cont. remove files

notebooks/notebook_utilities.py

-# -*- coding: utf-8 -*-
-"""
-Created on 2018-3-5
-
-@author: cheng.li
-"""
-
-import numpy as np
-import pandas as pd
-import statsmodels.api as sm
-from alphamind.api import (
-    SqlEngine, LinearConstraints, er_portfolio_analysis, alpha_logger
-)
-
-
-def cross_section_analysis(ref_date,
-                           factor_name,
-                           universe,
-                           horizon,
-                           constraint_risk,
-                           linear_bounds,
-                           lbound,
-                           ubound,
-                           engine):
-
-    codes = engine.fetch_codes(ref_date, universe)
-
-    risk_exposure = engine.fetch_risk_model(ref_date, codes)[1][['code'] + constraint_risk]
-    factor_data = engine.fetch_factor(ref_date, factor_name, codes)
-    industry_matrix = engine.fetch_industry_matrix(ref_date, codes, 'sw_adj', 1)
-
-    total_data = pd.merge(factor_data, risk_exposure, on='code')
-    total_data = pd.merge(total_data, industry_matrix, on='code').dropna()
-    total_risk_exp = total_data[constraint_risk]
-
-    constraints = LinearConstraints(linear_bounds, total_risk_exp)
-
-    er = total_data[factor_name].values
-    industry = total_data.industry_name.values
-
-    target_pos, _ = er_portfolio_analysis(er,
-                                          industry,
-                                          None,
-                                          constraints,
-                                          False,
-                                          None,
-                                          method='risk_neutral',
-                                          lbound=lbound*np.ones(len(er)),
-                                          ubound=ubound*np.ones(len(er)))
-
-    codes = total_data.code.tolist()
-    target_pos['code'] = codes
-
-    dx_returns = engine.fetch_dx_return(ref_date, codes, horizon=horizon, offset=1)
-    target_pos = pd.merge(target_pos, dx_returns, on=['code'])
-    activate_weight = target_pos.weight.values
-    excess_return = np.exp(target_pos.dx.values) - 1.
-    port_ret = np.log(activate_weight @ excess_return + 1.)
-    ic = np.corrcoef(excess_return, activate_weight)[0, 1]
-    x = sm.add_constant(activate_weight)
-    results = sm.OLS(excess_return, x).fit()
-    t_stats = results.tvalues[1]
-
-    alpha_logger.info(f"{ref_date} is finished with {len(target_pos)} stocks for {factor_name}")
-    return port_ret, ic, t_stats
-
-
-if __name__ == '__main__':
-
-    from alphamind.api import (
-        Universe, map_freq, risk_styles, industry_styles, macro_styles, BoundaryType, create_box_bounds
-    )
-
-    """
-    Back test parameter settings
-    """
-
-    start_date = '2010-01-01'
-    end_date = '2018-02-28'
-    category = 'sw_adj'
-    level = 1
-    freq = '20b'
-    universe = Universe('custom', ['zz800'])
-    data_source = 'postgres+psycopg2://postgres:A12345678!@10.63.6.220/alpha'
-    engine = SqlEngine(data_source)
-    horizon = map_freq(freq)
-
-    """
-    Factor Model
-    """
-    factor_name = 'SIZE'
-
-    """
-    Constraints
-    """
-
-    risk_names = list(set(risk_styles).difference({factor_name}))
-    industry_names = list(set(industry_styles).difference({factor_name}))
-    constraint_risk = risk_names + industry_names + macro_styles
-
-    b_type = []
-    l_val = []
-    u_val = []
-
-    for name in constraint_risk:
-        if name in set(risk_styles):
-            b_type.append(BoundaryType.ABSOLUTE)
-            l_val.append(0.0)
-            u_val.append(0.0)
-        else:
-            b_type.append(BoundaryType.RELATIVE)
-            l_val.append(1.0)
-            u_val.append(1.0)
-
-    linear_bounds = create_box_bounds(constraint_risk, b_type, l_val, u_val)
-
-    ref_date = '2018-02-08'
-    df = pd.DataFrame(columns=['ret', 'ic', 't.'])
-
-    print(cross_section_analysis(ref_date,
-                                 factor_name,
-                                 universe,
-                                 horizon,
-                                 constraint_risk,
-                                 linear_bounds,
-                                 lbound=-0.01,
-                                 ubound=0.01,
-                                 engine=engine))
\ No newline at end of file