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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.
34 lines
1.1 KiB
Python
34 lines
1.1 KiB
Python
from __future__ import annotations
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from datetime import date
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import polars as pl
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from app.backtest.matrix import build_market_data_matrix
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from app.strategy.builtin import high_turnover_surge
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def test_high_turnover_surge_uses_percent_value_turnover_rate():
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panel = pl.DataFrame({
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"symbol": ["low", "hit", "low", "hit"],
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"date": [date(2024, 1, 2), date(2024, 1, 2), date(2024, 1, 3), date(2024, 1, 3)],
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"open": [100.0, 100.0, 104.0, 104.0],
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"high": [100.0, 100.0, 104.0, 104.0],
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"low": [100.0, 100.0, 104.0, 104.0],
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"close": [100.0, 100.0, 104.0, 104.0],
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"volume": [1000.0, 1000.0, 1000.0, 1000.0],
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"turnover_rate": [4.9, 5.1, 4.9, 5.1],
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})
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market = build_market_data_matrix(panel, field_columns={"turnover_rate"})
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signals = high_turnover_surge.MATRIX_STRATEGY.compute_signals(
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market,
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{"min_turnover": 5.0, "min_change": 3.0},
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)
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selected = [
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symbol
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for symbol, hit in zip(market.symbols, signals.entry[-1], strict=True)
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if hit
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]
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assert selected == ["hit"]
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