"""因子计算与注册表测试（合成数据、确定性断言方向与相对排序）。"""

from __future__ import annotations

import pandas as pd
from app.quant.factors import (
    FactorDef,
    FactorError,
    compute_factor,
    get_factor,
    list_factors,
    register,
)

from conftest_quant import synthetic_daily


def test_registry_builtins_present() -> None:
    names = {f.name for f in list_factors()}
    assert {"momentum_20", "momentum_60", "volatility_20", "ma_bias_20"} <= names


def test_unknown_factor_raises() -> None:
    try:
        compute_factor("not_a_factor", synthetic_daily({"A": 0.0}))
    except FactorError:
        return
    raise AssertionError("应抛 FactorError")


def test_momentum_ordering_matches_drift() -> None:
    daily = synthetic_daily({"AAA": 0.002, "BBB": 0.0, "CCC": -0.002}, n=160)
    _defn, panel = compute_factor("momentum_20", daily)
    tail = panel.iloc[-1]
    assert tail["AAA"] > tail["BBB"] > tail["CCC"]
    assert tail["AAA"] > 0  # 上涨股 20 日动量为正


def test_volatility_ranks_noise() -> None:
    # 手写：SMOOTH 每日 +0.2%；WILD 在 ±5% 间摆动 → WILD 的 20 日波动率应显著更高

    from datetime import date, timedelta

    dates = [date(2024, 1, 1) + timedelta(days=i) for i in range(120)]
    rows = []
    smooth, wild = 100.0, 100.0
    for j, d in enumerate(dates):
        smooth *= 1.002
        wild *= 1.05 if j % 2 == 0 else 0.95
        for sym, px in (("SMOOTH", smooth), ("WILD", wild)):
            rows.append(
                {
                    "symbol": sym,
                    "trade_date": d,
                    "close": px,
                    "high": px,
                    "low": px,
                    "volume": 1e6,
                    "amount": 1e8,
                }
            )
    daily = pd.DataFrame(rows)
    _defn, panel = compute_factor("volatility_20", daily)
    assert float(panel["WILD"].iloc[-1]) > float(panel["SMOOTH"].iloc[-1]) * 5


def test_custom_factor_registration() -> None:
    @register(FactorDef("test_double_close", "close*2 测试因子", "close * 2", lookback=1))
    def _fn(fields: dict[str, pd.DataFrame]) -> pd.DataFrame:
        return fields["close"] * 2

    try:
        daily = synthetic_daily({"A": 0.001}, n=40)
        defn, panel = compute_factor("test_double_close", daily)
        assert defn.direction == "higher_is_better"
        assert float(panel.iloc[-1, 0]) > 200.0
    finally:
        # 清理注册表，避免污染其他测试
        from app.quant import factors as _factors

        _factors._REGISTRY.pop("test_double_close", None)  # noqa: SLF001


def test_factor_def_metadata_present() -> None:
    defn, _fn = get_factor("momentum_60")
    assert defn.description
    assert defn.formula
    assert defn.lookback == 60
    assert defn.direction in {"higher_is_better", "lower_is_better"}