feat: Add weekly performance metrics to Portfolio Overview section

src/pages/journal/trading_journal_page.py

@@ -1,6 +1,6 @@
 import streamlit as st
 import plotly.graph_objects as go
-from datetime import datetime
+from datetime import datetime, timedelta
 import pytz
 from trading.journal import (
     create_trades_table, get_open_trades, get_trade_history,
@@ -9,6 +9,44 @@ from trading.journal import (
     get_position_summary, get_latest_portfolio_value, update_portfolio_value
 )
 
+def calculate_weekly_metrics(trades) -> dict:
+    """Calculate weekly performance metrics"""
+    now = datetime.now(pytz.timezone('US/Pacific'))
+    week_start = (now - timedelta(days=now.weekday())).replace(hour=0, minute=0, second=0, microsecond=0)
+    
+    weekly_pl = 0
+    weekly_trades = []
+    
+    for trade in trades:
+        # For sells/exits that happened this week
+        if (trade.get('direction') == 'sell' or trade.get('exit_price')) and \
+           trade.get('exit_date', trade.get('entry_date')) >= week_start:
+            
+            price = float(trade.get('exit_price') or trade.get('entry_price'))
+            shares = float(trade['shares'])
+            position_id = trade['position_id']
+            
+            # Find matching buy order
+            buy_trades = [t for t in trades 
+                         if t['position_id'] == position_id and 
+                         (t.get('direction', '').lower() != 'sell' and not t.get('exit_price'))]
+            
+            if buy_trades:
+                # Calculate average entry price
+                total_cost = sum(float(t['entry_price']) * float(t['shares']) for t in buy_trades)
+                total_shares = sum(float(t['shares']) for t in buy_trades)
+                avg_entry_price = total_cost / total_shares if total_shares > 0 else 0
+                
+                # Calculate trade P/L
+                trade_pl = (price - avg_entry_price) * shares
+                weekly_pl += trade_pl
+                weekly_trades.append(trade)
+    
+    return {
+        'weekly_pl': weekly_pl,
+        'weekly_trade_count': len(weekly_trades)
+    }
+
 def calculate_portfolio_metrics(position_value: float, current_value: float, total_portfolio_value: float) -> dict:
     """Calculate portfolio metrics for a position"""
     allocation = (current_value / total_portfolio_value) * 100 if total_portfolio_value > 0 else 0
@@ -178,14 +216,34 @@ def trading_journal_page():
             total_portfolio_value = latest_portfolio['total_value'] if latest_portfolio else 0
             cash_balance = latest_portfolio['cash_balance'] if latest_portfolio else 0
             
+            # Get trade history for weekly metrics
+            trade_history = get_trade_history()
+            weekly_metrics = calculate_weekly_metrics(trade_history) if trade_history else {'weekly_pl': 0, 'weekly_trade_count': 0}
+            
             # Portfolio overview section
             st.subheader("Portfolio Overview")
-            col1, col2 = st.columns(2)
+            col1, col2, col3 = st.columns(3)
+            
             with col1:
                 st.metric("Total Portfolio Value", f"${total_portfolio_value:,.2f}")
                 st.metric("Cash Balance", f"${cash_balance:,.2f}", 
                          f"{(cash_balance/total_portfolio_value)*100:.1f}% of Portfolio" if total_portfolio_value > 0 else "")
             
+            with col2:
+                # Weekly metrics
+                weekly_pl_pct = (weekly_metrics['weekly_pl'] / total_portfolio_value * 100) if total_portfolio_value > 0 else 0
+                st.metric("Week P/L", 
+                         f"${weekly_metrics['weekly_pl']:,.2f}",
+                         f"{weekly_pl_pct:.2f}% | {weekly_metrics['weekly_trade_count']} trades")
+            
+            with col3:
+                # Overall P/L metrics (from trade history)
+                total_pl = sum(trade.get('position_pl', 0) for trade in trade_history) if trade_history else 0
+                total_pl_pct = (total_pl / total_portfolio_value * 100) if total_portfolio_value > 0 else 0
+                st.metric("Overall P/L", 
+                         f"${total_pl:,.2f}",
+                         f"{total_pl_pct:.2f}% since inception")
+            
             total_paper_pl = 0
             invested_value = 0