Stock Market Analysis using Python

Stock Market Analysis means analyzing the current and historical trends in the stock market to make future buying and selling decisions. Stock market analysis is one of the best use cases of Data Science in finance. So, if you want to learn to analyze the stock market, this article is for you. In this article, I will take you through the task of Stock Market Analysis using Python.

Stock Market Analysis using Python

To analyze the stock market, I will collect the stock price data of Google. At the end of this article, you will learn to analyze the stock market interactively using the Python programming language. Let’s start by collecting the stock price data of Google. I will use the yfinance API of Yahoo Finance for collecting the stock price data. You can learn more about this API here.

Now here’s how to collect Google’s stock price data:

import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
import plotly.graph_objects as go
import plotly.express as px

today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=365)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('GOOG', 
                      start=start_date, 
                      end=end_date, 
                      progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", 
             "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.head())

        Date         Open         High          Low        Close    Adj Close  \
0 2021-07-12  2596.669922  2615.399902  2592.000000  2611.280029  2611.280029   
1 2021-07-13  2617.629883  2640.840088  2612.739990  2619.889893  2619.889893   
2 2021-07-14  2638.030029  2659.919922  2637.959961  2641.649902  2641.649902   
3 2021-07-15  2650.000000  2651.899902  2611.959961  2625.330078  2625.330078   
4 2021-07-16  2632.820068  2643.659912  2616.429932  2636.909912  2636.909912   

   Volume  
0  847200  
1  830900  
2  895600  
3  829300  
4  742800  

Whenever you analyze the stock market, always start with a candlestick chart. A candlestick chart is a handy tool to analyze the price movements of stock prices. Here’s how you can visualize a candlestick chart of Google’s stock prices:

figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], high=data["High"],
                                        low=data["Low"], close=data["Close"])])
figure.update_layout(title = "Google Stock Price Analysis", xaxis_rangeslider_visible=False)
figure.show()

A bar plot is also a handy visualization to analyze the stock market, specifically in the long term. Here’s how to visualize the close prices of Google’s stock using a bar plot:

figure = px.bar(data, x = "Date", y= "Close")
figure.show()

One of the valuable tools to analyze the stock market is a range slider. It helps you analyze the stock market between two specific points by interactively selecting the time period. Here’s how you can add a range-slider to analyze the stock market:

figure = px.line(data, x='Date', y='Close', 
                 title='Stock Market Analysis with Rangeslider')
figure.update_xaxes(rangeslider_visible=True)
figure.show()

Another interactive feature you can add for stock market analysis is time period selectors. Time period selectors are like buttons that show you the graph of a specific time period. For example, a year, three months, six months, etc. Here is how you can add buttons for selecting the time period for stock market analysis:

figure = px.line(data, x='Date', y='Close', 
                 title='Stock Market Analysis with Time Period Selectors')

figure.update_xaxes(
    rangeselector=dict(
        buttons=list([
            dict(count=1, label="1m", step="month", stepmode="backward"),
            dict(count=6, label="6m", step="month", stepmode="backward"),
            dict(count=3, label="3m", step="month", stepmode="backward"),
            dict(count=1, label="1y", step="year", stepmode="backward"),
            dict(step="all")
        ])
    )
)
figure.show()

The weekend or holiday season always affects the stock market. So if you want to remove all the records of the weekend trends from your stock market visualization, below is how you can do it:

figure = px.scatter(data, x='Date', y='Close', range_x=['2021-07-12', '2022-07-11'],
                 title="Stock Market Analysis by Hiding Weekend Gaps")
figure.update_xaxes(
    rangebreaks=[
        dict(bounds=["sat", "sun"])
    ]
)
figure.show()

So that’s how you can analyze the stock market using Python. If you want to learn how to predict the stock market, you can learn here.

Summary

So this is how you can use the Python programming language to analyze the stock market interactively. Stock Market Analysis means analyzing the current and historical trends in the stock market to make future buying and selling decisions. I hope you liked this article on Stock Market Analysis using Python. Feel free to ask valuable questions in the comments section below.