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trading_strategy_fitting.py
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trading_strategy_fitting.py
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import numpy as np
from time import time
from data_input_processing import Data, train_test_validation_indices, generate_training_variables
from strategy_evaluation import post_process_training_results, output_strategy_results
from machine_learning import random_forest_fitting, svm_fitting, adaboost_fitting, gradient_boosting_fitting,\
extra_trees_fitting, tensorflow_fitting, tensorflow_sequence_fitting
SEC_IN_DAY = 86400
def meta_fitting(fitting_inputs, fitting_targets, strategy_dictionary):
error = []
fitting_dictionary = {}
train_indices, test_indices, validation_indices = train_test_validation_indices(
fitting_inputs, strategy_dictionary['train_test_validation_ratios'])
if strategy_dictionary['ml_mode'] == 'svm':
fitting_dictionary, error = svm_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'randomforest':
fitting_dictionary, error = random_forest_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'adaboost':
fitting_dictionary, error = adaboost_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'gradientboosting':
fitting_dictionary, error = gradient_boosting_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
elif strategy_dictionary['ml_mode'] == 'extratreesfitting':
fitting_dictionary, error = extra_trees_fitting(
fitting_inputs, fitting_targets, train_indices, test_indices, validation_indices, strategy_dictionary)
fitting_dictionary['train_indices'] = train_indices
fitting_dictionary['test_indices'] = test_indices
fitting_dictionary['validation_indices'] = validation_indices
fitting_dictionary['error'] = error
return fitting_dictionary
def input_processing(data_to_predict_local, data_input_2, strategy_dictionary):
fitting_inputs, continuous_targets, classification_targets = generate_training_variables(
data_to_predict_local, strategy_dictionary)
fitting_inputs_2, fitting_targets_2, classification_targets_2 = generate_training_variables(
data_input_2, strategy_dictionary, prior_data_obj=data_to_predict_local)
fitting_inputs, fitting_inputs_2 = trim_inputs(fitting_inputs, fitting_inputs_2)
fitting_inputs = np.hstack((fitting_inputs, fitting_inputs_2))
return fitting_inputs, continuous_targets, classification_targets
def trim_inputs(fitting_inputs, fitting_inputs_2):
length_1 = len(fitting_inputs)
length_2 = len(fitting_inputs_2)
min_length = np.minimum(length_1, length_2)
fitting_inputs = fitting_inputs[-min_length:]
fitting_inputs_2 = fitting_inputs_2[-min_length:]
return fitting_inputs, fitting_inputs_2
def tic():
t = time()
return lambda: (time() - t)
def retrieve_data(ticker, scraper_currency, strategy_dictionary, filename):
data_local = None
while data_local is None:
try:
if strategy_dictionary['web_flag']:
end = time() - strategy_dictionary['offset'] * SEC_IN_DAY
start = end - SEC_IN_DAY * strategy_dictionary['n_days']
data_local = Data(
ticker, scraper_currency, strategy_dictionary['candle_size'], strategy_dictionary['web_flag'],
start=start, end=end,
)
else:
data_local = Data(
ticker, scraper_currency, strategy_dictionary['candle_size'], strategy_dictionary['web_flag'],
offset=strategy_dictionary['offset'], filename=filename,
n_days=strategy_dictionary['n_days'])
except Exception:
pass
data_local.normalise_data()
return data_local
def offset_scan_validation(strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets, offsets):
strategy_dictionary['plot_flag'] = False
strategy_dictionary['ouput_flag'] = True
total_error = 0
total_profit = 0
for offset in offsets:
strategy_dictionary['offset'] = offset
fitting_dictionary, profit_fraction = fit_strategy(
strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets)
total_error += fitting_dictionary['error'] / len(offsets)
total_profit += profit_fraction
underlined_output('Averages: ')
print 'Total profit: ', total_profit
print 'Average error: ', total_error
def tensorflow_offset_scan_validation(strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets, offsets):
strategy_dictionary['plot_flag'] = False
strategy_dictionary['ouput_flag'] = True
total_error = 0
total_profit = 0
for offset in offsets:
strategy_dictionary['offset'] = offset
fitting_dictionary, error, profit_fraction = fit_tensorflow(
strategy_dictionary, data_to_predict, fitting_inputs, fitting_targets)
total_error += error
total_profit += profit_fraction
underlined_output('Averages: ')
print 'Total profit: ', total_profit
print 'Average error: ', total_error
def import_data(strategy_dictionary):
data_to_predict = retrieve_data(
strategy_dictionary['ticker_1'], strategy_dictionary['scraper_currency_1'], strategy_dictionary,
strategy_dictionary['filename1'])
data_2 = retrieve_data(strategy_dictionary['ticker_2'], strategy_dictionary['scraper_currency_2'],
strategy_dictionary, strategy_dictionary['filename2'])
return data_to_predict, data_2
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 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(
'/home/thomas/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 underlined_output(string):
print string
print '----------------------'
print '\n'