Update app.py

app.py

@@ -72,10 +72,10 @@ def plot_results(data, best_short_window, best_long_window, horizon_name, best_a
     buy_signals = data[data['positions'] == 2]  # 2 indicates a Buy signal after a Sell
     sell_signals = data[data['positions'] == -2]  # -2 indicates a Sell signal after a Buy
     fig.add_trace(go.Scatter(x=buy_signals.index, y=buy_signals['Close'], mode='markers',
-                             marker=dict(color='green', size=15, symbol='triangle-up'),  # Increased size
+                             marker=dict(color='green', size=15, symbol='triangle-up'),
                              name=f'Buy Signal (Win Rate: {buy_accuracy:.2f})', hovertemplate='%{x|%Y-%m-%d}'))
     fig.add_trace(go.Scatter(x=sell_signals.index, y=sell_signals['Close'], mode='markers',
-                             marker=dict(color='red', size=15, symbol='triangle-down'),  # Increased size
+                             marker=dict(color='red', size=15, symbol='triangle-down'),
                              name=f'Sell Signal (Win Rate: {sell_accuracy:.2f})', hovertemplate='%{x|%Y-%m-%d}'))
 
     # Set title and layout, including more detailed date formatting for x-axis
@@ -84,11 +84,11 @@ def plot_results(data, best_short_window, best_long_window, horizon_name, best_a
         xaxis_title='Date',
         yaxis_title='Price',
         xaxis=dict(
-            tickformat="%b %Y",  # Month and year format
-            dtick="M1",  # Set tick interval to every month
-            tickangle=45,  # Rotate the tick labels for better readability
+            tickformat="%b %Y",
+            dtick="M1",
+            tickangle=45,
         ),
-        autosize=True  # Enable autosizing
+        autosize=True
     )
     return fig
 
@@ -107,7 +107,7 @@ def plot_strategy_over_time(data, best_short_window, best_long_window):
         title='Strategy Accuracy Over Time',
         xaxis_title='Date',
         yaxis_title='Rolling Accuracy',
-        autosize=True  # Enable autosizing
+        autosize=True
     )
     return fig
 
@@ -118,9 +118,7 @@ st.set_page_config(layout="wide")
 with st.sidebar:
     st.header("Input Parameters")
 
-    
     with st.expander("How to Use", expanded=False):
-        #st.subheader("How to Use")
         st.write("""
         - Select the stock ticker.
         - Set the start and end dates.
@@ -155,7 +153,6 @@ This application optimizes a trading strategy based on the Hull Moving Average.
 """)
 
 with st.expander("Hull Moving Average Methodology", expanded=False):
-
     st.latex(r"""
     \text{HMA} = \text{WMA}(2 \times \text{WMA}(n/2) - \text{WMA}(n), \sqrt{n})
     """)
@@ -173,65 +170,76 @@ with st.expander("Hull Moving Average Methodology", expanded=False):
     st.write("""
     To read more about moving averages methodologies, visit [this link](https://entreprenerdly.com/top-36-moving-averages-methods-for-stock-prices-in-python/).
     """)
-    
+
 # Main application logic
 if run_button:
-    if 'data' not in st.session_state or st.session_state.get('ticker') != ticker or st.session_state.get('start_date') != start_date or st.session_state.get('end_date') != end_date:
-        st.session_state['data'] = yf.download(ticker, start=start_date, end=end_date)
-        st.session_state['ticker'] = ticker
-        st.session_state['start_date'] = start_date
-        st.session_state['end_date'] = end_date
-
-    data = st.session_state['data']
-
-    # Cache optimization results for each horizon
-    if f'{st.session_state.horizon_page}_results' not in st.session_state:
-        st.session_state[f'{st.session_state.horizon_page}_results'] = optimize_hma(data, selected_horizon['short_windows'], selected_horizon['long_windows'], selected_horizon['buy_threshold'], selected_horizon['sell_threshold'])
-
-    # Unpack the results from the session state
-    best_params, best_accuracy, results_df = st.session_state[f'{st.session_state.horizon_page}_results']
-    best_short_window, best_long_window, buy_accuracy, sell_accuracy = best_params
-
-    # Display results
-    st.write(f"**{st.session_state.horizon_page} Horizon - Best Short HMA**: {best_short_window}, **Best Long HMA**: {best_long_window}, **Best Accuracy**: {best_accuracy:.2f}")
-    st.write(f"**Buy Win Rate**: {buy_accuracy:.2f}, **Sell Win Rate**: {sell_accuracy:.2f}")
-
-    # Plot results within a container to limit the height
-    with st.container():
-        fig = plot_results(data, best_short_window, best_long_window, st.session_state.horizon_page, best_accuracy, buy_accuracy, sell_accuracy)
-        st.plotly_chart(fig, use_container_width=True, height=600)  # Specify height here
-
-    # Plot strategy performance over time within a container to limit the height
-    st.write("Strategy Performance Over Time")
-    with st.container():
-        strategy_fig = plot_strategy_over_time(data, best_short_window, best_long_window)
-        st.plotly_chart(strategy_fig, use_container_width=True, height=400)  # Specify height here
-
-    # Display heatmap of accuracy with annotations
-    st.write(f"{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations")
-    heatmap_df = results_df.pivot(index='Short_HMA', columns='Long_HMA', values='Accuracy')
-
-    # Create the heatmap with annotations
-    heatmap_fig = go.Figure(data=go.Heatmap(
-        z=heatmap_df.values,
-        x=heatmap_df.columns,
-        y=heatmap_df.index,
-        colorscale='YlGnBu',
-        text=heatmap_df.values,  # Use the values for the text inside the heatmap
-        texttemplate="%{text:.2f}",  # Format text with two decimal places
-        hovertemplate="Short HMA: %{y}<br>Long HMA: %{x}<br>Accuracy: %{text:.2f}<extra></extra>",
-        showscale=True
-    ))
-
-    heatmap_fig.update_layout(
-        title=f'{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations',
-        xaxis_title='Long HMA',
-        yaxis_title='Short HMA',
-        autosize=True  # Enable autosizing
-    )
+    try:
+        if 'data' not in st.session_state or st.session_state.get('ticker') != ticker or st.session_state.get('start_date') != start_date or st.session_state.get('end_date') != end_date:
+            data = yf.download(ticker, start=start_date, end=end_date, auto_adjust=False)
+            if isinstance(data.columns, pd.MultiIndex):
+                data.columns = data.columns.get_level_values(0)
+            if data.empty:
+                raise ValueError(f"No data retrieved for {ticker}")
+            if len(data) < max(selected_horizon['short_windows']) + max(selected_horizon['long_windows']):
+                raise ValueError(f"Insufficient data points for {ticker}. Need at least {max(selected_horizon['short_windows']) + max(selected_horizon['long_windows'])} days.")
+            st.session_state['data'] = data
+            st.session_state['ticker'] = ticker
+            st.session_state['start_date'] = start_date
+            st.session_state['end_date'] = end_date
+
+        data = st.session_state['data']
+
+        # Cache optimization results for each horizon
+        if f'{st.session_state.horizon_page}_results' not in st.session_state:
+            st.session_state[f'{st.session_state.horizon_page}_results'] = optimize_hma(data, selected_horizon['short_windows'], selected_horizon['long_windows'], selected_horizon['buy_threshold'], selected_horizon['sell_threshold'])
+
+        # Unpack the results from the session state
+        best_params, best_accuracy, results_df = st.session_state[f'{st.session_state.horizon_page}_results']
+        best_short_window, best_long_window, buy_accuracy, sell_accuracy = best_params
+
+        # Display results
+        st.write(f"**{st.session_state.horizon_page} Horizon - Best Short HMA**: {best_short_window}, **Best Long HMA**: {best_long_window}, **Best Accuracy**: {best_accuracy:.2f}")
+        st.write(f"**Buy Win Rate**: {buy_accuracy:.2f}, **Sell Win Rate**: {sell_accuracy:.2f}")
 
-    with st.container():
-        st.plotly_chart(heatmap_fig, use_container_width=True, height=600)  # Specify height here
+        # Plot results within a container to limit the height
+        with st.container():
+            fig = plot_results(data, best_short_window, best_long_window, st.session_state.horizon_page, best_accuracy, buy_accuracy, sell_accuracy)
+            st.plotly_chart(fig, use_container_width=True, height=600)
+
+        # Plot strategy performance over time within a container to limit the height
+        st.write("Strategy Performance Over Time")
+        with st.container():
+            strategy_fig = plot_strategy_over_time(data, best_short_window, best_long_window)
+            st.plotly_chart(strategy_fig, use_container_width=True, height=400)
+
+        # Display heatmap of accuracy with annotations
+        st.write(f"{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations")
+        heatmap_df = results_df.pivot(index='Short_HMA', columns='Long_HMA', values='Accuracy')
+
+        # Create the heatmap with annotations
+        heatmap_fig = go.Figure(data=go.Heatmap(
+            z=heatmap_df.values,
+            x=heatmap_df.columns,
+            y=heatmap_df.index,
+            colorscale='YlGnBu',
+            text=heatmap_df.values,
+            texttemplate="%{text:.2f}",
+            hovertemplate="Short HMA: %{y}<br>Long HMA: %{x}<br>Accuracy: %{text:.2f}<extra></extra>",
+            showscale=True
+        ))
+
+        heatmap_fig.update_layout(
+            title=f'{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations',
+            xaxis_title='Long HMA',
+            yaxis_title='Short HMA',
+            autosize=True
+        )
+
+        with st.container():
+            st.plotly_chart(heatmap_fig, use_container_width=True, height=600)
+
+    except Exception as e:
+        st.error(f"An error occurred while running the analysis: {e}")
 
 # Re-display the results if they exist and user switches pages without re-running
 else:
@@ -247,13 +255,13 @@ else:
         # Plot results within a container to limit the height
         with st.container():
             fig = plot_results(st.session_state['data'], best_short_window, best_long_window, st.session_state.horizon_page, best_accuracy, buy_accuracy, sell_accuracy)
-            st.plotly_chart(fig, use_container_width=True, height=600)  # Specify height here
+            st.plotly_chart(fig, use_container_width=True, height=600)
 
         # Plot strategy performance over time within a container to limit the height
         st.write("Strategy Performance Over Time")
         with st.container():
             strategy_fig = plot_strategy_over_time(st.session_state['data'], best_short_window, best_long_window)
-            st.plotly_chart(strategy_fig, use_container_width=True, height=400)  # Specify height here
+            st.plotly_chart(strategy_fig, use_container_width=True, height=400)
 
         # Display heatmap of accuracy with annotations
         st.write(f"{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations")
@@ -265,8 +273,8 @@ else:
             x=heatmap_df.columns,
             y=heatmap_df.index,
             colorscale='YlGnBu',
-            text=heatmap_df.values,  # Use the values for the text inside the heatmap
-            texttemplate="%{text:.2f}",  # Format text with two decimal places
+            text=heatmap_df.values,
+            texttemplate="%{text:.2f}",
             hovertemplate="Short HMA: %{y}<br>Long HMA: %{x}<br>Accuracy: %{text:.2f}<extra></extra>",
             showscale=True
         ))
@@ -275,12 +283,11 @@ else:
             title=f'{st.session_state.horizon_page} Horizon: Accuracy Heatmap of HMA Combinations',
             xaxis_title='Long HMA',
             yaxis_title='Short HMA',
-            autosize=True  # Enable autosizing
+            autosize=True
         )
 
         with st.container():
-            st.plotly_chart(heatmap_fig, use_container_width=True, height=600)  # Specify height here
-
+            st.plotly_chart(heatmap_fig, use_container_width=True, height=600)
 
 hide_streamlit_style = """
 <style>