return pool_long, pool_short
if __name__ == "__main__":
# parameters initialization
variable_list = ['quant_technical_st','quant_technical_it','quant_technical_lt','quant_sentiment_iv',\
'quant_sentiment_si','quant_sentiment_pc','quant_fundamental_pe','quant_fundamental_pb',\
'quant_fundamental_pcf','quant_fundamental_div','quant_global_sector','quant_global_country',\
'quant_quality_diversification','quant_quality_firm','quant_quality_liquidity']
start_day = '2012-01-01'
horizon = 21 * 1
freq = 21 * 1 # rebalance monthly
roll = 36 # rolling in x months
ben = 'ACWI' # benchmark
model_name = 'StepWise'
relative = True
# Back-test initialization
context = context(
ben,
start_day,
variable_list,
freq,
)
# Form training set and you just need run once and after that you can comment it until you change
# variable list or other parameters.
# context.generate_train(horizon, relative, normalize=True,method='98%shrink')
context.feature_selection(horizon,method=model_name,freq=freq,model_name = model_name,\
select_method=select_stocks,roll=roll)
start_day = '2012-01-01'
trading_days = 252.0
interest_rate = 0.0
horizon = 21 * 1 # prediction horizon
freq = 21 * 1 # rebalance monthly
roll = 36 # rolling in x months
ben = 'ACWI' # benchmark
model_name = 'Lasso'
relative = True
normalize = True
nor_method = '98%shrink'
thre = {'long_thre': (0.0, 0.2), 'short_thre': (0.0, 0.1)}
daily = False
get_data_method = 'last_date_monthly'
# Back-test initialization
context = context(ben, start_day, variable_list, freq, leverage, long_position, short_position, \
interest_rate, trading_days, daily=daily, method=get_data_method)
# Form training set and you just need run once and after that you can comment it until you change
# variable list or other parameters.
# context.generate_train(horizon, relative, normalize, method=nor_method)
# Name the results using parameters and so on
address = 'etf_T_0.001_long' + str(round(long_position, 1)) + '_' + 'short' + str(round(short_position, 1)) + \
'_' + model_name + '_' + str(thre['long_thre'][1]) + '_' + str(thre['short_thre'][1]) + '_' + \
str(horizon) + '_' + str(roll) + '_' + nor_method + '.csv'
context.back_test(horizon,
model_name,
address,
select_stocks,
order_method,
roll,
thre=thre,
filter_score=False)
variable_list = ['prr_ttm', 'eps_ttm', 'pb_lf', 'ps_ttm', 'np_growth_1y2']
leverage = 0.95
end_day = -1
start_day = '2008-01-01'
trading_days = 12.0 #because it's mnonthly data
horizon = 1
istech = 1
large = True
freq = 1
roll = 6
model_name = 'Lasso'
address = model_name + '_istech_large.csv'
relative = True
context = context(
start_day,
leverage,
trading_days,
)
# context.pre_processing()
context.import_trading_data()
context.import_features(variable_list)
# context.generate_train(horizon,relative,normalize =True)
context.back_test(horizon,
istech,
large,
freq,
model_name,
address,
roll,
threshold=0.5)