diff --git a/尾盘_数据统计_优化01.py b/尾盘_数据统计_优化01.py
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+++ b/尾盘_数据统计_优化01.py
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+import os
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+from concurrent.futures import ProcessPoolExecutor, as_completed
+import logging
+from numba import jit
+from datetime import datetime
+
+# ========== 环境配置 ==========
+plt.rcParams['font.sans-serif'] = ['SimHei']
+plt.rcParams['axes.unicode_minus'] = False
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+# ========== 策略参数配置 ==========
+# ========== 全局参数 ==========
+ATR_WINDOW = 5
+VOLATILITY_WINDOW = 10
+BULL_THRESHOLD = 0.83
+BEAR_THRESHOLD = 0.77
+NEUTRAL_THRESHOLD = 0.81
+HOLDING_DAYS_MAP = {
+    'bull': 4,
+    'bear': 2,
+    'neutral': 3
+}
+
+
+
+# ========== 核心策略逻辑 ==========
+@jit(nopython=True)
+def calculate_technical_indicators(close, high, low, volume,
+                                   atr_window, volatility_window):
+    """支持Numba的参数传递"""
+    n = len(close)
+    macd = np.zeros(n)
+    signal = np.zeros(n)
+    atr = np.zeros(n)
+
+    # MACD计算
+    ema12, ema26 = np.zeros(n), np.zeros(n)
+    for i in range(1, n):
+        ema12[i] = ema12[i - 1] * 11 / 13 + close[i] * 2 / 13
+        ema26[i] = ema26[i - 1] * 25 / 27 + close[i] * 2 / 27
+        macd[i] = ema12[i] - ema26[i]
+        signal[i] = signal[i - 1] * 0.8 + macd[i] * 0.2
+
+    # ATR计算
+    for i in range(1, n):
+        tr = max(high[i] - low[i],
+                 abs(high[i] - close[i - 1]),
+                 abs(low[i] - close[i - 1]))
+        atr[i] = atr[i-1] * (atr_window-1)/atr_window + tr/atr_window
+
+    return macd, signal, atr
+
+
+@jit(nopython=True)
+def generate_trading_signals(close, open_, high, low, volume, macd, signal, atr, threshold, volatility_window):
+    """生成交易信号"""
+    n = len(close)
+    signals = np.zeros(n, dtype=np.bool_)
+
+    for i in range(3, n):
+        # 基础K线形态条件
+        is_red = close[i] > open_[i]
+        upper_shadow = high[i] - max(close[i], open_[i])
+        lower_shadow = min(close[i], open_[i]) - low[i]
+        body_size = abs(close[i] - open_[i])
+
+        cond1 = is_red and (high[i] / close[i - 1] > 1.005)
+        cond2 = (body_size > upper_shadow) and (body_size > lower_shadow)
+        cond3 = (high[i] / low[i] < 1.12) and (high[i] / open_[i] > 1.036)
+        cond4 = close[i] < close[i - 1] * 1.10  # 排除涨停
+
+        # 技术指标条件
+        cond5 = atr[i] > np.mean(atr[i - 4:i + 1]) * 0.8
+        cond6 = (macd[i] - signal[i]) > (macd[i - 1] - signal[i - 1]) * 1.2
+
+        # 波动率条件
+        llv = np.min(low[max(0, i - volatility_window + 1):i + 1])
+        hhv = np.max(high[max(0, i - volatility_window + 1):i + 1])
+        cond7 = (llv / hhv) < threshold
+
+        # 量能条件
+        vol_cond1 = volume[i] < np.mean(volume[max(0, i - 10):i])
+        vol_cond2 = volume[i] < np.min(volume[max(0, i - 20):i - 1]) * 3.5
+
+        signals[i] = cond1 & cond2 & cond3 & cond4 & cond5 & cond6 & cond7 & vol_cond1 & vol_cond2
+
+    return signals
+
+
+# ========== 市场状态判断 ==========
+def get_market_condition(index_data):
+    """动态判断市场状态"""
+    if len(index_data) < 60:
+        return 'neutral'
+
+    ma20 = index_data['close'].rolling(20).mean().iloc[-1]
+    ma60 = index_data['close'].rolling(60).mean().iloc[-1]
+
+    if pd.isna(ma20) or pd.isna(ma60):
+        return 'neutral'
+
+    if ma20 > ma60 * 1.05:
+        return 'bull'
+    elif ma20 < ma60 * 0.95:
+        return 'bear'
+    else:
+        return 'neutral'
+
+
+# ========== 数据加载处理 ==========
+def load_index_data(index_path):
+    """加载并预处理指数数据"""
+    try:
+        # 自动检测日期列名
+        df = pd.read_csv(index_path, sep='\t', nrows=0)
+        date_col = 'date' if 'date' in df.columns else 'trade_date'
+
+        index_data = pd.read_csv(
+            index_path,
+            sep='\t',
+            usecols=[date_col, 'open', 'high', 'low', 'close', 'volume'],
+            parse_dates=[date_col],
+            date_parser=lambda x: pd.to_datetime(x, format='%Y%m%d')
+        )
+        # index_data.rename(columns={date_col: 'trade_date'}, inplace=True)
+        index_data.sort_values(date_col, inplace=True)
+
+        logging.info(
+            f"指数数据加载成功，时间范围: {index_data['trade_date'].min().date()} 至 {index_data['trade_date'].max().date()}")
+        return index_data
+    except Exception as e:
+        logging.error(f"指数数据加载失败: {str(e)}")
+        return None
+
+
+def process_stock_file(file_path, index_data):
+    """处理单个股票文件"""
+    try:
+        # 加载并预处理数据
+        df = pd.read_csv(file_path, sep='\t',
+                         usecols=['trade_date', 'open', 'high', 'low', 'close', 'vol'])
+        df = df.rename(columns={'vol': 'volume'})
+        df['trade_date'] = pd.to_datetime(df['trade_date'], format='%Y%m%d', errors='coerce')
+        df = df.dropna(subset=['trade_date']).sort_values('trade_date')
+
+        # 对齐指数时间范围
+        start_date = index_data['trade_date'].min()
+        end_date = index_data['trade_date'].max()
+        df = df[(df['trade_date'] >= start_date) & (df['trade_date'] <= end_date)]
+
+        # if len(df) < StrategyConfig.MIN_TRADE_DAYS:
+        #     return None
+
+        # 计算技术指标
+        close = df['close'].values.astype(np.float64)
+        high = df['high'].values.astype(np.float64)
+        low = df['low'].values.astype(np.float64)
+        volume = df['volume'].values.astype(np.float64)
+
+        macd, signal, atr = calculate_technical_indicators(
+            close, high, low, volume,
+            atr_window=ATR_WINDOW,
+            volatility_window=VOLATILITY_WINDOW
+        )
+
+        # # 获取市场状态
+        # market_condition = get_market_condition(index_data)
+        # threshold = StrategyConfig.THRESHOLDS[market_condition]
+
+        # 生成信号
+        signals = generate_trading_signals(
+            close, df['open'].values, high, low, volume,
+            macd, signal, atr, threshold=BULL_THRESHOLD,
+            volatility_window=VOLATILITY_WINDOW
+        )
+
+        df['signal'] = signals
+        return os.path.basename(file_path).split('_')[0], df
+
+    except Exception as e:
+        logging.error(f"处理文件 {os.path.basename(file_path)} 失败: {str(e)}")
+        return None
+
+
+# ========== 回测分析模块 ==========
+def backtest_strategy(all_data, index_data):
+    """执行动态持仓周期回测"""
+    results = []
+
+    for stock_code, data in all_data.items():
+        if data is None or 'signal' not in data.columns:
+            continue
+
+        signals = data[data['signal']]
+        for idx in signals.index:
+            # 动态获取市场状态
+            current_date = data.iloc[idx]['trade_date']
+            market_condition = get_market_condition(index_data)
+            holding_days = HOLDING_DAYS_MAP.get(market_condition, 2)
+
+            # 计算退出时间
+            exit_idx = idx + holding_days + 1  # 包含买入当天
+
+            if exit_idx >= len(data):
+                continue
+
+            # 计算收益
+            entry_price = data.loc[idx, 'close']
+            exit_prices = data.iloc[idx + 1:exit_idx]['close']
+
+            max_profit = (exit_prices.max() - entry_price) / entry_price
+            max_loss = (exit_prices.min() - entry_price) / entry_price
+            final_return = (exit_prices.iloc[-1] - entry_price) / entry_price
+
+            results.append({
+                'code': stock_code,
+                'date': current_date.strftime('%Y-%m-%d'),
+                'market': market_condition,
+                'holding_days': holding_days,
+                'return': final_return,
+                'max_profit': max_profit,
+                'max_loss': max_loss
+            })
+
+    return pd.DataFrame(results) if results else pd.DataFrame()
+
+
+def analyze_results(results_df):
+    """分析回测结果"""
+    if results_df.empty:
+        logging.warning("无有效交易记录")
+        return
+
+    # 基础统计
+    total_trades = len(results_df)
+    annual_return = results_df['return'].mean() * 252
+    win_rate = len(results_df[results_df['return'] > 0]) / total_trades
+    profit_factor = results_df[results_df['return'] > 0]['return'].mean() / \
+                    abs(results_df[results_df['return'] < 0]['return'].mean())
+
+    print(f"\n策略表现汇总:")
+    print(f"总交易次数: {total_trades}")
+    print(f"年化收益率: {annual_return:.2%}")
+    print(f"胜率: {win_rate:.2%}")
+    print(f"盈亏比: {profit_factor:.2f}")
+
+    # 分市场状态分析
+    if 'market' in results_df.columns:
+        market_stats = results_df.groupby('market').agg({
+            'return': ['mean', 'count'],
+            'holding_days': 'mean'
+        })
+        print("\n分市场状态表现:")
+        print(market_stats)
+
+    # 可视化
+    plt.figure(figsize=(12, 5))
+    plt.subplot(121)
+    results_df['return'].hist(bins=20, alpha=0.7)
+    plt.title('收益率分布')
+    plt.xlabel('收益率')
+    plt.ylabel('频次')
+
+    plt.subplot(122)
+    if 'market' in results_df.columns:
+        for condition, group in results_df.groupby('market'):
+            plt.scatter(group['holding_days'], group['return'], alpha=0.5, label=condition)
+        plt.legend()
+    plt.axhline(0, color='red', linestyle='--')
+    plt.title('持仓周期 vs 收益率')
+    plt.xlabel('持仓天数')
+    plt.ylabel('收益率')
+
+    plt.tight_layout()
+    plt.show()
+
+
+# ========== 主程序 ==========
+if __name__ == "__main__":
+    # 配置路径
+    STOCK_DIR = '/day/'
+    INDEX_PATH = '/index/000001.SH.txt'
+
+    # 加载指数数据
+    logging.info("正在加载指数数据...")
+    index_data = load_index_data(INDEX_PATH)
+    if index_data is None:
+        exit()
+
+    # 并行处理个股数据
+    logging.info("正在加载个股数据...")
+    stock_files = [os.path.join(STOCK_DIR, f) for f in os.listdir(STOCK_DIR)
+                   if f.endswith('.txt') and not any(kw in f for kw in ['ST', '*ST', '688'])]
+
+    all_data = {}
+    with ProcessPoolExecutor(max_workers=os.cpu_count()) as executor:
+        futures = {executor.submit(process_stock_file, f, index_data): f for f in stock_files}
+        for future in tqdm(as_completed(futures), total=len(futures)):
+            result = future.result()
+            if result:
+                code, data, _ = result
+                all_data[code] = data
+
+    if not all_data:
+        logging.error("没有加载到有效股票数据")
+        exit()
+
+    # 执行回测
+    logging.info("开始回测...")
+    results_df = backtest_strategy(all_data, index_data)
+
+    # 分析结果
+    analyze_results(results_df)
+
+    # 保存结果
+    if not results_df.empty:
+        results_df.to_csv('strategy_backtest_results.csv', index=False)
+        logging.info("回测结果已保存至 strategy_backtest_results.csv")
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