In [1]:
import json

In [2]:
with open('../../Random/2017-11-29-historical-data-of-ardor/notebook.ipynb') as fobj:
    notebook = json.load(fobj)

In [3]:
notebook


Out[3]:
{'cells': [{'cell_type': 'markdown',
   'metadata': {},
   'source': ['# Historical Data of Ardor (ARDR)\n',
    '\n',
    'From 28 April 2013 to `datetime.datetime.today()`.']},
  {'cell_type': 'code',
   'execution_count': 1,
   'metadata': {},
   'outputs': [],
   'source': ['import datetime\n',
    '\n',
    'import pandas as pd\n',
    'import numpy as np\n',
    'from fbprophet import Prophet']},
  {'cell_type': 'code',
   'execution_count': 2,
   'metadata': {},
   'outputs': [{'data': {'text/html': ['<div>\n',
       '<style scoped>\n',
       '    .dataframe tbody tr th:only-of-type {\n',
       '        vertical-align: middle;\n',
       '    }\n',
       '\n',
       '    .dataframe tbody tr th {\n',
       '        vertical-align: top;\n',
       '    }\n',
       '\n',
       '    .dataframe thead th {\n',
       '        text-align: right;\n',
       '    }\n',
       '</style>\n',
       '<table border="1" class="dataframe">\n',
       '  <thead>\n',
       '    <tr style="text-align: right;">\n',
       '      <th></th>\n',
       '      <th>Date</th>\n',
       '      <th>Open</th>\n',
       '      <th>High</th>\n',
       '      <th>Low</th>\n',
       '      <th>Close</th>\n',
       '      <th>Volume</th>\n',
       '      <th>Market Cap</th>\n',
       '    </tr>\n',
       '  </thead>\n',
       '  <tbody>\n',
       '    <tr>\n',
       '      <th>422</th>\n',
       '      <td>Jul 31, 2016</td>\n',
       '      <td>0.039315</td>\n',
       '      <td>0.039423</td>\n',
       '      <td>0.038534</td>\n',
       '      <td>0.039141</td>\n',
       '      <td>0</td>\n',
       '      <td>-</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>423</th>\n',
       '      <td>Jul 30, 2016</td>\n',
       '      <td>0.039250</td>\n',
       '      <td>0.039641</td>\n',
       '      <td>0.038929</td>\n',
       '      <td>0.039318</td>\n',
       '      <td>0</td>\n',
       '      <td>-</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>424</th>\n',
       '      <td>Jul 25, 2016</td>\n',
       '      <td>0.042403</td>\n',
       '      <td>0.042463</td>\n',
       '      <td>0.042117</td>\n',
       '      <td>0.042117</td>\n',
       '      <td>7</td>\n',
       '      <td>-</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>425</th>\n',
       '      <td>Jul 24, 2016</td>\n',
       '      <td>0.036912</td>\n',
       '      <td>0.042547</td>\n',
       '      <td>0.036834</td>\n',
       '      <td>0.042405</td>\n',
       '      <td>7</td>\n',
       '      <td>-</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>426</th>\n',
       '      <td>Jul 23, 2016</td>\n',
       '      <td>0.036725</td>\n',
       '      <td>0.036970</td>\n',
       '      <td>0.036284</td>\n',
       '      <td>0.036970</td>\n',
       '      <td>36</td>\n',
       '      <td>-</td>\n',
       '    </tr>\n',
       '  </tbody>\n',
       '</table>\n',
       '</div>'],
      'text/plain': ['             Date      Open      High       Low     Close  Volume Market Cap\n',
       '422  Jul 31, 2016  0.039315  0.039423  0.038534  0.039141       0          -\n',
       '423  Jul 30, 2016  0.039250  0.039641  0.038929  0.039318       0          -\n',
       '424  Jul 25, 2016  0.042403  0.042463  0.042117  0.042117       7          -\n',
       '425  Jul 24, 2016  0.036912  0.042547  0.036834  0.042405       7          -\n',
       '426  Jul 23, 2016  0.036725  0.036970  0.036284  0.036970      36          -']},
     'execution_count': 2,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ["dfs = pd.read_html('https://coinmarketcap.com/currencies/ardor/historical-data/?start=20130428&end={}'.format(datetime.datetime.today().strftime('%Y%m%d')))\n",
    '\n',
    'df = dfs[0]\n',
    '\n',
    'df.tail()']},
  {'cell_type': 'code',
   'execution_count': 3,
   'metadata': {},
   'outputs': [{'data': {'text/html': ['<div>\n',
       '<style scoped>\n',
       '    .dataframe tbody tr th:only-of-type {\n',
       '        vertical-align: middle;\n',
       '    }\n',
       '\n',
       '    .dataframe tbody tr th {\n',
       '        vertical-align: top;\n',
       '    }\n',
       '\n',
       '    .dataframe thead th {\n',
       '        text-align: right;\n',
       '    }\n',
       '</style>\n',
       '<table border="1" class="dataframe">\n',
       '  <thead>\n',
       '    <tr style="text-align: right;">\n',
       '      <th></th>\n',
       '      <th>ds</th>\n',
       '      <th>y</th>\n',
       '    </tr>\n',
       '  </thead>\n',
       '  <tbody>\n',
       '    <tr>\n',
       '      <th>422</th>\n',
       '      <td>Jul 31, 2016</td>\n',
       '      <td>0.039141</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>423</th>\n',
       '      <td>Jul 30, 2016</td>\n',
       '      <td>0.039318</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>424</th>\n',
       '      <td>Jul 25, 2016</td>\n',
       '      <td>0.042117</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>425</th>\n',
       '      <td>Jul 24, 2016</td>\n',
       '      <td>0.042405</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>426</th>\n',
       '      <td>Jul 23, 2016</td>\n',
       '      <td>0.036970</td>\n',
       '    </tr>\n',
       '  </tbody>\n',
       '</table>\n',
       '</div>'],
      'text/plain': ['               ds         y\n',
       '422  Jul 31, 2016  0.039141\n',
       '423  Jul 30, 2016  0.039318\n',
       '424  Jul 25, 2016  0.042117\n',
       '425  Jul 24, 2016  0.042405\n',
       '426  Jul 23, 2016  0.036970']},
     'execution_count': 3,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ["df = df.drop(columns=['Open', 'High', 'Low', 'Volume', 'Market Cap'])\n",
    "df = df.rename(columns={'Date': 'ds', 'Close': 'y'})\n",
    '\n',
    'df.tail()']},
  {'cell_type': 'code',
   'execution_count': 4,
   'metadata': {},
   'outputs': [{'data': {'text/html': ['<div>\n',
       '<style scoped>\n',
       '    .dataframe tbody tr th:only-of-type {\n',
       '        vertical-align: middle;\n',
       '    }\n',
       '\n',
       '    .dataframe tbody tr th {\n',
       '        vertical-align: top;\n',
       '    }\n',
       '\n',
       '    .dataframe thead th {\n',
       '        text-align: right;\n',
       '    }\n',
       '</style>\n',
       '<table border="1" class="dataframe">\n',
       '  <thead>\n',
       '    <tr style="text-align: right;">\n',
       '      <th></th>\n',
       '      <th>ds</th>\n',
       '      <th>y</th>\n',
       '    </tr>\n',
       '  </thead>\n',
       '  <tbody>\n',
       '    <tr>\n',
       '      <th>422</th>\n',
       '      <td>Jul 31, 2016</td>\n',
       '      <td>-3.240585</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>423</th>\n',
       '      <td>Jul 30, 2016</td>\n',
       '      <td>-3.236073</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>424</th>\n',
       '      <td>Jul 25, 2016</td>\n',
       '      <td>-3.167304</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>425</th>\n',
       '      <td>Jul 24, 2016</td>\n',
       '      <td>-3.160489</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>426</th>\n',
       '      <td>Jul 23, 2016</td>\n',
       '      <td>-3.297649</td>\n',
       '    </tr>\n',
       '  </tbody>\n',
       '</table>\n',
       '</div>'],
      'text/plain': ['               ds         y\n',
       '422  Jul 31, 2016 -3.240585\n',
       '423  Jul 30, 2016 -3.236073\n',
       '424  Jul 25, 2016 -3.167304\n',
       '425  Jul 24, 2016 -3.160489\n',
       '426  Jul 23, 2016 -3.297649']},
     'execution_count': 4,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ["df['y'] = np.log(df['y'])\n", '\n', 'df.tail()']},
  {'cell_type': 'code',
   'execution_count': 5,
   'metadata': {},
   'outputs': [{'name': 'stderr',
     'output_type': 'stream',
     'text': ['INFO:fbprophet.forecaster:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n',
      'INFO:fbprophet.forecaster:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n']},
    {'data': {'text/plain': ['<fbprophet.forecaster.Prophet at 0x10eeb5048>']},
     'execution_count': 5,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ['m = Prophet()\n', 'm.fit(df)']},
  {'cell_type': 'code',
   'execution_count': 6,
   'metadata': {},
   'outputs': [{'data': {'text/html': ['<div>\n',
       '<style scoped>\n',
       '    .dataframe tbody tr th:only-of-type {\n',
       '        vertical-align: middle;\n',
       '    }\n',
       '\n',
       '    .dataframe tbody tr th {\n',
       '        vertical-align: top;\n',
       '    }\n',
       '\n',
       '    .dataframe thead th {\n',
       '        text-align: right;\n',
       '    }\n',
       '</style>\n',
       '<table border="1" class="dataframe">\n',
       '  <thead>\n',
       '    <tr style="text-align: right;">\n',
       '      <th></th>\n',
       '      <th>ds</th>\n',
       '    </tr>\n',
       '  </thead>\n',
       '  <tbody>\n',
       '    <tr>\n',
       '      <th>787</th>\n',
       '      <td>2018-11-24</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>788</th>\n',
       '      <td>2018-11-25</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>789</th>\n',
       '      <td>2018-11-26</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>790</th>\n',
       '      <td>2018-11-27</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>791</th>\n',
       '      <td>2018-11-28</td>\n',
       '    </tr>\n',
       '  </tbody>\n',
       '</table>\n',
       '</div>'],
      'text/plain': ['            ds\n',
       '787 2018-11-24\n',
       '788 2018-11-25\n',
       '789 2018-11-26\n',
       '790 2018-11-27\n',
       '791 2018-11-28']},
     'execution_count': 6,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ['future = m.make_future_dataframe(periods=365)\n',
    'future.tail()']},
  {'cell_type': 'code',
   'execution_count': 7,
   'metadata': {},
   'outputs': [{'data': {'text/html': ['<div>\n',
       '<style scoped>\n',
       '    .dataframe tbody tr th:only-of-type {\n',
       '        vertical-align: middle;\n',
       '    }\n',
       '\n',
       '    .dataframe tbody tr th {\n',
       '        vertical-align: top;\n',
       '    }\n',
       '\n',
       '    .dataframe thead th {\n',
       '        text-align: right;\n',
       '    }\n',
       '</style>\n',
       '<table border="1" class="dataframe">\n',
       '  <thead>\n',
       '    <tr style="text-align: right;">\n',
       '      <th></th>\n',
       '      <th>ds</th>\n',
       '      <th>yhat</th>\n',
       '      <th>yhat_lower</th>\n',
       '      <th>yhat_upper</th>\n',
       '    </tr>\n',
       '  </thead>\n',
       '  <tbody>\n',
       '    <tr>\n',
       '      <th>787</th>\n',
       '      <td>2018-11-24</td>\n',
       '      <td>1.731838</td>\n',
       '      <td>-3.381154</td>\n',
       '      <td>7.026396</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>788</th>\n',
       '      <td>2018-11-25</td>\n',
       '      <td>1.746593</td>\n',
       '      <td>-3.427866</td>\n',
       '      <td>7.025637</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>789</th>\n',
       '      <td>2018-11-26</td>\n',
       '      <td>1.771612</td>\n',
       '      <td>-3.431581</td>\n',
       '      <td>6.944436</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>790</th>\n',
       '      <td>2018-11-27</td>\n',
       '      <td>1.768283</td>\n',
       '      <td>-3.348127</td>\n',
       '      <td>7.073092</td>\n',
       '    </tr>\n',
       '    <tr>\n',
       '      <th>791</th>\n',
       '      <td>2018-11-28</td>\n',
       '      <td>1.783292</td>\n',
       '      <td>-3.434536</td>\n',
       '      <td>7.128455</td>\n',
       '    </tr>\n',
       '  </tbody>\n',
       '</table>\n',
       '</div>'],
      'text/plain': ['            ds      yhat  yhat_lower  yhat_upper\n',
       '787 2018-11-24  1.731838   -3.381154    7.026396\n',
       '788 2018-11-25  1.746593   -3.427866    7.025637\n',
       '789 2018-11-26  1.771612   -3.431581    6.944436\n',
       '790 2018-11-27  1.768283   -3.348127    7.073092\n',
       '791 2018-11-28  1.783292   -3.434536    7.128455']},
     'execution_count': 7,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ['forecast = m.predict(future)\n',
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail()"]},
  {'cell_type': 'code',
   'execution_count': 8,
   'metadata': {},
   'outputs': [{'data': {'image/png': 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GUPFDHhnO44eGRFThuBkpIIuIiIgckSl7PD6Sx2Dob43XuhypEb0sEhEREWFm9zhb8hgt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\nyfl90rg5vxsDMv2H3FeSJJLc8YC4MRJj0ZYa0nwafTP8pHi0w+ZGC/+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/4sWL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\nAAAAAElFTkSuQmCC\n',
      'text/plain': ['<matplotlib.figure.Figure at 0x10eeb8ac8>']},
     'execution_count': 8,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ['m.plot(forecast)']},
  {'cell_type': 'code',
   'execution_count': 9,
   'metadata': {},
   'outputs': [{'data': {'image/png': 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QllW89EkLnvjgMD483DPoa8a++VRWkwFmo6EvNEpIs5qQ77TClWaByxn7lec8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\nAAAASUVORK5CYII=\n',
      'text/plain': ['<matplotlib.figure.Figure at 0x10f2b35f8>']},
     'execution_count': 9,
     'metadata': {},
     'output_type': 'execute_result'}],
   'source': ['m.plot_components(forecast)']},
  {'cell_type': 'code',
   'execution_count': None,
   'metadata': {},
   'outputs': [],
   'source': []}],
 'metadata': {'kernelspec': {'display_name': 'Python 3',
   'language': 'python',
   'name': 'python3'},
  'language_info': {'codemirror_mode': {'name': 'ipython', 'version': 3},
   'file_extension': '.py',
   'mimetype': 'text/x-python',
   'name': 'python',
   'nbconvert_exporter': 'python',
   'pygments_lexer': 'ipython3',
   'version': '3.6.3'}},
 'nbformat': 4,
 'nbformat_minor': 2}

In [4]:
notebook.keys()


Out[4]:
dict_keys(['cells', 'metadata', 'nbformat', 'nbformat_minor'])

In [5]:
notebook['metadata']


Out[5]:
{'kernelspec': {'display_name': 'Python 3',
  'language': 'python',
  'name': 'python3'},
 'language_info': {'codemirror_mode': {'name': 'ipython', 'version': 3},
  'file_extension': '.py',
  'mimetype': 'text/x-python',
  'name': 'python',
  'nbconvert_exporter': 'python',
  'pygments_lexer': 'ipython3',
  'version': '3.6.3'}}

In [6]:
notebook['nbformat']


Out[6]:
4

In [7]:
notebook['nbformat_minor']


Out[7]:
2

In [11]:
notebook['cells'][0]


Out[11]:
{'cell_type': 'markdown',
 'metadata': {},
 'source': ['# Historical Data of Ardor (ARDR)\n',
  '\n',
  'From 28 April 2013 to `datetime.datetime.today()`.']}

In [9]:
notebook['cells'][1]


Out[9]:
{'cell_type': 'code',
 'execution_count': 1,
 'metadata': {},
 'outputs': [],
 'source': ['import datetime\n',
  '\n',
  'import pandas as pd\n',
  'import numpy as np\n',
  'from fbprophet import Prophet']}

In [10]:
notebook['cells'][2]


Out[10]:
{'cell_type': 'code',
 'execution_count': 2,
 'metadata': {},
 'outputs': [{'data': {'text/html': ['<div>\n',
     '<style scoped>\n',
     '    .dataframe tbody tr th:only-of-type {\n',
     '        vertical-align: middle;\n',
     '    }\n',
     '\n',
     '    .dataframe tbody tr th {\n',
     '        vertical-align: top;\n',
     '    }\n',
     '\n',
     '    .dataframe thead th {\n',
     '        text-align: right;\n',
     '    }\n',
     '</style>\n',
     '<table border="1" class="dataframe">\n',
     '  <thead>\n',
     '    <tr style="text-align: right;">\n',
     '      <th></th>\n',
     '      <th>Date</th>\n',
     '      <th>Open</th>\n',
     '      <th>High</th>\n',
     '      <th>Low</th>\n',
     '      <th>Close</th>\n',
     '      <th>Volume</th>\n',
     '      <th>Market Cap</th>\n',
     '    </tr>\n',
     '  </thead>\n',
     '  <tbody>\n',
     '    <tr>\n',
     '      <th>422</th>\n',
     '      <td>Jul 31, 2016</td>\n',
     '      <td>0.039315</td>\n',
     '      <td>0.039423</td>\n',
     '      <td>0.038534</td>\n',
     '      <td>0.039141</td>\n',
     '      <td>0</td>\n',
     '      <td>-</td>\n',
     '    </tr>\n',
     '    <tr>\n',
     '      <th>423</th>\n',
     '      <td>Jul 30, 2016</td>\n',
     '      <td>0.039250</td>\n',
     '      <td>0.039641</td>\n',
     '      <td>0.038929</td>\n',
     '      <td>0.039318</td>\n',
     '      <td>0</td>\n',
     '      <td>-</td>\n',
     '    </tr>\n',
     '    <tr>\n',
     '      <th>424</th>\n',
     '      <td>Jul 25, 2016</td>\n',
     '      <td>0.042403</td>\n',
     '      <td>0.042463</td>\n',
     '      <td>0.042117</td>\n',
     '      <td>0.042117</td>\n',
     '      <td>7</td>\n',
     '      <td>-</td>\n',
     '    </tr>\n',
     '    <tr>\n',
     '      <th>425</th>\n',
     '      <td>Jul 24, 2016</td>\n',
     '      <td>0.036912</td>\n',
     '      <td>0.042547</td>\n',
     '      <td>0.036834</td>\n',
     '      <td>0.042405</td>\n',
     '      <td>7</td>\n',
     '      <td>-</td>\n',
     '    </tr>\n',
     '    <tr>\n',
     '      <th>426</th>\n',
     '      <td>Jul 23, 2016</td>\n',
     '      <td>0.036725</td>\n',
     '      <td>0.036970</td>\n',
     '      <td>0.036284</td>\n',
     '      <td>0.036970</td>\n',
     '      <td>36</td>\n',
     '      <td>-</td>\n',
     '    </tr>\n',
     '  </tbody>\n',
     '</table>\n',
     '</div>'],
    'text/plain': ['             Date      Open      High       Low     Close  Volume Market Cap\n',
     '422  Jul 31, 2016  0.039315  0.039423  0.038534  0.039141       0          -\n',
     '423  Jul 30, 2016  0.039250  0.039641  0.038929  0.039318       0          -\n',
     '424  Jul 25, 2016  0.042403  0.042463  0.042117  0.042117       7          -\n',
     '425  Jul 24, 2016  0.036912  0.042547  0.036834  0.042405       7          -\n',
     '426  Jul 23, 2016  0.036725  0.036970  0.036284  0.036970      36          -']},
   'execution_count': 2,
   'metadata': {},
   'output_type': 'execute_result'}],
 'source': ["dfs = pd.read_html('https://coinmarketcap.com/currencies/ardor/historical-data/?start=20130428&end={}'.format(datetime.datetime.today().strftime('%Y%m%d')))\n",
  '\n',
  'df = dfs[0]\n',
  '\n',
  'df.tail()']}

In [12]:
notebook['cells'][8]


Out[12]:
{'cell_type': 'code',
 'execution_count': 8,
 'metadata': {},
 'outputs': [{'data': {'image/png': 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GUPFDHhnO44eGRFThuBkpIIuIiIgckSl7PD6Sx2Dob43XuhypEb0sEhEREWFm9zhb8hgt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\nyfl90rg5vxsDMv2H3FeSJJLc8YC4MRJj0ZYa0nwafTP8pHi0w+ZGC/+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/4sWL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\nAAAAAElFTkSuQmCC\n',
    'text/plain': ['<matplotlib.figure.Figure at 0x10eeb8ac8>']},
   'execution_count': 8,
   'metadata': {},
   'output_type': 'execute_result'}],
 'source': ['m.plot(forecast)']}

In [ ]: