Update app.py

app.py

@@ -98,71 +98,80 @@ def interpret_rolling_window(window):
         return "A moderate rolling window balances recent trends with some degree of smoothing."
 
 if st.sidebar.button("Run Analysis"):
-    df = yf.download(symbol, start=start_date, end=end_date)
-    
-    df['hurst_exponent'] = rolling_hurst(df['Close'], rolling_window)
-    df.dropna(inplace=True)
-
-    df['sma'] = df['Close'].rolling(window=sma_window).mean()
-
-    hurst_95 = df['hurst_exponent'].quantile(quantile_high)
-    hurst_05 = df['hurst_exponent'].quantile(quantile_low)
-    current_hurst = df['hurst_exponent'].iloc[-1]
-
-    significant_dates_95 = df[df['hurst_exponent'] >= hurst_95].index
-    significant_dates_05 = df[df['hurst_exponent'] <= hurst_05].index
-
-    fig = make_subplots(rows=3, cols=1, shared_xaxes=True, 
-                        subplot_titles=(f"{symbol} Price and SMA", "Rolling Hurst Exponent", "Hurst Exponent Histogram"))
-
-    # Plot price and SMA
-    fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name=f'{symbol} Price', line=dict(color='blue')), row=1, col=1)
-    fig.add_trace(go.Scatter(x=df.index, y=df['sma'], name=f'{sma_window}-day SMA', line=dict(color='purple')), row=1, col=1)
-
-    # Add shaded areas for significant dates
-    for date in significant_dates_95:
-        fig.add_vrect(
-            x0=date,
-            x1=date + pd.Timedelta(days=1),  # Assuming you're shading a single day
-            fillcolor="red",
-            opacity=0.7,  # Adjust the opacity here
-            layer="below",
-            line_width=0
-        )
-    
-    for date in significant_dates_05:
-        fig.add_vrect(
-            x0=date,
-            x1=date + pd.Timedelta(days=1),
-            fillcolor="blue",
-            opacity=0.7,  # Adjust the opacity here
-            layer="below",
-            line_width=0
-        )
-
-    # Plot Hurst Exponent
-    fig.add_trace(go.Scatter(x=df.index, y=df['hurst_exponent'], name='Rolling Hurst Exponent', line=dict(color='white', width=5)), row=2, col=1)
-    fig.add_hline(y=hurst_95, line=dict(color='red', dash='dash', width=1), annotation_text=f'95th Percentile: {hurst_95:.2f}', row=2, col=1)
-    fig.add_hline(y=hurst_05, line=dict(color='blue', dash='dash', width=1), annotation_text=f'5th Percentile: {hurst_05:.2f}', row=2, col=1)
-    fig.add_hline(y=current_hurst, line=dict(color='orange', width=1), annotation_text=f'Current Hurst: {current_hurst:.2f}', row=2, col=1)
-
-    # Hurst interpretation ranges
-    fig.add_hline(y=0.5, line=dict(color='black', width=1), annotation_text='0.5: Random Walk', row=2, col=1)
-    fig.add_trace(go.Scatter(x=df.index, y=[0.5] * len(df.index), name='0.5: Random Walk', mode='lines', line=dict(color='black', width=3)), row=2, col=1)
-    fig.add_trace(go.Scatter(x=df.index, y=[0.0] * len(df.index), name='<0.5: Mean Reverting', fill='tonexty', mode='lines', line=dict(color='blue', width=0), opacity=0.01), row=2, col=1)
-    fig.add_trace(go.Scatter(x=df.index, y=[1.0] * len(df.index), name='>0.5: Persistent Trend', fill='tonexty', mode='lines', line=dict(color='red', width=0), opacity=0.01), row=2, col=1)
-
-    # Plot histogram of Hurst Exponent
-    fig.add_trace(go.Histogram(x=df['hurst_exponent'], nbinsx=30, marker=dict(color='grey', line=dict(color='black', width=1)), name='Hurst Exponent Histogram'), row=3, col=1)
-    fig.add_vline(x=current_hurst, line=dict(color='orange', width=1), annotation_text=f'Current Hurst: {current_hurst:.2f}', row=3, col=1)
-
-    fig.update_layout(height=900, width=1200, title_text=f"Hurst Exponent Analysis for {symbol}")
-    st.plotly_chart(fig)
-
-    hurst_interpretation = interpret_hurst(current_hurst)
-    window_interpretation = interpret_rolling_window(rolling_window)
-    st.markdown(f"**Rolling window interpretation:** {window_interpretation}")
-    st.markdown(f"**Hurst exponent interpretation:** {hurst_interpretation}")
+    try:
+        df = yf.download(symbol, start=start_date, end=end_date, auto_adjust=False)
+        if isinstance(df.columns, pd.MultiIndex):  # Flatten multi-index
+            df.columns = df.columns.get_level_values(0)
+        if df.empty:
+            raise ValueError(f"No data found for {symbol} from {start_date} to {end_date}")
+        if len(df) < rolling_window:
+            raise ValueError(f"Insufficient data points for {symbol}. Need at least {rolling_window} points.")
+
+        df['hurst_exponent'] = rolling_hurst(df['Close'], rolling_window)
+        df.dropna(inplace=True)
+
+        df['sma'] = df['Close'].rolling(window=sma_window).mean()
+
+        hurst_95 = df['hurst_exponent'].quantile(quantile_high)
+        hurst_05 = df['hurst_exponent'].quantile(quantile_low)
+        current_hurst = df['hurst_exponent'].iloc[-1]
+
+        significant_dates_95 = df[df['hurst_exponent'] >= hurst_95].index
+        significant_dates_05 = df[df['hurst_exponent'] <= hurst_05].index
+
+        fig = make_subplots(rows=3, cols=1, shared_xaxes=True, 
+                            subplot_titles=(f"{symbol} Price and SMA", "Rolling Hurst Exponent", "Hurst Exponent Histogram"))
+
+        # Plot price and SMA
+        fig.add_trace(go.Scatter(x=df.index, y=df['Close'], name=f'{symbol} Price', line=dict(color='blue')), row=1, col=1)
+        fig.add_trace(go.Scatter(x=df.index, y=df['sma'], name=f'{sma_window}-day SMA', line=dict(color='purple')), row=1, col=1)
+
+        # Add shaded areas for significant dates
+        for date in significant_dates_95:
+            fig.add_vrect(
+                x0=date,
+                x1=date + pd.Timedelta(days=1),
+                fillcolor="red",
+                opacity=0.7,
+                layer="below",
+                line_width=0
+            )
+        
+        for date in significant_dates_05:
+            fig.add_vrect(
+                x0=date,
+                x1=date + pd.Timedelta(days=1),
+                fillcolor="blue",
+                opacity=0.7,
+                layer="below",
+                line_width=0
+            )
+
+        # Plot Hurst Exponent
+        fig.add_trace(go.Scatter(x=df.index, y=df['hurst_exponent'], name='Rolling Hurst Exponent', line=dict(color='white', width=5)), row=2, col=1)
+        fig.add_hline(y=hurst_95, line=dict(color='red', dash='dash', width=1), annotation_text=f'95th Percentile: {hurst_95:.2f}', row=2, col=1)
+        fig.add_hline(y=hurst_05, line=dict(color='blue', dash='dash', width=1), annotation_text=f'5th Percentile: {hurst_05:.2f}', row=2, col=1)
+        fig.add_hline(y=current_hurst, line=dict(color='orange', width=1), annotation_text=f'Current Hurst: {current_hurst:.2f}', row=2, col=1)
+
+        # Hurst interpretation ranges
+        fig.add_hline(y=0.5, line=dict(color='black', width=1), annotation_text='0.5: Random Walk', row=2, col=1)
+        fig.add_trace(go.Scatter(x=df.index, y=[0.5] * len(df.index), name='0.5: Random Walk', mode='lines', line=dict(color='black', width=3)), row=2, col=1)
+        fig.add_trace(go.Scatter(x=df.index, y=[0.0] * len(df.index), name='<0.5: Mean Reverting', fill='tonexty', mode='lines', line=dict(color='blue', width=0), opacity=0.01), row=2, col=1)
+        fig.add_trace(go.Scatter(x=df.index, y=[1.0] * len(df.index), name='>0.5: Persistent Trend', fill='tonexty', mode='lines', line=dict(color='red', width=0), opacity=0.01), row=2, col=1)
+
+        # Plot histogram of Hurst Exponent
+        fig.add_trace(go.Histogram(x=df['hurst_exponent'], nbinsx=30, marker=dict(color='grey', line=dict(color='black', width=1)), name='Hurst Exponent Histogram'), row=3, col=1)
+        fig.add_vline(x=current_hurst, line=dict(color='orange', width=1), annotation_text=f'Current Hurst: {current_hurst:.2f}', row=3, col=1)
+
+        fig.update_layout(height=900, width=1200, title_text=f"Hurst Exponent Analysis for {symbol}")
+        st.plotly_chart(fig)
+
+        hurst_interpretation = interpret_hurst(current_hurst)
+        window_interpretation = interpret_rolling_window(rolling_window)
+        st.markdown(f"**Rolling window interpretation:** {window_interpretation}")
+        st.markdown(f"**Hurst exponent interpretation:** {hurst_interpretation}")
+    except Exception as e:
+        st.error(f"Error: {str(e)}. Check ticker symbol, date range, or window size.")
 
 hide_streamlit_style = """
 <style>
@@ -170,4 +179,4 @@ 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)