update example

alphamind/examples/example2.py

@@ -5,12 +5,14 @@ Created on 2017-7-10
 @author: cheng.li
 """
 
+import datetime as dt
 import pandas as pd
 from alphamind.analysis.factoranalysis import factor_analysis
 from alphamind.data.engines.sqlengine import SqlEngine
 from alphamind.data.engines.universe import Universe
 from alphamind.data.engines.sqlengine import industry_styles
 from PyFin.api import bizDatesList
+from PyFin.api import makeSchedule
 
 engine = SqlEngine('mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha')
 universe = Universe('custom', ['zz500'])
@@ -21,15 +23,16 @@ total_risks = used_risk_styles + industry_styles
 build_type = 'risk_neutral'
 
 
-def calculate_one_day(ref_date, factors, factor_weights):
+def calculate_one_day(ref_date, factors, factor_weights, horizon_end=None):
     print(ref_date)
+
     codes = engine.fetch_codes(ref_date, universe)
     total_data = engine.fetch_data(ref_date, factors, codes, 905)
 
     factor_data = total_data['factor']
     factor_df = factor_data[['Code', 'industry', 'weight', 'isOpen'] + total_risks + factors].dropna()
 
-    dx_return = engine.fetch_dx_return(ref_date, codes)
+    dx_return = engine.fetch_dx_return(ref_date, codes, expiry_date=horizon_end)
     factor_df = pd.merge(factor_df, dx_return, on=['Code'])
 
     weights, _ = factor_analysis(factor_df[factors],
@@ -51,15 +54,19 @@ if __name__ == '__main__':
 
     factors = ['BDTO', 'CFinc1', 'DivP', 'EPS', 'RVOL', 'DROEAfterNonRecurring']
     factor_weights = [0.10, 0.30, 0.15, 0.18, 0.11, 0.35]
-    biz_dates = bizDatesList('china.sse', '2017-01-01', '2017-07-07')
+    biz_dates = makeSchedule('2015-01-01', '2017-07-07', '1w', 'china.sse')
 
     ers = []
     dates = []
 
-    for ref_date in biz_dates:
-        ref_date, er = calculate_one_day(ref_date, factors, factor_weights)
-        dates.append(ref_date)
-        ers.append(er)
+    for i, ref_date in enumerate(biz_dates[:-1]):
+        ref_date = ref_date.strftime("%Y-%m-%d")
+        try:
+            ref_date, er = calculate_one_day(ref_date, factors, factor_weights, horizon_end=biz_dates[i+1])
+            dates.append(ref_date)
+            ers.append(er)
+        except Exception as e:
+            print(str(e) + ": {0}".format(ref_date))
 
     res = pd.Series(ers, index=dates)
     res.cumsum().plot()