Update app.py

app.py

@@ -7,11 +7,16 @@ import plotly.graph_objs as go
 import warnings
 import pandas as pd
 
-
 # Function to fetch stock or crypto data
 def fetch_data(ticker_name, start_date, end_date):
-    raw_data = yf.download(ticker_name, start=start_date, end=end_date)
+    raw_data = yf.download(ticker_name, start=start_date, end=end_date, auto_adjust=False)  # Unadjusted prices
+    if isinstance(raw_data.columns, pd.MultiIndex):  # Flatten multi-index
+        raw_data.columns = raw_data.columns.get_level_values(0)
+    if raw_data.empty:
+        raise ValueError(f"No data found for {ticker_name} from {start_date} to {end_date}")
     adjusted_close = raw_data['Adj Close'].dropna()
+    if len(adjusted_close) < 2:  # Need at least 2 points for log returns
+        raise ValueError(f"Insufficient data points for {ticker_name}")
     prices = adjusted_close.values
     log_returns = np.log(prices[1:] / prices[:-1])
     return adjusted_close, log_returns
@@ -70,14 +75,10 @@ with st.expander("Wasserstein Distance Methodology", expanded=False):
     A high distance indicates a significant shift in price dynamics, potentially due to a market event or a change in investor behavior.
     """)
 
-
 st.sidebar.title("""
 Input Parameters
 """)
 
-#st.write(f"Threshold: {threshold}")
-
-# Sidebar for "How to Use" instructions inside an expander, closed by default
 with st.sidebar.expander("How to Use", expanded=False):
     st.write("""
     **How to use this app:**
@@ -88,55 +89,58 @@ with st.sidebar.expander("How to Use", expanded=False):
     5. Click 'Run Analysis' to start.
     """)
 
-# Input parameters inside an expander, open by default
 with st.sidebar.expander("Input Parameters", expanded=True):
     ticker_name = st.text_input('Enter Stock or Crypto Symbol (e.g., AAPL or BTC-USD)', '^GSPC', help="Enter the ticker symbol for the stock or cryptocurrency you want to analyze.")
     start_date_string = st.date_input('Start Date', pd.to_datetime('2020-01-01'), help="Select the start date for the data range.")
     end_date_string = st.date_input('End Date', pd.to_datetime(pd.Timestamp.now().date() + pd.Timedelta(days=1)), help="Select the end date for the data range.")
 
-# Parameters for the selected method inside an expander, open by default
 with st.sidebar.expander("Parameters", expanded=True):
     window_size = st.slider('Window Size', min_value=5, max_value=50, value=20, help="Set the window size for the sliding window analysis.")
     threshold = st.slider('Alert Threshold', min_value=0.02, max_value=0.2, value=0.075, step=0.005, help="Set the threshold for detecting significant changes in price dynamics.")
 
-# Run Analysis button in the sidebar
 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)
-
-    st.write("""
-    **Plot Interpretation:**
-    - The first plot shows the asset 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.
-    """)
+    try:
+        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)
+        if len(log_returns) < 2 * window_size:
+            raise ValueError(f"Insufficient data: Need at least {2 * window_size} log returns, got {len(log_returns)}")
+
+        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)
+
+        st.write("""
+        **Plot Interpretation:**
+        - The first plot shows the asset 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.
+        """)
+    except Exception as e:
+        st.error(f"Error: {str(e)}. Check ticker symbol, date range, or window size.")
 
 st.markdown(
     """
@@ -157,4 +161,4 @@ hide_streamlit_style = """
 footer {visibility: hidden;}
 </style>
 """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)