cyb50-quant/cyb50_simple.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
创业板50指数点位量化交易策略回测 - 简化版（快速演示）
训练集：2018-2023 | 验证集：2024-2025
"""

import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from datetime import datetime
import warnings
warnings.filterwarnings('ignore')

# 设置图表
plt.rcParams['font.size'] = 10

# ==================== 1. 数据生成 ====================

def generate_index_data(start='2017-01-01', end='2025-12-31', seed=42):
    """生成模拟创业板50指数数据"""
    np.random.seed(seed)
    dates = pd.date_range(start=start, end=end, freq='D')
    dates = dates[dates.dayofweek < 5]  # 去掉周末
    n = len(dates)
    
    # 生成收益率序列（带趋势和波动）
    returns = np.random.normal(0.0002, 0.015, n)
    
    # 2019-2021牛市
    bull = (dates >= '2019-01-01') & (dates <= '2021-12-31')
    returns[bull] += 0.0008
    
    # 2022熊市
    bear = (dates >= '2022-01-01') & (dates <= '2022-12-31')
    returns[bear] -= 0.001
    
    # 2024-2025震荡
    osc = dates >= '2024-01-01'
    returns[osc] = np.random.normal(0, 0.012, sum(osc))
    
    # 计算价格
    price = 1800 * np.exp(np.cumsum(returns))
    
    df = pd.DataFrame(index=dates)
    df['close'] = price
    df['open'] = price * (1 + np.random.normal(0, 0.005, n))
    df['high'] = df[['open', 'close']].max(axis=1) * (1 + np.abs(np.random.normal(0, 0.008, n)))
    df['low'] = df[['open', 'close']].min(axis=1) * (1 - np.abs(np.random.normal(0, 0.008, n)))
    
    return df

# ==================== 2. 策略类 ====================

class SimpleCYBStrategy:
    """简化版策略：双均线 + 波动率控制"""
    
    def __init__(self, params=None):
        self.params = params or {
            'fast_ma': 20,      # 快线
            'slow_ma': 60,      # 慢线
            'volatility_period': 20,
            'max_position': 1.0,
            'stop_loss': 0.10,
        }
        self.position = 0
        self.entry_price = None
    
    def generate_signal(self, data):
        """生成交易信号"""
        close = data['close']
        high = data['high']
        low = data['low']
        
        p = self.params
        
        # 计算均线
        ma_fast = close.rolling(p['fast_ma']).mean().iloc[-1]
        ma_slow = close.rolling(p['slow_ma']).mean().iloc[-1]
        
        # 计算波动率（20日ATR/价格）
        tr = pd.concat([
            high - low,
            abs(high - close.shift(1)),
            abs(low - close.shift(1))
        ], axis=1).max(axis=1)
        atr = tr.rolling(p['volatility_period']).mean().iloc[-1]
        vol_pct = atr / close.iloc[-1] * 100
        
        # 趋势判断
        trend_up = (close.iloc[-1] > ma_fast) and (ma_fast > ma_slow)
        trend_down = (close.iloc[-1] < ma_fast) and (ma_fast < ma_slow)
        
        # 仓位决策
        if trend_up and vol_pct < 4:
            target_pos = p['max_position']  # 满仓
            state = "BULL"
        elif trend_down or vol_pct > 6:
            target_pos = 0  # 空仓
            state = "BEAR" if trend_down else "HIGH_VOL"
        else:
            target_pos = p['max_position'] * 0.5  # 半仓
            state = "OSCILLATE"
        
        # 止损检查
        if self.position > 0 and self.entry_price:
            drawdown = (close.iloc[-1] - self.entry_price) / self.entry_price
            if drawdown < -p['stop_loss']:
                target_pos = 0
        
        # 更新
        if target_pos > 0 and self.position == 0:
            self.entry_price = close.iloc[-1]
        if target_pos == 0:
            self.entry_price = None
        
        self.position = target_pos
        return target_pos, state

# ==================== 3. 回测引擎 ====================

def backtest(data, strategy, start_date=None, end_date=None, warmup=60):
    """回测引擎"""
    if start_date:
        data = data[data.index >= start_date]
    if end_date:
        data = data[data.index <= end_date]
    
    results = []
    nav = 1.0
    
    for i in range(warmup, len(data)):
        curr_data = data.iloc[:i+1]
        
        # 获取信号
        position, state = strategy.generate_signal(curr_data)
        
        # 计算收益
        if i > warmup:
            daily_return = data['close'].iloc[i] / data['close'].iloc[i-1] - 1
            strategy_return = daily_return * results[-1]['position'] if results else 0
            nav *= (1 + strategy_return)
        
        results.append({
            'date': data.index[i],
            'position': position,
            'nav': nav,
            'state': state,
            'close': data['close'].iloc[i]
        })
    
    df = pd.DataFrame(results).set_index('date')
    df['index_nav'] = df['close'] / df['close'].iloc[0]
    
    # 计算指标
    metrics = calculate_metrics(df['nav'], df['index_nav'])
    return df, metrics

def calculate_metrics(strategy_nav, index_nav):
    """计算绩效指标"""
    s_returns = strategy_nav.pct_change().dropna()
    
    total_return = strategy_nav.iloc[-1] - 1
    days = len(strategy_nav)
    annual_return = (1 + total_return) ** (252 / days) - 1
    
    index_return = index_nav.iloc[-1] - 1
    index_annual = (1 + index_return) ** (252 / days) - 1
    
    # 最大回撤
    running_max = strategy_nav.expanding().max()
    max_dd = ((strategy_nav - running_max) / running_max).min()
    
    # 波动率和夏普
    volatility = s_returns.std() * np.sqrt(252)
    sharpe = (annual_return - 0.03) / volatility if volatility > 0 else 0
    
    # 卡玛
    calmar = annual_return / abs(max_dd) if max_dd != 0 else 0
    
    # 胜率
    win_rate = (s_returns > 0).mean()
    
    return {
        'annual_return': annual_return,
        'index_annual': index_annual,
        'excess_annual': annual_return - index_annual,
        'max_drawdown': max_dd,
        'sharpe': sharpe,
        'calmar': calmar,
        'win_rate': win_rate,
        'total_return': total_return,
        'index_return': index_return
    }

# ==================== 4. 可视化 ====================

def plot_results(results, title, filename):
    """绘制回测图表"""
    fig, axes = plt.subplots(3, 1, figsize=(12, 9))
    
    # 净值曲线
    ax1 = axes[0]
    ax1.plot(results.index, results['nav'], label='Strategy', linewidth=2, color='blue')
    ax1.plot(results.index, results['index_nav'], label='Index', linewidth=1, color='gray', alpha=0.7)
    ax1.set_title(f'{title} - NAV Comparison')
    ax1.set_ylabel('NAV')
    ax1.legend()
    ax1.grid(True, alpha=0.3)
    
    # 仓位
    ax2 = axes[1]
    ax2.fill_between(results.index, 0, results['position'], alpha=0.3, color='green')
    ax2.set_ylabel('Position')
    ax2.set_ylim(0, 1.1)
    ax2.grid(True, alpha=0.3)
    
    # 回撤
    ax3 = axes[2]
    running_max = results['nav'].expanding().max()
    drawdown = (results['nav'] - running_max) / running_max
    ax3.fill_between(results.index, drawdown, 0, alpha=0.3, color='red')
    ax3.set_ylabel('Drawdown')
    ax3.set_xlabel('Date')
    ax3.grid(True, alpha=0.3)
    
    plt.tight_layout()
    plt.savefig(filename, dpi=150, bbox_inches='tight')
    print(f"  图表已保存: {filename}")

# ==================== 5. 主程序 ====================

def main():
    print("="*70)
    print("创业板50指数量化交易策略回测 - 简化版")
    print("="*70)
    
    # 生成数据
    print("\n[1] 生成模拟数据...")
    data = generate_index_data()
    print(f"    数据区间: {data.index[0].date()} ~ {data.index[-1].date()}")
    print(f"    共 {len(data)} 个交易日")
    
    # 训练阶段
    print("\n[2] 训练阶段 (2018-2023)...")
    strategy = SimpleCYBStrategy()
    train_results, train_metrics = backtest(data, strategy, 
                                           start_date='2018-01-01', 
                                           end_date='2023-12-31')
    
    print(f"\n    训练集表现:")
    print(f"    - 策略年化收益: {train_metrics['annual_return']*100:>7.2f}%")
    print(f"    - 指数年化收益: {train_metrics['index_annual']*100:>7.2f}%")
    print(f"    - 超额收益:     {train_metrics['excess_annual']*100:>7.2f}%")
    print(f"    - 最大回撤:     {train_metrics['max_drawdown']*100:>7.2f}%")
    print(f"    - 夏普比率:     {train_metrics['sharpe']:>7.2f}")
    print(f"    - 卡玛比率:     {train_metrics['calmar']:>7.2f}")
    print(f"    - 胜率:         {train_metrics['win_rate']*100:>7.1f}%")
    
    plot_results(train_results, "Training Set (2018-2023)", "train_results.png")
    
    # 验证阶段
    print("\n[3] 验证阶段 (2024-2025)...")
    strategy_val = SimpleCYBStrategy()  # 使用相同参数
    val_results, val_metrics = backtest(data, strategy_val,
                                       start_date='2024-01-01',
                                       end_date='2025-12-31')
    
    print(f"\n    验证集表现:")
    print(f"    - 策略年化收益: {val_metrics['annual_return']*100:>7.2f}%")
    print(f"    - 指数年化收益: {val_metrics['index_annual']*100:>7.2f}%")
    print(f"    - 超额收益:     {val_metrics['excess_annual']*100:>7.2f}%")
    print(f"    - 最大回撤:     {val_metrics['max_drawdown']*100:>7.2f}%")
    print(f"    - 夏普比率:     {val_metrics['sharpe']:>7.2f}")
    print(f"    - 卡玛比率:     {val_metrics['calmar']:>7.2f}")
    
    plot_results(val_results, "Validation Set (2024-2025)", "val_results.png")
    
    # 过拟合检测
    print("\n[4] 过拟合检测:")
    sharpe_decay = (train_metrics['sharpe'] - val_metrics['sharpe']) / train_metrics['sharpe'] if train_metrics['sharpe'] != 0 else 0
    print(f"    夏普比率衰减: {sharpe_decay*100:.1f}%")
    
    if sharpe_decay > 0.5:
        print("    ⚠️  警告：可能存在严重过拟合")
    elif sharpe_decay > 0.3:
        print("    ⚠️  注意：轻度过拟合")
    else:
        print("    ✓ 无过拟合，策略稳健")
    
    # 总结
    print("\n" + "="*70)
    print("回测完成")
    print("="*70)
    print(f"\n输出文件:")
    print(f"  - train_results.png (训练集图表)")
    print(f"  - val_results.png (验证集图表)")

if __name__ == "__main__":
    main()