34 lines
1.3 KiB
Python
34 lines
1.3 KiB
Python
import numpy as np
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import pandas as pd
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from src.live.tsmom_worker import TsmomWorker
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def _df(n=300, slope=1.0):
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c = np.linspace(100, 100 + slope * n, n)
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ts = (pd.date_range("2023-01-01", periods=n, freq="1D", tz="UTC").astype("int64") // 10**6)
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return pd.DataFrame({"timestamp": ts, "open": c, "high": c, "low": c, "close": c, "volume": 1.0})
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def test_tsmom_selects_full_consensus_uptrend(tmp_path):
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# tutti gli orizzonti positivi -> score=1>=thr; BTC su -> risk_on
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w = TsmomWorker(universe=["BTC", "AAA"], horizons=(63, 126, 252), thr=1.0,
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gross=0.30, data_dir=tmp_path)
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data = {"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)}
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w.tick(data)
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assert w.weights["BTC"] > 0 and w.weights["AAA"] > 0
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assert abs(sum(w.weights.values()) - 0.30) < 1e-9
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def test_tsmom_flat_when_risk_off(tmp_path):
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w = TsmomWorker(universe=["BTC", "AAA"], thr=1.0, gross=0.30, data_dir=tmp_path)
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data = {"BTC": _df(slope=-1.0), "AAA": _df(slope=2.0)}
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w.tick(data)
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assert sum(w.weights.values()) == 0.0
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def test_tsmom_persists_and_resumes(tmp_path):
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w = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
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w.tick({"BTC": _df(slope=1.0), "AAA": _df(slope=2.0)})
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w2 = TsmomWorker(universe=["BTC", "AAA"], gross=0.30, data_dir=tmp_path)
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assert w2.weights == w.weights
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