fixed bug

alphamind/analysis/crosssetctions.py

@@ -41,6 +41,7 @@ def cross_section_analysis(ref_date,
 
     dx_returns = engine.fetch_dx_return(ref_date, codes, horizon=horizon, offset=1)
     target_pos = pd.merge(target_pos, dx_returns, on=['code'])
+    target_pos = pd.merge(target_pos, total_data[['code'] + constraint_risk], on=['code'])
     activate_weight = target_pos.weight.values
     excess_return = np.exp(target_pos.dx.values) - 1.
     port_ret = np.log(activate_weight @ excess_return + 1.)
@@ -50,5 +51,29 @@ def cross_section_analysis(ref_date,
     t_stats = results.tvalues[1]
 
     alpha_logger.info(f"{ref_date} is finished with {len(target_pos)} stocks for {factor_name}")
-    alpha_logger.info(f"{ref_date} risk_exposure {target_pos.weight.values @ total_risk_exp.values}")
+    alpha_logger.info(f"{ref_date} risk_exposure \n {target_pos.weight.values @ target_pos[constraint_risk].values}")
     return port_ret, ic, t_stats
+
+
+if __name__ == '__main__':
+    import numpy as np
+    import pandas as pd
+    import statsmodels.api as sm
+    from alphamind.api import *
+    from PyFin.api import *
+
+    factor_name = 'SIZE'
+    data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'
+    engine = SqlEngine(data_source)
+    risk_names = list(set(risk_styles).difference({factor_name}))
+    industry_names = list(set(industry_styles).difference({factor_name}))
+    constraint_risk = risk_names + industry_names
+    universe = Universe('custom', ['ashare_ex'])
+    horizon = 9
+
+    cross_section_analysis('2018-02-08',
+                           factor_name,
+                           universe,
+                           horizon,
+                           constraint_risk,
+                           engine=engine)
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