def __init__(self, name, train_start_date_string,
train_end_test_start_date_string, test_end_date_string):
Company.__init__(self, name)
self.train_raw_series = self.get_share_prices(
train_start_date_string, train_end_test_start_date_string)
self.test_raw_series = self.get_share_prices(
train_end_test_start_date_string,
test_end_date_string,
start_delay=1)
def __init__(self, name, train_start_date_string,
train_end_test_start_date_string, test_end_date_string,
n_epochs, n_batch, n_neurons):
Company.__init__(self, name)
self.lstm_model = None
self.scaler = None
self.n_epochs = n_epochs
self.n_batch = n_batch
self.n_neurons = n_neurons
self.train_raw_series = self.get_share_prices(
train_start_date_string, train_end_test_start_date_string)
self.test_raw_series = self.get_share_prices(
train_end_test_start_date_string,
test_end_date_string,
start_delay=1)
self.train_scaled, self.test_scaled = self.preprocess_data()
# same as test_raw_series with the addition of the last element of train_raw_series
# Add the last training sample to the start of the test raw series so the invert differnce can work
self.invert_difference_series_values = [
self.train_raw_series.values[-1]
] + self.test_raw_series.values.tolist()
def __init__(self, name, train_start_date_string, train_end_test_start_date_string, test_end_date_string,
n_lag, n_seq, n_batch=None, tech_indicators=[], model_type="vanilla"):
Company.__init__(self, name)
self.scaler = None
self.lstm_model = None
self.train_scaled, self.test_scaled = None, None
self.supervised_pd = None
self.raw_pd = None
self.train_raw_series, self.test_raw_series = None, None
self.model_type = model_type
if tech_indicators == "all":
self.input_tech_indicators_list = self.all_tech_indicators
else:
self.input_tech_indicators_list = tech_indicators
self.number_of_indicators = len(self.input_tech_indicators_list)
self.train_start_date_string = train_start_date_string
self.train_end_test_start_date_string = train_end_test_start_date_string
self.test_end_date_string = test_end_date_string
self.n_lag = n_lag
self.n_seq = n_seq
if model_type == "vanilla":
self.n_epochs = 3238
elif model_type == "stacked":
self.n_epochs = 239
elif model_type == "bi":
self.n_epochs = 2097
elif model_type == "cnn":
self.n_epochs = 3238
elif model_type == "conv":
self.n_epochs = 100
else:
raise ValueError("model type", model_type, "does not exist!")
self.n_batch = n_batch
self.time_taken_to_train = 0
self.preprocess_data()
