Simon
ba52edc2d6
- SignalRules（买入 rank 阈值/趋势 MA/动量 + 卖出区间/破位警示）+ SignalEvent （BUY/WATCH/SELL，score/price/trigger_reason 可解释）+ SignalResult/Meta - quant/signal.generate_signals：与选股同一评分引擎取全市场 rank，按规则分类输出 - signal_snapshot/signal_event 表（migration d8e0b2f3c4d5，MySQL 已应用）+ Repo - SignalService + POST /api/signals（同步+落库）、GET 详情/列表 - tests/test_signals.py（引擎分类/排序/破位不 BUY、service、API 提交读回）；全量 pytest 通过
136 lines
4.9 KiB
Python
136 lines
4.9 KiB
Python
"""Signal Engine（v2 §15）—— 纯 pandas 执行。
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输入：行情长表（<=as_of）+ SelectionQuery（评分因子）+ SignalRules。
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流程：复合分 → 全市场 rank → 按规则判定 BUY / WATCH / SELL，
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事件带 trigger_reason 与 score/price（可解释）。回测的买入逻辑（TopK+可买过滤）
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与这里的 BUY 建议同源于同一评分引擎（v2 §25 一致）。
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"""
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from __future__ import annotations
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from datetime import date
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import pandas as pd
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from app.domain.entities.selection import SelectionQuery
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from app.domain.entities.signal import (
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SignalEvent,
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SignalResult,
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SignalRules,
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SignalStatistics,
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)
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from app.quant.composite import build_score_panel
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from app.quant.selection import resolve_observation_date
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def generate_signals(
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daily: pd.DataFrame,
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query: SelectionQuery,
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rules: SignalRules,
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as_of: date | None,
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) -> SignalResult:
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if not query.factors:
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raise ValueError("signal 需要评分因子（SelectionQuery.factors）")
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obs = resolve_observation_date(daily, as_of)
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resolved = (obs.date() if obs is not None else as_of) or date.today()
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if obs is None or daily.empty:
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return _empty(query, rules, resolved)
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view = daily[pd.to_datetime(daily["trade_date"]) <= obs]
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score = build_score_panel(view, query.factors).loc[obs].dropna().sort_values(ascending=False)
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close = view.pivot(index="trade_date", columns="symbol", values="close").sort_index()
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close.index = pd.to_datetime(close.index)
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c_d = close.loc[obs]
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ma_d = close.rolling(rules.trend_ma).mean().loc[obs]
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mom20_d = (close / close.shift(20) - 1.0).loc[obs]
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events: list[SignalEvent] = []
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stats = SignalStatistics(universe_size=int(len(score)))
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for rank, (sym, sc) in enumerate(score.items(), start=1):
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if rank > rules.max_output_rank:
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break
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c = _num(c_d.get(sym))
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ma = _num(ma_d.get(sym))
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mom = _num(mom20_d.get(sym))
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price = float(c) if c is not None else None
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reason: list[str] = []
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event_type = "WATCH"
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trend_ok = c is not None and ma is not None and c > ma
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momentum_ok = mom is not None and mom > 0
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if rank <= rules.buy_rank_threshold and (
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not rules.buy_require_trend or trend_ok
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) and (not rules.buy_require_momentum or momentum_ok):
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event_type = "BUY"
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reason = [f"综合分排名第 {rank}（≤买入阈值 {rules.buy_rank_threshold}）"]
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if rules.buy_require_trend:
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reason.append(f"close > MA{rules.trend_ma}（趋势向上）")
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if rules.buy_require_momentum:
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reason.append("close > 20 日前收盘（动量为正）")
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elif rank <= rules.buy_rank_threshold:
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event_type = "WATCH"
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reason = [f"综合分排名第 {rank}（买入区间）"]
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if rules.buy_require_trend and not trend_ok:
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reason.append(f"但 close < MA{rules.trend_ma}（趋势未确认）")
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elif rank <= rules.sell_rank_threshold:
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# 观望带
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if rules.sell_on_trend_break and c is not None and ma is not None and c < ma:
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event_type = "SELL"
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reason = [f"跌破 MA{rules.trend_ma}（持仓者应卖出/减仓），rank={rank}"]
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else:
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event_type = "WATCH"
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reason = [f"rank={rank}（买入区间外、卖出区间内：观望）"]
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else:
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event_type = "SELL"
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reason = [f"综合分排名第 {rank}（>卖出阈值 {rules.sell_rank_threshold}，持仓者应卖出）"]
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if c is not None and c < 0:
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continue # 防御负价
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events.append(
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SignalEvent(
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symbol=sym,
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signal_date=resolved,
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signal_type=event_type,
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score=round(float(sc), 6),
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price=round(price, 4) if price is not None else None,
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trigger_reason=reason,
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)
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)
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if event_type == "BUY":
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stats.buy += 1
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elif event_type == "SELL":
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stats.sell += 1
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else:
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stats.watch += 1
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return SignalResult(
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as_of_date=resolved,
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rules=rules,
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statistics=stats,
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events=events,
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config_snapshot={
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"as_of": resolved.isoformat(),
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"factors": [f.model_dump() for f in query.factors],
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"rules": rules.model_dump(mode="json"),
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},
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)
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def _num(v) -> float | None:
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if v is None:
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return None
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try:
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f = float(v)
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except (TypeError, ValueError):
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return None
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return None if f != f else f # NaN → None
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def _empty(query: SelectionQuery, rules: SignalRules, resolved: date) -> SignalResult:
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return SignalResult(
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as_of_date=resolved,
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rules=rules,
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statistics=SignalStatistics(),
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events=[],
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config_snapshot={"as_of": resolved.isoformat(), "rules": rules.model_dump(mode="json")},
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)
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