"""回归测试:

1. 2026-07-06 前 ST 5% 涨跌停限幅仅适用于主板风险警示股;
   新规生效后主板 ST 为 10%, 创业板/科创板 ST 始终执行 20%。
   (修正前 _is_st 无条件套 5%, 会误报/漏报这批股的涨停。)
2. 因子回测 Sharpe 的年化系数须匹配调仓频率 (月频 √12 / 周频 √52 / 日频 √252);
   (修正前一律 √252, 月频 Sharpe 被高估 √21 ≈ 4.6 倍。)
"""
from __future__ import annotations

from datetime import date, timedelta

import polars as pl
import pytest

from app.backtest.factor import FactorBacktestService
from app.backtest.matrix import build_market_data_matrix
from app.indicators.pipeline import compute_limit_signals
from app.strategy.builtin.near_limit_up import MATRIX_STRATEGY


def test_near_limit_pct_st_only_on_main_board():
    df = pl.DataFrame({
        "symbol": ["300001", "688001", "689001", "600001", "000001", "830001.BJ"],
        "name": ["*ST创业", "科创ST", "科创ST", "*ST主板", "平安银行", "北交ST"],
        "date": [date(2024, 1, 2)] * 6,
        "open": [10.0] * 6,
        "high": [10.0] * 6,
        "low": [10.0] * 6,
        "close": [10.0] * 6,
        "volume": [1000.0] * 6,
    })
    market = build_market_data_matrix(df, field_columns={"price_limit_pct"})
    limit_by_symbol = dict(
        zip(market.symbols, market.field("price_limit_pct")[0], strict=True)
    )
    assert limit_by_symbol["300001"] == pytest.approx(0.20)  # 创业板 ST → 20%
    assert limit_by_symbol["688001"] == pytest.approx(0.20)  # 科创板 ST → 20%
    assert limit_by_symbol["689001"] == pytest.approx(0.20)  # 科创板 689 → 20%
    assert limit_by_symbol["600001"] == pytest.approx(0.05)  # 主板 ST → 5%
    assert limit_by_symbol["000001"] == pytest.approx(0.10)  # 主板普通 → 10%
    assert limit_by_symbol["830001.BJ"] == pytest.approx(0.30)  # 北交所 → 30%


def _two_day(
    symbol: str,
    prev_close: float,
    today_close: float,
    trade_date: date = date(2024, 1, 3),
) -> pl.DataFrame:
    """2 日最小输入: 首日平收, 次日收于 today_close。"""
    return pl.DataFrame({
        "symbol": [symbol, symbol],
        "date": [trade_date - timedelta(days=1), trade_date],
        "raw_close": [prev_close, today_close],
        "close": [prev_close, today_close],
        "raw_high": [prev_close, today_close],
        "raw_low": [prev_close, today_close],
        "open": [prev_close, today_close],
        "high": [prev_close, today_close],
        "low": [prev_close, today_close],
        "change_pct": [0.0, today_close / prev_close - 1],
        "vol_ratio_5d": [1.0, 1.0],
    })


def _last_limit_up(
    symbol: str,
    name: str,
    prev_close: float,
    today_close: float,
    trade_date: date = date(2024, 1, 3),
):
    df = _two_day(symbol, prev_close, today_close, trade_date)
    inst = pl.DataFrame({"symbol": [symbol], "name": [name]})
    out = compute_limit_signals(df, inst).sort("date")
    return out["signal_limit_up"].to_list()[-1], out["consecutive_limit_ups"].to_list()[-1]


def test_st_chinext_limit_up_detected_at_20pct():
    # 创业板 *ST 昨收 10.00 → 今日 +20% 至 12.00 应识别为涨停 (修正前按 5% 会漏)
    sig, consec = _last_limit_up("300001", "*ST创业", 10.0, 12.0)
    assert sig is True
    assert consec == 1


def test_st_chinext_plus5pct_is_not_a_false_limit_up():
    # 同股仅 +5% 至 10.50 不应误报涨停 (修正前按 5% 会误报)
    sig, _ = _last_limit_up("300001", "*ST创业", 10.0, 10.5)
    assert sig is False


def test_st_main_board_historical_limit_is_5pct():
    # 新规前主板 *ST 昨收 10.00 → 今日 +5% 至 10.50 应识别为涨停
    sig, _ = _last_limit_up("600001", "*ST主板", 10.0, 10.5)
    assert sig is True


def test_st_main_board_limit_changes_to_10pct_on_2026_07_06():
    change_date = date(2026, 7, 6)
    plus_five, _ = _last_limit_up(
        "600001", "*ST主板", 10.0, 10.5, change_date,
    )
    plus_ten, _ = _last_limit_up(
        "600001", "*ST主板", 10.0, 11.0, change_date,
    )
    assert plus_five is False
    assert plus_ten is True


def test_near_limit_up_accepts_stocks_inside_configured_gap():
    dates = [date(2026, 6, 8) + timedelta(days=index) for index in range(21)]
    closes = [10.0] * 20 + [10.8]
    panel = pl.DataFrame({
        "symbol": ["600001.SH"] * len(dates),
        "name": ["普通股"] * len(dates),
        "date": dates,
        "open": closes,
        "high": closes,
        "low": closes,
        "close": closes,
        "volume": [1000.0] * len(dates),
    })
    market = build_market_data_matrix(panel, field_columns={"price_limit_pct"})
    signals = MATRIX_STRATEGY.compute_signals(market, {
        "min_change": 7.0,
        "limit_gap": 3.0,
    })
    assert signals.entry[-1, 0] == 1


def test_sharpe_annualization_matches_rebalance_frequency():
    nav = [
        {"date": "2024-01-31", "Q1": 1.00},
        {"date": "2024-02-29", "Q1": 1.02},
        {"date": "2024-03-29", "Q1": 1.01},
        {"date": "2024-04-30", "Q1": 1.05},
        {"date": "2024-05-31", "Q1": 1.04},
        {"date": "2024-06-28", "Q1": 1.08},
    ]
    start, end = date(2024, 1, 1), date(2024, 6, 30)
    m = FactorBacktestService._calc_group_stats(nav, start, end, "monthly")[0]["sharpe"]
    d = FactorBacktestService._calc_group_stats(nav, start, end, "daily")[0]["sharpe"]
    assert m != 0.0 and d != 0.0
    # 同一净值曲线, daily(√252) / monthly(√12) 的比值应为 √21 ≈ 4.58
    assert abs((d / m) - (252 / 12) ** 0.5) < 0.05