If I had told you a year ago that amidst a global pandemic, an online community of retail traders would take down a multi-billion dollar hedge fund, would you believe me? Probably not, and that is perfectly reasonable. This pandemic has led to some unprecedented market events. I suppose we can assume this is because everyone was bored quarantining at home and more willing to speculate on the market with their stimulus checks.
The increase in retail investors from all around the world helped the subreddit r/wallstreetbets, self-classified as “Like 4chan found a Bloomberg terminal”, grow by almost 10M users since the beginning of 2021. This is a special place where casual investors can turn companies into memes and raise their market cap by over 1000% by squeezing short-selling hedge funds to bankruptcy (See GME and AMC).
Anyways, the best investment decisions are only as good as the data they are derived from. This is why r/wallstreetbets decided to acquire market data from polygon.io. We can't take credit for "memestonk" gains, but let’s discuss what role polygon.io played in this movement.
When you have a large pool of accredited investors promising “runs to the moon”, and "Tendies", there has to be some way to ensure they’re not just pump-and-dump schemes. This is why the mods at r/wallstreetbets use polygon.io’s Daily Market Cap data to train their bots to detect and remove any spam posts shilling companies beneath a $1.5B Market Cap.
These mod bots are constantly sifting through new posts, identifying companies, then querying our Ticker Details vX endpoint to get the up-to-date market cap value, which ultimately determines if u/stonkrocket420 gets banned for posting about a ticker whose market cap is less than $1.5B.
We like to think that we have played a small part in protecting the retail trading community from gambling on inconspicuous pump-and-dumps, and are eager to continue doing so. Polygon.io believes in fair access to market data for all apes. ??
Let our team know if you have any interesting use cases like this one! We’d love to help out in any way we can.
This tutorial demonstrates how to detect short-lived statistical anomalies in historical US stock market data by building tools to identify unusual trading patterns and visualize them through a user-friendly web interface.
In this tutorial, we'll explore the capabilities of Polygon.io’s Related Companies API, where we learn how to identify and visualize intricate corporate relationships using Python and vis.js.