"""
回测引擎回归测试：T+1 成交、涨跌停拒成交、report DatetimeIndex、截面组合资金切分。
"""
import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

import numpy as np
import pandas as pd
import pytest

vectorbt = pytest.importorskip("vectorbt")

from backtest.vectorbt.engine import VectorBTEngine
from backtest.strategies.momentum_breakout import MomentumBreakoutStrategy


@pytest.fixture
def price_df():
    idx = pd.date_range('2024-01-01', '2024-06-30', freq='B').strftime('%Y%m%d')
    close = 100 * np.cumprod(1 + np.random.default_rng(42).normal(0.0005, 0.02, len(idx)))
    open_p = np.roll(close, 1); open_p[0] = close[0]
    pre_close = np.roll(close, 1); pre_close[0] = close[0]
    return pd.DataFrame({
        'open': open_p, 'close': close, 'pre_close': pre_close,
        'pct_chg': (close / pre_close - 1) * 100,
    }, index=idx)


def test_backtest_runs_with_t1(price_df):
    eng = VectorBTEngine()
    rep = eng.run(MomentumBreakoutStrategy(), price_df, t_plus_one=True, limit_check=True, slippage=0.0002)
    assert not rep.equity_curve.empty
    assert isinstance(rep.equity_curve.index, pd.DatetimeIndex)


def test_limit_up_blocks_buy(price_df):
    eng = VectorBTEngine()
    p = price_df.copy()
    i = p.index[-5]
    p.loc[i, 'close'] = p.loc[i, 'pre_close'] * 1.099
    p.loc[i, 'pct_chg'] = 9.9
    entries = pd.Series(True, index=p.index)
    exits = pd.Series(False, index=p.index)
    eff, _ = eng._apply_limit_filters(entries, exits, p)
    assert not bool(eff.loc[i]), "涨停日应抑制买入"
    assert eff.dtype == bool


def test_limit_down_blocks_sell(price_df):
    eng = VectorBTEngine()
    p = price_df.copy()
    i = p.index[-7]
    p.loc[i, 'close'] = p.loc[i, 'pre_close'] * 0.901
    p.loc[i, 'pct_chg'] = -9.9
    _, exf = eng._apply_limit_filters(
        pd.Series(False, index=p.index), pd.Series(True, index=p.index), p)
    assert not bool(exf.loc[i]), "跌停日应抑制卖出"


def test_normal_day_keeps_entry(price_df):
    eng = VectorBTEngine()
    p = price_df.copy()
    eff, _ = eng._apply_limit_filters(
        pd.Series(True, index=p.index), pd.Series(False, index=p.index), p)
    assert eff.sum() > 0


def test_cross_section_investable_curve():
    idx = pd.date_range('2024-01-01', '2024-06-30', freq='B').strftime('%Y%m%d')
    uni = {}
    for i in range(4):
        c = 50 * np.cumprod(1 + np.random.default_rng(i).normal(0.0003, 0.018, len(idx)))
        o = np.roll(c, 1); o[0] = c[0]
        uni[f"600{i:04d}.SH"] = pd.DataFrame({'open': o, 'close': c}, index=idx)
    eng = VectorBTEngine(initial_capital=100000)
    rep = eng.run_cross_section(MomentumBreakoutStrategy(), uni, rebalance_freq="M")
    assert not rep.equity_curve.empty
    assert isinstance(rep.equity_curve.index, pd.DatetimeIndex)
    # 组合净值可联合投资：末值与初始资本量级相当，不会因每股满额而超限
    assert rep.equity_curve.iloc[-1] > 0