From cd9b6f7969b104f16d5fb8808646e2b6988ff833 Mon Sep 17 00:00:00 2001
From: "Bobby (aider)" <bobby@abellana.work>
Date: Mon, 17 Feb 2025 15:45:24 -0800
Subject: [PATCH] feat: Add Monte Carlo stop loss calculation to trading system

---
 src/pages/analysis/monte_carlo_page.py   | 25 ++++++++++++
 src/pages/trading/trading_system_page.py | 50 +++++++++++++++++++++---
 2 files changed, 70 insertions(+), 5 deletions(-)

diff --git a/src/pages/analysis/monte_carlo_page.py b/src/pages/analysis/monte_carlo_page.py
index 8cff8b5..d0fa34d 100644
--- a/src/pages/analysis/monte_carlo_page.py
+++ b/src/pages/analysis/monte_carlo_page.py
@@ -47,6 +47,31 @@ class MonteCarloSimulator:
             
         return price_paths
 
+    def calculate_stop_loss(self, risk_percentage: float) -> float:
+        """
+        Calculate stop loss price based on Monte Carlo simulation and desired risk percentage
+        
+        Args:
+            risk_percentage (float): Maximum risk percentage willing to take
+            
+        Returns:
+            float: Recommended stop loss price
+        """
+        # Run a quick simulation
+        paths = self.run_simulation()
+        
+        # Get the first day's simulated prices
+        first_day_prices = paths[1]  # Using day 1 instead of day 0 to see immediate movement
+        
+        # Calculate the price that represents the risk percentage loss
+        potential_losses = (first_day_prices - self.last_price) / self.last_price * 100
+        
+        # Find the price level that corresponds to our risk percentage
+        stop_loss_percentile = np.percentile(potential_losses, risk_percentage)
+        stop_loss_price = self.last_price * (1 + stop_loss_percentile / 100)
+        
+        return stop_loss_price
+
     def calculate_metrics(self, paths: np.ndarray) -> Dict:
         """Calculate key metrics from simulation results"""
         final_prices = paths[-1]
diff --git a/src/pages/trading/trading_system_page.py b/src/pages/trading/trading_system_page.py
index af79d0d..83832c8 100644
--- a/src/pages/trading/trading_system_page.py
+++ b/src/pages/trading/trading_system_page.py
@@ -2,6 +2,9 @@ import streamlit as st
 from trading.journal import get_latest_portfolio_value, get_open_trades_summary
 from trading.position_calculator import PositionCalculator
 from utils.data_utils import get_current_prices
+from pages.analysis.monte_carlo_page import MonteCarloSimulator
+from datetime import datetime, timedelta
+from utils.common_utils import get_stock_data
 
 def trading_system_page():
     st.header("Trading System")
@@ -54,18 +57,44 @@ def trading_system_page():
                                         max_value=100.0, 
                                         value=1.0, 
                                         step=0.1)
-        stop_loss_percentage = st.number_input("Stop Loss Percentage (%)", 
-                                             min_value=0.1, 
-                                             max_value=100.0, 
-                                             value=7.0, 
-                                             step=0.1)
+        use_monte_carlo = st.checkbox("Use Monte Carlo for Stop Loss", value=True)
     
     with col2:
+        ticker = st.text_input("Ticker Symbol", value="").upper()
         entry_price = st.number_input("Entry Price ($)", min_value=0.01, step=0.01)
         target_price = st.number_input("Target Price ($)", min_value=0.01, step=0.01)
     
     if st.button("Calculate Position"):
         try:
+            if not ticker:
+                st.error("Please enter a ticker symbol")
+                return
+                
+            # Get historical data for Monte Carlo simulation
+            if use_monte_carlo:
+                with st.spinner("Calculating optimal stop loss..."):
+                    df = get_stock_data(
+                        ticker,
+                        datetime.now() - timedelta(days=30),  # Last 30 days of data
+                        datetime.now(),
+                        '1m'  # Minute data for more accurate simulation
+                    )
+                    
+                    if df.empty:
+                        st.error("No data available for the selected ticker")
+                        return
+                    
+                    # Initialize Monte Carlo simulator
+                    simulator = MonteCarloSimulator(df, num_simulations=1000, time_horizon=5)
+                    
+                    # Calculate stop loss price
+                    stop_loss_price = simulator.calculate_stop_loss(risk_percentage)
+                    
+                    # Calculate stop loss percentage
+                    stop_loss_percentage = abs((stop_loss_price - entry_price) / entry_price * 100)
+            else:
+                stop_loss_percentage = 7.0  # Default value if not using Monte Carlo
+            
             calculator = PositionCalculator(
                 account_size=account_size,
                 risk_percentage=risk_percentage,
@@ -118,5 +147,16 @@ def trading_system_page():
                     f"- {portfolio_usage:.1f}% of total portfolio"
                 )
                 
+            # Add Monte Carlo metrics if used
+            if use_monte_carlo:
+                st.subheader("Monte Carlo Analysis")
+                col1, col2 = st.columns(2)
+                with col1:
+                    st.metric("Calculated Stop Loss Price", f"${stop_loss_price:.2f}")
+                    st.metric("Stop Loss Percentage", f"{stop_loss_percentage:.2f}%")
+                with col2:
+                    st.metric("Based on Simulations", "1,000")
+                    st.metric("Simulation Timeframe", "5 days")
+                
         except Exception as e:
             st.error(f"Error calculating position: {str(e)}")