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shy3130
365f1ccbfb
1. 分钟K流式落盘 (修复1年全市场卡死)
- sync_minute_batch 加 on_segment 回调,每段拉完立即写盘
- 抽出 _write_minute_partition 公共函数,消除重复
- 内存峰值从全量(~25GB)降到单段,避免 OOM
2. 段大小可配 (默认20交易日,范围[5,30])
- 新增 minute_sync_segment_days 偏好项
- 「单次获取」与「获取1年」共用此分段设置
- 前端设置弹窗新增分段大小步进器
3. 回测分钟K精确成交修复 (从未成功跑通过)
- _resolve_minute_fill 改接受 DataFrame,避免 to_numpy() 退化为
object 数组后字段名索引抛 IndexError
- _load_minute_for_fills 用 partition_by 向量化分组替换逐行循环
- 新增 test_minute_fill.py 回归测试 (6 用例)
4. 卡死 watchdog 超时阈值按任务类型区分
- 普通任务 600s → 1200s;分钟K长任务 → 1800s
- create(timeout_s=) 支持按 job 存阈值,reap_stale 读 job 自身值
- 修复分钟K正常拉取被误杀导致僵尸线程继续写盘的问题
5. UI 优化
- 设置弹窗按 自动同步/手动获取/清空 三区块分组
- 「单次获取」改为按分段大小拉一段 (天数=分段设置)
- 状态文案: 盘后自动同步 → 自动同步已开启/已关闭
105 lines
4.2 KiB
Python
105 lines
4.2 KiB
Python
"""分钟K精确成交 (_resolve_minute_fill / _load_minute_for_fills) 回归测试。
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背景: 原实现用 df.to_numpy() 转 structured array 再按字段名索引 (arr["open"])。
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当 DataFrame 含 datetime 列 + float 列时, to_numpy() 退化为 dtype=object 的二维
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数组, 字段名索引抛 IndexError: "only integers, slices... are valid indices"。
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开启 minute_fill 的回测从未成功跑通过。此测试锁定该 bug 不再复发。
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"""
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from __future__ import annotations
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from datetime import date, datetime
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import polars as pl
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from app.backtest.engine import BacktestEngine
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def _sample_minute_df(symbol: str = "000001.SZ") -> pl.DataFrame:
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"""构造一份带 datetime 列 + float 列的分钟K (复现 to_numpy 退化的场景)。"""
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base = datetime(2024, 1, 2, 9, 31)
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return pl.DataFrame({
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"symbol": [symbol] * 4,
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"datetime": [base.replace(hour=h, minute=m) for h, m in
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[(9, 31), (10, 0), (14, 0), (14, 57)]],
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"open": [10.0, 10.5, 10.8, 10.6],
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"high": [10.6, 10.7, 10.9, 10.7],
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"low": [9.9, 10.4, 10.7, 10.5],
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"close": [10.2, 10.6, 10.85, 10.65],
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"volume": [100, 200, 150, 120],
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"amount": [1020.0, 2120.0, 1627.0, 1278.0],
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})
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def test_resolve_minute_fill_with_mixed_columns_no_index_error():
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"""混合列类型 (datetime + float) 不再抛 IndexError。
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这是原 bug 的精确复现点: 旧实现 _resolve_minute_fill 接收 ndarray,
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arr["open"] 在 object 数组上会炸。现在接受 DataFrame, 按列取值。
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"""
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mdf = _sample_minute_df()
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# 三种分支都应正常返回, 不抛 IndexError
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assert BacktestEngine._resolve_minute_fill(mdf, ref_price=10.5, side="buy") is not None
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assert BacktestEngine._resolve_minute_fill(mdf, ref_price=10.5, side="sell") is not None
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# 无参考线 → VWAP 分支
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vwap = BacktestEngine._resolve_minute_fill(mdf, ref_price=None, side="buy")
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assert vwap is not None and vwap > 0
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def test_resolve_minute_fill_buy_cross_above_ref():
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"""买入: 价格涨破参考线 → 开盘已高于则按开盘。"""
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mdf = _sample_minute_df()
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# ref=9.5, 开盘 10.0 已高于 → 按开盘
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assert BacktestEngine._resolve_minute_fill(mdf, 9.5, "buy") == 10.0
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def test_resolve_minute_fill_sell_cross_below_ref():
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"""卖出: 价格跌破参考线 → 开盘已低于则按开盘。"""
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mdf = _sample_minute_df()
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# ref=10.5, 开盘 10.0 已低于 → 按开盘
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assert BacktestEngine._resolve_minute_fill(mdf, 10.5, "sell") == 10.0
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def test_resolve_minute_fill_vwap():
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"""无参考线 → VWAP = 总成交额 / 总成交量。"""
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mdf = _sample_minute_df()
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total_amt = 1020.0 + 2120.0 + 1627.0 + 1278.0
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total_vol = 100 + 200 + 150 + 120
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expected = total_amt / total_vol
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assert BacktestEngine._resolve_minute_fill(mdf, None, "buy") == expected
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def test_resolve_minute_fill_empty_returns_none():
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"""空 DataFrame → None (降级到日K口径)。"""
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assert BacktestEngine._resolve_minute_fill(pl.DataFrame(), None, "buy") is None
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class _FakeRepo:
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"""最小 repo 桩: get_minute_range 直接返回预构造的混合列 DataFrame。"""
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def __init__(self, df: pl.DataFrame) -> None:
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self._df = df
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def get_minute_range(self, symbols, start, end, asset_type="stock") -> pl.DataFrame: # noqa: ANN001
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return self._df
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def test_load_minute_for_fills_returns_dataframe_dict():
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"""_load_minute_for_fills 返回 {(symbol, date_str): DataFrame}, 而非 ndarray。
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锁定: cache 值类型必须是 pl.DataFrame (旧实现返回的对象后续被 .to_numpy()
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退化成 object 数组触发 bug)。
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"""
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df = _sample_minute_df()
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repo = _FakeRepo(df)
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result = BacktestEngine._load_minute_for_fills(
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repo, ["000001.SZ"], {"2024-01-02"}, "stock",
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)
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assert ("000001.SZ", "2024-01-02") in result
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val = result[("000001.SZ", "2024-01-02")]
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# 关键断言: 返回的是 DataFrame, 可直接喂给 _resolve_minute_fill
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assert isinstance(val, pl.DataFrame)
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assert not val.is_empty()
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# 端到端: load → resolve 不抛异常
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price = BacktestEngine._resolve_minute_fill(val, None, "buy")
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assert price is not None and price > 0
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