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Edit and run



In [1]:

    
import pandas as pd
import numpy as np
from pandas_datareader import data



In [2]:

    
goog = data.DataReader('GOOG', start='2004', end='2018', data_source='yahoo')
goog.head()



In [3]:

    
goog_p = goog['Adj Close']



In [20]:

    
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-ticks')
import seaborn; seaborn.set_style('whitegrid')



In [21]:

    
goog_p.plot(figsize=(15,10))









    Out[21]:





<matplotlib.axes._subplots.AxesSubplot at 0x128dc0610>

Compute Yearly Return

A common context for financial timeseries data is computing difference over time. For example, we can calculate one-year returns using pandas.



In [25]:

    
goog_p = goog_p.asfreq('D', method='pad')    
ROI = 100 * (goog_p.tshift(-365)/goog_p - 1)

ROI.plot(figsize=(15, 10))
plt.ylabel('% Return on Investment');



In [26]:

    
y1_return = goog['Adj Close'].pct_change().rolling(365).sum().dropna()
y1_vol = goog['Adj Close'].pct_change().rolling(365).std().dropna()



In [27]:

    
plt.figure(figsize=(15, 10))
seaborn.distplot(y1_return*100)
plt.ylabel('% Yearly Rolling Return')
plt.title('GOOG Historical Returns');

	High	Low	Open	Close	Volume	Adj Close
Date
2004-08-19	51.835709	47.800831	49.813286	49.982655	44871300.0	49.982655
2004-08-20	54.336334	50.062355	50.316402	53.952770	22942800.0	53.952770
2004-08-23	56.528118	54.321388	55.168217	54.495735	18342800.0	54.495735
2004-08-24	55.591629	51.591621	55.412300	52.239193	15319700.0	52.239193
2004-08-25	53.798351	51.746044	52.284027	52.802086	9232100.0	52.802086