feat: 新增趋势质量评估器 (Trend Quality Evaluator)

多因子评分模型 (0-100分制)：
- ADX趋势强度: 30% (ADX>25得满分)
- 均线斜率: 25% (MA20斜率向上加速)
- 波动率收缩: 20% (ATR14/ATR50<0.8)
- 多时间框架共振: 15% (日线+周线共振)
- 成交量确认: 10% (成交量>20日均量1.5倍)

触发条件: ≥60分才建议交易

新增文件:
- trend_quality_evaluator.py: 评估器核心实现
- README.md: 使用说明

trend-quality-evaluator/README.md

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+# 趋势质量评估器 (Trend Quality Evaluator)
+
+多因子评分模型，0-100分制，≥60分触发交易。
+
+## 评分因子
+
+| 因子 | 权重 | 计算方式 | 阈值 | 说明 |
+|------|------|----------|------|------|
+| ADX趋势强度 | 30% | ADX(14) > 25 | 必须满足 | 趋势强度指标 |
+| 均线斜率 | 25% | (MA20/MA20[5]) > 1.002 | 向上加速 | 趋势动量 |
+| 波动率收缩 | 20% | ATR(14)/ATR(50) < 0.8 | 波动率压缩后爆发 |  volatility squeeze |
+| 多时间框架共振 | 15% | 日线突破+周线方向一致 | 趋势确认 | 多周期共振 |
+| 成交量确认 | 10% | 成交量>20日均量1.5倍 | 资金流入 | 量价配合 |
+
+## 使用方法
+
+```python
+from trend_quality_evaluator import TrendQualityEvaluator, fetch_stock_data
+
+# 获取数据
+df_daily = fetch_stock_data("399673", "2024-01-01", "2026-12-31", "d")
+df_weekly = fetch_stock_data("399673", "2023-01-01", "2026-12-31", "w")
+
+# 评估
+evaluator = TrendQualityEvaluator()
+score = evaluator.evaluate(df_daily, df_weekly)
+
+# 查看结果
+print(f"总分: {score.total_score}")
+print(f"可交易: {score.is_tradeable}")
+```
+
+## 评分标准
+
+| 分数 | 评级 | 建议 |
+|------|------|------|
+| 80-100 | 优秀 | 重仓 |
+| 70-79 | 良好 | 中等仓位 |
+| 60-69 | 及格 | 轻仓试探 |
+| 40-59 | 较差 | 观望 |
+| 0-39 | 混乱 | 避免交易 |
+
+## 核心类
+
+### TrendQualityScore
+
+评分结果数据类：
+- `total_score`: 总分 0-100
+- `adx_score`: ADX得分 0-30
+- `ma_slope_score`: 均线斜率得分 0-25
+- `volatility_score`: 波动率得分 0-20
+- `timeframe_score`: 时间框架得分 0-15
+- `volume_score`: 成交量得分 0-10
+- `is_tradeable`: 是否可交易 (≥60分)
+
+### TrendQualityEvaluator
+
+评估器主类：
+- `evaluate(df, df_weekly)`: 评估趋势质量

trend-quality-evaluator/trend_quality_evaluator.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+趋势质量评估器 (Trend Quality Evaluator)
+多因子评分模型：0-100分制，≥60分触发交易
+
+因子权重：
+- ADX趋势强度: 30%
+- 均线斜率: 25%
+- 波动率收缩: 20%
+- 多时间框架共振: 15%
+- 成交量确认: 10%
+"""
+
+import numpy as np
+import pandas as pd
+import baostock as bs
+from dataclasses import dataclass
+from typing import Optional, Tuple
+import warnings
+warnings.filterwarnings('ignore')
+
+
+@dataclass
+class TrendQualityScore:
+    """趋势质量评分结果"""
+    total_score: float          # 总分 0-100
+    adx_score: float            # ADX得分 0-30
+    ma_slope_score: float       # 均线斜率得分 0-25
+    volatility_score: float     # 波动率得分 0-20
+    timeframe_score: float      # 时间框架得分 0-15
+    volume_score: float         # 成交量得分 0-10
+    
+    is_tradeable: bool          # 是否可交易 (>=60分)
+    
+    adx_value: float            # 原始ADX值
+    ma_slope: float             # 均线斜率
+    volatility_ratio: float     # 波动率比率
+    volume_ratio: float         # 成交量比率
+
+
+class TrendQualityEvaluator:
+    """趋势质量评估器"""
+    
+    def __init__(self):
+        self.weights = {
+            'adx': 30,
+            'ma_slope': 25,
+            'volatility': 20,
+            'timeframe': 15,
+            'volume': 10
+        }
+    
+    def calculate_adx(self, df: pd.DataFrame, period: int = 14) -> pd.Series:
+        """计算ADX趋势强度指标"""
+        high, low, close = df['high'], df['low'], df['close']
+        
+        # +DM和-DM
+        plus_dm = high.diff()
+        minus_dm = low.diff().abs()
+        
+        plus_dm = plus_dm.where((plus_dm > minus_dm) & (plus_dm > 0), 0)
+        minus_dm = minus_dm.where((minus_dm > plus_dm) & (minus_dm > 0), 0)
+        
+        # 真实波幅 TR
+        tr1 = high - low
+        tr2 = (high - close.shift()).abs()
+        tr3 = (low - close.shift()).abs()
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        
+        # ATR
+        atr = tr.rolling(period).mean()
+        
+        # +DI和-DI
+        plus_di = 100 * (plus_dm.rolling(period).mean() / atr)
+        minus_di = 100 * (minus_dm.rolling(period).mean() / atr)
+        
+        # DX和ADX
+        dx = (abs(plus_di - minus_di) / (plus_di + minus_di + 1e-10)) * 100
+        adx = dx.rolling(period).mean()
+        
+        return adx
+    
+    def evaluate(self, df: pd.DataFrame, df_weekly: Optional[pd.DataFrame] = None) -> TrendQualityScore:
+        """
+        评估趋势质量
+        
+        Args:
+            df: 日线数据 DataFrame (需要包含 open, high, low, close, volume)
+            df_weekly: 周线数据 (可选，用于多时间框架共振)
+        
+        Returns:
+            TrendQualityScore: 评分结果
+        """
+        latest = df.iloc[-1]
+        
+        # 1. ADX趋势强度 (30分) - 阈值: ADX > 25
+        adx = self.calculate_adx(df, 14)
+        latest_adx = adx.iloc[-1]
+        # 评分: ADX 0-50映射到0-30分，>25得满分
+        if latest_adx >= 25:
+            adx_score = 30
+        else:
+            adx_score = min(30, latest_adx * 30 / 25)
+        
+        # 2. 均线斜率 (25分) - 阈值: MA20/MA20[5] > 1.002
+        ma20 = df['close'].rolling(20).mean()
+        ma20_current = ma20.iloc[-1]
+        ma20_5days_ago = ma20.iloc[-5] if len(ma20) >= 5 else ma20_current
+        ma_slope = ma20_current / ma20_5days_ago if ma20_5days_ago > 0 else 1
+        # 评分: 斜率 > 1.002得满分，线性递减
+        if ma_slope >= 1.005:
+            ma_slope_score = 25
+        elif ma_slope >= 1.002:
+            ma_slope_score = 25 * (ma_slope - 1.002) / (1.005 - 1.002) + 15
+        elif ma_slope >= 1.0:
+            ma_slope_score = 15 * (ma_slope - 1.0) / 0.002
+        else:
+            ma_slope_score = 0
+        
+        # 3. 波动率收缩 (20分) - 阈值: ATR(14)/ATR(50) < 0.8
+        atr14 = self._calculate_atr(df, 14)
+        atr50 = self._calculate_atr(df, 50)
+        volatility_ratio = atr14 / atr50 if atr50 > 0 else 1
+        # 评分: 比率 < 0.8得满分，越小越好
+        if volatility_ratio <= 0.6:
+            volatility_score = 20
+        elif volatility_ratio <= 0.8:
+            volatility_score = 20 - 10 * (volatility_ratio - 0.6) / 0.2
+        elif volatility_ratio <= 1.0:
+            volatility_score = 10 - 10 * (volatility_ratio - 0.8) / 0.2
+        else:
+            volatility_score = 0
+        
+        # 4. 多时间框架共振 (15分) - 日线突破+周线方向一致
+        timeframe_score = 0
+        if df_weekly is not None and len(df_weekly) >= 5:
+            # 日线突破: 收盘价 > MA20
+            daily_breakout = latest['close'] > ma20_current
+            # 周线方向: 周线MA5 > MA10
+            weekly_ma5 = df_weekly['close'].rolling(5).mean().iloc[-1]
+            weekly_ma10 = df_weekly['close'].rolling(10).mean().iloc[-1]
+            weekly_aligned = weekly_ma5 > weekly_ma10
+            
+            if daily_breakout and weekly_aligned:
+                timeframe_score = 15
+            elif daily_breakout or weekly_aligned:
+                timeframe_score = 7.5
+        else:
+            # 无周线数据时，仅看日线突破
+            daily_breakout = latest['close'] > ma20_current
+            timeframe_score = 15 if daily_breakout else 0
+        
+        # 5. 成交量确认 (10分) - 突破当日成交量 > 20日均量1.5倍
+        volume_ma20 = df['volume'].rolling(20).mean().iloc[-1]
+        volume_ratio = latest['volume'] / volume_ma20 if volume_ma20 > 0 else 1
+        # 评分: >1.5倍得满分
+        if volume_ratio >= 2.0:
+            volume_score = 10
+        elif volume_ratio >= 1.5:
+            volume_score = 10 - 5 * (2.0 - volume_ratio) / 0.5
+        elif volume_ratio >= 1.0:
+            volume_score = 5 - 5 * (1.5 - volume_ratio) / 0.5
+        else:
+            volume_score = 0
+        
+        # 计算总分
+        total_score = adx_score + ma_slope_score + volatility_score + timeframe_score + volume_score
+        
+        return TrendQualityScore(
+            total_score=round(total_score, 1),
+            adx_score=round(adx_score, 1),
+            ma_slope_score=round(ma_slope_score, 1),
+            volatility_score=round(volatility_score, 1),
+            timeframe_score=round(timeframe_score, 1),
+            volume_score=round(volume_score, 1),
+            is_tradeable=total_score >= 60,
+            adx_value=round(latest_adx, 2),
+            ma_slope=round(ma_slope, 4),
+            volatility_ratio=round(volatility_ratio, 3),
+            volume_ratio=round(volume_ratio, 2)
+        )
+    
+    def _calculate_atr(self, df: pd.DataFrame, period: int) -> float:
+        """计算ATR"""
+        high, low, close = df['high'], df['low'], df['close']
+        tr1 = high - low
+        tr2 = (high - close.shift()).abs()
+        tr3 = (low - close.shift()).abs()
+        tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
+        return tr.rolling(period).mean().iloc[-1]
+
+
+def fetch_stock_data(symbol: str, start_date: str, end_date: str, frequency: str = "d") -> Optional[pd.DataFrame]:
+    """获取股票数据"""
+    try:
+        bs.login()
+        
+        if symbol.startswith('6'):
+            code = f"sh.{symbol}"
+        elif symbol.startswith('0') or symbol.startswith('3'):
+            code = f"sz.{symbol}"
+        else:
+            code = symbol
+        
+        rs = bs.query_history_k_data_plus(
+            code,
+            "date,open,high,low,close,volume",
+            start_date=start_date,
+            end_date=end_date,
+            frequency=frequency,
+            adjustflag="3"
+        )
+        
+        data = []
+        while rs.error_code == '0' and rs.next():
+            row = rs.get_row_data()
+            data.append({
+                'date': row[0],
+                'open': float(row[1]),
+                'high': float(row[2]),
+                'low': float(row[3]),
+                'close': float(row[4]),
+                'volume': int(float(row[5]))
+            })
+        
+        bs.logout()
+        
+        if not data:
+            return None
+        
+        df = pd.DataFrame(data)
+        df['date'] = pd.to_datetime(df['date'])
+        df = df.set_index('date').sort_index()
+        return df
+    
+    except Exception as e:
+        print(f"数据获取失败: {e}")
+        return None
+
+
+def main():
+    """主函数 - 示例用法"""
+    print("="*70)
+    print("趋势质量评估器 (Trend Quality Evaluator)")
+    print("="*70)
+    
+    # 示例: 评估创业板50
+    symbol = "399673"  # 创业板50
+    
+    print(f"\n评估标的: 创业板50 ({symbol})")
+    print("-"*70)
+    
+    # 获取日线数据
+    df_daily = fetch_stock_data(symbol, "2024-01-01", "2026-12-31", "d")
+    if df_daily is None:
+        print("数据获取失败")
+        return
+    
+    # 获取周线数据（用于多时间框架共振）
+    df_weekly = fetch_stock_data(symbol, "2023-01-01", "2026-12-31", "w")
+    
+    # 评估趋势质量
+    evaluator = TrendQualityEvaluator()
+    score = evaluator.evaluate(df_daily, df_weekly)
+    
+    # 打印结果
+    print(f"\n📊 评估日期: {df_daily.index[-1].strftime('%Y-%m-%d')}")
+    print(f"📈 当前价格: {df_daily['close'].iloc[-1]:.2f}")
+    print()
+    print("="*50)
+    print("评分详情 (满分100分)")
+    print("="*50)
+    print(f"{'1. ADX趋势强度 (30分):':<25} {score.adx_score:>6.1f}分  (ADX={score.adx_value:.2f})")
+    print(f"{'2. 均线斜率 (25分):':<25} {score.ma_slope_score:>6.1f}分  (斜率={score.ma_slope:.4f})")
+    print(f"{'3. 波动率收缩 (20分):':<25} {score.volatility_score:>6.1f}分  (ATR比={score.volatility_ratio:.3f})")
+    print(f"{'4. 多时间框架共振 (15分):':<25} {score.timeframe_score:>6.1f}分")
+    print(f"{'5. 成交量确认 (10分):':<25} {score.volume_score:>6.1f}分  (量比={score.volume_ratio:.2f}x)")
+    print("-"*50)
+    print(f"{'总分:':<25} {score.total_score:>6.1f}分")
+    print("="*50)
+    
+    # 交易建议
+    print(f"\n🎯 交易建议:")
+    if score.is_tradeable:
+        print(f"   ✅ 趋势质量良好 (≥60分)，建议交易")
+        if score.total_score >= 80:
+            print(f"   💎 优秀趋势！建议重仓")
+        elif score.total_score >= 70:
+            print(f"   ⭐ 良好趋势！建议中等仓位")
+        else:
+            print(f"   📌 及格趋势！建议轻仓试探")
+    else:
+        print(f"   ❌ 趋势质量不足 (<60分)，建议观望")
+        if score.total_score < 40:
+            print(f"   ⚠️  趋势混乱，避免交易")
+    
+    print("\n" + "="*70)
+
+
+if __name__ == "__main__":
+    main()