AI Trading Bot for Bybit: How a Multi-Agent LLM System Actually Works
Most 'AI trading bots' are one indicator script with a marketing label glued on. Here's how a real multi-agent LLM trading system for Bybit, SharapovLab Trading Lab, actually decides whether to take a trade.
Overview
Search "AI trading bot for Bybit" and most of what comes up is the same product wearing different branding. One script, a handful of indicators, a fixed position size, and a claim about "AI" that usually just means someone wrote an if/else block after playing around with ChatGPT for an afternoon. That's not what the term should mean, and it's nowhere close to how a real multi-agent LLM trading system gets built.
This post walks through what an actual multi-agent trading architecture looks like, using SharapovLab Trading Lab (formerly S.A.M.S.) as the reference. It's a shipping Windows application trading live on Bybit right now, not a whiteboard sketch.
Why "one model, one decision" doesn't hold up
A single strategy function can't disagree with itself. It has one read of the market, one risk model, and nothing to catch a bad call before the order actually goes out. When it's wrong, there's no trail explaining why. Just a losing trade and, effectively, a shrug.
What a multi-agent trading system does differently
The decision gets split across specialized roles instead of resting on one model. A swarm of strategy agents generates trade candidates from different readings of the market. A structured debate argues each candidate for and against before anything is taken. A verdict agent then resolves that debate into either an approval or an explicit rejection with a stated reason, so a signal is never just quietly dropped. Position sizing adapts to account balance and real market structure after that, rather than applying the same fixed number no matter how big the account is.
The part most bots skip: checking their own confidence
A stated confidence score only means something if the system actually checks whether it's accurate. Trading Lab continuously compares its own confidence claims against the real track record, and it gets more conservative automatically once it's been overconfident a few times in a row. Most retail bots skip this entirely. They'll size the tenth trade exactly like the first, whether the last nine were right or dead wrong.
What this looks like in practice on Bybit
Signals come from a swarm of specialized agents. Each candidate trade gets argued over before it's executed. Position size is set by account balance rather than a flat percentage that ignores how much money is actually at stake. Every order gets checked against Bybit's own limits before it's sent, and a kill switch steps in if agent behavior starts looking anomalous. Paper mode is on by default, so nothing touches real money until someone flips that switch on purpose.
Read more
The full architecture, feature list, current pricing and FAQ live on the Trading Lab project page. Building your own multi-agent trading infrastructure instead of buying one? Take a look at Trading Systems and AI Agent Systems.
FAQ
Is Trading Lab free or open source?
No, it's closed source and sold as a single-seat license for a shipping Windows application. Current pricing is on the project page.
Does it trade automatically right out of the box?
It starts in paper mode. Live trading on Bybit only happens once you turn it on yourself and connect your own exchange API keys.
What actually separates it from an indicator-based bot?
Every trade goes through a Bull/Bear/Probability debate and a Judge verdict before anything executes, and sizing follows account size and real market structure instead of one fixed rule applied to everyone.