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112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import unittest
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import numpy as np
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from scipy.linalg import sqrtm
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from qlib.model.riskmodel import StructuredCovEstimator
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class TestStructuredCovEstimator(unittest.TestCase):
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def test_random_covariance(self):
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# Try to estimate the covariance from a randomly generated matrix.
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NUM_VARIABLE = 10
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NUM_OBSERVATION = 200
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EPS = 1e-6
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estimator = StructuredCovEstimator(scale_return=False, assume_centered=True)
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X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
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est_cov = estimator.predict(X, is_price=False)
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np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise.
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delta = abs(est_cov - np_cov)
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if_identical = (delta < EPS).all()
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self.assertTrue(if_identical)
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def test_nan_option_covariance(self):
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# Test if nan_option is correctly passed.
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NUM_VARIABLE = 10
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NUM_OBSERVATION = 200
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EPS = 1e-6
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estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill")
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X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
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est_cov = estimator.predict(X, is_price=False)
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np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise.
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delta = abs(est_cov - np_cov)
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if_identical = (delta < EPS).all()
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self.assertTrue(if_identical)
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def test_decompose_covariance(self):
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# Test if return_decomposed_components is correctly passed.
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NUM_VARIABLE = 10
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NUM_OBSERVATION = 200
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estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill")
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X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
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F, cov_b, var_u = estimator.predict(X, is_price=False, return_decomposed_components=True)
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self.assertTrue(F is not None and cov_b is not None and var_u is not None)
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def test_constructed_covariance(self):
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# Try to estimate the covariance from a specially crafted matrix.
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# There should be some significant correlation since X is specially crafted.
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NUM_VARIABLE = 7
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NUM_OBSERVATION = 500
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EPS = 0.1
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estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_VARIABLE - 1)
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sqrt_cov = None
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while sqrt_cov is None or (np.iscomplex(sqrt_cov)).any():
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cov = np.random.rand(NUM_VARIABLE, NUM_VARIABLE)
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for i in range(NUM_VARIABLE):
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cov[i][i] = 1
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sqrt_cov = sqrtm(cov)
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X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) @ sqrt_cov
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est_cov = estimator.predict(X, is_price=False)
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np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise.
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delta = abs(est_cov - np_cov)
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if_identical = (delta < EPS).all()
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self.assertTrue(if_identical)
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def test_decomposition(self):
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# Try to estimate the covariance from a specially crafted matrix.
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# The matrix is generated in the assumption that observations can be predicted by multiple factors.
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NUM_VARIABLE = 30
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NUM_OBSERVATION = 100
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NUM_FACTOR = 10
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EPS = 0.1
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estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_FACTOR)
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F = np.random.rand(NUM_VARIABLE, NUM_FACTOR)
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B = np.random.rand(NUM_FACTOR, NUM_OBSERVATION)
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U = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
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X = (F @ B).T + U
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est_cov = estimator.predict(X, is_price=False)
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np_cov = np.cov(X.T) # While numpy assume row means variable, qlib assume the other wise.
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delta = abs(est_cov - np_cov)
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if_identical = (delta < EPS).all()
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self.assertTrue(if_identical)
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if __name__ == "__main__":
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unittest.main()
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