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 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
if strategy_dictionary['fit_time']:
strategy_dictionary, fitting_dictionary, fitting_inputs, fitting_targets, data_to_predict = fit_time_scale(
strategy_dictionary, search_iterations, time_iterations, toc)
else:
strategy_dictionary, fitting_dictionary, fitting_inputs, fitting_targets, data_to_predict, test_profit \
= random_search(
strategy_dictionary,
search_iterations,
toc)
underlined_output('Best strategy fit')
if strategy_dictionary['plot_last']:
strategy_dictionary['plot_flag'] = True
output_strategy_results(strategy_dictionary,
fitting_dictionary,
data_to_predict,
toc,
momentum_dict=simple_momentum_comparison(
data_to_predict, strategy_dictionary,
fitting_dictionary))
underlined_output('Offset validation')
offsets = np.linspace(0, 300, 5)
offset_scan_validation(strategy_dictionary, data_to_predict,
fitting_inputs, fitting_targets, offsets)