计算策略统计指标

calculate_statistics函数是基于逐日盯市盈亏情况（DateFrame格式）来计算衍生指标，如最大回撤、年化收益、盈亏比、夏普比率等。

            df["balance"] = df["net_pnl"].cumsum() + self.capital
            df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0)
            df["highlevel"] = (
                df["balance"].rolling(
                    min_periods=1, window=len(df), center=False).max()
            )
            df["drawdown"] = df["balance"] - df["highlevel"]
            df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100
 
            # Calculate statistics value
            start_date = df.index[0]
            end_date = df.index[-1]
 
            total_days = len(df)
            profit_days = len(df[df["net_pnl"] > 0])
            loss_days = len(df[df["net_pnl"] < 0])
 
            end_balance = df["balance"].iloc[-1]
            max_drawdown = df["drawdown"].min()
            max_ddpercent = df["ddpercent"].min()
 
            total_net_pnl = df["net_pnl"].sum()
            daily_net_pnl = total_net_pnl / total_days
 
            total_commission = df["commission"].sum()
            daily_commission = total_commission / total_days
 
            total_slippage = df["slippage"].sum()
            daily_slippage = total_slippage / total_days
 
            total_turnover = df["turnover"].sum()
            daily_turnover = total_turnover / total_days
 
            total_trade_count = df["trade_count"].sum()
            daily_trade_count = total_trade_count / total_days
 
            total_return = (end_balance / self.capital - 1) * 100
            annual_return = total_return / total_days * 240
            daily_return = df["return"].mean() * 100
            return_std = df["return"].std() * 100
 
            if return_std:
                sharpe_ratio = daily_return / return_std * np.sqrt(240)
            else:
                sharpe_ratio = 0