AlphaForge — stock automation & an AI Lab.
A different beast than the rest: a market-automation platform built around capturing the evidence behind every decision — paired with an AI Lab for exploring strategies and a risk engine that keeps the whole thing honest. Same evidence-first instinct as my monitoring work, pointed at a much faster-moving problem.
TypeScript
ReactDecisions are only as good as the evidence behind them.
Markets generate noise faster than anything I've monitored. AlphaForge treats that the same way I treat infrastructure telemetry: capture the raw evidence first, keep its provenance intact, and never let a derived signal pretend to be ground truth. On top of that sits an AI Lab for testing ideas and a risk engine that scores and constrains what the automation is allowed to do.
The interesting parts — how data is captured and how the risk engine reasons — are what this architecture note surfaces directly, instead of asking the reader to take the claims on faith.
Three pillars.
Evidence capture
Every input and decision is recorded with its source and timing intact, so any outcome can be traced back to what was actually known at the time.
The risk engine
Scores and constrains automated actions against configurable risk rules — the governor that decides what's allowed to run, and what gets held back.
The AI Lab
A workspace for exploring and pressure-testing strategies against captured evidence, with the same source-aware boundaries I build into every assistant.
The current architecture, without implying a production screenshot.
An illustrative operator-console view of the AI Lab, evidence trail, and risk-engine boundary.
This page deliberately shares AlphaForge at the architecture level: evidence capture, the AI Lab, and the risk-engine boundary. The code remains closed, while the system design and operating principles are the publishable layer. Want to talk markets + automation? Reach out →