added tests for risk analysis

alphamind/analysis/riskanalysis.py

@@ -35,30 +35,3 @@ def risk_analysis(net_weight_series: pd.Series,
     explained_table = pd.DataFrame(explained_table * net_pos , columns=cols, index=net_weight_series.index)
     exposure_table = pd.DataFrame(exposure[:, :, 0] * net_pos, columns=risk_factor_cols, index=net_weight_series.index)
     return explained_table, exposure_table.groupby(level=0).first()
-
-
-if __name__ == '__main__':
-    from matplotlib import pyplot as plt
-
-    n_samples = 36000
-    n_dates = 20
-    n_risk_factors = 35
-
-    dates = np.sort(np.random.randint(n_dates, size=n_samples))
-    weights_series = pd.Series(data=np.random.randn(n_samples), index=dates)
-    bm_series = pd.Series(data=np.random.randn(n_samples), index=dates)
-    next_bar_return_series = pd.Series(data=np.random.randn(n_samples), index=dates)
-    risk_table = pd.DataFrame(data=np.random.randn(n_samples, n_risk_factors),
-                              columns=list(range(n_risk_factors)),
-                              index=dates)
-
-    explained_table, exposure_table = risk_analysis(weights_series - bm_series, next_bar_return_series, risk_table)
-
-    aggregated_bars = explained_table.groupby(level=0).sum()
-    top_sources = aggregated_bars.sum().abs().sort_values(ascending=False).index[:10]
-    aggregated_bars.sum().sort_values(ascending=False).plot(kind='bar', figsize=(16, 8))
-
-    exposure_table[top_sources.difference(['idiosyncratic'])].plot(figsize=(14, 7))
-
-    plt.show()
-

alphamind/tests/analysis/test_riskanalysis.py

@@ -5,6 +5,36 @@ Created on 2017-5-8
 @author: cheng.li
 """
 
-
+import unittest
+import numpy as np
+import pandas as pd
 from alphamind.analysis.riskanalysis import risk_analysis
 
+
+class TestRiskAnalysis(unittest.TestCase):
+
+    def test_risk_analysis(self):
+        n_samples = 36000
+        n_dates = 20
+        n_risk_factors = 35
+
+        dates = np.sort(np.random.randint(n_dates, size=n_samples))
+        weights_series = pd.Series(data=np.random.randn(n_samples), index=dates)
+        bm_series = pd.Series(data=np.random.randn(n_samples), index=dates)
+        next_bar_return_series = pd.Series(data=np.random.randn(n_samples), index=dates)
+        risk_table = pd.DataFrame(data=np.random.randn(n_samples, n_risk_factors),
+                                  columns=list(range(n_risk_factors)),
+                                  index=dates)
+
+        explained_table, exposure_table = risk_analysis(weights_series - bm_series,
+                                                        next_bar_return_series,
+                                                        risk_table)
+
+        to_explain = (weights_series - bm_series).multiply(next_bar_return_series, axis=0)
+        aggregated = explained_table.sum(axis=1)
+
+        np.testing.assert_array_almost_equal(to_explain.values, aggregated.values)
+
+
+if __name__ == '__main__':
+    unittest.main()

alphamind/tests/test_suite.py

@@ -18,6 +18,7 @@ from alphamind.tests.portfolio.test_rankbuild import TestRankBuild
 from alphamind.tests.portfolio.test_percentbuild import TestPercentBuild
 from alphamind.tests.portfolio.test_linearbuild import TestLinearBuild
 from alphamind.tests.settlement.test_simplesettle import TestSimpleSettle
+from alphamind.tests.analysis.test_riskanalysis import TestRiskAnalysis
 
 
 if __name__ == '__main__':
@@ -27,6 +28,7 @@ if __name__ == '__main__':
                          TestRankBuild,
                          TestPercentBuild,
                          TestLinearBuild,
-                         TestSimpleSettle],
+                         TestSimpleSettle,
+                         TestRiskAnalysis],
                         alpha_logger)
     runner.run()