def get_input_data(self, skip_preprocessing, preprocessing_constant, normalization_method, database_path, feature_names, datetime_interval, blockchain_indicators):
data = md.get_dataset_with_descriptors(skip_preprocessing = skip_preprocessing,
preproc_constant = preprocessing_constant,
normalization_method = normalization_method,
dataset_directory = database_path,
feature_names = feature_names,
datetime_interval = datetime_interval,
blockchain_indicators = blockchain_indicators)
self.data = data
self.feature_names = feature_names
return self.data
def plot_vertical_lines(data, desired_color='brown'):
## even data samples
data1 = [data[i] if ((i % 2) == 0) else 0 for i in range(len(data))]
#odd data samples
data2 = [data[i] if ((i % 2) != 0) else 0 for i in range(len(data))]
#plot them
plt.plot(data1, color=desired_color)
plt.plot(data2, color=desired_color)
directory = '/home/catalin/databases/klines_2014-2018_15min/'
hard_coded_file_number = 0
data = md.get_dataset_with_descriptors(
concatenate_datasets_preproc_flag=True,
preproc_constant=0.99,
normalization_method="rescale",
dataset_directory=directory,
hard_coded_file_number=hard_coded_file_number)
X = data['preprocessed_data'] ## this will be used for training
X_unprocessed = data['data']
close_prices = X_unprocessed[:, 0]
one_day_in_15_min_candles = 15 * 4 * 24
twelve_hrs_in_15_min_candles = 15 * 4 * 12
six_hrs_in_15_min_candles = 15 * 4 * 6
three_hrs_in_15_min_candles = 15 * 4 * 3
one_hr_in_15_min_candles = 15 * 4
SMA_12_day_values, EMA_12_day_values = SMA_EMA(close_prices, 12 * 1)
SMA_26_day_values, EMA_26_day_values = SMA_EMA(close_prices, 26 * 1)
# 'slope_VBP_smooth_24',
'nlms_indicator',
'nlms_smoothed_indicator',
# 'rls_indicator_error',
# 'rls_smoothed_indicator',
'ATR_EMA',
'ATR_EMA_Wilder',
'CMF_12h',
'CMF_12h_2',
# 'sentiment_indicator_positive',
# 'sentiment_indicator_negative'
]
data = md.get_dataset_with_descriptors(skip_preprocessing=False,
preproc_constant=0.99,
normalization_method="rescale",
dataset_directory=directory,
feature_names=feature_names)
X = data['preprocessed_data'] ## this will be used for training
X_unprocessed = data['data']
#sys.exit()
start_date = md.get_date_from_UTC_ms(data['dataset_dict']['UTC'][0])
end_date = md.get_date_from_UTC_ms(data['dataset_dict']['UTC'][-1])
#import random
#
#noise = np.array([np.array([random.uniform(0,1) for _ in range(X.shape[0])]) for i in range(5)])
#
#X = np.concatenate([X, noise.T], axis = 1)
#X_unprocessed = np.concatenate([X_unprocessed, noise.T], axis = 1)
