AI Agent vs Crypto Trading Bot (2026)
Traditional crypto bots vs AI agents: latency, backtests, risk, and the hybrid path that usually wins.
Library
Research articles
Deep dives on AI trading agents — from fundamentals and architecture to risk controls and rigorous evaluation.
Cluster overview. Start with definitions and bot vs agent comparisons, then architecture, data, build path, LLM analysis, risk, and evaluation. Last cluster update: .
AI Trading Agent Tools in 2026
Stratium, Robinhood/Webull/Coinbase MCP rails, and bot platforms compared by policy ownership, risk gates, and paper-before-capital design.
Robinhood Agentic Trading (2026)
MCP, sandboxed agentic accounts, crypto rollout, and why BYO AI agents on a major brokerage change retail automation risk.
Data Stack for AI Trading Agents
Ingest, quality flags, point-in-time features, and fail-closed behavior when market data goes bad.
Types of AI Trading Agents
A practical taxonomy: rule bots, ML signals, RL policies, LLM tool-users, multi-agent systems, market making, and on-chain agents.
How to Build an AI Trading Agent
Practical roadmap: scope, data, policy, risk gate, execution, paper trading, and promotion gates for production agents.
AI Trading Agent vs Trading Bot
Side-by-side comparison of bots and agents — autonomy spectrum, risk, and when each approach is justified.
What Is an AI Trading Agent?
A clear definition of autonomous AI trading agents, how they differ from classic bots, and the stack that makes them production-ready.
Architecture of Modern Trading Agents
Perception, reasoning, execution, and memory — a practical blueprint for multi-module AI agents in crypto markets.
LLMs for Crypto Market Analysis
How large language models parse news, on-chain narratives, and social signal — and where they still fail as trading oracles.
Risk Management for AI Traders
Position sizing, kill switches, drawdown guards, and why the best agents are defined by what they refuse to do.
Backtesting & Evaluating Trading Agents
Walk-forward tests, paper trading discipline, leakage traps, and metrics that actually matter for agent performance.