def tensorflow_fitting(strategy_dictionary_local):
toc = tic()
data_local = import_data(strategy_dictionary_local)
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, strategy_dictionary)
if strategy_dictionary_local['regression_mode'] == 'classification':
fitting_targets_local = classification_targets
elif strategy_dictionary_local['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local, strategy_dictionary_local = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary, error_loop, profit_factor = fit_tensorflow(
strategy_dictionary_local, data_local, fitting_inputs_local,
fitting_targets_local)
if strategy_dictionary_local['plot_last']:
strategy_dictionary_local['plot_flag'] = True
output_strategy_results(strategy_dictionary_local,
fitting_dictionary,
data_local,
toc,
momentum_dict=simple_momentum_comparison(
data_local, strategy_dictionary,
fitting_dictionary))
output_strategy_results(strategy_dictionary, fitting_dictionary,
data_local, toc)
return strategy_dictionary, data_local, fitting_inputs_local, fitting_targets_local
def random_search(strategy_dictionary_local, n_iterations):
toc = tic()
data_local, data_2 = import_data(strategy_dictionary)
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, data_2, strategy_dictionary)
counter = 0
error = 1e5
while counter < n_iterations:
counter += 1
strategy_dictionary_local = randomise_dictionary_inputs(strategy_dictionary_local)
if strategy_dictionary['regression_mode'] == 'classification':
fitting_targets_local = classification_targets
elif strategy_dictionary['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local = preprocessing_inputs(strategy_dictionary, fitting_inputs_local)
fitting_dictionary, profit_factor = fit_strategy(
strategy_dictionary, data_local, fitting_inputs_local, fitting_targets_local)
error_loop = fitting_dictionary['error']
if error_loop < error and fitting_dictionary['n_trades'] != 0:
error = error_loop
strategy_dictionary_local_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_local_optimum, fitting_dictionary_optimum, data_local, toc)
return strategy_dictionary_local_optimum, fitting_inputs_local, fitting_targets_local, data_local
def random_search(strategy_dictionary_local, n_iterations):
toc = tic()
counter = 0
error = 1e10
while counter < n_iterations:
counter += 1
#strategy_dictionary['sequence_flag'] = np.random.choice([True, False])
if strategy_dictionary['sequence_flag']:
strategy_dictionary_local = randomise_sequence_dictionary_inputs(strategy_dictionary_local)
else:
strategy_dictionary_local = randomise_dictionary_inputs(strategy_dictionary_local)
fitting_dictionary, data_to_predict, error_loop, profit_factor = fit_tensorflow(strategy_dictionary_local)
if error_loop < error:
error = error_loop
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_optimum, fitting_dictionary_optimum, data_to_predict, toc)
return strategy_dictionary_optimum
def random_search(strategy_dictionary_local, n_iterations):
print("Ticking")
toc = tic()
print("Importing data")
data_local, data_2 = import_data(strategy_dictionary_local)
print("Finished Imported data")
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, data_2, strategy_dictionary)
print("Targets determined")
counter = 0
error = 1e5
fitting_dictionary_optimum = []
strategy_dictionary_optimum = []
fitting_targets_local = []
while counter < n_iterations:
print("Starting iteration %s" % counter)
counter += 1
# strategy_dictionary['sequence_flag'] = np.random.choice([True, False])
strategy_dictionary['sequence_flag'] = False
if strategy_dictionary['sequence_flag']:
strategy_dictionary_local = randomise_sequence_dictionary_inputs(
strategy_dictionary_local)
else:
strategy_dictionary_local = randomise_dictionary_inputs(
strategy_dictionary_local)
if strategy_dictionary_local['regression_mode'] == 'classification':
fitting_targets_local = classification_targets
elif strategy_dictionary_local['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local, strategy_dictionary_local = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary, error_loop, profit_factor = fit_tensorflow(
strategy_dictionary_local, data_local, fitting_inputs_local,
fitting_targets_local)
if error_loop < error:
error = error_loop
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
print("Completed iteration")
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_optimum,
fitting_dictionary_optimum, data_local, toc)
return strategy_dictionary_optimum, data_local, fitting_inputs_local, fitting_targets_local
def fit_time_scale(strategy_dictionary_input, search_iterations_local,
time_iterations):
""" fit timescale variables"""
toc = tic()
counter = 0
strategy_dictionary_optimum = []
optimum_profit = -2
while counter < time_iterations:
strategy_dictionary_input = randomise_time_inputs(
strategy_dictionary_input)
strategy_dictionary_local,\
fitting_dictionary_local,\
fitting_inputs_local,\
fitting_targets_local,\
data_local,\
test_profit\
= random_search(
strategy_dictionary_input,
search_iterations_local,
toc)
if test_profit > optimum_profit:
optimum_profit = test_profit
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary_local
counter += 1
underlined_output('Best strategy fit')
if strategy_dictionary['plot_last']:
strategy_dictionary['plot_flag'] = True
output_strategy_results(strategy_dictionary_optimum,
fitting_dictionary_optimum,
data_local,
toc,
momentum_dict=simple_momentum_comparison(
data_local, strategy_dictionary_optimum,
fitting_dictionary_optimum))
return strategy_dictionary_optimum,\
fitting_inputs_local,\
fitting_targets_local,\
data_local
def random_search(strategy_dictionary_local, n_iterations):
toc = tic()
data_local, data_2 = import_data(strategy_dictionary_local)
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, data_2, strategy_dictionary)
counter = 0
error = 1e5
fitting_dictionary_optimum = []
strategy_dictionary_optimum = []
fitting_targets_local = []
while counter < n_iterations:
counter += 1
strategy_dictionary['sequence_flag'] = np.random.choice([True, False])
if strategy_dictionary['sequence_flag']:
strategy_dictionary_local = randomise_sequence_dictionary_inputs(
strategy_dictionary_local)
else:
strategy_dictionary_local = randomise_dictionary_inputs(
strategy_dictionary_local)
if strategy_dictionary_local['regression_mode'] == 'classification':
fitting_targets_local = classification_targets
elif strategy_dictionary_local['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local, strategy_dictionary_local = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary, error_loop, profit_factor = fit_tensorflow(
strategy_dictionary_local, data_local, fitting_inputs_local,
fitting_targets_local)
if error_loop < error:
error = error_loop
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_optimum,
fitting_dictionary_optimum, data_local, toc)
return strategy_dictionary_optimum, data_local, fitting_inputs_local, fitting_targets_local
def random_search(strategy_dictionary_local, n_iterations):
"""random search to find optimum machien learning algorithm and preprocessing"""
toc = tic()
data_local = import_data(strategy_dictionary_local)
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, strategy_dictionary_local)
counter = 0
error = 1e5
fitting_targets_local = []
fitting_dictionary_optimum = []
strategy_dictionary_optimum = []
while counter < n_iterations:
counter += 1
strategy_dictionary_local = randomise_dictionary_inputs(
strategy_dictionary_local)
if strategy_dictionary_local['regression_mode'] == 'classification':
fitting_targets_local = classification_targets.astype(int)
elif strategy_dictionary_local['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local, strategy_dictionary = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary, profit_factor = fit_strategy(
strategy_dictionary, data_local, fitting_inputs_local,
fitting_targets_local)
error_loop = fitting_dictionary['error']
if error_loop < error and fitting_dictionary['n_trades'] != 0:
error = error_loop
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_optimum,
fitting_dictionary_optimum, data_local, toc)
return strategy_dictionary_optimum, fitting_inputs_local, fitting_targets_local, data_local
def random_search(strategy_dictionary_local, n_iterations):
toc = tic()
counter = 0
error = -1e5
while counter < n_iterations:
counter += 1
strategy_dictionary_local = randomise_dictionary_inputs(strategy_dictionary_local)
fitting_dictionary, data_to_predict, profit_factor = fit_strategy(strategy_dictionary_local)
error_loop = fitting_dictionary['error']
if error_loop > error and fitting_dictionary['n_trades'] != 0:
error = error_loop
strategy_dictionary_local_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary_local_optimum, fitting_dictionary_optimum, data_to_predict, toc)
return strategy_dictionary_local_optimum
def tensorflow_fitting(strategy_dictionary_local):
toc = tic()
data_local, data_2 = import_data(strategy_dictionary_local)
fitting_inputs_local, continuous_targets, classification_targets = input_processing(
data_local, data_2, strategy_dictionary)
if strategy_dictionary_local['regression_mode'] == 'classification':
fitting_targets_local = classification_targets
elif strategy_dictionary_local['regression_mode'] == 'regression':
fitting_targets_local = continuous_targets
fitting_inputs_local, strategy_dictionary_local = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary, error_loop, profit_factor = fit_tensorflow(
strategy_dictionary_local, data_local, fitting_inputs_local,
fitting_targets_local)
underlined_output('Best strategy fit')
output_strategy_results(strategy_dictionary, fitting_dictionary,
data_local, toc)
return strategy_dictionary, data_local, fitting_inputs_local, fitting_targets_local
if test_profit > optimum_profit:
optimum_profit = test_profit
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary_local
counter += 1
return strategy_dictionary_optimum,\
fitting_dictionary_optimum,\
fitting_inputs_local,\
fitting_targets_local,\
data_local
if __name__ == '__main__':
toc = tic()
strategy_dictionary = {
'trading_currencies': ['XMR', 'DASH'],
'ticker_1': 'XMR_DASH',
'scraper_currency_1': 'DASH',
'candle_size': 300,
'n_days': 50,
'offset': 0,
'bid_ask_spread': 0.003,
'transaction_fee': 0.0025,
'train_test_validation_ratios': [0.5, 0.2, 0.3],
'output_flag': True,
'plot_flag': False,
'plot_last': True,
'ml_iterations': 15,
