} 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']) ]