75.5 结果分析与优化

def generate_performance_report(results):
    """
    生成绩效分析报告
    """
    report = {
        '收益分析': {},
        '风险分析': {},
        '交易分析': {},
        '因子分析': {}
    }

    # 收益分析
    report['收益分析']['年化收益'] = results['annual_return']
    report['收益分析']['累计收益'] = results['cumulative_return']
    report['收益分析']['月均收益'] = results['monthly_return_mean']
    report['收益分析']['胜率'] = results['win_rate']

    # 风险分析
    report['风险分析']['夏普比率'] = results['sharpe_ratio']
    report['风险分析']['最大回撤'] = results['max_drawdown']
    report['风险分析']['波动率'] = results['volatility']
    report['风险分析']['VaR'] = results['var_95']

    # 交易分析
    report['交易分析']['换手率'] = results['turnover_rate']
    report['交易分析']['交易次数'] = results['trade_count']
    report['交易分析']['平均持仓天数'] = results['avg_holding_period']
    report['交易分析']['交易成本占比'] = results['cost_ratio']

    # 因子分析
    report['因子分析']['IC均值'] = results['ic_mean']
    report['因子分析']['IC标准差'] = results['ic_std']
    report['因子分析']['信息比率'] = results['information_ratio']
    report['因子分析']['因子衰减'] = results['factor_decay']

    return report

# 生成报告
report = generate_performance_report(results)
print("========== 绩效分析报告 ==========")
for category, metrics in report.items():
    print(f"\n{category}:")
    for metric, value in metrics.items():
        print(f"  {metric}: {value:.2%}" if isinstance(value, float) else f"  {metric}: {value}")

from sklearn.model_selection import ParameterGrid

def parameter_optimization():
    """
    参数优化
    """
    # 定义参数网格
    param_grid = {
        'lookback': [5, 10, 20, 40],
        'top_n': [30, 50, 80, 100],
        'rebalance_freq': [5, 10, 20, 40],
        'stop_loss': [0.05, 0.10, 0.15],
        'max_position': [0.05, 0.10, 0.15]
    }

    # 遍历参数组合
    results = []
    for params in ParameterGrid(param_grid):
        try:
            # 运行回测
            backtest_result = run_backtest_with_params(params)

            # 记录结果
            results.append({
                'params': params,
                'sharpe_ratio': backtest_result['sharpe_ratio'],
                'annual_return': backtest_result['annual_return'],
                'max_drawdown': backtest_result['max_drawdown'],
                'turnover': backtest_result['turnover_rate']
            })

        except Exception as e:
            print(f"参数组合 {params} 失败: {e}")
            continue

    # 转换为DataFrame
    results_df = pd.DataFrame(results)

    # 找到最优参数
    results_df['score'] = (
        results_df['sharpe_ratio'] * 0.4 +
        results_df['annual_return'] * 0.3 -
        results_df['max_drawdown'] * 0.2 -
        results_df['turnover'] * 0.1
    )

    best_params = results_df.loc[results_df['score'].idxmax(), 'params']

    print("最优参数组合:")
    for key, value in best_params.items():
        print(f"  {key}: {value}")

    return best_params, results_df

from scipy.optimize import minimize

def optimize_factor_weights(factor_returns):
    """
    因子权重优化
    """
    n_factors = len(factor_returns.columns)

    # 目标函数：最大化夏普比率
    def objective(weights):
        portfolio_return = (weights * factor_returns).sum(axis=1)
        sharpe = portfolio_return.mean() / portfolio_return.std()
        return -sharpe  # 最小化负夏普比率

    # 约束条件
    constraints = ({
        'type': 'eq',
        'fun': lambda w: np.sum(w) - 1  # 权重和为1
    })

    # 权重边界
    bounds = tuple((0, 1) for _ in range(n_factors))

    # 初始权重
    initial_weights = np.array([1/n_factors] * n_factors)

    # 优化
    result = minimize(
        objective,
        initial_weights,
        method='SLSQP',
        bounds=bounds,
        constraints=constraints
    )

    optimal_weights = result.x

    print("优化后的因子权重:")
    for i, factor in enumerate(factor_returns.columns):
        print(f"  {factor}: {optimal_weights[i]:.2%}")

    return optimal_weights