
The complete guide to building Ethereum trading & arbitrage bots.
| Platform | Pricing | Freemium | Publishes | Weekly | |
|---|---|---|---|---|---|
| Issues | 220 | Founded | 4 years ago | Last Issue | 5 days ago |
| Active | |||||

The Answer Hiding in Plain Sight
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...
Standing on the Shoulders of Giants
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 flexi...
More Pools (Maybe) More Profits
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.
Black Box Optimization
Part I outlined the structure of our optimization effort before concluding with a simple benchmark of SciPy’s minimize_scalar methods.
Who Optimizes the Optimizers?
MEV searching boils down to a set of critical elements: pathfinding, numerical optimization, execution.
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The writers behind this newsletter.
Self-taught coder, engineer, blockchain enthusiast, technical writer.
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