Update app.py

app.py

@@ -7,19 +7,23 @@ from datetime import datetime, timedelta
 
 # Fetch stock data
 def get_stock_data(ticker, start_date, end_date):
-    stock_data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)  # Set auto_adjust=False for unadjusted closes
-    return stock_data['Close']
+    data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)  # Unadjusted prices
+    if isinstance(data.columns, pd.MultiIndex):  # Flatten multi-index columns
+        data.columns = data.columns.get_level_values(0)
+    if data.empty:
+        raise ValueError(f"No data found for {ticker} from {start_date} to {end_date}")
+    return data['Close']
 
 # Bootstrapping simulation function
 def bootstrap_simulation(data, days, n_iterations=10000):
     daily_returns = data.pct_change().dropna()
-    # Convert to 1D NumPy array to fix previous ValueError
-    daily_returns = daily_returns.values.flatten()
+    daily_returns = daily_returns.values.flatten()  # Ensure 1D array
     simulations = np.zeros((n_iterations, days))
+    last_price = data.iloc[-1].item()  # Scalar last price
 
     for i in range(n_iterations):
         sample = np.random.choice(daily_returns, size=days, replace=True)
-        simulations[i] = np.cumprod(1 + sample) * data.iloc[-1].item()  # Use .item() to get scalar value
+        simulations[i] = np.cumprod(1 + sample) * last_price
 
     return simulations
 
@@ -29,7 +33,6 @@ def calculate_probabilities(simulations, thresholds):
     below = np.mean(final_prices < thresholds[0])
     above = np.mean(final_prices > thresholds[1])
     between = np.mean((final_prices >= thresholds[0]) & (final_prices <= thresholds[1]))
-
     return {'below': below, 'between': between, 'above': above}
 
 # Calculate percentiles
@@ -37,99 +40,62 @@ def calculate_percentiles(simulations):
     percentiles = np.percentile(simulations, [2.5, 16, 50, 84, 97.5], axis=0)
     return percentiles
 
+# Plot distribution of final prices
 def plot_distributions(bootstrap_simulations, data, thresholds, bootstrap_probabilities):
     final_bootstrap_prices = bootstrap_simulations[:, -1]
-
     mean_bootstrap_price = np.mean(final_bootstrap_prices)
     median_bootstrap_price = np.median(final_bootstrap_prices)
     ci_68_bootstrap = np.percentile(final_bootstrap_prices, [16, 84])
     ci_95_bootstrap = np.percentile(final_bootstrap_prices, [2.5, 97.5])
-    latest_price = data.iloc[-1].item()  # Convert Series to scalar with .item()
+    latest_price = data.iloc[-1].item()  # Scalar for plotting
 
     fig = go.Figure()
-
-    # Plot for Bootstrapping
-    fig.add_trace(go.Histogram(x=final_bootstrap_prices, nbinsx=50, name='Simulated Final Prices',
-                               marker_color='blue', opacity=0.7))
-
-    # Replace add_vline with add_trace for vertical lines with legends
-    fig.add_trace(go.Scatter(
-        x=[mean_bootstrap_price, mean_bootstrap_price],
-        y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()],
-        mode='lines',
-        line=dict(color='red', dash='dash'),
-        name=f'Mean: {mean_bootstrap_price:.2f}'
-    ))
-
-    fig.add_trace(go.Scatter(
-        x=[median_bootstrap_price, median_bootstrap_price],
-        y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()],
-        mode='lines',
-        line=dict(color='orange', dash='dash'),
-        name=f'Median: {median_bootstrap_price:.2f}'
-    ))
-
-    fig.add_trace(go.Scatter(
-        x=[latest_price, latest_price],
-        y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()],
-        mode='lines',
-        line=dict(color='green', dash='dash'),
-        name=f'Latest Price: {latest_price:.2f}'
-    ))
-
-    # Add confidence intervals as vertical shaded regions
+    fig.add_trace(go.Histogram(x=final_bootstrap_prices, nbinsx=50, name='Simulated Final Prices', marker_color='blue', opacity=0.7))
+    fig.add_trace(go.Scatter(x=[mean_bootstrap_price, mean_bootstrap_price], y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()], mode='lines', line=dict(color='red', dash='dash'), name=f'Mean: {mean_bootstrap_price:.2f}'))
+    fig.add_trace(go.Scatter(x=[median_bootstrap_price, median_bootstrap_price], y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()], mode='lines', line=dict(color='orange', dash='dash'), name=f'Median: {median_bootstrap_price:.2f}'))
+    fig.add_trace(go.Scatter(x=[latest_price, latest_price], y=[0, np.histogram(final_bootstrap_prices, bins=50)[0].max()], mode='lines', line=dict(color='green', dash='dash'), name=f'Latest Price: {latest_price:.2f}'))
     fig.add_vrect(x0=ci_68_bootstrap[0], x1=ci_68_bootstrap[1], fillcolor='yellow', opacity=0.2, layer="below", line_width=0)
     fig.add_vrect(x0=ci_95_bootstrap[0], x1=ci_95_bootstrap[1], fillcolor='grey', opacity=0.2, layer="below", line_width=0)
 
-    # Annotations for probabilities
-    textstr = f'P(>{thresholds[1]:.2f}): {bootstrap_probabilities["above"]:.2%}<br>' + \
-              f'P(<{thresholds[0]:.2f}): {bootstrap_probabilities["below"]:.2%}<br>' + \
-              f'P({thresholds[0]:.2f} - {thresholds[1]:.2f}): {bootstrap_probabilities["between"]:.2%}'
-    fig.add_annotation(xref='paper', yref='paper', x=0.98, y=0.02, text=textstr, showarrow=False,
-                       bordercolor="black", borderwidth=1, borderpad=4, bgcolor="white", opacity=0.4, font=dict(color="black"))
-
-    # Update layout
-    fig.update_layout(
-        title='Bootstrapping Simulation',
-        xaxis_title='Final Price',
-        yaxis_title='Frequency',
-        showlegend=True
-    )
+    textstr = (f'P(>{thresholds[1]:.2f}): {bootstrap_probabilities["above"]:.2%}<br>'
+               f'P(<{thresholds[0]:.2f}): {bootstrap_probabilities["below"]:.2%}<br>'
+               f'P({thresholds[0]:.2f} - {thresholds[1]:.2f}): {bootstrap_probabilities["between"]:.2%}')
+    fig.add_annotation(xref='paper', yref='paper', x=0.98, y=0.02, text=textstr, showarrow=False, bordercolor="black", borderwidth=1, borderpad=4, bgcolor="white", opacity=0.4)
 
+    fig.update_layout(title='Bootstrapping Simulation', xaxis_title='Final Price', yaxis_title='Frequency', showlegend=True)
     return fig, mean_bootstrap_price, median_bootstrap_price, ci_68_bootstrap, ci_95_bootstrap, latest_price
 
-
 # Plot price data with simulation cones
 def plot_price_with_cones(data, bootstrap_percentiles, days, thresholds, bootstrap_probabilities):
     last_date = data.index[-1]
     future_dates = pd.date_range(start=last_date + pd.Timedelta(days=1), periods=days, freq='D')
 
     fig = go.Figure()
-
-    # Plot historical prices
+    # Historical prices
     fig.add_trace(go.Scatter(x=data.index, y=data, mode='lines', name='Historical Prices', line=dict(color='white')))
-
-    # Plot bootstrapping simulation cone
-    fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[2], mode='lines', name='Bootstrap Median', line=dict(color='red', dash='dash')))
-    fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[0], fill=None, mode='lines', line=dict(color='lightgrey'), showlegend=False))
-    fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[4], fill='tonexty', mode='lines', line=dict(color='lightgrey'), name='Bootstrap 95% CI'))
-    fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[1], fill=None, mode='lines', line=dict(color='lightyellow'), showlegend=False))
-    fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[3], fill='tonexty', mode='lines', line=dict(color='lightyellow'), name='Bootstrap 68% CI'))
-
-    # Annotate the thresholds
+    
+    # Simulation cone (check for valid percentiles)
+    if bootstrap_percentiles.shape[1] == len(future_dates):
+        fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[2], mode='lines', name='Bootstrap Median', line=dict(color='red', dash='dash')))
+        fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[0], fill=None, mode='lines', line=dict(color='lightgrey'), showlegend=False))
+        fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[4], fill='tonexty', mode='lines', line=dict(color='lightgrey'), name='Bootstrap 95% CI'))
+        fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[1], fill=None, mode='lines', line=dict(color='lightyellow'), showlegend=False))
+        fig.add_trace(go.Scatter(x=future_dates, y=bootstrap_percentiles[3], fill='tonexty', mode='lines', line=dict(color='lightyellow'), name='Bootstrap 68% CI'))
+    else:
+        st.warning("Simulation percentiles length does not match future dates. Check simulation output.")
+
+    # Thresholds
     fig.add_hline(y=thresholds[0], line=dict(color='blue', dash='dash'), annotation_text=f'Threshold 1: {thresholds[0]}', annotation_position="top left")
     fig.add_hline(y=thresholds[1], line=dict(color='green', dash='dash'), annotation_text=f'Threshold 2: {thresholds[1]}', annotation_position="top left")
 
-    # Add probability annotations
-    textstr_bootstrap = f'Bootstrap Probabilities:<br>Below {thresholds[0]}: {bootstrap_probabilities["below"]:.2%}<br>' + \
-                        f'Between {thresholds[0]} and {thresholds[1]}: {bootstrap_probabilities["between"]:.2%}<br>' + \
-                        f'Above {thresholds[1]}: {bootstrap_probabilities["above"]:.2%}'
-    fig.add_annotation(xref='paper', yref='paper', x=0.98, y=0.02, text=textstr_bootstrap, showarrow=False, 
-                       bordercolor="black", borderwidth=1, borderpad=4, bgcolor="white", opacity=0.4, font=dict(color="black"))
+    # Probability annotations
+    textstr_bootstrap = (f'Bootstrap Probabilities:<br>Below {thresholds[0]}: {bootstrap_probabilities["below"]:.2%}<br>'
+                        f'Between {thresholds[0]} and {thresholds[1]}: {bootstrap_probabilities["between"]:.2%}<br>'
+                        f'Above {thresholds[1]}: {bootstrap_probabilities["above"]:.2%}')
+    fig.add_annotation(xref='paper', yref='paper', x=0.98, y=0.02, text=textstr_bootstrap, showarrow=False, bordercolor="black", borderwidth=1, borderpad=4, bgcolor="white", opacity=0.4)
 
     fig.update_layout(title='Bootstrapping Simulation Cone', xaxis_title='Date', yaxis_title='Price', showlegend=True)
     fig.update_xaxes(type='date')
-
     return fig
 
 # Streamlit app
@@ -138,7 +104,6 @@ st.title('Future Asset Prices Bootstrap Simulation')
 
 st.sidebar.header('Input Parameters')
 
-# How to use (in sidebar, closed by default)
 with st.sidebar.expander("How to Use", expanded=False):
     st.write("""
     1. Enter the stock ticker or crypto pair (e.g., 'AAPL' or 'BTC-USD') in the 'Ticker' field.
@@ -149,7 +114,6 @@ with st.sidebar.expander("How to Use", expanded=False):
     6. Analyze the resulting charts and statistics.
     """)
 
-# Ticker and dates in an expander (open by default)
 with st.sidebar.expander("Symbol and Dates", expanded=True):
     ticker = st.text_input('Enter Asset Symbol', 'ASML.AS', help="Enter a stock ticker (e.g., AAPL) or a crypto pair (e.g., BTC-USD)")
     start_date = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for historical data")
@@ -168,7 +132,6 @@ This application simulates future asset prices using bootstrapping simulation me
 You can specify the stock ticker or crypto pair, the date range, the number of simulation days, the number of simulations, and price thresholds.
 The simulation results will show the probability of the price falling below, between, or above the specified thresholds.""")
 
-
 with st.expander("Click here to read the description"):
     st.write("""
     ### Description
@@ -203,8 +166,6 @@ with st.expander("Click here to read the description"):
     4. **Estimate**: Use the bootstrap statistics to estimate the mean, standard error, and confidence intervals of \( \theta \).
     """)
 
-
-
 st.write("""**Results:**
 The app will display two charts:
 1. The distribution of the final simulated prices with key statistical measures.
@@ -212,42 +173,55 @@ The app will display two charts:
 """)
 
 if st.sidebar.button('Run Simulation'):
-    data = get_stock_data(ticker, start_date, end_date)
-
-    bootstrap_simulations = bootstrap_simulation(data, days, n_iterations)
-
-    bootstrap_probabilities = calculate_probabilities(bootstrap_simulations, thresholds)
-
-    bootstrap_percentiles = calculate_percentiles(bootstrap_simulations)
-
-    fig1, mean_bootstrap_price, median_bootstrap_price, ci_68_bootstrap, ci_95_bootstrap, latest_price = plot_distributions(bootstrap_simulations, data, thresholds, bootstrap_probabilities)
-    fig2 = plot_price_with_cones(data, bootstrap_percentiles, days, thresholds, bootstrap_probabilities)
-
-    st.plotly_chart(fig1)
-    st.plotly_chart(fig2)
-
-    st.write(f"""
-    ### Interpretation of Results
-
-    **Distribution of Final Simulated Prices:**
-    - **Mean Final Price:** {mean_bootstrap_price:.2f}
-    - **Median Final Price:** {median_bootstrap_price:.2f}
-    - **68% Confidence Interval (CI):** [{ci_68_bootstrap[0]:.2f}, {ci_68_bootstrap[1]:.2f}]
-    - **95% Confidence Interval (CI):** [{ci_95_bootstrap[0]:.2f}, {ci_95_bootstrap[1]:.2f}]
-    - **Latest Price:** {latest_price:.2f}
-
-    **Bootstrapping Simulation Cone:**
-    - **Bootstrap Median:** The median of the simulated future prices for each day.
-    - **Bootstrap 68% CI:** The 68% confidence interval for the simulated future prices.
-    - **Bootstrap 95% CI:** The 95% confidence interval for the simulated future prices.
-    - **Threshold 1 and Threshold 2:** {threshold1:.2f}, {threshold2:.2f}
-    - **Probability Annotations:**
-      - The probability of the price being below Threshold 1: {bootstrap_probabilities["below"]:.2%}
-      - The probability of the price being between Threshold 1 and Threshold 2: {bootstrap_probabilities["between"]:.2%}
-      - The probability of the price being above Threshold 2: {bootstrap_probabilities["above"]:.2%}
-
-    These results help in understanding the potential future movements of the stock or cryptocurrency price based on historical data and bootstrapping simulation.
-    """)
+    try:
+        st.write(f"Fetching data for {ticker}...")
+        data = get_stock_data(ticker, start_date, end_date)
+        
+        # Debug data
+        st.write(f"Data shape: {data.shape}")
+        st.write(f"Last few rows: {data.tail()}")
+
+        bootstrap_simulations = bootstrap_simulation(data, days, n_iterations)
+        bootstrap_probabilities = calculate_probabilities(bootstrap_simulations, thresholds)
+        bootstrap_percentiles = calculate_percentiles(bootstrap_simulations)
+
+        # Debug simulation output
+        st.write(f"Simulations shape: {bootstrap_simulations.shape}")
+        st.write(f"Percentiles shape: {bootstrap_percentiles.shape}")
+
+        fig1, mean_bootstrap_price, median_bootstrap_price, ci_68_bootstrap, ci_95_bootstrap, latest_price = plot_distributions(bootstrap_simulations, data, thresholds, bootstrap_probabilities)
+        fig2 = plot_price_with_cones(data, bootstrap_percentiles, days, thresholds, bootstrap_probabilities)
+
+        st.plotly_chart(fig1)
+        if bootstrap_percentiles.shape[1] == days:
+            st.plotly_chart(fig2)
+        else:
+            st.error("Time series plot failed: Percentiles length does not match simulation days.")
+
+        st.write(f"""
+        ### Interpretation of Results
+
+        **Distribution of Final Simulated Prices:**
+        - **Mean Final Price:** {mean_bootstrap_price:.2f}
+        - **Median Final Price:** {median_bootstrap_price:.2f}
+        - **68% Confidence Interval (CI):** [{ci_68_bootstrap[0]:.2f}, {ci_68_bootstrap[1]:.2f}]
+        - **95% Confidence Interval (CI):** [{ci_95_bootstrap[0]:.2f}, {ci_95_bootstrap[1]:.2f}]
+        - **Latest Price:** {latest_price:.2f}
+
+        **Bootstrapping Simulation Cone:**
+        - **Bootstrap Median:** The median of the simulated future prices for each day.
+        - **Bootstrap 68% CI:** The 68% confidence interval for the simulated future prices.
+        - **Bootstrap 95% CI:** The 95% confidence interval for the simulated future prices.
+        - **Threshold 1 and Threshold 2:** {threshold1:.2f}, {threshold2:.2f}
+        - **Probability Annotations:**
+          - The probability of the price being below Threshold 1: {bootstrap_probabilities["below"]:.2%}
+          - The probability of the price being between Threshold 1 and Threshold 2: {bootstrap_probabilities["between"]:.2%}
+          - The probability of the price being above Threshold 2: {bootstrap_probabilities["above"]:.2%}
+
+        These results help in understanding the potential future movements of the stock or cryptocurrency price based on historical data and bootstrapping simulation.
+        """)
+    except Exception as e:
+        st.error(f"Error: {str(e)}. Check ticker, date range, or try again.")
 
 hide_streamlit_style = """
 <style>
@@ -257,7 +231,6 @@ footer {visibility: hidden;}
 """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
-
 st.markdown("""
 <style>
     .stSelectbox, .stNumberInput {