feat: add daily P&L calculator with weekend gap handling

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Co-Authored-By: Claude <noreply@anthropic.com>

tests/unit/test_pnl_calculator.py

@@ -0,0 +1,152 @@
+import pytest
+from agent.pnl_calculator import DailyPnLCalculator
+
+
+class TestDailyPnLCalculator:
+
+    def test_first_day_zero_pnl(self):
+        """First trading day should have zero P&L."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        result = calculator.calculate(
+            previous_day=None,
+            current_date="2025-01-15",
+            current_prices={"AAPL": 150.0}
+        )
+
+        assert result["daily_profit"] == 0.0
+        assert result["daily_return_pct"] == 0.0
+        assert result["starting_portfolio_value"] == 10000.0
+        assert result["days_since_last_trading"] == 0
+
+    def test_positive_pnl_from_price_increase(self):
+        """Portfolio gains value when holdings appreciate."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        # Previous day: 10 shares of AAPL at $100, cash $9000
+        previous_day = {
+            "date": "2025-01-15",
+            "ending_cash": 9000.0,
+            "ending_portfolio_value": 10000.0,  # 10 * $100 + $9000
+            "holdings": [{"symbol": "AAPL", "quantity": 10}]
+        }
+
+        # Current day: AAPL now $150
+        current_prices = {"AAPL": 150.0}
+
+        result = calculator.calculate(
+            previous_day=previous_day,
+            current_date="2025-01-16",
+            current_prices=current_prices
+        )
+
+        # New value: 10 * $150 + $9000 = $10,500
+        # Profit: $10,500 - $10,000 = $500
+        assert result["daily_profit"] == 500.0
+        assert result["daily_return_pct"] == 5.0
+        assert result["starting_portfolio_value"] == 10500.0
+        assert result["days_since_last_trading"] == 1
+
+    def test_negative_pnl_from_price_decrease(self):
+        """Portfolio loses value when holdings depreciate."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        previous_day = {
+            "date": "2025-01-15",
+            "ending_cash": 9000.0,
+            "ending_portfolio_value": 10000.0,
+            "holdings": [{"symbol": "AAPL", "quantity": 10}]
+        }
+
+        # AAPL drops from $100 to $80
+        current_prices = {"AAPL": 80.0}
+
+        result = calculator.calculate(
+            previous_day=previous_day,
+            current_date="2025-01-16",
+            current_prices=current_prices
+        )
+
+        # New value: 10 * $80 + $9000 = $9,800
+        # Loss: $9,800 - $10,000 = -$200
+        assert result["daily_profit"] == -200.0
+        assert result["daily_return_pct"] == -2.0
+
+    def test_weekend_gap_calculation(self):
+        """Calculate P&L correctly across weekend."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        # Friday
+        previous_day = {
+            "date": "2025-01-17",  # Friday
+            "ending_cash": 9000.0,
+            "ending_portfolio_value": 10000.0,
+            "holdings": [{"symbol": "AAPL", "quantity": 10}]
+        }
+
+        # Monday (3 days later)
+        current_prices = {"AAPL": 120.0}
+
+        result = calculator.calculate(
+            previous_day=previous_day,
+            current_date="2025-01-20",  # Monday
+            current_prices=current_prices
+        )
+
+        # New value: 10 * $120 + $9000 = $10,200
+        assert result["daily_profit"] == 200.0
+        assert result["days_since_last_trading"] == 3
+
+    def test_multiple_holdings(self):
+        """Calculate P&L with multiple stock positions."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        previous_day = {
+            "date": "2025-01-15",
+            "ending_cash": 8000.0,
+            "ending_portfolio_value": 10000.0,
+            "holdings": [
+                {"symbol": "AAPL", "quantity": 10},  # Was $100
+                {"symbol": "MSFT", "quantity": 5}    # Was $200
+            ]
+        }
+
+        # Prices change
+        current_prices = {
+            "AAPL": 110.0,  # +$10
+            "MSFT": 190.0   # -$10
+        }
+
+        result = calculator.calculate(
+            previous_day=previous_day,
+            current_date="2025-01-16",
+            current_prices=current_prices
+        )
+
+        # AAPL: 10 * $110 = $1,100 (was $1,000, +$100)
+        # MSFT: 5 * $190 = $950 (was $1,000, -$50)
+        # Cash: $8,000 (unchanged)
+        # New total: $10,050
+        # Profit: $50
+        assert result["daily_profit"] == 50.0
+
+    def test_missing_price_raises_error(self):
+        """Raise error if price data missing for holding."""
+        calculator = DailyPnLCalculator(initial_cash=10000.0)
+
+        previous_day = {
+            "date": "2025-01-15",
+            "ending_cash": 9000.0,
+            "ending_portfolio_value": 10000.0,
+            "holdings": [{"symbol": "AAPL", "quantity": 10}]
+        }
+
+        # Missing AAPL price
+        current_prices = {"MSFT": 150.0}
+
+        with pytest.raises(ValueError, match="Missing price data for AAPL"):
+            calculator.calculate(
+                previous_day=previous_day,
+                current_date="2025-01-16",
+                current_prices=current_prices
+            )