Sharpe-like Ratio（夏普比率）算法解析

import math from typing import List, Dict def calculate_sharpe_ratio(historical_data: Dict) -> float: """ 基于JS文件 (Ze 函数) 解析的夏普比率 (Sharpe-like Ratio) 算法。 计算公式: (每日百分比收益率的平均值) / (每日百分比收益率的标准差) (非年化) Args: historical_data: 包含 'accountValueHistory' 和 'pnlHistory' 的字典， time1, time2... 必须按时间顺序排列，pnlHistory 中的 PnL 是累积的。 例如: {"accountValueHistory": [[time1, value1], ...], "pnlHistory": [[time1, pnl1], ...]} Returns: 夏普比率的数值。 """ account_value_history = historical_data.get("accountValueHistory", []) pnl_history = historical_data.get("pnlHistory", []) # 检查基本数据长度  if len(account_value_history) < 1 or len(pnl_history) < 2: return 0.0 # 1. 计算每日 PnL (Daily PnL)  daily_pnls = [] # pnl_history 是累积 PnL: pnl[t] - pnl[t-1]  for t in range(1, len(pnl_history)): try: current_pnl = float(pnl_history[t][1]) previous_pnl = float(pnl_history[t-1][1]) daily_pnls.append(current_pnl - previous_pnl) except (ValueError, TypeError): # 忽略数据格式错误的点  continue # 2. 计算平均账户价值 (Average Account Value)  total_value_sum = sum(float(item[1]) for item in account_value_history if isinstance(item[1], (int, float, str))) count = len(account_value_history) if count == 0: return 0.0 avg_value = total_value_sum / count if avg_value == 0: return 0.0 # 3. 计算每日百分比收益率 (Daily Return %)  # R_t = 100 * Daily PnL / AvgValue  returns_percent = [] for daily_pnl in daily_pnls: daily_return_percent = 100 * daily_pnl / avg_value returns_percent.append(daily_return_percent) # 检查收益率序列长度 (JS: r.length < 2)  if len(returns_percent) < 2: return 0.0 # 4. 计算均值 (Mean)  mean_return = sum(returns_percent) / len(returns_percent) # 5. 计算样本标准差 (Sample Standard Deviation) (N-1 分母)  variance_sum = sum(math.pow(r - mean_return, 2) for r in returns_percent) # 分母是 N - 1  sample_stdev = math.sqrt(variance_sum / (len(returns_percent) - 1)) # 6. 计算比率  # JS: 0===u?0:o/u  if sample_stdev == 0: return 0.0 else: return mean_return / sample_stdev