}
train_options = {
'upper_th': 0.3,
'lower_th': -0.3,
'z_score_mean_window': 3,
'z_score_std_window': 6,
'slsp': (-100,2000), # None means no constraint
'close_change': 1, # 0 = close; 1 = change
}
# tensorboard --logdir C:\Users\Chris\projects\210215_mt5\production\docs\1\runs --host localhost
with mt5Model.csv_Writer_Helper():
prices_loader = prices.Prices_Loader(symbols=data_options['symbols'],
timeframe=data_options['timeframe'],
start=data_options['start'],
end=data_options['end'],
timezone=data_options['timezone'],
data_path=data_options['local_min_path'],
deposit_currency=data_options['deposit_currency'])
# get the data
prices_loader.get_data(data_options['local'])
# Prices = prices_loader.get_Prices_format(options['local'])
# split into train set and test set
Train_Prices, Test_Prices = prices.split_Prices(prices_loader.Prices, percentage=data_options['trainTestSplit'])
dependent_variable = Train_Prices.c
if train_options['close_change'] == 1:
dependent_variable = Train_Prices.cc
# define the model, optimizer, trainer, writer
myModel = None
-0.98662]), # will be round to 2 decimal
'upper_th': 1.5, # 1.5
'lower_th': -1.5, # -1.5
'z_score_mean_window': 5,
'z_score_std_window': 20,
'slsp': (-50000, 50000), # None means no constraint
'close_change': 1, # 0 = close; 1 = change
}
with mt5Model.Trader(dt_string=options['dt'],
history_path=trader_options["history_path"],
type_filling=trader_options['type_filling']) as trader:
prices_loader = prices.Prices_Loader(
symbols=trader_options['symbols'],
timeframe=trader_options['timeframe'],
count=trader_options['count'],
timezone=trader_options['timezone'],
deposit_currency=trader_options['deposit_currency'])
long_lots = [
round(i, 2) for i in coinModel.get_modified_coefficient_vector(
coin_option['coefficient_vector'],
long_mode=True,
lot_times=trader_options['lot_times'])
]
short_lots = [
round(i, 2) for i in coinModel.get_modified_coefficient_vector(
coin_option['coefficient_vector'],
long_mode=False,
lot_times=trader_options['lot_times'])
]