139 lines
5.9 KiB
Python
139 lines
5.9 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any
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from backend.bootstrap.config import display_compact_date as _display_date
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from backend.features.screener.backtest import BacktestRunner
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from backend.features.screener.catalog import REGIMES
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from backend.features.screener.factors import FactorBuilder
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from backend.features.screener.formula import FormulaEvaluator
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from database import ReviewDatabase
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class SelectionRunner:
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def __init__(
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self,
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database: ReviewDatabase,
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factor_builder: FactorBuilder,
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formula_evaluator: FormulaEvaluator,
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backtest_runner: BacktestRunner,
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) -> None:
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self.database = database
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self.factor_builder = factor_builder
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self.formula_evaluator = formula_evaluator
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self.backtest_runner = backtest_runner
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def validate_formula(self, formula: dict[str, Any]) -> dict[str, Any]:
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return self.formula_evaluator.validate_formula(formula)
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def build_factors(
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self,
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trade_date: str,
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realtime_snapshot: dict[str, Any] | None,
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history_days: int,
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) -> tuple[list[dict[str, Any]], str]:
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return self.factor_builder.build_factors(
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trade_date, realtime_snapshot, history_days
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)
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def apply_formula(
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self, rows: list[dict[str, Any]], formula: dict[str, Any], regime: str
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) -> list[dict[str, Any]]:
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return self.formula_evaluator.apply_formula(rows, formula, regime)
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def backtest(self, trade_date: str, formula: dict[str, Any]) -> dict[str, Any]:
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return self.backtest_runner.backtest(trade_date, formula)
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def screen(
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self, user_id: int, trade_date: str, formula: dict[str, Any], regime: str,
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strategy_name: str, run_backtest: bool = True,
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realtime_snapshot: dict[str, Any] | None = None,
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mode: str = "smart",
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prepared_factors: list[dict[str, Any]] | None = None,
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prepared_date: str = "",
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) -> dict[str, Any]:
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mode = mode if mode in {"smart", "curated", "quant"} else "smart"
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formula = self.validate_formula(formula)
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if prepared_factors is None:
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history_days = int((formula.get("meta") or {}).get("history_days") or 80)
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factors, actual_date = self.build_factors(
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trade_date, realtime_snapshot, history_days
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)
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else:
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factors = prepared_factors
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actual_date = prepared_date or trade_date
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candidates = self.apply_formula(factors, formula, regime)
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backtest = self.backtest(actual_date, formula) if run_backtest else None
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required_fields = sorted({
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str(item.get("field") or "")
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for item in list(formula.get("filters") or []) + list(formula.get("score") or [])
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if item.get("field")
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})
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complete_rows = sum(
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1 for row in factors
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if all(row.get(field) is not None for field in required_fields)
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)
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coverage = round(complete_rows / len(factors) * 100, 1) if factors else 0.0
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health_status = "normal" if candidates else "no_signal"
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if backtest and backtest["samples"] >= 20:
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for candidate in candidates:
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estimate = backtest["win_rate"] * 0.65 + candidate["score"] * 100 * 0.35
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candidate["historical_probability"] = round(min(95, max(5, estimate)), 1)
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candidate["probability_samples"] = backtest["samples"]
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else:
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for candidate in candidates:
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candidate["historical_probability"] = None
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candidate["probability_samples"] = backtest["samples"] if backtest else 0
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result = {
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"meta": {
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"trade_date": _display_date(actual_date),
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"regime": regime,
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"regime_label": REGIMES.get(regime, regime),
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"strategy_name": strategy_name,
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"mode": mode,
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"library_version": int(
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(formula.get("meta") or {}).get("library_version") or 0
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),
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"universe_count": len(factors),
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"candidate_count": len(candidates),
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"updated_at": datetime.now().astimezone().isoformat(timespec="seconds"),
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"health": {
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"status": health_status,
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"required_field_count": len(required_fields),
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"complete_rows": complete_rows,
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"universe_rows": len(factors),
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"coverage": coverage,
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"signal_count": len(candidates),
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},
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"selection_source": (
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"tushare_rt_k+history" if realtime_snapshot else "historical_eod"
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),
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"realtime": bool(realtime_snapshot),
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"history_cutoff": (
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str(realtime_snapshot.get("previous_trade_date") or "")
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if realtime_snapshot else actual_date
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),
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"factor_freshness": {
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"realtime": [
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"价格", "涨跌幅", "成交量", "成交额", "换手率",
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"均线位置", "5/10日动量", "板块强度", "开盘竞价",
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] if realtime_snapshot else [],
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"historical": ["历史波动率", "流通市值", "资金流", "竞价因子", "回测"],
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},
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},
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"formula": formula,
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"candidates": candidates,
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"backtest": backtest,
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"disclaimer": (
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"候选仅由策略条件与当日数据计算；历史统计不代表未来收益。"
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if mode == "curated"
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else "概率为历史条件估计，不代表未来收益；退潮或样本不足时允许无候选。"
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),
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}
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run_id = self.database.save_screener_run(
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user_id, actual_date, regime, strategy_name, formula, result, mode
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)
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result["meta"]["run_id"] = run_id
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return result
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