Merge pull request #280 from yongzhengqi/main

Implement Enhanced Indexing as a Portfolio Optimizer

tests/test_structured_cov_estimator.py

@@ -0,0 +1,111 @@
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import unittest
+import numpy as np
+from scipy.linalg import sqrtm
+
+from qlib.model.riskmodel import StructuredCovEstimator
+
+
+class TestStructuredCovEstimator(unittest.TestCase):
+    def test_random_covariance(self):
+        # Try to estimate the covariance from a randomly generated matrix.
+        NUM_VARIABLE = 10
+        NUM_OBSERVATION = 200
+        EPS = 1e-6
+
+        estimator = StructuredCovEstimator(scale_return=False, assume_centered=True)
+
+        X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
+
+        est_cov = estimator.predict(X, is_price=False)
+        np_cov = np.cov(X.T)  # While numpy assume row means variable, qlib assume the other wise.
+
+        delta = abs(est_cov - np_cov)
+        if_identical = (delta < EPS).all()
+
+        self.assertTrue(if_identical)
+
+    def test_nan_option_covariance(self):
+        # Test if nan_option is correctly passed.
+        NUM_VARIABLE = 10
+        NUM_OBSERVATION = 200
+        EPS = 1e-6
+
+        estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill")
+
+        X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
+
+        est_cov = estimator.predict(X, is_price=False)
+        np_cov = np.cov(X.T)  # While numpy assume row means variable, qlib assume the other wise.
+
+        delta = abs(est_cov - np_cov)
+        if_identical = (delta < EPS).all()
+
+        self.assertTrue(if_identical)
+
+    def test_decompose_covariance(self):
+        # Test if return_decomposed_components is correctly passed.
+        NUM_VARIABLE = 10
+        NUM_OBSERVATION = 200
+
+        estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, nan_option="fill")
+
+        X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
+
+        F, cov_b, var_u = estimator.predict(X, is_price=False, return_decomposed_components=True)
+
+        self.assertTrue(F is not None and cov_b is not None and var_u is not None)
+
+    def test_constructed_covariance(self):
+        # Try to estimate the covariance from a specially crafted matrix.
+        # There should be some significant correlation since X is specially crafted.
+        NUM_VARIABLE = 7
+        NUM_OBSERVATION = 500
+        EPS = 0.1
+
+        estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_VARIABLE - 1)
+
+        sqrt_cov = None
+        while sqrt_cov is None or (np.iscomplex(sqrt_cov)).any():
+            cov = np.random.rand(NUM_VARIABLE, NUM_VARIABLE)
+            for i in range(NUM_VARIABLE):
+                cov[i][i] = 1
+            sqrt_cov = sqrtm(cov)
+        X = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE) @ sqrt_cov
+
+        est_cov = estimator.predict(X, is_price=False)
+        np_cov = np.cov(X.T)  # While numpy assume row means variable, qlib assume the other wise.
+
+        delta = abs(est_cov - np_cov)
+        if_identical = (delta < EPS).all()
+
+        self.assertTrue(if_identical)
+
+    def test_decomposition(self):
+        # Try to estimate the covariance from a specially crafted matrix.
+        # The matrix is generated in the assumption that observations can be predicted by multiple factors.
+        NUM_VARIABLE = 30
+        NUM_OBSERVATION = 100
+        NUM_FACTOR = 10
+        EPS = 0.1
+
+        estimator = StructuredCovEstimator(scale_return=False, assume_centered=True, num_factors=NUM_FACTOR)
+
+        F = np.random.rand(NUM_VARIABLE, NUM_FACTOR)
+        B = np.random.rand(NUM_FACTOR, NUM_OBSERVATION)
+        U = np.random.rand(NUM_OBSERVATION, NUM_VARIABLE)
+        X = (F @ B).T + U
+
+        est_cov = estimator.predict(X, is_price=False)
+        np_cov = np.cov(X.T)  # While numpy assume row means variable, qlib assume the other wise.
+
+        delta = abs(est_cov - np_cov)
+        if_identical = (delta < EPS).all()
+
+        self.assertTrue(if_identical)
+
+
+if __name__ == "__main__":
+    unittest.main()