Stock Market Performance Analysis using Python

Stock Market Performance Analysis involves calculating moving averages, measuring volatility, conducting correlation analysis and analyzing various aspects of the stock market to gain a deeper understanding of the factors that affect stock prices and the relationships between the stock prices of different companies. So, if you want to learn how to analyze the stock market performance for a given period, this article is for you. In this article, I will take you through the task of Stock Market Performance Analysis using Python.

Stock Market Performance Analysis: Overview

Stock market performance analysis can be used to inform investment decisions and help investors make informed decisions about buying or selling stocks. Suppose you work as a data science professional in a company that provides services based on investment decisions. As a data science professional, you can help your business by analyzing the historical performance of different companies, identifying potential opportunities and risks in the stock market, and adjusting your clients’ investment strategies accordingly.

As a data science professional, you can go through a structured process of stock market performance analysis, which involves collecting historical stock price data of different companies from trusted sources such as Yahoo Finance, visualizing data using various charts, calculating movements, averages and volatility for each company, and performing correlation analysis to analyze the relationships between different stock prices.

In the section below, I will take you through the task of Stock Market Performance Analysis using Python step by step.

Stock Market Performance Analysis using Python

Let’s start the task of Stock Market Performance Analysis by importing the necessary Python libraries and the dataset. For this task, I will use the Yahoo finance API (yfinance) to collect real-time stock market data for the past three months.

It’s important to collect real-time data for this task, but still, if you are a complete beginner and want a dataset only to practice the concepts covered in this article, you can download the dataset from here. But it’s recommended to use the yfinance API to collect and work on real-time data. You can install the yfinace API in your Python environment using the pip command mentioned below (run the command below on your command prompt or terminal):

• for command prompt or terminal: pip install yfinance
• for Google Colab or Jupyter notebooks: !pip install yfinance

Now below is how we can collect real-time stock market data using the yfinance API:

import pandas as pd
import yfinance as yf
from datetime import datetime

start_date = datetime.now() - pd.DateOffset(months=3)
end_date = datetime.now()

tickers = ['AAPL', 'MSFT', 'NFLX', 'GOOG']

df_list = []

for ticker in tickers:
    data = yf.download(ticker, start=start_date, end=end_date)
    df_list.append(data)

df = pd.concat(df_list, keys=tickers, names=['Ticker', 'Date'])
print(df.head())

                         Open        High         Low       Close   Adj Close  \
Ticker Date                                                                     
AAPL   2023-02-08  153.880005  154.580002  151.169998  151.919998  151.688400   
       2023-02-09  153.779999  154.330002  150.419998  150.869995  150.639999   
       2023-02-10  149.460007  151.339996  149.220001  151.009995  151.009995   
       2023-02-13  150.949997  154.259995  150.919998  153.850006  153.850006   
       2023-02-14  152.119995  153.770004  150.860001  153.199997  153.199997   

                     Volume  
Ticker Date                  
AAPL   2023-02-08  64120100  
       2023-02-09  56007100  
       2023-02-10  57450700  
       2023-02-13  62199000  
       2023-02-14  61707600  

In the above code, we first imported the necessary Python libraries and downloaded the historical stock price data for four companies: Apple, Microsoft, Netflix, and Google, for the last three months.

In this dataset, the Date column is the index column in the DataFrame. We need to reset the index before moving forward:

df = df.reset_index()
print(df.head())

  Ticker       Date        Open        High         Low       Close  \
0   AAPL 2023-02-08  153.880005  154.580002  151.169998  151.919998   
1   AAPL 2023-02-09  153.779999  154.330002  150.419998  150.869995   
2   AAPL 2023-02-10  149.460007  151.339996  149.220001  151.009995   
3   AAPL 2023-02-13  150.949997  154.259995  150.919998  153.850006   
4   AAPL 2023-02-14  152.119995  153.770004  150.860001  153.199997   

    Adj Close    Volume  
0  151.688400  64120100  
1  150.639999  56007100  
2  151.009995  57450700  
3  153.850006  62199000  
4  153.199997  61707600  

Now let’s have a look at the performance in the stock market of all the companies:

import plotly.express as px
fig = px.line(df, x='Date', 
              y='Close', 
              color='Ticker', 
              title="Stock Market Performance for the Last 3 Months")
fig.show()

Now let’s look at the faceted area chart, which makes it easy to compare the performance of different companies and identify similarities or differences in their stock price movements:

fig = px.area(df, x='Date', y='Close', color='Ticker',
              facet_col='Ticker',
              labels={'Date':'Date', 'Close':'Closing Price', 'Ticker':'Company'},
              title='Stock Prices for Apple, Microsoft, Netflix, and Google')
fig.show()

Now let’s analyze moving averages, which provide a useful way to identify trends and patterns in each company’s stock price movements over a period of time:

df['MA10'] = df.groupby('Ticker')['Close'].rolling(window=10).mean().reset_index(0, drop=True)
df['MA20'] = df.groupby('Ticker')['Close'].rolling(window=20).mean().reset_index(0, drop=True)

for ticker, group in df.groupby('Ticker'):
    print(f'Moving Averages for {ticker}')
    print(group[['MA10', 'MA20']])

Moving Averages for AAPL
          MA10      MA20
0          NaN       NaN
1          NaN       NaN
2          NaN       NaN
3          NaN       NaN
4          NaN       NaN
..         ...       ...
56  166.631000  165.2730
57  166.837999  165.3915
58  166.819998  165.4825
59  166.733998  165.5840
60  167.588998  166.0295

[61 rows x 2 columns]
Moving Averages for GOOG
           MA10        MA20
183         NaN         NaN
184         NaN         NaN
185         NaN         NaN
186         NaN         NaN
187         NaN         NaN
..          ...         ...
239  106.209000  106.416500
240  106.295000  106.470000
241  106.405001  106.520000
242  106.336001  106.533001
243  106.366500  106.398750

[61 rows x 2 columns]
Moving Averages for MSFT
           MA10        MA20
61          NaN         NaN
62          NaN         NaN
63          NaN         NaN
64          NaN         NaN
65          NaN         NaN
..          ...         ...
117  291.889999  289.487000
118  293.594000  290.395999
119  295.188998  291.256999
120  297.119000  292.310500
121  299.607999  293.262999

[61 rows x 2 columns]
Moving Averages for NFLX
           MA10        MA20
122         NaN         NaN
123         NaN         NaN
124         NaN         NaN
125         NaN         NaN
126         NaN         NaN
..          ...         ...
178  326.276999  333.262498
179  324.661996  331.725998
180  324.279996  330.353497
181  323.822995  329.274997
182  323.300995  328.446498

[61 rows x 2 columns]

Now here’s how to visualize the moving averages of all companies:

for ticker, group in df.groupby('Ticker'):
    fig = px.line(group, x='Date', y=['Close', 'MA10', 'MA20'], 
                  title=f"{ticker} Moving Averages")
    fig.show()

The output shows four separate graphs for each company. When the MA10 crosses above the MA20, it is considered a bullish signal indicating that the stock price will continue to rise. Conversely, when the MA10 crosses below the MA20, it is a bearish signal that the stock price will continue falling.

Let us now analyze the volatility of all companies. Volatility is a measure of how much and how often the stock price or market fluctuates over a given period of time. Here’s how to visualize the volatility of all companies:

df['Volatility'] = df.groupby('Ticker')['Close'].pct_change().rolling(window=10).std().reset_index(0, drop=True)
fig = px.line(df, x='Date', y='Volatility', 
              color='Ticker', 
              title='Volatility of All Companies')
fig.show()

High volatility indicates that the stock or market experiences large and frequent price movements, while low volatility indicates that the market experiences smaller or less frequent price movements.

Now let’s analyze the correlation between the stock prices of Apple and Microsoft:

# create a DataFrame with the stock prices of Apple and Microsoft
apple = df.loc[df['Ticker'] == 'AAPL', ['Date', 'Close']].rename(columns={'Close': 'AAPL'})
microsoft = df.loc[df['Ticker'] == 'MSFT', ['Date', 'Close']].rename(columns={'Close': 'MSFT'})
df_corr = pd.merge(apple, microsoft, on='Date')

# create a scatter plot to visualize the correlation
fig = px.scatter(df_corr, x='AAPL', y='MSFT', 
                 trendline='ols', 
                 title='Correlation between Apple and Microsoft')
fig.show()

There is a strong linear relationship between the stock prices of Apple and Microsoft, which means that when the stock price of Apple increases, the stock price of Microsoft also tends to increase. It is a sign of a strong correlation or similarity between the two companies, which can be due to factors such as industry trends, market conditions, or common business partners or customers. For investors, this positive correlation may indicate an opportunity to diversify their portfolio by investing in both companies, as both stocks may offer similar potential returns and risks.

So this is how you can perform stock market analysis using Python.

Summary

Stock Market Performance Analysis involves calculating moving averages, measuring volatility, conducting correlation analysis and analyzing various aspects of the stock market to gain a deeper understanding of the factors that affect stock prices and the relationships between the stock prices of different companies. I hope you liked this article on Stock Market Performance Analysis using Python. Feel free to ask valuable questions in the comments section below.