added example_101

alphamind/examples/example_101.py

+# -*- coding: utf-8 -*-
+"""
+Created on 2017-12-30
+
+@author: cheng.li
+"""
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+from PyFin.api import *
+from alphamind.api import *
+
+engine = SqlEngine()
+
+start_date = '2017-01-01'
+end_date = '2017-12-25'
+universe = Universe('custom', ['zz800'])
+neutralize_risk = ['SIZE'] + industry_styles
+factors = [CSRes(LAST('closePrice') / LAST('openPrice'), LAST('turnoverVol')),
+           LAST('lowestPrice')]
+benchmark = 300
+build_type = 'risk_neutral'
+
+freq = '5b'
+horizon = map_freq(freq)
+
+factors_data = fetch_data_package(engine,
+                                  alpha_factors=factors,
+                                  start_date=start_date,
+                                  end_date=end_date,
+                                  frequency=freq,
+                                  universe=universe,
+                                  benchmark=benchmark,
+                                  batch=1,
+                                  neutralized_risk=neutralize_risk,
+                                  pre_process=[winsorize_normal, standardize],
+                                  post_process=[winsorize_normal, standardize])
+
+x_names = factors_data['x_names']
+train_x = factors_data['train']['x']
+train_y = factors_data['train']['y']
+ref_dates = sorted(train_x.keys())
+
+predict_x = factors_data['predict']['x']
+settlement = factors_data['settlement']
+benchmark_w = settlement['weight'].values
+industry_names = settlement['industry'].values
+realized_r = settlement['dx'].values
+risk_exp = settlement[neutralize_risk].values
+
+"""
+Training phase
+"""
+
+models_series = pd.Series()
+
+for date in ref_dates:
+    x = train_x[date]
+    y = train_y[date].flatten()
+
+    model = LinearRegression(fit_intercept=False, features=x_names)
+    model.fit(x, y)
+    models_series.loc[date] = model
+    alpha_logger.info('trade_date: {0} training finished'.format(date))
+
+"""
+Predicting and re-balance phase
+"""
+
+index_dates = []
+final_res = np.zeros(len(ref_dates))
+
+for i, date in enumerate(ref_dates):
+    this_date_x = predict_x[date]
+    index = settlement.trade_date == date
+    this_benchmark_w = benchmark_w[index]
+    this_industry_names = industry_names[index]
+    this_realized_r = realized_r[index]
+
+    # linear regression model
+    model = models_series[date]
+    predict_y = model.predict(this_date_x)
+
+    # set constraint
+    this_risk_exp = risk_exp[index]
+
+    lbound = np.zeros(len(this_date_x))
+    ubound = 0.02 * np.ones(len(this_date_x))
+
+    cons = Constraints()
+    cons.add_exposure(neutralize_risk, this_risk_exp)
+    risk_target = this_risk_exp.T @ this_benchmark_w
+
+    for k, name in enumerate(neutralize_risk):
+        cons.set_constraints(name, risk_target[k], risk_target[k])
+
+    weights, analysis = er_portfolio_analysis(predict_y,
+                                              this_industry_names,
+                                              this_realized_r,
+                                              constraints=cons,
+                                              detail_analysis=True,
+                                              benchmark=this_benchmark_w,
+                                              method=build_type)
+
+    final_res[i] = analysis['er']['total']
+    alpha_logger.info('trade_date: {0} predicting finished'.format(date))
+
+
+# Plot the cumulative returns
+df = pd.Series(final_res, index=ref_dates)
+df.sort_index(inplace=True)
+df.cumsum().plot()
+plt.title('Factors model {1} ({0})'.format(build_type, models_series.iloc[0].__class__.__name__))
+plt.show()
\ No newline at end of file