from decimal import Decimal

from app.service.asset_allocation_service import AssetAllocationService
from app.service.dynamic_allocation_optimizer import (
    AssetClassMarketMetric,
    DynamicAllocationOptimizer,
)


def metric(
    asset_class: str, return_pct: str, drawdown: str, turnover: str
) -> AssetClassMarketMetric:
    return AssetClassMarketMetric(
        asset_class=asset_class,
        trailing_120d_return_pct=Decimal(return_pct),
        max_drawdown_pct=Decimal(drawdown),
        average_daily_turnover_amount=Decimal(turnover),
        product_count=2,
    )


def test_strategic_weights_apply_horizon_liquidity_and_drawdown_constraints() -> None:
    weights = AssetAllocationService._strategic_weights("C1", 6, "daily", Decimal("10"))
    assert weights == {"cash_management_etf": 65, "bond_etf": 35, "equity_etf": 0}

    weights = AssetAllocationService._strategic_weights("C5", 72, "over_30_days", Decimal("30"))
    assert weights == {"cash_management_etf": 5, "bond_etf": 10, "equity_etf": 85}


def test_optimizer_uses_return_drawdown_and_liquidity_evidence() -> None:
    metrics = [
        metric("cash_management_etf", "2", "1", "20000000"),
        metric("bond_etf", "6", "8", "5000000"),
        metric("equity_etf", "12", "25", "1000000"),
    ]
    result = DynamicAllocationOptimizer.optimize(
        {"cash_management_etf": 15, "bond_etf": 45, "equity_etf": 40},
        metrics,
        return_target_lower_pct=Decimal("6"),
        max_drawdown_pct=Decimal("15"),
        liquidity_requirement="within_7_days",
    )

    assert result.dynamic is True
    assert sum(result.weights.values()) == 100
    assert result.weights["equity_etf"] <= 40
    assert result.metric_coverage_pct == Decimal("100")
    evidence = {item["asset_class"]: item for item in result.factors}
    assert evidence["equity_etf"]["composite_score"] is not None


def test_optimizer_falls_back_to_static_weights_when_coverage_is_insufficient() -> None:
    strategic = {"cash_management_etf": 30, "bond_etf": 50, "equity_etf": 20}
    result = DynamicAllocationOptimizer.optimize(
        strategic,
        [metric("bond_etf", "6", "8", "5000000")],
        return_target_lower_pct=Decimal("6"),
        max_drawdown_pct=Decimal("15"),
        liquidity_requirement="within_7_days",
    )

    assert result.dynamic is False
    assert result.weights == strategic
    assert result.metric_coverage_pct == Decimal("33.33333333333333333333333333")
    assert result.factors[0]["composite_score"] is None