def transform(self, X):
fe = make_features(X, self.target_col, self.moving_averages)
if self.production:
X = series_to_predict_matrix(fe, n_in=self.Tx, dropnan=True)
return self._reshape(X)
else:
X, y = data_to_supervised(fe, self.Tx, self.Ty)
return self._reshape(X), y
def main():
print('Making features from raw data...')
data_dir = join(get_project_path(), 'data', 'raw')
output_dir = join(get_project_path(), 'data', 'processed')
makedirs(output_dir, exist_ok=True)
coins = ['BTC', 'ETH']
TARGET = 'close'
Tx = 72
Ty = 1
TEST_SIZE = 0.05
for SYM in coins:
raw_data_path = join(data_dir, SYM + '.csv')
print('Featurizing raw {} data from {}...'.format(SYM, raw_data_path))
raw_df = pd.read_csv(raw_data_path, index_col=0)
feature_df = make_features(
raw_df,
target_col=TARGET,
keep_cols=['close', 'high', 'low', 'volumeto', 'volumefrom'],
ma_lags=[6, 12, 24, 48],
ma_cols=['close', 'volumefrom', 'volumeto'])
X, y = data_to_supervised(feature_df, target_ix=-1, Tx=Tx, Ty=Ty)
num_features = int(X.shape[1] / Tx)
X = make_3d(X, tx=Tx, num_channels=num_features)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=TEST_SIZE, shuffle=False)
np.save(arr=X_train, file=join(output_dir, 'X_train_{}'.format(SYM)))
np.save(arr=X_test, file=join(output_dir, 'X_test_{}'.format(SYM)))
np.save(arr=y_train, file=join(output_dir, 'y_train_{}'.format(SYM)))
np.save(arr=y_test, file=join(output_dir, 'y_test_{}'.format(SYM)))
def predict(coin=None):
coin = coin or 'BTC'
Tx = 72
target = 'close'
cryptocompare_data = retrieve_all_data(coin, Tx + 1 + 48)
preprocessed_data = make_features(cryptocompare_data, target_col=target,
keep_cols=['close', 'high', 'low', 'volumeto', 'volumefrom'],
ma_lags=[6, 12, 24, 48], ma_cols=['close', 'volumefrom', 'volumeto'])
time_series_data = series_to_predict_matrix(preprocessed_data, Tx)
n_features = int(time_series_data.shape[1]/Tx)
model_input_data = make_3d(arr=time_series_data, tx=Tx, num_channels=n_features)
if coin == 'ETH':
model = eth_model
elif coin == 'BTC':
model = btc_model
else:
abort(404) # FIXME: More descriptive error
with model.graph.as_default():
prediction = model.estimator.predict(model_input_data)
def parse_keras_prediction(keras_prediction):
"""Handles multi-output Keras models"""
if len(keras_prediction) == 2:
return keras_prediction[1][0]
else:
return keras_prediction[0]
parsed_prediction = parse_keras_prediction(prediction)
last_value = cryptocompare_data[target].iloc[-1]
last_time = cryptocompare_data['timestamp'].iloc[-1]
prediction_val = [last_value + pred/100*last_value for pred in parsed_prediction]
time_val = [last_time + pd.Timedelta(hours=ix + 1) for ix in range(len(parsed_prediction))]
return dict(prediction=prediction_val, time=time_val)
"""
data_train, data_test = train_test_split(data,
test_size=TEST_SIZE,
shuffle=False)
data_train = data_train.dropna()
data_test = data_test.dropna().iloc[:-1]
p(data_train.shape, data_test.shape)
data_test.head()
# In[3]:
"""
Get features.
"""
feature_data_train = make_features(input_df=data_train,
target_col='close',
ma_cols=['volumeto', 'volumefrom'],
ma_lags=[3, 6, 12])
feature_data_test = make_features(input_df=data_test,
target_col='close',
ma_cols=['volumeto', 'volumefrom'],
ma_lags=[3, 6, 12])
feature_data_train.dropna(inplace=True)
feature_data_test.dropna(inplace=True)
feature_data_test.head()
# In[4]:
"""
Apply DWT Smooth.
"""
SYM = 'BTC' TARGET = 'close' Tx = 72 Ty = 1 TEST_SIZE = 0.05 data_path = os.path.join(get_project_path(), 'data', 'raw', SYM + '.csv') data = pd.read_csv(data_path, index_col=0) data.head() # In[3]: """ Get percent change feature and target data. """ df = make_features(input_df=data, target_col='close', moving_average_lags=[]) X, y = data_to_supervised(input_df=df, Tx=Tx, Ty=Ty) p(X.shape, y.shape) X.head() # In[4]: """ Confirm data reshape and target/feature creation was done correctly. """ y_values_except_last = np.squeeze(y.iloc[:-1].values) t_minus_1_x_values_except_first = X.iloc[1:, -1].values y_values_except_last.all() == t_minus_1_x_values_except_first.all() # In[5]: """