Simon
0d3e123de3
- quant/selection.score_panel_for_factors：复合分面板构建收敛为共享函数； LocalEngine.run_backtest 与 SelectionEngine.run_score_selection 均调它 —— 消除「回测一套评分、选股另一套」的隐患 - tests/test_selection_backtest_consistency.py：对回测每个调仓日验证 SelectionService.select(as_of=d, top_n) 候选 == 该日回测实际持仓（月调仓多时点）， 排序方向一致性亦验证；全量 pytest 通过
67 lines
2.4 KiB
Python
67 lines
2.4 KiB
Python
"""研究引擎抽象与默认实现（业务层依赖本接口，可替换引擎）。
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切换引擎（如未来在支持平台启用 Qlib）只需注入不同实现 —— 业务代码不变。
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"""
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from __future__ import annotations
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from typing import Protocol
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import pandas as pd
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from app.domain.entities.research import BacktestResult, FactorTestReport, ResearchSpec
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from app.quant.factors import FactorError, get_factor
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from app.quant.local_engine import TopKBacktestRunner, run_spec_factor_test
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from app.quant.selection import score_panel_for_factors
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# LocalEngine 路径恒需 close（TopK 收盘撮合 / 前瞻收益）
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_CLOSE = {"close"}
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def factor_required_columns(spec: ResearchSpec) -> set[str]:
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"""spec 因子计算 + 回测撮合所需的行情数值列（含 close）。"""
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needed = set(_CLOSE)
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for fs in spec.factors:
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try:
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defn, _fn = get_factor(fs.name)
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except FactorError:
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continue # 未知因子由执行期统一报错
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needed.update(defn.requires)
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return needed
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class QuantEngine(Protocol):
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"""研究引擎端口：因子面板构建 / 因子测试 / 回测。"""
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name: str
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def required_columns(self, spec: ResearchSpec) -> set[str]:
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"""执行该 spec 所需的行情数值列（数据装配按此裁剪，控制内存）。"""
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def run_factor_test(
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self, daily: pd.DataFrame, spec: ResearchSpec, horizon_days: int = 21
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) -> FactorTestReport: ...
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def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult: ...
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class LocalEngine:
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"""默认引擎：纯 pandas 实现（无 Qlib 依赖），见 local_engine.py 的纪律说明。"""
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name = "local"
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def required_columns(self, spec: ResearchSpec) -> set[str]:
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return factor_required_columns(spec)
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def run_factor_test(
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self, daily: pd.DataFrame, spec: ResearchSpec, horizon_days: int = 21
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) -> FactorTestReport:
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report, _panels = run_spec_factor_test(daily, spec, horizon_days)
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return report
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def run_backtest(self, daily: pd.DataFrame, spec: ResearchSpec) -> BacktestResult:
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# 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）
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score = score_panel_for_factors(daily, spec.factors)
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close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
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return TopKBacktestRunner(spec, score, close).run()
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