Parallel Claude agents that generate, test, and optimize trading strategies — and won a hackathon doing it. Read it for the parallel-AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → search pattern, which generalizes well beyond trading.
Key takeaways · AI-distilled
The winning market maker's first rule is refusal: skip the tightest-spread regime entirely, because the informed arbitrageur sweeps your quote before retail arrives. Not trading there is the profitable move.
Order size scales with the modeled probability that an arbitrageur hits you. Quote large at extreme prices and wide spreads where that's unlikely, small near 50% where it's dangerous.
An empty book is the best moment, not a reason to pause. When the competitor's orders get consumed, the strategy quotes at tick 1 or 99 rather than cancelling, capturing the widest edge available.
The loop ran 8 to 20 parallel Claude Code sessions, each assigned one hypothesis or search space: read the current best strategy, build variants, evaluate, report. The human's job shrank to folding winners back in.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
Key quotes
“I won a hackathon by @paradigm, but I have no idea what the winning strategy was.”
“Every step of the solution was made by AI.”
“At peak, I had 20 agents running simultaneously, each sweeping different parameter spaces or testing structural changes. This massively parallelized the search — equivalent to weeks of manual experimentation compressed into hours.”
“Early on, I optimized only on seed=0 and hit +$44 locally. But when I tested on other seeds, performance varied wildly (+$34 to +$70).”
