"""src/shared/alpha_factors.py 单元测试（v1.2.0 Phase 0 第一批）。"""

from __future__ import annotations

import numpy as np
import pandas as pd
import pytest

from quant_engine.alpha_factors import (
    ALPHA158_REGISTRY,
    alpha_001,
    alpha_002,
    alpha_003,
    alpha_004,
    alpha_005,
    alpha_006,
    alpha_007,
    alpha_008,
    alpha_009,
    alpha_010,
    alpha_011,
    alpha_012,
    alpha_013,
    alpha_014,
    alpha_015,
    alpha_016,
    alpha_017,
    alpha_018,
    alpha_019,
    alpha_020,
    alpha_021,
    alpha_022,
    alpha_023,
    alpha_024,
    alpha_025,
    alpha_026,
    alpha_027,
    alpha_028,
    alpha_029,
    alpha_030,
    alpha_031,
    alpha_032,
    alpha_033,
    alpha_034,
    alpha_035,
    alpha_036,
    alpha_037,
    alpha_038,
    alpha_039,
    alpha_040,
    alpha_041,
    alpha_042,
    alpha_043,
    alpha_044,
    alpha_045,
    alpha_046,
    alpha_047,
    alpha_048,
    alpha_049,
    alpha_050,
    alpha_051,
    alpha_052,
    alpha_053,
    alpha_054,
    alpha_055,
    alpha_056,
    alpha_057,
    alpha_058,
    alpha_059,
    alpha_060,
    alpha_061,
    alpha_062,
    alpha_063,
    alpha_064,
    alpha_065,
    alpha_066,
    alpha_067,
    alpha_068,
    alpha_069,
    alpha_070,
    alpha_071,
    alpha_072,
    alpha_073,
    alpha_074,
    alpha_075,
    alpha_076,
    alpha_077,
    alpha_078,
    alpha_079,
    alpha_080,
    alpha_081,
    alpha_082,
    alpha_083,
    alpha_084,
    alpha_085,
    alpha_086,
    alpha_087,
    alpha_088,
    alpha_089,
    alpha_090,
    alpha_091,
    alpha_092,
    alpha_093,
    alpha_094,
    alpha_095,
    alpha_096,
    alpha_097,
    alpha_098,
    alpha_099,
    alpha_100,
    alpha_101,
    alpha_102,
    alpha_103,
    alpha_104,
    alpha_105,
    alpha_106,
    alpha_107,
    alpha_108,
    alpha_109,
    alpha_110,
    alpha_111,
    alpha_112,
    alpha_113,
    alpha_114,
    alpha_115,
    alpha_116,
    alpha_117,
    alpha_118,
    alpha_119,
    alpha_120,
    alpha_121,
    alpha_122,
    alpha_123,
    alpha_124,
    alpha_125,
    alpha_126,
    alpha_127,
    alpha_128,
    alpha_129,
    alpha_130,
    alpha_131,
    alpha_132,
    alpha_133,
    alpha_134,
    alpha_135,
    alpha_136,
    alpha_137,
    alpha_138,
    alpha_139,
    alpha_140,
    alpha_141,
    alpha_142,
    alpha_143,
    alpha_144,
    alpha_145,
    alpha_146,
    alpha_147,
    alpha_148,
    alpha_149,
    alpha_150,
    alpha_151,
    alpha_152,
    alpha_153,
    alpha_154,
    alpha_155,
    alpha_156,
    alpha_157,
    alpha_158,
    correlation,
    covariance,
    decay_linear,
    delta,
    get_alpha_meta,
    indneutralize,
    log,
    max_pair,
    min_pair,
    product,
    rank,
    returns,
    scale,
    sign,
    signed_power,
    stddev,
    ts_argmax,
    ts_argmin,
    ts_max,
    ts_mean,
    ts_min,
    ts_rank,
    ts_std,
    ts_sum,
)


# ── rank 基础算子 ──────────────────────────────


def test_rank_uniform_input():
    """全相同输入 → 所有位置相同（pandas average tie-breaking）。"""
    s = pd.Series([1.0, 1.0, 1.0, 1.0])
    result = rank(s)
    # pandas rank default method='average': ties get average rank
    # 4 ties at positions 1,2,3,4 → average 2.5 → 2.5/4 = 0.625
    expected = pd.Series([0.625, 0.625, 0.625, 0.625])
    pd.testing.assert_series_equal(result, expected)


def test_rank_basic():
    """5 个升序值 → 0.2, 0.4, 0.6, 0.8, 1.0（pandas rank pct=True）。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    result = rank(s)
    expected = pd.Series([0.2, 0.4, 0.6, 0.8, 1.0])
    pd.testing.assert_series_equal(result, expected)


def test_rank_empty():
    """空 series 返回空 series。"""
    s = pd.Series([], dtype=float)
    result = rank(s)
    assert len(result) == 0


# ── delta 基础算子 ──────────────────────────────


def test_delta_basic():
    """5 期 delta。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
    result = delta(s, 5)
    expected = pd.Series([np.nan, np.nan, np.nan, np.nan, np.nan, 5.0])
    pd.testing.assert_series_equal(result, expected)


def test_delta_zero():
    """delta n=0 → 全部 NaN（无前移）。"""
    s = pd.Series([1.0, 2.0, 3.0])
    result = delta(s, 0)
    # shift(0) is identity, so result is all zeros except first NaN
    assert result.iloc[0] == 0.0 or pd.isna(result.iloc[0])


def test_delta_negative_raises():
    """delta n<0 应报错。"""
    s = pd.Series([1.0, 2.0, 3.0])
    with pytest.raises(ValueError, match="non-negative"):
        delta(s, -1)


# ── ts_mean 基础算子 ──────────────────────────────


def test_ts_mean_basic():
    """20 期均值。"""
    s = pd.Series(range(20), dtype=float)
    result = ts_mean(s, 5)
    # 最后 5 个值 15,16,17,18,19 均值 17
    assert result.iloc[-1] == 17.0


def test_ts_mean_n_1_raises():
    """ts_mean n<=0 应报错。"""
    s = pd.Series([1.0, 2.0, 3.0])
    with pytest.raises(ValueError, match="positive"):
        ts_mean(s, 0)


# ── ts_std 基础算子 ──────────────────────────────


def test_ts_std_basic():
    """常量 series → std = 0。"""
    s = pd.Series([5.0] * 20)
    result = ts_std(s, 5)
    assert result.iloc[-1] == 0.0


def test_ts_std_n_1_raises():
    """ts_std n<=0 应报错。"""
    s = pd.Series([1.0, 2.0, 3.0])
    with pytest.raises(ValueError, match="positive"):
        ts_std(s, -1)


# ── ts_rank 基础算子 ──────────────────────────────


def test_ts_rank_basic():
    """时序 rank：最后一个值在窗口内应排 1.0。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    result = ts_rank(s, 5)
    # 最后一个值 5 在 [1,2,3,4,5] 内 rank = 1.0
    assert result.iloc[-1] == 1.0


def test_ts_rank_n_1_raises():
    """ts_rank n<=0 应报错。"""
    s = pd.Series([1.0, 2.0, 3.0])
    with pytest.raises(ValueError, match="positive"):
        ts_rank(s, 0)


# ── correlation 基础算子 ──────────────────────────────


def test_correlation_perfect_positive():
    """完全正相关 → 1.0。"""
    s1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
    s2 = s1 * 2 + 1
    result = correlation(s1, s2, 5)
    assert result.iloc[-1] == pytest.approx(1.0)


def test_correlation_perfect_negative():
    """完全负相关 → -1.0。"""
    s1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
    s2 = -s1
    result = correlation(s1, s2, 5)
    assert result.iloc[-1] == pytest.approx(-1.0)


def test_correlation_n_1_raises():
    """correlation n<=0 应报错。"""
    s1 = pd.Series([1.0, 2.0, 3.0])
    s2 = pd.Series([3.0, 2.0, 1.0])
    with pytest.raises(ValueError, match="positive"):
        correlation(s1, s2, 0)


# ── alpha_001 公式 ──────────────────────────────


def test_alpha_001_smoke():
    """alpha_001 端到端 smoke test。"""
    idx = pd.MultiIndex.from_product(
        [["d1", "d2", "d3", "d4", "d5"], ["s1", "s2", "s3"]],
        names=["date", "stock"],
    )
    close = pd.Series(np.random.rand(15), index=idx)
    result = alpha_001(close)
    assert isinstance(result, pd.Series)
    assert result.shape == close.shape


def test_alpha_001_constant_input():
    """常量输入 → ts_rank 在窗口内全 0.5 → rank 后仍全相同。"""
    s = pd.Series([5.0] * 10)
    result = alpha_001(s)
    # 全 0.5 输入 → ts_rank = 0.5 → rank 在 6 个 0.5 之间取平均
    # 6 个 ties at positions 1..6 → average 3.5 → 3.5/6 ≈ 0.5833
    expected_value = 3.5 / 6.0
    valid = result.dropna()
    for v in valid:
        assert v == pytest.approx(expected_value)


# ── alpha_002 公式 ──────────────────────────────


def test_alpha_002_matches_delta():
    """alpha_002 应等于 delta(close, 5)。"""
    close = pd.Series(range(20), dtype=float)
    assert alpha_002(close).equals(delta(close, 5))


# ── alpha_003 公式 ──────────────────────────────


def test_alpha_003_matches_ts_mean():
    """alpha_003 应等于 ts_mean(close, 20)。"""
    close = pd.Series(range(30), dtype=float)
    assert alpha_003(close).equals(ts_mean(close, 20))


# ── alpha_004 公式 ──────────────────────────────


def test_alpha_004_matches_ts_std():
    """alpha_004 应等于 ts_std(close, 20)。"""
    close = pd.Series(range(30), dtype=float)
    assert alpha_004(close).equals(ts_std(close, 20))


# ── alpha_005 公式 ──────────────────────────────


def test_alpha_005_matches_correlation():
    """alpha_005 应等于 correlation(close, volume, 10)。"""
    close = pd.Series(range(20), dtype=float)
    volume = pd.Series([1.0] * 10 + [2.0] * 10)
    assert alpha_005(close, volume).equals(correlation(close, volume, 10))


# ── 元数据 ──────────────────────────────


def test_alpha_registry_complete():
    """158 个 alpha 算子全部在 registry 中（v1.2.0 第八批后——达成完整 alpha158）。"""
    expected = {f"alpha_{i:03d}" for i in range(1, 159)}
    assert set(ALPHA158_REGISTRY.keys()) == expected


def test_alpha_registry_required_fields():
    """每个 registry 条目都包含必需字段。"""
    required = {"formula", "category", "complexity", "params", "description", "inputs"}
    for alpha_id, meta in ALPHA158_REGISTRY.items():
        assert required <= set(meta.keys()), f"{alpha_id} missing fields"


def test_get_alpha_meta_success():
    """已知 alpha_id 返回完整 meta。"""
    meta = get_alpha_meta("alpha_001")
    assert meta["formula"] == "rank(ts_rank(close, 5))"
    assert meta["category"] == "momentum_rank"


def test_get_alpha_meta_unknown():
    """未知 alpha_id 抛 KeyError。"""
    with pytest.raises(KeyError, match="alpha_999"):
        get_alpha_meta("alpha_999")


def test_alpha_meta_jsonb_serializable():
    """元数据可以 JSON 序列化（PostgreSQL JSONB 落库前置验证）。"""
    import json

    for alpha_id, meta in ALPHA158_REGISTRY.items():
        # 抛异常即失败
        json.dumps(meta)


# ── v1.2.0 第二批基础算子（ts_min / ts_max / ts_sum / ts_argmin / ts_argmax / decay_linear / product / returns / scale / signed_power） ──


def test_ts_min_basic():
    """5 期滚动最小值。"""
    s = pd.Series([5.0, 3.0, 8.0, 1.0, 6.0, 2.0])
    assert ts_min(s, 5).iloc[-1] == 1.0


def test_ts_max_basic():
    """5 期滚动最大值。"""
    s = pd.Series([5.0, 3.0, 8.0, 1.0, 6.0, 2.0])
    assert ts_max(s, 5).iloc[-1] == 8.0


def test_ts_sum_basic():
    """5 期滚动求和。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
    assert ts_sum(s, 5).iloc[-1] == 20.0  # 2+3+4+5+6


def test_ts_argmin_first_position():
    """最小值在窗口起点 → argmin = 0。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    assert ts_argmin(s, 5).iloc[-1] == 0.0


def test_ts_argmax_last_position():
    """最大值在窗口终点 → argmax = 4。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    assert ts_argmax(s, 5).iloc[-1] == 4.0


def test_decay_linear_recent_weighted():
    """线性衰减：近期权重大。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    weights = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
    weights /= weights.sum()  # [0.0667, 0.1333, 0.2, 0.2667, 0.3333]
    expected = float(np.dot(s.values, weights))
    assert decay_linear(s, 5).iloc[-1] == pytest.approx(expected)


def test_product_basic():
    """5 期滚动乘积。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    assert product(s, 5).iloc[-1] == 120.0


def test_returns_basic():
    """简单收益率。"""
    s = pd.Series([100.0, 110.0, 121.0])
    assert returns(s).iloc[1] == pytest.approx(0.1)
    assert returns(s).iloc[2] == pytest.approx(0.1)


def test_scale_sum_to_one():
    """scale 让 |x| 之和 = 1。"""
    s = pd.Series([1.0, -2.0, 3.0, -4.0])
    result = scale(s)
    assert result.abs().sum() == pytest.approx(1.0)


def test_scale_zero_input():
    """scale 全 0 输入 → 原样返回（避免除零）。"""
    s = pd.Series([0.0, 0.0, 0.0])
    result = scale(s)
    assert (result == 0.0).all()


def test_signed_power_positive():
    """正数 signed_power(2) → x^2。"""
    s = pd.Series([3.0, 4.0])
    result = signed_power(s, 2)
    assert result.iloc[0] == pytest.approx(9.0)
    assert result.iloc[1] == pytest.approx(16.0)


def test_signed_power_negative():
    """负数 signed_power(2) → -|x|^2。"""
    s = pd.Series([-3.0])
    result = signed_power(s, 2)
    assert result.iloc[0] == pytest.approx(-9.0)


def test_ts_min_n_1_raises():
    """ts_min n<=0 应报错。"""
    with pytest.raises(ValueError, match="positive"):
        ts_min(pd.Series([1.0]), 0)


def test_ts_max_n_1_raises():
    with pytest.raises(ValueError, match="positive"):
        ts_max(pd.Series([1.0]), -1)


def test_decay_linear_n_1_raises():
    with pytest.raises(ValueError, match="positive"):
        decay_linear(pd.Series([1.0]), 0)


# ── v1.2.0 第二批 alpha 公式（alpha_006 – alpha_015） ─────


def test_alpha_006_smoke():
    """alpha_006 = rank(close - open) smoke test。"""
    open_ = pd.Series([10.0, 11.0, 12.0])
    close = pd.Series([11.0, 10.0, 13.0])  # diff: +1, -1, +1
    result = alpha_006(open_, close)
    # diff 序列 [1, -1, 1] → pandas rank pct=True：ranks [2.5, 1, 2.5] → pct [0.833, 0.333, 0.833]
    assert result.iloc[0] == pytest.approx(5.0 / 6)
    assert result.iloc[1] == pytest.approx(1.0 / 3)
    assert result.iloc[2] == pytest.approx(5.0 / 6)


def test_alpha_007_volume_trend():
    """alpha_007 = mean(volume, 7) - mean(volume, 14)。"""
    volume = pd.Series(range(20), dtype=float)
    result = alpha_007(volume)
    expected_last = volume.iloc[-7:].mean() - volume.iloc[-14:].mean()
    assert result.iloc[-1] == pytest.approx(expected_last)


def test_alpha_008_smoke():
    """alpha_008 = rank(delta(vwap, 5))。"""
    vwap = pd.Series(range(20), dtype=float)
    result = alpha_008(vwap)
    assert isinstance(result, pd.Series)


def test_alpha_009_min_rank():
    """alpha_009 = rank(ts_min(low, 5))。"""
    low = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    result = alpha_009(low)
    # 5 期 ts_min 都 = 1.0 → rank 全相同
    valid = result.dropna()
    assert valid.nunique() == 1


def test_alpha_010_max_rank():
    """alpha_010 = rank(ts_max(high, 5))。"""
    high = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    result = alpha_010(high)
    # 5 期 ts_max 都 = 5.0 → rank 全相同
    valid = result.dropna()
    assert valid.nunique() == 1


def test_alpha_011_constant_range():
    """alpha_011 高=低时（无振幅）→ 返回 0（safe_diff 处理）。"""
    open_ = pd.Series([10.0, 11.0])
    close = pd.Series([10.0, 11.0])
    high = pd.Series([10.0, 11.0])
    low = pd.Series([10.0, 11.0])
    result = alpha_011(open_, close, high, low)
    assert (result == 0.0).all()


def test_alpha_011_bullish():
    """alpha_011 阳线 close > open → 正值（多头强度）。"""
    open_ = pd.Series([10.0] * 5)
    close = pd.Series([10.5] * 5)
    high = pd.Series([11.0] * 5)
    low = pd.Series([9.5] * 5)
    result = alpha_011(open_, close, high, low)
    # close - low = 1.0; high - close = 0.5; high - low = 1.5
    # (1.0 - 0.5) / 1.5 = 0.333
    for v in result:
        assert v == pytest.approx(1.0 / 3.0)


def test_alpha_012_volume_rank_change():
    """alpha_012 = rank(volume) - rank(volume.shift(5))。"""
    volume = pd.Series(range(20), dtype=float)
    result = alpha_012(volume)
    # 前 5 个应为 NaN
    assert result.iloc[:5].isna().all()


def test_alpha_013_returns_rank_change():
    """alpha_013 = rank(returns) - rank(returns.shift(3))。"""
    close = pd.Series(range(20), dtype=float)
    result = alpha_013(close)
    # 前 4 个 NaN（returns 第一行 + shift(3) 3 行）
    assert result.iloc[:4].isna().all()


def test_alpha_014_combined_momentum():
    """alpha_014 = -rank(delta(returns(open_), 3)) * correlation(open_, volume, 10)。"""
    open_ = pd.Series(range(20), dtype=float)
    volume = pd.Series([1.0] * 20)
    result = alpha_014(open_, volume)
    assert isinstance(result, pd.Series)


def test_alpha_015_high_volume_combo():
    """alpha_015 = -1 * ts_sum(rank(correlation(rank(high), rank(volume), 5)), 5)。

    完全相关输入（high 和 volume 同方向）→ correlation = 1.0；
    但 ts_sum 5 期窗口内 rank(1.0) 因 tie-breaking 在 [0, 1] 之间分布，
    所以最后 5 期总和约为 -3.6875（实测值）。
    """
    high = pd.Series(range(20), dtype=float)
    volume = pd.Series(range(20), dtype=float)
    result = alpha_015(high, volume)
    # 取最后一个完全有效值（rolling warm-up 后）
    valid_last = result.dropna().iloc[-1]
    # 实测：-3.6875（具体数值取决于 pandas rank tie-breaking）
    assert valid_last < 0  # 公式有 -1 系数
    assert valid_last > -5.5  # 不会低于完全负相关
    assert valid_last == pytest.approx(-3.6875, abs=1e-4)


def test_get_alpha_meta_alpha_006_to_015():
    """alpha_006 到 alpha_015 全部在 registry 中。"""
    for i in range(6, 16):
        alpha_id = f"alpha_{i:03d}"
        meta = get_alpha_meta(alpha_id)
        assert "formula" in meta
        assert "inputs" in meta
        assert len(meta["inputs"]) >= 1


# ── v1.2.0 第三批基础算子（stddev / covariance / log / abs / sign / max_pair / min_pair / indneutralize） ──


def test_stddev_alias():
    """stddev 与 ts_std 同义。"""
    s = pd.Series(range(20), dtype=float)
    assert stddev(s, 5).equals(ts_std(s, 5))


def test_covariance_perfect():
    """完全正相关 → 协方差 ≈ var(x)。"""
    s1 = pd.Series([1.0, 2.0, 3.0, 4.0, 5.0])
    s2 = s1 * 2 + 1
    cov = covariance(s1, s2, 5).iloc[-1]
    var = s1.var()
    # cov = corr * std1 * std2 = 1 * std1 * (2*std1) = 2*var
    assert cov == pytest.approx(2 * var)


def test_log_basic():
    """log(e) ≈ 1。"""
    s = pd.Series([1.0, np.e, np.e**2])
    assert log(s).iloc[0] == pytest.approx(0.0)
    assert log(s).iloc[1] == pytest.approx(1.0)
    assert log(s).iloc[2] == pytest.approx(2.0)


def test_log_negative_input():
    """log(<=0) → NaN。"""
    s = pd.Series([-1.0, 0.0, 1.0])
    assert pd.isna(log(s).iloc[0])
    assert pd.isna(log(s).iloc[1])
    assert log(s).iloc[2] == pytest.approx(0.0)


def test_abs_basic():
    """abs_series: |−3| = 3。"""
    from quant_engine.alpha_factors import abs_series

    assert abs_series(pd.Series([-3.0, 0.0, 5.0])).tolist() == [3.0, 0.0, 5.0]


def test_sign_basic():
    """sign 函数。"""
    assert sign(pd.Series([-5.0, 0.0, 5.0])).tolist() == [-1.0, 0.0, 1.0]


def test_max_pair():
    """逐元素 max。"""
    s1 = pd.Series([1.0, 5.0, 3.0])
    s2 = pd.Series([4.0, 2.0, 6.0])
    assert max_pair(s1, s2).tolist() == [4.0, 5.0, 6.0]


def test_min_pair():
    """逐元素 min。"""
    s1 = pd.Series([1.0, 5.0, 3.0])
    s2 = pd.Series([4.0, 2.0, 6.0])
    assert min_pair(s1, s2).tolist() == [1.0, 2.0, 3.0]


def test_indneutralize():
    """组内中性化：减去组均值。"""
    s = pd.Series([1.0, 2.0, 3.0, 4.0], index=["a", "a", "b", "b"])
    groups = pd.Series(["g1", "g1", "g2", "g2"], index=s.index)
    result = indneutralize(s, groups)
    # g1 均值 = 1.5；g2 均值 = 3.5
    assert result.iloc[0] == pytest.approx(-0.5)
    assert result.iloc[1] == pytest.approx(0.5)
    assert result.iloc[2] == pytest.approx(-0.5)
    assert result.iloc[3] == pytest.approx(0.5)


# ── v1.2.0 第三批 alpha 公式（alpha_016 – alpha_035） ─────


def test_alpha_016_smoke():
    """alpha_016 = rank(decay_linear(correlation(rank(high), rank(volume), 5), 5))。"""
    high = pd.Series(np.random.rand(30).cumsum())
    volume = pd.Series(np.random.rand(30) * 100 + 1.0)
    result = alpha_016(high, volume)
    assert isinstance(result, pd.Series)
    assert result.dropna().shape[0] > 0


def test_alpha_017_smoke():
    """alpha_017 smoke test。"""
    close = pd.Series(np.random.rand(30).cumsum() + 10)
    volume = pd.Series(np.random.rand(30) * 100 + 1.0)
    result = alpha_017(close, volume)
    assert isinstance(result, pd.Series)


def test_alpha_018_smoke():
    """alpha_018 smoke test。"""
    open_ = pd.Series(np.random.rand(30).cumsum() + 10)
    volume = pd.Series(np.random.rand(30) * 100 + 1.0)
    result = alpha_018(open_, volume)
    assert isinstance(result, pd.Series)


def test_alpha_019_smoke():
    """alpha_019 smoke test。"""
    close = pd.Series(np.random.rand(30).cumsum() + 10)
    open_ = pd.Series(np.random.rand(30).cumsum() + 10)
    result = alpha_019(close, open_)
    assert isinstance(result, pd.Series)


def test_alpha_020_smoke():
    """alpha_020 smoke test。"""
    open_ = pd.Series(np.random.rand(30).cumsum() + 10)
    close = pd.Series(np.random.rand(30).cumsum() + 10)
    result = alpha_020(open_, close)
    assert isinstance(result, pd.Series)


def test_alpha_021_volume_ratio():
    """alpha_021 = ts_mean(volume, 20) / ts_mean(volume, 60)。"""
    volume = pd.Series([1.0] * 60 + [3.0] * 20)
    result = alpha_021(volume).dropna()
    # 后 20 个 volume=3, 前 40 个=1, ts_mean 20 = 3, ts_mean 60 = (40+60)/60 = 100/60
    expected = 3.0 / (100.0 / 60.0)
    assert result.iloc[-1] == pytest.approx(expected)


def test_alpha_021_zero_long_avg():
    """alpha_021 分母为 0 → NaN（safe 处理）。"""
    volume = pd.Series([0.0] * 80)
    result = alpha_021(volume)
    assert result.dropna().empty


def test_alpha_022_smoke():
    """alpha_022 smoke test。"""
    high = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_022(high, volume, close)
    assert result.dropna().shape[0] > 0


def test_alpha_023_smoke():
    """alpha_023 = -1 * ts_mean(delta(close, 5), 20) * delta(close, 5) / close。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_023(close)
    assert result.dropna().shape[0] > 0


def test_alpha_023_zero_close():
    """close 中含 0 → 跳过（safe_close）。"""
    close = pd.Series([10.0, 11.0, 0.0, 12.0, 13.0] * 10)
    result = alpha_023(close)
    # 不抛异常；NaN 在 0 处
    assert not result.isna().all()


def test_alpha_024_smoke():
    """alpha_024 smoke test。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_024(close, volume)
    assert result.dropna().shape[0] > 0


def test_alpha_025_smoke():
    """alpha_025 = rank(decay_linear(correlation(vwap, volume, 4), 8))。"""
    vwap = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_025(vwap, volume)
    assert result.dropna().shape[0] > 0


def test_alpha_026_smoke():
    """alpha_026 smoke test。"""
    open_ = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_026(open_, volume, close)
    assert result.dropna().shape[0] > 0


def test_alpha_027_smoke():
    """alpha_027 smoke test。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_027(close, volume)
    assert result.dropna().shape[0] > 0


def test_alpha_028_scale():
    """alpha_028 = scale(...) 应满足 |sum| ≈ 1。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    open_ = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_028(close, open_)
    valid = result.dropna()
    assert valid.abs().sum() == pytest.approx(1.0, abs=1e-6)


def test_alpha_029_smoke():
    """alpha_029 嵌套组合 smoke。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_029(close)
    assert result.dropna().shape[0] > 0


def test_alpha_030_smoke():
    """alpha_030 = -1 * rank(decay_linear(correlation(rank(low), rank(volume), 5), 5))。"""
    low = pd.Series(np.random.rand(50).cumsum() + 5)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_030(low, volume)
    assert result.dropna().shape[0] > 0


def test_alpha_031_smoke():
    """alpha_031 smoke test。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_031(close, volume)
    assert result.dropna().shape[0] > 0


def test_alpha_032_scale():
    """alpha_032 = scale(...) → |sum| ≈ 1。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    volume = pd.Series(np.random.rand(50) * 100 + 1.0)
    result = alpha_032(close, volume)
    valid = result.dropna()
    assert valid.abs().sum() == pytest.approx(1.0, abs=1e-6)


def test_alpha_033_scale():
    """alpha_033 = scale(...) → |sum| ≈ 1。"""
    close = pd.Series(np.random.rand(50).cumsum() + 10)
    result = alpha_033(close)
    valid = result.dropna()
    assert valid.abs().sum() == pytest.approx(1.0, abs=1e-6)


def test_alpha_034_volume_ratio():
    """alpha_034 = ts_mean(volume, 12) / ts_mean(volume, 26)。"""
    volume = pd.Series([1.0] * 50)
    result = alpha_034(volume).dropna()
    # 全部 = 1，ratio = 1.0
    assert result.iloc[-1] == pytest.approx(1.0)


def test_alpha_035_volume_ratio():
    """alpha_035 = ts_mean(volume, 6) / ts_mean(volume, 12)。"""
    close = pd.Series([1.0] * 30)
    volume = pd.Series([1.0] * 30)
    result = alpha_035(close, volume).dropna()
    assert result.iloc[-1] == pytest.approx(1.0)


# ── v1.2.0 第四批 alpha 公式（alpha_036 – alpha_060） ─────

# 共 25 个 smoke test（大多数是组合公式，行为正确即可）


@pytest.mark.parametrize(
    "alpha_fn,args_fn",
    [
        ("alpha_036", lambda c, v, o: (o, c)),
        ("alpha_037", lambda c, v, o: (o, c)),
        ("alpha_038", lambda c, v, o: (c,)),
        ("alpha_039", lambda c, v, o: (v,)),
        ("alpha_040", lambda c, v, o: (o * 1.1, c * 0.9, v)),
        ("alpha_041", lambda c, v, o: (o * 1.1, c * 0.9)),
        ("alpha_042", lambda c, v, o: (c, o * 1.1, c * 0.9)),
        ("alpha_043", lambda c, v, o: (c, v)),
        ("alpha_044", lambda c, v, o: (o, v)),
        ("alpha_045", lambda c, v, o: (c, v)),
        ("alpha_046", lambda c, v, o: (c,)),
        ("alpha_047", lambda c, v, o: (v, c)),
        ("alpha_048", lambda c, v, o: (c,)),
        ("alpha_049", lambda c, v, o: (c, v)),
        ("alpha_050", lambda c, v, o: (c, v)),
        ("alpha_051", lambda c, v, o: (o * 1.1, c * 0.9)),
        ("alpha_052", lambda c, v, o: (c,)),
        ("alpha_053", lambda c, v, o: (c, o * 1.1, c * 0.9)),
        ("alpha_054", lambda c, v, o: (o, c, c * 0.9)),
        ("alpha_055", lambda c, v, o: (o, o * 1.1, c * 0.9, v, c)),
        ("alpha_056", lambda c, v, o: (c, v)),
        ("alpha_057", lambda c, v, o: (c, v)),
        ("alpha_058", lambda c, v, o: (v, c)),
        ("alpha_059", lambda c, v, o: (c, v)),
        ("alpha_060", lambda c, v, o: (c, v)),
    ],
)
def test_alpha_036_to_060_smoke(alpha_fn, args_fn):
    """alpha_036 到 alpha_060 全部能跑通（smoke test）。"""
    import sys

    module = sys.modules["quant_engine.alpha_factors"]
    fn = getattr(module, alpha_fn)
    close = pd.Series(np.random.rand(60).cumsum() + 10)
    volume = pd.Series(np.random.rand(60) * 100 + 1.0)
    open_ = close.shift(1).fillna(close.iloc[0])
    args = args_fn(close, volume, open_)
    result = fn(*args)
    assert isinstance(result, pd.Series)
    # 大多数公式应该至少有少量有效值
    assert result.dropna().shape[0] > 0


# ── v1.2.0 第五批 alpha 公式（alpha_061 – alpha_085） ─────


@pytest.mark.parametrize(
    "alpha_fn,args_fn",
    [
        ("alpha_061", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_062", lambda c, v, o, h, lo: (c, v)),
        ("alpha_063", lambda c, v, o, h, lo: (c, v)),
        ("alpha_064", lambda c, v, o, h, lo: (o, c, v)),
        ("alpha_065", lambda c, v, o, h, lo: (c, v)),
        ("alpha_066", lambda c, v, o, h, lo: (c, v)),
        ("alpha_067", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_068", lambda c, v, o, h, lo: (c, v)),
        ("alpha_069", lambda c, v, o, h, lo: (c, v)),
        ("alpha_070", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_071", lambda c, v, o, h, lo: (o, c, lo, v)),
        ("alpha_072", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_073", lambda c, v, o, h, lo: (c, v)),
        ("alpha_074", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_075", lambda c, v, o, h, lo: (c, v)),
        ("alpha_076", lambda c, v, o, h, lo: (c, v)),
        ("alpha_077", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_078", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_079", lambda c, v, o, h, lo: (c, h, lo)),
        ("alpha_080", lambda c, v, o, h, lo: (o, c, v)),
        ("alpha_081", lambda c, v, o, h, lo: (c, v)),
        ("alpha_082", lambda c, v, o, h, lo: (o, v)),
        ("alpha_083", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_084", lambda c, v, o, h, lo: (c, v)),
        ("alpha_085", lambda c, v, o, h, lo: (c, v)),
    ],
)
def test_alpha_061_to_085_smoke(alpha_fn, args_fn):
    """alpha_061 到 alpha_085 smoke test。"""
    import sys

    module = sys.modules["quant_engine.alpha_factors"]
    fn = getattr(module, alpha_fn)
    close = pd.Series(np.random.rand(60).cumsum() + 10)
    volume = pd.Series(np.random.rand(60) * 100 + 1.0)
    high = close * 1.05
    low = close * 0.95
    open_ = close.shift(1).fillna(close.iloc[0])
    args = args_fn(close, volume, open_, high, low)
    result = fn(*args)
    assert isinstance(result, pd.Series)
    assert result.dropna().shape[0] > 0


def test_get_alpha_meta_alpha_061_to_085():
    """alpha_061 到 alpha_085 全部在 registry 中。"""
    for i in range(61, 86):
        alpha_id = f"alpha_{i:03d}"
        meta = get_alpha_meta(alpha_id)
        assert "formula" in meta
        assert "inputs" in meta
        assert len(meta["inputs"]) >= 1


# ── v1.2.0 第六批 alpha 公式（alpha_086 – alpha_110） ─────


@pytest.mark.parametrize(
    "alpha_fn,args_fn",
    [
        ("alpha_086", lambda c, v, o, h, lo: (c, v)),
        ("alpha_087", lambda c, v, o, h, lo: (o, c, v)),
        ("alpha_088", lambda c, v, o, h, lo: (c, v)),
        ("alpha_089", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_090", lambda c, v, o, h, lo: (c, v)),
        ("alpha_091", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_092", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_093", lambda c, v, o, h, lo: (c, v)),
        ("alpha_094", lambda c, v, o, h, lo: (c, v)),
        ("alpha_095", lambda c, v, o, h, lo: (c, v)),
        ("alpha_096", lambda c, v, o, h, lo: (c, v)),
        ("alpha_097", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_098", lambda c, v, o, h, lo: (c, v)),
        ("alpha_099", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_100", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_101", lambda c, v, o, h, lo: (c, h, lo)),
        ("alpha_102", lambda c, v, o, h, lo: (c, v)),
        ("alpha_103", lambda c, v, o, h, lo: (c, v)),
        ("alpha_104", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_105", lambda c, v, o, h, lo: (c, v)),
        ("alpha_106", lambda c, v, o, h, lo: (c, v)),
        ("alpha_107", lambda c, v, o, h, lo: (c, v)),
        ("alpha_108", lambda c, v, o, h, lo: (h, lo, v)),
        ("alpha_109", lambda c, v, o, h, lo: (c, h, lo, v)),
        ("alpha_110", lambda c, v, o, h, lo: (c, h, lo, v)),
    ],
)
def test_alpha_086_to_110_smoke(alpha_fn, args_fn):
    """alpha_086 到 alpha_110 smoke test。"""
    import sys

    module = sys.modules["quant_engine.alpha_factors"]
    fn = getattr(module, alpha_fn)
    close = pd.Series(np.random.rand(60).cumsum() + 10)
    volume = pd.Series(np.random.rand(60) * 100 + 1.0)
    high = close * 1.05
    low = close * 0.95
    open_ = close.shift(1).fillna(close.iloc[0])
    args = args_fn(close, volume, open_, high, low)
    result = fn(*args)
    assert isinstance(result, pd.Series)
    assert result.dropna().shape[0] > 0


# ── v1.2.0 第七批 alpha 公式（alpha_111 – alpha_135） ─────


@pytest.mark.parametrize(
    "alpha_id,args_fn",
    [
        (
            f"alpha_{i:03d}",
            lambda c, v, o, h, lo, _i=i: (
                (c, v) if _i % 3 == 0 else (c, v) if _i % 3 == 1 else (h, lo, v)
            ),
        )
        for i in range(111, 136)
    ],
)
def test_alpha_111_to_135_smoke(alpha_id, args_fn):
    """alpha_111 到 alpha_135 smoke test（模板化 25 个）。"""
    import sys

    fn = getattr(sys.modules["quant_engine.alpha_factors"], alpha_id)
    close = pd.Series(np.random.rand(60).cumsum() + 10)
    volume = pd.Series(np.random.rand(60) * 100 + 1.0)
    high = close * 1.05
    low = close * 0.95
    open_ = close.shift(1).fillna(close.iloc[0])
    args = args_fn(close, volume, open_, high, low)
    result = fn(*args)
    assert isinstance(result, pd.Series)
    assert result.dropna().shape[0] > 0


# ── v1.2.0 第八批 alpha 公式（alpha_136 – alpha_158） ─────
# 这是 alpha158 完整版的最后一批——达成 158 个公式


@pytest.mark.parametrize(
    "alpha_id",
    [f"alpha_{i:03d}" for i in range(136, 159)],
)
def test_alpha_136_to_158_smoke(alpha_id):
    """alpha_136 到 alpha_158 smoke test（达成 158 完整版）。"""
    import sys

    fn = getattr(sys.modules["quant_engine.alpha_factors"], alpha_id)
    close = pd.Series(np.random.rand(60).cumsum() + 10)
    volume = pd.Series(np.random.rand(60) * 100 + 1.0)
    high = close * 1.05
    low = close * 0.95
    args = fn.__code__.co_varnames[: fn.__code__.co_argcount]
    args_series = {
        "close": close,
        "volume": volume,
        "high": high,
        "low": low,
        "open": close.shift(1).fillna(close.iloc[0]),
    }
    fn_args = tuple(args_series[a] for a in args if a in args_series)
    result = fn(*fn_args)
    assert isinstance(result, pd.Series)
    assert result.dropna().shape[0] > 0


def test_alpha_158_complete():
    """确认 alpha158 完整版：158 个算子全部可用 + 全部元数据。"""
    assert len(ALPHA158_REGISTRY) == 158
    for i in range(1, 159):
        alpha_id = f"alpha_{i:03d}"
        meta = get_alpha_meta(alpha_id)
        assert "formula" in meta
        assert "inputs" in meta
        assert len(meta["inputs"]) >= 1


# ── v1.2.0 Phase 2: dump/load / JSONB 兼容工具 ─────


def test_dump_alpha_registry_jsonl(tmp_path):
    """dump_alpha_registry_jsonl 应产生 JSONL 文件。"""
    from quant_engine.alpha_factors import dump_alpha_registry_jsonl, load_alpha_registry_jsonl

    output_path = tmp_path / "alpha_registry.jsonl"
    count = dump_alpha_registry_jsonl(str(output_path))
    assert count == 158
    assert output_path.exists()
    # 加载回来验证
    loaded = load_alpha_registry_jsonl(str(output_path))
    assert len(loaded) == 158
    assert "alpha_001" in loaded
    assert "alpha_158" in loaded


def test_dump_load_roundtrip(tmp_path):
    """dump + load 应保持数据完整性。"""
    from quant_engine.alpha_factors import (
        ALPHA158_REGISTRY,
        dump_alpha_registry_jsonl,
        load_alpha_registry_jsonl,
    )

    output_path = tmp_path / "roundtrip.jsonl"
    dump_alpha_registry_jsonl(str(output_path))
    loaded = load_alpha_registry_jsonl(str(output_path))
    # 抽样验证
    for alpha_id in ["alpha_001", "alpha_050", "alpha_158"]:
        assert alpha_id in loaded
        original = ALPHA158_REGISTRY[alpha_id]
        # 注意：load 后 id 字段已剥离，其余字段应完全一致
        for key in original:
            assert loaded[alpha_id].get(key) == original[key], f"Mismatch in {alpha_id}.{key}"


def test_alpha_registry_to_pg_rows():
    """alpha_registry_to_pg_rows 应输出 PG INSERT 行结构。"""
    from quant_engine.alpha_factors import alpha_registry_to_pg_rows

    rows = alpha_registry_to_pg_rows()
    assert len(rows) == 158
    # 验证第一行结构
    row = rows[0]
    assert "name" in row
    assert "formula" in row
    assert "metadata" in row
    assert "version" in row
    assert row["version"] == "1.2.0"
    assert isinstance(row["metadata"], dict)
    assert "category" in row["metadata"]


def test_parse_alpha_formula_basic():
    """parse_alpha_formula 应提取函数名与变量。"""
    from quant_engine.alpha_factors import parse_alpha_formula

    parsed = parse_alpha_formula("rank(ts_rank(close, 5))")
    assert "raw" in parsed
    assert "operators" in parsed
    assert "functions" in parsed
    assert "variables" in parsed
    assert "close" in parsed["variables"]
    assert "rank" in parsed["operators"]


def test_parse_alpha_formula_complex():
    """复杂公式解析应识别多函数 + 多变量。"""
    from quant_engine.alpha_factors import parse_alpha_formula

    parsed = parse_alpha_formula("-1 * rank(decay_linear(correlation(close, volume, 10), 5))")
    # 应识别 correlation, decay_linear 函数调用
    func_names = [f["name"] for f in parsed["functions"]]
    assert "correlation" in func_names
    assert "decay_linear" in func_names
    # 应识别 close, volume 变量
    assert "close" in parsed["variables"]
    assert "volume" in parsed["variables"]


def test_parse_alpha_formula_round_trip_jsonb():
    """parse_alpha_formula 输出可 json.dumps → JSONB 落库。"""
    import json
    from quant_engine.alpha_factors import parse_alpha_formula

    parsed = parse_alpha_formula("rank(decay_linear(rank(ts_rank(close, 5)), 5))")
    # 应能 JSON 序列化
    serialized = json.dumps(parsed)
    assert isinstance(serialized, str)
    assert "ts_rank" in serialized