61 lines
2.0 KiB
Python
61 lines
2.0 KiB
Python
"""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）。"""
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from __future__ import annotations
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from datetime import datetime
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import pytest
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from app.backtest.events import _entry_exit_indices, _stats_block
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from app.schemas import EventBacktestSpec
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def _spec(holding_days: int = 5) -> EventBacktestSpec:
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return EventBacktestSpec.model_validate({
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"holding_days": holding_days,
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"entry": {"indicator": []},
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})
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def test_entry_exit_next_day_and_hold():
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spec = _spec(holding_days=5)
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assert _entry_exit_indices(10, spec, n=100) == (11, 16) # 次日入，持有 5 日出
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def test_entry_exit_out_of_range_none():
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spec = _spec(holding_days=5)
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# 出场索引越界（exit_i >= n）
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assert _entry_exit_indices(94, spec, n=100) is None
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assert _entry_exit_indices(99, spec, n=100) is None
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def test_entry_exit_boundary_exact_fit():
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spec = _spec(holding_days=5)
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# exit_i == n-1 恰好可用
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assert _entry_exit_indices(93, spec, n=100) == (94, 99)
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def test_stats_block_empty():
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s = _stats_block([])
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assert s["samples"] == 0 and s["stocks"] == 0
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assert s["by_year"] == []
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assert s["win_rate"] == 0.0
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def test_stats_block_aggregates():
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trades = [
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{"ts_code": "000001.SZ", "ret_pct": 10.0,
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"entry_date": datetime(2024, 1, 5), "entry_price": 10, "exit_price": 11},
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{"ts_code": "000001.SZ", "ret_pct": -4.0,
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"entry_date": datetime(2024, 3, 6), "entry_price": 10, "exit_price": 9.6},
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{"ts_code": "600519.SH", "ret_pct": 2.0,
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"entry_date": datetime(2023, 5, 10), "entry_price": 10, "exit_price": 10.2},
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]
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s = _stats_block(trades)
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assert s["samples"] == 3
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assert s["stocks"] == 2
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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
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assert s["win_rate"] == round(2 / 3 * 100, 2)
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assert [y["year"] for y in s["by_year"]] == [2023, 2024] # 分年升序
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assert s["by_year"][1]["samples"] == 2
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assert s["max_pct"] == 10.0 and s["min_pct"] == -4.0
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