feat: add period metrics calculation for date range queries

api/routes/period_metrics.py

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+"""Period metrics calculation for date range queries."""
+
+from datetime import datetime
+
+
+def calculate_period_metrics(
+    starting_value: float,
+    ending_value: float,
+    start_date: str,
+    end_date: str,
+    trading_days: int
+) -> dict:
+    """Calculate period return and annualized return.
+
+    Args:
+        starting_value: Portfolio value at start of period
+        ending_value: Portfolio value at end of period
+        start_date: Start date (YYYY-MM-DD)
+        end_date: End date (YYYY-MM-DD)
+        trading_days: Number of actual trading days in period
+
+    Returns:
+        Dict with period_return_pct, annualized_return_pct, calendar_days, trading_days
+    """
+    # Calculate calendar days (inclusive)
+    start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+    end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+    calendar_days = (end_dt - start_dt).days + 1
+
+    # Calculate period return
+    if starting_value == 0:
+        period_return_pct = 0.0
+    else:
+        period_return_pct = ((ending_value - starting_value) / starting_value) * 100
+
+    # Calculate annualized return
+    if calendar_days == 0 or starting_value == 0 or ending_value <= 0:
+        annualized_return_pct = 0.0
+    else:
+        # Formula: ((ending / starting) ** (365 / days) - 1) * 100
+        annualized_return_pct = ((ending_value / starting_value) ** (365 / calendar_days) - 1) * 100
+
+    return {
+        "starting_portfolio_value": starting_value,
+        "ending_portfolio_value": ending_value,
+        "period_return_pct": round(period_return_pct, 2),
+        "annualized_return_pct": round(annualized_return_pct, 2),
+        "calendar_days": calendar_days,
+        "trading_days": trading_days
+    }

tests/api/__init__.py

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+"""API tests."""

tests/api/test_period_metrics.py

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+"""Tests for period metrics calculations."""
+
+from datetime import datetime
+from api.routes.period_metrics import calculate_period_metrics
+
+
+def test_calculate_period_metrics_basic():
+    """Test basic period metrics calculation."""
+    metrics = calculate_period_metrics(
+        starting_value=10000.0,
+        ending_value=10500.0,
+        start_date="2025-01-16",
+        end_date="2025-01-20",
+        trading_days=3
+    )
+
+    assert metrics["starting_portfolio_value"] == 10000.0
+    assert metrics["ending_portfolio_value"] == 10500.0
+    assert metrics["period_return_pct"] == 5.0
+    assert metrics["calendar_days"] == 5
+    assert metrics["trading_days"] == 3
+    # annualized_return = ((10500/10000) ** (365/5) - 1) * 100 = ~3422%
+    assert 3400 < metrics["annualized_return_pct"] < 3450
+
+
+def test_calculate_period_metrics_zero_return():
+    """Test period metrics when no change."""
+    metrics = calculate_period_metrics(
+        starting_value=10000.0,
+        ending_value=10000.0,
+        start_date="2025-01-16",
+        end_date="2025-01-16",
+        trading_days=1
+    )
+
+    assert metrics["period_return_pct"] == 0.0
+    assert metrics["annualized_return_pct"] == 0.0
+    assert metrics["calendar_days"] == 1
+
+
+def test_calculate_period_metrics_negative_return():
+    """Test period metrics with loss."""
+    metrics = calculate_period_metrics(
+        starting_value=10000.0,
+        ending_value=9500.0,
+        start_date="2025-01-16",
+        end_date="2025-01-23",
+        trading_days=5
+    )
+
+    assert metrics["period_return_pct"] == -5.0
+    assert metrics["calendar_days"] == 8
+    # Negative annualized return
+    assert metrics["annualized_return_pct"] < 0