Initial commit: stock analysis backend and prototype UI.

Co-authored-by: Cursor <cursoragent@cursor.com>

backend/signals.py

@@ -0,0 +1,194 @@
+"""信号历史胜率回测 + AI 预测留痕核验。
+
+- 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」，
+  作为 AI 证据链『历史命中率』的客观依据。
+- record_prediction / verify_predictions / accuracy: 记录每次 AI 诊断，N 日后核验真实涨跌，
+  形成可回溯的『实测准确率』。
+"""
+from __future__ import annotations
+
+import datetime as dt
+
+import numpy as np
+import pandas as pd
+from sqlalchemy import select, func, distinct
+
+from db import get_session
+from models import DailyQuote, StockMetric, SignalStat, Prediction
+
+SIGNAL_DEFS = {
+    "ma_bull": "均线多头排列(MA5>MA10>MA20)",
+    "macd_gold": "MACD 金叉",
+    "up_streak3": "三连阳",
+    "vol_breakout": "放量上涨(量比>2)",
+    "rsi_oversold": "RSI 超卖(<30)抄底",
+    "new_high60": "创60日新高",
+}
+
+
+def _indicators(df: pd.DataFrame) -> pd.DataFrame:
+    c = df["close"]
+    df["ma5"] = c.rolling(5).mean()
+    df["ma10"] = c.rolling(10).mean()
+    df["ma20"] = c.rolling(20).mean()
+    e12 = c.ewm(span=12, adjust=False).mean()
+    e26 = c.ewm(span=26, adjust=False).mean()
+    df["dif"] = e12 - e26
+    df["dea"] = df["dif"].ewm(span=9, adjust=False).mean()
+    delta = c.diff()
+    gain = delta.clip(lower=0)
+    loss = -delta.clip(upper=0)
+    ag = gain.rolling(14).mean()
+    al = loss.rolling(14).mean()
+    df["rsi"] = 100 - 100 / (1 + ag / al.replace(0, np.nan))
+    df["vavg5"] = df["volume"].rolling(5).mean().shift(1)
+    df["high60"] = c.rolling(60).max()
+    return df
+
+
+def _masks(df: pd.DataFrame) -> dict:
+    c = df["close"]
+    c1 = c.shift(1)
+    return {
+        "ma_bull": (df.ma5 > df.ma10) & (df.ma10 > df.ma20),
+        "macd_gold": (df.dif.shift(1) < df.dea.shift(1)) & (df.dif >= df.dea),
+        "up_streak3": (c > c1) & (c1 > c.shift(2)) & (c.shift(2) > c.shift(3)),
+        "vol_breakout": (df.volume / df.vavg5 > 2) & (c > c1),
+        "rsi_oversold": df.rsi < 30,
+        "new_high60": c >= df.high60,
+    }
+
+
+def compute_signal_stats(sample_limit: int = 500, horizon: int = 5):
+    """对样本股历史回测各信号 N 日后表现并落库。"""
+    with get_session() as s:
+        codes = [r[0] for r in s.execute(select(StockMetric.code).limit(sample_limit)).all()]
+        if not codes:
+            codes = [r[0] for r in s.execute(select(distinct(DailyQuote.code)).limit(sample_limit)).all()]
+
+    agg = {k: {"n": 0, "win": 0, "sum": 0.0} for k in SIGNAL_DEFS}
+    used = 0
+    with get_session() as s:
+        for code in codes:
+            rows = s.execute(select(DailyQuote.date, DailyQuote.close, DailyQuote.volume)
+                             .where(DailyQuote.code == code).order_by(DailyQuote.date)).all()
+            if len(rows) < 80:
+                continue
+            used += 1
+            df = pd.DataFrame(rows, columns=["date", "close", "volume"])
+            df["close"] = df["close"].astype(float)
+            df["volume"] = df["volume"].astype(float)
+            df = _indicators(df)
+            fwd = df["close"].shift(-horizon) / df["close"] - 1
+            for k, m in _masks(df).items():
+                m = m & fwd.notna()
+                r = fwd[m]
+                if len(r):
+                    agg[k]["n"] += int(len(r))
+                    agg[k]["win"] += int((r > 0).sum())
+                    agg[k]["sum"] += float(r.sum())
+
+    with get_session() as s:
+        for k, a in agg.items():
+            if a["n"] == 0:
+                continue
+            wr = round(a["win"] / a["n"] * 100, 1)
+            ar = round(a["sum"] / a["n"] * 100, 2)
+            row = s.execute(select(SignalStat).where(SignalStat.signal == k, SignalStat.horizon == horizon)).scalar_one_or_none()
+            if row:
+                row.samples, row.win_rate, row.avg_ret, row.updated_at = a["n"], wr, ar, dt.datetime.now()
+            else:
+                s.add(SignalStat(signal=k, horizon=horizon, samples=a["n"], win_rate=wr, avg_ret=ar))
+        s.commit()
+    return {"ok": True, "horizon": horizon, "sampled": used,
+            "result": {k: {"samples": a["n"], "win_rate": round(a["win"] / a["n"] * 100, 1) if a["n"] else None}
+                       for k, a in agg.items()}}
+
+
+def get_stats(horizon: int = 5) -> dict:
+    with get_session() as s:
+        rows = s.execute(select(SignalStat).where(SignalStat.horizon == horizon)).scalars().all()
+    return {r.signal: {"label": SIGNAL_DEFS.get(r.signal, r.signal), "win_rate": r.win_rate,
+                       "avg_ret": r.avg_ret, "samples": r.samples} for r in rows}
+
+
+def active_signals(m: StockMetric) -> list[str]:
+    """根据最新因子快照判断当前激活的信号。"""
+    out = []
+    if m.ma_bull:
+        out.append("ma_bull")
+    if m.macd_gold:
+        out.append("macd_gold")
+    if m.up_streak >= 3:
+        out.append("up_streak3")
+    if m.vol_ratio > 2 and m.pct > 0:
+        out.append("vol_breakout")
+    if m.rsi14 < 30:
+        out.append("rsi_oversold")
+    if m.pos60 >= 0.99:
+        out.append("new_high60")
+    return out
+
+
+# ---------------- 预测留痕与核验 ----------------
+def record_prediction(code, name, date, score, confidence, direction, base_close, horizon=5, kind="diagnose"):
+    with get_session() as s:
+        exist = s.execute(select(Prediction).where(
+            Prediction.code == code, Prediction.date == date, Prediction.kind == kind)).scalar_one_or_none()
+        if exist:
+            return False
+        s.add(Prediction(date=date, code=code, name=name, kind=kind, score=score,
+                         confidence=confidence, direction=direction, horizon=horizon, base_close=base_close))
+        s.commit()
+        return True
+
+
+def verify_predictions():
+    """对到期(已有 horizon 个交易日)的 open 预测核验真实涨跌。"""
+    with get_session() as s:
+        opens = s.execute(select(Prediction).where(Prediction.status == "open")).scalars().all()
+        closed = 0
+        for p in opens:
+            future = s.execute(select(DailyQuote.close).where(
+                DailyQuote.code == p.code, DailyQuote.date > p.date)
+                .order_by(DailyQuote.date).limit(p.horizon)).all()
+            if len(future) < p.horizon:
+                continue
+            end = float(future[-1][0])
+            ret = (end / p.base_close - 1) * 100 if p.base_close else 0.0
+            if p.direction == "up":
+                hit = ret > 0
+            elif p.direction == "down":
+                hit = ret < 0
+            else:
+                hit = abs(ret) < 2
+            p.actual_ret = round(ret, 2)
+            p.hit = bool(hit)
+            p.status = "closed"
+            closed += 1
+        s.commit()
+    return {"ok": True, "closed": closed}
+
+
+def accuracy():
+    with get_session() as s:
+        rows = s.execute(select(Prediction).where(Prediction.status == "closed")).scalars().all()
+        opens = s.execute(select(func.count()).select_from(Prediction).where(Prediction.status == "open")).scalar()
+    n = len(rows)
+    hits = sum(1 for r in rows if r.hit)
+    by_dir = {}
+    for r in rows:
+        d = by_dir.setdefault(r.direction, {"n": 0, "hit": 0})
+        d["n"] += 1
+        d["hit"] += 1 if r.hit else 0
+    recent = sorted(rows, key=lambda r: (r.date, r.id), reverse=True)[:25]
+    return {
+        "closed": n, "open": opens or 0,
+        "hit_rate": round(hits / n * 100, 1) if n else None,
+        "avg_ret": round(sum(r.actual_ret for r in rows) / n, 2) if n else None,
+        "by_direction": {k: {"n": v["n"], "hit_rate": round(v["hit"] / v["n"] * 100, 1)}
+                         for k, v in by_dir.items()},
+        "recent": [{"date": r.date.isoformat(), "code": r.code, "name": r.name,
+                    "direction": r.direction, "score": r.score, "confidence": r.confidence,
+                    "actual_ret": r.actual_ret, "hit": r.hit, "horizon": r.horizon} for r in recent],
+    }