Library

Research articles

Deep dives on AI trading agents — from fundamentals and architecture to risk controls and rigorous evaluation.

Data Stack for AI Trading Agents

Ingest, quality flags, point-in-time features, and fail-closed behavior when market data goes bad.

A practical taxonomy: rule bots, ML signals, RL policies, LLM tool-users, multi-agent systems, market making, and on-chain agents.

Practical roadmap: scope, data, policy, risk gate, execution, paper trading, and promotion gates for production agents.

Side-by-side comparison of bots and agents — autonomy spectrum, risk, and when each approach is justified.

A clear definition of autonomous AI trading agents, how they differ from classic bots, and the stack that makes them production-ready.

Perception, reasoning, execution, and memory — a practical blueprint for multi-module AI agents in crypto markets.

How large language models parse news, on-chain narratives, and social signal — and where they still fail as trading oracles.

Position sizing, kill switches, drawdown guards, and why the best agents are defined by what they refuse to do.

Walk-forward tests, paper trading discipline, leakage traps, and metrics that actually matter for agent performance.