modified risk analysis implementation

alphamind/analysis/riskanalysis.py

@@ -11,16 +11,16 @@ import pandas as pd
 from alphamind.data.neutralize import neutralize
 
 
-def risk_analysis(return_series: pd.Series,
-                  risk_return_table: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
+def risk_analysis(net_weight_series: pd.Series,
+                  next_bar_return_series: pd.Series,
+                  risk_table: pd.DataFrame) -> Tuple[pd.DataFrame, pd.DataFrame]:
 
-    group_idx = return_series.index.values.astype(int)
-    return_values = return_series.values
-    risk_return_values = risk_return_table.values
-    risk_factor_cols = risk_return_table.columns
+    group_idx = net_weight_series.index.values.astype(int)
+    net_pos = net_weight_series.values.reshape((-1, 1))
+    risk_factor_cols = risk_table.columns
 
-    idiosyncratic, other_stats = neutralize(risk_return_values,
-                                            return_values,
+    idiosyncratic, other_stats = neutralize(risk_table.values,
+                                            next_bar_return_series.values,
                                             group_idx,
                                             output_exposure=True,
                                             output_explained=True)
@@ -32,25 +32,27 @@ def risk_analysis(return_series: pd.Series,
     cols = ['idiosyncratic']
     cols.extend(risk_factor_cols)
 
-    explained_table = pd.DataFrame(explained_table, columns=cols, index=return_series.index)
-    exposure_table = pd.DataFrame(exposure[:, :, 0], columns=risk_factor_cols, index=return_series.index)
+    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 = 360000
-    n_dates = 200
+    n_samples = 36000
+    n_dates = 20
     n_risk_factors = 35
 
     dates = np.sort(np.random.randint(n_dates, size=n_samples))
-    return_series = pd.Series(data=np.random.randn(n_samples), index=dates)
-    risk_return_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(return_series, risk_return_table)
+    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]

alphamind/tests/analysis/__init__.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-5-8
+
+@author: cheng.li
+"""

alphamind/tests/analysis/test_riskanalysis.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-5-8
+
+@author: cheng.li
+"""
+
+
+from alphamind.analysis.riskanalysis import risk_analysis
+