Simon
ed54096331
- inspect_factor：因子目录元数据（公式/方向/lookback/输入列） - create_composite_factor：解析 name:weight 组件并保存（方向由注册表填充，未注册 400 语义） - get_backtest_result：回测 Experiment 详细结果（收益/回撤/交易/意图与成交统计） - create_experiment：成功 Job 兜底归档为 Experiment（幂等提示） - 白名单工具总数 14（= v3 §25 清单 + get_market_data）；tests/test_agent_v3_tools.py 5 例（数量/各工具行为/幂等）；全量 pytest 通过
596 lines
25 KiB
Python
596 lines
25 KiB
Python
"""受控工具集实现（AGENT.md §28）：Agent 只能调用这里的白名单工具。
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全部工具经 Job/Experiment 链路或只读查询执行：
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- 不提供 shell / 任意代码执行 / 修改配置与凭证 / 删除数据
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- 任何研究都会产出 Experiment 归档（可复现）
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"""
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from __future__ import annotations
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import json
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from datetime import date
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from app.agent.tools import Tool
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from app.application.services.job_executor import default_factories, submit_and_run
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from app.application.services.selection_service import SelectionService
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from app.application.services.signal_service import SignalService
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from app.domain.entities.composite import CompositeComponent, CompositeDefinition
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from app.domain.entities.research import (
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BacktestResult,
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FactorTestReport,
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ResearchSpec,
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UniverseSpec,
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)
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from app.domain.entities.selection import SelectionQuery
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from app.domain.entities.signal import SignalRules
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from app.domain.entities.strategy import StrategyDefinition
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from app.infrastructure.persistence.sqlalchemy.repositories.composite_impl import (
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SqlAlchemyCompositeRepository,
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)
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from app.infrastructure.persistence.sqlalchemy.repositories.factor_impl import (
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SqlAlchemyFactorRepository,
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)
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from app.infrastructure.persistence.sqlalchemy.repositories.selection_impl import (
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SqlAlchemySelectionRepository,
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)
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from app.infrastructure.persistence.sqlalchemy.repositories.strategy_impl import (
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SqlAlchemyStrategyRepository,
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)
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from app.quant.factors import FactorError, get_factor
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def _day(text: str) -> date:
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return date.fromisoformat(text)
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def _pick(mapping: dict, key: str, default=None):
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val = mapping.get(key, default)
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if isinstance(val, str):
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val = val.strip()
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if val == "":
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return default
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return val
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def build_tools(factories: dict | None = None) -> list[Tool]:
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facts = factories or default_factories()
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session_factory = facts["session_factory"]
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stock_repo_f = facts["stock_repo_factory"]
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daily_repo_f = facts["daily_repo_factory"]
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exp_repo_f = facts["experiment_repo_factory"]
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def search_stocks(args: dict) -> str:
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q = str(_pick(args, "q", "") or "").upper()
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with session_factory() as session:
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stocks = stock_repo_f(session).list()
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rows = [
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s for s in stocks if (not q) or q in s.symbol.upper() or q in (s.name or "").upper()
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][:15]
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if not rows:
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return "未找到匹配股票"
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return "\n".join(
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f"{s.symbol} {s.name} 行业={s.industry or '-'} 上市={s.list_date}" for s in rows
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)
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def get_market_data(args: dict) -> str:
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symbol = str(_pick(args, "symbol", "")).upper()
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start = _day(str(_pick(args, "start", "2024-01-01")))
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end = _day(str(_pick(args, "end", date.today().isoformat())))
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with session_factory() as session:
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bars = daily_repo_f(session).get_range(symbol, start, end)
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if not bars:
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return f"{symbol} 在 {start}~{end} 无日线数据（可能未同步）"
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head, tail = bars[0], bars[-1]
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last = "\n".join(f"{b.trade_date} close={b.close}" for b in bars[-8:])
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change = float(tail.close) / float(head.close) - 1 if head.close and tail.close else None
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return (
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f"{symbol} {start}~{end} 共 {len(bars)} 根日线；"
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f"区间 {head.trade_date}→{tail.trade_date} 收盘 {head.close}→{tail.close}"
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f"（涨跌 {change * 100:.2f}% 若数据完整）；最近 8 根：\n{last}"
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)
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def _run_spec(spec: ResearchSpec, desc: str) -> str:
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job = submit_and_run(spec, factories=facts)
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if job.status != "success":
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return f"{desc} 执行失败：{job.error}"
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if spec.type == "backtest":
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result = BacktestResult.model_validate_json(job.result_json or "{}")
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s = result.summary
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return (
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f"回测完成（Experiment {job.experiment_id}，代码版本 {_code_version(job, exp_repo_f)}）。"
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f"总收益 {s.total_return_pct:.2f}%，年化 {s.annual_return_pct:.2f}%，"
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f"Sharpe {s.sharpe:.2f}，最大回撤 {s.max_drawdown_pct:.2f}%，"
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f"交易 {s.total_trades} 笔，平均换手 {s.avg_turnover_pct:.1f}%。"
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f"未建模约束 {len(result.unimplemented)} 项（成本/涨跌停近似见实验详情）。"
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)
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report = FactorTestReport.model_validate_json(job.result_json or "{}")
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qs = ", ".join(f"Q{q.quantile + 1}: {q.return_pct:.2f}%" for q in report.quantile_returns)
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return (
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f"因子测试完成（Experiment {job.experiment_id}）。IC {report.ic_mean:.4f}，"
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f"RankIC {report.rank_ic_mean:.4f}，ICIR {report.icir:.2f}，正收益占比 "
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f"{report.positive_ratio_pct:.1f}%，样本 {report.sample_days} 日；分层未来收益 {qs}。"
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f"注意：单因子测试不代表策略有效，需结合稳健性分析。"
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)
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def test_factor(args: dict) -> str:
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name = str(_pick(args, "name", ""))
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start = _day(str(_pick(args, "start", "2024-01-01")))
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end = _day(str(_pick(args, "end", "2024-12-31")))
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spec = ResearchSpec(
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type="factor_test",
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universe={"exclude_st": True, "min_listing_days": 0},
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factors=[{"name": name, "weight": 1.0}],
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selection={"top_n": 10},
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rebalance="monthly",
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period=(start, end),
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)
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return _run_spec(spec, f"因子 {name} 测试")
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def run_backtest(args: dict) -> str:
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factor_names = [f.strip() for f in str(_pick(args, "factors", "momentum_60")).split(",")]
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top_n = int(_pick(args, "top_n", 5) or 5)
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rebalance = str(_pick(args, "rebalance", "monthly"))
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exclude_st = bool(_pick(args, "exclude_st", True))
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start = _day(str(_pick(args, "start", "2024-01-01")))
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end = _day(str(_pick(args, "end", "2024-12-31")))
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spec = ResearchSpec(
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type="backtest",
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universe={"exclude_st": exclude_st, "min_listing_days": 0},
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factors=[{"name": n, "weight": 1.0} for n in factor_names],
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selection={"top_n": top_n},
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rebalance=rebalance,
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period=(start, end),
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)
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return _run_spec(spec, "回测")
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def get_experiment(args: dict) -> str:
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exp_id = str(_pick(args, "experiment_id", "")).upper()
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with session_factory() as session:
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exp = exp_repo_f(session).get(exp_id)
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if exp is None:
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return f"Experiment {exp_id} 不存在（可用列表：GET /api/experiments）"
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spec = json.loads(exp.spec_json)
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return (
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f"Experiment {exp.id} [{exp.kind}] 因子={[f['name'] for f in spec.get('factors', [])]} "
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f"区间={spec.get('period')} 调仓={spec.get('rebalance')}；摘要：{exp.summary_text or '-'} "
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f"代码版本={exp.code_version or '-'} 创建={exp.created_at}"
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)
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def compare_experiments(args: dict) -> str:
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ids = [
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x.strip().upper()
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for x in str(_pick(args, "experiment_ids", "")).split(",")
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if x.strip()
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]
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if not ids:
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return "请提供 experiment_ids（逗号分隔）"
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with session_factory() as session:
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repo = exp_repo_f(session)
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rows = [(i, repo.get(i)) for i in ids]
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out = []
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for exp_id, exp in rows:
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if exp is None:
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out.append(f"{exp_id}: 不存在")
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else:
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spec = json.loads(exp.spec_json)
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out.append(
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f"{exp.id}: 因子={[f['name'] for f in spec.get('factors', [])]} "
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f"区间={spec.get('period')} → {exp.summary_text or '-'}"
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)
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return "\n".join(out)
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def _scope_symbols(raw: str | None) -> list[str]:
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"""白名单（可选）：避免全市场长任务拖垮同步对话（全市场可用 Web 页异步）。"""
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if not raw:
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return []
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return [x.strip().upper() for x in raw.split(",") if x.strip()][:60]
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def screen_stocks(args: dict) -> str:
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factors = [x.strip() for x in str(_pick(args, "factors", "momentum_60")).split(",") if x.strip()]
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top_n = int(_pick(args, "top_n", 10) or 10)
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as_of = _day(str(_pick(args, "as_of", date.today().isoformat())))
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symbols = _scope_symbols(str(_pick(args, "symbols", "") or ""))
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if not symbols:
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return (
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"为避免全市场长任务（>1 分钟），请传 symbols 白名单（≤60，逗号分隔）"
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"或使用 Web 选股页执行全市场选股。"
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)
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query = SelectionQuery(
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universe=UniverseSpec(
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exclude_st=bool(_pick(args, "exclude_st", True)),
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min_listing_days=0,
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symbols=symbols,
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),
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factors=[{"name": f, "weight": 1.0} for f in factors],
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top_n=top_n,
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as_of=as_of,
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)
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with session_factory() as session:
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service = SelectionService(
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stock_repo_f(session), daily_repo_f(session)
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)
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result = service.select(query)
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if not result.candidates:
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return (
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f"{as_of} 无候选（范围 {result.statistics.universe_size} 只，"
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f"可评分 {result.statistics.evaluated}）。如需白名单可传 symbols（≤60）。"
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)
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lines = [f"as_of={result.as_of_date} 选出 Top{len(result.candidates)}："]
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for c in result.candidates:
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vals = ", ".join(f"{k}={v:.4f}" for k, v in c.factor_values.items())
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lines.append(f" #{c.rank} {c.symbol} score={c.score:.4f}（{vals}）")
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lines.append("入选理由见 explain_selection（selection_id）。")
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return "\n".join(lines)
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def explain_selection(args: dict) -> str:
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sel_id = str(_pick(args, "selection_id", "")).upper()
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with session_factory() as session:
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repo = SqlAlchemySelectionRepository(session)
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result = repo.get(sel_id)
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if result is None:
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return f"选股记录 {sel_id} 不存在（先通过 Web 选股页或 screen_stocks 生成）"
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out = [f"选股 {sel_id} as_of={result.as_of_date}（{result.method}，选出 {len(result.candidates)} 只）"]
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for c in result.candidates[:10]:
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reasons = "; ".join(c.selection_reason[:3])
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out.append(f" #{c.rank} {c.symbol} score={c.score:.4f} — {reasons}")
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return "\n".join(out)
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def generate_signals(args: dict) -> str:
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factors = [x.strip() for x in str(_pick(args, "factors", "momentum_60")).split(",") if x.strip()]
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as_of = _day(str(_pick(args, "as_of", date.today().isoformat())))
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symbols = _scope_symbols(str(_pick(args, "symbols", "") or ""))
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query = SelectionQuery(
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universe=UniverseSpec(
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exclude_st=bool(_pick(args, "exclude_st", True)),
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min_listing_days=0,
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symbols=symbols,
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),
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factors=[{"name": f, "weight": 1.0} for f in factors],
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top_n=int(_pick(args, "top_n", 50) or 50),
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as_of=as_of,
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)
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rules = SignalRules(
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buy_rank_threshold=int(_pick(args, "buy_rank", 20) or 20),
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sell_rank_threshold=int(_pick(args, "sell_rank", 50) or 50),
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)
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with session_factory() as session:
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res = SignalService(stock_repo_f(session), daily_repo_f(session)).signal(query, rules)
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out = [
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f"信号 as_of={res.as_of_date}: BUY {res.statistics.buy} / WATCH {res.statistics.watch} / "
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f"SELL {res.statistics.sell}（前 8 条）"
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]
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for e in res.events[:8]:
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out.append(f" {e.signal_type} {e.symbol} score={e.score:.4f} — {e.trigger_reason[0] if e.trigger_reason else ''}")
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return "\n".join(out)
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def create_strategy(args: dict) -> str:
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name = str(_pick(args, "name", ""))
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if not name:
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return "请提供 name"
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factors = [
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{"name": x.strip(), "weight": 1.0}
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for x in str(_pick(args, "factors", "momentum_60")).split(",")
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if x.strip()
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]
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if not factors:
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return "请提供至少一个 factors（逗号分隔）"
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description = str(_pick(args, "description", "") or "")
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st = StrategyDefinition(
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name=name,
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description=description,
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universe=UniverseSpec(
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exclude_st=bool(_pick(args, "exclude_st", True)), min_listing_days=0
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),
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factors=factors,
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selection={"top_n": int(_pick(args, "top_n", 10) or 10)},
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rebalance=str(_pick(args, "rebalance", "monthly")),
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)
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from app.application.services.job_executor import new_id
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with session_factory() as session:
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saved = SqlAlchemyStrategyRepository(session).save(
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st.model_copy(update={"id": new_id("STG")})
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)
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session.commit()
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return f"策略已保存：{saved.id} {saved.name}（factors={[f.name for f in saved.factors]}）"
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def inspect_factor(args: dict) -> str:
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name = str(_pick(args, "name", ""))
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with session_factory() as session:
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row = SqlAlchemyFactorRepository(session).get(name)
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if row is None:
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return f"因子 {name} 不在目录（可用列表：GET /api/factors）"
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return (
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f"{row.name}：{row.description}\n公式：{row.formula}\n方向："
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f"{'越高越好' if row.direction == 'higher_is_better' else '越低越好'}"
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f"（lookback {row.lookback}，输入 {row.requires}）\n简介：{row.brief}"
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)
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def create_composite_factor(args: dict) -> str:
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name = str(_pick(args, "name", ""))
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raw = str(_pick(args, "factors", ""))
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if not name or not raw:
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return "请提供 name 与 factors（格式：momentum_60:0.7,volatility_60:0.3）"
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comps: list[CompositeComponent] = []
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for part in raw.split(","):
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if not part.strip():
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continue
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seg = part.strip().split(":")
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fname = seg[0].strip()
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weight = float(seg[1]) if len(seg) > 1 and seg[1].strip() else 1.0
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if not fname:
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continue
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try:
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defn, _fn = get_factor(fname)
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except FactorError as exc:
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return f"无法创建：{exc}"
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comps.append(CompositeComponent(name=fname, weight=weight, direction=defn.direction))
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if not comps:
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return "未解析到任何因子组件"
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from app.application.services.job_executor import new_id
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cf = CompositeDefinition(
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name=name, description=str(_pick(args, "description", "") or ""), components=comps
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)
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with session_factory() as session:
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saved = SqlAlchemyCompositeRepository(session).save(
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cf.model_copy(update={"id": new_id("CF")})
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)
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session.commit()
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return (
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f"组合已保存：{saved.id} {saved.name}（"
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+ ", ".join(f"{c.name}:{c.weight}" for c in saved.components)
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+ "）"
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)
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def get_backtest_result(args: dict) -> str:
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exp_id = str(_pick(args, "experiment_id", "")).upper()
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with session_factory() as session:
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exp = exp_repo_f(session).get(exp_id)
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if exp is None:
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return f"Experiment {exp_id} 不存在"
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try:
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result = BacktestResult.model_validate_json(exp.result_json)
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except Exception: # noqa: BLE001
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return f"{exp_id} 不是回测结果"
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sm = result.summary
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return (
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f"回测 {exp_id} {sm.start}~{sm.end}：总收益 {sm.total_return_pct:.2f}%，"
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f"年化 {sm.annual_return_pct:.2f}%，Sharpe {sm.sharpe:.2f}，"
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f"最大回撤 {sm.max_drawdown_pct:.2f}%，期末 {sm.final_equity:,.0f} 元；"
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f"交易 {sm.total_trades} 笔胜率 {sm.win_rate_pct:.1f}%；"
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f"选股记录 {len(result.selection_history)} / 信号 {len(result.signal_history)} / "
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f"成交 {len(result.fills)}；未建模 {len(result.unimplemented)} 项"
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)
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def create_experiment(args: dict) -> str:
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"""把成功 Job 兜底归档为 Experiment（研究工具已自动归档；本工具用于补档）。"""
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job_id = str(_pick(args, "job_id", "")).upper()
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if not job_id:
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return "请提供 job_id"
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from app.application.services.job_executor import new_id
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from app.domain.entities.research import ExperimentRecord
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from app.infrastructure.persistence.sqlalchemy.repositories.jobs_impl import (
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SqlAlchemyJobRepository,
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)
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with session_factory() as session:
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job = SqlAlchemyJobRepository(session).get(job_id)
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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）"
|
||
if job.experiment_id:
|
||
return f"Job {job_id} 已归档为 {job.experiment_id}"
|
||
exp_repo = exp_repo_f(session)
|
||
summary = None
|
||
try:
|
||
if job.kind == "backtest":
|
||
r = BacktestResult.model_validate_json(job.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}%"
|
||
)
|
||
except Exception: # noqa: BLE001
|
||
pass
|
||
exp = ExperimentRecord(
|
||
id=new_id("EXP"),
|
||
kind=job.kind,
|
||
spec_json=job.spec_json,
|
||
result_json=job.result_json,
|
||
summary_text=summary,
|
||
job_id=job.id,
|
||
created_at=job.created_at,
|
||
)
|
||
exp_repo.save(exp)
|
||
job.experiment_id = exp.id
|
||
SqlAlchemyJobRepository(session).update(job)
|
||
session.commit()
|
||
return f"已归档：{exp.id}（Job {job_id} → Experiment）"
|
||
|
||
return [
|
||
Tool(
|
||
"search_stocks",
|
||
"按代码或名称搜索股票，返回基础信息（只读）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"q": {"type": "string", "description": "代码或名称关键字"}},
|
||
},
|
||
search_stocks,
|
||
),
|
||
Tool(
|
||
"get_market_data",
|
||
"读取一只股票一段区间的日线行情摘要（只读，不复权）",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"symbol": {"type": "string", "description": "如 600519.SH"},
|
||
"start": {"type": "string", "description": "YYYY-MM-DD"},
|
||
"end": {"type": "string", "description": "YYYY-MM-DD"},
|
||
},
|
||
"required": ["symbol"],
|
||
},
|
||
get_market_data,
|
||
),
|
||
Tool(
|
||
"test_factor",
|
||
"对单个因子做 IC/RankIC/分层测试并归档 Experiment",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"name": {
|
||
"type": "string",
|
||
"description": "因子名（momentum_60 / volatility_20 等）",
|
||
},
|
||
"start": {"type": "string"},
|
||
"end": {"type": "string"},
|
||
},
|
||
"required": ["name"],
|
||
},
|
||
test_factor,
|
||
),
|
||
Tool(
|
||
"run_backtest",
|
||
"运行 TopK 低频回测并归档 Experiment（成本/涨跌停近似建模）",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"factors": {"type": "string", "description": "逗号分隔的因子名"},
|
||
"top_n": {"type": "integer"},
|
||
"rebalance": {"type": "string", "enum": ["monthly", "weekly"]},
|
||
"exclude_st": {"type": "boolean"},
|
||
"start": {"type": "string"},
|
||
"end": {"type": "string"},
|
||
},
|
||
},
|
||
run_backtest,
|
||
),
|
||
Tool(
|
||
"get_experiment",
|
||
"读取已归档实验的摘要",
|
||
{
|
||
"type": "object",
|
||
"properties": {"experiment_id": {"type": "string"}},
|
||
"required": ["experiment_id"],
|
||
},
|
||
get_experiment,
|
||
),
|
||
Tool(
|
||
"compare_experiments",
|
||
"对比多个实验（因子/区间/收益摘要）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"experiment_ids": {"type": "string"}},
|
||
"required": ["experiment_ids"],
|
||
},
|
||
compare_experiments,
|
||
),
|
||
Tool(
|
||
"screen_stocks",
|
||
"按因子评分筛选股票（TopN；传 symbols 白名单避免全市场长任务）",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"factors": {"type": "string", "description": "逗号分隔因子名"},
|
||
"top_n": {"type": "integer"},
|
||
"as_of": {"type": "string", "description": "YYYY-MM-DD"},
|
||
"symbols": {"type": "string", "description": "逗号分隔白名单（可选，≤60）"},
|
||
},
|
||
},
|
||
screen_stocks,
|
||
),
|
||
Tool(
|
||
"explain_selection",
|
||
"解释一次选股结果：为什么选这些股票（含因子值与理由）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"selection_id": {"type": "string"}},
|
||
"required": ["selection_id"],
|
||
},
|
||
explain_selection,
|
||
),
|
||
Tool(
|
||
"generate_signals",
|
||
"基于选股评分+趋势生成 BUY/WATCH/SELL 信号",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"factors": {"type": "string"},
|
||
"as_of": {"type": "string"},
|
||
"symbols": {"type": "string", "description": "白名单（可选）"},
|
||
"buy_rank": {"type": "integer"},
|
||
"sell_rank": {"type": "integer"},
|
||
},
|
||
},
|
||
generate_signals,
|
||
),
|
||
Tool(
|
||
"create_strategy",
|
||
"创建/保存一个命名策略（可随后展开为回测）",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"name": {"type": "string"},
|
||
"description": {"type": "string"},
|
||
"factors": {"type": "string"},
|
||
"top_n": {"type": "integer"},
|
||
"rebalance": {"type": "string", "enum": ["monthly", "weekly"]},
|
||
},
|
||
"required": ["name", "factors"],
|
||
},
|
||
create_strategy,
|
||
),
|
||
Tool(
|
||
"inspect_factor",
|
||
"查看因子目录元数据（公式/方向/lookback/输入列）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"name": {"type": "string"}},
|
||
"required": ["name"],
|
||
},
|
||
inspect_factor,
|
||
),
|
||
Tool(
|
||
"create_composite_factor",
|
||
"创建并保存多因子组合（factors 格式：momentum_60:0.7,volatility_60:0.3）",
|
||
{
|
||
"type": "object",
|
||
"properties": {
|
||
"name": {"type": "string"},
|
||
"factors": {"type": "string"},
|
||
"description": {"type": "string"},
|
||
},
|
||
"required": ["name", "factors"],
|
||
},
|
||
create_composite_factor,
|
||
),
|
||
Tool(
|
||
"get_backtest_result",
|
||
"读取回测 Experiment 的详细结果（收益/回撤/交易/意图与成交统计）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"experiment_id": {"type": "string"}},
|
||
"required": ["experiment_id"],
|
||
},
|
||
get_backtest_result,
|
||
),
|
||
Tool(
|
||
"create_experiment",
|
||
"把成功 Job 兜底归档为 Experiment（补档；研究工具已自动归档）",
|
||
{
|
||
"type": "object",
|
||
"properties": {"job_id": {"type": "string"}},
|
||
"required": ["job_id"],
|
||
},
|
||
create_experiment,
|
||
),
|
||
]
|
||
|
||
def _code_version(job, exp_repo_f) -> str:
|
||
try:
|
||
with default_factories()["session_factory"]() as session:
|
||
exp = exp_repo_f(session).get(job.experiment_id or "")
|
||
return exp.code_version or "-" if exp else "-"
|
||
except Exception: # noqa: BLE001
|
||
return "-"
|