同步本地回测与tqsdk回测

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。
@@ -124,27 +124,30 @@ 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
+        "initial_capital": initial_capital,
+        "final_capital": final_value,
+        "total_return": total_return,
+        "annualized_return": annualized_return,
+        "max_drawdown": max_drawdown,
+        "sharpe_ratio": sharpe_ratio,
+        "calmar_ratio": calmar_ratio,
+        "total_trades": len(trades),  # All buy and sell trades
+        "transaction_costs": total_commissions,
+        "total_realized_pnl": total_realized_pnl,  # New
+        "win_rate": win_rate,
+        "profit_loss_ratio": profit_loss_ratio,
+        "winning_trades_count": winning_count,
+        "losing_trades_count": losing_count,
+        "avg_profit_per_trade": avg_profit_per_trade,
+        "avg_loss_per_trade": 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:
+def plot_equity_and_drawdown_chart(
+    snapshots: List[PortfolioSnapshot],
+    initial_capital: float,
+    title: str = "Portfolio Equity and Drawdown Curve",
+) -> None:
     """
     Plots the portfolio equity curve and drawdown. X-axis points are equally spaced.
 
@@ -157,35 +160,45 @@ def plot_equity_and_drawdown_chart(snapshots: List[PortfolioSnapshot], initial_c
         print("No portfolio snapshots available to plot equity and drawdown.")
         return
 
-    df_equity = pd.DataFrame([
-        {'datetime': s.datetime, 'total_value': s.total_value}
-        for s in snapshots
-    ])
+    df_equity = pd.DataFrame(
+        [{"datetime": s.datetime, "total_value": s.total_value} for s in snapshots]
+    )
 
-    equity_curve = df_equity['total_value'] / initial_capital
+    equity_curve = df_equity["total_value"] / initial_capital
 
     rolling_max = equity_curve.cummax()
     drawdown = (rolling_max - equity_curve) / rolling_max
 
-    plt.style.use('seaborn-v0_8-darkgrid')
-    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True, gridspec_kw={'height_ratios': [3, 1]})
+    plt.style.use("seaborn-v0_8-darkgrid")
+    fig, (ax1, ax2) = plt.subplots(
+        2, 1, figsize=(14, 10), sharex=True, gridspec_kw={"height_ratios": [3, 1]}
+    )
 
     x_axis_indices = np.arange(len(df_equity))
 
     # Equity Curve Plot
-    ax1.plot(x_axis_indices, equity_curve, label='Equity Curve', color='blue', linewidth=1.5)
-    ax1.set_ylabel('Equity', fontsize=12)
-    ax1.legend(loc='upper left')
+    ax1.plot(
+        x_axis_indices, equity_curve, label="Equity Curve", color="blue", linewidth=1.5
+    )
+    ax1.set_ylabel("Equity", fontsize=12)
+    ax1.legend(loc="upper left")
     ax1.grid(True)
     ax1.set_title(title, fontsize=16)
 
     # Drawdown Curve Plot
-    ax2.fill_between(x_axis_indices, 0, drawdown, color='red', alpha=0.3)
-    ax2.plot(x_axis_indices, drawdown, color='red', linewidth=1.0, linestyle='--', label='Drawdown')
-    ax2.set_ylabel('Drawdown Rate', fontsize=12)
-    ax2.set_xlabel('Data Point Index (Date Labels Below)', fontsize=12)
-    ax2.set_title('Portfolio Drawdown Curve', fontsize=14)
-    ax2.legend(loc='upper left')
+    ax2.fill_between(x_axis_indices, 0, drawdown, color="red", alpha=0.3)
+    ax2.plot(
+        x_axis_indices,
+        drawdown,
+        color="red",
+        linewidth=1.0,
+        linestyle="--",
+        label="Drawdown",
+    )
+    ax2.set_ylabel("Drawdown Rate", fontsize=12)
+    ax2.set_xlabel("Data Point Index (Date Labels Below)", fontsize=12)
+    ax2.set_title("Portfolio Drawdown Curve", fontsize=14)
+    ax2.legend(loc="upper left")
     ax2.grid(True)
     ax2.set_ylim(0, max(drawdown.max() * 1.1, 0.05))
 
@@ -193,9 +206,12 @@ def plot_equity_and_drawdown_chart(snapshots: List[PortfolioSnapshot], initial_c
     num_ticks = 10
     if len(df_equity) > 0:
         tick_positions = np.linspace(0, len(df_equity) - 1, num_ticks, dtype=int)
-        tick_labels = [df_equity['datetime'].iloc[i].strftime('%Y-%m-%d %H:%M') for i in tick_positions]
+        tick_labels = [
+            df_equity["datetime"].iloc[i].strftime("%Y-%m-%d %H:%M")
+            for i in tick_positions
+        ]
         ax1.set_xticks(tick_positions)
-        ax1.set_xticklabels(tick_labels, rotation=45, ha='right')
+        ax1.set_xticklabels(tick_labels, rotation=45, ha="right")
 
     plt.tight_layout()
     plt.show()
@@ -213,30 +229,38 @@ def plot_close_price_chart(bars: List[Bar], title: str = "Close Price Chart") ->
         print("No bar data available to plot close price.")
         return
 
-    df_prices = pd.DataFrame([
-        {'datetime': b.datetime, 'close_price': b.close}
-        for b in bars
-    ])
+    df_prices = pd.DataFrame(
+        [{"datetime": b.datetime, "close_price": b.close} for b in bars]
+    )
 
-    plt.style.use('seaborn-v0_8-darkgrid')
+    plt.style.use("seaborn-v0_8-darkgrid")
     fig, ax = plt.subplots(1, 1, figsize=(14, 7))  # Single subplot
 
     x_axis_indices = np.arange(len(df_prices))
 
-    ax.plot(x_axis_indices, df_prices['close_price'], label='Close Price', color='orange', linewidth=1.5)
-    ax.set_ylabel('Price', fontsize=12)
-    ax.set_xlabel('Data Point Index (Date Labels Below)', fontsize=12)
+    ax.plot(
+        x_axis_indices,
+        df_prices["close_price"],
+        label="Close Price",
+        color="orange",
+        linewidth=1.5,
+    )
+    ax.set_ylabel("Price", fontsize=12)
+    ax.set_xlabel("Data Point Index (Date Labels Below)", fontsize=12)
     ax.set_title(title, fontsize=16)
-    ax.legend(loc='upper left')
+    ax.legend(loc="upper left")
     ax.grid(True)
 
     # Set X-axis ticks to show actual dates at intervals
     num_ticks = 10
     if len(df_prices) > 0:
         tick_positions = np.linspace(0, len(df_prices) - 1, num_ticks, dtype=int)
-        tick_labels = [df_prices['datetime'].iloc[i].strftime('%Y-%m-%d %H:%M') for i in tick_positions]
+        tick_labels = [
+            df_prices["datetime"].iloc[i].strftime("%Y-%m-%d %H:%M")
+            for i in tick_positions
+        ]
         ax.set_xticks(tick_positions)
-        ax.set_xticklabels(tick_labels, rotation=45, ha='right')
+        ax.set_xticklabels(tick_labels, rotation=45, ha="right")
 
     plt.tight_layout()
     plt.show()
@@ -244,7 +268,7 @@ 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