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Trading Systems

Autonomous trading system architecture. Multi-agent decision pipelines, execution infrastructure and risk management, not another indicator script.

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Overview

I design and build autonomous trading systems as software architecture: a decision pipeline with memory and risk management built in, not a single indicator-based script pretending to be a system.

How I Build This

Market analysis, risk assessment and execution get separated into their own agents or stages, so no single failure point controls the whole pipeline end to end. Knowledge storage stays persistent, a graph-based structure paired with vector-based similarity memory, so the system can actually reason about market conditions instead of just reacting to whatever the latest price tick happens to be.

Live and paper-trading execution runs through direct integration with real exchange APIs, Bybit being the current one. And deployment stays containerized and reproducible, so the exact same system behaves the same way in development and in production, not two different beasts wearing the same name.

This is the architecture behind SharapovLab Trading Lab (formerly S.A.M.S.), a live multi-agent trading system running on crypto markets right now.

Who This Is For

Traders and firms who've outgrown a single strategy script and need something that can hold state, manage risk explicitly, and get audited after the fact instead of just trusted blindly.

FAQ

Do you build strategies, or the system around them?

Both, though the real emphasis sits on the system architecture. How analysis, risk and execution get separated and coordinated is what actually makes a strategy survivable once it's live in real markets.

Which markets/exchanges do you work with?

Crypto markets through exchange APIs like Bybit currently, and the same architecture generalizes cleanly to other venues when needed.