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
input_train, input_test, input_cv, output_train, output_test, output_cv, price_train, price_test, price_cv = \
train_test_validation_split(
[input_data_scaled, output_data, price_data], split=split)
# get dims
_, input_dim = np.shape(input_train)
_, output_dim = np.shape(output_train)
# forward-propagation
def do_stuff_every_period():
global log
global start_time
global last_complete_candle_stamp
global margin_rate
current_time = str(datetime.datetime.now(tz))[:-13]
# estimate position size
account_balance = np.around(
float(trading_sess.check_account_summary()['account']['balance']), 0)
funds_to_commit = account_balance * (1 / margin_rate)
# download latest data
# always check if new candle is present, because even after 5 seconds, it might be not formed if market is very calm
# make sure this loop does not loop endlessly on weekends (this is configured in scheduler)
while True:
oanda_data = get_latest_oanda_data(
'EUR_USD', 'H1',
300) # many data-points to increase EMA and such accuracy
current_complete_candle_stamp = oanda_data[-1]['time']
if current_complete_candle_stamp != last_complete_candle_stamp: # if new candle is complete
break
time.sleep(5)
last_complete_candle_stamp = current_complete_candle_stamp
# get features
input_data_raw, input_data_dummy = get_features(oanda_data)
input_data, input_data_dummy = remove_nan_rows(
[input_data_raw, input_data_dummy])
input_data_scaled_no_dummy = (input_data - min_max_scaling[1, :]) / (
min_max_scaling[0, :] - min_max_scaling[1, :])
input_data_scaled = np.concatenate(
[input_data_scaled_no_dummy, input_data_dummy], axis=1)
# estimate signal
y_pred = sess.run(y_,
feed_dict={
x: input_data_scaled[-1:, :],
drop_out: 1
})
order_signal_id = y_pred.argmax()
order_signal = [
1, -1, 0
][order_signal_id] # 0 stands for buy, 1 for sell, 2 for hold
# manage trading positions
current_position = trading_sess.order_book['EUR_USD']['order_type']
if current_position != order_signal:
if current_position is not None:
trading_sess.close_order('EUR_USD')
trading_sess.open_order('EUR_USD', funds_to_commit * order_signal)
else:
print('{}: EUR_USD (holding)'.format(['Long', 'Short',
'Nothing'][order_signal_id]))
# log
new_log = pd.DataFrame(
[[current_time, oanda_data[-1]['closeMid'], y_pred]],
columns=['Datetime', 'Last input Price', 'y_pred'])
log = log.append(new_log)
log.to_csv('logs/log {}.csv'.format(start_time))
print(
'{} | price: {:.5f} | signal: buy: {:.2f}, sell: {:.2f}, nothing: {:.2f}'
.format(current_time, oanda_data[-1]['closeMid'], y_pred[0][0],
y_pred[0][1], y_pred[0][2]))
split = (0.5, 0.3, 0.2)
plotting = False
saving = False
time_steps = 4
# load data
oanda_data = np.load('data\\EUR_USD_H1.npy') # [-50000:]
price_data_raw = extract_timeseries_from_oanda_data(oanda_data, ['closeMid'])
input_data_raw, input_data_dummy = 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, price_data, input_data_dummy = remove_nan_rows(
[input_data_raw, price_data_raw, input_data_dummy])
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)
input_data, _ = get_cnn_input_output(input_data,
np.zeros_like(input_data),
time_steps=time_steps)
price_data = price_data[-len(input_data):]
# split to train,test and cross validation
input_train, input_test, input_cv, price_train, price_test, price_cv = \
train_test_validation_split([input_data, price_data], split=split)
# get dims
_, input_dim, _, _ = np.shape(input_train)
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)
# pre-process data: scale, pca, polynomial
input_train, input_test, input_cv = min_max_scale(input_train,
input_test,
input_cv,
std_dev_threshold=2.5)
# input_train, input_test, input_cv = get_pca(input_train, input_test, input_cv, threshold=0.01)
input_train, input_test, input_cv = get_poloynomials(input_train,
input_test,
input_cv,
degree=2)
