from __future__ import annotations

import zipfile

from scripts import run_fundamentals_research as runner


def _window(name, sharpe, drawdown):
    return {
        "window": name,
        "sharpe": sharpe,
        "sharpe_se": 0.2,
        "dsr": 0.8,
        "cagr_pct": 10.0,
        "max_drawdown_pct": drawdown,
        "calmar": 1.0,
        "trades": 20,
    }


def test_arm_matrix_is_bounded_and_pre_registered():
    assert runner.N_TRIALS == 13
    assert runner.ARMS[0]["id"] == "control_w00"
    assert {arm["weight"] for arm in runner.ARMS[1:]} == {0.1, 0.2, 0.3, 0.4}
    assert {arm["composite"] for arm in runner.ARMS[1:]} == {
        "quality",
        "growth",
        "balanced",
    }


def test_development_grade_does_not_read_test_window():
    control = {
        "windows": [
            _window("train", 1.0, 10.0),
            _window("validation", 1.0, 10.0),
            _window("test", 9.0, 1.0),
        ]
    }
    arm = {
        "windows": [
            _window("train", 1.1, 10.0),
            _window("validation", 1.2, 11.0),
            _window("test", -9.0, 90.0),
        ]
    }
    assert runner._development_grade(control, arm)["pass"] is True


def test_output_bundle_is_self_contained(tmp_path):
    report = {
        "generated_at": "2026-07-23T00:00:00Z",
        "splits": {"train_end": "2024-01-01", "test_start": "2025-01-01"},
        "n_trials": 13,
        "warnings": ["survivorship bias"],
        "factor_ic": {"full": []},
        "arms": [],
        "development_selection": None,
        "final_check": None,
    }
    output = tmp_path / "result.json"
    runner._write_outputs(report, output, bundle=True)
    with zipfile.ZipFile(output.with_suffix(".zip")) as archive:
        names = set(archive.namelist())
    assert {
        "result.json",
        "result.md",
        "result-arms.csv",
        "result-factor-ic.csv",
        "result-trades.csv",
    } <= names


def test_winner_concentration_exposes_top_five_dependence():
    details = [
        {"pnl": value, "r": value / 10} for value in (100, 90, 80, 70, 60, -10, -20)
    ]
    result = runner._winner_concentration(details)
    assert result["top5_pnl"] == 400
    assert result["net_pnl_ex_top5"] == -30
    assert result["avg_r_ex_top5"] == -1.5