Problem and Motivation

The big trading firms use custom-built AI to get an edge. Independent traders are left behind, without access to the same infrastructure or tooling.

Trying to build your own trading AI is a losing battle for most. You have to fight messy data from exchanges, deal with surprise cloud computing bills, and constantly worry that your back-test is lying to you. This often leads to overfitting, where a model looks perfect on historical data but fails the moment it sees a live market.

So, what happens? Traders give up. They go back to drawing lines on charts or using copy-trade services. The gap between what the pros can do and what's available to everyone else just keeps getting wider.

This isn't to say AI is absent from a trader's workflow. You can ask a language model about a token or upload a screenshot of a chart, but this approach is flawed. These models can hallucinate market data or, worse, simply confirm your existing biases. Show one a chart with a pattern you think you see, and it's likely to agree with you, serving as an echo chamber rather than a source of objective truth. It's a research assistant, not a quantitative engine.

That's why, for trading, a true building tool doesn't exist yet. There's no public platform where you can provide data, define a mathematical goal, and receive a trained model with transparent, honest metrics.

SlinkyLayer changes this. We are doing for trading models what platforms like Cursor did for software: creating the fastest path from an idea to a working AI, with transparent results and fair rewards.