Guillaume Redoulès

Moving average with pandas

  2. Python
  3. Data Wrangling
# Import modules
import pandas as pd
from pandas_datareader import data, wb

#import packages

from pandas_datareader import data

aapl = data.DataReader('AAPL', 'quandl', '1980-01-01')
aapl.head()
Open High Low Close Volume ExDividend SplitRatio AdjOpen AdjHigh AdjLow AdjClose AdjVolume
Date
2018-03-27 173.68 175.15 166.92 168.340 38962839.0 0.0 1.0 173.68 175.15 166.92 168.340 38962839.0
2018-03-26 168.07 173.10 166.44 172.770 36272617.0 0.0 1.0 168.07 173.10 166.44 172.770 36272617.0
2018-03-23 168.39 169.92 164.94 164.940 40248954.0 0.0 1.0 168.39 169.92 164.94 164.940 40248954.0
2018-03-22 170.00 172.68 168.60 168.845 41051076.0 0.0 1.0 170.00 172.68 168.60 168.845 41051076.0
2018-03-21 175.04 175.09 171.26 171.270 35247358.0 0.0 1.0 175.04 175.09 171.26 171.270 35247358.0

In order to computer the moving average, we will use the rolling function.

#120 days moving average
moving_averages = aapl[["Open" ,"High" ,"Low","Close" ,"Volume"]].rolling(window=120).mean()
moving_averages.tail()
Open High Low Close Volume
Date
1980-12-18 28.457667 28.551917 28.385000 28.385000 139495.000000
1980-12-17 28.410750 28.502917 28.338083 28.338083 141772.500000
1980-12-16 28.362833 28.453917 28.289167 28.289167 141256.666667
1980-12-15 28.335750 28.426833 28.262083 28.262083 144321.666667
1980-12-12 28.310750 28.402833 28.238167 28.238167 159625.000000 
%matplotlib inline
import matplotlib.pyplot as plt


plt.plot(aapl.index, aapl.Open, label='Open price')
plt.plot(moving_averages.index, moving_averages.Open, label="120 MA Open price")
plt.legend()
plt.show()

png