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()