added cross section analysis

alphamind/analysis/crosssetctions.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.portfolio.constraints import LinearConstraints
+from alphamind.analysis.factoranalysis import er_portfolio_analysis
+from alphamind.utilities import 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
+    target_pos['weight'] = target_pos['weight'] / target_pos['weight'].abs().sum()
+
+    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
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