Qovaryx
Sovereign-trained compact AI for trading and finance.
Random-init scratch substrates. Audited compact specialists. Local-first deployment. No borrowed foundation weights, no closed APIs in the inference path. A research project building toward a frontier-grade decision system that runs on hardware a single person can own.
🏆 2026-07-20 — Qovaryx 3.0 milestone shipped
A 5-year full-pipeline backtest (2021–2026, $500 starting capital, real + synthetic option corpus, all wired safety gates engaged) on the four public risk tiers:
| Tier | Total return (5 yrs) | Positive every year? |
|---|---|---|
| Conservative | +523% | ✅ |
| Standard | +1,793% | ✅ |
| Aggressive | +22,785% | ✅ |
| Extreme | +47,326% | ✅ |
Every profile positive every year — including 2022 (bear), 2020-style vol shocks in the corpus, and 2025 chop. Aggressive and Extreme require typed operator acknowledgement and enforce mandatory milestone profit-pulling. Universal circuit breakers active on every tier: daily loss halt, correlation cap, drawdown ack, slippage governor. Numbers describe the backtest; live paper begins Q3 2026.
What actually changed in 3.0:
- Four-tier execution-authority taxonomy on every specialist head.
REAL_VALIDATED_SCOPEDheads can influence a named consumer strategy.SYNTH_VALIDATED_SHADOWruns alongside live decisions, logged, never mutating.VERIFIED_FORECAST_CONTEXTheads are feature-input only.OFFLINE_FORECAST_ADVISORYis research-only. Nothing overrides an order without a signed head→consumer→mutation contract. Full write-up: Head Architecture Advances. - A proprietary data moat. Multi-decade daily equity coverage (thousands of tickers), multi-year real options-chain snapshots for the highly liquid names, app-faithful multi-symbol option-PnL labels, calibrated synthetic bridges with label-bridging validation, intraday minute-bar snapshots, and a privacy-preserving decision-outcome corpus. Full write-up: The Data Moat.
- BYO-AI chat with 7 providers. OpenAI, Anthropic, Google, xAI, OpenRouter, Ollama (local), and custom OpenAI-compatible. Keys encrypted via OS keychain. Per-context opt-in for what the AI can see. BYO-AI is for chart analysis and position review — it is not on the order-execution path. The trending "ChatGPT / Claude will trade for you" wrappers are not consistently profitable when benchmarked out-of-sample; general-purpose LLMs weren't trained to price options or size risk. Different job, different tool.
- CPU-only inference. No GPU, no cloud round-trip. The full trading cluster runs on any x86-64 from 2018 or newer with 8 GB RAM.
- 13 shipping options strategies dispatched under the same risk gate: iron condors, credit spreads (both directional variants), PEAD drift (30-DTE and 45-DTE), breakouts, VPA long calls/puts, tactical allocator, VIX-crush credit spread, tail hedges.
- Deep cleanup. 22 previously-dormant safety features wired end-to-end and ~1,750 lines of dead code removed. 80/80 focused test suite. Codex ship audit: APPROVE / HIGH CONFIDENCE.
Implementation specifics — exact training recipes, routing heuristics, sizing math, gate thresholds, corpus labeling functions — are intentionally withheld. The framings publish; the recipes do not.
Trading options involves substantial risk of loss. Backtest results are historical simulation, not a forecast. Not financial advice.
What lives here
This organization is the home for the Qovaryx model lineage — the published artifacts of a project arguing that compact, locally-trainable AI is a distinct research target, not a smaller version of frontier scaling.
Scratch-base substrates (random-init — designed to be trained, not chatted with)
These are directed blanks: the architecture, the decision surfaces, and the design choices are baked in; the weights are random. They exist so a single researcher on a single consumer GPU can train a specialist model end-to-end without renting a datacenter.
- qovaryx-1b-scratch-base — 1B parameter own-base decoder. Fits on a 16 GB consumer card under QLoRA NF4. The publication target of the substrate line.
- qovaryx-350m-scratch-base — 350M parameter mid-size substrate. Fits on a 12 GB card. The deployment-friendly size.
- qovaryx-50m-scratch-base — 50M parameter proxy substrate. Fits anywhere. Used for pipeline validation before the larger sizes commit to training time.
- qovaryx-3b-scratch-base — 3B parameter scratch-base extension of the same lineage.
All four share a modern compact stack: Grouped-Query Attention (GQA), Rotary Position Embeddings (RoPE), pre-norm RMSNorm, SwiGLU feed-forward, tied embeddings, native multi-token-prediction (MTP) heads, a four-class decision head, optional chart-patch encoder for vision input. Apache-2.0 weights, Apache-2.0 reference trainer.
Trading-application lineage (trained, working, local-first deployable)
The 9B chart-reading lineage that operationalized the same disciplines on a larger backbone before the current Qovaryx app surface:
- vfai-x-3.5-9b-options — legacy VFAi-X lineage page retained for reproducibility and research.
- vfaix-vpa-options-trader — VPA-flavored legacy variant.
Deployed: Q-Chat router
The first operational artifact in the lineage — a sovereign-trained compact specialist intent router, audited and deployed live on free Hugging Face CPU infrastructure, serving a real community Discord. Read more in the public devlog entry on the deployment.
You can interact with it directly through the project's Discord community — /qchat ask <question> — and the model will route, retrieve, and respond. No closed model anywhere in the loop.
Discipline
This project is built under six explicit commitments:
- Local sovereign AI — the deployed system runs on hardware the operator owns. No silent dependency on a remote API.
- Compact cognition — small is not the apology. Density per stored bit, routing per active parameter, structure per training row.
- Verifier-governed inference — the wrapper around the model is part of the architecture, not a deployment afterthought.
- Adaptive compute — the model thinks harder on the inputs that deserve it and cheaper on the inputs that do not.
- Execution-aware intelligence — gross alpha is not a result. Numbers must survive realistic costs.
- Intelligence per watt — the only honest unit of progress under a power-, memory-, and bit-constrained budget.
These are first-class architectural decisions made at the very first commit. They are not deployment optimizations.
Where to go next
| Surface | Link |
|---|---|
| Website | qovaryx.jehorizon.com |
| 📜 Public research devlog | github.com/thron-j/qovaryx-ai-research |
| 🎫 Community Discord (builders training their own trading/finance models, no signals) | discord.gg/PtuHZDv5ju |
| 🧪 Deployed Q-Chat router | type /qchat ask <question> in the Discord |
| 🤗 Founder profile | huggingface.co/tjarvis91 |
| 📦 VFAi-X desktop releases | github.com/thron-j/vfai-x-releases |
| ☕ Support the next training run | ko-fi.com/tjarvis91 |
| 📧 Contact | thomasjarvis2026@gmail.com |
Implementation specifics — exact training recipes, routing heuristics, gate thresholds, curriculum mixing ratios, verifier internals — are intentionally withheld. The framings are publishable; the recipes are not.
Qovaryx is an ongoing research effort. The work is in motion. The claims are provisional. The constraint is the point.
This is research and infrastructure writing. Not financial advice. Not a trading signal.