Pandas: Create a plot of adjusted closing prices, thirty days simple moving average and exponential moving average

Last update on December 21 2024 07:54:10 (UTC/GMT +8 hours)

Write a Pandas program to create a plot of adjusted closing prices, 30 days simple moving average and exponential moving average of Alphabet Inc. between two specific dates.

Use the alphabet_stock_data.csv file to extract data.

What is Simple Moving Average (SMA)?
In financial applications a simple moving average (SMA) is the unweighted mean of the previous n data. However, in science and engineering, the mean is normally taken from an equal number of data on either side of a central value. This ensures that variations in the mean are aligned with the variations in the data rather than being shifted in time.

What Is an Exponential Moving Average (EMA)?
From investopedia.com - An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average (SMA), which applies an equal weight to all observations in the period.

alphabet_stock_data:

alphabet_stock_data Table

Sample Solution:

Python Code :

import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("alphabet_stock_data.csv")
start_date = pd.to_datetime('2020-4-1')
end_date = pd.to_datetime('2020-9-30')                         
df['Date'] = pd.to_datetime(df['Date']) 
new_df = (df['Date']>= start_date) & (df['Date']<= end_date)
df1 = df.loc[new_df]
stock_data = df1.set_index('Date')
close_px = stock_data['Adj Close']
stock_data['SMA_30_days'] = stock_data.iloc[:,4].rolling(window=30).mean() 
stock_data['EMA_20_days'] = stock_data.iloc[:,4].ewm(span=20,adjust=False).mean()
plt.figure(figsize=[15,10])
plt.grid(True)
plt.title('Historical stock prices of Alphabet Inc. [01-04-2020 to 30-09-2020]\n',fontsize=18, color='black')
plt.plot(stock_data['Adj Close'],label='Adjusted Closing Price', color='black')
plt.plot(stock_data['SMA_30_days'],label='30 days Simple moving average', color='red')
plt.plot(stock_data['EMA_20_days'],label='20 days Exponential moving average', color='green')
plt.legend(loc=2)
plt.show()

Sample Output:

Click for download alphabet_stock_data.csv

Python Code Editor:

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