def train(self, data: DataFrame):
"""
Train networks to the data (OHLC) passed
:param data: Data in OHLC format from the ticks module.
:return: the NN trained, and the encoder used
"""
# Remove the "Date" Column
ticks = data.copy(deep=True).drop([self.params.csv_dict['d']], axis=1)
# Train
encoder = CSEncoder(self.params).fit(ticks)
cse = encoder.ticks2cse(ticks)
dataset = self.prepare_input(encoder, cse, self.params.subtypes)
nn = self.train_nn(dataset, self.params.subtypes)
encoder.save()
return nn, encoder
tf.compat.v1.logging.set_verbosity(tf.logging.ERROR)
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.random.seed(1)
params = Params(args=sys.argv)
log = CSLogger(params._log_level)
ticks = Ticks()
ohlc_data = ticks.read_ohlc()
if params.do_train is True:
encoder = CSEncoder().fit(ohlc_data)
cse = encoder.ticks2cse(ohlc_data)
dataset = split_datasets(encoder, cse, params.subtypes)
nn = train_nn(dataset, params.subtypes)
encoder.save()
else:
nn = load_nn(params.model_names, params.subtypes)
encoder = load_encoders(params.model_names)
predictions = pd.DataFrame([])
if params._predict_training:
# for from_idx in range(0, ticks.shape[0] - params._window_size + 1):
for from_idx in range(0, 50 - params._window_size + 1):
tick_group = ohlc_data.iloc[from_idx:from_idx +
params._window_size]
prediction = single_prediction(tick_group, nn, encoder, params)
prediction = add_supervised_info(
prediction,
ohlc_data.iloc[from_idx + params._window_size]['c'], params)
predictions = predictions.append(prediction)