Field notes on autonomous trading agents, agentic markets, and turning plain-English ideas into systematic strategies.
Agentic trading has moved from research labs to retail portfolios. Here is what changed in 2026, why it matters, and what autonomous trading agents actually do differently from the bots that came before.
Running several autonomous trading agents at once works like managing a team of specialists. Here is how to allocate capital across strategies, read realized versus unrealized PnL, and move money toward what is working.
AI trading agents are not limited to stocks. Here is a tour of the markets autonomous agents can work in — equities and ETFs, 24/7 crypto, and prediction or event markets — and what changes in each.
Thematic trading turns a story about the world into a position in the market. Here is how autonomous AI agents make it practical to run themes like congressional trades or AI infrastructure without watching the market all day.
Autonomous trading agents can act without you, which makes risk controls the whole game. Here is an honest look at what can go wrong and the guardrails that actually matter.
Algorithmic trading and agentic AI both automate decisions, but they are not the same thing. Here is how rule-based algorithms, machine-learning models, and reasoning agents actually differ — and why the distinction matters.
A practical, step-by-step guide to going from a trading idea written in plain English to a backtested, risk-controlled autonomous agent that runs on its own.
A clear, jargon-free explanation of autonomous trading agents: what they are, how they differ from trading bots, how they work, and what to know before using one.
This blog is for educational purposes only and does not constitute investment advice or a recommendation to buy or sell any security. Trading involves risk, including the possible loss of principal. Past performance does not guarantee future results.