42 lines
1.3 KiB
Python
42 lines
1.3 KiB
Python
import numpy as np
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import pandas as pd
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import pytest
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from src.portfolio import weighting as W
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def test_family_of():
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assert W.family_of("PR_ETHBTC") == "PAIRS"
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assert W.family_of("SH_BTC") == "SHAPE"
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assert W.family_of("TSM01") == "TSM"
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assert W.family_of("MR01_BTC") == "FADE"
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assert W.family_of("DIP01_BTC") == "HONEST"
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def test_equal_sums_to_one():
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w = W.equal(["a", "b", "c", "d"])
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assert pytest.approx(sum(w.values())) == 1.0
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assert all(abs(v - 0.25) < 1e-9 for v in w.values())
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def test_manual_normalizes():
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w = W.manual(["a", "b"], {"a": 3, "b": 1})
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assert pytest.approx(w["a"]) == 0.75 and pytest.approx(w["b"]) == 0.25
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def test_cap_limits_family_and_redistributes():
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ids = ["PR_ETHBTC", "PR_LTCETH", "MR01_BTC", "MR02_BTC"]
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w = W.cap(ids, caps={"PAIRS": 0.30})
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pairs_w = w["PR_ETHBTC"] + w["PR_LTCETH"]
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assert pytest.approx(pairs_w, abs=1e-9) == 0.30
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assert pytest.approx(sum(w.values())) == 1.0
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assert w["MR01_BTC"] > 0.25
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def test_inverse_vol_prefers_low_vol():
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idx = pd.date_range("2024-01-01", periods=100, freq="D", tz="UTC")
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rng = np.random.default_rng(0)
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df = pd.DataFrame({"lo": rng.normal(0, 0.01, 100), "hi": rng.normal(0, 0.05, 100)}, index=idx)
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w = W.inverse_vol(["lo", "hi"], df, lookback=90)
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assert w["lo"] > w["hi"]
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assert pytest.approx(sum(w.values())) == 1.0
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