pl.rcParams.update({'font.size': 6})
np.random.seed(0)
tf.set_random_seed(0)
# hyper-params
batch_size = 1024
learning_rate = 0.002
drop_keep_prob = 1
value_moving_average = 50
split = (0.5, 0.3, 0.2)
plotting = False
saving = False
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
output_data_raw = price_to_binary_target(oanda_data, delta=0.0001)
price_data_raw = extract_timeseries_from_oanda_data(oanda_data, ['closeMid'])
input_data_raw, input_data_dummy_raw = get_features(oanda_data)
price_data_raw = np.concatenate([[[0]],
(price_data_raw[1:] - price_data_raw[:-1]) / (price_data_raw[1:] + 1e-10)], axis=0)
# prepare data
input_data, output_data, input_data_dummy, price_data = \
remove_nan_rows([input_data_raw, output_data_raw,
input_data_dummy_raw, price_data_raw])
input_data_scaled_no_dummies = (
input_data - min_max_scaling[1, :]) / (min_max_scaling[0, :] - min_max_scaling[1, :])
input_data_scaled = np.concatenate(
[input_data_scaled_no_dummies, input_data_dummy], axis=1)
# split to train, test and cross validation
import tensorflow as tf
from models import logistic_regression
# hyper-params
batch_size = 1024
learning_rate = 0.002
drop_keep_prob = 0.4
value_moving_average = 50
split = (0.5, 0.3, 0.2)
plotting = False
saving = False
transaction_c = 0.000
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy')[-50000:]
y_data = price_to_binary_target(oanda_data, delta=0.000275)
x_data = get_features_v2(oanda_data,
time_periods=[10, 25, 50, 120, 256],
return_numpy=False)
# separate, rearrange and remove nans
price = x_data['price'].as_matrix().reshape(-1, 1)
price_change = x_data['price_delta'].as_matrix().reshape(-1, 1)
x_data = x_data.drop(['price', 'price_delta'], axis=1).as_matrix()
price, price_change, x_data, y_data = remove_nan_rows(
[price, price_change, x_data, y_data])
# split to train, test and cross validation
input_train, input_test, input_cv, output_train, output_test, output_cv, price_train, price_test, price_cv = \
train_test_validation_split([x_data, y_data, price_change], split=split)
