By BowTiedDevil on Substack · 7K+ subscribers · 1 free and 11 paid among the latest 12.
A deeply autistic exploration of Ethereum and building automated trading bots with Python, Rust, and AI.
Performance matters and I’m not afraid to say it! degenbot was a pure Python package for many years. I love Python but it’s not a high performance language, so I mostly focused on speeding up the slowest parts where I could while…
The most painful part of high-volume searching is simulation. Before every value capture can occur, you must run it against your best view of the chain state. The boundary between your efficient searcher and the generalized…
This entry builds on the theoretical concepts explored in the Numerical Optimization series. If you didn’t read those, shame on you! But for your convenience, here they are again: Liquidity Pool Structure A given liquidity pool…
We have already established a long term goal: build a Rust core driven by a thin Python shell. Communication with the blockchain is done via Remote Procedure Call (RPC) exposed by a node directly, or through some gateway service.…
I love graphs and I’m not afraid to admit it. They are an ideal data structure for arbitrage pathfinding and optimization for certain multi-market problems. I primarily use NetworkX for working with graphs. It’s easy to use, but…
The architectural rewrite has allowed me to revisit some design decisions that have been bugging me. I started developing degenbot to speed up my day-to-day bot building. Those bots were focused on calculating cyclic arbitrage…
Last year I wrote a short Rust series (Parts I , II , III , IV ) surveying the basics of the language. I was particularly excited about the ability to integrate Rust directly into degenbot , which was pure Python at its core and…
I’ve been using AutoResearch (AR) to improve smart contracts. I don’t see AR discussed often enough, and I think it suffers from a branding problem. The name suggests it is a tool for finding obscure info, digging into scientific…
In Part IV , while deriving analytical gradients for the Newtonian methods benchmark, we paused on the simple case of two V2 pools with the same token pair. The profit function P ( x ) and its derivative dP/dx both had clean…
The derivative-free methods covered in Parts II and III treat the objective function as a black box, probing inputs and observing outputs without requiring knowledge of the internal structure. This flexibility is valuable, but it…
Part II covered derivative-free methods for the simple arbitrage problem: two pools holding the same token pair, optimizing for maximum profit from a single trade. Now we’ll turn our attention to a complex arbitrage problem:…
Part I outlined the structure of our optimization effort before concluding with a simple benchmark of SciPy’s minimize_scalar methods. To understand how I landed on Brent as the default solver in degenbot , we need to understand…