构建期货数据采集与三层打分系统

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

tushare/src/scorer.py

@@ -0,0 +1,149 @@
+import pandas as pd
+
+from .models import ScoreDetail, ScoreResult
+
+
+def _daily_short_score(row: pd.Series) -> int:
+    """单日短期动力打分。"""
+    oi = float(row["oi"])
+    oi_chg = float(row["oi_chg"])
+    close = float(row["close"])
+    pre_close = float(row["pre_close"])
+
+    oi_change_pct = abs(oi_chg / oi) if oi != 0 else 0
+    price_up = close >= pre_close
+
+    if oi_change_pct < 0.01:
+        return 60 if price_up else 40
+
+    oi_increasing = oi_chg > 0
+    if oi_increasing and price_up:
+        return 100
+    if oi_increasing and not price_up:
+        return 0
+    if not oi_increasing and price_up:
+        return 70
+    return 30
+
+
+def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]:
+    recent = df.iloc[-window:].copy()
+    scores = []
+    details = []
+    for _, row in recent.iterrows():
+        score = _daily_short_score(row)
+        scores.append(score)
+        details.append({
+            "trade_date": str(row["trade_date"]),
+            "close": float(row["close"]),
+            "pre_close": float(row["pre_close"]),
+            "oi": float(row["oi"]),
+            "oi_chg": float(row["oi_chg"]),
+            "score": score,
+        })
+    return sum(scores) / len(scores), details
+
+
+def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
+    if len(df) < window + 1:
+        raise ValueError(f"数据不足,需要至少 {window + 1} 行")
+
+    recent = df.iloc[-window:].copy()
+    close_now = float(df.iloc[-1]["close"])
+    close_before = float(df.iloc[-window - 1]["close"])
+
+    price_return = (close_now - close_before) / close_before if close_before != 0 else 0
+    price_score = max(0.0, min(100.0, 50.0 + price_return * 500))
+
+    long_up = 0
+    long_down = 0
+    for _, row in recent.iterrows():
+        if row["oi_chg"] > 0:
+            if row["close"] >= row["pre_close"]:
+                long_up += 1
+            else:
+                long_down += 1
+
+    fund_score = 80 if long_up > long_down else (20 if long_up < long_down else 50)
+    score = price_score * 0.6 + fund_score * 0.4
+
+    detail = {
+        "price_return_pct": round(price_return * 100, 2),
+        "price_signal": round(price_score, 1),
+        "long_up_days": long_up,
+        "long_down_days": long_down,
+        "fund_signal": fund_score,
+    }
+    return score, detail
+
+
+def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]:
+    if len(df) < window + 1:
+        raise ValueError(f"数据不足,需要至少 {window + 1} 行")
+
+    recent_oi = df.iloc[-window:]["oi"]
+    avg_oi = recent_oi.mean()
+    oi_before = float(df.iloc[-window - 1]["oi"])
+
+    change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0
+
+    if change_pct > 0.10:
+        score = 90
+    elif change_pct > 0.05:
+        score = 70
+    elif change_pct > -0.05:
+        score = 50
+    elif change_pct > -0.10:
+        score = 30
+    else:
+        score = 10
+
+    detail = {
+        "avg_oi": round(float(avg_oi), 0),
+        "oi_before": round(oi_before, 0),
+        "change_pct": round(change_pct * 100, 2),
+    }
+    return score, detail
+
+
+def _interpret(composite: float) -> str:
+    if composite >= 80:
+        return "强烈看多区域 — 价格与资金共振,趋势多头的温床"
+    if composite >= 50:
+        return "偏多/震荡偏强 — 上涨但资金犹豫,或空头离场反弹"
+    if composite >= 40:
+        return "偏空/震荡偏弱 — 多头止损,或缺乏资金的阴跌"
+    return "强烈看空区域 — 资金主动且持续地打压价格"
+
+
+def score_daily(df: pd.DataFrame) -> ScoreResult:
+    """对 DataFrame 中最新一条记录打分。"""
+    if len(df) < 31:
+        raise ValueError(f"数据量不足(仅 {len(df)} 行),需要至少 31 行")
+
+    latest = df.iloc[-1]
+
+    short, short_details = calc_short_term(df, 7)
+    medium, medium_detail = calc_medium_term(df, 15)
+    long_, long_detail = calc_long_term(df, 30)
+
+    composite = short * 0.4 + medium * 0.35 + long_ * 0.25
+    signal = _interpret(composite)
+
+    return ScoreResult(
+        ts_code=str(latest["ts_code"]),
+        trade_date=str(latest["trade_date"]),
+        close=float(latest["close"]),
+        oi=float(latest["oi"]),
+        oi_chg=float(latest["oi_chg"]),
+        short_term=round(short, 1),
+        medium_term=round(medium, 1),
+        long_term=round(long_, 1),
+        composite=round(composite, 1),
+        signal=signal,
+        detail=ScoreDetail(
+            short_details=short_details,
+            medium_detail=medium_detail,
+            long_detail=long_detail,
+        ),
+    )