"""Kevin-extension tests for compute_metrics (alpha/beta/winners/losers/best/worst)."""

from datetime import datetime, timedelta, timezone
from decimal import Decimal

import pandas as pd

from backtester.metrics import compute_metrics


def _ts(day):
    return datetime(2026, 5, 15, tzinfo=timezone.utc) + timedelta(days=day)


def test_compute_metrics_returns_alpha_and_beta_with_benchmark():
    spy = pd.DataFrame(
        {
            "open": [500, 500, 510, 510, 520],
            "high": [500] * 5,
            "low": [500] * 5,
            "close": [500, 500, 510, 510, 520],
            "volume": [0] * 5,
        },
        index=pd.date_range("2026-05-15", periods=5, freq="D", tz="UTC"),
    )
    trades = [
        {
            "symbol": "NVDA",
            "source_mention_id": 1,
            "entry_at": _ts(0),
            "entry_price": Decimal("100"),
            "exit_at": _ts(4),
            "exit_price": Decimal("110"),
            "qty": Decimal("10"),
            "pnl_usd": Decimal("100"),
            "pnl_pct": Decimal("10"),
            "holding_days_actual": 4,
        }
    ]
    curve = [(_ts(i), Decimal(str(100000 + 25 * i))) for i in range(5)]

    result = compute_metrics(
        trade_log=trades,
        equity_curve=curve,
        initial_capital=Decimal("100000"),
        benchmark_bars=spy,
    )

    assert result.alpha_vs_spy_pct is not None
    assert result.beta_vs_spy is not None


def test_compute_metrics_returns_winners_losers_best_worst():
    trades = [
        {
            "symbol": "NVDA",
            "source_mention_id": 1,
            "entry_at": _ts(0),
            "entry_price": Decimal("100"),
            "exit_at": _ts(5),
            "exit_price": Decimal("110"),
            "qty": Decimal("10"),
            "pnl_usd": Decimal("100"),
            "pnl_pct": Decimal("10"),
            "holding_days_actual": 5,
        },
        {
            "symbol": "INTC",
            "source_mention_id": 2,
            "entry_at": _ts(0),
            "entry_price": Decimal("50"),
            "exit_at": _ts(5),
            "exit_price": Decimal("45"),
            "qty": Decimal("20"),
            "pnl_usd": Decimal("-100"),
            "pnl_pct": Decimal("-10"),
            "holding_days_actual": 5,
        },
    ]
    result = compute_metrics(
        trade_log=trades,
        equity_curve=[(_ts(0), Decimal("100000"))],
        initial_capital=Decimal("100000"),
    )

    assert result.avg_winner_pct == Decimal("10")
    assert result.avg_loser_pct == Decimal("-10")
    assert result.best_trade == {"symbol": "NVDA", "pnl_pct": Decimal("10")}
    assert result.worst_trade == {"symbol": "INTC", "pnl_pct": Decimal("-10")}


def test_compute_metrics_no_benchmark_omits_alpha_beta():
    result = compute_metrics(
        trade_log=[],
        equity_curve=[(_ts(0), Decimal("100000"))],
        initial_capital=Decimal("100000"),
    )
    assert result.alpha_vs_spy_pct is None
    assert result.beta_vs_spy is None