Update app.py

app.py

@@ -351,7 +351,8 @@ with st.sidebar.expander("How to Use", expanded=False):
     4. Configure buffer days for fetching stock data.
     5. Enter the implied volatility and days until earnings for Monte Carlo simulation.
     6. Set the number of simulations for more precise results.
-    7. Click the "Run Analysis" button to start the analysis.
+    7. Check the box if you wish to run the Monte Carlo simulations (may slow down the app).
+    8. Click the "Run Analysis" button to start the analysis.
     """)
 
 # Ticker and date inputs inside an expander, open by default
@@ -368,6 +369,10 @@ with st.sidebar.expander("Analysis Parameters", expanded=True):
     days_until_earnings = st.number_input("Days Until Earnings", value=10, min_value=1, help="Enter the number of days until the earnings announcement for the Monte Carlo simulation.")
     num_simulations = st.number_input("Number of Simulations for Monte Carlo", value=10000, min_value=100, help="Set the number of simulations for the Monte Carlo analysis.")
 
+# Checkbox to run Monte Carlo simulations
+with st.sidebar.expander("Monte Carlo Simulation", expanded=False):
+    run_simulation = st.checkbox("Run Monte Carlo Simulations (may slow down the app)", value=False)
+
 if st.sidebar.button("Run Analysis"):
     try:
         if not FMP_API_KEY:
@@ -440,12 +445,13 @@ if st.sidebar.button("Run Analysis"):
                     st.markdown("This scatter plot shows the relationship between earnings surprise percentages and the resulting price effects.")
                     st.plotly_chart(plot_surprise_vs_price_effect(earnings_dates), use_container_width=True)
 
-                    up_target = latest_close_price * upper_threshold
-                    down_target = latest_close_price * lower_threshold
+                    if run_simulation:
+                        up_target = latest_close_price * upper_threshold
+                        down_target = latest_close_price * lower_threshold
 
-                    st.subheader("Monte Carlo Simulation for Price Movements")
-                    st.markdown("We simulate multiple price paths using the stock's implied volatility to estimate the probabilities of the stock price reaching given targets.")
-                    st.plotly_chart(monte_carlo_simulation(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data, num_simulations), use_container_width=True)
+                        st.subheader("Monte Carlo Simulation for Price Movements")
+                        st.markdown("We simulate multiple price paths using the stock's implied volatility to estimate the probabilities of the stock price reaching given targets.")
+                        st.plotly_chart(monte_carlo_simulation(ticker, implied_volatility, days_until_earnings, up_target, down_target, stock_data, num_simulations), use_container_width=True)
 
     except Exception as e:
         st.error(f"An error occurred while running the analysis: {e}")