Can a sealed agent fleet trade unsupervised — on paper?

Hypothesis

Trading is the harshest testbed for autonomous agents: adversarial, numeric, unforgiving of bugs. Run many strategies simultaneously in a paper-money arena with honest attribution, and let the data — not the vibes — say which deserve to exist. Real money is gated behind a pre-committed contract the system itself enforces.

What we built

A fleet on a sealed server: a momentum autopilot rebalancing a paper account monthly; an arena of 19 virtual strategies (momentum variants, risk parity, theme baskets, an ML price-forecast ensemble, an LLM that scores news headlines weekly) with simulated fills and a weekly Telegram leaderboard; a nightly ML forecast job; and an end-of-day report that aggregates everything — including a self-tuning loop that may only adjust parameters once enough evidence accumulates, double-clamped, with fixed-parameter control arms.

Graduation to real money has a written contract: six months of live paper track record, risk and drawdown limits, a kill rule — and the AI is instructed to enforce it against its own operator.

Learnings

• The bugs are the curriculum. A symbol-format mismatch made one bot liquidate another bot's position; a lookahead bug flattered a forecast; a dropped cash sleeve silently bled a backtest 2% a day. Every one was invisible until honest attribution surfaced it.
• Fine-tuned models only paid via ensembling with the base model — the standalone fine-tune underperformed both. Measure the combination, not the component.
• Pre-committed rules (rebalance dates, kill thresholds, graduation gates) are what make an autonomous money system safe to leave alone. Discretion is where both humans and agents go wrong.

Log

• 2026-07-07 — 19th strategy live (LLM news sentiment); learning-loop scoreboard, weekly research routine, and self-tuning EOD report shipped.
• 2026-07-01 — Momentum autopilot live on a paper account; arena design locked: one arena, strategies as pure functions, honest attribution.