Update app.py

app.py

@@ -34,98 +34,95 @@ def compute_wasserstein_distances(log_returns, window_size, rips):
     return distances
 
 # Streamlit app
-def main():
-    st.set_page_config(layout="wide")
+st.set_page_config(layout="wide")
+
+st.title("Market Crash Analysis with Topology")
+st.write("""
+This application analyzes asset price data using Wasserstein distances to detect changes in price dynamics over time.
+Wasserstein distances, derived from persistence diagrams in Topological Data Analysis (TDA), help identify significant shifts in stock price behaviors.
+""")
+
+st.sidebar.title('Input Parameters')
+
+# Input fields for user
+ticker_name = st.sidebar.text_input('Enter Ticker Symbol', '^GSPC')
+start_date_string = st.sidebar.text_input('Start Date (YYYY-MM-DD)', '2020-01-01')
+end_date_string = st.sidebar.text_input('End Date (YYYY-MM-DD)', '2025-01-01')
+window_size = st.sidebar.slider('Window Size', min_value=5, max_value=50, value=20)
+threshold = st.sidebar.slider('Alert Threshold', min_value=0.05, max_value=0.2, value=0.075, step=0.005)
+
+st.sidebar.write("""
+**How to use:**
+1. Enter the stock ticker symbol (e.g., `^GSPC` for S&P 500).
+2. Specify the start and end dates for the analysis period.
+3. Adjust the window size for the sliding window analysis.
+4. Set the alert threshold for detecting significant changes.
+5. Click 'Run Analysis' to start.
+""")
+
+if st.sidebar.button('Run Analysis'):
+    st.write(f"Analyzing {ticker_name} from {start_date_string} to {end_date_string} with window size {window_size} and threshold {threshold}")
+
+    # Fetch data
+    prices, log_returns = fetch_data(ticker_name, start_date_string, end_date_string)
+    rips = Rips(maxdim=2)
+    wasserstein_dists = compute_wasserstein_distances(log_returns, window_size, rips)
+
+    # Plotting with Plotly
+    dates = prices.index[window_size:-window_size]
+    valid_indices = ~np.isnan(wasserstein_dists.flatten())
+    valid_dates = dates[valid_indices]
+    valid_distances = wasserstein_dists[valid_indices].flatten()
+
+    alert_indices = [i for i, d in enumerate(valid_distances) if d > threshold]
+    alert_dates = [valid_dates[i] for i in alert_indices]
+    alert_values = [prices.iloc[i + window_size] for i in alert_indices]
+
+    # Plot price and alerts
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=valid_dates, y=prices.iloc[window_size:-window_size], mode='lines', name='Price'))
+    fig.add_trace(go.Scatter(x=alert_dates, y=alert_values, mode='markers', name='Alert', marker=dict(color='red', size=8)))
+    fig.update_layout(title=f'{ticker_name} Prices Over Time', xaxis_title='Date', yaxis_title='Price')
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Plot Wasserstein distances
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=valid_dates, y=valid_distances, mode='lines', name='Wasserstein Distance', line=dict(color='blue', width=2)))
+    fig.add_hline(y=threshold, line_dash='dash', line_color='red', annotation_text=f'Threshold: {threshold}', annotation_position='bottom right')
+    fig.update_layout(title='Wasserstein Distances Over Time', xaxis_title='Date', yaxis_title='Wasserstein Distance')
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Interpretation of results
+    st.subheader("Interpretation of Results")
+    
+    st.write("""
+    **Wasserstein Distance Analysis:**
+    The Wasserstein distance measures the difference between two distributions. In this context, it quantifies changes in the log returns of stock prices over time.
+    A high Wasserstein distance indicates a significant change in the price dynamics, which might suggest a market event or shift in investor sentiment.
+    """)
 
-    st.title('Stock Analysis Using Topological Data Analysis')
+    st.latex(r'''
+    W(P, Q) = \inf_{\gamma \in \Pi(P, Q)} \mathbb{E}_{(x,y) \sim \gamma} [d(x, y)]
+    ''')
+    
     st.write("""
-    This application analyzes stock data using Wasserstein distances to detect changes in price dynamics over time.
-    Wasserstein distances, derived from persistence diagrams in Topological Data Analysis (TDA), help identify significant shifts in stock price behaviors.
+    - Where \( W(P, Q) \) is the Wasserstein distance between distributions \( P \) and \( Q \).
+    - \( d(x, y) \) is the distance between points \( x \) and \( y \).
+    - \( \gamma \) is a joint distribution with marginals \( P \) and \( Q \).
+    
+    **Alert Threshold:**
+    The alert threshold is set to identify significant changes in the Wasserstein distances. Alerts are triggered when the distance exceeds the threshold.
     """)
+    st.write(f"Threshold: {threshold}")
 
-    st.sidebar.title('Parameters')
-
-    # Input fields for user
-    ticker_name = st.sidebar.text_input('Enter Ticker Symbol', '^GSPC')
-    start_date_string = st.sidebar.text_input('Start Date (YYYY-MM-DD)', '2020-01-01')
-    end_date_string = st.sidebar.text_input('End Date (YYYY-MM-DD)', '2025-01-01')
-    window_size = st.sidebar.slider('Window Size', min_value=5, max_value=50, value=20)
-    threshold = st.sidebar.slider('Alert Threshold', min_value=0.05, max_value=0.2, value=0.075, step=0.005)
-
-    st.sidebar.write("""
-    **How to use:**
-    1. Enter the stock ticker symbol (e.g., `^GSPC` for S&P 500).
-    2. Specify the start and end dates for the analysis period.
-    3. Adjust the window size for the sliding window analysis.
-    4. Set the alert threshold for detecting significant changes.
-    5. Click 'Run Analysis' to start.
+    st.write("""
+    **Plot Interpretation:**
+    - The first plot shows the stock price over time with alerts marked in red.
+    - The second plot displays the Wasserstein distances over time, with the threshold indicated by a dashed red line. Peaks above this line represent significant changes in price dynamics.
     """)
 
-    if st.sidebar.button('Run Analysis'):
-        st.write(f"Analyzing {ticker_name} from {start_date_string} to {end_date_string} with window size {window_size} and threshold {threshold}")
-
-        # Fetch data
-        prices, log_returns = fetch_data(ticker_name, start_date_string, end_date_string)
-        rips = Rips(maxdim=2)
-        wasserstein_dists = compute_wasserstein_distances(log_returns, window_size, rips)
-
-        # Plotting with Plotly
-        dates = prices.index[window_size:-window_size]
-        valid_indices = ~np.isnan(wasserstein_dists.flatten())
-        valid_dates = dates[valid_indices]
-        valid_distances = wasserstein_dists[valid_indices].flatten()
-
-        alert_indices = [i for i, d in enumerate(valid_distances) if d > threshold]
-        alert_dates = [valid_dates[i] for i in alert_indices]
-        alert_values = [prices.iloc[i + window_size] for i in alert_indices]
-
-        # Plot price and alerts
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=valid_dates, y=prices.iloc[window_size:-window_size], mode='lines', name='Price'))
-        fig.add_trace(go.Scatter(x=alert_dates, y=alert_values, mode='markers', name='Alert', marker=dict(color='red', size=8)))
-        fig.update_layout(title=f'{ticker_name} Prices Over Time', xaxis_title='Date', yaxis_title='Price')
-        st.plotly_chart(fig, use_container_width=True)
-
-        # Plot Wasserstein distances
-        fig = go.Figure()
-        fig.add_trace(go.Scatter(x=valid_dates, y=valid_distances, mode='lines', name='Wasserstein Distance', line=dict(color='blue', width=2)))
-        fig.add_hline(y=threshold, line_dash='dash', line_color='red', annotation_text=f'Threshold: {threshold}', annotation_position='bottom right')
-        fig.update_layout(title='Wasserstein Distances Over Time', xaxis_title='Date', yaxis_title='Wasserstein Distance')
-        st.plotly_chart(fig, use_container_width=True)
-
-        # Interpretation of results
-        st.subheader("Interpretation of Results")
-        
-        st.write("""
-        **Wasserstein Distance Analysis:**
-        The Wasserstein distance measures the difference between two distributions. In this context, it quantifies changes in the log returns of stock prices over time.
-        A high Wasserstein distance indicates a significant change in the price dynamics, which might suggest a market event or shift in investor sentiment.
-        """)
-
-        st.latex(r'''
-        W(P, Q) = \inf_{\gamma \in \Pi(P, Q)} \mathbb{E}_{(x,y) \sim \gamma} [d(x, y)]
-        ''')
-        
-        st.write("""
-        - Where \( W(P, Q) \) is the Wasserstein distance between distributions \( P \) and \( Q \).
-        - \( d(x, y) \) is the distance between points \( x \) and \( y \).
-        - \( \gamma \) is a joint distribution with marginals \( P \) and \( Q \).
-        
-        **Alert Threshold:**
-        The alert threshold is set to identify significant changes in the Wasserstein distances. Alerts are triggered when the distance exceeds the threshold.
-        """)
-        st.write(f"Threshold: {threshold}")
-
-        st.write("""
-        **Plot Interpretation:**
-        - The first plot shows the stock price over time with alerts marked in red.
-        - The second plot displays the Wasserstein distances over time, with the threshold indicated by a dashed red line. Peaks above this line represent significant changes in price dynamics.
-        """)
-
-# Main function call
-if __name__ == "__main__":
-    warnings.filterwarnings('ignore')
-    main()
+
+
 
 hide_streamlit_style = """
 <style>