u/EntertainmentLow7952

I’ve spent the past few weeks diving deep into the codebases of the most popular open-source arbitrage and trading systems. Most are either outdated or complete spaghetti code, but the underlying math and engineering are fascinating.

To make sense of it all, I compiled a comprehensive document that breaks down everything from AMM constant product formulas to Bellman-Ford pathfinding for negative cycles.

Repo Link: https://github.com/TejasS1233/aribitrage_trading_system

If you're interested in algorithmic trading or high-concurrency Python, I hope this saves you the hours I spent digging through the repos.

Hey,
I have been researching a bunch of popular arbitrage repos and their features and decided to make my own modular system where u can add all sources through plugins.

Contrary to my expectations it actually does decently well , its able to find 1-2$ worth of arbitrage (pretty less ik but still)

It supports 100+ exchanges and handles both token-based and probability-based arbitrage with deterministic slippage taken into account.

The only things that still feel iffy are that to actually get bare minimal profits I had to assume lower fees similar to high-volume traders instead of retail users and when there is no volume info of the stock it assumes minimal volume amount instead.
Looking for advice on whether these assumptions are reasonable or if there’s a better way to handle them.

And yeah, if u want to learn about things in this field do check out the mastery concepts markdown - it has a compilation of all the things in the projects I used to make so that u can develop a similar system.