Stock price analysis with Python

Hello readers! We all know how the Stock Market works. A share is the small part of the property of the company. The company's share price reflects the net evaluation of the company and also gives a small idea of ​​its performance.. These shares are traded on exchanges and their prices are constantly changing due to their demand and supply in the market.. If a stock has high demand and low supply, namely, more people want to buy it and fewer people are willing to sell it, then the price of the stock will rise and similarly if the stock has low demand and high supply, which means more people are willing to sell it, but fewer people are willing to buy it and its prices go down.

The sudden increase in demand for stocks can be due to a number of reasons, including positive news about the company or a company announcement. After a period of time in which the demand for the shares disappears, their prices slowly decline as the investor loses interest in them. These rising and falling stock prices are an iterative and repeated process. This volatility in stocks makes investors nervous when investing in a company. Then, to understand the risk associated with it, there must be a proper analysis of the shares before buying them. In this article, we would try to explore only the tip of the iceberg for stock market analysis, since technical value analysis is a vast field. This blog can be the starting point in this industry.

The tool is not important for analysis, can be done in any statistical software like Python, R o Excel, but for the sake of this article, we are demonstrating parsing in python.

Contents

• Libraries used
• Description of data
• Exploratory analysis
• Scattered plot matrix
• Moving averages
• Percentage increase in the value of shares
• Conclution

Libraries used

The following are the libraries that must be installed beforehand and can be easily downloaded with the help of the pip function. the following is a brief description of the name of the library and its application.

import pandas as pd

import datetime

import numpy as np

import matplotlib.pyplot as plt

from pandas.plotting import scatter_matrix

!pip install yfinance

import yfinance as yf

%matplotlib inline

Description of data:

We have downloaded daily stock price data using Yahoo Finance API functionality. It is a data capture of five years of openness, maximum, minimum, closure and volume.

• Opened: The price of the stock when the market opens in the morning.
• Closing: the price of the stock when the market closed overnight.
• Alto: highest price the stock reached on that day
• Low: lowest price the stock is trading that day
• Volume: the total number of shares traded that day.

Here, we will take the example of three companies, TCS, Infosys and Wipro, who are industry leaders in IT service delivery.

start = "2014-01-01"
end = '2019-1-01'
tcs = yf.download('TCS',start,end)
infy = yf.download('INFY',start,end)
wipro = yf.download('WIPRO. NS',start,end)

Exploratory analysis

Tcs['Open'].plot(label="TCS", figsize = (15,7))
infy['Open'].plot(label = "Infosys")
wipro['Open'].plot(label="Wipro")
plt.title('Stock Prices of TCS, Infosys and Wipro')

Tcs['Volume'].plot(label="TCS", figsize = (15,7))
infy['Volume'].plot(label = "Infosys")
wipro['Volume'].plot(label="Wipro")
plt.title('Volume of Stock traded')
plt.legend()

#Market Capitalisation
tcs['MarktCap'] = tcs['Open'] * Tcs['Volume']
infy['MarktCap'] = infy['Open'] * infy['Volume']
wipro['MarktCap'] = wipro['Open'] * wipro['Volume']
Tcs['MarktCap'].plot(label="TCS", figsize = (15,7))
infy['MarktCap'].plot(label="Infosys")
wipro['MarktCap'].plot(label="Wipro")
plt.title('Market Cap')
plt.legend()

Tcs['MA50'] = tcs['Open'].rolling(50).mean()
Tcs['MA200'] = tcs['Open'].rolling(200).mean()
Tcs['Open'].plot(figsize = (15,7))
Tcs['MA50'].plot()
Tcs['MA200'].plot()

data = pd.concat([Tcs['Open'],infy['Open'],wipro['Open']],axis = 1)
data.columns = ['TCSOpen','InfosysOpen','WiproOpen']
scatter_matrix(data, figsize = (8,8), hist_kwds= {'bins':250})

#Volatility
tcs['returns'] = (Tcs['Close']/Tcs['Close'].shift(1)) -1
infy['returns'] = (infy['Close']/infy['Close'].shift(1))-1
wipro['returns'] = (wipro['Close']/wipro['Close'].shift(1)) - 1
Tcs['returns'].hist(bins = 100, label="TCS", alpha = 0.5, figsize = (15,7))
infy['returns'].hist(bins = 100, label="Infosysy", alpha = 0.5)
wipro['returns'].hist(bins = 100, label="Wipro", alpha = 0.5)
plt.legend()