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https://ghfast.top/https://github.com/aeroxw/tick-stock-panel.git
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lytem28
b6cf0495e1
Unify strategy execution across backtest, screener, and monitoring; isolate backtest workloads in spawn workers; and add shared matrix caching plus valid-bar indicator acceleration.
63 lines
2.4 KiB
Python
63 lines
2.4 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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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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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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