update examples

.gitignore

@@ -10,5 +10,4 @@ Alpha_Mind.egg-info/*
 *.nbc
 *.nbi
 /notebooks/.ipynb_checkpoints
-alphamind/examples/*
 notebooks/*
\ No newline at end of file

alphamind/examples/__init__.py

-# -*- coding: utf-8 -*-
-"""
-Created on 2017-5-15
-
-@author: cheng.li
-"""
\ No newline at end of file

alphamind/examples/config.py

-# -*- coding: utf-8 -*-
-"""
-Created on 2017-5-15
-
-@author: cheng.li
-"""
-
-risk_factors_500 = ['CommunicationsAndTransportation',
-                    'LeisureServices',
-                    'MultiMedia',
-                    'PublicUtility',
-                    'Agriculture',
-                    'ChemicalIndustry',
-                    'MedicationAndBio',
-                    'CommercialTrade',
-                    'DefenseIndustry',
-                    'HouseholdAppliances',
-                    'ConstructionAndMaterial',
-                    'BuildingDecoration',
-                    'RealEstate',
-                    'DiversifiedMetal',
-                    'Machinary',
-                    'MotorVehicle',
-                    'ElectronicIndustry',
-                    'ElectricalEquip',
-                    'TextileAndGarment',
-                    'Synthetics',
-                    'Computer',
-                    'LightManufacturing',
-                    'Telecoms',
-                    'ExtractiveIndustry',
-                    'Metal',
-                    'FoodAndBeverage',
-                    'Size']
-
-
-risk_factors_300 = ['CommunicationsAndTransportation',
-                    'LeisureServices',
-                    'MultiMedia',
-                    'PublicUtility',
-                    'Agriculture',
-                    'ChemicalIndustry',
-                    'MedicationAndBio',
-                    'CommercialTrade',
-                    'DefenseIndustry',
-                    'HouseholdAppliances',
-                    'ConstructionAndMaterial',
-                    'BuildingDecoration',
-                    'RealEstate',
-                    'DiversifiedMetal',
-                    'Machinary',
-                    'MotorVehicle',
-                    'ElectronicIndustry',
-                    'ElectricalEquip',
-                    'TextileAndGarment',
-                    'Synthetics',
-                    'Computer',
-                    'Telecoms',
-                    'ExtractiveIndustry',
-                    'Metal',
-                    'FoodAndBeverage',
-                    'Size']

alphamind/examples/example2.py → alphamind/examples/factor_analysis_example.py

 # -*- coding: utf-8 -*-
 """
-Created on 2017-7-10
+Created on 2017-8-16
 
 @author: cheng.li
 """
 
 import numpy as np
 import pandas as pd
-from alphamind.analysis.factoranalysis import factor_analysis
-from alphamind.data.engines.sqlengine import risk_styles
-from alphamind.data.engines.sqlengine import industry_styles
-from alphamind.portfolio.constraints import Constraints
-from alphamind.data.engines.sqlengine import SqlEngine
-from alphamind.data.engines.universe import Universe
-from PyFin.api import bizDatesList
-
-engine = SqlEngine('mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha')
-universe = Universe('custom', ['zz500'])
-dates = bizDatesList('china.sse', '2017-01-01', '2017-08-05')
-factors = ['EPS', 'FEARNG', 'VAL', 'NIAP']
-f_weights = np.array([1., 1., 1., 1.])
+from matplotlib import pyplot as plt
+from PyFin.api import makeSchedule
+from alphamind.api import *
 
+engine = SqlEngine("mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha")
+universe = Universe('custom', ['zz500'])
 neutralize_risk = ['SIZE'] + industry_styles
-constraint_risk = []
+constraint_risk = ['SIZE'] + industry_styles
+n_bins = 5
+factors = ['ROEDiluted']
+factor_weights = np.array([1.])
+
+dates = makeSchedule('2017-01-01',
+                     '2017-08-14',
+                     tenor='1w',
+                     calendar='china.sse')
+
+final_res = np.zeros((len(dates), n_bins))
 
 rets = []
 
-for date in dates:
+for i, date in enumerate(dates):
     print(date)
     ref_date = date.strftime('%Y-%m-%d')
-    codes = engine.fetch_codes(ref_date, universe)
-    data = engine.fetch_data(ref_date, factors, codes, 905, risk_model='short')
-    returns = engine.fetch_dx_return(ref_date, codes, 0)
-
-    total_data = pd.merge(data['factor'], returns, on=['Code']).dropna()
-    risk_cov = data['risk_cov']
 
-    total_risks = risk_cov.Factor
-    risk_cov = risk_cov[total_risks]
-    risk_exp = total_data[total_risks]
-    stocks_cov = ((risk_exp.values @ risk_cov.values @ risk_exp.values.T) + np.diag(total_data.SRISK ** 2)) / 10000.
+    codes = engine.fetch_codes(ref_date, universe)
 
-    f_data = total_data[factors]
+    data = engine.fetch_data(ref_date, factors, codes, 905)
+    returns = engine.fetch_dx_return(ref_date, codes, horizon=4)
 
+    total_data = pd.merge(data['factor'], returns, on=['Code']).dropna()
+    risk_exp = total_data[neutralize_risk].values.astype(float)
     industry = total_data.industry_code.values
     dx_return = total_data.dx.values
     benchmark = total_data.weight.values
-    risk_exp = total_data[neutralize_risk].values
+
     constraint_exp = total_data[constraint_risk].values
     risk_exp_expand = np.concatenate((constraint_exp, np.ones((len(risk_exp), 1))), axis=1).astype(float)
 
     risk_names = constraint_risk + ['total']
     risk_target = risk_exp_expand.T @ benchmark
 
-    lbound = 0.
-    ubound = 0.05 + benchmark
+    lbound = np.zeros(len(total_data))
+    ubound = 0.01 + benchmark
 
     constraint = Constraints(risk_exp_expand, risk_names)
     for i, name in enumerate(risk_names):
         constraint.set_constraints(name, lower_bound=risk_target[i], upper_bound=risk_target[i])
 
+    f_data = total_data[factors]
     try:
         pos, analysis = factor_analysis(f_data,
-                                        f_weights,
-                                        industry,
-                                        dx_return,
-                                        benchmark=benchmark,
+                                        factor_weights,
+                                        industry=industry,
+                                        d1returns=dx_return,
                                         risk_exp=risk_exp,
+                                        benchmark=benchmark,
                                         is_tradable=total_data.isOpen.values.astype(bool),
-                                        method='mv',
-                                        constraints=constraint,
-                                        cov=stocks_cov,
-                                        use_rank=100,
-                                        lam=100.,
-                                        lbound=lbound,
-                                        ubound=ubound)
+                                        method='risk_neutral',
+                                        constraints=constraint)
     except:
+        print(date, ' is error!')
         rets.append(0.)
-        print("{0} is error!".format(date))
     else:
         rets.append(analysis.er[-1])
 
-ret_series = pd.Series(rets, dates)
+ret_series = pd.Series(rets, dates)
\ No newline at end of file
+ret_series.cumsum().plot(figsize=(12, 6))
+plt.title('{0} weekly reblance'.format(factors))
+plt.show()

alphamind/examples/quantile_analysis_example.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-8-16
+
+@author: cheng.li
+"""
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from PyFin.api import makeSchedule
+from alphamind.api import *
+
+engine = SqlEngine("mssql+pymssql://licheng:A12345678!@10.63.6.220/alpha")
+universe = Universe('custom', ['zz500'])
+neutralize_risk = ['SIZE'] + industry_styles
+n_bins = 5
+factors = ['ROEDiluted']
+factor_weights = np.array([1.])
+
+dates = makeSchedule('2017-01-01',
+                     '2017-08-14',
+                     tenor='1w',
+                     calendar='china.sse')
+
+final_res = np.zeros((len(dates), n_bins))
+
+for i, date in enumerate(dates):
+    print(date)
+    ref_date = date.strftime('%Y-%m-%d')
+
+    codes = engine.fetch_codes(ref_date, universe)
+
+    data = engine.fetch_data(ref_date, factors, codes, 905)
+    returns = engine.fetch_dx_return(ref_date, codes, horizon=4)
+
+    total_data = pd.merge(data['factor'], 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:
+        print(date, ' is error!')
+        res = np.zeros(n_bins)
+
+    final_res[i] = res
+
+df = pd.DataFrame(final_res, index=dates)
+df.cumsum().plot(figsize=(12, 6))
+plt.title('{0} weekly reblance'.format(factors))
+plt.show()
\ No newline at end of file

alphamind/examples/single_day_portfolio.py

-# -*- coding: utf-8 -*-
-"""
-Created on 2017-5-15
-
-@author: cheng.li
-"""
-
-import sqlalchemy as sa
-from alphamind.data.engines.sqlengine import risk_styles
-from alphamind.data.engines.sqlengine import industry_styles
-from alphamind.data.engines.sqlengine import SqlEngine
-from alphamind.data.engines.universe import Universe
-from alphamind.data.neutralize import neutralize
-from alphamind.data.standardize import standardize
-from alphamind.data.winsorize import winsorize_normal
-from PyFin.api import bizDatesList
-
-
-universe = Universe("zz500", ['zz500'])
-engine = SqlEngine("mysql+pymysql://root:we083826@localhost/alpha?charset=utf8")
-dates = bizDatesList('china.sse', '2017-01-01', '2017-07-10')
-
-for date in dates:
-    print(date)
-    ref_date = date.strftime("%Y-%m-%d")
-
-    codes = engine.fetch_codes(ref_date, universe)
-    engine.fetch_data(ref_date, ['EPS'], codes)
\ No newline at end of file