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 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):
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 fit_tensorflow(strategy_dictionary, data_to_predict, fitting_inputs,
fitting_targets):
toc = tic()
train_indices, test_indices, validation_indices = train_test_validation_indices(
fitting_inputs, strategy_dictionary['train_test_validation_ratios'])
if strategy_dictionary['sequence_flag']:
fitting_dictionary, error = tensorflow_sequence_fitting(
'/tmp/test', train_indices, test_indices, validation_indices,
fitting_inputs, fitting_targets, strategy_dictionary)
else:
fitting_dictionary, error = tensorflow_fitting(train_indices,
test_indices,
validation_indices,
fitting_inputs,
fitting_targets)
fitting_dictionary['train_indices'] = train_indices
fitting_dictionary['test_indices'] = test_indices
fitting_dictionary['validation_indices'] = validation_indices
fitting_dictionary = post_process_training_results(strategy_dictionary,
fitting_dictionary,
data_to_predict)
profit_factor = output_strategy_results(strategy_dictionary,
fitting_dictionary,
data_to_predict, toc)
return fitting_dictionary, error, profit_factor
def fit_tensorflow(strategy_dictionary):
toc = tic()
data_to_predict, data_2 = import_data(strategy_dictionary)
fitting_inputs, fitting_targets = input_processing(data_to_predict, data_2,
strategy_dictionary)
train_indices, test_indices = train_test_indices(
fitting_inputs, strategy_dictionary['train_test_ratio'])
if strategy_dictionary['sequence_flag']:
fitting_dictionary, error = tensorflow_sequence_fitting(
'/home/thomas/test', train_indices, test_indices, fitting_inputs,
fitting_targets, strategy_dictionary)
else:
fitting_dictionary, error = tensorflow_fitting(train_indices,
test_indices,
fitting_inputs,
fitting_targets)
fitting_dictionary['train_indices'] = train_indices
fitting_dictionary['test_indices'] = test_indices
fitting_dictionary = post_process_training_results(strategy_dictionary,
fitting_dictionary,
data_to_predict)
profit_factor = output_strategy_results(strategy_dictionary,
fitting_dictionary,
data_to_predict, toc)
return fitting_dictionary, data_to_predict, error, profit_factor
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 fit_strategy(strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets):
toc = tic()
fitting_dictionary = meta_fitting(fitting_inputs, fitting_targets, strategy_dictionary)
fitting_dictionary = post_process_training_results(strategy_dictionary, fitting_dictionary, data_to_predict)
profit_factor = output_strategy_results(strategy_dictionary, fitting_dictionary, data_to_predict, toc)
return fitting_dictionary, profit_factor
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 fit_strategy(strategy_dictionary):
toc = tic()
data_to_predict, data_2 = import_data(strategy_dictionary)
fitting_dictionary = meta_fitting(data_to_predict, data_2, strategy_dictionary)
fitting_dictionary = post_process_training_results(strategy_dictionary, fitting_dictionary, data_to_predict)
profit_factor = output_strategy_results(strategy_dictionary, fitting_dictionary, data_to_predict, toc)
return fitting_dictionary, data_to_predict, profit_factor
def fit_strategy(strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets):
"""fit machine learning algorithm to data and return predictions and profit"""
toc = tic()
fitting_dictionary = meta_fitting(fitting_inputs, fitting_targets, strategy_dictionary)
fitting_dictionary = post_process_training_results(strategy_dictionary, fitting_dictionary, data_to_predict)
profit_factor = output_strategy_results(strategy_dictionary, fitting_dictionary, data_to_predict, toc)
return fitting_dictionary, profit_factor
def random_search(strategy_dictionary_local, n_iterations, toc):
"""random search to find optimum machine learning algorithm and preprocessing"""
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_local = preprocessing_inputs(
strategy_dictionary_local, fitting_inputs_local)
fitting_dictionary_local, _, _, strategy_dictionary_local = fit_strategy(
strategy_dictionary_local, data_local, fitting_inputs_local,
fitting_targets_local)
error_loop = fitting_dictionary_local['error']
if error_loop < error and fitting_dictionary_local['n_trades'] != 0:
error = error_loop
strategy_dictionary_optimum = strategy_dictionary_local
fitting_dictionary_optimum = fitting_dictionary_local
if strategy_dictionary_optimum:
profit, test_profit = output_strategy_results(
strategy_dictionary_optimum, fitting_dictionary_optimum,
data_local, toc)
else:
test_profit = -2
return strategy_dictionary_optimum,\
fitting_dictionary_optimum,\
fitting_inputs_local,\
fitting_targets_local,\
data_local,\
test_profit
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 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)
