update example

alphamind/examples/factor_analysis_example.py

@@ -29,31 +29,39 @@ universe = Universe('custom', ['zz500'])
 benchmark_code = 905
 neutralize_risk = ['SIZE'] + industry_styles
 constraint_risk = ['SIZE'] + industry_styles
-n_bins = 5
+freq = '1w'
+
+if freq == '1m':
+    horizon = 21
+elif freq == '1w':
+    horizon = 4
+elif freq == '1d':
+    horizon = 0
+
 dates = makeSchedule('2012-01-14',
                      '2017-08-14',
-                     tenor='1w',
+                     tenor=freq,
                      calendar='china.sse')
 
-final_res = np.zeros((len(dates), n_bins))
-
-
 total_data_dict = {}
 
 for strategy in strategies:
     factors = strategies[strategy]['factors']
     factor_weights = strategies[strategy]['weights']
 
+    all_data = engine.fetch_data_range(universe, factors, dates=dates, benchmark=905)
+    factor_all_data = all_data['factor']
+    factor_groups = factor_all_data.groupby('Date')
+
     rets = []
-    for i, date in enumerate(dates):
+    for i, value in enumerate(factor_groups):
+        date = value[0]
+        data = value[1]
+        codes = data.Code.tolist()
         ref_date = date.strftime('%Y-%m-%d')
+        returns = engine.fetch_dx_return(ref_date, codes, horizon=horizon)
 
-        codes = engine.fetch_codes(ref_date, universe)
-
-        data = engine.fetch_data(ref_date, factors, codes, benchmark_code)
-        returns = engine.fetch_dx_return(ref_date, codes, horizon=4)
-
-        total_data = pd.merge(data['factor'], returns, on=['Code']).dropna()
+        total_data = pd.merge(data, returns, on=['Code']).dropna()
         print(date, ': ', len(total_data))
         risk_exp = total_data[neutralize_risk].values.astype(float)
         industry = total_data.industry_code.values
@@ -97,3 +105,4 @@ for strategy in strategies:
 ret_df = pd.DataFrame(total_data_dict, index=dates)
 ret_df.cumsum().plot(figsize=(12, 6))
 plt.savefig("backtest_big_universe_20170814.png")
+plt.show()

alphamind/examples/quantile_analysis_example.py

@@ -11,15 +11,14 @@ from matplotlib import pyplot as plt
 from PyFin.api import *
 from alphamind.api import *
 
-#engine = SqlEngine("mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha")
-engine = SqlEngine('postgresql+psycopg2://postgres:we083826@localhost/alpha')
+engine = SqlEngine("mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha")
 universe = Universe('custom', ['zz500'])
 neutralize_risk = ['SIZE'] + industry_styles
 n_bins = 5
 
 factor_weights = np.array([1.])
 
-freq = '1d'
+freq = '1w'
 
 if freq == '1m':
     horizon = 21
@@ -40,7 +39,6 @@ prod_factors = ['EARNYILD', 'ROAEBIT', 'CHV', 'CFinc1']
 
 all_data = engine.fetch_data_range(universe, prod_factors, dates=dates, benchmark=905)
 factor_all_data = all_data['factor']
-return_all_data = engine.fetch_dx_return_range(universe, start_date, end_date, dates, horizon=horizon)
 
 for factor in prod_factors:
 
@@ -48,14 +46,12 @@ for factor in prod_factors:
     final_res = np.zeros((len(dates), n_bins))
 
     factor_groups = factor_all_data.groupby('Date')
-    return_groups = return_all_data.groupby('Date')
-
     for i, value in enumerate(factor_groups):
         date = value[0]
         data = value[1]
         codes = data.Code.tolist()
         ref_date = value[0].strftime('%Y-%m-%d')
-        returns = return_groups.get_group(date)
+        returns = engine.fetch_dx_return(date, codes, horizon=horizon)
 
         total_data = pd.merge(data, returns, on=['Code']).dropna()
         print(date, ': ', len(total_data))