Skip to main content

Stock Market using Python

 "The stock market is a device for transferring money from the impatient to the patient." - Warren Buffett


Today we'll look into few ways for accessing the stock market. And we'll do this using Python!

Now, as we know that there are 2 stock exchange in India; BSE and NSE

So we'll get the data from both!

To begin with let's access the data from BSE first.

(P.S: I certainly like the 2nd and the 3rd method to access stock market!)


* So, to import the BSE data we need to "pip install bsedata".

=> And then import the module,


=> Create an object to store the Driver Class




=> Then we need to do "getQuote('script_code')" where we need to provide a script code of a company which we need to access.
Just like here we have given;


=> And from here we can see that the script code was for the company named "V-MART". But we can't remember all the script code hence we need to download this script file from the BSE website.


* The second method is from NSE, for this we need to "pip install nsepy".
=> We'll import the module;

=> And, with "get_history(symbol=' ', start=' ', end=' ')" (providing the essential parameters) save it into a variable called 'data'.



=> Now, here we are taking 'Previous Close'.
(``I find this method to be convenient!``)
And likewise we can get data of any stock;
Like here we've taken a stock of "SBI" from 31-01-2023 to 01-02-2023


And here we've taken 2  columns into account.

Let's take data of 'TATA MOTORS';




* For the third method we'll use nsetools for which we need to "pip install nsetools".
=> Then let's first import required modules;



=> And just by 'get_quote(''company_name)' we can get the stock price of any company.


=> Now here we can also do top_gainers;


=> And top_losers of the day;



And by this we can see that accessing the stock market is pretty easy! 👍

To see the full document I'm providing the github link here: click here

Comments

Popular posts from this blog

Predicting whether the bank will give loan to its customers based on their credit score (Logistic Regression)

"The best thing about data is that it tells a story." - Naveen Jain Today let's have a look at another machine learning model know as Logistic Regression. Logistic Regression is a statistical model where the outcome is predicted as binary such as YES or NO, based on the previous/train_dataset. ( Please open the images in a new tab or try to zoom-in ) import required models read your dataframe visualize your dataset create Logistic Regression model split your data into train and test dataset fit your train dataset into the regression model predict future values visualize your predicted values Github link for pdf:  click here

Machine Learning model for predicting 'Salary' of an Employee based on 'YearsofExperience'

“ Data really powers everything that we do .” — Jeff Weiner In the 21st century, Data is one of the most valuable entity anyone can have! There is loads-and-loads of data generated everyday. And to process this huge amount of data we need people who have expertise in it, who by the way are called as Data Engineers. Data Engineer collects the raw data, process it for further use; but we need an Analytic process which will automatically predict the data based on the previous one. And here's how 'Machine Learning' comes into the picture. "Machine Learning allows us to make highly accurate predictions based on the Historical Dataset which is used to train the machine learning model." Today let us look at a similar ML model to predict the 'Salary' of Employees based on 'YearsofExperience'. (P.S: I've provided pdf link at the very bottom of this page for clear understanding) 1) import the required modules 2) read the csv file 3) plot the graph 4) us...