price_array, price_array_training = build_price_arrays(
input_data,
time_lag=TIME_LAG)
#train_indices, validation_indices, test_indices = train_validation_test_indices(
# price_array_training,
# [0.9, 0.05, 0.0001])
train_indices = np.linspace(0, len(price_array_training)-100, len(price_array_training)-100, dtype=int)
test_indices = np.linspace(len(price_array_training) - 100, len(price_array_training) - 75, 25, dtype=int)
validation_indices = np.linspace(len(price_array_training) -5, len(price_array_training)-1, 5, dtype=int)
fitting_dictionary = tensorflow_cnn_fitting(
train_indices,
test_indices,
validation_indices,
price_array_training,
price_array,
load_net='model.h5')
portfolio_value, cum_log_return = calculate_portfolio_value(
fitting_dictionary['fitted_strategy_score'],
price_array[test_indices, :])
final_portfolio_value = portfolio_value[-1]
running_portfolio_value *= final_portfolio_value
print('Run profit fraction: ', final_portfolio_value - 1)
print('Offset tests cumulative profit fraction:', running_portfolio_value - 1)
from matplotlib import pyplot as plt
if idx == int(len(price_array) / 2):
cnn_kwargs = {
'retrain': 80,
}
else:
fit_kwargs = {'steps_per_epoch': 85}
cnn_kwargs = {
'retrain': 1,
'fit_kwargs': fit_kwargs,
'load_net': 'model.h5'
}
fitting_dictionary = tensorflow_cnn_fitting(train_indices,
test_indices, [],
price_array_training,
price_array, **cnn_kwargs)
print(fitting_dictionary['fitted_strategy_score'])
if len(portfolio_allocations) != 0:
portfolio_allocations = np.vstack([
portfolio_allocations,
fitting_dictionary['fitted_strategy_score']
])
else:
portfolio_allocations = fitting_dictionary['fitted_strategy_score']
plot_portfolio_value, cum_log_return = calculate_portfolio_value(
portfolio_allocations, price_array[initial_idx:(idx + 1), :],
liquidation_factor[(initial_idx + 1):(idx + 1), :])
train_indices = np.linspace(0,
len(price_array_training) - 100,
len(price_array_training) - 100,
dtype=int)
test_indices = np.linspace(len(price_array_training) - 100,
len(price_array_training) - 75,
25,
dtype=int)
validation_indices = np.linspace(len(price_array_training) - 5,
len(price_array_training) - 1,
5,
dtype=int)
fitting_dictionary = tensorflow_cnn_fitting(train_indices,
test_indices,
validation_indices,
price_array_training,
price_array)
portfolio_value, cum_log_return = calculate_portfolio_value(
fitting_dictionary['fitted_strategy_score'],
price_array[test_indices, :])
final_portfolio_value = portfolio_value[-1]
running_portfolio_value *= final_portfolio_value
print('Run profit fraction: ', final_portfolio_value - 1)
print('Offset tests cumulative profit fraction:',
running_portfolio_value - 1)
from matplotlib import pyplot as plt