"""ResearchSpec 校验与因子评估（IC/RankIC/分层）测试。"""

from __future__ import annotations

from datetime import date

import numpy as np
import pandas as pd
import pytest
from app.domain.entities.research import (
    CostSpec,
    FactorSpec,
    ResearchSpec,
    SelectionSpec,
    UniverseSpec,
)
from app.quant.composite import composite_score, cross_sectional_zscore
from app.quant.evaluation import run_factor_test
from app.quant.factors import compute_factor
from app.quant.local_engine import rebalance_dates
from pydantic import ValidationError

from conftest_quant import synthetic_daily


def _spec(start: date = date(2024, 3, 1), end: date = date(2024, 12, 31), **kw) -> ResearchSpec:
    base = dict(
        type="backtest",
        universe=UniverseSpec(exclude_st=False, min_listing_days=0),
        factors=[FactorSpec(name="momentum_20")],
        selection=SelectionSpec(top_n=10),
        rebalance="monthly",
        period=(start, end),
    )
    base.update(kw)
    return ResearchSpec(**base)


class TestSpecValidation:
    def test_inverted_period_rejected(self) -> None:
        with pytest.raises(ValidationError, match="start < end"):
            _spec(start=date(2024, 12, 1), end=date(2024, 1, 1))

    def test_duplicate_factors_rejected(self) -> None:
        with pytest.raises(ValidationError, match="重复"):
            _spec(factors=[FactorSpec(name="momentum_20"), FactorSpec(name="momentum_20")])

    def test_nonpositive_weight_rejected(self) -> None:
        with pytest.raises(ValidationError):
            _spec(factors=[FactorSpec(name="momentum_20", weight=0)])

    def test_cost_bounds(self) -> None:
        with pytest.raises(ValidationError):
            CostSpec(commission_rate=0.5)  # 超过 1%
        with pytest.raises(ValidationError):
            CostSpec(slippage_rate=-0.01)


class TestEvaluation:
    def _panels(self):
        # 强趋势 + 弱噪声，确保截面排序稳定（drift 差异远大于噪声）
        drifts = {f"S{i:02d}": v for i, v in enumerate(np.linspace(0.006, -0.006, 12), start=1)}
        daily = synthetic_daily(drifts, n=260)
        _d, f20 = compute_factor("momentum_20", daily)
        _d, f60 = compute_factor("momentum_60", daily)
        close = daily.pivot(index="trade_date", columns="symbol", values="close").sort_index()
        close.index = pd.to_datetime(close.index)
        return daily, f20, f60, close

    def test_cross_sectional_zscore_standardized(self) -> None:
        _daily, f20, _f60, _close = self._panels()
        z = cross_sectional_zscore(f20).dropna(how="all")
        row = z.iloc[60].dropna()
        assert abs(float(row.mean())) < 1e-9
        assert abs(float(row.std()) - 1.0) < 1e-6

    def test_composite_score_respects_direction(self) -> None:
        _daily, f20, _f60, _close = self._panels()
        pos = composite_score([("m", f20, 1.0, "higher_is_better")])
        neg = composite_score([("m", f20, 1.0, "lower_is_better")])
        row_date = f20.dropna(how="all").iloc[100].name
        sym = f20.loc[row_date].dropna().index[0]
        assert float(pos.loc[row_date, sym]) == pytest.approx(-float(neg.loc[row_date, sym]))

    def test_momentum_ic_positive_on_trend_data(self) -> None:
        daily, f20, _f60, close = self._panels()
        forward = close.shift(-21) / close - 1.0
        report = run_factor_test(f20, forward, factor_name="momentum_20")
        assert report.sample_days > 10
        assert report.ic_mean > 0
        assert report.rank_ic_mean > 0
        assert report.positive_ratio_pct > 50

    def test_quantile_monotonic_on_trend(self) -> None:
        daily, f20, _f60, close = self._panels()
        forward = close.shift(-21) / close - 1.0
        report = run_factor_test(f20, forward, factor_name="momentum_20", quantiles=5)
        qs = {q.quantile: q.return_pct for q in report.quantile_returns}
        assert qs[4] > qs[0]  # 高动量层未来收益高于低动量层
        assert report.spread_quantile is not None

    def test_rebalance_dates_monthly_first(self) -> None:
        idx = pd.bdate_range("2024-03-01", "2024-05-31")
        out = rebalance_dates(idx, "monthly", date(2024, 3, 1))
        assert [d.strftime("%Y-%m-%d") for d in out][:3] == [
            "2024-03-01",
            "2024-04-01",
            "2024-05-01",
        ]

    def test_rebalance_dates_respects_start(self) -> None:
        idx = pd.bdate_range("2024-03-01", "2024-05-31")
        out = rebalance_dates(idx, "monthly", date(2024, 4, 10))
        assert out and out[0] >= pd.Timestamp("2024-04-10")


class TestSingleStockDegradation:
    def test_zscore_single_stock_keeps_candidate(self) -> None:
        from app.quant.composite import cross_sectional_zscore

        daily = synthetic_daily({"ONLY": 0.001}, n=80)
        _d, panel = compute_factor("momentum_20", daily)
        z = cross_sectional_zscore(panel)
        valid = z.dropna(how="all")
        assert not valid.empty
        assert (valid == 0.0).all().all()  # 单股退化为 0，而非 NaN