简单波动率策略，实现+网格搜索

src/analysis/analysis_utils.py

@@ -9,7 +9,7 @@ from ..core_data import PortfolioSnapshot, Trade, Bar
 
 
 def calculate_metrics(
-    snapshots: List[PortfolioSnapshot], trades: List[Trade], initial_capital: float
+        snapshots: List[PortfolioSnapshot], trades: List[Trade], initial_capital: float
 ) -> Dict[str, Any]:
     """
     纯函数：根据投资组合快照和交易历史计算关键绩效指标。
@@ -124,11 +124,27 @@ def calculate_metrics(
         "亏损交易次数": losing_count,
         "平均每次盈利": avg_profit_per_trade,
         "平均每次亏损": avg_loss_per_trade,  # 这个值是负数
+        "InitialCapital": initial_capital,
+        "FinalCapital": final_value,
+        "TotalReturn": total_return,
+        "AnnualizedReturn": annualized_return,
+        "MaxDrawdown": max_drawdown,
+        "SharpeRatio": sharpe_ratio,
+        "CalmarRatio": calmar_ratio,
+        "TotalTrades": len(trades),  # All buy and sell trades
+        "TransactionCosts": total_commissions,
+        "TotalRealizedPNL": total_realized_pnl,  # New
+        "WinRate": win_rate,
+        "ProfitLossRatio": profit_loss_ratio,
+        "WinningTradesCount": winning_count,
+        "LosingTradesCount": losing_count,
+        "AvgProfitPerTrade": avg_profit_per_trade,
+        "AvgLossPerTrade": avg_loss_per_trade,  # This value is negative
     }
 
 
 def plot_equity_and_drawdown_chart(snapshots: List[PortfolioSnapshot], initial_capital: float,
-                                    title: str = "Portfolio Equity and Drawdown Curve") -> None:
+                                   title: str = "Portfolio Equity and Drawdown Curve") -> None:
     """
     Plots the portfolio equity curve and drawdown. X-axis points are equally spaced.
 
@@ -145,7 +161,7 @@ def plot_equity_and_drawdown_chart(snapshots: List[PortfolioSnapshot], initial_c
         {'datetime': s.datetime, 'total_value': s.total_value}
         for s in snapshots
     ])
-    
+
     equity_curve = df_equity['total_value'] / initial_capital
 
     rolling_max = equity_curve.cummax()
@@ -203,7 +219,7 @@ def plot_close_price_chart(bars: List[Bar], title: str = "Close Price Chart") ->
     ])
 
     plt.style.use('seaborn-v0_8-darkgrid')
-    fig, ax = plt.subplots(1, 1, figsize=(14, 7)) # Single subplot
+    fig, ax = plt.subplots(1, 1, figsize=(14, 7))  # Single subplot
 
     x_axis_indices = np.arange(len(df_prices))
 
@@ -228,7 +244,7 @@ def plot_close_price_chart(bars: List[Bar], title: str = "Close Price Chart") ->
 
 # 辅助函数：计算单笔交易的盈亏
 def calculate_trade_pnl(
-    trade: Trade, entry_price: float, exit_price: float, direction: str
+        trade: Trade, entry_price: float, exit_price: float, direction: str
 ) -> float:
     if direction == "LONG":
         pnl = (exit_price - entry_price) * trade.volume