Update app.py

app.py

@@ -5,13 +5,14 @@ import yfinance as yf
 import datetime
 import plotly.graph_objs as go
 
-# ---------- Page config (must be first) ----------
+# ---------- Page config (must be the first Streamlit call) ----------
 st.set_page_config(layout="wide")
 
-# ---------- Stable CSS for wider sidebar ----------
+# ---------- Stable CSS for wider sidebar (avoid fragile class names) ----------
 st.markdown(
     """
     <style>
+    /* Make the sidebar wider in a stable way */
     [data-testid="stSidebar"] {
         width: 350px;
         min-width: 350px;
@@ -21,15 +22,11 @@ st.markdown(
     unsafe_allow_html=True,
 )
 
-# ---------- Session state ----------
+# ---------- Session state for persistent "Run Analysis" ----------
 if "run_analysis" not in st.session_state:
     st.session_state.run_analysis = False
-if "combined_data" not in st.session_state:
-    st.session_state.combined_data = None
-if "selection_signature" not in st.session_state:
-    st.session_state.selection_signature = None
 
-# ---------- App title / description ----------
+# ---------- 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.
@@ -37,7 +34,7 @@ This tool allows you to visualize and analyze various recession indicators over
 - Use the checkboxes in the sidebar to choose the indicators you'd like to explore.
 """)
 
-# ---------- Sidebar ----------
+# ---------- Sidebar controls ----------
 with st.sidebar.expander("How to Use", expanded=False):
     st.write("""
     **How to use this app:**
@@ -53,8 +50,8 @@ with st.sidebar.expander("Indicators", expanded=True):
         'Sahm Recession Indicator': 'SAHMREALTIME',
         'U.S. Recession Probabilities': 'RECPROUSM156N',
         'Yield Spread (10Y - 2Y)': 'Yield_Spread',  # Calculated, not fetched
-        'Stock Market (S&P 500)': 'SP500',          # yfinance (^GSPC)
-        'VIX': 'VIX',                               # yfinance (^VIX)
+        'Stock Market (S&P 500)': 'SP500',          # Fetched from yfinance
+        'VIX': 'VIX',                               # Fetched from yfinance
         'Treasury Rates': ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'),
         'Federal Funds Rate': 'FEDFUNDS',
         'Unemployment Rate': 'UNRATE',
@@ -68,7 +65,7 @@ with st.sidebar.expander("Indicators", expanded=True):
     }
     selected_indicators = {key: st.checkbox(key, value=True) for key in indicators.keys()}
 
-# Single Run button
+# Single Run button (no clear buttons)
 if st.sidebar.button("Run Analysis"):
     st.session_state.run_analysis = True
 
@@ -95,192 +92,166 @@ crash_periods = {
     '2020-02-01': '2020-04-01'
 }
 
-# ---------- Cached batch fetchers ----------
-@st.cache_data(ttl=6*60*60, show_spinner=False)
-def fetch_fred_batch(series_codes, start, end) -> pd.DataFrame:
-    """Batch fetch from FRED; falls back to per-series if batch fails."""
-    if not series_codes:
-        return pd.DataFrame()
+# ---------- Cached data fetchers ----------
+@st.cache_data(ttl=6 * 60 * 60, show_spinner=False)
+def fetch_fred_series(series_code: str, start: datetime.datetime, end: datetime.datetime) -> pd.Series:
+    """Fetch a single FRED series as a named Series (empty Series if fails)."""
     try:
-        df = web.DataReader(series_codes, "fred", start, end)
-        # DataReader may return a Series when series_codes has length 1
-        if isinstance(df, pd.Series):
-            df = df.to_frame(series_codes[0])
-        return df
+        df = web.DataReader(series_code, 'fred', start, end)
+        if isinstance(df, pd.DataFrame):
+            s = df.squeeze("columns")
+        else:
+            s = df
+        s = s.rename(series_code)
+        return s
     except Exception as e:
-        # Fallback: try individually, salvage what we can
-        frames = []
-        for code in series_codes:
-            try:
-                s = web.DataReader(code, "fred", start, end).squeeze("columns").rename(code)
-                frames.append(s)
-            except Exception as ie:
-                st.warning(f"Failed to fetch {code} from FRED: {ie}")
-        return pd.concat(frames, axis=1) if frames else pd.DataFrame()
-
-@st.cache_data(ttl=6*60*60, show_spinner=False)
-def fetch_yf_batch(tickers, start, end) -> pd.DataFrame:
-    """Batch fetch Adj Close from Yahoo Finance, rename to friendly labels."""
-    if not tickers:
-        return pd.DataFrame()
+        st.warning(f"Failed to fetch {series_code} from FRED: {e}")
+        return pd.Series(name=series_code, dtype="float64")
+
+@st.cache_data(ttl=6 * 60 * 60, show_spinner=False)
+def fetch_yf_series(ticker: str, label: str, start: datetime.datetime, end: datetime.datetime) -> pd.Series:
+    """Fetch Adj Close from Yahoo Finance as a named Series."""
     try:
-        df = yf.download(
-            tickers=" ".join(tickers),
-            start=start, end=end,
-            auto_adjust=False, progress=False, threads=False,
-        )
-        # Normalize to a flat DataFrame of Adj Close columns
+        df = yf.download(ticker, start=start, end=end, auto_adjust=False, progress=False, threads=False)
         if isinstance(df.columns, pd.MultiIndex):
-            # pick Adj Close for each ticker
-            if "Adj Close" in df.columns.get_level_values(0):
-                wide = df["Adj Close"].copy()
-            else:
-                # Fallback: try Close
-                wide = df["Close"].copy()
-        else:
-            # Single ticker -> just one column set
-            wide = df[["Adj Close"]] if "Adj Close" in df.columns else df[["Close"]]
-            wide.columns = [tickers[0]]
-        # Rename to app labels
-        rename_map = {"^GSPC": "SP500", "^VIX": "VIX"}
-        wide = wide.rename(columns=rename_map)
-        return wide
+            df.columns = df.columns.get_level_values(0)
+        s = df.get('Adj Close', pd.Series(dtype="float64")).rename(label)
+        return s
     except Exception as e:
-        st.warning(f"Failed to fetch from Yahoo Finance: {e}")
-        return pd.DataFrame()
-
-# ---------- Build everything up-front ----------
-def selection_signature(selected: dict) -> tuple:
-    """Immutable key for the current selection (used to decide if we refetch)."""
-    return tuple(sorted([k for k, v in selected.items() if v]))
+        st.warning(f"Failed to fetch {label} ({ticker}) from Yahoo Finance: {e}")
+        return pd.Series(name=label, dtype="float64")
 
-@st.cache_data(ttl=6*60*60, show_spinner=False)
-def build_dataset_all(selected_keys: tuple) -> pd.DataFrame:
-    """Build the full dataset for the given selection (batched)."""
-    # Recreate indicator mapping (cache functions need pure inputs)
-    indicators_local = {
-        'Sahm Recession Indicator': 'SAHMREALTIME',
-        'U.S. Recession Probabilities': 'RECPROUSM156N',
-        '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')
-    }
+# ---------- Build dataset ----------
+def build_dataset(selected: dict) -> pd.DataFrame:
+    series_list = []
 
-    fred_series = []
-    yf_tickers = []
-    for key in selected_keys:
-        col = indicators_local[key]
+    # FRED (skip derived)
+    for key, col in indicators.items():
+        if not selected.get(key, False):
+            continue
         if isinstance(col, tuple):
             for c in col:
-                if c in ("INDPRO_PCT", "CPIAUCSL_PCT"):  # derived later
-                    continue
-                fred_series.append(c)
+                if c in ["INDPRO_PCT", "CPIAUCSL_PCT"]:
+                    continue  # derived later
+                s = fetch_fred_series(c, start_date, end_date)
+                if not s.empty:
+                    series_list.append(s)
         else:
-            if col == "SP500":
-                yf_tickers.append("^GSPC")
-            elif col == "VIX":
-                yf_tickers.append("^VIX")
-            elif col == "Yield_Spread":
-                pass  # derived later
-            else:
-                fred_series.append(col)
-
-    fred = fetch_fred_batch(sorted(set(fred_series)), start_date, end_date)
-    yfdf = fetch_yf_batch(sorted(set(yf_tickers)), start_date, end_date)
-
-    frames = []
-    if isinstance(fred, pd.DataFrame) and not fred.empty:
-        frames.append(fred)
-    if isinstance(yfdf, pd.DataFrame) and not yfdf.empty:
-        frames.append(yfdf)
-
-    if not frames:
+            if col in ["Yield_Spread", "SP500", "VIX"]:
+                continue  # handled separately / derived
+            s = fetch_fred_series(col, start_date, end_date)
+            if not s.empty:
+                series_list.append(s)
+
+    # YFinance
+    if selected.get('Stock Market (S&P 500)', False):
+        s = fetch_yf_series('^GSPC', 'SP500', start_date, end_date)
+        if not s.empty:
+            series_list.append(s)
+    if selected.get('VIX', False):
+        s = fetch_yf_series('^VIX', 'VIX', start_date, end_date)
+        if not s.empty:
+            series_list.append(s)
+
+    if not series_list:
         return pd.DataFrame()
 
-    combined = pd.concat(frames, axis=1).sort_index()
+    combined = pd.concat(series_list, axis=1).sort_index()
 
-    # Derived series
-    if "INDPRO" in combined.columns and "Industrial Production" in selected_keys:
-        combined["INDPRO_PCT"] = combined["INDPRO"].pct_change() * 100
-    if "CPIAUCSL" in combined.columns and "Inflation (CPI)" in selected_keys:
-        combined["CPIAUCSL_PCT"] = combined["CPIAUCSL"].pct_change() * 100
+    # Derived columns
+    if selected.get('Industrial Production', False) and 'INDPRO' in combined.columns:
+        combined['INDPRO_PCT'] = combined['INDPRO'].pct_change() * 100
+    if selected.get('Inflation (CPI)', False) and 'CPIAUCSL' in combined.columns:
+        combined['CPIAUCSL_PCT'] = combined['CPIAUCSL'].pct_change() * 100
 
-    # Interpolate for alignment
-    combined = combined.interpolate(method="time")
+    # Interpolate (time index required)
+    combined = combined.interpolate(method='time')
 
     # Yield spread
-    if "Yield Spread (10Y - 2Y)" in selected_keys and {"GS10", "DGS2"}.issubset(combined.columns):
-        combined["Yield_Spread"] = combined["GS10"] - combined["DGS2"]
+    if selected.get('Yield Spread (10Y - 2Y)', False) and {'GS10', 'DGS2'}.issubset(combined.columns):
+        combined['Yield_Spread'] = combined['GS10'] - combined['DGS2']
 
     return combined
 
-# ---------- Plot helpers ----------
+# ---------- Plotting helpers ----------
 def add_recession_shading(fig: go.Figure):
     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,
+            type="rect",
+            xref="x",
+            yref="paper",
+            x0=peak,
+            y0=0,
+            x1=trough,
+            y1=1,
+            fillcolor="gray",
+            opacity=0.3,
+            layer="below",
+            line_width=0,
         )
 
 def finalize_layout(fig: go.Figure, title: str, ytitle: str):
     fig.update_layout(
         title=title,
-        xaxis_title="Date",
+        xaxis_title='Date',
         yaxis_title=ytitle,
+        template='plotly_dark',                 # dark-friendly defaults
+        paper_bgcolor='rgba(0,0,0,0)',          # transparent to match Streamlit theme
+        plot_bgcolor='rgba(0,0,0,0)',           # transparent to match Streamlit theme
+        font=dict(color="white"),
         xaxis=dict(
-            tickformat="%Y", tickmode="linear", dtick="M36",
-            showspikes=True, spikemode="across", spikesnap="cursor", spikethickness=1
+            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"),
+        hoverlabel=dict(
+            bgcolor="rgba(14,17,23,0.95)",      # blends with backgroundColor "#0e1117"
+            font_size=12,
+            font_family="Rockwell",
+            font_color="white"
+        ),
         legend=dict(
-            x=0.02, y=0.95, traceorder="normal",
-            bgcolor="rgba(255,255,255,0.5)", bordercolor="rgba(0,0,0,0)"
+            x=0.02,
+            y=0.95,
+            traceorder='normal',
+            bgcolor='rgba(0,0,0,0)',            # transparent legend
+            bordercolor='rgba(0,0,0,0)',
+            font=dict(color="white"),
+            title_font=dict(color="white")
         ),
-        margin=dict(l=60, r=20, t=40, b=40),
+        margin=dict(l=60, r=20, t=40, b=40)
     )
     fig.update_xaxes(
-        showgrid=True, gridwidth=1, gridcolor="LightGray", tickangle=45,
+        showgrid=True,
+        gridwidth=1,
+        gridcolor='rgba(255,255,255,0.12)',     # subtle grid for dark
+        tickangle=45,
         tickformatstops=[
             dict(dtickrange=[None, "M1"], value="%b %d, %Y"),
-            dict(dtickrange=["M1", None], value="%Y"),
-        ],
+            dict(dtickrange=["M1", None], value="%Y")
+        ]
     )
-    fig.update_traces(hovertemplate="%{x|%b %d, %Y}<br>%{y}<extra></extra>")
+    fig.update_yaxes(
+        showgrid=True,
+        gridwidth=1,
+        gridcolor='rgba(255,255,255,0.12)'
+    )
+    fig.update_traces(hovertemplate='%{x|%b %d, %Y}<br>%{y}<extra></extra>')
 
 # ---------- Main render ----------
 if st.session_state.run_analysis:
-    # Compute a signature of the current selection to decide if we need to rebuild
-    sig = selection_signature(selected_indicators)
-
-    need_fetch = (
-        st.session_state.combined_data is None
-        or st.session_state.selection_signature != sig
-    )
-
-    if need_fetch:
-        with st.spinner("Fetching all selected data (batched) and building dataset..."):
-            combined_data = build_dataset_all(sig)
-        st.session_state.combined_data = combined_data
-        st.session_state.selection_signature = sig
-    else:
-        combined_data = st.session_state.combined_data
+    with st.spinner("Fetching data and building charts..."):
+        combined_data = build_dataset(selected_indicators)
 
-    if combined_data is None or combined_data.empty:
+    if combined_data.empty:
         st.error("No data was successfully fetched for the selected indicators.")
     else:
-        # Plot everything AFTER data is fully ready
+        # Loop through selections and plot
         for key, column in indicators.items():
             if not selected_indicators.get(key, False):
                 continue
@@ -289,7 +260,7 @@ if st.session_state.run_analysis:
             add_recession_shading(fig)
 
             if isinstance(column, tuple):
-                # Industrial Production (level + % change on y2)
+                # Industrial Production: level + % change on y2
                 if column == ('INDPRO', 'INDPRO_PCT') and 'INDPRO' in combined_data.columns:
                     fig.add_trace(go.Scatter(
                         x=combined_data.index, y=combined_data['INDPRO'],
@@ -301,11 +272,12 @@ if st.session_state.run_analysis:
                             mode='lines', name='Industrial Production % Change', yaxis='y2'
                         ))
                         fig.update_layout(yaxis2=dict(
-                            title="Industrial Production % Change", overlaying='y', side='right'
+                            title="Industrial Production % Change",
+                            overlaying='y', side='right'
                         ))
                     finalize_layout(fig, key, key)
 
-                # Inflation CPI (level + % change on y2)
+                # Inflation: CPI + % change on y2
                 elif column == ('CPIAUCSL', 'CPIAUCSL_PCT') and 'CPIAUCSL' in combined_data.columns:
                     fig.add_trace(go.Scatter(
                         x=combined_data.index, y=combined_data['CPIAUCSL'],
@@ -317,25 +289,25 @@ if st.session_state.run_analysis:
                             mode='lines', name='Inflation % Change', yaxis='y2'
                         ))
                         fig.update_layout(yaxis2=dict(
-                            title="Inflation % Change", overlaying='y', side='right'
+                            title="Inflation % Change",
+                            overlaying='y', side='right'
                         ))
                     finalize_layout(fig, key, key)
 
-                # Treasury rates
+                # Treasury rates: plot each available
                 elif column == ('GS10', 'DGS2', 'DGS1MO', 'TB3MS'):
                     any_added = False
                     for col in column:
                         if col in combined_data.columns:
+                            any_added = True
                             fig.add_trace(go.Scatter(
-                                x=combined_data.index, y=combined_data[col],
-                                mode='lines', name=col
+                                x=combined_data.index, y=combined_data[col], mode='lines', name=col
                             ))
-                            any_added = True
                     if any_added:
                         finalize_layout(fig, key, key)
 
                 else:
-                    # Generic multi-series, if ever needed
+                    # Generic multi-series if needed
                     for col in column:
                         if col in combined_data.columns:
                             fig.add_trace(go.Scatter(
@@ -351,7 +323,7 @@ if st.session_state.run_analysis:
                     ))
                     if column == 'SAHMREALTIME':
                         fig.add_hline(
-                            y=0.5, line=dict(color="red", dash="dash"),
+                            y=0.5, line=dict(color="#ff6b6b", dash="dash"),
                             annotation_text="Recession Threshold",
                             annotation_position="bottom right"
                         )
@@ -364,16 +336,15 @@ if st.session_state.run_analysis:
                     ))
                     finalize_layout(fig, key, key)
 
+            # Only render if we actually added something beyond the shading
             if fig.data:
                 st.plotly_chart(fig, use_container_width=True)
 
-# ---------- Hide Streamlit branding ----------
-st.markdown(
-    """
-    <style>
-    #MainMenu {visibility: hidden;}
-    footer {visibility: hidden;}
-    </style>
-    """,
-    unsafe_allow_html=True,
-)
+# ---------- Hide default Streamlit branding ----------
+hide_streamlit_style = """
+<style>
+#MainMenu {visibility: hidden;}
+footer {visibility: hidden;}
+</style>
+"""
+st.markdown(hide_streamlit_style, unsafe_allow_html=True)