added one more example

alphamind/data/engines/sqlengine.py

@@ -23,6 +23,7 @@ from alphamind.data.dbmodel.models import IndexComponent
 from alphamind.data.dbmodel.models import Uqer
 from alphamind.data.dbmodel.models import Tiny
 from alphamind.data.dbmodel.models import LegacyFactor
+from alphamind.data.dbmodel.models import Experimental
 from alphamind.data.dbmodel.models import SpecificRiskDay
 from alphamind.data.dbmodel.models import SpecificRiskShort
 from alphamind.data.dbmodel.models import SpecificRiskLong
@@ -79,7 +80,7 @@ macro_styles = ['COUNTRY']
 
 total_risk_factors = risk_styles + industry_styles + macro_styles
 
-factor_tables = [Uqer, Tiny, LegacyFactor]
+factor_tables = [Uqer, Tiny, LegacyFactor, Experimental]
 
 
 def append_industry_info(df):

alphamind/examples/plot_quantile_res.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-8-16
+
+@author: cheng.li
+"""
+
+import datetime as dt
+import numpy as np
+import pandas as pd
+from PyFin.api import *
+from alphamind.api import *
+from matplotlib import pyplot as plt
+
+start = dt.datetime.now()
+
+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 = '1w'
+
+if freq == '1m':
+    horizon = 21
+elif freq == '1w':
+    horizon = 4
+elif freq == '1d':
+    horizon = 0
+
+start_date = '2016-04-01'
+end_date = '2017-08-17'
+
+dates = makeSchedule(start_date,
+                     end_date,
+                     tenor=freq,
+                     calendar='china.sse')
+
+prod_factors = ['IVR', 'RVOL']
+
+all_data = engine.fetch_data_range(universe, prod_factors, dates=dates, benchmark=905)
+factor_all_data = all_data['factor']
+
+total_df = pd.DataFrame()
+
+for factor in prod_factors:
+
+    factors = [factor]
+    final_res = np.zeros((len(dates), n_bins))
+
+    factor_groups = factor_all_data.groupby('Date')
+    for i, value in enumerate(factor_groups):
+        date = value[0]
+        data = value[1][['Code', factor, 'isOpen', 'weight'] + neutralize_risk]
+        codes = data.Code.tolist()
+        ref_date = value[0].strftime('%Y-%m-%d')
+        returns = engine.fetch_dx_return(date, codes, horizon=horizon)
+
+        total_data = pd.merge(data, returns, on=['Code']).dropna()
+        risk_exp = total_data[neutralize_risk].values.astype(float)
+        dx_return = total_data.dx.values
+        benchmark = total_data.weight.values
+
+        f_data = total_data[factors]
+        try:
+            res = quantile_analysis(f_data,
+                                    factor_weights,
+                                    dx_return,
+                                    risk_exp=risk_exp,
+                                    n_bins=n_bins,
+                                    benchmark=benchmark)
+        except Exception as e:
+            print(e)
+            res = np.zeros(n_bins)
+
+        final_res[i] = res / benchmark.sum()
+
+    df = pd.DataFrame(final_res, index=dates)
+
+    start_date = advanceDateByCalendar('china.sse', dates[0], '-1w')
+    df.loc[start_date] = 0.
+    df.sort_index(inplace=True)
+    df = df.cumsum().plot()
+    plt.title('{0} weekly re-balance'.format(factors[0]))
+    plt.savefig('{0}_big_universe_20170814.png'.format(factors[0]))
+    print('{0} is finished'.format(factor))
+
+print(dt.datetime.now() - start)
+plt.show()
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