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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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shy3130
716897f41a
- intraday_features: 会话对齐特征帧构造器(vwap/涨跌幅/1·3·5分钟放量比/ 日内与开盘30分钟高低点距离, 滚动窗口不跨午休, 只用已收盘bar防未来函数) - custom_signals: timeframe=daily|intraday 双 schema, 盘中条件支持 cross_up/cross_down 穿越算子; 输出为当日条件上升沿(首bar不触发, null特征判false绝不误报) - 旧4个分时穿越信号列名零迁移(评估器回映射历史列名, 存量监控规则不动) - 引擎单点注入 csgi_ 列: 监控/分钟策略/分钟回测共用同一构造器; 日线策略引用盘中信号显式报错 - 回放验证 API /api/custom-signals/intraday/replay(本地历史分钟K重放, 区间≤60天标的≤200, 先验证再配置监控) - 能力门槛: 分钟K能力(订阅池)或全量分钟能力(本地分区); 回放仅需本地历史 验证: 新增16个测试(特征数值/边界/旧4信号黄金等价/引擎注入/回放端点), 受影响回归109个全过, ruff对齐基线, docs/features.md 同步
372 lines
17 KiB
Python
372 lines
17 KiB
Python
"""盘中信号(分钟K)测试 — 特征帧 / 编译 / 旧 4 信号等价 / 引擎注入 / 回放。
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口径单源的核心保证: 测试内复刻 v1 盘中评估器的累计循环作为黄金参照,
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新评估器(v2, 特征帧 + 表达式)必须与它对任意序列产出完全一致的触发集合。
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"""
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from __future__ import annotations
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import json
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from datetime import date, datetime, timedelta
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from pathlib import Path
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from types import SimpleNamespace
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import polars as pl
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import pytest
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from app.strategy import custom_signals
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from app.strategy.intraday_features import build_feature_frame
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from app.strategy.intraday_signals import IntradaySignalEvaluator, uses_intraday_signals
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SYMBOL = "000001.SZ"
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DAY = date(2026, 9, 4)
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def _bars(
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prices: list[float],
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volumes: list[float] | None = None,
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start: datetime = datetime(2026, 9, 4, 9, 30),
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symbol: str = SYMBOL,
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) -> pl.DataFrame:
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"""构造规范的上午分钟序列: volume 单位为手, amount = close*volume*100(元)。"""
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volumes = volumes or [100.0] * len(prices)
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# 跳过午休: 09:30+120 根后进入 13:00
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times = []
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for i in range(len(prices)):
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if i < 120:
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times.append(start + timedelta(minutes=i))
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else:
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times.append(datetime(start.year, start.month, start.day, 13, 0) + timedelta(minutes=i - 120))
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return pl.DataFrame({
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"symbol": [symbol] * len(prices),
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"datetime": times,
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"open": prices,
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"high": [p * 1.01 for p in prices],
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"low": [p * 0.99 for p in prices],
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"close": prices,
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"volume": volumes,
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"amount": [p * v * 100.0 for p, v in zip(prices, volumes, strict=True)],
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})
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# ══ 特征帧 ═══════════════════════════════════════════════
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def test_feature_frame_vwap_and_pct():
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df = _bars([10.0, 10.2, 9.8])
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frame = build_feature_frame(df, prev_close={SYMBOL: 10.0})
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# vwap = 累计成交额 / (累计量(手)*100)
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assert frame["vwap"][2] == pytest.approx((10.0 + 10.2 + 9.8) / 3)
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assert frame["pct_vs_prev_close"][2] == pytest.approx(9.8 / 10.0 - 1)
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assert frame["pct_from_open"][2] == pytest.approx(9.8 / 10.0 - 1)
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assert frame["price"][2] == 9.8
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# 缺昨收 → null
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frame2 = build_feature_frame(df)
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assert frame2["pct_vs_prev_close"][2] is None
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def test_feature_frame_vol_ratio_windows_and_nulls():
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# 量比: 当前N根量和 / 此前N根量和; 前 2N-1 根为 null
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prices = [10.0] * 12
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volumes = [100.0, 100.0, 100.0, 100.0, 100.0, 300.0, 100.0, 100.0, 900.0, 100.0, 100.0, 100.0]
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frame = build_feature_frame(_bars(prices, volumes))
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vr1 = frame["vol_ratio_1m_today"].to_list()
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assert vr1[0] is None # 无前值
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assert vr1[1] == pytest.approx(100.0 / 100.0)
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assert vr1[5] == pytest.approx(300.0 / 100.0)
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assert vr1[8] == pytest.approx(900.0 / 100.0)
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vr3 = frame["vol_ratio_3m_today"].to_list()
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assert vr3[0] is None and vr3[4] is None # 3+3-1=5 根前不足
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# 第 6 根(idx5): [3,4,5]=100+100+300 vs [0,1,2]=300
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assert vr3[5] == pytest.approx(500.0 / 300.0)
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# 前窗口量为 0 → null(不伪装成 0)
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zero_start = build_feature_frame(_bars([10.0, 10.0, 10.0], [0.0, 0.0, 500.0]))
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assert zero_start["vol_ratio_1m_today"][2] is None
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def test_feature_frame_rolling_window_never_crosses_lunch():
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# 上午最后一根的 5 分钟窗口不回看跨日; 下午重新预热
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n_am, n_pm = 8, 4
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am = _bars([10.0] * n_am, [100.0] * n_am) # 09:30-09:37
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pm = _bars([10.0] * n_pm, [100.0] * n_pm, start=datetime(2026, 9, 4, 13, 0))
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frame = build_feature_frame(pl.concat([am, pm]))
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vr = frame["vol_ratio_3m_today"].to_list()
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# 上午 8 根: idx5 起 3/3 窗口成立(idx5,6,7 非 null); 下午 4 根全部 null(窗口不足)
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assert vr[5] is not None and vr[7] is not None
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assert all(v is None for v in vr[n_am:n_am + n_pm])
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def test_feature_frame_open_30m_and_day_extremes():
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frame = build_feature_frame(_bars([10.0 + 0.1 * i for i in range(32)]))
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o30h = frame["open_30m_high_dist"].to_list()
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assert all(v is None for v in o30h[:29]) # 开盘未满 30 分钟
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# _bars 里 high = price*1.01 → 第 30 根 close/开盘30分钟最高 = 1/1.01
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assert o30h[29] == pytest.approx(1.0 / 1.01 - 1)
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assert o30h[31] == pytest.approx(13.1 / (12.9 * 1.01) - 1)
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assert frame["day_high_dist"][5] == pytest.approx(1.0 / 1.01 - 1)
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# 累计最低 = 首根 low(10.0*0.99), close_5 = 10.5
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assert frame["day_low_dist"][5] == pytest.approx(10.5 / 9.9 - 1)
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def test_feature_frame_cutoff_drops_incomplete_bar():
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df = _bars([10.0, 10.1, 10.2])
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frame = build_feature_frame(df, cutoff=datetime(2026, 9, 4, 9, 32))
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assert frame.height == 2
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# ══ 校验与编译 ═══════════════════════════════════════════
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def _intraday_sig(**overrides) -> dict:
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sig = {
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"id": "test_sig", "name": "测试", "kind": "entry",
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"timeframe": "intraday",
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"conditions": [{"left": "price", "op": "cross_up", "right": "field:vwap"}],
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}
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sig.update(overrides)
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return sig
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def test_validate_intraday_accepts_and_rejects():
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custom_signals.validate(_intraday_sig())
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custom_signals.validate(_intraday_sig(
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conditions=[{"left": "vol_ratio_3m_today", "op": ">", "right": "3"}],
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min_bars=10,
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))
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with pytest.raises(ValueError, match="盘中字段"):
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custom_signals.validate(_intraday_sig(conditions=[{"left": "ma5", "op": ">", "right": "1"}]))
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with pytest.raises(ValueError, match="运算符"):
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custom_signals.validate(_intraday_sig(conditions=[{"left": "price", "op": "~", "right": "1"}]))
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with pytest.raises(ValueError, match="日期偏移"):
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custom_signals.validate(_intraday_sig(
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conditions=[{"left": "price", "op": ">", "right": "1", "leftDays": 1}]
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))
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with pytest.raises(ValueError, match="min_bars"):
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custom_signals.validate(_intraday_sig(min_bars=500))
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with pytest.raises(ValueError, match="timeframe"):
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custom_signals.validate(_intraday_sig(timeframe="weekly"))
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def test_build_intraday_expressions_edge_semantics():
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# 价格下探再上穿 vwap: 上升沿恰好只在上穿那根 bar 为 true
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prices = [10.0, 10.0, 10.0, 10.0, 9.8, 9.7, 9.6, 9.6, 9.6, 10.0, 10.2, 10.3]
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frame = build_feature_frame(_bars(prices))
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exprs = custom_signals.build_intraday_expressions([_intraday_sig()])
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col = custom_signals.intraday_column_name("test_sig")
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assert col == "csgi_test_sig"
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out = custom_signals.apply_intraday_edges(frame, exprs)
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fired = [i for i, v in enumerate(out[col].to_list()) if v]
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assert len(fired) == 1
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# idx8: 9.6 < vwap; idx9: 10.0 > vwap → 上穿在第 9 根(0 起)
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assert fired[0] == 9
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# 比较条件的上升沿: 持续满足只触发一次(本序列条件自首根即为真 → 只在回升的 idx9 触发)
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sig2 = _intraday_sig(id="test_state", conditions=[{"left": "price", "op": ">", "right": "9.65"}])
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exprs2 = custom_signals.build_intraday_expressions([sig2])
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out2 = custom_signals.apply_intraday_edges(frame, exprs2)
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fired2 = [i for i, v in enumerate(out2["csgi_test_state"].to_list()) if v]
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assert fired2 == [9]
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# 振荡序列: 每次 false→true 各触发一次
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osc = build_feature_frame(_bars([10.0, 9.6, 10.0, 9.6, 10.0]))
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exprs3 = custom_signals.build_intraday_expressions([sig2])
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out3 = custom_signals.apply_intraday_edges(osc, exprs3)
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assert [i for i, v in enumerate(out3["csgi_test_state"].to_list()) if v] == [2, 4]
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# ══ v1 黄金等价: 旧累计循环算法作参照 ═════════════════════
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def _legacy_v1_triggers(prices: list[float], prev_close: float | None) -> dict[str, int]:
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"""复刻 v1 IntradaySignalEvaluator 的判定(逐 bar 累计, 边沿触发)。"""
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cum_vol = cum_amt = 0.0
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fired: dict[str, int] = {}
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prev_price = prev_vwap = None
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for i, p in enumerate(prices):
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cum_vol += 100.0
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cum_amt += p * 100.0 * 100.0
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vwap = cum_amt / (cum_vol * 100.0)
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if i >= 1:
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if prev_price <= prev_vwap and p > vwap:
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fired.setdefault("avg_up", i)
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if prev_price >= prev_vwap and p < vwap:
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fired.setdefault("avg_down", i)
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if prev_close and prev_price <= prev_close and p > prev_close:
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fired.setdefault("zero_up", i)
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if prev_close and prev_price >= prev_close and p < prev_close:
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fired.setdefault("zero_down", i)
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prev_price, prev_vwap = p, vwap
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return fired
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@pytest.mark.parametrize("prices,prev_close", [
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([10.0, 10.0, 10.0, 10.0, 9.8, 9.7, 9.6, 9.6, 9.6, 10.0, 10.2, 10.3], 10.0),
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([9.0, 9.1, 9.2, 9.3, 9.4, 9.3, 9.2, 9.1, 9.0, 8.9, 8.8, 8.7], 9.25),
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([10.0, 9.9, 10.1, 9.8, 10.2, 9.7, 10.3, 9.6, 10.4, 9.5, 10.5, 9.4], 9.95),
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])
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def test_evaluator_matches_legacy_v1(prices, prev_close):
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"""新评估器逐 bar 喂入, 触发时点必须与 v1 算法完全一致。"""
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df = _bars(prices)
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evaluator = IntradaySignalEvaluator()
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got: dict[str, int] = {}
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for t in range(1, len(prices) + 1):
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now = df["datetime"][t - 1] + timedelta(minutes=1)
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rows = evaluator.evaluate(
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df.head(t), symbols={SYMBOL}, prev_close={SYMBOL: prev_close},
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asset_type="stock", now=now,
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)
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for r in rows:
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if r.get("signal_intraday_avg_cross_up"):
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got.setdefault("avg_up", t - 1)
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if r.get("signal_intraday_avg_cross_down"):
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got.setdefault("avg_down", t - 1)
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if r.get("signal_intraday_zero_cross_up"):
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got.setdefault("zero_up", t - 1)
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if r.get("signal_intraday_zero_cross_down"):
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got.setdefault("zero_down", t - 1)
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assert got == _legacy_v1_triggers(prices, prev_close)
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def test_evaluator_no_refire_without_new_bar():
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prices = [10.0, 9.0, 10.5]
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df = _bars(prices)
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evaluator = IntradaySignalEvaluator()
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# 逐根喂入: 首轮只建状态; 新 bar 出现才可能触发; 同批 bar 重跑不重复触发
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fired: list[dict] = []
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for t in range(1, len(prices) + 1):
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now = df["datetime"][t - 1] + timedelta(minutes=1)
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fired += evaluator.evaluate(
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df.head(t), symbols={SYMBOL}, prev_close={SYMBOL: 10.0}, asset_type="stock", now=now,
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)
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assert fired # 9.0 下穿 / 10.5 上穿均有触发
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now3 = df["datetime"][2] + timedelta(minutes=1)
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again = evaluator.evaluate(df, symbols={SYMBOL}, prev_close={SYMBOL: 10.0}, asset_type="stock", now=now3)
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assert again == [] # 无新 bar → 不重复触发
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def test_evaluator_custom_csgi_signal_and_inject():
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sig = _intraday_sig(id="my_intraday", conditions=[{"left": "price", "op": "cross_up", "right": 10.05}])
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prices = [10.0, 9.9, 9.8, 10.1, 10.2, 10.3]
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df = _bars(prices)
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evaluator = IntradaySignalEvaluator()
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fired_rows = []
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for t in range(1, len(prices) + 1):
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now = df["datetime"][t - 1] + timedelta(minutes=1)
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fired_rows += evaluator.evaluate(
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df.head(t), symbols={SYMBOL}, prev_close={}, asset_type="stock",
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now=now, signals=[sig],
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)
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# 上穿 10.05 发生在 idx3 (10.1)
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assert any(r.get("csgi_my_intraday") for r in fired_rows)
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assert len([r for r in fired_rows if r.get("csgi_my_intraday")]) == 1
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# inject 的契约是"单桶结果": 只传最后一个触发桶的行
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last_bucket = fired_rows[-1:] if fired_rows else []
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enriched = pl.DataFrame({"symbol": [SYMBOL, "999999.SZ"], "close": [10.0, 5.0]})
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injected = evaluator.inject(enriched, last_bucket)
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assert injected.height == 2 # 单桶单行, join 不膨胀
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assert injected["csgi_my_intraday"].to_list() == [True, False] # 未触发标的补 False
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assert set(injected.columns) >= {"signal_intraday_avg_cross_up", "signal_intraday_avg_cross_down"}
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def test_uses_intraday_signals_matches_csgi_fields():
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assert uses_intraday_signals({"conditions": [{"op": "truth", "field": "signal_intraday_avg_cross_up"}]})
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assert uses_intraday_signals({"conditions": [{"op": "truth", "field": "csgi_my_intraday"}]})
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assert not uses_intraday_signals({"conditions": [{"op": "truth", "field": "csg_daily_sig"}]})
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assert not uses_intraday_signals({"conditions": [{"op": ">", "field": "close", "value": 1}]})
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# ══ 引擎注入 + 加载缓存 ═══════════════════════════════════
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def _engine(tmp_path: Path):
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from app.strategy.engine import StrategyEngine
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custom_dir = tmp_path / "strategies" / "custom"
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custom_dir.mkdir(parents=True, exist_ok=True)
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return StrategyEngine(strategy_dirs=[custom_dir])
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def test_engine_injects_csgi_columns_into_minute_frame(tmp_path: Path):
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sig = _intraday_sig(id="eng_sig", conditions=[{"left": "price", "op": "cross_up", "right": "field:vwap"}])
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sig_dir = tmp_path / "user_data" / "custom_signals"
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sig_dir.mkdir(parents=True, exist_ok=True)
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(sig_dir / "eng_sig.json").write_text(json.dumps(sig), encoding="utf-8")
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engine = _engine(tmp_path)
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assert engine._user_data_dir() == tmp_path
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prices = [10.0, 10.0, 9.0, 9.0, 10.5, 10.6]
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injected = engine._inject_intraday_signal_columns(_bars(prices))
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assert "csgi_eng_sig" in injected.columns
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fired = [i for i, v in enumerate(injected["csgi_eng_sig"].to_list()) if v]
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assert fired == [4] # idx4 上穿 vwap
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# 定义缓存指纹失效: 改文件后引擎立刻读到新定义
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(sig_dir / "eng_sig.json").write_text(json.dumps(
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_intraday_sig(id="eng_sig", conditions=[{"left": "price", "op": "<", "right": "9.5"}]),
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), encoding="utf-8")
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injected2 = engine._inject_intraday_signal_columns(_bars(prices))
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assert injected2["csgi_eng_sig"].to_list()[2] is True or injected2["csgi_eng_sig"].to_list()[2] == True # noqa: E712
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def test_engine_skips_injection_without_definitions(tmp_path: Path):
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engine = _engine(tmp_path)
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df = _bars([10.0, 10.1])
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out = engine._inject_intraday_signal_columns(df)
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assert out is df or out.equals(df)
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# ══ 回放 API ═════════════════════════════════════════════
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def _seed_repo(tmp_path: Path):
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from app.tickflow.repository import DataStore, KlineRepository
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def minute(day: str, prices: list[float]):
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part = tmp_path / "kline_minute" / f"date={day}" / "part.parquet"
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part.parent.mkdir(parents=True, exist_ok=True)
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_bars(prices, start=datetime.fromisoformat(f"{day}T09:30:00")).with_columns(
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pl.col("datetime").cast(pl.Datetime("us"))
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).write_parquet(part)
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def daily(day: str, closes: dict[str, float]):
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part = tmp_path / "kline_daily" / f"date={day}" / "part.parquet"
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part.parent.mkdir(parents=True, exist_ok=True)
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pl.DataFrame({"symbol": list(closes), "close": list(closes.values())}).write_parquet(part)
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minute("2026-09-03", [10.0, 9.9, 9.8, 9.9, 10.0, 10.1])
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minute("2026-09-04", [10.0, 10.0, 9.0, 9.0, 10.5, 10.6])
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daily("2026-09-02", {SYMBOL: 9.95})
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daily("2026-09-03", {SYMBOL: 10.0})
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return KlineRepository(DataStore(tmp_path))
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def test_intraday_replay_endpoint(tmp_path: Path):
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from app.api.signals import IntradayReplayRequest, intraday_replay
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repo = _seed_repo(tmp_path)
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request = SimpleNamespace(app=SimpleNamespace(state=SimpleNamespace(repo=repo)))
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sig = _intraday_sig(id="replay_sig")
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sig_dir = tmp_path / "user_data" / "custom_signals"
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sig_dir.mkdir(parents=True, exist_ok=True)
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(sig_dir / "replay_sig.json").write_text(json.dumps(sig), encoding="utf-8")
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daily_sig = {
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"id": "daily_sig", "name": "日线信号", "kind": "entry",
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"conditions": [{"left": "close", "op": ">", "right": "1"}],
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}
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(sig_dir / "daily_sig.json").write_text(json.dumps(daily_sig), encoding="utf-8")
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|
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result = intraday_replay(IntradayReplayRequest(
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signal_id="replay_sig", start_date="2026-09-03", end_date="2026-09-04",
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symbols=[SYMBOL],
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), request)
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assert result["days_scanned"] == 2
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times = [(t["date"], t["time"]) for t in result["triggers"]]
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# 09-03: 10.0→9.x→10.0 上穿在 idx4(09:34); 09-04: 上穿在 idx4(09:34)
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assert times == [("2026-09-03", "09:34:00"), ("2026-09-04", "09:34:00")]
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|
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# 日线信号不可回放
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with pytest.raises(Exception, match="盘中"):
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intraday_replay(IntradayReplayRequest(
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signal_id="daily_sig", start_date="2026-09-03", end_date="2026-09-04",
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|
symbols=[SYMBOL],
|
|
), request)
|