How to Plot 5‑Day and 20‑Day Moving Averages for Stock Prices with Python

Using Python, you can fetch historical stock data, compute 5‑day and 20‑day moving averages, and visualize them to aid short‑term and medium‑term trading decisions.

First, import pandas, numpy, pandas_datareader, datetime, and matplotlib.pyplot. Then retrieve the last 100 days of price data for stock 601127.SS from Yahoo Finance.

import pandas as pd
import numpy as np
from pandas_datareader import data
import datetime
import matplotlib.pyplot as plt

end_date = datetime.date.today()
start_date = end_date - datetime.timedelta(days=100)
price = data.DataReader('601127.ss','yahoo', start_date, end_date)
price.head()

Calculate the moving averages:

price['ma5'] = price['Adj Close'].rolling(5).mean()
price['ma20'] = price['Adj Close'].rolling(20).mean()
price.tail()

Plot the adjusted close price together with the two moving averages:

fig = plt.figure(figsize=(16,9))
ax1 = fig.add_subplot(111, ylabel='Price')
price['Adj Close'].plot(ax=ax1, color='g', lw=2, legend=True)
price.ma5.plot(ax=ax1, color='r', lw=2, legend=True)
price.ma20.plot(ax=ax1, color='b', lw=2, legend=True)
plt.grid()
plt.show()

The resulting chart clearly shows the price line along with the 5‑day (red) and 20‑day (blue) moving averages, providing a visual basis for designing simple moving‑average trading strategies.