优化三层打分模型：短期引入幅度因子与量能确认，中期资金信号连续化，长期加入价格维度，新增波动率调整

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

tushare/src/main.py

@@ -1,5 +1,6 @@
 import argparse
 import sys
+from typing import Optional
 
 from . import contracts, fetcher, scorer, storage
 
@@ -37,15 +38,19 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int:
     print(f"\n信号: {result.signal}")
     print("=" * 65)
 
+    quadrant_names = {
+        "accumulation": "增仓上涨", "distribution": "增仓下跌",
+        "covering": "减仓上涨", "liquidation": "减仓下跌", "flat": "持仓持平",
+    }
     print("\n[短期动力] 近7日逐日打分:")
-    print("-" * 65)
+    print("-" * 80)
     for d in result.detail.short_details:
-        tag = "增仓" if d["oi_chg"] > 0 else "减仓"
+        q = quadrant_names.get(d["quadrant"], d["quadrant"])
-        if abs(d["oi_chg"] / d["oi"]) < 0.01:
+        print(f"  {d['trade_date']}  {q}  "
-            tag = "持平"
+              f"涨跌{d['price_chg_pct']*100:>+.2f}%  "
-        price_dir = "涨" if d["close"] >= d["pre_close"] else "跌"
+              f"OI变化{d['oi_chg_pct']*100:>+.2f}%  "
-        print(f"  {d['trade_date']}  {tag:>4} + {price_dir}  "
+              f"量比{d['vol_ratio']:.2f}  "
-              f"持仓{d['oi_chg']:>+8,.0f}  得分: {d['score']:>3}")
+              f"得分: {d['score']:>5.1f}")
 
     md = result.detail.medium_detail
     print(f"\n[中期趋势] 明细:")
@@ -53,14 +58,24 @@ def run(ts_code: str, trade_date: Optional[str] = None) -> int:
     print(f"  价格信号得分:   {md['price_signal']:.1f}")
     print(f"  增仓上涨天数:   {md['long_up_days']} 天")
     print(f"  增仓下跌天数:   {md['long_down_days']} 天")
-    print(f"  资金意愿得分:   {md['fund_signal']} 分")
+    print(f"  资金意愿得分:   {md['fund_signal']:.1f} 分")
 
     ld = result.detail.long_detail
     print(f"\n[长期结构] 明细:")
+    print(f"  OI趋势得分:     {ld['oi_score']:.1f} (权重 60%)")
+    print(f"  价格趋势得分:   {ld['price_score']:.1f} (权重 40%)")
+    print(f"  30日价格收益:   {ld['price_return_30d_pct']:+.2f}%")
+    print(f"  30日前收盘价:   {ld['price_before_30d']:.2f}")
     print(f"  近30日日均持仓: {ld['avg_oi']:,.0f}")
     print(f"  30日前持仓量:   {ld['oi_before']:,.0f}")
     print(f"  持仓变化幅度:   {ld['change_pct']:+.2f}%")
 
+    vd = result.detail.volatility
+    print(f"\n[波动率调整]")
+    print(f"  日波动率(30d std): {vd['daily_vol_pct']*100:.2f}%")
+    print(f"  ATR%:              {vd['atr_pct']*100:.2f}%")
+    print(f"  惩罚系数:          {vd['vol_penalty']:.3f}")
+
     print(f"\n[OK] 数据已持久化到 PostgreSQL")
 
     return 0

tushare/src/models.py

@@ -22,6 +22,7 @@ class ScoreDetail:
     short_details: list = field(default_factory=list)
     medium_detail: dict = field(default_factory=dict)
     long_detail: dict = field(default_factory=dict)
+    volatility: dict = field(default_factory=dict)
 
 
 @dataclass

tushare/src/scorer.py

@@ -5,44 +5,79 @@ import pandas as pd
 from .models import ScoreDetail, ScoreResult
 
 
-def _daily_short_score(row: pd.Series) -> int:
+def _daily_short_score(row: pd.Series, avg_vol_7d: float) -> dict:
-    """单日短期动力打分。"""
+    """单日短期动力打分（连续值 + 幅度因子 + 量能确认）。"""
     oi = float(row["oi"])
     oi_chg = float(row["oi_chg"])
     close = float(row["close"])
     pre_close = float(row["pre_close"])
+    vol = float(row.get("vol", 0))
 
-    oi_change_pct = abs(oi_chg / oi) if oi != 0 else 0
+    oi_chg_pct = oi_chg / oi if oi != 0 else 0.0
+    price_chg_pct = (close - pre_close) / pre_close if pre_close != 0 else 0.0
     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
+    # 象限基础分
-    if oi_increasing and not price_up:
+    if abs(oi_chg_pct) < 0.01:
-        return 0
+        base = 60.0 if price_up else 40.0
-    if not oi_increasing and price_up:
+        quadrant = "flat"
-        return 70
+    elif oi_increasing and price_up:
-    return 30
+        base = 75.0
+        quadrant = "accumulation"
+    elif oi_increasing and not price_up:
+        base = 25.0
+        quadrant = "distribution"
+    elif not oi_increasing and price_up:
+        base = 65.0
+        quadrant = "covering"
+    else:
+        base = 20.0
+        quadrant = "liquidation"
+
+    # 幅度加成：OI 变化率封顶 5%，价格涨跌幅封顶 3%
+    oi_mag = min(1.0, abs(oi_chg_pct) / 0.05)
+    price_mag = min(1.0, abs(price_chg_pct) / 0.03)
+
+    if quadrant in ("accumulation", "liquidation"):
+        boost = (oi_mag + price_mag) / 2.0 * 20.0
+    elif quadrant == "flat":
+        boost = price_mag * 10.0
+    else:
+        boost = 0.0
+
+    # 量能确认
+    vol_ratio = vol / avg_vol_7d if avg_vol_7d > 0 else 1.0
+    vol_factor = 0.9 + 0.2 * min(vol_ratio, 1.5)
+
+    score = max(0.0, min(100.0, (base + boost) * vol_factor))
+
+    return {
+        "trade_date": str(row["trade_date"]),
+        "close": close,
+        "pre_close": pre_close,
+        "oi": oi,
+        "oi_chg": oi_chg,
+        "oi_chg_pct": round(float(oi_chg_pct), 4),
+        "price_chg_pct": round(float(price_chg_pct), 4),
+        "vol": float(vol),
+        "vol_ratio": round(float(vol_ratio), 2),
+        "quadrant": quadrant,
+        "score": round(float(score), 1),
+    }
 
 
 def calc_short_term(df: pd.DataFrame, window: int = 7) -> tuple[float, list]:
     recent = df.iloc[-window:].copy()
+    avg_vol_7d = float(recent["vol"].mean()) if "vol" in recent.columns else 0.0
+
     scores = []
     details = []
     for _, row in recent.iterrows():
-        score = _daily_short_score(row)
+        detail = _daily_short_score(row, avg_vol_7d)
-        scores.append(score)
+        scores.append(detail["score"])
-        details.append({
+        details.append(detail)
-            "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
 
 
@@ -66,7 +101,7 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
             else:
                 long_down += 1
 
-    fund_score = 80 if long_up > long_down else (20 if long_up < long_down else 50)
+    fund_score = 50.0 + (long_up - long_down) / window * 50.0
     score = price_score * 0.6 + fund_score * 0.4
 
     detail = {
@@ -74,7 +109,8 @@ def calc_medium_term(df: pd.DataFrame, window: int = 15) -> tuple[float, dict]:
         "price_signal": round(price_score, 1),
         "long_up_days": long_up,
         "long_down_days": long_down,
-        "fund_signal": fund_score,
+        "fund_signal": round(fund_score, 1),
+        "window": window,
     }
     return score, detail
 
@@ -84,26 +120,31 @@ def calc_long_term(df: pd.DataFrame, window: int = 30) -> tuple[float, dict]:
         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()
+    avg_oi = float(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
+    oi_change_pct = (avg_oi - oi_before) / oi_before if oi_before != 0 else 0.0
 
-    if change_pct > 0.10:
+    # OI 趋势分 (60%)
-        score = 90
+    oi_score = max(0.0, min(100.0, 50.0 + oi_change_pct * 250))
-    elif change_pct > 0.05:
+
-        score = 70
+    # 价格趋势分 (40%)
-    elif change_pct > -0.05:
+    close_now = float(df.iloc[-1]["close"])
-        score = 50
+    price_before = float(df.iloc[-window - 1]["close"])
-    elif change_pct > -0.10:
+    price_return_30d = (close_now - price_before) / price_before if price_before != 0 else 0.0
-        score = 30
+    price_score = max(0.0, min(100.0, 50.0 + price_return_30d * 200))
-    else:
+
-        score = 10
+    score = oi_score * 0.6 + price_score * 0.4
 
     detail = {
-        "avg_oi": round(float(avg_oi), 0),
+        "avg_oi": round(avg_oi, 0),
         "oi_before": round(oi_before, 0),
-        "change_pct": round(change_pct * 100, 2),
+        "change_pct": round(oi_change_pct * 100, 2),
+        "oi_score": round(oi_score, 1),
+        "price_score": round(price_score, 1),
+        "price_return_30d_pct": round(price_return_30d * 100, 2),
+        "price_before_30d": round(price_before, 2),
+        "window": window,
     }
     return score, detail
 
@@ -139,7 +180,31 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
     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
+    # 波动率调整
+    recent_30 = df.iloc[-30:].copy()
+    recent_30["ret"] = recent_30["close"].pct_change()
+    daily_vol = float(recent_30["ret"].std())
+
+    recent_30["tr"] = recent_30.apply(
+        lambda r: max(
+            r["high"] - r["low"],
+            abs(r["high"] - r["pre_close"]),
+            abs(r["low"] - r["pre_close"]),
+        ),
+        axis=1,
+    )
+    atr = float(recent_30["tr"].mean())
+    avg_close_30 = float(recent_30["close"].mean())
+    atr_pct = (atr / avg_close_30) if avg_close_30 else 0.0
+
+    vol_ref = 0.015
+    if daily_vol <= vol_ref:
+        vol_penalty = 1.0
+    else:
+        vol_penalty = max(0.85, 1.0 - (daily_vol - vol_ref) * 10)
+
+    composite_raw = short * 0.4 + medium * 0.35 + long_ * 0.25
+    composite = round(composite_raw * vol_penalty, 1)
     signal = _interpret(composite)
 
     return ScoreResult(
@@ -157,5 +222,10 @@ def score_daily(df: pd.DataFrame, trade_date: Optional[str] = None) -> ScoreResu
             short_details=short_details,
             medium_detail=medium_detail,
             long_detail=long_detail,
+            volatility={
+                "daily_vol_pct": round(float(daily_vol), 4),
+                "atr_pct": round(float(atr_pct), 4),
+                "vol_penalty": round(float(vol_penalty), 4),
+            },
         ),
     )

web/frontend/src/api/scores.ts

@@ -7,6 +7,11 @@ export interface ShortDetail {
   oi: number
   oi_chg: number
   score: number
+  oi_chg_pct: number
+  price_chg_pct: number
+  vol: number
+  vol_ratio: number
+  quadrant: string
 }
 
 export interface MediumDetail {
@@ -15,18 +20,31 @@ export interface MediumDetail {
   long_up_days: number
   long_down_days: number
   fund_signal: number
+  window: number
 }
 
 export interface LongDetail {
   avg_oi: number
   oi_before: number
   change_pct: number
+  oi_score: number
+  price_score: number
+  price_return_30d_pct: number
+  price_before_30d: number
+  window: number
+}
+
+export interface VolatilityDetail {
+  daily_vol_pct: number
+  atr_pct: number
+  vol_penalty: number
 }
 
 export interface ScoreDetail {
   short_details?: ShortDetail[]
   medium_detail?: MediumDetail
   long_detail?: LongDetail
+  volatility?: VolatilityDetail
 }
 
 export interface Score {

web/frontend/src/components/ScoreDetailDrawer.vue

@@ -34,10 +34,32 @@ watch(
     }
   },
 )
+
+const quadrantTag = (q: string) => {
+  const map: Record<string, string> = {
+    accumulation: 'success',
+    distribution: 'warning',
+    covering: 'info',
+    liquidation: 'danger',
+    flat: '',
+  }
+  return map[q] ?? ''
+}
+
+const quadrantLabel = (q: string) => {
+  const map: Record<string, string> = {
+    accumulation: '增仓涨',
+    distribution: '增仓跌',
+    covering: '减仓涨',
+    liquidation: '减仓跌',
+    flat: '持平',
+  }
+  return map[q] ?? q
+}
 </script>
 
 <template>
-  <el-drawer v-model="visible" title="打分明细" :size="isMobile ? '92%' : '640px'" destroy-on-close>
+  <el-drawer v-model="visible" title="打分明细" :size="isMobile ? '92%' : '680px'" destroy-on-close>
     <div v-loading="loading" v-if="score">
       <el-descriptions :column="isMobile ? 1 : 2" border>
         <el-descriptions-item label="品种">{{ parseTsCode(score.ts_code).symbol }}</el-descriptions-item>
@@ -60,20 +82,47 @@ watch(
 
       <h4 class="section">短期 7 日逐日打分</h4>
       <div class="table-wrapper">
-        <el-table :data="score.detail?.short_details ?? []" size="small" border class="detail-table">
+        <el-table
+          :data="score.detail?.short_details ?? []"
+          size="small"
+          border
+          class="detail-table"
+          max-height="400"
+        >
           <el-table-column prop="trade_date" label="日期" width="100" />
-          <el-table-column prop="close" label="收盘" />
+          <el-table-column prop="close" label="收盘" width="70" />
-          <el-table-column prop="pre_close" label="昨收" />
+          <el-table-column label="涨跌幅" width="80">
-          <el-table-column prop="oi" label="持仓" />
+            <template #default="{ row }">
-          <el-table-column prop="oi_chg" label="持仓变化" />
+              <span :style="{ color: row.price_chg_pct >= 0 ? '#e4393c' : '#1ca11c' }">
-          <el-table-column prop="score" label="单日得分" />
+                {{ ((row.price_chg_pct ?? 0) * 100).toFixed(2) }}%
+              </span>
+            </template>
+          </el-table-column>
+          <el-table-column label="OI变化%" width="90">
+            <template #default="{ row }">
+              {{ ((row.oi_chg_pct ?? 0) * 100).toFixed(2) }}%
+            </template>
+          </el-table-column>
+          <el-table-column label="量比" width="65">
+            <template #default="{ row }">
+              {{ row.vol_ratio ?? '-' }}
+            </template>
+          </el-table-column>
+          <el-table-column label="象限" width="85">
+            <template #default="{ row }">
+              <el-tag :type="quadrantTag(row.quadrant)" size="small">
+                {{ quadrantLabel(row.quadrant) }}
+              </el-tag>
+            </template>
+          </el-table-column>
+          <el-table-column prop="score" label="得分" width="65" />
         </el-table>
       </div>
 
       <h4 class="section">中期(15d)细节</h4>
       <el-descriptions :column="isMobile ? 1 : 2" border v-if="score.detail?.medium_detail">
         <el-descriptions-item label="价格收益率">
-          {{ (score.detail.medium_detail.price_return_pct * 100).toFixed(2) }}%
+          {{ score.detail.medium_detail.price_return_pct.toFixed(2) }}%
         </el-descriptions-item>
         <el-descriptions-item label="价格信号分">
           {{ score.detail.medium_detail.price_signal.toFixed(2) }}
@@ -85,20 +134,48 @@ watch(
           {{ score.detail.medium_detail.long_down_days }}
         </el-descriptions-item>
         <el-descriptions-item label="资金意愿分" :span="isMobile ? 1 : 2">
-          {{ score.detail.medium_detail.fund_signal }}
+          {{ score.detail.medium_detail.fund_signal.toFixed(1) }}
+          <span class="formula-hint">(50 + (增仓涨 - 增仓跌)/{{ score.detail.medium_detail.window ?? 15 }} × 50)</span>
         </el-descriptions-item>
       </el-descriptions>
 
       <h4 class="section">长期(30d)细节</h4>
       <el-descriptions :column="isMobile ? 1 : 2" border v-if="score.detail?.long_detail">
+        <el-descriptions-item label="OI 趋势分">
+          {{ score.detail.long_detail.oi_score?.toFixed(1) ?? '-' }}
+          <span class="formula-hint">(权重 60%)</span>
+        </el-descriptions-item>
+        <el-descriptions-item label="价格趋势分">
+          {{ score.detail.long_detail.price_score?.toFixed(1) ?? '-' }}
+          <span class="formula-hint">(权重 40%)</span>
+        </el-descriptions-item>
+        <el-descriptions-item label="30 日价格收益">
+          {{ score.detail.long_detail.price_return_30d_pct?.toFixed(2) ?? '-' }}%
+        </el-descriptions-item>
+        <el-descriptions-item label="30 日前收盘">
+          {{ score.detail.long_detail.price_before_30d?.toFixed(2) ?? '-' }}
+        </el-descriptions-item>
         <el-descriptions-item label="30 日均持仓">
           {{ score.detail.long_detail.avg_oi.toFixed(0) }}
         </el-descriptions-item>
         <el-descriptions-item label="30 日前持仓">
           {{ score.detail.long_detail.oi_before.toFixed(0) }}
         </el-descriptions-item>
-        <el-descriptions-item label="变化幅度" :span="isMobile ? 1 : 2">
+        <el-descriptions-item label="OI 变化幅度" :span="isMobile ? 1 : 2">
-          {{ (score.detail.long_detail.change_pct * 100).toFixed(2) }}%
+          {{ score.detail.long_detail.change_pct.toFixed(2) }}%
+        </el-descriptions-item>
+      </el-descriptions>
+
+      <h4 class="section">波动率调整</h4>
+      <el-descriptions :column="isMobile ? 1 : 2" border v-if="score.detail?.volatility">
+        <el-descriptions-item label="日波动率(30d std)">
+          {{ (score.detail.volatility.daily_vol_pct * 100).toFixed(2) }}%
+        </el-descriptions-item>
+        <el-descriptions-item label="ATR%">
+          {{ (score.detail.volatility.atr_pct * 100).toFixed(2) }}%
+        </el-descriptions-item>
+        <el-descriptions-item label="波动率惩罚系数" :span="isMobile ? 1 : 2">
+          {{ score.detail.volatility.vol_penalty.toFixed(3) }}
         </el-descriptions-item>
       </el-descriptions>
     </div>
@@ -113,6 +190,11 @@ watch(
   overflow-x: auto;
 }
 .detail-table {
-  min-width: 520px;
+  min-width: 620px;
+}
+.formula-hint {
+  color: var(--el-text-color-secondary);
+  font-size: 12px;
+  margin-left: 6px;
 }
 </style>