"""事件回测纯函数：入场/出场索引语义、汇总统计（含空样本与分年）。"""
from __future__ import annotations

from datetime import datetime

import pytest

from app.backtest.events import _entry_exit_indices, _stats_block
from app.schemas import EventBacktestSpec


def _spec(holding_days: int = 5) -> EventBacktestSpec:
    return EventBacktestSpec.model_validate({
        "holding_days": holding_days,
        "entry": {"indicator": []},
    })


def test_entry_exit_next_day_and_hold():
    spec = _spec(holding_days=5)
    assert _entry_exit_indices(10, spec, n=100) == (11, 16)  # 次日入，持有 5 日出


def test_entry_exit_out_of_range_none():
    spec = _spec(holding_days=5)
    # 出场索引越界（exit_i >= n）
    assert _entry_exit_indices(94, spec, n=100) is None
    assert _entry_exit_indices(99, spec, n=100) is None


def test_entry_exit_boundary_exact_fit():
    spec = _spec(holding_days=5)
    # exit_i == n-1 恰好可用
    assert _entry_exit_indices(93, spec, n=100) == (94, 99)


def test_stats_block_empty():
    s = _stats_block([])
    assert s["samples"] == 0 and s["stocks"] == 0
    assert s["by_year"] == []
    assert s["win_rate"] == 0.0


def test_stats_block_aggregates():
    trades = [
        {"ts_code": "000001.SZ", "ret_pct": 10.0,
         "entry_date": datetime(2024, 1, 5), "entry_price": 10, "exit_price": 11},
        {"ts_code": "000001.SZ", "ret_pct": -4.0,
         "entry_date": datetime(2024, 3, 6), "entry_price": 10, "exit_price": 9.6},
        {"ts_code": "600519.SH", "ret_pct": 2.0,
         "entry_date": datetime(2023, 5, 10), "entry_price": 10, "exit_price": 10.2},
    ]
    s = _stats_block(trades)
    assert s["samples"] == 3
    assert s["stocks"] == 2
    assert s["mean_pct"] == pytest.approx((10.0 - 4.0 + 2.0) / 3, abs=1e-3)  # 统计块保留 3 位小数
    assert s["win_rate"] == round(2 / 3 * 100, 2)
    assert [y["year"] for y in s["by_year"]] == [2023, 2024]  # 分年升序
    assert s["by_year"][1]["samples"] == 2
    assert s["max_pct"] == 10.0 and s["min_pct"] == -4.0