I tested 4 strategy families out-of-sample, found no edge, and built guardrails for AI trading agents instead (free, open)
Long story short: I wanted an AI agent trading for me. Before risking a dollar
I built an honest backtester (next-bar fills, fees, slippage, walk-forward,
buy-and-hold benchmark).
EMA/RSI, Donchian breakouts, time-series momentum and RSI(2) mean reversion
on BTC and ETH, 1h and 4h, 2022–2026:
**none beat buy and hold out of sample**
.
In-sample Sharpe of 1–2 collapsed to between −1.5 and 0.4.
What did pass a pre-registered test was boring: volatility-targeted holding
with a trend filter on daily BTC (Sharpe 1.03 vs 0.85 for buy and hold, max
drawdown 42% vs 76%). Not alpha, just less pain.
The more useful output was the guardrail. If an LLM agent is going to trade,
it needs a mandate it can't break and a record nobody can rewrite. So I built
IRL, an MCP gateway: every order is checked against the agent's mandate before
it's sent, the agent's reasoning is sealed first, the fill comes back MATCHED
or DIVERGENT, and the records are anchored on Bitcoin.
Free to try in a minute: `uvx irl-gateway init` (paper trading, no signup
form). Site with a live, editable demo: https://norve.dev. Gateway:
https://github.com/norve-labs/irl-gateway
Research write-ups are in the repo. Happy to be told where my backtests are wrong.
u/GabrielGauss — 7 days ago