195 lines
7.7 KiB
Python
195 lines
7.7 KiB
Python
"""信号历史胜率回测 + AI 预测留痕核验。
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- compute_signal_stats: 对全市场历史日线回测各技术信号「N 日后上涨概率/平均收益」，
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作为 AI 证据链『历史命中率』的客观依据。
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- record_prediction / verify_predictions / accuracy: 记录每次 AI 诊断，N 日后核验真实涨跌，
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形成可回溯的『实测准确率』。
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"""
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from __future__ import annotations
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import datetime as dt
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import numpy as np
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import pandas as pd
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from sqlalchemy import select, func, distinct
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from db import get_session
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from models import DailyQuote, StockMetric, SignalStat, Prediction
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SIGNAL_DEFS = {
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"ma_bull": "均线多头排列(MA5>MA10>MA20)",
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"macd_gold": "MACD 金叉",
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"up_streak3": "三连阳",
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"vol_breakout": "放量上涨(量比>2)",
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"rsi_oversold": "RSI 超卖(<30)抄底",
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"new_high60": "创60日新高",
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}
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def _indicators(df: pd.DataFrame) -> pd.DataFrame:
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c = df["close"]
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df["ma5"] = c.rolling(5).mean()
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df["ma10"] = c.rolling(10).mean()
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df["ma20"] = c.rolling(20).mean()
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e12 = c.ewm(span=12, adjust=False).mean()
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e26 = c.ewm(span=26, adjust=False).mean()
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df["dif"] = e12 - e26
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df["dea"] = df["dif"].ewm(span=9, adjust=False).mean()
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delta = c.diff()
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gain = delta.clip(lower=0)
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loss = -delta.clip(upper=0)
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ag = gain.rolling(14).mean()
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al = loss.rolling(14).mean()
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df["rsi"] = 100 - 100 / (1 + ag / al.replace(0, np.nan))
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df["vavg5"] = df["volume"].rolling(5).mean().shift(1)
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df["high60"] = c.rolling(60).max()
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return df
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def _masks(df: pd.DataFrame) -> dict:
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c = df["close"]
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c1 = c.shift(1)
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return {
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"ma_bull": (df.ma5 > df.ma10) & (df.ma10 > df.ma20),
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"macd_gold": (df.dif.shift(1) < df.dea.shift(1)) & (df.dif >= df.dea),
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"up_streak3": (c > c1) & (c1 > c.shift(2)) & (c.shift(2) > c.shift(3)),
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"vol_breakout": (df.volume / df.vavg5 > 2) & (c > c1),
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"rsi_oversold": df.rsi < 30,
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"new_high60": c >= df.high60,
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}
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def compute_signal_stats(sample_limit: int = 500, horizon: int = 5):
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"""对样本股历史回测各信号 N 日后表现并落库。"""
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with get_session() as s:
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codes = [r[0] for r in s.execute(select(StockMetric.code).limit(sample_limit)).all()]
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if not codes:
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codes = [r[0] for r in s.execute(select(distinct(DailyQuote.code)).limit(sample_limit)).all()]
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agg = {k: {"n": 0, "win": 0, "sum": 0.0} for k in SIGNAL_DEFS}
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used = 0
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with get_session() as s:
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for code in codes:
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rows = s.execute(select(DailyQuote.date, DailyQuote.close, DailyQuote.volume)
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.where(DailyQuote.code == code).order_by(DailyQuote.date)).all()
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if len(rows) < 80:
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continue
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used += 1
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df = pd.DataFrame(rows, columns=["date", "close", "volume"])
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df["close"] = df["close"].astype(float)
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df["volume"] = df["volume"].astype(float)
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df = _indicators(df)
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fwd = df["close"].shift(-horizon) / df["close"] - 1
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for k, m in _masks(df).items():
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m = m & fwd.notna()
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r = fwd[m]
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if len(r):
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agg[k]["n"] += int(len(r))
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agg[k]["win"] += int((r > 0).sum())
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agg[k]["sum"] += float(r.sum())
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with get_session() as s:
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for k, a in agg.items():
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if a["n"] == 0:
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continue
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wr = round(a["win"] / a["n"] * 100, 1)
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ar = round(a["sum"] / a["n"] * 100, 2)
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row = s.execute(select(SignalStat).where(SignalStat.signal == k, SignalStat.horizon == horizon)).scalar_one_or_none()
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if row:
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row.samples, row.win_rate, row.avg_ret, row.updated_at = a["n"], wr, ar, dt.datetime.now()
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else:
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s.add(SignalStat(signal=k, horizon=horizon, samples=a["n"], win_rate=wr, avg_ret=ar))
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s.commit()
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return {"ok": True, "horizon": horizon, "sampled": used,
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"result": {k: {"samples": a["n"], "win_rate": round(a["win"] / a["n"] * 100, 1) if a["n"] else None}
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for k, a in agg.items()}}
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def get_stats(horizon: int = 5) -> dict:
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with get_session() as s:
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rows = s.execute(select(SignalStat).where(SignalStat.horizon == horizon)).scalars().all()
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return {r.signal: {"label": SIGNAL_DEFS.get(r.signal, r.signal), "win_rate": r.win_rate,
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"avg_ret": r.avg_ret, "samples": r.samples} for r in rows}
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def active_signals(m: StockMetric) -> list[str]:
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"""根据最新因子快照判断当前激活的信号。"""
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out = []
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if m.ma_bull:
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out.append("ma_bull")
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if m.macd_gold:
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out.append("macd_gold")
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if m.up_streak >= 3:
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out.append("up_streak3")
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if m.vol_ratio > 2 and m.pct > 0:
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out.append("vol_breakout")
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if m.rsi14 < 30:
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out.append("rsi_oversold")
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if m.pos60 >= 0.99:
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out.append("new_high60")
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return out
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# ---------------- 预测留痕与核验 ----------------
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def record_prediction(code, name, date, score, confidence, direction, base_close, horizon=5, kind="diagnose"):
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with get_session() as s:
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exist = s.execute(select(Prediction).where(
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Prediction.code == code, Prediction.date == date, Prediction.kind == kind)).scalar_one_or_none()
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if exist:
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return False
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s.add(Prediction(date=date, code=code, name=name, kind=kind, score=score,
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confidence=confidence, direction=direction, horizon=horizon, base_close=base_close))
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s.commit()
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return True
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def verify_predictions():
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"""对到期(已有 horizon 个交易日)的 open 预测核验真实涨跌。"""
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with get_session() as s:
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opens = s.execute(select(Prediction).where(Prediction.status == "open")).scalars().all()
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closed = 0
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for p in opens:
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future = s.execute(select(DailyQuote.close).where(
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DailyQuote.code == p.code, DailyQuote.date > p.date)
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.order_by(DailyQuote.date).limit(p.horizon)).all()
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if len(future) < p.horizon:
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continue
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end = float(future[-1][0])
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ret = (end / p.base_close - 1) * 100 if p.base_close else 0.0
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if p.direction == "up":
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hit = ret > 0
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elif p.direction == "down":
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hit = ret < 0
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else:
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hit = abs(ret) < 2
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p.actual_ret = round(ret, 2)
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p.hit = bool(hit)
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p.status = "closed"
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closed += 1
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s.commit()
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return {"ok": True, "closed": closed}
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def accuracy():
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with get_session() as s:
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rows = s.execute(select(Prediction).where(Prediction.status == "closed")).scalars().all()
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opens = s.execute(select(func.count()).select_from(Prediction).where(Prediction.status == "open")).scalar()
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n = len(rows)
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hits = sum(1 for r in rows if r.hit)
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by_dir = {}
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for r in rows:
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d = by_dir.setdefault(r.direction, {"n": 0, "hit": 0})
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d["n"] += 1
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d["hit"] += 1 if r.hit else 0
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recent = sorted(rows, key=lambda r: (r.date, r.id), reverse=True)[:25]
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return {
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"closed": n, "open": opens or 0,
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"hit_rate": round(hits / n * 100, 1) if n else None,
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"avg_ret": round(sum(r.actual_ret for r in rows) / n, 2) if n else None,
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"by_direction": {k: {"n": v["n"], "hit_rate": round(v["hit"] / v["n"] * 100, 1)}
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for k, v in by_dir.items()},
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"recent": [{"date": r.date.isoformat(), "code": r.code, "name": r.name,
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"direction": r.direction, "score": r.score, "confidence": r.confidence,
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"actual_ret": r.actual_ret, "hit": r.hit, "horizon": r.horizon} for r in recent],
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}
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