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- import numpy as np
- import pandas as pd
- import pandas_datareader as pdr
- from statsmodels.tsa.stattools import adfuller
- pd.core.common.is_list_like = pd.api.types.is_list_like
- import matplotlib.pyplot as plt
- from datetime import datetime
- import time
- %matplotlib inline
- start, end = datetime(2016, 1, 1), time.strftime("%x")
- aapl = pdr.DataReader(['AAPL'],
- 'yahoo',
- start,
- end)
- aapl.columns = [col[0].lower().replace(' ', '_')
- for col in aapl.columns]
- aapl_close = aapl[['close']]
- aapl_close = aapl_close.apply(lambda x: np.log(x) - np.log(x.shift(1)))
- aapl_close.dropna(inplace=True)
- _ = aapl_close.plot(figsize=(20, 10),
- linewidth=3,
- fontsize=14)
- result = adfuller(aapl_close['close'].values)
- print('Augmented Dickey-Fuller test statistic: {}'.format(result[0]))
- print('p-value: {}'.format(result[1]))
- print('Critical Values:')
- for key, value in result[4].items():
- print('\t{}: {}'. format(key, value))
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