import json

from scripts.render_factor_weight_grid_report import generate_html_report, render_report


def _grid_payload():
    candidate = {
        "config": {"buy_bull_weight": 0.03, "buy_bear_weight": -0.01},
        "passed": True,
        "gate": {
            "checks": {
                "accuracy_delta": True,
                "buy_precision_delta": True,
                "signal_ratio": True,
                "buy_count_ratio": True,
            },
            "signal_ratio": 1.04,
            "buy_count_ratio": 1.25,
        },
        "validation": {
            "metrics": {
                "accuracy": 41.16,
                "direction_accuracy": 46.27,
                "buy_precision": 50.46,
                "sell_precision": 39.83,
                "signal_count": 2671,
                "coverage": 0.7949,
            },
            "deltas": {
                "accuracy_delta_pp": 2.83,
                "direction_accuracy_delta_pp": 2.09,
                "buy_precision_delta_pp": 1.24,
                "sell_precision_delta_pp": 0.79,
                "signal_count_delta": 111,
                "coverage_delta": 0.033,
            },
        },
    }
    return {
        "experiment": "factor_weight_grid_search",
        "generated_at": "2026-05-18T00:00:00+00:00",
        "stocks": ["2330", "0050"],
        "factors": {"bull_column": "oldwang_bull_score", "bear_column": "oldwang_bear_score"},
        "grid_preset": "wide",
        "result": {
            "split": {
                "tune_events": 10,
                "validation_events": 8,
                "tune_date_range": ["2025-01-01", "2025-06-01"],
                "validation_date_range": ["2025-06-02", "2025-12-31"],
            },
            "grid_size": 63504,
            "thresholds": {
                "min_accuracy_delta_pp": 2.0,
                "min_buy_precision_delta_pp": 0.0,
                "min_signal_ratio": 0.7,
                "max_buy_count_ratio": 1.3,
            },
            "baseline": {
                "validation": {
                    "accuracy": 38.33,
                    "direction_accuracy": 44.18,
                    "buy_precision": 49.23,
                    "sell_precision": 39.04,
                    "signal_count": 2560,
                    "coverage": 0.7619,
                }
            },
            "passing_count": 139,
            "best_passing": candidate,
            "top_passing": [candidate],
            "top_overall": [candidate],
        },
    }


def test_generate_html_report_contains_validation_details():
    html = generate_html_report(
        _grid_payload(),
        popular_payload={
            "source": "yahoo_rank",
            "count": 2,
            "fallback_used": True,
            "stocks": [{"rank": 1, "code": "2330", "name": "台積電", "source": "yahoo_rank"}],
        },
        raw_json_path="docs/result.json",
        command="venv/bin/python scripts/search_factor_weight_grid.py ...",
    )

    assert "Factor Weight Grid Search Report" in html
    assert "Grid Size" in html
    assert "63504" in html
    assert "oldwang_bull_score" in html
    assert "buy_bull_weight" in html
    assert "台積電" in html
    assert "venv/bin/python" in html


def test_render_report_writes_html(tmp_path):
    input_json = tmp_path / "grid.json"
    popular_json = tmp_path / "popular.json"
    output_html = tmp_path / "report.html"
    input_json.write_text(json.dumps(_grid_payload(), ensure_ascii=False))
    popular_json.write_text(
        json.dumps({"source": "fixture", "stocks": [{"code": "2330", "rank": 1}]}, ensure_ascii=False)
    )

    output = render_report(input_json=input_json, popular_json=popular_json, output_html=output_html)

    assert output.exists()
    assert "Top Passing Candidates" in output.read_text()


def test_generate_html_report_includes_external_factor_metadata():
    payload = _grid_payload()
    payload["experiment"] = "external_factor_weight_grid_search"
    payload["external_factors"] = {
        "csv_paths": ["data/external/chipk_scores.csv", "data/external/crowd_scores.csv"],
        "stock_column": "stock",
        "date_column": "date",
        "bull_column": "bull_score",
        "bear_column": "bear_score",
        "combine": "sum",
        "merge_summary": {"event_count": 100, "matched_event_count": 80, "coverage": 0.8},
        "promotion_note": "validation-only",
    }

    html = generate_html_report(payload)

    assert "External Factor Source" in html
    assert "chipk_scores.csv" in html
    assert "80 / 100" in html
    assert "validation-only" in html