Update app.py

app.py

@@ -12,27 +12,59 @@ from Gradio_UI import GradioUI
 
 # Below is an example of a tool that does nothing. Amaze us with your creativity !
 @tool
-def calculate_historical_var(returns: np.ndarray, var_level: float = 0.95) -> float:
+def calculate_risk_metrics(
+    returns: np.ndarray,
+    var_level: float = 0.95,
+    n_simulations: int = 10000,
+    bootstrap: bool = False,
+    random_seed: Optional[int] = None
+) -> Tuple[float, float]:
     """
-    Calculate the Value at Risk (VaR) using the historical method.
-    
+    Calculate Value at Risk (VaR) and Expected Shortfall (ES) using the historical method, with an option for bootstrapped historical simulation.
+
     Args:
-    returns: Array of daily returns.
-    var_level: VaR level (e.g., 0.95 for 95%).
-    
+        returns: Array of daily returns. Each value represents the percentage return for a single day.
+        var_level: VaR level (e.g., 0.95 for 95% confidence). Defaults to 0.95.
+        n_simulations: Number of bootstrap simulations. Defaults to 10000.
+        bootstrap: If True, use bootstrapped historical simulation. Defaults to False.
+        random_seed: Seed for random number generation to ensure reproducibility. Defaults to None.
+
     Returns:
-    float: VaR value.
+        Tuple[float, float]: A tuple containing the VaR and Expected Shortfall (ES) values.
     """
-    # Sort the returns in ascending order
-    sorted_returns = np.sort(returns)
-    
-    # Determine the index for the VaR level
-    index = int((1 - var_level) * len(returns))
-    
-    # Get the VaR value
-    var_value = sorted_returns[index]
-    
-    return var_value
+    if random_seed is not None:
+        np.random.seed(random_seed)
+
+    if bootstrap:
+        # Perform bootstrapped historical simulation
+        simulated_var = np.zeros(n_simulations)
+        simulated_es = np.zeros(n_simulations)
+
+        for i in range(n_simulations):
+            # Resample returns with replacement
+            resampled_returns = np.random.choice(returns, size=len(returns), replace=True)
+            # Sort the resampled returns
+            sorted_returns = np.sort(resampled_returns)
+            # Determine the index for the VaR level
+            index = int((1 - var_level) * len(sorted_returns))
+            # Calculate VaR for this simulation
+            simulated_var[i] = sorted_returns[index]
+            # Calculate ES for this simulation (average of returns below VaR)
+            simulated_es[i] = np.mean(sorted_returns[:index])
+
+        # Calculate the average VaR and ES across all simulations
+        var_value = np.mean(simulated_var)
+        es_value = np.mean(simulated_es)
+    else:
+        # Use the standard historical method
+        sorted_returns = np.sort(returns)
+        index = int((1 - var_level) * len(returns))
+        # Calculate VaR
+        var_value = sorted_returns[index]
+        # Calculate ES (average of returns below VaR)
+        es_value = np.mean(sorted_returns[:index])
+
+    return var_value, es_value
 
 @tool
 def get_current_time_in_timezone(timezone: str) -> str:
@@ -73,7 +105,7 @@ with open("prompts.yaml", 'r') as stream:
     
 agent = CodeAgent(
     model=model,
-    tools=[final_answer,calculate_historical_var,get_current_time_in_timezone,visit_webpage,web_search], ## add your tools here (don't remove final answer)
+    tools=[final_answer,calculate_risk_metrics,get_current_time_in_timezone,visit_webpage,web_search], ## add your tools here (don't remove final answer)
     max_steps=6,
     verbosity_level=1,
     grammar=None,