Moving Average- Pandas

Question

I would like to add a moving average calculation to my exchange time series.

Original data from Quandl

Exchange = Quandl.get("BUNDESBANK/BBEX3_D_SEK_USD_CA_AC_000", authtoken="xxxxxxx")

    Value
Date               
1989-01-02  6.10500
1989-01-03  6.07500
1989-01-04  6.10750
1989-01-05  6.15250
1989-01-09  6.25500
1989-01-10  6.24250
1989-01-11  6.26250
1989-01-12  6.23250
1989-01-13  6.27750
1989-01-16  6.31250

Calculating Moving Avarage

MovingAverage = pd.rolling_mean(Exchange,5)

              Value
Date          
1989-01-02      NaN
1989-01-03      NaN
1989-01-04      NaN
1989-01-05      NaN
1989-01-09  6.13900
1989-01-10  6.16650
1989-01-11  6.20400
1989-01-12  6.22900
1989-01-13  6.25400
1989-01-16  6.26550

I would like to add the calculated Moving Average as a new column to the right after 'Value' using the same index (Date). Preferably i would also like to rename the calculated moving average to 'MA'

Answer 1

The rolling mean returns a Series you only have to add it as a new column of your DataFrame (MA) as described below.

For information, the rolling_mean function has been deprecated in pandas newer versions. I have used the new method in my example, see below a quote from the pandas documentation

Warning Prior to version 0.18.0, pd.rolling_*, pd.expanding_*, and pd.ewm* were module level functions and are now deprecated. These are replaced by using the Rolling, Expanding and EWM. objects and a corresponding method call.

df['MA'] = df.rolling(window=5).mean()

print(df)
#             Value    MA
# Date                   
# 1989-01-02   6.11   NaN
# 1989-01-03   6.08   NaN
# 1989-01-04   6.11   NaN
# 1989-01-05   6.15   NaN
# 1989-01-09   6.25  6.14
# 1989-01-10   6.24  6.17
# 1989-01-11   6.26  6.20
# 1989-01-12   6.23  6.23
# 1989-01-13   6.28  6.25
# 1989-01-16   6.31  6.27

Answer 2

In case you are calculating more than one moving average:

for i in range(2,10):
   df['MA{}'.format(i)] = df.rolling(window=i).mean()

Then you can do an aggregate average of all the MA

df[[f for f in list(df) if "MA" in f]].mean(axis=1)

Answer 3

A moving average can also be calculated and visualized directly in a line chart by using the following code:

Example using stock price data:

import pandas_datareader.data as web
import matplotlib.pyplot as plt
import datetime
plt.style.use('ggplot')

# Input variables
start = datetime.datetime(2016, 1, 01)
end = datetime.datetime(2018, 3, 29)
stock = 'WFC'

# Extrating data
df = web.DataReader(stock,'morningstar', start, end)
df = df['Close']

print df 

plt.plot(df['WFC'],label= 'Close')
plt.plot(df['WFC'].rolling(9).mean(),label= 'MA 9 days')
plt.plot(df['WFC'].rolling(21).mean(),label= 'MA 21 days')
plt.legend(loc='best')
plt.title('Wells Fargo\nClose and Moving Averages')
plt.show()

Tutorial on how to do this: https://youtu.be/XWAPpyF62Vg