# 回测策略引擎简化示例
class BacktestingEngine:
    def __init__(self, data):
        self.data = data  # 历史市场数据
        self.portfolio = Portfolio()  # 投资组合管理
        self.strategy = None  # 交易策略
        self.transaction_cost = 0.001  # 交易成本
        self.slippage = 0.0005  # 滑点
        self.trades = []  # 交易记录
        
    def set_strategy(self, strategy):
        self.strategy = strategy
        
    def run(self):
        for i in range(len(self.data)):
            # 获取当前市场状态
            current_data = self.data.iloc[i]
            
            # 策略生成信号
            signals = self.strategy.generate_signals(current_data, self.portfolio)
            
            # 执行交易
            for signal in signals:
                # 考虑交易成本和滑点
                executed_price = self.calculate_executed_price(signal)
                
                # 记录交易
                trade = self.execute_trade(signal, executed_price)
                self.trades.append(trade)
                
            # 更新投资组合价值
            self.portfolio.update_value(current_data.close)
        
    def calculate_executed_price(self, signal):
        # 简化的执行价格计算，考虑滑点
        if signal['type'] == 'buy':
            return signal['price'] * (1 + self.slippage)
        else:
            return signal['price'] * (1 - self.slippage)
        
    def execute_trade(self, signal, executed_price):
        # 执行交易并返回交易记录
        # ...交易执行逻辑...
        return trade_record

# 注意：这只是一个简化的示例，实际回测系统要复杂得多

# 参数敏感度分析示例
def parameter_sensitivity_analysis(engine, base_params, param_ranges):
    results = {}
    
    # 对每个参数进行分析
    for param_name, param_range in param_ranges.items():
        param_results = []
        
        # 遍历参数的不同取值
        for param_value in param_range:
            # 设置当前参数值，保持其他参数不变
            current_params = base_params.copy()
            current_params[param_name] = param_value
            
            # 设置策略参数
            engine.strategy.set_params(current_params)
            
            # 运行回测
            engine.run()
            
            # 获取回测结果
            performance = engine.get_performance_metrics()
            param_results.append({
                'param_value': param_value,
                'performance': performance
            })
        
        results[param_name] = param_results
    
    return results

# 使用示例
param_ranges = {
    'lookback_period': [10, 20, 30, 40, 50],
    'threshold': [0.01, 0.02, 0.03, 0.04, 0.05]
}

sensitivity_results = parameter_sensitivity_analysis(
    engine, base_params, param_ranges
)