添加年度收益对比脚本和数据获取脚本

新增文件:
- quant/compare_yearly_returns.py: 全策略年度收益对比分析
- quant/fetch_data.py: 从baostock获取真实数据脚本

功能:
- 对比5种策略（趋势跟踪、双均线、动量、多因子、RSI）年度收益
- 计算超额收益和跑赢年数
- 提供数据获取参考实现

quant/compare_yearly_returns.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数 - 全策略年度收益对比（真实数据）
+对比策略：趋势跟踪、双均线、动量、多因子、RSI
+"""
+
+import pandas as pd
+import numpy as np
+import warnings
+warnings.filterwarnings('ignore')
+
+print("="*90)
+print("创业板50指数 - 全策略年度收益对比（真实数据）")
+print("="*90)
+
+# ==================== 1. 加载真实数据 ====================
+def load_real_data():
+    df = pd.read_csv('cyb50_baostock.csv')
+    df['date'] = pd.to_datetime(df['date'])
+    df = df.set_index('date').sort_index()
+    for col in ['open', 'high', 'low', 'close', 'volume']:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    return df
+
+# ==================== 2. 策略定义 ====================
+
+# 策略1: 趋势跟踪
+def strategy_trend(data, pos):
+    close = data['close'].values
+    high = data['high'].values
+    low = data['low'].values
+    if len(close) < 60:
+        return 0, "INIT"
+    
+    ma10 = np.mean(close[-10:])
+    ma30 = np.mean(close[-30:])
+    ret10 = (close[-1] / close[-10] - 1) if len(close) >= 10 else 0
+    high_20 = np.max(high[-20:])
+    low_20 = np.min(low[-20:])
+    curr = close[-1]
+    
+    buy = (curr > ma10 > ma30) and (curr >= high_20 * 0.995) and (ret10 > 0.02)
+    sell = (curr < ma30) or (curr <= low_20 * 1.005)
+    
+    if buy and pos == 0:
+        return 1.0, "ENTRY"
+    elif sell and pos > 0:
+        return 0.0, "EXIT"
+    return pos, "HOLD" if pos > 0 else "EMPTY"
+
+# 策略2: 双均线
+def strategy_ma_cross(data, pos):
+    close = data['close'].values
+    if len(close) < 60:
+        return 0, "INIT"
+    
+    ma20 = np.mean(close[-20:])
+    ma60 = np.mean(close[-60:])
+    curr = close[-1]
+    
+    if curr > ma20 > ma60:
+        return 1.0, "BULL"
+    elif curr < ma60:
+        return 0.0, "BEAR"
+    return pos, "HOLD"
+
+# 策略3: 动量
+def strategy_momentum(data, pos):
+    close = data['close']
+    if len(close) < 60:
+        return 0, "INIT"
+    
+    ma5 = close.rolling(5).mean().iloc[-1]
+    ma20 = close.rolling(20).mean().iloc[-1]
+    ma60 = close.rolling(60).mean().iloc[-1]
+    
+    momentum = (close.iloc[-1] / close.iloc[-10] - 1) * 100
+    
+    trend_strong = (close.iloc[-1] > ma5) and (ma5 > ma20) and (ma20 > ma60)
+    trend_weak = (close.iloc[-1] < ma5) and (ma5 < ma20)
+    
+    if trend_strong and momentum > 2:
+        return 1.0, "STRONG_UP"
+    elif trend_strong and momentum > 0:
+        return 0.8, "UP"
+    elif trend_weak or momentum < -3:
+        return 0.0, "DOWN"
+    return 0.5, "OSCILLATE"
+
+# 策略4: 多因子
+def strategy_multifactor(data, pos):
+    c = data['close']
+    h = data['high']
+    l = data['low']
+    
+    if len(c) < 60:
+        return 0, "INIT"
+    
+    # 趋势因子
+    ma5 = c.rolling(5).mean()
+    ma20 = c.rolling(20).mean()
+    ma60 = c.rolling(60).mean()
+    
+    trend_score = 0
+    if c.iloc[-1] > ma5.iloc[-1]: trend_score += 1
+    if ma5.iloc[-1] > ma20.iloc[-1]: trend_score += 1
+    if ma20.iloc[-1] > ma60.iloc[-1]: trend_score += 1
+    trend_score = trend_score / 3
+    
+    # 动量因子
+    ret20 = (c.iloc[-1] / c.iloc[-20] - 1) if len(c) >= 20 else 0
+    mom_score = np.clip((ret20 + 0.2) / 0.4, 0, 1)
+    
+    # 波动率因子
+    atr = pd.concat([h-l, (h-c.shift(1)).abs(), (l-c.shift(1)).abs()], axis=1).max(axis=1)
+    atr_mean = atr.rolling(20).mean().iloc[-1]
+    vol_pct = atr_mean / c.iloc[-1]
+    vol_score = 1 - np.clip((vol_pct - 0.015) / 0.025, 0, 1)
+    
+    # 突破因子
+    high_20 = h.rolling(20).max()
+    breakout = 1 if c.iloc[-1] >= high_20.iloc[-1] * 0.99 else 0
+    
+    # 综合得分
+    total_score = trend_score * 0.35 + mom_score * 0.25 + vol_score * 0.25 + breakout * 0.15
+    
+    if total_score > 0.7:
+        return 1.0, "STRONG"
+    elif total_score > 0.5:
+        return 0.6, "MEDIUM"
+    elif total_score > 0.3:
+        return 0.3, "WEAK"
+    return 0.0, "EMPTY"
+
+# 策略5: RSI
+def strategy_rsi(data, pos):
+    close = data['close']
+    if len(close) < 20:
+        return 0, "INIT"
+    
+    delta = close.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
+    rs = gain / loss
+    rsi = 100 - (100 / (1 + rs))
+    
+    curr_rsi = rsi.iloc[-1]
+    
+    if pd.isna(curr_rsi):
+        return 0, "INIT"
+    
+    if curr_rsi < 30:
+        return 1.0, "OVERSOLD"
+    elif curr_rsi > 70:
+        return 0.0, "OVERBOUGHT"
+    return pos, "HOLD"
+
+# ==================== 3. 回测引擎 ====================
+def backtest_yearly(data, strategy_func, year, warmup=60):
+    """年度回测"""
+    year_data = data[data.index.year == year].copy()
+    if len(year_data) < warmup + 5:
+        return None
+    
+    nav = 1.0
+    position = 0
+    
+    for i in range(warmup, len(year_data)):
+        curr_data = data.loc[:year_data.index[i]]
+        new_pos, state = strategy_func(curr_data, position)
+        
+        if i > warmup:
+            daily_ret = year_data['close'].iloc[i] / year_data['close'].iloc[i-1] - 1
+            nav *= (1 + daily_ret * position)
+        
+        position = new_pos if new_pos is not None else position
+    
+    return (nav - 1) * 100  # 返回百分比收益
+
+# ==================== 4. 主程序 ====================
+def main():
+    # 加载数据
+    data = load_real_data()
+    
+    strategies = [
+        ("趋势跟踪", strategy_trend),
+        ("双均线", strategy_ma_cross),
+        ("动量", strategy_momentum),
+        ("多因子", strategy_multifactor),
+        ("RSI", strategy_rsi),
+    ]
+    
+    years = list(range(2018, 2026))
+    
+    # 收集结果
+    results = {name: {} for name, _ in strategies}
+    results["买入持有"] = {}
+    
+    print("\n开始年度回测...")
+    print("-"*90)
+    
+    for year in years:
+        year_data = data[data.index.year == year]
+        if len(year_data) == 0:
+            continue
+        
+        # 买入持有收益
+        start_price = year_data['close'].iloc[60]  # 考虑warmup
+        end_price = year_data['close'].iloc[-1]
+        buyhold_ret = (end_price / start_price - 1) * 100
+        results["买入持有"][year] = buyhold_ret
+        
+        # 各策略收益
+        for name, strategy_func in strategies:
+            ret = backtest_yearly(data, strategy_func, year)
+            results[name][year] = ret if ret is not None else 0
+    
+    # 打印结果
+    print("\n" + "="*90)
+    print("年度收益对比表 (%)")
+    print("="*90)
+    
+    # 表头
+    header = f"{'策略':<10}"
+    for year in years:
+        header += f" | {year:>8}"
+    header += f" | {'平均':>8} | {'跑赢年数':>8}"
+    print(header)
+    print("-"*90)
+    
+    # 各策略
+    all_strategies = list(results.keys())
+    for name in all_strategies:
+        row = f"{name:<10}"
+        returns = []
+        win_count = 0
+        
+        for year in years:
+            if year in results[name]:
+                ret = results[name][year]
+                returns.append(ret)
+                row += f" | {ret:>+7.1f}"
+                
+                # 判断是否跑赢指数
+                if name != "买入持有" and year in results["买入持有"]:
+                    if ret > results["买入持有"][year]:
+                        win_count += 1
+            else:
+                row += f" | {'--':>8}"
+        
+        avg_ret = np.mean(returns) if returns else 0
+        row += f" | {avg_ret:>+7.1f}"
+        
+        if name != "买入持有":
+            row += f" | {win_count:>6}/8"
+        else:
+            row += f" | {'--':>8}"
+        
+        print(row)
+    
+    print("-"*90)
+    
+    # 超额收益统计
+    print("\n" + "="*90)
+    print("超额收益对比 (策略 - 买入持有)")
+    print("="*90)
+    
+    header = f"{'策略':<10}"
+    for year in years:
+        header += f" | {year:>8}"
+    header += f" | {'平均超额':>8}"
+    print(header)
+    print("-"*90)
+    
+    for name in all_strategies:
+        if name == "买入持有":
+            continue
+        
+        row = f"{name:<10}"
+        excess_returns = []
+        
+        for year in years:
+            if year in results[name] and year in results["买入持有"]:
+                excess = results[name][year] - results["买入持有"][year]
+                excess_returns.append(excess)
+                marker = "⭐" if excess > 10 else "✓" if excess > 0 else ""
+                row += f" | {excess:>+7.1f}{marker}"
+            else:
+                row += f" | {'--':>8}"
+        
+        avg_excess = np.mean(excess_returns) if excess_returns else 0
+        row += f" | {avg_excess:>+7.1f}"
+        print(row)
+    
+    print("-"*90)
+    
+    # 汇总评价
+    print("\n" + "="*90)
+    print("策略评价")
+    print("="*90)
+    
+    for name in all_strategies:
+        if name == "买入持有":
+            continue
+        
+        returns = [results[name][y] for y in years if y in results[name]]
+        excess_list = [results[name][y] - results["买入持有"][y] for y in years if y in results[name] and y in results["买入持有"]]
+        
+        win_years = sum(1 for e in excess_list if e > 0)
+        avg_ret = np.mean(returns)
+        avg_excess = np.mean(excess_list)
+        
+        print(f"\n【{name}】")
+        print(f"  年均收益: {avg_ret:+.1f}%")
+        print(f"  年均超额: {avg_excess:+.1f}%")
+        print(f"  跑赢年数: {win_years}/8 ({win_years/8*100:.0f}%)")
+        
+        if avg_excess > 10:
+            print(f"  评价: 优秀 ⭐⭐⭐")
+        elif avg_excess > 5:
+            print(f"  评价: 良好 ⭐⭐")
+        elif avg_excess > 0:
+            print(f"  评价: 一般 ⭐")
+        else:
+            print(f"  评价: 不佳")
+    
+    print("\n" + "="*90)
+
+if __name__ == "__main__":
+    main()

quant/fetch_data.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+创业板50指数数据获取脚本
+数据来源：baostock (证券宝) - http://www.baostock.com
+"""
+
+import baostock as bs
+import pandas as pd
+from datetime import datetime
+
+def fetch_cyb50_data():
+    """
+    获取创业板50指数历史数据
+    指数代码：sz.399673
+    """
+    # 登录baostock
+    lg = bs.login()
+    if lg.error_code != '0':
+        print(f"登录失败: {lg.error_msg}")
+        return None
+    
+    print("登录成功，开始获取数据...")
+    
+    # 获取创业板50指数日线数据
+    # 参数说明：
+    # - code: 指数代码 sz.399673 (深圳.创业板50)
+    # - fields: 获取的字段 (日期, 开盘价, 最高价, 最低价, 收盘价, 成交量)
+    # - start_date: 开始日期
+    # - end_date: 结束日期
+    rs = bs.query_history_k_data_plus(
+        "sz.399673",
+        "date,open,high,low,close,volume",
+        start_date='2017-01-01',
+        end_date='2025-12-31',
+        frequency='d',  # 日频
+        adjustflag='3'  # 不复权 (指数不需要复权)
+    )
+    
+    if rs.error_code != '0':
+        print(f"数据获取失败: {rs.error_msg}")
+        bs.logout()
+        return None
+    
+    # 解析数据
+    data_list = []
+    while rs.next():
+        data_list.append(rs.get_row_data())
+    
+    # 转换为DataFrame
+    df = pd.DataFrame(data_list, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
+    
+    # 数据类型转换
+    for col in ['open', 'high', 'low', 'close', 'volume']:
+        df[col] = pd.to_numeric(df[col], errors='coerce')
+    
+    # 保存为CSV
+    df.to_csv('cyb50_baostock.csv', index=False)
+    
+    print(f"数据获取成功!")
+    print(f"  数据区间: {df['date'].iloc[0]} ~ {df['date'].iloc[-1]}")
+    print(f"  总交易日: {len(df)} 天")
+    print(f"  保存文件: cyb50_baostock.csv")
+    
+    # 登出
+    bs.logout()
+    return df
+
+if __name__ == "__main__":
+    df = fetch_cyb50_data()
+    
+    if df is not None:
+        print("\n数据预览:")
+        print(df.head())
+        print("...")
+        print(df.tail())