Commit 6d06e29d authored by Dr.李's avatar Dr.李

update models

parent 7b4198be
......@@ -31,14 +31,14 @@ training - every 4 week
engine = SqlEngine('postgresql+psycopg2://postgres:A12345678!@10.63.6.220/alpha')
universe = Universe('zz500', ['zz500'])
neutralize_risk = industry_styles
neutralize_risk = ['SIZE'] + industry_styles
alpha_factors = ['RVOL', 'EPS', 'CFinc1', 'BDTO', 'VAL', 'GREV',
'ROEDiluted'] # ['BDTO', 'RVOL', 'CHV', 'VAL', 'CFinc1'] # risk_styles
benchmark = 905
n_bins = 5
frequency = '1w'
batch = 4
start_date = '2012-01-01'
start_date = '2011-01-05'
end_date = '2017-08-31'
'''
......@@ -146,6 +146,7 @@ for i, predict_date in enumerate(dates):
is_tradable=is_tradable)
final_res[i] = analysis['er']['total'] / benchmark_w.sum()
print('trade_date: {0} predicting finished'.format(train_date))
last_date = advanceDateByCalendar('china.sse', dates[-1], frequency)
......
......@@ -139,8 +139,6 @@ def fetch_data_package(engine: SqlEngine,
benchmark,
warm_start)
alpha_logger.info("Loading data is finished")
if neutralized_risk:
risk_df = engine.fetch_risk_model_range(universe, dates=dates, risk_model=risk_model)[1]
used_neutralized_risk = list(set(neutralized_risk).difference(transformer.names))
......@@ -168,6 +166,8 @@ def fetch_data_package(engine: SqlEngine,
return_df['industry_code'] = train_x['industry_code']
return_df['isOpen'] = train_x['isOpen']
alpha_logger.info("Loading data is finished")
train_x_buckets, train_y_buckets, predict_x_buckets = batch_processing(x_values,
y_values,
dates,
......
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