!pip install --upgrade quandl
!pip install --upgrade jhtalib

import quandl
import jhtalib as jhta
import matplotlib.pyplot as plt


#quandl_data = quandl.get('BCHARTS/BITSTAMPUSD', start_date='2011-01-01', end_date='2018-11-01', order='asc', collapse='daily', returns='numpy', authtoken='YOUR_AUTH_TOKEN')
quandl_data = quandl.get('BCHARTS/BITSTAMPUSD', start_date='2011-01-01', end_date='2018-11-01', order='asc', collapse='daily', returns='numpy')

df = {'datetime': [], 'Open': [], 'High': [], 'Low': [], 'Close': [], 'Volume': []}
i = 0
while i < len(quandl_data['Close']):
  df['datetime'].append(str(quandl_data['Date'][i]))
  df['Open'].append(float(quandl_data['Open'][i]))
  df['High'].append(float(quandl_data['High'][i]))
  df['Low'].append(float(quandl_data['Low'][i]))
  df['Close'].append(float(quandl_data['Close'][i]))
  df['Volume'].append(int(quandl_data['Volume (BTC)'][i]))
  i += 1

x = df['datetime']

plt.figure(1, figsize=(20, 10))

plt.subplot(211)
plt.title('Time / Price')
plt.xlabel('Time')
plt.ylabel('Price')
plt.grid(True)
plt.plot(x, df['Close'], color='blue')
plt.plot(x, df['High'], color='grey')
plt.plot(x, df['Low'], color='grey')
plt.plot(x, jhta.ATH(df)['ath'], color='red')
plt.plot(x, jhta.LMC(df)['lmc'], color='green')
plt.legend(['Close', 'High', 'Low', 'ATH', 'LMC'], loc='upper left')

plt.show()