import importlib.util
import sys
from pathlib import Path
from types import SimpleNamespace

import pytest


def load_module():
    path = Path(__file__).resolve().parents[1] / "scripts" / "search_eth_nextgen_micro_portfolio.py"
    spec = importlib.util.spec_from_file_location("search_eth_nextgen_micro_portfolio", path)
    assert spec is not None
    module = importlib.util.module_from_spec(spec)
    assert spec.loader is not None
    sys.modules[spec.name] = module
    spec.loader.exec_module(module)
    return module


def test_trade_returns_apply_selected_cost_model():
    module = load_module()
    result = SimpleNamespace(
        trades=[
            {
                "return_pct": 1.0,
                "cost_weight": 0.5,
                "exit_time": "2026-04-30 12:00",
            }
        ]
    )

    maker = module.nextgen_trade_returns(result, 1.0, 0.0021)
    taker = module.nextgen_trade_returns(result, 1.0, 0.0030)

    assert maker[0].value == pytest.approx(0.01 - 0.0021 * 0.5)
    assert taker[0].value == pytest.approx(0.01 - 0.0030 * 0.5)


def test_monthly_stability_groups_by_name_and_cost_model():
    module = load_module()
    pandas = pytest.importorskip("pandas")
    frame = pandas.DataFrame(
        [
            {"cost_model": "maker_taker", "name": "a", "return": 0.02},
            {"cost_model": "maker_taker", "name": "a", "return": -0.01},
            {"cost_model": "taker_taker", "name": "a", "return": -0.03},
        ]
    )

    stability = module.monthly_stability(frame).sort_values(["cost_model", "name"]).reset_index(drop=True)

    assert stability.loc[0, "cost_model"] == "maker_taker"
    assert stability.loc[0, "months"] == 2
    assert stability.loc[0, "positive_month_rate"] == pytest.approx(0.5)
    assert stability.loc[1, "cost_model"] == "taker_taker"
    assert stability.loc[1, "positive_month_rate"] == pytest.approx(0.0)


def test_robust_survivors_requires_positive_returns_and_calmar_per_cost_model():
    module = load_module()
    pandas = pytest.importorskip("pandas")
    rows = []
    for cost_model, ret_3m in (("maker_taker", 0.04), ("taker_taker", -0.01)):
        for horizon, total_return in (("full", 0.30), ("3y", 0.20), ("1y", 0.10), ("6m", 0.08), ("3m", ret_3m)):
            rows.append(
                {
                    "cost_model": cost_model,
                    "name": "candidate",
                    "horizon": horizon,
                    "net_total_return": total_return,
                    "net_calmar": 0.50,
                }
            )

    survivors = module.robust_survivors(pandas.DataFrame(rows))

    assert survivors["cost_model"].tolist() == ["maker_taker"]