A multi-agent AI trading system
Five specialist LLM agents propose trades, a critic validates them, my phone gets a veto window, 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.
The strategy — how & why
It's not one bet — it's a diversified ensemble of five classic equity strategies run in parallel on the S&P 500, long and short, on a multi-day (swing) horizon. The key idea: the edge is not an AI predicting prices. It comes from two things a human desk can't match at scale.
A local NVIDIA GPU reads the entire universe every day — news, 8-K filings, social chatter, analyst revisions, search-attention spikes — and scores it. The agents start each morning having "read" far more than any analyst could.
Every closed trade is attributed to the agent that made it. Capital is re-weighted toward whichever strategies actually earn it, and a RAG memory recalls how similar past setups resolved. The system is built to measure edge, not assume it.
The five specialist agentsi
Longs breakouts backed by volume; shorts relative weakness / broken uptrends.
Longs names in regime-favored sectors; shorts laggards in out-of-favor ones.
Shorts the rich leg and longs the cheap leg of a diverged, correlated pair.
Buys irrational panic; shorts broken theses (high-severity news / downgrades).
Longs positive catalysts (beats, raises); shorts misses and guidance cuts.
Long/flat swing trades on ~30 major pairs, stops sized at ~2σ of each pair's own volatility.
Officer/director open-market buys, cluster-weighted, 10b5-1 plan trades discounted. Trades zero capital until 20+ settled shadow trades prove a positive expectancy CI.
Every trade clears four gatesi
Five isolated agents each read a different slice of the market and emit structured trade ideas.
An LLM critic scores every idea against an investment memo and kills the weak ones (~60%).
Every survivor is auto-approved and announced to my phone with a veto button — I can stop any trade before it executes, without being a bottleneck.
A pure-Python, no-LLM guard has final veto: 10% position / 30% sector / 1.0× gross. Then capital flows to whichever agents actually earn it.
Why this way: diversified strategies + hard risk limits + measured attribution means no single idea can sink the book, and the data tells us which approaches have edge instead of us guessing. Gross leverage is capped at 1.0× on purpose — the goal right now is to prove edge, not amplify an unproven one. That's what the scorecard below is for.
Live system feedi
loading…Equity curve (paper, $25k start)i
Governance funneli
How many ideas survive each gate from proposal to execution.
Per-agent realized P&Li
Alpha factory throughputi
Component healthi
▉ DGX Spark — live GPU loadi
—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 tradingi
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 veto window, 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.
⚡ Intraday book i
Deterministic opening-range breakouts on the ~150 most liquid S&P names, flat by close every day. Its job is velocity of evidence: same-day settlement means the n≥50 verdict arrives in weeks. Judged on its own numbers — never pooled with the swing books.
How trades ended (fill-based only) i
| Exit | Count | P&L |
|---|
Recent intraday trades
| Ticker | Side | Entry | Exit | P&L | Why |
|---|
🛞 Wheel book i
Open option legs i
| Contract | Premium | Close at |
|---|
Resolved legs
| Contract | P&L | How |
|---|
🪙 Crypto book i
Runs on its own account and its own clock — same five gates (propose → critic → veto window → risk guard), long-only, exits managed in software.
Crypto equity curve i
$15,000 start · marked 24/7 · dashed line = break-even
Open crypto positions
| Pair | Side | Entry | Target | Stop |
|---|
Recent crypto trades
| Pair | Entry | Exit | P&L | Why |
|---|
Does it actually have edge?i
A trading system that shows a win rate without a confidence interval is lying to you. This scorecard measures whether closed-trade returns are statistically distinguishable from zero — and refuses to claim edge until the sample is big enough.
Per-agent edgei
| Agent | Closed | Hit rate (95% CI) | Expectancy/trade | Total P&L | Profit factor |
|---|
Live system feed EVERY OWNER MESSAGE i
The same messages that reach the owner's phone, unedited: proposals, verdicts, exits, alerts, recoveries.
🧪 Research lane HYPOTHETICAL — NO CAPITAL i
Before any strategy idea touches money, it runs here: real bars, real fees, zero capital. Eight signal families and counting — stop geometry, insider flow (SEC Form 4), social momentum, hour-scale breakouts and panic-dip reversion on minute bars, weekly reversal, turn-of-month — each with matched controls where beta could masquerade as edge, and a multiple-testing bar before anything is promoted. Most cells measure NEGATIVE or flat, which is the point: every cheap no permanently closes a temptation, and the rare near-positive (turn-of-month, 7 years of month-ends, CI a hair under zero) earns patient monitoring instead of blind capital. These numbers are simulations and are never blended with the live books above.
| Cohort | Settled | Still open | Win rate | Stopped out | Verdict |
|---|
💰 Institutional money flow i
Where the big orders went today — net $ millions by order-size class, across the whole S&P 500. Context the agents read, not holdings.
Top institutional inflows
| Symbol | Institutional net | Retail net |
|---|
Top institutional outflows
| Symbol | Institutional net | Retail net |
|---|
Methodology: hit rate uses the Wilson score interval; per-trade expectancy is the mean return on deployed capital with a 95% t-interval. Paper trading — not investment advice, not a track record.
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.
Git as the control plane
The unusual part of this platform isn't that it deploys from git — it's who commits. Two autonomous AI agents work on this repo around the clock: a maintenance agent that finds and fixes bugs every six hours with full merge authority, and an independent supervisor that audits the first agent's work twice a day. Both ship through exactly the same gate a human does.
The repo is the constitution
Two charter files govern the agents: one defines what they may change alone (bugs, observability, research) versus what always needs a human (risk limits, money movement); the other gates what the system may become on measured evidence. Agents read these at the start of every run — the rules version with the code they govern.
Merge = production deploy
A push to main runs lint plus 590+ tests, then a self-hosted runner rebuilds the image and recreates all 19 service containers — re-running the full suite first. The deploy log echoes every container back up with its exact command, because a green workflow only proves the script exited; the roll-call proves the fleet is actually running the new code.
Strategy proves itself first
No strategy change goes straight to money. Variants run
in the shadow research lane — same setups, real bars, real fees, zero orders — and promotion to live
requires 20+ settled trades with a positive expectancy confidence interval, filed as a pull request with
the evidence in the body. The audit trail of an AI-operated trading system is just git log.
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 is announced on Telegram with a human veto window before it executes.
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.
Research lane (Stage 0R)
New strategies prove themselves on real bars with zero capital before touching money — including a deterministic insider-flow agent built from SEC Form 4 filings, entered only at prices a filing reader could actually have obtained.
Autonomous maintenance
Two cloud AI agents work the repo around the clock — one fixes bugs with full merge authority inside a written charter, one audits it independently. Every change ships through the same gated pipeline a human uses.