Pandas: Create a plot of adjusted closing prices and simple 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, thirty days and forty days simple 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)?
A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing that figure by the number of time periods in the calculation average. For example, one could add the closing price of a security for a number of time periods and then divide this total by that same number of periods. Short-term averages respond quickly to changes in the price of the underlying security, while long-term averages are slower to react.

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['SMA_40_days'] = stock_data.iloc[:,4].rolling(window=40).mean()
plt.figure(figsize=[10,8])
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['SMA_40_days'],label='40 days simple 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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