'input_size': len(data_options['symbols']) - 1,
'hidden_size': 64,
'layer': 2,
'batch_first': True,
}
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:
train_long_stat_file_name = "{}_{}_{}_Long_Limit{}_From{}_To{}_Train.csv".format(
curr_time_string, data_options['symbols'][0], data_options['timeframe'],
data_options['max_limit_range'], start_string, end_string)
train_short_stat_file_name = "{}_{}_{}_Short_Limit{}_From{}_To{}_Train.csv".format(
curr_time_string, data_options['symbols'][0], data_options['timeframe'],
data_options['max_limit_range'], start_string, end_string)
test_long_stat_file_name = "{}_{}_{}_Long_Limit{}_From{}_To{}_Test.csv".format(
curr_time_string, data_options['symbols'][0], data_options['timeframe'],
data_options['max_limit_range'], start_string, end_string)
test_short_stat_file_name = "{}_{}_{}_Short_Limit{}_From{}_To{}_Test.csv".format(
curr_time_string, data_options['symbols'][0], data_options['timeframe'],
data_options['max_limit_range'], start_string, end_string)
with mt5Model.csv_Writer_Helper(
csv_save_path=data_options['csv_save_path'],
csv_file_names=[
train_long_stat_file_name, train_short_stat_file_name,
test_long_stat_file_name, test_short_stat_file_name
],
append_checkpoint=data_options["append_checkpoint"]) as helper:
# define loader
prices_loader = prices.Prices_Loader(
symbols=data_options['symbols'],
timeframe=data_options['timeframe'],
data_path=data_options['local_min_path'],
start=data_options['start'],
end=data_options['end'],
timezone=data_options['timezone'],
deposit_currency=data_options['deposit_currency'])
# get the data
prices_loader.get_data(data_options['local'])
