A multi-agent AI trading system
Five specialist LLM agents propose trades, a critic validates them, I approve on my phone, and a deterministic risk guard has final veto — with a local NVIDIA DGX Spark reading the whole market every morning. Everything below is a live feed from the system running as an educational real-world proof-of-concept on a paper account.
Paper trading only — for research and demonstration. Not investment advice.
Live system feed
loading…Equity curve (paper, $25k start)
Per-agent realized P&L
Alpha factory throughput
Component health
▉ DGX Spark — live GPU load
—The local NVIDIA GB10 (spanky1) running vLLM — it classifies and embeds the market's news & filings all day. This is its real workload, scraped from the model server every minute.
What the system is trading
No black box — these are the actual open paper positions and recently closed trades, straight from the live feed. Equities only (long/short bracket orders); every one cleared the critic, a human approval, and the deterministic risk guard.
Open positions
| Ticker | Side | Entry | Target | Stop | Agent |
|---|
Recent closed trades
| Ticker | Side | Entry | Exit | P&L | Why |
|---|
Paper trading — for research and demonstration. The account identifier and order IDs are never exposed.
How this live feed reaches the page
The system runs entirely on my own hardware — there's no public database. Here's how I safely surface a live view of it on this public site:
The same database that powers my internal Grafana dashboards stores every trade, snapshot and equity point.
A tiny read-only service publishes only curated aggregates — equity curve, returns, per-agent P&L, health. No account number, IPs or secrets.
A zero-trust tunnel exposes that one endpoint at trading-api.vitalemazo.com — no ports opened, nothing else on my network reachable.
Your browser fetches that feed directly and re-renders every 60 seconds — so the numbers above are always live.
Internal observability (Grafana, alerting, the auto-healing health agent) stays private behind Cloudflare Access — only the read-only aggregate feed is public.
How it works
The design principle: agentic models reason, the local GPU grinds volume. The edge comes from processing the entire market cheaply and locally, plus tight feedback loops — not from an LLM guessing stock prices. Every trade passes through a human and a deterministic guard.
Infrastructure & delivery
This is an educational real-world proof-of-concept — a full platform-engineering pipeline running on my own hardware: GitOps deploys, self-hosted compute, secret management, and zero-trust exposure.
Built
Data layer
Nightly S&P 500 snapshot — Alpaca OHLCV + Benzinga news, SEC EDGAR filings, FRED macro — into Postgres.
Alpha factory (DGX)
A local NVIDIA GB10 classifies every news article & filing and embeds them for RAG memory, plus an O(N²) pairs scan.
Five specialist agents
Momentum, Macro, StatArb, Contrarian, Exotic — isolated and parallel, each an agentic model reasoning independently, emitting structured JSON trades.
Critic + human gate
A critic validates against an investment memo; every trade needs a human thumb via Telegram before it can execute.
Deterministic RiskGuard
A pure-Python, unit-tested guard has final veto: 10% position / 30% sector / 1.0× leverage. No LLM in the loop.
Feedback loops
Per-agent P&L attribution → dynamic capital weights, plus a self-healing health agent and full Grafana observability.