Update app.py

app.py

@@ -4,60 +4,70 @@ import yfinance as yf
 import plotly.graph_objects as go
 import streamlit as st
 
-st.set_page_config(page_title = "Analyzing Future Stock Price Movements with Monte Carlo Simulations", layout = "wide")
-st.title('Analyzing Future Stock Price Movements with Monte Carlo Simulations')
-
-# Sidebar inputs
-st.sidebar.header('Input Parameters')
-ticker = st.sidebar.text_input('Enter Stock Ticker', 'AFX.DE')
-start_date = st.sidebar.date_input('Start Date', pd.to_datetime('2020-01-01'))
-end_date = st.sidebar.date_input('End Date', pd.to_datetime('2024-12-31'))
-days_to_forecast = st.sidebar.slider('Time Horizon (Days)', min_value=1, max_value=60, value=30)
-num_simulations = st.sidebar.number_input('Number of Simulations', min_value=100, max_value=100000, value=10000, step=100)
-lower_threshold = st.sidebar.number_input('Lower Threshold', min_value=0, max_value=10000, value=90)
-upper_threshold = st.sidebar.number_input('Upper Threshold', min_value=0, max_value=10000, value=110)
-volatility_bsm = st.sidebar.number_input('BSM Volatility (Annualized)', min_value=0.01, max_value=10.0, value=0.29, step=0.01)
-
-st.write("""
-This analysis estimates potential price movements of a selected stock over a specified time horizon. The estimates are based on historical volatility and the implied volatility derived fromthe Black-Scholes-Merton model. You can adjust the time horizon, number of simulations, and implied volatility measure to explore different scenarios of price dynamics.
+# Set up the Streamlit app configuration
+st.set_page_config(page_title="Analyzing Future Price Movements with Monte Carlo Simulations", layout="wide")
+st.title('Analyzing Future Price Movements with Monte Carlo Simulations')
+
+# Sidebar instructions
+st.sidebar.title('Input Parameters')
+st.sidebar.subheader('How to Use This App')
+st.sidebar.markdown("""
+1. **Enter Ticker and Date**: Type the stock or cryptocurrency ticker and select the date range.
+2. **Set Parameters**: Adjust the time horizon, number of simulations, and other parameters to customize the analysis.
+   - **For Cryptocurrencies**: The BSM analysis is not applicable. Set the BSM volatility to 0 to exclude the BSM-based analysis.
 """)
 
-st.markdown("""
-### How to Use This App
-1. **Input Parameters**: Use the sidebar to enter the stock ticker, date range, and other parameters for the analyses.
-2. **Run the Analysis**: Click the "Run" button to perform the analyses and visualize the results.
-Each analysis is accompanied by a detailed explanation and visual representation, providing insights into the stock's price behavior and helping traders make informed decisions regarding potential price movements.
+# Wrapping ticker and date settings in an expander
+with st.sidebar.expander("Ticker and Date Settings", expanded=True):
+    ticker = st.text_input('Enter Stock/Crypto Ticker', 'AFX.DE', help="Enter the ticker symbol of the stock or cryptocurrency pair you want to analyze (e.g., 'AAPL' for Apple, 'BTC-USD' for Bitcoin).")
+    start_date = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for fetching historical data.")
+    end_date = st.date_input('End Date', pd.to_datetime('today') + pd.DateOffset(1), help="Select the end date for fetching historical data (default is today plus one day).")
+
+# Wrapping parameter settings in an expander
+with st.sidebar.expander("Parameter Settings", expanded=True):
+    days_to_forecast = st.slider('Time Horizon (Days)', min_value=1, max_value=60, value=30, help="Select the number of days into the future for the simulation.")
+    num_simulations = st.number_input('Number of Simulations', min_value=100, max_value=100000, value=10000, step=100, help="Set the number of Monte Carlo simulations to run.")
+    lower_threshold = st.number_input('Lower Threshold', min_value=0, max_value=10000, value=50, help="Set the lower price threshold for probability calculation.")
+    upper_threshold = st.number_input('Upper Threshold', min_value=0, max_value=10000, value=70, help="Set the upper price threshold for probability calculation.")
+    volatility_bsm = st.number_input('BSM Volatility (Annualized)', min_value=0.0, max_value=10.0, value=0.29, step=0.01, help="Set the annualized volatility for the Black-Scholes-Merton model. Set to 0 if analyzing a cryptocurrency.")
+
+# Detect if the asset is a cryptocurrency
+is_crypto = '-' in ticker
+
+# Automatically set BSM volatility to 0 if it's a cryptocurrency
+if is_crypto:
+    volatility_bsm = 0.0
+
+# Description of the analysis
+st.write("""
+This tool estimates potential price movements of a selected stock or cryptocurrency over a specified time horizon using Monte Carlo Simulations.
+The estimates are based on historical volatility and the implied volatility derived from the Black-Scholes-Merton model. 
+You can adjust the time horizon, number of simulations, and volatility measures to explore different scenarios of price dynamics.
 """)
 
 # Adding LaTeX formatted formulas
-# Display the main formula for Monte Carlo simulations with clear, basic LaTeX
 st.latex(r'''
 P_t = P_0 \times e^{(\mu - \frac{1}{2} \sigma^2) \times t + \sigma \times \sqrt{t} \times Z}
 ''')
 
-# Provide detailed explanations using plain text
 st.markdown("""
-**Monte Carlo Simulation Explained:**
-- **(P_t)**: Estimated stock price at time (t).
-- **(P_0)**: Current stock price.
-- **(mu)**: Mean of the log returns.
-- **(sigma)**: Standard deviation of the log returns, representing historical volatility.
+**Monte Carlo Simulation Inputs:**
+- **(Pt)**: Estimated asset price at time (t).
+- **(P0)**: Current asset price.
+- **(μ)**: Mean of the log returns.
+- **(σ)**: Standard deviation of the log returns, representing historical volatility.
 - **(t)**: Time horizon in days.
 - **(Z)**: Random variable from the standard normal distribution.
-This formula models future stock prices using a stochastic process known as the Monte Carlo simulation. It considers both the average return and the variability in returns to project future price movements. The random component (Z) introduces randomness reflecting the unpredictable nature of stock price movements.
-""")
-
-# Note on usage
-st.markdown("""
-Use this tool to simulate different scenarios by varying the number of simulations and observing the distribution of possible future prices. Adjust the time horizon and volatility to see how these factors influence the projected price range. This analysis is crucial for assessing potential investment risks and rewards.
 """)
 
 st.write(f"""
 ### Monte Carlo Simulations
-These simulations project multiple potential future price paths for the stock based on the volatility models described. By running {num_simulations} simulations, the tool generates a distribution of possible future prices, allowing us to calculate confidence intervals and probabilities for various price levels.
+These simulations project multiple potential future price paths for the asset based on the volatility models described. 
+By running {num_simulations} simulations, the tool generates a distribution of possible future prices. This allows us to calculate confidence intervals and probabilities for various price levels.
 """)
 
-if st.sidebar.button('Run'):
+# Running the analysis when the button is pressed
+if st.sidebar.button('Run Analysis'):
     stock_data = yf.download(ticker, start=start_date, end=end_date)
     
     if not stock_data.empty:
@@ -79,74 +89,128 @@ if st.sidebar.button('Run'):
                 simulated_prices[t] = simulated_prices[t - 1] * np.exp(random_walk)
             return simulated_prices
 
+        # Run simulations based on historical volatility
+        st.write(f"### Simulation with Historical Volatility for {ticker}")
         simulated_prices_historical = run_simulation(log_returns.std(), 1)
-        simulated_prices_bsm = run_simulation(volatility_bsm, 1, annualized=True)
 
         fig1 = go.Figure()
-        fig2 = go.Figure()
 
-        for simulated_prices, fig, title in zip([simulated_prices_historical, simulated_prices_bsm],
-                                                [fig1, fig2], ['Historical Volatility', 'BSM Volatility']):
-            mean_price_path = np.mean(simulated_prices, axis=1)
-            median_price_path = np.median(simulated_prices, axis=1)
-            lower_bound_68 = np.percentile(simulated_prices, 16, axis=1)
-            upper_bound_68 = np.percentile(simulated_prices, 84, axis=1)
-            lower_bound_95 = np.percentile(simulated_prices, 2.5, axis=1)
-            upper_bound_95 = np.percentile(simulated_prices, 97.5, axis=1)
+        mean_price_path = np.mean(simulated_prices_historical, axis=1)
+        median_price_path = np.median(simulated_prices_historical, axis=1)
+        lower_bound_68 = np.percentile(simulated_prices_historical, 16, axis=1)
+        upper_bound_68 = np.percentile(simulated_prices_historical, 84, axis=1)
+        lower_bound_95 = np.percentile(simulated_prices_historical, 2.5, axis=1)
+        upper_bound_95 = np.percentile(simulated_prices_historical, 97.5, axis=1)
+
+        for i in range(min(num_simulations, 100)):  # Plot a subset of paths for clarity
+            fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=simulated_prices_historical[:, i], mode='lines', line=dict(color='lightgray', width=0.5), opacity=0.3, showlegend=False))
+
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=mean_price_path, mode='lines', name='Mean Price Path', line=dict(color='black')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=median_price_path, mode='lines', name='Median Price Path', line=dict(color='blue')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_68, mode='lines', name='68% confidence interval (Lower)', line=dict(color='green', dash='dash')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_68, mode='lines', name='68% confidence interval (Upper)', line=dict(color='green', dash='dash')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_95, mode='lines', name='95% confidence interval (Lower)', line=dict(color='blue', dash='dash')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_95, mode='lines', name='95% confidence interval (Upper)', line=dict(color='red', dash='dash')))
+
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[upper_threshold] * days_to_forecast, mode='lines', name='Upper Threshold', line=dict(color='purple')))
+        fig1.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[lower_threshold] * days_to_forecast, mode='lines', name='Lower Threshold', line=dict(color='orange')))
+
+        above_upper_threshold_prob = (simulated_prices_historical[-1] > upper_threshold).sum() / num_simulations
+        below_lower_threshold_prob = (simulated_prices_historical[-1] < lower_threshold).sum() / num_simulations
+        between_thresholds_prob = 1 - above_upper_threshold_prob - below_lower_threshold_prob
+
+        fig1.update_layout(
+            title=f'Monte Carlo Confidence Cone for {ticker} using Historical Volatility',
+            xaxis_title='Days',
+            yaxis_title='Price',
+            legend_title='Legend'
+        )
+
+        fig1.add_annotation(text=f'P(>{upper_threshold:.2f}): {above_upper_threshold_prob:.2%}<br>'
+                                f'P(<{lower_threshold:.2f}): {below_lower_threshold_prob:.2%}<br>'
+                                f'P({lower_threshold:.2f} - {upper_threshold:.2f}): {between_thresholds_prob:.2%}',
+                            xref='paper', yref='paper', x=0.02, y=0.95, showarrow=False, bordercolor="black", borderwidth=1)
+
+        st.plotly_chart(fig1)
+
+        st.markdown(f"""
+        <h4 style='font-size: 16px;'>Interpretation of Results</h4>
+        <ul style='font-size: 14px;'>
+            <li><strong>Mean Price Path:</strong> The average simulated price path over the forecast period.</li>
+            <li><strong>Median Price Path:</strong> The median simulated price path over the forecast period.</li>
+            <li><strong>68% Confidence Interval:</strong> The range within which 68% of the simulated prices fall.</li>
+            <li><strong>95% Confidence Interval:</strong> The range within which 95% of the simulated prices fall.</li>
+            <li><strong>Probability of Exceeding Upper Threshold ({upper_threshold}):</strong> {above_upper_threshold_prob:.2%}</li>
+            <li><strong>Probability of Falling Below Lower Threshold ({lower_threshold}):</strong> {below_lower_threshold_prob:.2%}</li>
+            <li><strong>Probability of Staying Between Thresholds ({lower_threshold} - {upper_threshold}):</strong> {between_thresholds_prob:.2%}</li>
+        </ul>
+        """, unsafe_allow_html=True)
+
+        # Run BSM-based simulation only if volatility_bsm is greater than 0
+        if volatility_bsm > 0:
+            st.write(f"### Simulation with BSM Volatility for {ticker}")
+            simulated_prices_bsm = run_simulation(volatility_bsm, 1, annualized=True)
+
+            fig2 = go.Figure()
+
+            mean_price_path = np.mean(simulated_prices_bsm, axis=1)
+            median_price_path = np.median(simulated_prices_bsm, axis=1)
+            lower_bound_68 = np.percentile(simulated_prices_bsm, 16, axis=1)
+            upper_bound_68 = np.percentile(simulated_prices_bsm, 84, axis=1)
+            lower_bound_95 = np.percentile(simulated_prices_bsm, 2.5, axis=1)
+            upper_bound_95 = np.percentile(simulated_prices_bsm, 97.5, axis=1)
 
             for i in range(min(num_simulations, 100)):  # Plot a subset of paths for clarity
-                fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=simulated_prices[:, i], mode='lines', line=dict(color='lightgray', width=0.5), opacity=0.3, showlegend=False))
+                fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=simulated_prices_bsm[:, i], mode='lines', line=dict(color='lightgray', width=0.5), opacity=0.3, showlegend=False))
 
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=mean_price_path, mode='lines', name='Mean Price Path', line=dict(color='black')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=median_price_path, mode='lines', name='Median Price Path', line=dict(color='blue')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_68, mode='lines', name='68% confidence interval (Lower)', line=dict(color='green', dash='dash')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_68, mode='lines', name='68% confidence interval (Upper)', line=dict(color='green', dash='dash')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_95, mode='lines', name='95% confidence interval (Lower)', line=dict(color='blue', dash='dash')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_95, mode='lines', name='95% confidence interval (Upper)', line=dict(color='red', dash='dash')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=mean_price_path, mode='lines', name='Mean Price Path', line=dict(color='black')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=median_price_path, mode='lines', name='Median Price Path', line=dict(color='blue')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_68, mode='lines', name='68% confidence interval (Lower)', line=dict(color='green', dash='dash')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_68, mode='lines', name='68% confidence interval (Upper)', line=dict(color='green', dash='dash')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=lower_bound_95, mode='lines', name='95% confidence interval (Lower)', line=dict(color='blue', dash='dash')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=upper_bound_95, mode='lines', name='95% confidence interval (Upper)', line=dict(color='red', dash='dash')))
 
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[upper_threshold] * days_to_forecast, mode='lines', name='Upper Threshold', line=dict(color='purple')))
-            fig.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[lower_threshold] * days_to_forecast, mode='lines', name='Lower Threshold', line=dict(color='orange')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[upper_threshold] * days_to_forecast, mode='lines', name='Upper Threshold', line=dict(color='purple')))
+            fig2.add_trace(go.Scatter(x=np.arange(days_to_forecast), y=[lower_threshold] * days_to_forecast, mode='lines', name='Lower Threshold', line=dict(color='orange')))
 
-            above_upper_threshold_prob = (simulated_prices[-1] > upper_threshold).sum() / num_simulations
-            below_lower_threshold_prob = (simulated_prices[-1] < lower_threshold).sum() / num_simulations
+            above_upper_threshold_prob = (simulated_prices_bsm[-1] > upper_threshold).sum() / num_simulations
+            below_lower_threshold_prob = (simulated_prices_bsm[-1] < lower_threshold).sum() / num_simulations
             between_thresholds_prob = 1 - above_upper_threshold_prob - below_lower_threshold_prob
 
-            fig.update_layout(
-                title=f'Monte Carlo Confidence Cone for {ticker} using {title}',
+            fig2.update_layout(
+                title=f'Monte Carlo Confidence Cone for {ticker} using BSM Volatility',
                 xaxis_title='Days',
-                yaxis_title='Stock Price',
+                yaxis_title='Price',
                 legend_title='Legend'
             )
-                                          
-                              
-            fig.add_annotation(text=f'P(>{upper_threshold:.2f}): {above_upper_threshold_prob:.2%}<br>'
+
+            fig2.add_annotation(text=f'P(>{upper_threshold:.2f}): {above_upper_threshold_prob:.2%}<br>'
                                     f'P(<{lower_threshold:.2f}): {below_lower_threshold_prob:.2%}<br>'
                                     f'P({lower_threshold:.2f} - {upper_threshold:.2f}): {between_thresholds_prob:.2%}',
-                               xref='paper', yref='paper', x=0.02, y=0.95, showarrow=False, bordercolor="black", borderwidth=1)
+                                xref='paper', yref='paper', x=0.02, y=0.95, showarrow=False, bordercolor="black", borderwidth=1)
+
+            st.plotly_chart(fig2)
 
-            # Add elaboration text below each graph
-            st.plotly_chart(fig)
             st.markdown(f"""
             <h4 style='font-size: 16px;'>Interpretation of Results</h4>
             <ul style='font-size: 14px;'>
-                <li><strong>Mean Price Path:</strong> The average simulated stock price path over the forecast period.</li>
-                <li><strong>Median Price Path:</strong> The median simulated stock price path over the forecast period.</li>
-                <li><strong>68% Confidence Interval:</strong> The range within which 68% of the simulated stock prices fall.</li>
-                <li><strong>95% Confidence Interval:</strong> The range within which 95% of the simulated stock prices fall.</li>
+                <li><strong>Mean Price Path:</strong> The average simulated price path over the forecast period.</li>
+                <li><strong>Median Price Path:</strong> The median simulated price path over the forecast period.</li>
+                <li><strong>68% Confidence Interval:</strong> The range within which 68% of the simulated prices fall.</li>
+                <li><strong>95% Confidence Interval:</strong> The range within which 95% of the simulated prices fall.</li>
                 <li><strong>Probability of Exceeding Upper Threshold ({upper_threshold}):</strong> {above_upper_threshold_prob:.2%}</li>
                 <li><strong>Probability of Falling Below Lower Threshold ({lower_threshold}):</strong> {below_lower_threshold_prob:.2%}</li>
                 <li><strong>Probability of Staying Between Thresholds ({lower_threshold} - {upper_threshold}):</strong> {between_thresholds_prob:.2%}</li>
             </ul>
             """, unsafe_allow_html=True)
-
     else:
         st.write("No data found for the given ticker and date range.")
 
-
+# Hide the Streamlit main menu and footer for a cleaner interface
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True) 
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)