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shy3130
918c7ade7e
- 策略: 引擎只加载 data/strategies/{custom,ai,composite} 与研究模板目录,
原 25 个内置策略一次性迁移为用户自定义文件(ID 不变, 引用零迁移);
研究模板移至 app/strategy/research, 打包 spec 同步; 新装环境策略库为空
- 信号: 管线不再硬编码任何信号表达式; 涨跌停判定价成为运行时列
limit_up_price/limit_down_price(生效价∓0.005 容差), 20 个信号转为
data/user_data/custom_signals 定义(显式 column 沿用 signal_* 原列名);
依赖解析/注入顺序/当日路径 prev-join 全部定义驱动; 语义统一为历史路径
口径(顺带修正当日路径 BOLL 比较符与翘板/炸板空值两处既有偏差)
- 前端: 信号库单列表(全部可编辑), SignalPicker/监控选项/信号标签全走
定义接口; 回测页新增免责声明(模拟性质/数据源准确性不作保证/不构成
投资建议)
- 测试: 25 个策略夹具与 20 个信号种子入库 tests/fixtures, 受影响测试
全部自包含化(不依赖运行目录用户数据); 77 万行 MA 死叉新旧表达式对拍
逐行一致; 回测实跑 45 笔卖点信号出场验证矩阵链路
- 同批包含工作区既有进度: 策略结果 ext 列同日投影修复(含测试)、数据源插件、fuyao/auth/api_gateway/data_integrity 调整、README 信号库
描述与 strategy.md 导入路径补充
验证: 后端 2592 passed(空数据目录 CI 模拟 2588 passed, 4 例为模拟环境
伪影复验排除); 前端 vitest 125 passed + tsc 0 错 + vite build 通过
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
from __future__ import annotations
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from datetime import date, timedelta
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import polars as pl
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import pytest
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from app.indicators.pipeline import compute_indicators, compute_limit_signals, compute_signals
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def _bars(n: int = 90) -> pl.DataFrame:
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rows = []
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for symbol, offset in (("600000", 0.0), ("300001", 2.0)):
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for i in range(n):
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close = 10.0 + offset + i * 0.03 + ((i % 7) - 3) * 0.04
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rows.append({
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"symbol": symbol,
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"date": date(2024, 1, 1) + timedelta(days=i),
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"open": close - 0.02,
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"high": close + 0.10,
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"low": close - 0.10,
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"close": close,
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"volume": 1000 + i * 10,
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"amount": close * (1000 + i * 10),
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"raw_close": close,
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"raw_high": close + 0.10,
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"raw_low": close - 0.10,
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})
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return pl.DataFrame(rows)
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@pytest.fixture(autouse=True)
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def _migrated_signal_definitions(monkeypatch):
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"""注入迁移自内置的信号定义 (信号列定义驱动, 不依赖运行目录)。"""
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from tests.signal_seeds import install_pipeline_caches
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install_pipeline_caches(monkeypatch)
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def test_compute_indicators_assume_sorted_matches_default_values():
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bars = _bars().sort(["symbol", "date"])
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default = compute_indicators(bars, needed={"macd_hist", "rsi_14", "momentum_20d"})
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fast = compute_indicators(
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bars,
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needed={"macd_hist", "rsi_14", "momentum_20d"},
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assume_sorted=True,
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)
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assert fast.equals(default)
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def test_compute_signals_subset_matches_full_values():
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indicators = compute_indicators(_bars())
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full = compute_signals(indicators)
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subset = compute_signals(indicators, needed={"signal_macd_golden", "signal_volume_surge"})
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assert "signal_macd_golden" in subset.columns
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assert "signal_volume_surge" in subset.columns
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assert "signal_ma20_breakout" not in subset.columns
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assert subset["signal_macd_golden"].equals(full["signal_macd_golden"])
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assert subset["signal_volume_surge"].equals(full["signal_volume_surge"])
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def test_compute_signals_empty_needed_adds_no_signals():
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indicators = compute_indicators(_bars(), needed={"ma20"})
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result = compute_signals(indicators, needed=set())
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assert not any(col.startswith(("signal_", "csg_")) for col in result.columns)
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def test_compute_limit_signals_subset_matches_full_and_prunes_other_outputs():
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bars = compute_indicators(_bars(), needed={"change_pct"})
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instruments = pl.DataFrame({
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"symbol": ["600000", "300001"],
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"name": ["浦发银行", "测试股份"],
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"float_shares": [1_000_000_000.0, 500_000_000.0],
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})
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full = compute_limit_signals(bars, instruments)
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subset = compute_limit_signals(bars, instruments, needed={"signal_limit_up"})
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assert "signal_limit_up" in subset.columns
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assert "signal_limit_down" not in subset.columns
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assert "signal_broken_limit_up" not in subset.columns
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assert subset["signal_limit_up"].equals(full["signal_limit_up"])
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