Create app.py

app.py

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+import streamlit as st
+import pandas_datareader.data as web
+import yfinance as yf
+import datetime
+import plotly.graph_objs as go
+
+# Set up the layout
+st.set_page_config(layout="wide")
+
+# App title and description
+st.title("Key Economic Recession Indicators")
+st.markdown("""
+This tool allows you to visualize and analyze various recession indicators over time.
+The shaded areas in the charts represent historical recession periods.
+Use the checkboxes in the sidebar to choose the indicators you'd like to explore.
+""")
+
+# Sidebar for chart selection
+st.sidebar.header("Select Indicators")
+indicators = {
+    'Sahm Recession Indicator': 'SAHMREALTIME',
+    'U.S. Recession Probabilities': 'Recession_Probabilities',
+    'Yield Spread (10Y - 2Y)': 'Yield_Spread',
+    'Stock Market (S&P 500)': 'SP500',
+    'VIX': 'VIX',
+    'Treasury Rates': ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'),
+    'Federal Funds Rate': 'FEDFUNDS',
+    'Unemployment Rate': 'UNRATE',
+    'Nonfarm Payrolls': 'PAYEMS',
+    'Jobless Claims': 'ICSA',
+    'Retail Sales': 'RSXFS',
+    'Industrial Production': ('INDPRO', 'INDPRO_PCT'),
+    'Housing Starts': 'HOUST',
+    'Consumer Confidence': 'UMCSENT',
+    'Inflation (CPI)': ('CPIAUCSL', 'CPIAUCSL_PCT')
+}
+
+selected_indicators = {key: st.sidebar.checkbox(key, value=True) for key in indicators.keys()}
+
+# Define start and end dates
+start_date = datetime.datetime(1920, 1, 1)
+end_date = datetime.datetime.today()
+extended_end_date = end_date + datetime.timedelta(days=120)
+
+# Define recession periods (as before)
+crash_periods = {
+    '1929-08-01': '1933-03-01',
+    '1937-05-01': '1938-06-01',
+    '1945-02-01': '1945-10-01',
+    '1948-11-01': '1949-10-01',
+    '1953-07-01': '1954-05-01',
+    '1957-08-01': '1958-04-01',
+    '1960-04-01': '1961-02-01',
+    '1969-12-01': '1970-11-01',
+    '1973-11-01': '1975-03-01',
+    '1980-01-01': '1980-07-01',
+    '1981-07-01': '1982-11-01',
+    '1990-07-01': '1991-03-01',
+    '2001-03-01': '2001-11-01',
+    '2007-12-01': '2009-06-01',
+    '2020-02-01': '2020-04-01'
+}
+
+# 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)
+
+    # Rename columns for clarity
+    recession_probabilities.columns = ['Recession_Probabilities']
+
+    # 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']
+
+    # Calculate percentage changes for relevant indicators
+    industrial_production_pct = industrial_production.pct_change() * 100
+    inflation_pct = inflation.pct_change() * 100
+
+    # Rename columns for clarity
+    industrial_production_pct = industrial_production_pct.rename(columns={'INDPRO': 'INDPRO_PCT'})
+    inflation_pct = inflation_pct.rename(columns={'CPIAUCSL': 'CPIAUCSL_PCT'})
+
+    # 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')
+
+    # Interpolate missing values for alignment
+    combined_data = combined_data.interpolate(method='time')
+
+    # Calculate the yield spread
+    combined_data['Yield_Spread'] = combined_data['GS10'] - combined_data['DGS2']
+
+    # 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'))
+
+                    # 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'))
+
+                    # 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)'
+                        )
+                    )
+                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)'
+                        )
+                    )
+            else:
+                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",  # 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"
+                )
+            )
+            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)
+
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)