💡 多策略对比评估框架

本章讲解了策略回测与评估，这里提供一个完整的多策略对比框架：

1. 策略评估类

class StrategyEvaluator:
    """策略评估器"""
    
    def __init__(self, returns, benchmark_returns=None):
        self.returns = returns
        self.benchmark = benchmark_returns
        self.cumulative = (1 + returns).cumprod()
    
    def metrics(self):
        """计算所有指标"""
        return {
            '总收益': f"{(self.cumulative.iloc[-1] - 1) * 100:.2f}%",
            '年化收益': f"{self.annualized_return() * 100:.2f}%",
            '年化波动': f"{self.volatility() * 100:.2f}%",
            '夏普比率': f"{self.sharpe():.2f}",
            '最大回撤': f"{self.max_drawdown() * 100:.2f}%",
            '卡玛比率': f"{self.calmar():.2f}",
            '胜率': f"{self.win_rate() * 100:.1f}%"
        }
    
    def annualized_return(self):
        total = self.cumulative.iloc[-1] - 1
        days = len(self.returns)
        return (1 + total) ** (252 / days) - 1
    
    def volatility(self):
        return self.returns.std() * np.sqrt(252)
    
    def sharpe(self, rf=0.03):
        return (self.annualized_return() - rf) / self.volatility()
    
    def max_drawdown(self):
        peak = self.cumulative.cummax()
        drawdown = (self.cumulative - peak) / peak
        return drawdown.min()
    
    def calmar(self):
        return self.annualized_return() / abs(self.max_drawdown())
    
    def win_rate(self):
        return (self.returns > 0).sum() / len(self.returns)

2. 多策略对比

def compare_strategies(data, strategies):
    """对比多个策略"""
    results = {}
    
    for name, strategy in strategies.items():
        # 生成信号
        signals = strategy.generate_signals(data)
        positions = signals.shift(1)
        
        # 计算收益
        returns = data['close'].pct_change()
        strategy_returns = positions * returns
        
        # 评估
        evaluator = StrategyEvaluator(strategy_returns.dropna())
        results[name] = evaluator.metrics()
    
    return pd.DataFrame(results).T

3. 可视化对比

def plot_strategy_comparison(results_dict):
    """可视化对比"""
    import matplotlib.pyplot as plt
    
    fig, axes = plt.subplots(2, 2, figsize=(14, 10))
    
    # 净值曲线
    ax1 = axes[0, 0]
    for name, results in results_dict.items():
        ax1.plot(results['cumulative'], label=name)
    ax1.set_title('净值曲线对比')
    ax1.legend()
    
    # 回撤对比
    ax2 = axes[0, 1]
    for name, results in results_dict.items():
        peak = results['cumulative'].cummax()
        drawdown = (results['cumulative'] - peak) / peak
        ax2.fill_between(drawdown.index, drawdown, 0, alpha=0.3, label=name)
    ax2.set_title('回撤对比')
    ax2.legend()
    
    # 风险收益散点图
    ax3 = axes[1, 0]
    for name, metrics in metrics_dict.items():
        ax3.scatter(metrics['年化波动'], metrics['年化收益'], s=100, label=name)
    ax3.set_xlabel('年化波动率')
    ax3.set_ylabel('年化收益率')
    ax3.set_title('风险收益分布')
    ax3.legend()
    
    # 指标雷达图
    ax4 = axes[1, 1]
    # ... 雷达图代码
    
    plt.tight_layout()
    plt.show()

4. 策略排名

def rank_strategies(metrics_df, weights=None):
    """策略综合排名"""
    if weights is None:
        weights = {
            '夏普比率': 0.3,
            '年化收益': 0.25,
            '最大回撤': 0.25,
            '胜率': 0.2
        }
    
    scores = {}
    for name in metrics_df.index:
        score = 0
        for metric, weight in weights.items():
            value = float(metrics_df.loc[name, metric].replace('%', ''))
            score += value * weight
        scores[name] = score
    
    ranked = pd.Series(scores).sort_values(ascending=False)
    return ranked

5. 关键指标对比表

6. 报告生成

def generate_report(results, output='report.html'):
    """生成HTML报告"""
    import plotly.graph_objects as go
    from plotly.subplots import make_subplots
    
    fig = make_subplots(
        rows=3, cols=2,
        subplot_titles=['净值曲线', '回撤', '月度收益', '风险收益', '持仓分布', '交易统计']
    )
    
    # 添加图表...
    
    fig.write_html(output)
    print(f"报告已保存: {output}")

核心建议：

1. 至少对比3个策略以上
2. 使用多维度指标评估
3. 风险调整收益最重要
4. 生成可视化报告便于分析