Hi

Author: MeridianAlgo-Developer

meridianalgo/analytics/tear_sheets.py

@@ -482,18 +482,41 @@ def plot_position_exposure(self, ax: Optional[Any] = None) -> Any:
         else:
             gross_exposure = self.gross_lev
             # Estimate net from positions if available, else assume 0
-            net_exposure = self.positions.sum(axis=1) if not self.positions.empty else pd.Series(0, index=self.returns.index)
+            net_exposure = (
+                self.positions.sum(axis=1)
+                if not self.positions.empty
+                else pd.Series(0, index=self.returns.index)
+            )
+
+        ax.plot(
+            gross_exposure.index,
+            gross_exposure.values,
+            label="Gross Exposure",
+            color="black",
+            linewidth=1,
+        )
+        ax.plot(
+            net_exposure.index,
+            net_exposure.values,
+            label="Net Exposure",
+            color=self.COLORS["strategy"],
+            linewidth=1,
+            linestyle="--",
+        )
 
-        ax.plot(gross_exposure.index, gross_exposure.values, label="Gross Exposure", color="black", linewidth=1)
-        ax.plot(net_exposure.index, net_exposure.values, label="Net Exposure", color=self.COLORS["strategy"], linewidth=1, linestyle="--")
-        
-        ax.fill_between(net_exposure.index, net_exposure.values, 0, alpha=0.1, color=self.COLORS["strategy"])
+        ax.fill_between(
+            net_exposure.index,
+            net_exposure.values,
+            0,
+            alpha=0.1,
+            color=self.COLORS["strategy"],
+        )
 
         ax.set_xlabel("Date")
         ax.set_ylabel("Exposure")
         ax.set_title("Portfolio Exposure")
         ax.legend(loc="upper left")
-        
+
         return ax
 
     def plot_top_positions(self, top_n: int = 10, ax: Optional[Any] = None) -> Any:
@@ -508,84 +531,105 @@ def plot_top_positions(self, top_n: int = 10, ax: Optional[Any] = None) -> Any:
             fig, ax = plt.subplots(figsize=(10, 6))
 
         # Calculate average absolute allocation
-        avg_allocation = self.positions.abs().mean().sort_values(ascending=False).head(top_n)
-        
-        sns.barplot(x=avg_allocation.values, y=avg_allocation.index, ax=ax, palette="viridis", orient="h")
-        
+        avg_allocation = (
+            self.positions.abs().mean().sort_values(ascending=False).head(top_n)
+        )
+
+        sns.barplot(
+            x=avg_allocation.values,
+            y=avg_allocation.index,
+            ax=ax,
+            palette="viridis",
+            orient="h",
+        )
+
         ax.set_xlabel("Average Absolute Allocation")
         ax.set_title(f"Top {top_n} Positions by Allocation")
-        
+
         return ax
 
     def plot_trade_pnl_distribution(self, ax: Optional[Any] = None) -> Any:
         """Plot distribution of trade P&L."""
         if not HAS_MATPLOTLIB:
             raise ImportError("matplotlib required for plotting")
-            
+
         if self.transactions is None or "pnl" not in self.transactions.columns:
             return None
-            
+
         if ax is None:
             fig, ax = plt.subplots(figsize=(10, 6))
-            
+
         pnls = self.transactions["pnl"].dropna()
         if pnls.empty:
             return None
-            
+
         sns.histplot(pnls, kde=True, ax=ax, color=self.COLORS["strategy"])
         ax.axvline(0, color="black", linestyle="--", linewidth=1)
-        
+
         ax.set_xlabel("Trade P&L")
         ax.set_title("Trade P&L Distribution")
-        
+
         # Add stats
         win_rate = (pnls > 0).mean()
         avg_win = pnls[pnls > 0].mean() if not pnls[pnls > 0].empty else 0
         avg_loss = pnls[pnls < 0].mean() if not pnls[pnls < 0].empty else 0
-        profit_factor = abs(pnls[pnls > 0].sum() / pnls[pnls < 0].sum()) if pnls[pnls < 0].sum() != 0 else float('inf')
-        
+        profit_factor = (
+            abs(pnls[pnls > 0].sum() / pnls[pnls < 0].sum())
+            if pnls[pnls < 0].sum() != 0
+            else float("inf")
+        )
+
         text = f"Win Rate: {win_rate:.1%}\nAvg Win: ${avg_win:.2f}\nAvg Loss: ${avg_loss:.2f}\nProfit Factor: {profit_factor:.2f}"
         self._create_text_box(ax, text, (0.05, 0.95))
-        
+
         return ax
 
-    def plot_bayesian_cone(self, ax: Optional[Any] = None, n_samples: int = 1000) -> Any:
+    def plot_bayesian_cone(
+        self, ax: Optional[Any] = None, n_samples: int = 1000
+    ) -> Any:
         """Plot Bayesian cone for Sharpe ratio uncertainty."""
         if not HAS_MATPLOTLIB:
             raise ImportError("matplotlib required for plotting")
-            
-            
+
         if ax is None:
             fig, ax = plt.subplots(figsize=(12, 6))
-            
+
         # Bootstrap resampling for Sharpe ratio distribution
         sharpe_dist = []
         returns_array = self.returns.values
-        
+
         for _ in range(n_samples):
             # Simple bootstrap
-            sample = np.random.choice(returns_array, size=len(returns_array), replace=True)
+            sample = np.random.choice(
+                returns_array, size=len(returns_array), replace=True
+            )
             if np.std(sample) > 0:
-                sharpe = np.mean(sample) / np.std(sample) * np.sqrt(self.periods_per_year)
+                sharpe = (
+                    np.mean(sample) / np.std(sample) * np.sqrt(self.periods_per_year)
+                )
                 sharpe_dist.append(sharpe)
-                
+
         sharpe_dist = np.array(sharpe_dist)
         mean_sharpe = np.mean(sharpe_dist)
-        
+
         # Plot cone
         # We project the uncertainty into the future
         # This is a simplified visualization - typically cones are for cumulative returns
         # Here we plot the distribution of likely Sharpe ratios
-        
+
         sns.histplot(sharpe_dist, kde=True, ax=ax, color=self.COLORS["strategy"])
-        ax.axvline(mean_sharpe, color="red", linestyle="--", label=f"Mean: {mean_sharpe:.2f}")
-        ax.axvline(np.percentile(sharpe_dist, 5), color="orange", linestyle=":", label="95% CI")
+        ax.axvline(
+            mean_sharpe, color="red", linestyle="--", label=f"Mean: {mean_sharpe:.2f}"
+        )
+        ax.axvline(
+            np.percentile(sharpe_dist, 5), color="orange", linestyle=":", label="95% CI"
+        )
         ax.axvline(np.percentile(sharpe_dist, 95), color="orange", linestyle=":")
-        
+
         ax.set_title(f"Bootstrap Sharpe Ratio Distribution (N={n_samples})")
         ax.set_xlabel("Sharpe Ratio")
         ax.legend()
-        
+
         return ax
 
     # =========================================================================
@@ -707,97 +751,109 @@ def create_full_tear_sheet(
             plt.show()
 
         return fig
-        
-    def create_position_tear_sheet(self, filename: Optional[str] = None, show: bool = True) -> Optional[Any]:
+
+    def create_position_tear_sheet(
+        self, filename: Optional[str] = None, show: bool = True
+    ) -> Optional[Any]:
         """Create position analysis tear sheet."""
         if not HAS_MATPLOTLIB:
             raise ImportError("matplotlib required for tear sheets")
 
         self._setup_plot_style()
-        
+
         fig = plt.figure(figsize=(14, 12))
         gs = gridspec.GridSpec(3, 1, figure=fig, hspace=0.4)
-        
+
         # Exposure
         ax1 = fig.add_subplot(gs[0, :])
         self.plot_position_exposure(ax1)
-        
+
         # Top Positions
         ax2 = fig.add_subplot(gs[1, :])
         self.plot_top_positions(ax=ax2)
-        
+
         # Returns (for context)
         ax3 = fig.add_subplot(gs[2, :])
         self.plot_cumulative_returns(ax3)
-        
-        plt.suptitle("Position Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01)
-        
+
+        plt.suptitle(
+            "Position Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01
+        )
+
         if filename:
             fig.savefig(filename, dpi=150, bbox_inches="tight")
-            
+
         if show:
             plt.show()
-            
+
         return fig
 
-    def create_round_trip_tear_sheet(self, filename: Optional[str] = None, show: bool = True) -> Optional[Any]:
+    def create_round_trip_tear_sheet(
+        self, filename: Optional[str] = None, show: bool = True
+    ) -> Optional[Any]:
         """Create trade analysis tear sheet."""
         if not HAS_MATPLOTLIB:
             raise ImportError("matplotlib required for tear sheets")
 
         self._setup_plot_style()
-        
+
         fig = plt.figure(figsize=(14, 12))
         gs = gridspec.GridSpec(2, 2, figure=fig, hspace=0.4, wspace=0.3)
-        
+
         # Trade P&L Distribution
         ax1 = fig.add_subplot(gs[0, 0])
         self.plot_trade_pnl_distribution(ax1)
-        
+
         # Cumulative Returns (for context)
         ax2 = fig.add_subplot(gs[0, 1])
         self.plot_cumulative_returns(ax2)
-        
+
         # Rolling Sharpe
         ax3 = fig.add_subplot(gs[1, :])
         self.plot_rolling_sharpe(ax=ax3)
-        
-        plt.suptitle("Trade Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01)
-        
+
+        plt.suptitle(
+            "Trade Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01
+        )
+
         if filename:
             fig.savefig(filename, dpi=150, bbox_inches="tight")
-            
+
         if show:
             plt.show()
-            
+
         return fig
-        
-    def create_bayesian_tear_sheet(self, filename: Optional[str] = None, show: bool = True) -> Optional[Any]:
+
+    def create_bayesian_tear_sheet(
+        self, filename: Optional[str] = None, show: bool = True
+    ) -> Optional[Any]:
         """Create Bayesian analysis tear sheet."""
         if not HAS_MATPLOTLIB:
             raise ImportError("matplotlib required for tear sheets")
 
         self._setup_plot_style()
-        
+
         fig = plt.figure(figsize=(14, 10))
         gs = gridspec.GridSpec(2, 1, figure=fig, hspace=0.4)
-        
+
         # Bayesian Cone / Sharpe Distribution
         ax1 = fig.add_subplot(gs[0, :])
         self.plot_bayesian_cone(ax1)
-        
+
         # Returns Distribution (for context)
         ax2 = fig.add_subplot(gs[1, :])
         self.plot_returns_distribution(ax2)
-        
-        plt.suptitle("Bayesian Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01)
-        
+
+        plt.suptitle(
+            "Bayesian Analysis Tear Sheet", fontsize=14, fontweight="bold", y=1.01
+        )
+
         if filename:
             fig.savefig(filename, dpi=150, bbox_inches="tight")
-            
+
         if show:
             plt.show()
-            
+
         return fig
 
     def get_metrics_summary(self) -> pd.DataFrame: