from __future__ import annotations

from datetime import UTC, datetime, timedelta

from arbitrade.backtesting.replay import ReplayBookEvent
from arbitrade.backtesting.sweep import (
    PromotionCriteria,
    SweepResult,
    build_parameter_grid,
    run_parameter_search,
    split_events_time_windows,
)
from arbitrade.detection.graph import CurrencyGraph
from arbitrade.exchange.models import BookLevel


def _build_cycles() -> dict[str, list]:
    graph = CurrencyGraph()
    graph.add_pair("USD", "BTC", "BTC/USD")
    graph.add_pair("BTC", "ETH", "ETH/BTC")
    graph.add_pair("ETH", "USD", "ETH/USD")
    return graph.index_cycles_by_pair(graph.triangular_cycles())


def _events() -> list[ReplayBookEvent]:
    base_time = datetime(2026, 6, 1, 12, 0, tzinfo=UTC)
    rows: list[ReplayBookEvent] = []
    for index in range(12):
        tick = base_time + timedelta(seconds=index)
        rows.extend(
            [
                ReplayBookEvent(
                    occurred_at=tick,
                    symbol="BTC/USD",
                    bids=(BookLevel(price=99.5, volume=10.0),),
                    asks=(BookLevel(price=100.0, volume=10.0),),
                ),
                ReplayBookEvent(
                    occurred_at=tick,
                    symbol="ETH/BTC",
                    bids=(BookLevel(price=0.051, volume=10.0),),
                    asks=(BookLevel(price=0.050, volume=10.0),),
                ),
                ReplayBookEvent(
                    occurred_at=tick,
                    symbol="ETH/USD",
                    bids=(BookLevel(price=110.0, volume=10.0),),
                    asks=(BookLevel(price=110.5, volume=10.0),),
                ),
            ]
        )
    return rows


def test_split_events_time_windows_returns_non_empty_train_and_test() -> None:
    train, test = split_events_time_windows(_events(), train_ratio=0.7)

    assert train
    assert test
    assert train[-1].occurred_at <= test[0].occurred_at


def test_build_parameter_grid_expands_combinations() -> None:
    grid = build_parameter_grid(
        theta_values=[0.0005, 0.001],
        trade_capital_values=[100.0],
        pair_universes=[["BTC/USD", "ETH/BTC", "ETH/USD"]],
        staleness_threshold_values=[3.0, 5.0],
    )

    assert len(grid) == 4


def test_run_parameter_search_produces_ranked_results_with_overfit_guard() -> None:
    artifacts = run_parameter_search(
        events=_events(),
        cycles_by_pair=_build_cycles(),
        parameter_grid=build_parameter_grid(
            theta_values=[0.0005, 0.001],
            trade_capital_values=[75.0, 100.0],
            pair_universes=[["BTC/USD", "ETH/BTC", "ETH/USD"]],
            staleness_threshold_values=[5.0],
        ),
        starting_balances={"USD": 2000.0},
        train_ratio=0.7,
        promotion_criteria=PromotionCriteria(
            min_test_realized_pnl_usd=-1000.0,
            min_test_win_rate=0.0,
            min_test_fill_rate=0.0,
            max_test_drawdown_usd=1_000_000.0,
            max_generalization_gap_ratio=0.9,
        ),
    )

    assert artifacts.results
    assert artifacts.results[0].test_score >= artifacts.results[-1].test_score

    first: SweepResult = artifacts.results[0]
    assert first.train_event_count > 0
    assert first.test_event_count > 0
    assert first.generalization_gap_ratio >= 0.0
    assert isinstance(first.promotion_ready, bool)