Hi, when you are getting into algo-trading or just technical analysis of any stock/ticker, it is imperative to have the right piece of data to do that. Otherwise, all your effort will be horse-shit.
And it is no secret that the stock market data provider industry thrives on monthly subscriptions because they know all their customers want is the good quality of data, and as long as they provide that, they will thrive!
But, quite often, these data providers have expensive subscriptions, and it might not really be affordable when you are just trying to get proof of concept on your algo trading strategy.
Hence, I am creating this new series for my readers interested in the Indian Stock Exchanges and want to collect EOD stock prices for each stock listed on the National Stock Exchange of India.
This series will span over a couple of articles, and we will be covering the following points in detail in each article.
- How to download the NSE Bhavcopy File programmatically via Python for the last 5 years. You can look at the sample Bhavcopy Report here.
- How to store all the downloaded data in one single file in time-series format.
- How to adjust your data for symbol changes over the last 5 years?
Work In Progress
- How to adjust your data for all corporate actions over the last 5 years? (e.g., stock splits, dividends, mergers)
As you must have realized, we will start with baby steps and go from 0 to 100 in this series. Over the last few months, I have tried and tested various methods of what works and what does not work, and I am excited to share all my learnings with you all.
The best part I think about this approach is that we are directly downloading the raw data from the official exchange without any third-party manipulations, giving us reliability on the data.
I hope this makes you excited as well, and you will follow me throughout this series, do give me any suggestions you have as I genuinely value them.
I highly recommend you to subscribe to my newsletter 📬 at the top of the page, this way, you will get updates on all new articles in this series.
If you like it until now, consider buying me a coffee ☕ by clicking here or the button below.
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