Update app.py

app.py

@@ -13,17 +13,17 @@ import plotly.colors as pc
 warnings.filterwarnings("ignore")
 
 # Set wide layout
-st.set_page_config(layout="wide", page_title="Yield Curve Analysis")
+st.set_page_config(layout="wide", page_title="U.S Treasury Yield Curve Analysis")
 
 # Sidebar menu
-st.sidebar.title("Yield Curve Analysis App")
-st.sidebar.write("""
-### Overview
-This application analyzes U.S. Treasury yield curves, yield spreads, and trends to provide insights into economic conditions.
+st.sidebar.title("Input Parameters")
+#st.sidebar.write("""
+#### Overview
+#This application analyzes U.S. Treasury yield curves, yield spreads, and trends to provide insights into economic conditions.
 ### How to Use
-1. Select the start and end dates for the analysis.
-2. Click on 'Run Analysis' to generate the plots and interpretations.
-""")
+#1. Select the start and end dates for the analysis.
+#2. Click on 'Run Analysis' to generate the plots and interpretations.
+#""")
 
 # Input fields for start and end dates
 start_date = st.sidebar.date_input("Start Date", datetime(2023, 1, 1))
@@ -45,11 +45,21 @@ labels = {
     'DGS30': '30 Years'
 }
 
+# Overview and Purpose
+st.title("Yield Curve Analysis")
+st.write("""
+The U.S. Treasury yield curve is a graph that plots the yields of Treasury securities at fixed maturities.
+Yield curves are crucial for understanding the term structure of interest rates and assessing economic conditions.
+This application analyzes the yield curve, yield spreads, and trends to provide insights into the economic outlook.
+""")
+
+
 if st.sidebar.button("Run Analysis"):
     yield_data = get_data_fred(tickers, start=start_date, end=end_date).dropna()
     yield_data.index = yield_data.index.date
 
     # Plot the time series for each term using Plotly
+    st.subheader("U.S. Treasury Yield Curve Time Series")
     fig = go.Figure()
     for ticker in tickers:
         fig.add_trace(go.Scatter(x=yield_data.index, y=yield_data[ticker], mode='lines', name=labels[ticker]))
@@ -75,6 +85,12 @@ if st.sidebar.button("Run Analysis"):
     slope_indicator = yield_data['DGS30'] - yield_data['DGS1MO']
     average_slope = slope_indicator.mean()
 
+    st.subheader("Yield Curve Slope Interpretation")
+    st.write("The slope of the yield curve is an important indicator of economic expectations:")
+    st.latex(r'''
+    \text{Slope} = \text{Yield}_{30\text{Year}} - \text{Yield}_{1\text{Month}}
+    ''')
+
     if average_slope > 0:
         st.write("On average, the yield curve shows an upward slope, which often indicates positive economic growth expectations.")
     else:
@@ -85,6 +101,8 @@ if st.sidebar.button("Run Analysis"):
     positive_trend_count = (trend > 0).sum()
     negative_trend_count = (trend < 0).sum()
 
+    st.subheader("Yield Curve Trend Interpretation")
+    st.write("Analyzing the trend of the yield curve helps in understanding the direction of interest rates:")
     if positive_trend_count > negative_trend_count:
         st.write("Overall, yields are increasing over time, indicating rising interest rates.")
     else:
@@ -101,6 +119,7 @@ if st.sidebar.button("Run Analysis"):
     })
 
     # Plot the spreads over time using Plotly
+    st.subheader("Yield Spreads Over Time")
     fig2 = go.Figure()
     for spread in spreads.columns:
         fig2.add_trace(go.Scatter(x=spreads.index, y=spreads[spread], mode='lines', name=spread))
@@ -114,8 +133,11 @@ if st.sidebar.button("Run Analysis"):
     st.plotly_chart(fig2)
 
     # Dynamic interpretation for spreads
+    st.subheader("Interpretation of Yield Spreads")
+    st.write("""
+    Yield spreads are the differences between yields on different maturities of bonds. They are often used to predict economic changes:
+    """)
     spread_stats = spreads.describe()
-    st.write("Spread Statistics:")
     st.write(spread_stats)
 
     for spread in spreads.columns:
@@ -146,6 +168,7 @@ if st.sidebar.button("Run Analysis"):
                 st.write("Negative spreads here indicate that yields decrease with maturity, reflecting investor pessimism about future economic conditions. This situation is also referred to as a yield inversion.")
 
     # Correlation matrix for the yield data
+    st.subheader("Correlation Heatmap of U.S. Treasury Yields")
     correlation_matrix = yield_data.corr()
 
     # Plot the heatmap of the correlation matrix using Plotly
@@ -171,6 +194,7 @@ if st.sidebar.button("Run Analysis"):
     yield_data_long = yield_data.melt(id_vars='DATE', var_name='Maturity', value_name='Yield')
 
     # Create the initial figure
+    st.subheader("Interactive U.S. Treasury Yield Curve")
     fig4 = go.Figure()
 
     # Add traces, one for each date
@@ -266,6 +290,13 @@ if st.sidebar.button("Run Analysis"):
     yield_data['10Y-2Y Spread'] = yield_data['DGS10'] - yield_data['DGS2']
 
     # Fit a Markov Switching Model
+    st.subheader("Markov Switching Model Analysis")
+    st.write("We use a Markov Switching Model to identify different regimes in the yield spread data:")
+    st.latex(r'''
+    y_t = \mu_{s_t} + \epsilon_t \\
+    \epsilon_t \sim N(0, \sigma_{s_t}^2)
+    ''')
+
     mod = sm.tsa.MarkovRegression(yield_data['10Y-2Y Spread'], k_regimes=2, trend='c')
     res = mod.fit()
 
@@ -323,6 +354,7 @@ if st.sidebar.button("Run Analysis"):
         st.write("Regime 2 tends to last longer, indicating longer periods of economic stress or recession.")
 
 
+
 hide_streamlit_style = """
 <style>
 #MainMenu {visibility: hidden;}