labels_2.py

# compute bands
window = 50
data['avg'], data['upper'], data['lower'] = bbands(data['close'],window, no_of_stdev=1.5)

# compute sides
data['side'] = np.nan
long_signals = (data['close'] <= data['lower'])
short_signals = (data['close'] >= data['upper'])
data.loc[long_signals, 'side'] = 1
data.loc[short_signals, 'side'] = -1

print(data.side.value_counts())

# remove Look ahead bias by lagging the signal
data['side'] = data['side'].shift(1)

# drop the NaN values from our data set
data.dropna(axis=0, how='any', inplace=True)