short_position = -0.5
leverage = 1.0
# end_day = -1 # -1 means till today
start_day = '2012-01-01'
trading_days = 252.0
interest_rate = 0.0
horizon = 21 * 1
freq = 21 * 1 # rebalance monthly
roll = 36 # rolling in x months
ben = 'ACWI' # benchmark
model_name = ['SVR', 'Lasso']
# model_name = 'Ridge'
relative = True
normalize = True
nor_method = '98%shrink'
thre = {'long_thre': (0.0, 0.2), 'short_thre': (0.0, 0.05)}
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_long_' + str(round(long_position, 1)) + '_' + 'short' + str(round(short_position, 1)) + \
'_' + str(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,com_method='score')
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)