
+        
+        
+          择股 {s.selection_interval_months ?? "跟随 y"} 月 / 调仓{" "}
+          {s.rebalance_interval_months ?? "跟随 m"} 月
+        
+        
+          复权 {adjLabel(s.price_adjustment)}
+        
+        
+          {s.universe?.exclude_st ? "剔除 ST" : "含 ST"} · 费率{" "}
+          {((costs.commission_rate ?? 0) * 100).toFixed(3)}% + 印花{" "}
+          {((costs.stamp_tax_rate ?? 0) * 100).toFixed(2)}%
+        
+        {(s.conditions ?? []).length ? (
+          
+            条件 {(s.conditions ?? []).map((c) => `${c.field} ${opLabel(c.op)} ${c.value ?? ""}`).join(" 且 ")}
+          
+        ) : null}
+      

+
+      

+        
+          {running ? "后台运行中…" : "一键回测"}
+        
+        
+          载入回测页（可微调）
+        
+        
+          看说明/公式
+        
+        
+          编辑
+        
+        
+          删除
+        
+      

+
+      {/* 运行区间（默认 2020-01-01 ~ 最近交易日） */}
+      
 setOpen((e.target as HTMLDetailsElement).open)}>
+        
+          回测区间与资金（一键回测使用）
+        
+        

+          
+             setStart(e.target.value)} disabled={running} />
+          
+          
+             setEnd(e.target.value)} disabled={running} />
+          
+          
+             setCapital(Number(e.target.value))}
+              disabled={running}
+            />
+          
+        

+      

+
+      {running ? (
+        
+              任务 {jobId || "排队中…"} 后台执行中（全市场回测通常 1~5 分钟）
+            
+          }
+        />
+      ) : null}
+      {error ? {error} : null}
+
+      {result ? (
+        

+          

+            
+              回测完成
+            
+            
+              在实验中对比
+            
+            {expId ? (
+              <>
+                
+                  打开归档（完整快照）
+                
+                
+                  以此参数再跑
+                
+              
+            ) : null}
+          

+          
+        

+      ) : null}
+    

+  );
+}
+
+function adjLabel(m?: string): string {
+  return m === "hfq" ? "后复权" : m === "qfq" ? "前复权" : "不复权";
+}
+
+function opLabel(op: string): string {
+  return (
+    { gt: ">", gte: "≥", lt: "<", lte: "≤", eq: "=", ne: "≠", in: "属于", not_in: "不属于" }[op] ?? op
+  );
+}
diff --git a/frontend/web/components/ArchiveActions.tsx b/frontend/web/components/ArchiveActions.tsx
new file mode 100644
index 0000000..2a26fc2
--- /dev/null
+++ b/frontend/web/components/ArchiveActions.tsx
@@ -0,0 +1,99 @@
+"use client";
+
+/**
+ * 归档页的**交互动作**（数据在服务端渲染，这里只管「页面之外」的动作）。
+ *
+ * 拆出来的原因：归档详情页改为 Server Component（只读内容必须能被 SSR 直出，
+ * 否则不带 JS 的抓取/分享预览看不到「选股条件」与「交易执行依据」）；
+ * 而删除、导出、折叠 spec 这些必须有事件处理，就集中放在这个客户端组件里。
+ *
+ * 删除的确认文案必须写清后果：结果只存归档一份（`job.result_json` 对新记录为 NULL，
+ * `GET /api/jobs/{id}` 是从归档回读的），所以删归档 = 该次回测结果彻底消失。
+ * 先前的文案写成「不影响作业记录」，那是**误导**。
+ */
+import { useState } from "react";
+import Link from "next/link";
+import { useRouter } from "next/navigation";
+import { apiDelete, apiGetText } from "@/lib/api";
+import { Banner, Btn, Card } from "@/components/ui";
+/**
+ * 刻意只收 `id` / `spec`（**不接收完整 result**）：归档结果可达数 MB，而服务端已经把它
+ * 传给图表组件了；再传一份给本组件会让 RSC 载荷翻倍。导出时按需拉取。
+ */
+export function ArchiveActions({ id, spec }: { id: string; spec: unknown }) {
+  const router = useRouter();
+  const [deleting, setDeleting] = useState(false);
+  const [error, setError] = useState("");
+  const [showSpec, setShowSpec] = useState(false);
+
+  async function downloadJson() {
+    setError("");
+    try {
+      const text = await apiGetText(`/experiments/${encodeURIComponent(id)}`);
+      const blob = new Blob([text], { type: "application/json" });
+      const url = URL.createObjectURL(blob);
+      const a = document.createElement("a");
+      a.href = url;
+      a.download = `${id}-archive.json`;
+      a.click();
+      URL.revokeObjectURL(url);
+    } catch (e) {
+      setError(`导出失败：${(e as Error).message}`);
+    }
+  }
+
+  async function remove() {
+    const ok = window.confirm(
+      `删除归档 ${id}？\n\n` +
+        `· 删除后这份快照（结果 + spec）无法再查看，也不可恢复；\n` +
+        `· 结果只存归档这一份 —— 删除后连该次执行的作业记录也读不回结果\n` +
+        `  （完整存档上线前的早期归档在作业表里另有副本，但不应依赖）；\n` +
+        `· 想留底请先点左侧「导出完整 JSON」。\n\n确认删除？`
+    );
+    if (!ok) return;
+    setDeleting(true);
+    setError("");
+    try {
+      await apiDelete(`/experiments/${encodeURIComponent(id)}`);
+      router.push("/experiments");
+    } catch (e) {
+      setError((e as Error).message);
+      setDeleting(false);
+    }
+  }
+
+  return (
+    <>
+      {error ? {error} : null}
+      

+        
+          实验对比
+        
+        
+          加入对比
+        
+        
+          以此参数再跑
+        
+        
+          导出完整 JSON
+        
+         setShowSpec((v) => !v)}>
+          {showSpec ? "收起原始 spec" : "查看原始 spec（JSON）"}
+        
+        
+          删除归档
+        
+      

+      {showSpec ? (
+        
+          
{JSON.stringify(spec ?? {}, null, 2)}

+        
+      ) : null}
+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/components/ArchiveResultView.tsx b/frontend/web/components/ArchiveResultView.tsx
new file mode 100644
index 0000000..a1c29bc
--- /dev/null
+++ b/frontend/web/components/ArchiveResultView.tsx
@@ -0,0 +1,222 @@
+"use client";
+
+/**
+ * 归档结果的**按类型分发**视图：归档页只依赖这一个入口，不在页面里猜结果结构。
+ *
+ * 为什么必须存在（真实缺陷驱动）：归档详情页原先在 `result` 非空时**无条件**渲染
+ * `BacktestResultView`，而 `factor_test` / `selection` 归档的结果结构与回测完全不同
+ * （没有 `summary` / `equity_curve` / `positions`）。实测后果：库里 4 条因子测试归档
+ * 与 1 条选股归档，从列表点「打开归档」**整页 500**（SSR 阶段 `result.positions.at(-1)`
+ * 抛 TypeError，且没有 error.tsx 兜底）——列表对每一行都给链接，所以这是必现路径。
+ *
+ * 现在按后端 `kind` 显式分发：
+ * - `backtest`    → `BacktestResultView`（净值/回撤/个股曲线/持仓/成交/月度年度/未建模）
+ * - `factor_test` → IC / RankIC / ICIR / 正收益占比 / 样本日数 + **分层收益柱状图**
+ * - `selection`   → 选股统计 + 候选明细表（代码**带名称**且可点击进个股页）
+ * - 其它未知类型  → 如实说明「暂不支持预览」并引导导出 JSON，**不假装能渲染**
+ *
+ * 纯派生逻辑（字段计数）不在这里，而在 `lib/archive.ts` —— Server Component 不能调用
+ * `"use client"` 模块导出的普通函数，放错位置会让归档页整页 500（已实测踩过）。
+ *
+ * 与 `/factors`、`/selection` 页面各自的渲染器并存是刻意的：那两个页面带交互
+ * （运行、直通回测、导出），归档页是**只读快照**，两类用途的文案与动作不同。
+ */
+
+import type { BacktestResult, FactorTestReport, SelectionResult } from "@/lib/types";
+import type { AnyArchiveResult } from "@/lib/archive";
+import { BacktestResultView } from "@/components/BacktestResultView";
+import { RichText } from "@/components/RichText";
+import { Card, Empty, Metric, UnimplementedNote } from "@/components/ui";
+import { SymbolLink } from "@/lib/symbols";
+
+export interface ArchiveInfo {
+  id: string;
+  created_at?: string | null;
+  code_version?: string | null;
+  data_version?: string | null;
+  job_id?: string | null;
+}
+
+export function ArchiveResultView({
+  kind,
+  result,
+  archive,
+}: {
+  kind: string;
+  result: AnyArchiveResult;
+  archive: ArchiveInfo;
+}) {
+  if (kind === "backtest") {
+    return ;
+  }
+  if (kind === "factor_test") {
+    return ;
+  }
+  if (kind === "selection") {
+    return ;
+  }
+  return (
+    
+      
+    
+  );
+}
+
+/* ------------------------------------------------------------------ */
+
+function FactorReportView({ report }: { report: FactorTestReport }) {
+  const qs = report.quantile_returns ?? [];
+  const maxAbs = Math.max(0.001, ...qs.map((q) => Math.abs(q.return_pct)));
+  return (
+    

+      归档快照，只读}
+      >
+        

+          = 0 ? "pos" : "neg"}
+          />
+          = 0 ? "pos" : "neg"}
+          />
+          = 1 ? "pos" : "plain"}
+            sub="越大越稳定"
+          />
+          = 50 ? "pos" : "warn"}
+          />
+          
+        

+
+        {qs.length > 0 ? (
+          

+            

+              分层表现（Q1 最低因子值 → Q{qs.length} 最高）
+            

+            

+              {qs.map((q) => {
+                const v = q.return_pct;
+                const h = Math.max(4, Math.round((Math.abs(v) / maxAbs) * 72));
+                return (
+                  

+                    
+                      {v >= 0 ? "+" : ""}
+                      {v.toFixed(2)}%
+                    
+                    
= 0 ? "is-pos" : "is-neg"}`}
+                      style={{ height: h }}
+                    />
+                    Q{q.quantile + 1}
+                  

+                );
+              })}
+            

+          

+        ) : null}
+
+        

+          
+        

+      
+      
+    

+  );
+}
+
+function SelectionSnapshotView({ result }: { result: SelectionResult }) {
+  const c = result.candidates ?? [];
+  return (
+    

+      归档快照，只读}
+      >
+        

+          
+          
+          
+          
+        

+
+        {c.length > 0 ? (
+          

+            

             
               
代码名称代码 / 名称
                 市场
                 行业
                 地区
{s.symbol}{s.name}
                   
                     {marketOf(s.symbol)}
                   

+              
+                
+                  
+                  
+                  
+                  
+                  
+                
+              
+              
+                {c.map((row) => (
+                  
+                    
+                    
+                    
+                    
+                    
+                  
+                ))}
+              
+            
#代码 / 名称得分因子值入选理由
{row.rank}
+                      
+                    {row.score.toFixed(4)}
+                      {Object.entries(row.factor_values ?? {})
+                        .map(([k, v]) =>
+                          `${k}=${typeof v === "number" ? v.toFixed(4) : v}`
+                        )
+                        .join("  ")}
+                    
+                      

+                        {(row.selection_reason ?? []).map((r) => (
+                          
+                            {r.slice(0, 40)}
+                          
+                        ))}
+                      

+                    

+          

+        ) : (
+          

+            该归档没有候选明细（结果为空或历史版本）。
+          

+        )}
+      
+      
+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/components/BacktestResultView.tsx b/frontend/web/components/BacktestResultView.tsx
new file mode 100644
index 0000000..da4aa59
--- /dev/null
+++ b/frontend/web/components/BacktestResultView.tsx
@@ -0,0 +1,656 @@
+"use client";
+
+/**
+ * 回测结果视图（**回测页**与**实验归档页**共用同一组件）。
+ *
+ * 共用是刻意的：归档要能被「往复查看」，就必须和刚跑完时看到的形态完全一致——
+ * 两处各写一套迟早会漂移，导致「归档里少了一张图/多了一列」这种隐性不一致。
+ *
+ * 三处模式差异用 props 表达：
+ * - `name`：策略名（运行时的策略）；归档页传归档里记录的策略名（若有）。
+ * - `archive`：传入后额外展示归档元数据（实验 id / 数据版本 / 代码版本 / 归档时间）
+ *   与**归档完整度**（`result.archive_meta.truncated` 为真时显式提示曲线被裁剪，不静默）。
+ */
+
+import { useMemo, useRef, useState } from "react";
+
+import {
+  Card,
+  Pill,
+  Btn,
+  Empty,
+  BacktestMetrics,
+  MonthlyReturnsTable,
+  UnimplementedNote,
+} from "@/components/ui";
+import { LwChart, type LwMarker, type LwSeries } from "@/components/charts/LwChart";
+import { CHART, fmtNum, fmtPct } from "@/components/charts/theme";
+import Link from "next/link";
+import { SymbolLink, useSymbolNames } from "@/lib/symbols";
+import { adjustLabel } from "@/lib/labels";
+import type { ActionRecord, BacktestResult, SymbolCurve } from "@/lib/types";
+
+export interface ArchiveInfo {
+  id: string;
+  created_at?: string | null;
+  code_version?: string | null;
+  data_version?: string | null;
+  job_id?: string | null;
+}
+
+function pad(n: number): string {
+  return String(n).padStart(2, "0");
+}
+
+function fmtDateTime(v?: string | null): string {
+  if (!v) return "—";
+  const d = new Date(v);
+  if (Number.isNaN(d.getTime())) return v;
+  return `${d.getFullYear()}-${pad(d.getMonth() + 1)}-${pad(d.getDate())} ${pad(d.getHours())}:${pad(d.getMinutes())}`;
+}
+
+export function BacktestResultView({
+  result,
+  name,
+  archive,
+}: {
+  result: BacktestResult;
+  name?: string;
+  archive?: ArchiveInfo;
+}) {
+  const s = result.summary;
+  const last = result.positions.at(-1);
+  const holdings = result.positions.filter((p) => p.date === last?.date);
+  const curves = result.symbol_curves ?? [];
+  const [active, setActive] = useState(curves[0]?.symbol ?? "");
+  const [curveQuery, setCurveQuery] = useState("");
+  // 老实验（后端填充 name 之前归档的结果）里 curve.name 为空：
+  // 名称统一从 useSymbolNames 缓存取，保证「有代码必有名称」在任何历史结果上都成立
+  const { names: nameCache } = useSymbolNames();
+  const nameOf = (c: SymbolCurve) => c.name ?? nameCache[c.symbol] ?? "";
+  const topRef = useRef(null);
+
+  // 组合净值上的买卖点：同一日的成交合并成一个标记，落在当日净值上
+  const equitySeries = useMemo(
+    () => [
+      {
+        key: "equity",
+        label: "组合净值（元）",
+        type: "area",
+        color: CHART.pos,
+        data: result.equity_curve.map((p) => ({ time: p.date, value: p.value })),
+        lastValueVisible: true,
+      },
+    ],
+    [result.equity_curve]
+  );
+
+  const equityMarkers = useMemo(() => {
+    const byDate = new Map();
+    for (const f of result.fills ?? []) {
+      const cur = byDate.get(f.date) ?? { BUY: false, SELL: false };
+      cur[f.signal] = true;
+      byDate.set(f.date, cur);
+    }
+    const equity = new Set(result.equity_curve.map((p) => p.date));
+    const out: LwMarker[] = [];
+    for (const [d, kinds] of byDate) {
+      if (!equity.has(d)) continue;
+      if (kinds.BUY) out.push({ time: d, kind: "BUY", text: "买" });
+      if (kinds.SELL) out.push({ time: d, kind: "SELL", text: "卖" });
+    }
+    return out;
+  }, [result.fills, result.equity_curve]);
+
+  const filteredCurves = useMemo(() => {
+    const q = curveQuery.trim().toLowerCase();
+    if (!q) return curves;
+    return curves.filter(
+      (c) => c.symbol.toLowerCase().includes(q) || nameOf(c).toLowerCase().includes(q)
+    );
+    // nameOf 只依赖 nameCache 与 c，二者都在依赖里
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [curves, curveQuery, nameCache]);
+
+  const activeCurve = curves.find((c) => c.symbol === active) ?? filteredCurves[0] ?? curves[0];
+
+  const buyDays = equityMarkers.filter((m) => m.kind === "BUY").length;
+  const sellDays = equityMarkers.filter((m) => m.kind === "SELL").length;
+
+  const SECTIONS = [
+    { id: "sec-equity", label: "整体收益" },
+    { id: "sec-symbols", label: "个股曲线" },
+    { id: "sec-monthly", label: "月度/年度" },
+    { id: "sec-holdings", label: "持仓" },
+    { id: "sec-trades", label: "成交明细" },
+  ];
+
+  return (
+    <>
+      

+      

+        结果分区
+        {SECTIONS.map((s) => (
+          
+            {s.label}
+          
+        ))}
+        共 {result.equity_curve.length} 个交易日 · {result.trades.length} 笔成交
+      

+
+      {archive ?  : null}
+      
+      

+        

+          
+            完成
+          
+          {name ? (
+            
+              {name}
+            
+          ) : null}
+          
+            {poolLabel(result)}
+          
+          {adjustLabelFromSnapshot(result.config_snapshot)} 口径
+          {stBasisNote(result) && (
+            
+              {stBasisNote(result)}
+            
+          )}
+          
+            {s.start} ~ {s.end}
+          
+        

+        

+          总收益 
+        

+      

+
+      
+
+      

+        
+              期末 {fmtNum(s.final_equity)} · 买入 {buyDays} 日 / 卖出 {sellDays} 日
+            
+          }
+        >
+           fmtNum(v)}
+            ariaLabel="组合净值曲线与买卖点"
+          />
+          

+            ▲ 绿 = 当日有买入成交，▼ 红 = 当日有卖出成交；点位取当日组合净值。
+            悬停可看任意日期的净值；拖动/滚轮可缩放区间，双击图例可临时隐藏曲线。
+          

+        
+        最大 {s.max_drawdown_pct.toFixed(2)}%}
+        >
+           ({ time: p.date, value: p.value })),
+                lastValueVisible: true,
+              },
+            ]}
+            height={320}
+            valueFormat={(v) => `${v.toFixed(2)}%`}
+            zeroLine
+            ariaLabel="回撤曲线"
+          />
+        
+      

+
+      
+             setCurveQuery(e.target.value)}
+              aria-label="搜索个股"
+            />
+            {curves.length} 只有成交
+          

+        }
+      >
+        {curves.length === 0 ? (
+          
+        ) : (
+          <>
+            

+              
+              
+              
+                曲线口径：该股被持有期间按日复利累计（建仓当日为 0%）；未持有期间不绘制，
+                分段间以直线连接，请以买卖点区分持仓区间。
+              
+            

+            {activeCurve ?  : null}
+            

+              
+                
+                  
+                    
+                    
+                    
+                    
+                    
+                  
+                
+                
+                  {filteredCurves.map((c) => (
+                    
+                      
+                      
+                      
+                      
+                      
+                    
+                  ))}
+                
+              
股票持仓期累计收益买点卖点操作

+                        
+                      = 0 ? "tone-pos" : "tone-neg"}>
+                        {c.final_return_pct.toFixed(2)}%
+                      
+                        {(c.marks ?? []).filter((m) => m.signal === "BUY").length}
+                      
+                        {(c.marks ?? []).filter((m) => m.signal === "SELL").length}
+                      
+                         setActive(c.symbol)}>
+                          查看曲线
+                        
+                      

+            

+          
+        )}
+      
+
+      
+         ({
+                time: `${m.year}-${String(m.month).padStart(2, "0")}-01`,
+                value: m.return_pct,
+              })),
+            },
+          ]}
+          height={200}
+          valueFormat={(v) => `${v.toFixed(2)}%`}
+          zeroLine
+          legend={false}
+          ariaLabel="月度收益柱状图"
+        />
+        

+          
+            查看月度收益明细表
+          
+          
+        

+      
+
+      {holdings.length} 只}
+      >
+        {holdings.length === 0 ? (
+          
+        ) : (
+          

+            
+              
+                
+                  
+                  
+                
+              
+              
+                {holdings.map((p) => (
+                  
+                    
+                    
+                  
+                ))}
+              
+            
股票权重

+                      
+                    {(p.weight * 100).toFixed(2)}%

+          

+        )}
+      
+
+      {result.yearly_returns.length ? (
+        
+              均值{" "}
+              {(
+                result.yearly_returns.reduce((a, y) => a + y.return_pct, 0) /
+                result.yearly_returns.length
+              ).toFixed(2)}
+              %
+            
+          }
+        >
+          

+            {result.yearly_returns.map((y) => (
+              
+                {y.year}
+                = 0 ? "tone-pos" : "tone-neg"}>
+                  {y.return_pct.toFixed(2)}%
+                
+              
+            ))}
+          

+        
+      ) : null}
+
+      {result.trades.length ? (
+        {result.turnover_pct.toFixed(2)}% 累计换手}
+        >
+          

+            
+              
+                
+                  
+                  
+                  
+                  
+                  
+                  
+                
+              
+              
+                {result.trades.map((t) => (
+                  
+                    
+                    
+                    
+                    
+                    
+                    
+                  
+                ))}
+              
+            
买入日卖出日股票买价卖价收益
{t.entry_date}{t.exit_date}
+                      
+                    {t.entry_price?.toFixed(2) ?? "-"}{t.exit_price?.toFixed(2) ?? "-"}= 0 ? "tone-pos" : "tone-neg"}>
+                      {t.return_pct.toFixed(2)}%
+                    

+          

+        
+      ) : null}
+
+      
+
+      
+    
+  );
+}
+
+/** 未成交意图（涨停/停牌/顺延）透明化，避免「信号有了却没买」无法解释。 */
+function NotFilledCard({ signals }: { signals: ActionRecord[] }) {
+  const rejects = signals.filter((a) => !a.filled && a.reject_reason);
+  if (rejects.length === 0) return null;
+  const byReason = new Map();
+  for (const r of rejects) {
+    const key = (r.reject_reason ?? "").replace(/，?顺延.*$/, "（顺延）");
+    byReason.set(key, (byReason.get(key) ?? 0) + 1);
+  }
+  return (
+    
+      

+        {[...byReason.entries()].map(([reason, n]) => (
+          
+            {reason} {n}
+          
+        ))}
+      

+      

+        涨停/停牌导致的未成交按「信号已记录、成交未发生」处理（v3 §20.3）：顺延买入会在之后首个
+        可成交交易日按收盘价成交；到下一次调仓仍未成交则作废。
+      

+    
+  );
+}
+
+function SymbolCurveChart({ curve }: { curve: SymbolCurve }) {
+  const valueByDate = useMemo(() => new Map(curve.points.map((p) => [p.date, p.value])), [curve]);
+  const markers = useMemo(
+    () =>
+      (curve.marks ?? [])
+        .filter((a) => valueByDate.has(a.date))
+        .map((a) => ({
+          time: a.date,
+          kind: a.signal,
+          text: a.signal === "BUY" ? "买" : "卖",
+        })),
+    [curve, valueByDate]
+  );
+  return (
+     ({ time: p.date, value: p.value })),
+          lastValueVisible: true,
+        },
+      ]}
+      markers={markers}
+      height={300}
+      valueFormat={(v) => `${v.toFixed(2)}%`}
+      zeroLine
+      ariaLabel={`${curve.symbol} 持仓期收益曲线与买卖点`}
+    />
+  );
+}
+
+function poolLabel(result: BacktestResult): string {
+  const sel = result.config_snapshot?.selection as
+    | { top_n?: number; hold_top_x?: number | null }
+    | undefined;
+  if (!sel) return "候选池 —";
+  return `候选池 ${sel.top_n ?? "—"} → 持仓 ${sel.hold_top_x ?? sel.top_n ?? "—"}`;
+}
+
+/**
+ * ST（风险警示）剔除口径提示。
+ *
+ * 后端逐择股日按**当时名称**判定 exclude_st（依赖 `sync namechange`）；未同步名称历史时
+ * 回退「最新名称快照」，会把「曾为高股息、后来才 ST/退市」的股息陷阱样本整段排除，
+ * 收益被高估（实测案例 +24.86% → +35.71%）。此处把口径显式暴露给使用者，避免误读。
+ */
+function stBasisNote(result: BacktestResult): string | null {
+  const basis = result.config_snapshot?.price_basis as
+    | { name_basis?: { point_in_time?: boolean } | null }
+    | undefined;
+  const nb = basis?.name_basis;
+  if (!nb) return null; // 未启用 exclude_st
+  return nb.point_in_time ? "ST 按时点名称判定" : "ST 用最新名称（可能高估）";
+}
+
+function adjustLabelFromSnapshot(snapshot: Record): string {
+  const basis = snapshot?.price_basis as { adjust_mode?: string } | undefined;
+  const mode = basis?.adjust_mode ?? "none";
+  return mode === "hfq" ? "后复权" : mode === "qfq" ? "前复权" : "不复权";
+}
+
+function SignedText({ value }: { value: number }) {
+  const cls = value >= 0 ? "tone-pos" : "tone-neg";
+  return (
+    
+      {value > 0 ? "+" : ""}
+      {value.toFixed(2)}%
+    
+  );
+}
+/**
+ * 归档元数据条：**归档能否被信任地复现，取决于这几项**，所以放在最显眼的位置。
+ * - 代码版本：跑出这个结果时的 git short rev
+ * - 数据版本：当时的数据快照指纹（由后端在归档时写入；老归档可能为空 → 如实显示「—」）
+ * - 实验 / 作业 id：可追到执行记录
+ */
+function ArchiveMetaBar({ archive }: { archive: ArchiveInfo }) {
+  return (
+    

+      
+        归档 {archive.id}
+      
+      
+        归档时间 {fmtDateTime(archive.created_at)} · 代码版本{" "}
+        {archive.code_version ?? "—"} · 数据版本{" "}
+        {archive.data_version ?? "未记录"}
+        {archive.job_id ? (
+          <>
+            {" "}
+            · 作业 {archive.job_id}
+          
+        ) : null}
+      
+    

+  );
+}
+
+/**
+ * 归档完整度提示。
+ *
+ * `archive_meta.truncated` 为真表示：归档时结果 JSON 超过安全预算，个股曲线被裁剪
+ * （后端会在 `unimplemented` 里同时留下说明）。**必须显式告知**，否则用户会以为
+ * 「这只股票没被买过」，而实际只是没存下来。
+ */
+function ArchiveCompletenessNote({
+  result,
+  archive,
+}: {
+  result: BacktestResult;
+  archive?: ArchiveInfo;
+}) {
+  const meta = result.archive_meta as
+    | { curves_stored?: number; curves_total?: number; truncated?: boolean; budget_chars?: number }
+    | undefined;
+  const legacyNote = (result.unimplemented ?? []).find((u) => u.includes("个股收益曲线"));
+
+  // 情况 1：归档带完整度元数据（本轮之后产生的归档）
+  if (meta && meta.curves_total !== undefined) {
+    const total = meta.curves_total ?? 0;
+    const stored = meta.curves_stored ?? 0;
+    return (
+      

+        {meta.truncated ? (
+          
+            归档不完整
+          
+        ) : (
+          
+            归档完整
+          
+        )}
+        
+          个股曲线已存 {stored} / 期内持有 {total} 只
+          {meta.truncated
+            ? "（超出结果体积预算被裁剪：请在下方用「成交明细 / 未成交意图」查缺失个股，或缩短区间后重跑以得到完整曲线）"
+            : "（全部持有过的股票都可在此查看）"}
+        
+      

+    );
+  }
+
+  // 情况 2：老归档没有元数据，但结果里留了截断说明 → **不能装作完整**
+  if (legacyNote) {
+    const held = /期内共持有\s*(\d+)\s*只/.exec(legacyNote)?.[1];
+    const kept = /最大的\s*(\d+)\s*只/.exec(legacyNote)?.[1];
+    return (
+      

+        
+          归档不完整
+        
+        
+          该归档产生于「完整存档」上线之前：个股曲线只有 {kept ?? "60"} 只（期内共持有{" "}
+          {held ?? "更多"} 只），换手但收益排名不靠前的个股在这里看不到曲线。
+          成交明细 / 未成交意图仍是完整的。要看全部个股曲线，请
+          {archive ? (
+            <>
+              {" "}
+              
+                以此参数重跑
+              {" "}
+              生成一份完整归档。
+            
+          ) : (
+            "以此参数重跑一次（新归档将保存全部持有过的个股曲线）。"
+          )}
+        
+      

+    );
+  }
+
+  // 情况 3：既无元数据也无截断说明 → 无从判断，不猜（只在老归档上出现）
+  if (archive) {
+    return (
+      

+        
+          完整度未标注
+        
+        
+          该归档早于「归档完整度元数据」上线，无法确认个股曲线是否被裁剪过
+          （成交明细 / 未成交意图始终是完整的）。
+        
+      

+    );
+  }
+  return null;
+}
diff --git a/frontend/web/components/LineChart.tsx b/frontend/web/components/LineChart.tsx
deleted file mode 100644
index 37e42a6..0000000
--- a/frontend/web/components/LineChart.tsx
+++ /dev/null
@@ -1,117 +0,0 @@
-"use client";
-
-/** 基于 ECharts 的折线图（净值 / 回撤），按需引入模块以压缩 bundle。 */
-import { useEffect, useRef } from "react";
-import * as echarts from "echarts/core";
-import { LineChart as EChartsLine } from "echarts/charts";
-import { GridComponent, TitleComponent, TooltipComponent } from "echarts/components";
-import { CanvasRenderer } from "echarts/renderers";
-
-echarts.use([EChartsLine, GridComponent, TitleComponent, TooltipComponent, CanvasRenderer]);
-
-export interface XY {
-  date: string;
-  value: number;
-}
-
-const C = {
-  text: "#a8b3c9",
-  faint: "#8b98b2",
-  line: "rgba(150,165,195,0.13)",
-  tooltipBg: "#141d30",
-  tooltipBorder: "rgba(150,165,195,0.28)",
-};
-
-export function LineChart({
-  title,
-  data,
-  color = "#3fb6ff",
-  fill = false,
-  height = 320,
-  yFmt,
-  xFmt,
-}: {
-  title?: string;
-  data: XY[];
-  color?: string;
-  /** 线下渐变面积（净值类曲线推荐开启） */
-  fill?: boolean;
-  height?: number;
-  yFmt?: (v: number) => string;
-  xFmt?: (v: string) => string;
-}) {
-  const ref = useRef(null);
-
-  useEffect(() => {
-    if (!ref.current || data.length === 0) return;
-    const chart = echarts.init(ref.current);
-    const areaColor = {
-      type: "linear",
-      x: 0,
-      y: 0,
-      x2: 0,
-      y2: 1,
-      colorStops: [
-        { offset: 0, color: color + "3d" },
-        { offset: 1, color: color + "00" },
-      ],
-    };
-    chart.setOption({
-      backgroundColor: "transparent",
-      title: title
-        ? {
-            text: title,
-            textStyle: { color: "#e9eef7", fontSize: 13, fontWeight: 600 },
-          }
-        : undefined,
-      tooltip: {
-        trigger: "axis",
-        backgroundColor: C.tooltipBg,
-        borderColor: C.tooltipBorder,
-        textStyle: { color: "#e9eef7", fontSize: 12 },
-        valueFormatter: (v: unknown) =>
-          typeof v === "number" ? (yFmt ? yFmt(v) : String(v)) : String(v),
-      },
-      grid: { left: 8, right: 14, top: title ? 42 : 18, bottom: 8, containLabel: true },
-      xAxis: {
-        type: "category",
-        boundaryGap: false,
-        data: data.map((p) => p.date),
-        axisLine: { lineStyle: { color: C.line } },
-        axisTick: { show: false },
-        axisLabel: {
-          color: C.faint,
-          fontSize: 10,
-          formatter: (v: string) => (xFmt ? xFmt(v) : v),
-        },
-      },
-      yAxis: {
-        type: "value",
-        scale: true,
-        axisLabel: { color: C.faint, fontSize: 10, formatter: yFmt },
-        splitLine: { lineStyle: { color: C.line } },
-      },
-      series: [
-        {
-          type: "line",
-          showSymbol: false,
-          symbol: "circle",
-          data: data.map((p) => p.value),
-          lineStyle: { color, width: 1.8 },
-          itemStyle: { color },
-          areaStyle: fill ? { color: areaColor } : undefined,
-          emphasis: { focus: "series" },
-        },
-      ],
-    });
-    const onResize = () => chart.resize();
-    window.addEventListener("resize", onResize);
-    return () => {
-      window.removeEventListener("resize", onResize);
-      chart.dispose();
-    };
-  }, [data, color, fill, title, height, yFmt, xFmt]);
-
-  if (data.length === 0) return 
暂无数据
;
-  return 
;
-}
diff --git a/frontend/web/components/RichText.tsx b/frontend/web/components/RichText.tsx
new file mode 100644
index 0000000..824271d
--- /dev/null
+++ b/frontend/web/components/RichText.tsx
@@ -0,0 +1,36 @@
+import type { ReactNode } from "react";
+
+/**
+ * 富文本渲染：把后端说明文本里的 **粗体** 与 `行内代码` 渲染成真实样式。
+ *
+ * 为什么需要：`describe_strategy`（以及引擎的 `unimplemented` 说明）用 Markdown 的
+ * `**…**` / `` `…` `` 做**强调协议**——纯文本接口下这是合理的表达方式。但 React 不会
+ * 解析 Markdown，直接 `{text}` 会把星号和反引号**原样显示**出来（实测归档页/策略页
+ * 有 10 处这样的漏字），让「说明」看起来像没写完的草稿。
+ *
+ * 为什么不引 markdown 渲染器：说明文本来自后端（可信），但仍不需要为两种标记引入
+ * `dangerouslySetInnerHTML` 与整个解析器；这里只做**结构化切分后生成 React 元素**，
+ * 不产生 HTML 注入面，也不支持链接/图片等其它语法（不支持就按字面显示，不猜）。
+ */
+export function RichText({ text }: { text?: string | null }): ReactNode {
+  if (!text) return null;
+  // 一次切分同时处理 **粗体** 与 `代码`（分隔符保留在结果里以便识别）
+  const parts = text.split(/(\*\*[^*]+\*\*|`[^`]+`)/g).filter((p) => p !== "");
+  return (
+    <>
+      {parts.map((p, i) => {
+        if (p.length > 4 && p.startsWith("**") && p.endsWith("**")) {
+          return {p.slice(2, -2)};
+        }
+        if (p.length > 2 && p.startsWith("`") && p.endsWith("`")) {
+          return (
+            
+              {p.slice(1, -1)}
+            
+          );
+        }
+        return {p};
+      })}
+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/components/StockChart/CandleChart.tsx b/frontend/web/components/StockChart/CandleChart.tsx
deleted file mode 100644
index efa82c1..0000000
--- a/frontend/web/components/StockChart/CandleChart.tsx
+++ /dev/null
@@ -1,200 +0,0 @@
-"use client";
-
-/**
- * K 线图（v3 §20 Chart）：蜡烛图 + 成交量 + MA 指标 + 事件标记。
- *
- * 标记语义（v3 §20.3 Signal↔Fill 严格区分）：
- * - fills（实际成交）：实心标记 —— fill_buy 红色 ▲ / fill_sell 绿色 ▼
- * - signals（未成交意图）：空心标记 —— signal_buy 红圈 ▲ / signal_sell 绿圈
- * 组件只做展示，绝不自行计算（数据来自后端 Chart API）。
- *
- * 基于 ECharts candlestick（ECharts 实现）；另一套实现见 CandleChartLW.tsx（TradingView
- * Lightweight Charts），两套共用同一 props 接口（chartTypes.ts）供对比。
- */
-import { useEffect, useRef } from "react";
-import * as echarts from "echarts/core";
-import { BarChart, CandlestickChart, LineChart, ScatterChart } from "echarts/charts";
-import { GridComponent, TooltipComponent } from "echarts/components";
-import { CanvasRenderer } from "echarts/renderers";
-import type { ChartMarker } from "@/lib/types";
-import type { CandleChartProps } from "./chartTypes";
-export type { CandleDatum } from "./chartTypes";
-
-echarts.use([
-  CandlestickChart,
-  LineChart,
-  BarChart,
-  ScatterChart,
-  GridComponent,
-  TooltipComponent,
-  CanvasRenderer,
-]);
-
-const C = {
-  faint: "#8b98b2",
-  line: "rgba(150,165,195,0.13)",
-  tooltipBg: "#141d30",
-  tooltipBorder: "rgba(150,165,195,0.28)",
-  up: "#e5484d",
-  down: "#2fb36b",
-  ma20: "#3fb6ff",
-  ma60: "#d4a72c",
-};
-
-export function CandleChart({
-  data,
-  volume = [],
-  indicators = {},
-  fills = [],
-  signals = [],
-  height = 480,
-  name = "",
-}: CandleChartProps) {
-  const ref = useRef(null);
-
-  useEffect(() => {
-    if (!ref.current || data.length === 0) return;
-    const chart = echarts.init(ref.current);
-    const dates = data.map((d) => d.time);
-    const vol = volume ?? [];
-
-    // echarts option 结构较宽，series 用宽松类型（组件层实现细节）
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    const series: any[] = [
-      {
-        name: "K线",
-        type: "candlestick",
-        data: data.map((d) => [d.open, d.close, d.low, d.high]),
-        itemStyle: {
-          color: C.up, // 阳线（收盘>开盘）红涨
-          color0: C.down,
-          borderColor: C.up,
-          borderColor0: C.down,
-        },
-      },
-    ];
-    for (const [name, pts] of Object.entries(indicators)) {
-      const vals = dates.map((t) => {
-        const p = pts.find((x) => x.time === t);
-        return p?.value ?? null;
-      });
-      series.push({
-        name,
-        type: "line",
-        data: vals,
-        smooth: false,
-        showSymbol: false,
-        lineStyle: { width: 1.2 },
-        itemStyle: { color: C[name as keyof typeof C] ?? "#b98bff" },
-        connectNulls: true,
-      });
-    }
-    // 成交标记（实心）与未成交信号（空心）：以散点叠加在价格坐标
-    // eslint-disable-next-line @typescript-eslint/no-explicit-any
-    const mk = (m: ChartMarker, filled: boolean): any => {
-      const buy = m.kind.endsWith("buy");
-      return {
-        value: [m.time, m.price ?? 0],
-        symbol: filled ? (buy ? "triangle" : "triangle") : buy ? "triangle" : "triangle",
-        symbolRotate: buy ? 0 : 180,
-        symbolSize: 13,
-        itemStyle: {
-          color: filled ? (buy ? C.up : C.down) : "transparent",
-          borderColor: buy ? C.up : C.down,
-          borderWidth: 1.6,
-        },
-        tooltip: { formatter: m.text.join("
") },
-      };
-    };
-    series.push(
-      { name: "成交", type: "scatter", data: fills.map((m) => mk(m, true)), z: 6 },
-      { name: "信号(未成交)", type: "scatter", data: signals.map((m) => mk(m, false)), z: 5 }
-    );
-
-    const hasVol = vol.length > 0;
-    const mainGridBottom = hasVol ? 58 : 14;
-    series.push({
-      name: "成交量",
-      type: "bar",
-      xAxisIndex: 1,
-      yAxisIndex: 1,
-      data: dates.map((t) => {
-        const p = vol.find((x) => x.time === t);
-        return p?.value ?? 0;
-      }),
-      itemStyle: { color: "rgba(150,165,195,0.35)" },
-    });
-
-    chart.setOption({
-      backgroundColor: "transparent",
-      tooltip: {
-        trigger: "axis",
-        axisPointer: { type: "cross" },
-        backgroundColor: C.tooltipBg,
-        borderColor: C.tooltipBorder,
-        textStyle: { color: "#e9eef7", fontSize: 12 },
-      },
-      axisPointer: { link: [{ xAxisIndex: "all" }] },
-      grid: [
-        { left: 8, right: 18, top: 16, height: hasVol ? "62%" : "82%", containLabel: true },
-        ...(hasVol
-          ? [{ left: 8, right: 18, top: hasVol ? "76%" : undefined, height: "14%", containLabel: true }]
-          : []),
-      ],
-      xAxis: [
-        {
-          type: "category",
-          data: dates,
-          boundaryGap: true,
-          axisLine: { lineStyle: { color: C.line } },
-          axisTick: { show: false },
-          axisLabel: { color: C.faint, fontSize: 10 },
-          splitLine: { show: false },
-        },
-        ...(hasVol
-          ? [{
-              type: "category",
-              gridIndex: 1,
-              data: dates,
-              boundaryGap: true,
-              axisLine: { lineStyle: { color: C.line } },
-              axisTick: { show: false },
-              axisLabel: { show: false },
-              splitLine: { show: false },
-            }]
-          : []),
-      ],
-      yAxis: [
-        {
-          type: "value",
-          scale: true,
-          splitNumber: 4,
-          axisLabel: { color: C.faint, fontSize: 10 },
-          splitLine: { lineStyle: { color: C.line } },
-        },
-        ...(hasVol
-          ? [{
-              type: "value",
-              gridIndex: 1,
-              scale: true,
-              axisLabel: { color: C.faint, fontSize: 9 },
-              splitLine: { show: false },
-            }]
-          : []),
-      ],
-      series,
-      legend: {
-        show: false,
-      },
-    });
-    const onResize = () => chart.resize();
-    window.addEventListener("resize", onResize);
-    return () => {
-      window.removeEventListener("resize", onResize);
-      chart.dispose();
-    };
-  }, [data, volume, indicators, fills, signals, height]);
-
-  if (data.length === 0) return 
暂无 K 线数据
;
-  return 
;
-}
diff --git a/frontend/web/components/StockChart/StockChart.tsx b/frontend/web/components/StockChart/StockChart.tsx
index b80bc0d..5cddb0e 100644
--- a/frontend/web/components/StockChart/StockChart.tsx
+++ b/frontend/web/components/StockChart/StockChart.tsx
@@ -1,12 +1,16 @@
 "use client";
 
-/** K 线图统一入口：可切换 ECharts / TradingView Lightweight Charts 两套实现（对比期）。 */
+/**
+ * K 线图统一入口（TradingView Lightweight Charts）。
+ *
+ * 为什么去掉 `library` 参数：ECharts 版（CandleChart.tsx）已随「图表统一到
+ * lightweight-charts」下线并删除；如果继续保留切换参数，调用方会以为还有第二套实现可用
+ * （AGENT §24：未实现必须如实标注，不许假装）。props 仍沿用 chartTypes.ts 的 CandleChartProps，
+ * 单只股票页与后续调用方无需改接口。
+ */
 import type { CandleChartProps } from "./chartTypes";
-import { CandleChart } from "./CandleChart";
 import { CandleChartLW } from "./CandleChartLW";
 
-export type ChartLibrary = "echarts" | "lightweight";
-
-export function StockChart({ library = "lightweight", ...props }: CandleChartProps & { library?: ChartLibrary }) {
-  return library === "echarts" ?  : ;
+export function StockChart(props: CandleChartProps) {
+  return ;
 }
diff --git a/frontend/web/components/StrategyDocCard.tsx b/frontend/web/components/StrategyDocCard.tsx
new file mode 100644
index 0000000..04ee598
--- /dev/null
+++ b/frontend/web/components/StrategyDocCard.tsx
@@ -0,0 +1,97 @@
+"use client";
+
+/**
+ * 策略说明卡片：一句话说明 + 计算公式 + 执行步骤 + 注意事项。
+ *
+ * 内容全部来自后端 describe_strategy（依 spec 真实推导）；本组件只负责呈现，
+ * 并用 `RichText` 渲染后端使用的 **粗体** / `代码` 强调协议（否则星号会字面漏出）。
+ * 接口不可用时**如实显示「说明暂不可用」并给出原因**，不伪造文案（AGENT §24）。
+ */
+import { RichText } from "@/components/RichText";
+import { Card, Pill } from "@/components/ui";
+import type { StrategyDoc } from "@/lib/types";
+
+export function StrategyDocBody({ doc }: { doc: StrategyDoc }) {
+  return (
+    

+      

+        💡
+        
+      

+      {doc.formula ? (
+        

+          计算公式
+          

+        

+      ) : null}
+      {doc.steps?.length ? (
+        

+          执行步骤
+          
    
+            {doc.steps.map((s, i) => (
+              
    2. 
+            ))}
+          

+        

+      ) : null}
+      {doc.warnings?.length ? (
+        

+          {doc.warnings.map((w, i) => (
+            
+              注意 
+            
+          ))}
+        

+      ) : null}
+    

+  );
+}
+
+export function StrategyDocCard({
+  doc,
+  loading,
+  error,
+  title = "策略说明与计算公式",
+  compact = false,
+}: {
+  doc: StrategyDoc | null;
+  loading: boolean;
+  error: string;
+  title?: string;
+  compact?: boolean;
+}) {
+  return (
+    
+            生成中
+          
+        ) : doc ? (
+          
+            由后端依参数推导
+          
+        ) : null
+      }
+    >
+      {doc ? (
+        
+      ) : loading ? (
+        
正在生成说明与公式…

+      ) : (
+        

+          说明暂不可用{error ? `：${error}` : "（后端 /api/strategies/describe 未就绪）"}。
+          策略仍可正常保存与运行；说明与公式由后端依 spec 推导，前端不自行拼造文案。
+        

+      )}
+      {!compact && doc && (
+        

+          
+          因此不会出现「文档写一套、代码跑另一套」。
+        

+      )}
+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/components/StrategyParamsForm.tsx b/frontend/web/components/StrategyParamsForm.tsx
new file mode 100644
index 0000000..e2e6ad6
--- /dev/null
+++ b/frontend/web/components/StrategyParamsForm.tsx
@@ -0,0 +1,717 @@
+"use client";
+
+/**
+ * 策略参数表单（受控组件）—— 策略库 / 回测页 / 选股直通 共用同一份参数模型。
+ *
+ * 为什么抽出来：策略库要「新建/编辑策略」、回测页要「保存为策略/从策略载入」、
+ * 选股页要「按此条件回测」，三处字段与校验完全同构。若各写一遍，必然出现
+ * 「选股页能设的条件在回测页设不了」这类口径漂移（本平台的核心风险）。
+ * 因此参数只有一个模型 `StrategyParams`，一个表单组件，一套校验。
+ *
+ * 组件**不持有业务状态**：value/onChange 由父组件控制，父组件负责提交与落库。
+ */
+import type { FactorMeta, ResearchCondition, ResearchSpec, StrategyDefinition } from "@/lib/types";
+import { Btn, Field } from "@/components/ui";
+
+export type Op = ResearchCondition["op"];
+
+export const OPS: { value: Op; label: string }[] = [
+  { value: "gt", label: ">" },
+  { value: "gte", label: "≥" },
+  { value: "lt", label: "<" },
+  { value: "lte", label: "≤" },
+  { value: "eq", label: "=" },
+  { value: "ne", label: "≠" },
+  { value: "in", label: "属于" },
+  { value: "not_in", label: "不属于" },
+];
+
+export interface StrategyParams {
+  name: string;
+  description: string;
+  /** 加权因子列表（打分公式 = Σ weight × factor） */
+  factors: { name: string; weight: number }[];
+  priceAdjustment: "none" | "qfq" | "hfq";
+  /** n：候选池（择股条件选出的股数） */
+  topN: number;
+  /** x：实际持仓数，必须 ≤ n */
+  holdX: number;
+  /** m：择股间隔（月），0 = 不单独设（跟随 y） */
+  mMonths: number;
+  /** y：调仓间隔（月），0 = 跟随 m */
+  yMonths: number;
+  rebalance: "monthly" | "weekly";
+  /**
+   * 买不进（涨停/停牌）时的补位策略 —— 三态，与后端两个互斥字段一一对应：
+   * - `substitute`：allow_substitute=true, defer_buy=false（换一只买）
+   * - `defer`：allow_substitute=false, defer_buy=true（顺延到之后首个不涨停的交易日）
+   * - `none`：两者皆 false（不补位，可能少持几只）
+   * 后端拒绝两者同时为 true，因此这里不提供「都选」的组合。
+   */
+  fillPolicy: FillPolicy;
+  conditions: ResearchCondition[];
+  excludeSt: boolean;
+  minListingDays: number;
+  commission: number; // %
+  stamp: number; // %
+  slippage: number; // %
+  minCommission: number; // 元/笔
+  capital: number;
+  start: string;
+  end: string;
+}
+
+/** 用户案例默认参数：全市场股息率最高的 n 只 → 持仓前 x 只，每 m 个月择股、每 y 个月调仓。 */
+export type FillPolicy = "substitute" | "defer" | "none";
+
+export const CASE_PRESET: Omit = {
+  factors: [{ name: "dividend_yield", weight: 1 }],
+  priceAdjustment: "hfq",
+  topN: 20,
+  holdX: 20,
+  mMonths: 6,
+  yMonths: 6,
+  rebalance: "monthly",
+  fillPolicy: "defer",
+  conditions: [{ field: "dv_ratio", op: "lte", value: 30 }],
+  excludeSt: true,
+  minListingDays: 250,
+  commission: 0.03,
+  stamp: 0.05,
+  slippage: 0.1,
+  minCommission: 5,
+  capital: 1_000_000,
+};
+
+export function emptyParams(range: { start: string; end: string }): StrategyParams {
+  return {
+    ...CASE_PRESET,
+    name: "",
+    description: "",
+    factors: [{ name: "momentum_60", weight: 1 }],
+    conditions: [],
+    start: range.start,
+    end: range.end,
+  };
+}
+
+export function casePreset(range: { start: string; end: string }): StrategyParams {
+  return { ...CASE_PRESET, name: "", description: "", start: "2020-01-01", end: range.end };
+}
+
+/** 参数 → ResearchSpec（回测提交体）。m/y 的 0 语义与后端一致。 */
+export function paramsToSpec(p: StrategyParams): ResearchSpec {
+  const factors = p.factors.filter((f) => f.name.trim() !== "");
+  return {
+    type: "backtest",
+    universe: { exclude_st: p.excludeSt, min_listing_days: p.minListingDays },
+    price_adjustment: p.priceAdjustment,
+    factors: factors.length ? factors : [{ name: "momentum_60", weight: 1 }],
+    conditions: p.conditions.filter((c) => c.field.trim() !== ""),
+    selection: {
+      top_n: p.topN,
+      hold_top_x: p.holdX,
+      allow_substitute: p.fillPolicy === "substitute",
+      defer_buy: p.fillPolicy === "defer",
+    },
+    rebalance: p.rebalance,
+    // m=0 表示「每次调仓都择股」：若同时给了 y>0，则择股间隔跟随 y
+    // （后端禁止只给 y 而不给 m —— 无锚点无法确定择股日集合）
+    selection_interval_months: p.mMonths > 0 ? p.mMonths : p.yMonths > 0 ? p.yMonths : null,
+    rebalance_interval_months: p.yMonths > 0 ? p.yMonths : p.mMonths > 0 ? p.mMonths : null,
+    costs: {
+      commission_rate: p.commission / 100,
+      stamp_tax_rate: p.stamp / 100,
+      slippage_rate: p.slippage / 100,
+      min_commission: p.minCommission,
+    },
+    initial_capital: p.capital,
+    period: [p.start, p.end],
+  };
+}
+
+/** ResearchSpec → 参数（从实验详情「以此参数回测」时使用） */
+export function paramsFromSpec(
+  spec: Partial & { config_snapshot?: Record },
+  base: StrategyParams
+): StrategyParams {
+  const snap = (spec.config_snapshot ?? {}) as Partial;
+  const s = (snap.factors ? snap : spec) as Partial;
+  const sel: NonNullable = s.selection ?? { top_n: base.topN };
+  const costs: NonNullable = s.costs ?? {};
+  return {
+    ...base,
+    factors: s.factors?.length ? s.factors.map((f) => ({ ...f })) : base.factors,
+    priceAdjustment: s.price_adjustment ?? base.priceAdjustment,
+    topN: sel.top_n ?? base.topN,
+    holdX: sel.hold_top_x ?? sel.top_n ?? base.holdX,
+    mMonths: s.selection_interval_months ?? base.mMonths,
+    yMonths: s.rebalance_interval_months ?? base.yMonths,
+    rebalance: s.rebalance ?? base.rebalance,
+    fillPolicy: sel.defer_buy ? "defer" : sel.allow_substitute === false ? "none" : "substitute",
+    conditions: (s.conditions ?? []).map((c) => ({ ...c })),
+    excludeSt: s.universe?.exclude_st ?? base.excludeSt,
+    minListingDays: s.universe?.min_listing_days ?? base.minListingDays,
+    commission: (costs.commission_rate ?? base.commission / 100) * 100,
+    stamp: (costs.stamp_tax_rate ?? base.stamp / 100) * 100,
+    slippage: (costs.slippage_rate ?? base.slippage / 100) * 100,
+    minCommission: costs.min_commission ?? base.minCommission,
+    capital: s.initial_capital ?? base.capital,
+    start: s.period?.[0] ?? base.start,
+    end: s.period?.[1] ?? base.end,
+  };
+}
+
+/** 已保存策略 → 参数 */
+export function paramsFromStrategy(st: StrategyDefinition, base: StrategyParams): StrategyParams {
+  const sel = st.selection ?? {};
+  const costs = st.costs ?? {};
+  return {
+    ...base,
+    name: st.name,
+    description: st.description ?? "",
+    factors: st.factors?.length ? st.factors.map((f) => ({ ...f })) : base.factors,
+    priceAdjustment: st.price_adjustment ?? base.priceAdjustment,
+    topN: sel.top_n ?? base.topN,
+    holdX: sel.hold_top_x ?? sel.top_n ?? base.holdX,
+    mMonths: st.selection_interval_months ?? base.mMonths,
+    yMonths: st.rebalance_interval_months ?? base.yMonths,
+    rebalance: st.rebalance ?? base.rebalance,
+    fillPolicy: sel.defer_buy ? "defer" : sel.allow_substitute === false ? "none" : "substitute",
+    conditions: (st.conditions ?? []).map((c) => ({ ...c })),
+    excludeSt: st.universe?.exclude_st ?? base.excludeSt,
+    minListingDays: st.universe?.min_listing_days ?? base.minListingDays,
+    commission: (costs.commission_rate ?? base.commission / 100) * 100,
+    stamp: (costs.stamp_tax_rate ?? base.stamp / 100) * 100,
+    slippage: (costs.slippage_rate ?? base.slippage / 100) * 100,
+    minCommission: costs.min_commission ?? base.minCommission,
+  };
+}
+
+/** 参数 → 策略定义（保存到策略库；period/capital 不入库，回测时再补） */
+export function strategyFromParams(p: StrategyParams, id?: string): StrategyDefinition {
+  const factors = p.factors.filter((f) => f.name.trim() !== "");
+  return {
+    ...(id ? { id } : {}),
+    name: p.name.trim(),
+    description: p.description.trim(),
+    spec_type: "backtest",
+    universe: { exclude_st: p.excludeSt, min_listing_days: p.minListingDays },
+    price_adjustment: p.priceAdjustment,
+    factors: factors.length ? factors : [{ name: "momentum_60", weight: 1 }],
+    conditions: p.conditions.filter((c) => c.field.trim() !== ""),
+    selection: {
+      top_n: p.topN,
+      hold_top_x: p.holdX,
+      allow_substitute: p.fillPolicy === "substitute",
+      defer_buy: p.fillPolicy === "defer",
+    },
+    rebalance: p.rebalance,
+    selection_interval_months: p.mMonths > 0 ? p.mMonths : p.yMonths > 0 ? p.yMonths : null,
+    rebalance_interval_months: p.yMonths > 0 ? p.yMonths : p.mMonths > 0 ? p.mMonths : null,
+    costs: {
+      commission_rate: p.commission / 100,
+      stamp_tax_rate: p.stamp / 100,
+      slippage_rate: p.slippage / 100,
+      min_commission: p.minCommission,
+    },
+    portfolio: {},
+  };
+}
+
+/** 表单校验（返回 field → 错误文案；空对象 = 通过） */
+export function validateParams(
+  p: StrategyParams,
+  opts: { requireMeta?: boolean } = {}
+): Record {
+  const e: Record = {};
+  if (opts.requireMeta) {
+    if (!p.name.trim()) e.name = "策略名必填（便于在策略库中识别）";
+    else if (p.name.trim().length > 64) e.name = "策略名最多 64 字";
+    if (!p.description.trim()) e.description = "一句话说明必填：说清这个策略做什么";
+    // strategy.description 落库列为 String(300)：超长会被 MySQL 严格模式拒绝，
+    // 因此在表单层就拦住并说明原因（而不是让用户在保存时吃一个 500）
+    else if (p.description.trim().length > 300)
+      e.description = `一句话说明最长 300 字（当前 ${p.description.trim().length} 字）`;
+  }
+  if (!p.factors.some((f) => f.name.trim())) e.factors = "至少选择一个因子";
+  else {
+    // 后端 ResearchSpec 校验会拒绝重复因子名（z-score 叠加两次没有意义且易误读），
+    // 这里提前拦住，避免用户填完参数后才吃一个 400
+    const used = p.factors.map((f) => f.name.trim()).filter(Boolean);
+    const dup = used.find((n, i) => used.indexOf(n) !== i);
+    if (dup) e.factors = `因子「${dup}」重复了：同一因子只应出现一次（想加权重请调权重值）`;
+  }
+  if (p.topN < 1) e.topN = "候选池 n 至少为 1";
+  if (p.holdX < 1) e.holdX = "持仓数 x 至少为 1";
+  if (p.holdX > p.topN) e.holdX = `持仓数 x=${p.holdX} 不能大于候选池 n=${p.topN}`;
+  if (p.mMonths < 0 || p.mMonths > 60) e.mMonths = "m 需在 0~60 之间";
+  if (p.yMonths < 0 || p.yMonths > 60) e.yMonths = "y 需在 0~60 之间";
+  if (p.commission < 0 || p.stamp < 0 || p.slippage < 0) e.costs = "费率不能为负";
+  if (p.minCommission < 0) e.minCommission = "最低佣金不能为负";
+  if (p.capital < 10000) e.capital = "初始资金建议 ≥ 1 万";
+  if (!p.start || !p.end) e.period = "起止日期都必填";
+  else if (p.start >= p.end) e.period = "开始日期必须早于结束日期";
+  for (const c of p.conditions) {
+    if (!c.field.trim()) {
+      e.conditions = "存在空的条件字段：请填写字段名或删除该条件";
+      break;
+    }
+  }
+  return e;
+}
+
+export interface StrategyParamsFormProps {
+  value: StrategyParams;
+  onChange: (next: StrategyParams) => void;
+  factorOptions: FactorMeta[];
+  /** 显示策略名 + 一句话说明（策略库编辑/保存为策略时） */
+  showMeta?: boolean;
+  /** 显示回测区间与初始资金（回测执行时才需要） */
+  showPeriod?: boolean;
+  /** 显示操作按钮区（表单内提交按钮） */
+  disabled?: boolean;
+  errors?: Record;
+  /** 因子选择是否允许加权多项（默认允许） */
+  multiFactor?: boolean;
+}
+
+export function StrategyParamsForm({
+  value: p,
+  onChange,
+  factorOptions,
+  showMeta = false,
+  showPeriod = true,
+  disabled = false,
+  errors = {},
+  multiFactor = true,
+}: StrategyParamsFormProps) {
+  const set = (patch: Partial) => onChange({ ...p, ...patch });
+  const setFactor = (i: number, patch: Partial<{ name: string; weight: number }>) =>
+    set({ factors: p.factors.map((f, j) => (j === i ? { ...f, ...patch } : f)) });
+
+  return (
+    <>
+      {showMeta && (
+        

+          
+             set({ name: e.target.value })}
+            />
+            {errors.name && (
+              

+                {errors.name}
+              

+            )}
+          
+          
+             set({ description: e.target.value })}
+            />
+            {errors.description && (
+              

+                {errors.description}
+              

+            )}
+          
+        

+      )}
+
+      {/* ---------- 因子（打分公式） ---------- */}
+      

+        打分因子（score = Σ 权重 × 因子值，越大越优先）
+        {multiFactor && (
+           set({ factors: [...p.factors, { name: factorOptions[0]?.name ?? "", weight: 1 }] })}
+          >
+            添加因子
+          
+        )}
+      

+      

+        {p.factors.map((f, i) => (
+          

+            
+             setFactor(i, { weight: Number(e.target.value) })}
+            />
+            {multiFactor && p.factors.length > 1 && (
+               set({ factors: p.factors.filter((_, j) => j !== i) })}
+              >
+                删除
+              
+            )}
+          

+        ))}
+      

+      {errors.factors && (
+        

+          {errors.factors}
+        

+      )}
+
+      {/* ---------- 选股规模与周期 ---------- */}
+      

+        
+           set({ topN: Number(e.target.value) })}
+          />
+        
+        
+           set({ holdX: Number(e.target.value) })}
+          />
+          {errors.holdX && (
+            

+              {errors.holdX}
+            

+          )}
+        
+        
+           set({ mMonths: Number(e.target.value) })}
+          />
+          {errors.mMonths && (
+            

+              {errors.mMonths}
+            

+          )}
+        
+        
+           set({ yMonths: Number(e.target.value) })}
+          />
+          {errors.yMonths && (
+            

+              {errors.yMonths}
+            

+          )}
+        
+        
+          
+        
+        
+          

+            {(
+              [
+                ["substitute", "换一只买", "从候选池之外按复合分往下找可买标的，补足持仓数"],
+                ["defer", "顺延买入", "等它到之后首个不涨停的交易日再按收盘价买入（到下次调仓仍未成交则作废）"],
+                ["none", "不补位", "买不进就空着，实际持仓可能少于持仓数 x"],
+              ] as const
+            ).map(([val, label, desc]) => (
+              
+            ))}
+          

+        
+      

+
+      {/* ---------- 口径与成本 ---------- */}
+      

+        
+          
+        
+        
+          
+        
+        
+           set({ minListingDays: Number(e.target.value) })}
+          />
+        
+        
+           set({ commission: Number(e.target.value) })}
+          />
+        
+        
+           set({ stamp: Number(e.target.value) })}
+          />
+        
+        
+           set({ slippage: Number(e.target.value) })}
+          />
+        
+        
+           set({ minCommission: Number(e.target.value) })}
+          />
+          {errors.minCommission && (
+            

+              {errors.minCommission}
+            

+          )}
+        
+        {showPeriod && (
+          <>
+            
+               set({ capital: Number(e.target.value) })}
+              />
+              {errors.capital && (
+                

+                  {errors.capital}
+                

+              )}
+            
+            
+               set({ start: e.target.value })}
+              />
+            
+            
+               set({ end: e.target.value })}
+              />
+            
+          
+        )}
+      

+      {errors.period && (
+        

+          {errors.period}
+        

+      )}
+
+      {/* ---------- 选股过滤条件 ---------- */}
+      

+        

+          选股过滤条件（AND，universe 之后、因子排序之前执行）
+           set({ conditions: [...p.conditions, { field: "", op: "gte", value: 0 }] })}
+          >
+            添加条件
+          
+        

+        {p.conditions.length === 0 ? (
+          
未设置条件：候选池 = universe 内因子分最高的 n 只。

+        ) : (
+          

+            {p.conditions.map((c, i) => (
+              

+                
+                    set({
+                      conditions: p.conditions.map((x, j) =>
+                        j === i ? { ...x, field: e.target.value } : x
+                      ),
+                    })
+                  }
+                />
+                
+                 {
+                    const raw = e.target.value;
+                    const num = Number(raw);
+                    set({
+                      conditions: p.conditions.map((x, j) =>
+                        j === i
+                          ? { ...x, value: raw !== "" && !Number.isNaN(num) ? num : raw }
+                          : x
+                      ),
+                    });
+                  }}
+                />
+                 set({ conditions: p.conditions.filter((_, j) => j !== i) })}
+                >
+                  删除
+                
+              

+            ))}
+          

+        )}
+        {errors.conditions && (
+          

+            {errors.conditions}
+          

+        )}
+        

+          可用字段：每日指标 dv_ratio / dv_ttm / pe / pb / total_mv、行情 close / volume /
+          amount、技术 ma20 / ma60、已注册因子名、static.industry 等、fundamental.roe 等
+          （财务按公告日 ≤ 择股日取用）。
+        

+      

+    
+  );
+}
\ No newline at end of file
diff --git a/frontend/web/components/app-shell.tsx b/frontend/web/components/app-shell.tsx
index 840499c..bfbf4cb 100644
--- a/frontend/web/components/app-shell.tsx
+++ b/frontend/web/components/app-shell.tsx
@@ -12,6 +12,11 @@ interface NavItem {
   icon: IconName;
 }
 
+/**
+ * 导航分组：按「研究闭环」的真实使用顺序排列 ——
+ * 找标的（股票池）→ 出候选（筛选）→ 定规则（策略库）→ 验规则（回测）→ 复盘（实验）。
+ * 「交易信号」「因子研究」是可选支路，放在同组但靠后；单独建组会让闭环读起来断裂。
+ */
 const GROUPS: { title: string; items: NavItem[] }[] = [
   {
     title: "研究",
@@ -19,15 +24,23 @@ const GROUPS: { title: string; items: NavItem[] }[] = [
       { href: "/", label: "总览", icon: "grid" },
       { href: "/stocks", label: "股票池", icon: "candles" },
       { href: "/selection", label: "股票筛选", icon: "target" },
-      { href: "/signals", label: "交易信号", icon: "scale" },
-      { href: "/factors", label: "因子研究", icon: "flask" },
-      { href: "/factors/compose", label: "因子组合", icon: "layers" },
-      { href: "/backtest", label: "选股回测", icon: "gauge" },
     ],
   },
   {
-    title: "归档",
-    items: [{ href: "/experiments", label: "实验", icon: "archive" }],
+    title: "策略",
+    items: [
+      { href: "/strategies", label: "策略库", icon: "book" },
+      { href: "/backtest", label: "选股回测", icon: "gauge" },
+      { href: "/experiments", label: "实验对比", icon: "archive" },
+    ],
+  },
+  {
+    title: "因子与信号",
+    items: [
+      { href: "/factors", label: "因子研究", icon: "flask" },
+      { href: "/factors/compose", label: "因子组合", icon: "layers" },
+      { href: "/signals", label: "交易信号", icon: "scale" },
+    ],
   },
 ];
 
diff --git a/frontend/web/components/charts/LwChart.tsx b/frontend/web/components/charts/LwChart.tsx
new file mode 100644
index 0000000..e1843d8
--- /dev/null
+++ b/frontend/web/components/charts/LwChart.tsx
@@ -0,0 +1,382 @@
+"use client";
+
+/**
+ * 通用折线/面积/柱状图 —— TradingView Lightweight Charts 实现（唯一图表基座）。
+ *
+ * 为什么自建而不直接用 LW 原语：LW 没有内置 tooltip 与图例，且硬性要求
+ *   ① 标记时间必须存在于序列数据中，② 标记必须按时间升序。
+ * 本组件把这两条约束在内部处理掉（过滤 + 排序），避免每个页面各写一遍而踩坑
+ * —— 回测买卖点必须精确落在净值曲线日期上（v3 §20.3）。
+ *
+ * 设计要点：
+ * - 图表实例只在「结构签名」（序列个数/类型/高度）变化时重建；数据变化走 setData
+ *   + 数值校验和作为数据签名，避免每次渲染重建导致闪烁、丢失缩放位置。
+ * - 多序列共用价格轴（实验对比均为收益率 %，可直接叠加比较）。
+ * - 图例可点击隐藏/显示单条序列；tooltip 显示十字光标处全部序列数值。
+ */
+import { useCallback, useEffect, useMemo, useRef, useState } from "react";
+import {
+  ColorType,
+  CrosshairMode,
+  LineStyle,
+  createChart,
+  type HistogramData,
+  type IChartApi,
+  type ISeriesApi,
+  type LineData,
+  type MouseEventParams,
+  type SeriesMarker,
+  type Time,
+} from "lightweight-charts";
+
+import { CHART, MARKER_COLORS } from "./theme";
+import { isBuyMarker, prepareMarkers } from "./markers";
+
+export interface LwPoint {
+  time: string;
+  value: number;
+}
+
+export interface LwSeries {
+  key: string;
+  label: string;
+  data: LwPoint[];
+  color?: string;
+  /** line=折线；area=带渐变面积；bar=柱状（月度收益等） */
+  type?: "line" | "area" | "bar";
+  lineWidth?: 1 | 2 | 3 | 4;
+  dashed?: boolean;
+  /** 是否在价格轴显示最新值标签（多序列对比时只留一条即可） */
+  lastValueVisible?: boolean;
+}
+
+export interface LwMarker {
+  time: string;
+  kind: keyof typeof MARKER_COLORS;
+  label?: string;
+  /** 覆盖默认文本（默认买 B / 卖 S） */
+  text?: string;
+}
+
+export interface LwChartProps {
+  series: LwSeries[];
+  markers?: LwMarker[];
+  height?: number;
+  /** 悬浮提示与价格轴的数值格式 */
+  valueFormat?: (v: number) => string;
+  /** 画一条 0 基准虚线（收益率曲线推荐开启） */
+  zeroLine?: boolean;
+  legend?: boolean;
+  ariaLabel?: string;
+  emptyHint?: string;
+}
+
+type AnySeries = ISeriesApi<"Line"> | ISeriesApi<"Area"> | ISeriesApi<"Histogram">;
+
+function toTime(t: string): Time {
+  return t as Time;
+}
+
+/** 结构签名：序列个数/类型/高度 —— 变化才重建图表实例 */
+function structureKey(series: LwSeries[], height: number): string {
+  return `${height}|${series.map((s) => `${s.key}:${s.type ?? "line"}`).join(",")}`;
+}
+
+/**
+ * 数据签名：长度 + 首末时间 + **数值校验和**。
+ * 必须含数值校验和：只改成本/滑点后曲线数值变了但日期与长度不变，
+ * 若签名只看长度就会漏更新（图表停留在上一次结果）。
+ */
+function dataKey(series: LwSeries[], markers: LwMarker[]): string {
+  const parts = series.map((s) => {
+    let sum = 0;
+    for (const p of s.data) sum += p.value;
+    return `${s.key}:${s.data.length}:${s.data[0]?.time ?? ""}:${s.data.at(-1)?.time ?? ""}:${sum.toFixed(4)}`;
+  });
+  const mk = markers.map((m) => `${m.time}${m.kind}`).join(",");
+  return `${parts.join("|")}#${mk}`;
+}
+
+export function LwChart({
+  series,
+  markers = [],
+  height = 320,
+  valueFormat,
+  zeroLine = false,
+  legend = true,
+  ariaLabel,
+  emptyHint = "暂无数据",
+}: LwChartProps) {
+  const wrapRef = useRef(null);
+  const boxRef = useRef(null);
+  const chartRef = useRef(null);
+  const seriesRef = useRef>(new Map());
+  const zeroLineDrawn = useRef(false);
+  /** 图例隐藏集合：tooltip 订阅里读取，用 ref 避免闭包过期 */
+  const hiddenRef = useRef>(new Set());
+  const fmtRef = useRef(valueFormat);
+  fmtRef.current = valueFormat;
+
+  const [hidden, setHidden] = useState>(new Set());
+  const [tip, setTip] = useState<{
+    x: number;
+    y: number;
+    time: string;
+    rows: { label: string; color: string; text: string }[];
+  } | null>(null);
+
+  hiddenRef.current = hidden;
+
+  const struct = structureKey(series, height);
+  const dkey = dataKey(series, markers);
+
+  const fmt = useCallback(
+    (v: number) => (fmtRef.current ? fmtRef.current(v) : v.toFixed(2)),
+    []
+  );
+
+  /* ---------- 创建/销毁图表实例 ---------- */
+  useEffect(() => {
+    const el = boxRef.current;
+    if (!el) return;
+
+    const chart = createChart(el, {
+      autoSize: true,
+      height,
+      layout: {
+        background: { type: ColorType.Solid, color: CHART.background },
+        textColor: CHART.text,
+        fontSize: 11,
+      },
+      grid: {
+        vertLines: { color: CHART.grid },
+        horzLines: { color: CHART.grid },
+      },
+      rightPriceScale: {
+        borderColor: CHART.border,
+        scaleMargins: { top: 0.12, bottom: 0.12 },
+      },
+      timeScale: { borderColor: CHART.border, timeVisible: false, secondsVisible: false },
+      crosshair: {
+        mode: CrosshairMode.Normal,
+        vertLine: { color: CHART.crosshair, width: 1, style: LineStyle.Dashed, labelVisible: true },
+        horzLine: { color: CHART.crosshair, width: 1, style: LineStyle.Dashed, labelVisible: true },
+      },
+    });
+    chartRef.current = chart;
+    zeroLineDrawn.current = false;
+
+    const map = new Map();
+    series.forEach((s, i) => {
+      const color = s.color ?? CHART.series[i % CHART.series.length];
+      let api: AnySeries;
+      if (s.type === "bar") {
+        api = chart.addHistogramSeries({
+          color,
+          priceLineVisible: false,
+          lastValueVisible: false,
+        });
+      } else if (s.type === "area") {
+        api = chart.addAreaSeries({
+          lineColor: color,
+          topColor: `${color}55`,
+          bottomColor: `${color}05`,
+          lineWidth: s.lineWidth ?? 2,
+          priceLineVisible: false,
+          lastValueVisible: s.lastValueVisible ?? false,
+        });
+      } else {
+        api = chart.addLineSeries({
+          color,
+          lineWidth: s.lineWidth ?? 2,
+          lineStyle: s.dashed ? LineStyle.Dashed : LineStyle.Solid,
+          priceLineVisible: false,
+          lastValueVisible: s.lastValueVisible ?? series.length === 1,
+          crosshairMarkerVisible: true,
+          crosshairMarkerRadius: 4,
+        });
+      }
+      map.set(s.key, api);
+    });
+    seriesRef.current = map;
+
+    // tooltip：LW 无内置 tooltip，订阅十字光标自行渲染
+    const onMove = (param: MouseEventParams) => {
+      if (!param.time || !param.point) {
+        setTip(null);
+        return;
+      }
+      const rows: { label: string; color: string; text: string }[] = [];
+      series.forEach((s, i) => {
+        const api = map.get(s.key);
+        if (!api || hiddenRef.current.has(s.key)) return;
+        const v = param.seriesData.get(api as unknown as ISeriesApi<"Line">) as
+          | LineData
+          | HistogramData
+          | undefined;
+        if (!v || typeof v.value !== "number") return;
+        rows.push({
+          label: s.label,
+          color: s.color ?? CHART.series[i % CHART.series.length],
+          text: fmt(v.value),
+        });
+      });
+      if (!rows.length) {
+        setTip(null);
+        return;
+      }
+      setTip({ x: param.point.x, y: param.point.y, time: String(param.time), rows });
+    };
+    chart.subscribeCrosshairMove(onMove);
+
+    const onLeave = () => setTip(null);
+    el.addEventListener("mouseleave", onLeave);
+
+    return () => {
+      el.removeEventListener("mouseleave", onLeave);
+      chart.unsubscribeCrosshairMove(onMove);
+      chart.remove();
+      chartRef.current = null;
+      seriesRef.current = new Map();
+    };
+    // 仅结构变化才重建；数据/标记由下方 effect 推入
+    // eslint-disable-next-line react-hooks/exhaustive-deps
+  }, [struct, fmt]);
+
+  /* ---------- 推入数据与标记 ---------- */
+  useEffect(() => {
+    const chart = chartRef.current;
+    if (!chart) return;
+
+    series.forEach((s) => {
+      const api = seriesRef.current.get(s.key);
+      if (!api) return;
+      if (s.type === "bar") {
+        api.setData(
+          s.data.map((p) => ({ time: toTime(p.time), value: p.value })) as HistogramData[]
+        );
+      } else {
+        api.setData(s.data.map((p) => ({ time: toTime(p.time), value: p.value })) as LineData[]);
+      }
+    });
+
+    // 0 基准线：每个图表实例只画一次（重复调用会叠加多条价格线）
+    if (zeroLine && !zeroLineDrawn.current) {
+      const first = seriesRef.current.get(series[0]?.key ?? "");
+      first?.createPriceLine({
+        price: 0,
+        color: CHART.faint,
+        lineWidth: 1,
+        lineStyle: LineStyle.Dotted,
+        axisLabelVisible: false,
+        title: "",
+      });
+      zeroLineDrawn.current = true;
+    }
+
+    // 标记只能挂在某个序列上；统一挂第一条非柱状序列（净值/收益曲线）
+    const primaryKey = series.find((s) => s.type !== "bar")?.key ?? series[0]?.key;
+    const primary = primaryKey ? seriesRef.current.get(primaryKey) : undefined;
+    if (primary && "setMarkers" in primary) {
+      const validTimes = new Set(
+        series.find((s) => s.key === primaryKey)?.data.map((p) => p.time) ?? []
+      );
+      // LW 硬约束：标记时间必须存在且升序 → 先过滤再排序，否则买卖点会丢或抛错。
+      // 传空数组同样重要：结果切换后必须清掉上一次的标记。
+      const ms: SeriesMarker