Update insights page on app to enhance portfolio metrics. Fix app bugs.

pyproject.toml

@@ -32,7 +32,8 @@ dependencies = [
     "shap",
     "pre-commit",
     "pydantic",
-    "openpyxl"
+    "openpyxl",
+    "optuna"
 ]
 
 [project.optional-dependencies]

stocksense/app/home.py

@@ -11,22 +11,60 @@ def run():
 
     st.set_page_config(
         layout="wide",
-        page_title="Stocksense Home",
-        page_icon="🏠",
+        page_title="Stocksense",
+        page_icon="📈",
+        initial_sidebar_state="expanded",
     )
     st.sidebar.title("Stocksense App")
     st.sidebar.success("Select page")
 
     st.sidebar.page_link("home.py", label="Home", icon="🏠")
     st.sidebar.page_link("pages/overview.py", label="Market Overview", icon="🌎")
     st.sidebar.page_link("pages/analytics.py", label="Stock Analytics", icon="📈")
-    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="🔮")
+    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="💼")
     st.sidebar.divider()
-    st.header(
-        """
-        Welcome to Stocksense Analytics App!
-        """
-    )
+
+    st.header("Welcome to Stocksense Analytics App!")
+    st.divider()
+
+    # Overview section
+    st.subheader("📊 App Features")
+
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.markdown("### 🌎 Market Overview")
+        st.markdown("""
+        - Real-time S&P 500 market summary
+        - Sector distribution analysis
+        - Latest earnings reports tracking
+        - Key market metrics and statistics
+        """)
+
+        st.markdown("### 📈 Stock Analytics")
+        st.markdown("""
+        - Detailed individual stock analysis
+        - Technical and fundamental indicators
+        - Financial statements analysis
+        - Insider trading tracking
+        """)
+
+    with col2:
+        st.markdown("### 💼 Portfolio Insights")
+        st.markdown("""
+        - Model-driven stock selection
+        - Portfolio composition analysis
+        - Sector allocation visualization
+        - Performance metrics tracking
+        """)
+
+        st.markdown("### 🔍 Key Features")
+        st.markdown("""
+        - Interactive data visualization
+        - Accurate market data
+        - Curated financial metrics
+        - User-friendly interface
+        """)
 
 
 def main():

stocksense/app/pages/analytics.py

@@ -239,7 +239,7 @@ def main():
     st.sidebar.page_link("home.py", label="Home", icon="🏠")
     st.sidebar.page_link("pages/overview.py", label="Market Overview", icon="🌎")
     st.sidebar.page_link("pages/analytics.py", label="Stock Analytics", icon="📈")
-    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="🔮")
+    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="💼")
     st.sidebar.divider()
     st.sidebar.header("Options: ")
 

stocksense/app/pages/insights.py

@@ -18,94 +18,149 @@ def load_stock_data():
     return db.fetch_stock().to_pandas()
 
 
-def get_available_dates():
+def get_available_portfolios():
     """
-    Get all available trade dates from score files.
+    Get all available portfolio files.
     """
-    score_files = list(SCORES_DIR.glob("scores_*.csv"))
-    dates = [dt.datetime.strptime(f.stem.split("_")[1], "%Y-%m-%d").date() for f in score_files]
+    portfolio_files = list(PORTFOLIOS_DIR.glob("portfolio_*.xlsx"))
+    dates = [dt.datetime.strptime(f.stem.split("_")[1], "%Y-%m-%d").date() for f in portfolio_files]
     return sorted(dates, reverse=True)
 
 
-def load_scores(trade_date):
+def load_portfolio(trade_date):
     """
-    Load scores for a specific trade date.
+    Load portfolio for a specific trade date.
     """
-    score_file = SCORES_DIR / f"scores_{trade_date}.csv"
-    if not score_file.exists():
-        st.error(f"No scores found for trade date {trade_date}")
+    portfolio_file = PORTFOLIOS_DIR / f"portfolio_{trade_date}.xlsx"
+    if not portfolio_file.exists():
+        st.error(f"No portfolio found for trade date {trade_date}")
         return None
-    return pd.read_csv(score_file)
+    return pd.read_excel(portfolio_file)
 
 
 def plot_sector_distribution(portfolio_data):
     """
     Plot sector distribution of selected stocks.
     """
-    sector_dist = portfolio_data.groupby("sector")["weight"].sum().reset_index()
+    sector_dist = portfolio_data.groupby("Sector")["Weight"].sum().reset_index()
     fig = px.pie(
         sector_dist,
-        values="weight",
-        names="sector",
+        values="Weight",
+        names="Sector",
         title="Sector Distribution",
         template="plotly_dark",
     )
     fig.update_traces(textposition="inside", textinfo="percent+label")
-    st.plotly_chart(fig, use_container_width=True)
+    return fig
+
+
+def plot_weight_distribution(portfolio_data):
+    """
+    Plot weight distribution of top 10 stocks.
+    """
+    top_10 = portfolio_data.nlargest(10, "Weight")
+    fig = px.pie(
+        top_10,
+        values="Weight",
+        names="Ticker",
+        title="Top 10 Holdings by Weight",
+        template="plotly_dark",
+    )
+    fig.update_traces(textposition="inside", textinfo="percent+label")
+    return fig
 
 
 def display_portfolio_metrics(portfolio_data):
     """
     Display key portfolio metrics.
     """
-    total_stocks = len(portfolio_data)
-    avg_score = portfolio_data["pred"].mean()
-    avg_price = portfolio_data["adj_close"].mean()
+    # Convert percentage strings to floats for calculations
+    portfolio_data['Weight'] = portfolio_data['Weight'].str.rstrip('%').astype(float) / 100
+
+    avg_model_score = portfolio_data['Model Score'].mean()
+    num_sectors = portfolio_data['Sector'].nunique()
 
-    col1, col2, col3 = st.columns(3)
+    col1, col2 = st.columns(2)
     with col1:
-        st.metric("Number of Stocks", total_stocks)
+        st.metric("Average Model Score", f"{avg_model_score:.1f}")
     with col2:
-        st.metric("Average Model Score", f"{avg_score:.3f}")
-    with col3:
-        st.metric("Average Stock Price", f"${avg_price:.2f}")
+        st.metric("Sectors", num_sectors)
 
 
 def main():
     """Insights main script."""
+    st.set_page_config(layout="wide", page_title="Portfolio Insights", page_icon="💼")
 
-    st.set_page_config(layout="wide", page_title="Stock Picks", page_icon="🔮")
+    # Sidebar navigation
     st.sidebar.title("Stocksense App")
     st.sidebar.success("Select page")
-
     st.sidebar.page_link("home.py", label="Home", icon="🏠")
     st.sidebar.page_link("pages/overview.py", label="Market Overview", icon="🌎")
     st.sidebar.page_link("pages/analytics.py", label="Stock Analytics", icon="📈")
-    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="🔮")
+    st.sidebar.page_link("pages/insights.py", label="Portfolio Insights", icon="💼")
     st.sidebar.divider()
 
-    st.title("Stock Picks Insights")
+    # Main content
+    st.title("Portfolio Insights 💼")
+    st.divider()
 
-    stock_data = load_stock_data()
-    available_dates = get_available_dates()
-    trade_date = st.selectbox("Select Trade Date", available_dates)
-    scores = load_scores(trade_date)
-    scores = scores.join(stock_data, on="tic", rsuffix="_stock")
+    # Portfolio selection
+    available_dates = get_available_portfolios()
+    if not available_dates:
+        st.warning("No portfolio files found.")
+        return
 
-    if scores is not None:
-        display_portfolio_metrics(scores)
-        plot_sector_distribution(scores)
-
-        st.subheader("Top 30 Selected Stocks")
-        columns_to_display = ["symbol", "company_name", "sector", "pred", "adj_close", "weight"]
-        formatted_scores = scores[columns_to_display].head(30)
-
-        formatted_scores["pred"] = formatted_scores["pred"].round(3)
-        formatted_scores["adj_close"] = formatted_scores["adj_close"].round(2)
-        formatted_scores["weight"] = (formatted_scores["weight"] * 100).round(2).astype(str) + "%"
-        formatted_scores.columns = ["Symbol", "Company", "Sector", "Score", "Price ($)", "Weight"]
+    trade_date = st.selectbox(
+        "Select Portfolio Date",
+        available_dates,
+        format_func=lambda x: x.strftime("%B %d, %Y")
+    )
 
-        st.dataframe(formatted_scores, use_container_width=True)
+    portfolio = load_portfolio(trade_date)
+
+    if portfolio is not None:
+        st.divider()
+        display_portfolio_metrics(portfolio)
+        st.divider()
+
+        col1, col2 = st.columns(2)
+        with col1:
+            weight_fig = plot_weight_distribution(portfolio)
+            st.plotly_chart(weight_fig, use_container_width=True)
+
+        with col2:
+            sector_fig = plot_sector_distribution(portfolio)
+            st.plotly_chart(sector_fig, use_container_width=True)
+
+        st.subheader("Portfolio Composition")
+        # Style the dataframe
+        portfolio["Weight"] = portfolio["Weight"].astype(float) * 100
+        st.dataframe(
+            portfolio,
+            column_config={
+                "Ticker": st.column_config.TextColumn("Ticker", width="small"),
+                "Company": st.column_config.TextColumn("Company", width="medium"),
+                "Sector": st.column_config.TextColumn("Sector", width="medium"),
+                "Strike Price ($)": st.column_config.NumberColumn(
+                    "Strike Price ($)",
+                    format="$%.2f",
+                    width="small"
+                ),
+                "Weight": st.column_config.NumberColumn(
+                    "Weight",
+                    format="%.2f%%",
+                    width="small",
+                    help="Portfolio weight in percentage"
+                ),
+                "Model Score": st.column_config.NumberColumn(
+                    "Model Score",
+                    format="%.1f",
+                    width="small"
+                ),
+            },
+            hide_index=True,
+            use_container_width=True,
+        )
     else:
         st.warning("No data available for the selected date.")
 

stocksense/app/pages/overview.py

@@ -125,7 +125,7 @@ def main():
     st.sidebar.page_link("home.py", label="Home", icon="🏠")
     st.sidebar.page_link("pages/overview.py", label="Market Overview", icon="🌎")
     st.sidebar.page_link("pages/analytics.py", label="Stock Analytics", icon="📈")
-    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="🔮")
+    st.sidebar.page_link("pages/insights.py", label="Stock Picks", icon="💼")
     st.sidebar.divider()
 
     data = load_sp500_data()

stocksense/model/portfolio.py

@@ -194,31 +194,52 @@ def _save_portfolio_excel(self, portfolio: pl.DataFrame, trade_date: dt.datetime
             Trade date.
         """
         excel_path = self.portfolios_dir / f"portfolio_{trade_date.date()}.xlsx"
+
+        # Convert to pandas once and rename columns
+        portfolio_pd = portfolio.rename({
+            "tic": "Ticker",
+            "name": "Company",
+            "sector": "Sector",
+            "adj_close": "Strike Price ($)",
+            "mkt_cap": "Market Cap ($M)",
+            "avg_score": "Model Score",
+            "weight": "Weight",
+            "max_return_4Q": "Max Return 1Y",
+            "fwd_return_4Q": "Forward Return 1Y"
+        }, strict=False).to_pandas()
+
+        # Format numeric columns
+        portfolio_pd["Weight"] = portfolio_pd["Weight"].map("{:.2%}".format)
+        portfolio_pd["Model Score"] = portfolio_pd["Model Score"].round(2)
+        portfolio_pd["Market Cap ($M)"] = portfolio_pd["Market Cap ($M)"].round(2)
+        portfolio_pd["Strike Price ($)"] = portfolio_pd["Strike Price ($)"].round(1)
+        if "Max Return 1Y" in portfolio_pd.columns:
+            portfolio_pd["Max Return 1Y"] = portfolio_pd["Max Return 1Y"].round(2)
+            portfolio_pd["Forward Return 1Y"] = portfolio_pd["Forward Return 1Y"].round(2)
+
         with pd.ExcelWriter(excel_path, engine="openpyxl") as writer:
             # Sheet 1: Full Portfolio
-            portfolio_df = portfolio.sort("weight", descending=True).to_pandas()
-            portfolio_df["weight"] = portfolio_df["weight"].map("{:.2%}".format)
-            portfolio_df["avg_score"] = portfolio_df["avg_score"].round(2)
-            portfolio_df["mkt_cap"] = portfolio_df["mkt_cap"].round(2)
-            portfolio_df["adj_close"] = portfolio_df["adj_close"].round(1)
-            if "max_return_4Q" in portfolio_df.columns:
-                portfolio_df["max_return_4Q"] = portfolio_df["max_return_4Q"].round(2)
-                portfolio_df["fwd_return_4Q"] = portfolio_df["fwd_return_4Q"].round(2)
-            portfolio_df.to_excel(writer, sheet_name="Full Portfolio", index=False)
+            portfolio_pd.sort_values("Weight", ascending=False).to_excel(
+                writer, sheet_name="Full Portfolio", index=False
+            )
 
             # Sheet 2: Sector Allocations
+            # Create a copy of the dataframe before formatting weights for aggregation
+            portfolio_numeric = portfolio_pd.copy()
+            portfolio_numeric["Weight"] = portfolio_pd["Weight"].str.rstrip('%').astype(float) / 100
+
             sector_alloc = (
-                portfolio.group_by("sector")
-                .agg(pl.col("weight").sum())
-                .sort("weight", descending=True)
-                .to_pandas()
+                portfolio_numeric.groupby("Sector")["Weight"]
+                .sum()
+                .reset_index()
+                .sort_values("Weight", ascending=False)
             )
-            sector_alloc["weight"] = sector_alloc["weight"].map("{:.2%}".format)
+            sector_alloc["Weight"] = sector_alloc["Weight"].map("{:.2%}".format)
             sector_alloc.to_excel(writer, sheet_name="Sector Allocations", index=False)
 
             # Sheet 3: Top Holdings
-            top_positions = portfolio.sort("weight", descending=True).head(5).to_pandas()
-            top_positions["weight"] = top_positions["weight"].map("{:.2%}".format)
+            # Use original dataframe with formatted weights
+            top_positions = portfolio_pd.sort_values("Weight", ascending=False).head(5)
             top_positions.to_excel(writer, sheet_name="Top Holdings", index=False)
 
         logger.info(f"Portfolio saved to {excel_path}")