Quant Funds, Sleep Quality, Free Data Sources, And More Inside.
How does Jane Street work? How to drastically improve the quality of your sleep? How do you build a mini-Berkshire Hathaway? All that and more inside this week's newsletter.
This is my first ever newsletter I am publishing. So, I am going to keep it very very brief and useful.
Drastically improve your sleep quality
Sleep quality has been something I have struggled with, for the past year or two. I always end up needing more sleep due to poor quality of sleep during the sleep time.
I absolutely loved this thread by Jack on improving sleep quality. Every single tweet in the thread is very actionable, and has a lot of common sense per tweet.
Build your own mini-Berkshire Hathaway
For those who aspire to build their own fund some day - be it family office, or a value investing fund open for investors, this small thread contains amazing and actionable information (based in US) on how you can build your own without much tax burden.

An Inside Look at Jane Street Capital
Last week I was reading this very detailed and extensive post by Bryne Hobart of “The Diff” on how Jane Street (and similar quant funds) works.
It’s a very detailed look into
how they hire
what kind of strategies they use
how they approach the trading part systematically
how they go about creating alpha
how their workflow, and teams are structured
how they do market making
and a lot more nuanced discussion around their day to day working.
I once applied for Masters in CS with hopes that I could do a PhD research in Automated Learning topic, and eventual dream was to work for the likes of Two Sigma, JS, Virtu, etc., as a quant researcher.
That dream didn’t materialise, but I still continue to study and read as much as possible about how these funds operate. And, this post was a very insightful one.
For the aspiring quants reading this, I hope it gave you a fantastic inside-view.
Python API for free data (multiple sources)
You may already be aware of this.
But Pandas has a module called “datareader” which has functions to fetch historical data across markets, different timeframes (even intraday) using several data providers.
Most importantly, this data is free.
There are different sources of data, from Alphavantage to Yahoo Finance.
The data quality may not be as good as the paid data you might acquire from sources like Reuters, Factset, etc.
But for retail investors/traders, the data that you can acquire through these providers would be good enough to make decisions on, especially higher timeframe data such as DailyTF and above.
Check the documentation here.
https://pandas-datareader.readthedocs.io/
That’s it for this week’s newsletter.
If you want me to write on a topic, do DM me on twitter (twitter.com/thebuoyantman) and tell me what you want me to write on. Or you can comment the same here too. I’d love to hear from you.
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Until next week,
Shravan