Update app.py

app.py

@@ -7,7 +7,6 @@ import plotly.graph_objs as go
 # Set up the layout
 st.set_page_config(layout="wide")
 
-
 # Custom CSS to adjust the sidebar width
 st.markdown(
     """
@@ -26,7 +25,6 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-
 # App title and description
 st.title("Key Economic Recession Indicators")
 st.markdown("""
@@ -94,175 +92,201 @@ crash_periods = {
 
 # Run button
 if st.sidebar.button('Run Analysis'):
-    # Fetch the data from FRED
-    sahm_recession_indicator = web.DataReader('SAHMREALTIME', 'fred', start_date, end_date)
-    retail_sales = web.DataReader('RSXFS', 'fred', start_date, end_date)
-    industrial_production = web.DataReader('INDPRO', 'fred', start_date, end_date)
-    unemployment_rate = web.DataReader('UNRATE', 'fred', start_date, end_date)
-    inflation = web.DataReader('CPIAUCSL', 'fred', start_date, end_date)
-    nonfarm_payrolls = web.DataReader('PAYEMS', 'fred', start_date, end_date)
-    jobless_claims = web.DataReader('ICSA', 'fred', start_date, end_date)
-    consumer_confidence = web.DataReader('UMCSENT', 'fred', start_date, end_date)
-    housing_starts = web.DataReader('HOUST', 'fred', start_date, end_date)
-    treasury_rate_10y = web.DataReader('GS10', 'fred', start_date, end_date)
-    treasury_rate_2y = web.DataReader('DGS2', 'fred', start_date, end_date)
-    treasury_rate_1m = web.DataReader('DGS1MO', 'fred', start_date, end_date)
-    treasury_rate_3m = web.DataReader('TB3MS', 'fred', start_date, end_date)
-    federal_funds_rate = web.DataReader('FEDFUNDS', 'fred', start_date, end_date)
-    recession_probabilities = web.DataReader('RECPROUSM156N', 'fred', start_date, end_date)
+    # Initialize combined_data as an empty DataFrame
+    combined_data = pd.DataFrame()
 
-    # Rename columns for clarity
-    recession_probabilities.columns = ['Recession_Probabilities']
+    # Fetch FRED data with error handling
+    fred_data = {}
+    for key, column in indicators.items():
+        if selected_indicators[key]:
+            if isinstance(column, tuple):
+                for col in column:
+                    if col not in ['INDPRO_PCT', 'CPIAUCSL_PCT']:  # Skip derived columns
+                        try:
+                            fred_data[col] = web.DataReader(col, 'fred', start_date, end_date)
+                        except Exception as e:
+                            st.warning(f"Failed to fetch {col} from FRED: {e}")
+                            fred_data[col] = pd.DataFrame()
+            else:
+                try:
+                    fred_data[column] = web.DataReader(column, 'fred', start_date, end_date)
+                except Exception as e:
+                    st.warning(f"Failed to fetch {column} from FRED: {e}")
+                    fred_data[column] = pd.DataFrame()
 
-    # Fetch the data from Yahoo Finance
-    sp500 = yf.download('^GSPC', start=start_date, end=end_date)['Adj Close']
-    vix = yf.download('^VIX', start=start_date, end=end_date)['Adj Close']
+    # Fetch Yahoo Finance data with yfinance adjustments
+    if selected_indicators['Stock Market (S&P 500)']:
+        try:
+            sp500 = yf.download('^GSPC', start=start_date, end=end_date, auto_adjust=False)
+            if isinstance(sp500.columns, pd.MultiIndex):
+                sp500.columns = sp500.columns.get_level_values(0)
+            sp500 = sp500['Adj Close'].rename('SP500')
+            fred_data['SP500'] = sp500
+        except Exception as e:
+            st.warning(f"Failed to fetch S&P 500 data: {e}")
+            fred_data['SP500'] = pd.DataFrame()
 
-    # Calculate percentage changes for relevant indicators
-    industrial_production_pct = industrial_production.pct_change() * 100
-    inflation_pct = inflation.pct_change() * 100
+    if selected_indicators['VIX']:
+        try:
+            vix = yf.download('^VIX', start=start_date, end=end_date, auto_adjust=False)
+            if isinstance(vix.columns, pd.MultiIndex):
+                vix.columns = vix.columns.get_level_values(0)
+            vix = vix['Adj Close'].rename('VIX')
+            fred_data['VIX'] = vix
+        except Exception as e:
+            st.warning(f"Failed to fetch VIX data: {e}")
+            fred_data['VIX'] = pd.DataFrame()
 
-    # Rename columns for clarity
-    industrial_production_pct = industrial_production_pct.rename(columns={'INDPRO': 'INDPRO_PCT'})
-    inflation_pct = inflation_pct.rename(columns={'CPIAUCSL': 'CPIAUCSL_PCT'})
+    # Rename recession probabilities for clarity
+    if 'RECPROUSM156N' in fred_data:
+        fred_data['RECPROUSM156N'].columns = ['Recession_Probabilities']
+        fred_data['Recession_Probabilities'] = fred_data.pop('RECPROUSM156N')
 
-    # Combine all data into a single DataFrame, aligning by date
-    combined_data = sahm_recession_indicator.join([
-        retail_sales, industrial_production, industrial_production_pct,
-        unemployment_rate, inflation, inflation_pct,
-        sp500.rename('SP500'), nonfarm_payrolls, jobless_claims,
-        consumer_confidence, housing_starts, treasury_rate_10y, treasury_rate_2y, treasury_rate_1m, treasury_rate_3m, federal_funds_rate, vix.rename('VIX'),
-        recession_probabilities
-    ], how='outer')
+    # Combine all FRED and Yahoo Finance data
+    if fred_data:
+        combined_data = pd.concat(fred_data.values(), axis=1, join='outer')
+        combined_data.columns = [col for col in fred_data.keys() if not col.endswith('_PCT')]  # Exclude derived columns for now
 
-    # Interpolate missing values for alignment
-    combined_data = combined_data.interpolate(method='time')
+    if not combined_data.empty:
+        # Calculate percentage changes for relevant indicators
+        if selected_indicators['Industrial Production']:
+            industrial_production = combined_data.get('INDPRO', pd.Series())
+            if not industrial_production.empty:
+                combined_data['INDPRO_PCT'] = industrial_production.pct_change() * 100
 
-    # Calculate the yield spread
-    combined_data['Yield_Spread'] = combined_data['GS10'] - combined_data['DGS2']
+        if selected_indicators['Inflation (CPI)']:
+            inflation = combined_data.get('CPIAUCSL', pd.Series())
+            if not inflation.empty:
+                combined_data['CPIAUCSL_PCT'] = inflation.pct_change() * 100
 
-    # Plot each selected series
-    for key, column in indicators.items():
-        if selected_indicators[key]:
-            fig = go.Figure()
-            
-            # Add recession shading
-            for peak, trough in crash_periods.items():
-                fig.add_shape(
-                    type="rect",
-                    xref="x",
-                    yref="paper",
-                    x0=peak,
-                    y0=0,
-                    x1=trough,
-                    y1=1,
-                    fillcolor="gray",
-                    opacity=0.3,
-                    layer="below",
-                    line_width=0,
-                )
-            
-            if isinstance(column, tuple):
-                if column == ('INDPRO', 'INDPRO_PCT'):
-                    # Plot industrial production and its percentage change on dual y-axes
-                    fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['INDPRO'], mode='lines', name='Industrial Production'))
-                    fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['INDPRO_PCT'], mode='lines', name='Industrial Production % Change', yaxis='y2'))
+        # Interpolate missing values for alignment
+        combined_data = combined_data.interpolate(method='time')
 
-                    # Create a secondary y-axis and place the legend inside the plot area
-                    fig.update_layout(
-                        yaxis2=dict(
-                            title="Industrial Production % Change",
-                            overlaying='y',
-                            side='right'
-                        ),
-                        legend=dict(
-                            x=0.02,
-                            y=0.95,
-                            traceorder='normal',
-                            bgcolor='rgba(255, 255, 255, 0.5)',
-                            bordercolor='rgba(0, 0, 0, 0)'
-                        )
-                    )
-                elif column == ('CPIAUCSL', 'CPIAUCSL_PCT'):
-                    # Plot inflation and its percentage change on dual y-axes
-                    fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['CPIAUCSL'], mode='lines', name='Inflation (CPI)'))
-                    fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['CPIAUCSL_PCT'], mode='lines', name='Inflation % Change', yaxis='y2'))
+        # Calculate the yield spread
+        if selected_indicators['Yield Spread (10Y - 2Y)'] and 'GS10' in combined_data and 'DGS2' in combined_data:
+            combined_data['Yield_Spread'] = combined_data['GS10'] - combined_data['DGS2']
 
-                    # Create a secondary y-axis and place the legend inside the plot area
-                    fig.update_layout(
-                        yaxis2=dict(
-                            title="Inflation % Change",
-                            overlaying='y',
-                            side='right'
-                        ),
-                        legend=dict(
-                            x=0.02,
-                            y=0.95,
-                            traceorder='normal',
-                            bgcolor='rgba(255, 255, 255, 0.5)',
-                            bordercolor='rgba(0, 0, 0, 0)'
-                        )
+        # Plot each selected series
+        for key, column in indicators.items():
+            if selected_indicators[key]:
+                fig = go.Figure()
+                
+                # Add recession shading
+                for peak, trough in crash_periods.items():
+                    fig.add_shape(
+                        type="rect",
+                        xref="x",
+                        yref="paper",
+                        x0=peak,
+                        y0=0,
+                        x1=trough,
+                        y1=1,
+                        fillcolor="gray",
+                        opacity=0.3,
+                        layer="below",
+                        line_width=0,
                     )
-                elif column == ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'):
-                    # Plot multiple Treasury rates in the same subplot
-                    for col in column:
-                        fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data[col], mode='lines', name=col))
-
-                    # Place the legend inside the plot area
-                    fig.update_layout(
-                        legend=dict(
-                            x=0.02,
-                            y=0.95,
-                            traceorder='normal',
-                            bgcolor='rgba(255, 255, 255, 0.5)',
-                            bordercolor='rgba(0, 0, 0, 0)'
+                
+                if isinstance(column, tuple):
+                    if column == ('INDPRO', 'INDPRO_PCT') and 'INDPRO' in combined_data:
+                        # Plot industrial production and its percentage change on dual y-axes
+                        fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['INDPRO'], mode='lines', name='Industrial Production'))
+                        if 'INDPRO_PCT' in combined_data:
+                            fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['INDPRO_PCT'], mode='lines', name='Industrial Production % Change', yaxis='y2'))
+                        fig.update_layout(
+                            yaxis2=dict(
+                                title="Industrial Production % Change",
+                                overlaying='y',
+                                side='right'
+                            ),
+                            legend=dict(
+                                x=0.02,
+                                y=0.95,
+                                traceorder='normal',
+                                bgcolor='rgba(255, 255, 255, 0.5)',
+                                bordercolor='rgba(0, 0, 0, 0)'
+                            )
+                        )
+                    elif column == ('CPIAUCSL', 'CPIAUCSL_PCT') and 'CPIAUCSL' in combined_data:
+                        # Plot inflation and its percentage change on dual y-axes
+                        fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['CPIAUCSL'], mode='lines', name='Inflation (CPI)'))
+                        if 'CPIAUCSL_PCT' in combined_data:
+                            fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data['CPIAUCSL_PCT'], mode='lines', name='Inflation % Change', yaxis='y2'))
+                        fig.update_layout(
+                            yaxis2=dict(
+                                title="Inflation % Change",
+                                overlaying='y',
+                                side='right'
+                            ),
+                            legend=dict(
+                                x=0.02,
+                                y=0.95,
+                                traceorder='normal',
+                                bgcolor='rgba(255, 255, 255, 0.5)',
+                                bordercolor='rgba(0, 0, 0, 0)'
+                            )
+                        )
+                    elif column == ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'):
+                        # Plot multiple Treasury rates in the same subplot
+                        for col in column:
+                            if col in combined_data:
+                                fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data[col], mode='lines', name=col))
+                        fig.update_layout(
+                            legend=dict(
+                                x=0.02,
+                                y=0.95,
+                                traceorder='normal',
+                                bgcolor='rgba(255, 255, 255, 0.5)',
+                                bordercolor='rgba(0, 0, 0, 0)'
+                            )
                         )
+                else:
+                    if column in combined_data:
+                        fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data[column], mode='lines', name=key))
+                        # Add horizontal threshold line for Sahm Recession Indicator
+                        if column == 'SAHMREALTIME':
+                            fig.add_hline(y=0.5, line=dict(color="red", dash="dash"), annotation_text="Recession Threshold", annotation_position="bottom right")
+                
+                # Update layout with detailed date formatting
+                fig.update_layout(
+                    title=key,
+                    xaxis_title='Date',
+                    yaxis_title=key,
+                    xaxis=dict(
+                        tickformat="%Y",
+                        tickmode="linear",
+                        dtick="M36",
+                        showspikes=True,
+                        spikemode='across',
+                        spikesnap='cursor',
+                        spikethickness=1
+                    ),
+                    hovermode="x unified",
+                    hoverlabel=dict(
+                        bgcolor="white",
+                        font_size=12,
+                        font_family="Rockwell"
                     )
-            else:
-                fig.add_trace(go.Scatter(x=combined_data.index, y=combined_data[column], mode='lines', name=key))
+                )
+                fig.update_xaxes(
+                    showgrid=True,
+                    gridwidth=1,
+                    gridcolor='LightGray',
+                    tickangle=45,
+                    tickformatstops=[
+                        dict(dtickrange=[None, "M1"], value="%b %d, %Y"),
+                        dict(dtickrange=["M1", None], value="%Y")
+                    ]
+                )
                 
-                # Add horizontal threshold line for Sahm Recession Indicator
-                if column == 'SAHMREALTIME':
-                    fig.add_hline(y=0.5, line=dict(color="red", dash="dash"), annotation_text="Recession Threshold", annotation_position="bottom right")
-            
-            # Update layout with detailed date formatting
-            fig.update_layout(
-                title=key,
-                xaxis_title='Date',
-                yaxis_title=key,
-                xaxis=dict(
-                    tickformat="%Y",  # Yearly ticks
-                    tickmode="linear",
-                    dtick="M36",  # Ticks every 12 months, M24 Every 2 years and 5 years is M60
-                    showspikes=True,
-                    spikemode='across',
-                    spikesnap='cursor',
-                    spikethickness=1
-                ),
-                hovermode="x unified",
-                hoverlabel=dict(
-                    bgcolor="white",
-                    font_size=12,
-                    font_family="Rockwell"
+                # Ensure full date is shown on hover
+                fig.update_traces(
+                    hovertemplate='%{x|%b %d, %Y}<br>%{y}<extra></extra>'
                 )
-            )
-            fig.update_xaxes(
-                showgrid=True,
-                gridwidth=1,
-                gridcolor='LightGray',
-                tickangle=45,
-                tickformatstops=[
-                    dict(dtickrange=[None, "M1"], value="%b %d, %Y"),  # Format as "Month Day, Year" for better granularity
-                    dict(dtickrange=["M1", None], value="%Y")  # Year format for larger intervals
-                ]
-            )
-            
-            # Ensure full date is shown on hover
-            fig.update_traces(
-                hovertemplate='%{x|%b %d, %Y}<br>%{y}<extra></extra>'
-            )
-            
-            st.plotly_chart(fig, use_container_width=True)
+                
+                st.plotly_chart(fig, use_container_width=True)
+    else:
+        st.error("No data was successfully fetched for the selected indicators.")
 
 hide_streamlit_style = """
 <style>
@@ -270,4 +294,4 @@ 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)