start_date = '2015-01-01'
end_date = '2016-12-31'
fig, ax = plt.subplots(figsize=(16,9))
ax.plot(data.loc[start_date:end_date, :].index, data.loc[start_date:end_date, 'MSFT'], label='Price')
ax.plot(long_rolling.loc[start_date:end_date, :].index, long_rolling.loc[start_date:end_date, 'MSFT'], label = '100-days SMA')
ax.plot(short_rolling.loc[start_date:end_date, :].index, short_rolling.loc[start_date:end_date, 'MSFT'], label = '20-days SMA')
ax.legend(loc='best')
ax.set_ylabel('Price in $')
ax.xaxis.set_major_formatter(my_year_month_fmt)
# Calculating the long-window simple moving average
long_rolling = data.rolling(window=100).mean()
long_rolling.tail()
# Calculating the short-window simple moving average
short_rolling = data.rolling(window=20).mean()
short_rolling.head(20)
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
%matplotlib inline
import seaborn as sns
sns.set(style='darkgrid', context='talk', palette='Dark2')
my_year_month_fmt = mdates.DateFormatter('%m/%y')
data = pd.read_pickle('./data.pkl')
data.head(10)