Yuxuan Yan
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
"""Tests for cross-sectional IC evaluation."""
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import numpy as np
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import pandas as pd
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from eval.metrics import evaluate_cross_sectional
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def test_cross_sectional_keys_present():
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dates = pd.date_range("2024-01-01", periods=10)
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cols = ["a", "b", "c"]
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rng = np.random.default_rng(0)
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signals = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
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returns = pd.DataFrame(rng.standard_normal((10, 3)), index=dates, columns=cols)
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res = evaluate_cross_sectional(signals, returns)
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for key in (
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"ic_mean", "ic_std", "ir", "rank_ic_mean", "rank_ic_std",
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"rank_ir", "hit_rate", "n_periods",
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):
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assert key in res
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def test_perfect_signal_has_positive_rank_ic():
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# When the signal equals next-period returns, rank IC should be ~1 each day.
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dates = pd.date_range("2024-01-01", periods=8)
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cols = ["a", "b", "c"]
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rng = np.random.default_rng(42)
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returns = pd.DataFrame(rng.standard_normal((8, 3)), index=dates, columns=cols)
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signals = returns.copy() # perfect foresight
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res = evaluate_cross_sectional(signals, returns)
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assert res["rank_ic_mean"] > 0.99
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assert res["hit_rate"] == 1.0
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assert res["n_periods"] == 8
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def test_inverted_signal_has_negative_rank_ic():
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dates = pd.date_range("2024-01-01", periods=6)
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cols = ["a", "b", "c"]
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rng = np.random.default_rng(7)
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returns = pd.DataFrame(rng.standard_normal((6, 3)), index=dates, columns=cols)
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signals = -returns # perfectly wrong
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res = evaluate_cross_sectional(signals, returns)
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assert res["rank_ic_mean"] < -0.99
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def test_single_stock_falls_back_to_rolling():
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dates = pd.date_range("2024-01-01", periods=40)
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rng = np.random.default_rng(1)
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signals = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
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returns = pd.DataFrame({"a": rng.standard_normal(40)}, index=dates)
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res = evaluate_cross_sectional(signals, returns)
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# Rolling fallback still yields the standard metric keys.
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assert "rank_ic_mean" in res
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assert res["n_periods"] > 0
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