"""组合回测引擎单测（合成数据，确定性，不依赖数据库/因子注册表）。

验证三件用户确认的语义：
1. Borda 秩和打分：两策略排名不同 → 综合排序可手算预测。
2. 持仓天数区间 [Tmin, Tmax]：超 Tmax 强制了结；未满 Tmin 即使掉出 TopN 也暂留。
3. 调仓时机 daily/weekly/monthly 产生不同的调仓次数。
"""

from __future__ import annotations

from datetime import date, timedelta

import pandas as pd
import pytest
from app.domain.entities.combo import BacktestCombo
from app.domain.entities.research import CostSpec
from app.quant.combo_engine import HoldingBandRunner, borda_combine


def _business_days(start: date, n: int) -> list[date]:
    """生成 n 个连续工作日（跳过周末），用作合成行情索引。"""
    out: list[date] = []
    d = start
    while len(out) < n:
        if d.weekday() < 5:
            out.append(d)
        d += timedelta(days=1)
    return out


def _flat_close(symbols: list[str], days: list[date], price: float = 100.0) -> pd.DataFrame:
    """所有股票恒定价格的面板（收益为 0，便于隔离「选股/调仓」逻辑）。"""
    idx = pd.to_datetime(days)
    return pd.DataFrame(price, index=idx, columns=symbols)


# ---------- 1. Borda 秩和 ----------


def test_borda_combine_hand_computed():
    """两策略排名不同，综合分 = Σ(1/名次)，可手算。"""
    day = pd.Timestamp("2024-01-02")
    # 策略1：A > B > C；策略2：C > A > B
    p1 = pd.DataFrame({"A": [3.0], "B": [2.0], "C": [1.0]}, index=[day])
    p2 = pd.DataFrame({"A": [2.0], "B": [1.0], "C": [3.0]}, index=[day])
    combined = borda_combine([p1, p2]).loc[day]
    # A: 1/1 + 1/2 = 1.5；C: 1/3 + 1/1 = 1.333；B: 1/2 + 1/3 = 0.833
    assert combined["A"] == pytest.approx(1.5)
    assert combined["C"] == pytest.approx(1.0 / 3 + 1.0)
    assert combined["B"] == pytest.approx(1.0 / 2 + 1.0 / 3)
    order = combined.sort_values(ascending=False).index.tolist()
    assert order == ["A", "C", "B"]  # 并集后统一排序：A、C 进 Top2，B 落选


def test_borda_missing_symbol_contributes_zero():
    """某策略面板里没有某股票（NaN）→ 该策略对它贡献 0，但不影响其它策略的贡献。"""
    day = pd.Timestamp("2024-01-02")
    p1 = pd.DataFrame({"A": [3.0], "B": [2.0]}, index=[day])  # 策略1 只有 A、B
    p2 = pd.DataFrame({"A": [1.0], "C": [2.0]}, index=[day])  # 策略2 只有 A、C
    combined = borda_combine([p1, p2]).loc[day]
    assert combined["A"] == pytest.approx(1.0 + 1.0 / 2)  # 两策略都覆盖 A
    assert combined["B"] == pytest.approx(1.0 / 2)  # 只被策略1 覆盖
    assert combined["C"] == pytest.approx(1.0)  # 只被策略2 覆盖（在其面板里排第 1）


# ---------- 2. 持仓天数区间 ----------


def _make_combo(**overrides) -> BacktestCombo:
    base = dict(
        name="t", strategy_ids=["S1"], initial_capital=1_000_000.0,
        hold_count=1, hold_min_days=0, hold_max_days=None,
        rebalance_freq="daily", period=(date(2024, 1, 2), date(2024, 1, 31)),
    )
    base.update(overrides)
    return BacktestCombo(**base)


def test_tmax_force_exit_respected():
    """恒价 + N=1 + 永远选 A + Tmax=5 + 日频：A 持有超过 5 天即被强制卖出再买回，
    任何一笔交易的持有天数都不应明显超过 Tmax。"""
    symbols = ["A", "B", "C"]
    days = _business_days(date(2024, 1, 2), 30)
    close = _flat_close(symbols, days)
    # A 永远最高分 → 永远 Top1
    score = pd.DataFrame(
        {"A": [3.0] * len(days), "B": [2.0] * len(days), "C": [1.0] * len(days)},
        index=pd.to_datetime(days),
    )
    combo = _make_combo(hold_count=1, hold_min_days=0, hold_max_days=5, rebalance_freq="daily")
    runner = HoldingBandRunner(
        combo=combo, costs=CostSpec(min_commission=0.0), score=score, close=close,
    )
    result = runner.run()

    assert result.trades, "应产生交易"
    # 交易日索引：用引擎同一口径（交易日）验证「任何一笔持仓都不超过 Tmax 个交易日」
    tday_pos = {pd.Timestamp(d): i for i, d in enumerate(close.index)}
    def trading_span(a, b):
        return tday_pos[pd.Timestamp(b)] - tday_pos[pd.Timestamp(a)]
    for t in result.trades:
        span = trading_span(t.entry_date, t.exit_date)
        # 卖出发生在「held > Tmax」的第一个交易日 → 跨度最多 Tmax+1 个交易日
        assert span <= 5 + 1, f"持仓跨 {span} 个交易日 > Tmax+1，Tmax 安全阀失效：{t}"
    # 确实反复「卖后再买」—— 证明 Tmax 在强制换手，而不是一直死拿
    buys = [a for a in result.signal_history if a.signal == "BUY" and a.filled]
    assert len(buys) >= 4, f"Tmax=5 在 30 个交易日内应触发多次重买，实际仅 {len(buys)} 次"


def test_tmin_protects_against_churn():
    """N=1，第 2 天起 B 变成最高分（A 掉出 Top1），但 Tmin=10 → A 在满 10 天前不被卖出。"""
    symbols = ["A", "B"]
    days = _business_days(date(2024, 1, 2), 20)
    close = _flat_close(symbols, days)
    # 第 0 天 A 最高；第 1 天起 B 最高
    a_scores = [3.0] + [1.0] * (len(days) - 1)
    b_scores = [1.0] + [3.0] * (len(days) - 1)
    score = pd.DataFrame({"A": a_scores, "B": b_scores}, index=pd.to_datetime(days))
    combo = _make_combo(hold_count=1, hold_min_days=10, hold_max_days=None, rebalance_freq="daily")
    runner = HoldingBandRunner(
        combo=combo, costs=CostSpec(min_commission=0.0), score=score, close=close,
    )
    result = runner.run()

    # A 应在第 0 天买入
    a_buys = [a for a in result.signal_history if a.symbol == "A" and a.signal == "BUY" and a.filled]
    assert a_buys, "A 应在首日买入"
    a_sells = [t for t in result.trades if t.symbol == "A"]
    if a_sells:
        # 若最终卖出，持有天数必须 ≥ Tmin（不能在满 10 天前因掉出 TopN 被卖）
        for t in a_sells:
            assert (t.exit_date - t.entry_date).days >= 10, (
                f"A 仅持 {(t.exit_date - t.entry_date).days} 天就被卖，违反 Tmin=10 保护"
            )
    # 关键断言：前 9 个工作日内 A 不应被卖出（Tmin 保护生效）
    tday_pos = {pd.Timestamp(d): i for i, d in enumerate(close.index)}
    early_sells = [
        a for a in result.signal_history
        if a.symbol == "A" and a.signal == "SELL" and a.filled
        and tday_pos[pd.Timestamp(a.date)] - tday_pos[pd.Timestamp(days[0])] < 10
    ]
    assert not early_sells, f"Tmin 保护失效：A 在 10 个交易日内被卖出 {early_sells}"


# ---------- 3. 调仓时机 ----------


def test_rebalance_freq_changes_cadence():
    """同一份数据，daily 的调仓日数 > weekly > monthly（用 selection_history 的 distinct 日期数衡量）。"""
    symbols = ["A", "B", "C"]
    days = _business_days(date(2024, 1, 2), 60)
    close = _flat_close(symbols, days)
    score = pd.DataFrame(
        {"A": [3.0] * len(days), "B": [2.0] * len(days), "C": [1.0] * len(days)},
        index=pd.to_datetime(days),
    )

    def run_with(freq: str) -> int:
        combo = _make_combo(
            hold_count=2, rebalance_freq=freq,
            period=(days[0], days[-1]),
        )
        r = HoldingBandRunner(
            combo=combo, costs=CostSpec(min_commission=0.0), score=score, close=close,
        ).run()
        return len({p.date for p in r.selection_history})

    daily_n, weekly_n, monthly_n = run_with("daily"), run_with("weekly"), run_with("monthly")
    assert daily_n > weekly_n > monthly_n, (
        f"调仓频次应 daily({daily_n}) > weekly({weekly_n}) > monthly({monthly_n})"
    )


# ---------- 实体校验 ----------


def test_combo_validates_hold_band_and_freq():
    with pytest.raises(ValueError):
        BacktestCombo(
            name="x", strategy_ids=["A"], hold_count=1,
            hold_min_days=20, hold_max_days=5,  # Tmax < Tmin
            rebalance_freq="monthly", period=(date(2024, 1, 1), date(2024, 2, 1)),
        )
    with pytest.raises(ValueError):
        BacktestCombo(
            name="x", strategy_ids=["A"], hold_count=1,
            rebalance_freq="yearly",  # 非法频率
            period=(date(2024, 1, 1), date(2024, 2, 1)),
        )
    with pytest.raises(ValueError):
        BacktestCombo(
            name="x", strategy_ids=["A", "A"], hold_count=1,  # 重复策略
            rebalance_freq="monthly", period=(date(2024, 1, 1), date(2024, 2, 1)),
        )