Simon
23972e7063
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）。
244 lines
10 KiB
Python
244 lines
10 KiB
Python
"""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。
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不连真库，用确定性合成行情 + 内存 SQLite（覆盖 daily_basic 与 adjust_factor
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两条新链路），验证：
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- dv_ratio 因子参与评分（dividend_yield）
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- conditions（dv_ratio <= 30）真正过滤掉特殊分红股
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- hfq 复权生效（除权日不再被计为亏损）
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- m/y 双周期、x<=n、顺延买入的端到端组合行为
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- 最低佣金 min_commission
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"""
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from __future__ import annotations
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from datetime import date
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from decimal import Decimal
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import pandas as pd
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import pytest
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from app.infrastructure.persistence.sqlalchemy.base import Base
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from app.infrastructure.persistence.sqlalchemy.models.market import (
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AdjustFactorModel,
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DailyBasicModel,
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StockDailyModel,
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StockModel,
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)
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from app.quant.engine import LocalEngine
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from app.quant.service import ResearchService
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from sqlalchemy import create_engine
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from sqlalchemy.orm import Session
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_SYMS = ["600000.SH", "600001.SH", "600002.SH", "600003.SH"]
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_START = date(2024, 1, 1)
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def _seed(session: Session, *, spike_date: date | None = None) -> None:
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"""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。
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`spike_date`：把 A 股在该日的 dv_ratio 抬到 45%（特殊分红尖峰，
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用于验证 `dv_ratio <= 30` 条件确实把它剔除）。
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"""
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session.add_all(
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[
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StockModel(
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symbol=s, name=f"股票{s[:6]}", industry="银行" if i < 2 else "制造",
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market="主板", area="深圳", list_date=date(2000, 1, 1), status="L",
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)
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for i, s in enumerate(_SYMS)
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]
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)
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# 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
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dv = {"600000.SH": 8.0, "600001.SH": 6.0, "600002.SH": 4.0, "600003.SH": 2.0}
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dates = pd.bdate_range(_START, periods=130)
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bars, basics, factors = [], [], []
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for i, sym in enumerate(_SYMS):
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price = 10.0 + i
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for d in dates:
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price = price * (1 + 0.0008 + 0.0003 * i)
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bars.append(
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StockDailyModel(
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symbol=sym, trade_date=d.date(), source="tushare", adjust="none",
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open=Decimal(str(price)), high=Decimal(str(price)),
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low=Decimal(str(price)), close=Decimal(str(price)),
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volume=Decimal("1000000"), amount=Decimal(str(price * 1e6)),
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)
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)
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rate = dv[sym]
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if spike_date is not None and sym == "600000.SH" and d.date() == spike_date:
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rate = 45.0 # 特殊分红尖峰（应按条件在该择股日被剔除）
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basics.append(
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DailyBasicModel(
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symbol=sym, trade_date=d.date(), source="tushare",
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close=Decimal(str(price)), dv_ratio=Decimal(str(rate)),
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dv_ttm=Decimal(str(rate)), pe=Decimal("8"), pb=Decimal("1"),
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total_mv=Decimal("1e11"),
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)
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)
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# 2024-06-03 起因子 1.1（模拟一次除权）：hfq 价格应整体上移 10%
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f = 1.1 if d.date() >= date(2024, 6, 3) else 1.0
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factors.append(
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AdjustFactorModel(symbol=sym, trade_date=d.date(), factor=Decimal(str(f)))
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)
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session.add_all(bars + basics + factors)
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session.commit()
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@pytest.fixture
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def service(tmp_path) -> ResearchService:
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engine = create_engine(f"sqlite:///{tmp_path / 'case.db'}", future=True)
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Base.metadata.create_all(engine)
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session = Session(engine)
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_seed(session, spike_date=date(2024, 3, 1))
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from app.infrastructure.persistence.sqlalchemy.repositories.market_impl import (
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SqlAlchemyDailyBarRepository,
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SqlAlchemyDailyBasicRepository,
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SqlAlchemyStockRepository,
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)
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# 复权折算发生在 SqlAlchemyDailyBarRepository 的 SQL 内（price_adjust=...），
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# 业务层无需 adjust_factor 仓储
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yield ResearchService(
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SqlAlchemyStockRepository(session),
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SqlAlchemyDailyBarRepository(session),
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LocalEngine(),
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basic_repo=SqlAlchemyDailyBasicRepository(session),
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)
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session.close()
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def _spec(**kw):
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from app.domain.entities.research import (
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ConditionSpec,
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CostSpec,
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FactorSpec,
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ResearchSpec,
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SelectionSpec,
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UniverseSpec,
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)
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base = dict(
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type="backtest",
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universe=UniverseSpec(exclude_st=False, min_listing_days=0),
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price_adjustment="hfq",
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factors=[FactorSpec(name="dividend_yield")],
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conditions=[ConditionSpec(field="dv_ratio", op="lte", value=30)],
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selection=SelectionSpec(
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top_n=3, hold_top_x=2, allow_substitute=False, defer_buy=True
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),
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rebalance="monthly",
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selection_interval_months=3,
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rebalance_interval_months=3,
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period=(date(2024, 3, 1), date(2024, 6, 28)),
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costs=CostSpec(
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commission_rate=0.0003, stamp_tax_rate=0.0005, slippage_rate=0.001,
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min_commission=5.0,
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),
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)
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base.update(kw)
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return ResearchSpec(**base)
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class TestUserCaseEndToEnd:
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def test_backtest_runs_with_all_new_knobs(self, service) -> None:
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result = service.run_backtest(_spec())
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assert result.summary.total_trades >= 1
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# 配置可溯源（v3 §20.5）
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snap = result.config_snapshot
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assert snap["price_adjustment"] == "hfq"
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assert snap["price_basis"]["execution_price_basis"] == "close_adj"
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assert snap["selection_interval_months"] == 3
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assert snap["selection"]["hold_top_x"] == 2
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assert result.symbol_curves, "应输出个股收益曲线"
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def test_condition_excludes_special_dividend_spike(self, service) -> None:
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"""A 股在 2024-03-01 的 dv_ratio=45% > 30% → 该日不得进入候选池；
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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）。"""
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result = service.run_backtest(
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_spec(period=(date(2024, 3, 1), date(2024, 6, 28)),
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selection_interval_months=3, rebalance_interval_months=3)
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)
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by_day: dict = {}
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for p in result.selection_history:
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by_day.setdefault(p.date, []).append(p.symbol)
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assert date(2024, 3, 1) in by_day
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assert "600000.SH" not in by_day[date(2024, 3, 1)], "尖峰日应按条件剔除"
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later = [d for d in by_day if d > date(2024, 3, 1)]
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assert later, "应存在后续择股日"
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assert all("600000.SH" in by_day[d] for d in later), "尖峰解除后应恢复入选"
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def test_top_x_cap_and_pool_recorded(self, service) -> None:
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result = service.run_backtest(_spec())
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# 候选池 = n = 3（4 只中剔除 1 只）
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per_day: dict = {}
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for p in result.selection_history:
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per_day.setdefault(p.date, []).append(p.symbol)
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assert per_day and all(len(v) <= 3 for v in per_day.values())
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# 持仓 ≤ x = 2
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held: dict = {}
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for pos in result.positions:
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held.setdefault(pos.date, []).append(pos.symbol)
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assert held and all(len(v) <= 2 for v in held.values())
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def test_hfq_reflects_dividend_jump(self, service) -> None:
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"""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）。"""
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from app.quant.service import load_daily_df
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daily = load_daily_df(
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service._daily_repo, ["600000.SH"], date(2024, 5, 31), date(2024, 6, 4),
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["close"], price_adjust="hfq",
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)
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raw = load_daily_df(
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service._daily_repo, ["600000.SH"], date(2024, 5, 31), date(2024, 6, 4),
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["close"], price_adjust="none",
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)
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adj = daily.sort_values("trade_date")["close"].tolist()
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rawp = raw.sort_values("trade_date")["close"].tolist()
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# 6/3 之前因子 1.0；之后 1.1 → 复权价整体上移
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assert adj[0] == pytest.approx(rawp[0])
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assert adj[-1] == pytest.approx(rawp[-1] * 1.1, rel=1e-9)
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def test_min_commission_increases_cost(self, service) -> None:
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"""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。"""
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free = service.run_backtest(
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_spec(
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initial_capital=20_000.0,
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costs=_spec().costs.model_copy(update={"min_commission": 0.0}),
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)
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)
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costly = service.run_backtest(_spec(initial_capital=20_000.0)) # min_commission=5.0
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assert costly.summary.final_equity < free.summary.final_equity
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def test_unimplemented_notes_surfaced(self, service) -> None:
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result = service.run_backtest(_spec())
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joined = " | ".join(result.unimplemented)
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assert "后复权" in joined
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assert "顺延买入" in joined
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assert "退市" in joined # 幸存者偏差显式标注
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class TestSelectionBacktestConsistencyWithConditions:
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"""v3 §28：回测择股日候选池 == 同日 /api/selections（同一求值器）。"""
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def test_pool_matches_selection_service(self, service) -> None:
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from app.application.services.selection_service import SelectionService
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from app.domain.entities.selection import SelectionQuery
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result = service.run_backtest(
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_spec(selection_interval_months=3, rebalance_interval_months=3)
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)
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sel_service = SelectionService(
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service._stock_repo, service._daily_repo, basic_repo=service._basic_repo
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)
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first_day = min(p.date for p in result.selection_history)
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query = SelectionQuery(
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method="condition",
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price_adjustment="hfq",
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conditions=[{"field": "dv_ratio", "op": "lte", "value": 30}],
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as_of=first_day,
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)
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sel = sel_service.select(query)
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eligible = {c.symbol for c in sel.candidates}
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# 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
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pool = {p.symbol for p in result.selection_history if p.date == first_day}
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assert pool <= eligible, f"候选池 {pool} 不在条件合格集 {eligible} 内"
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assert eligible, "条件合格集不应为空（B/C/D 股 dv_ratio ≤ 30）" |