README / README.md

Add Qovaryx 3.0 milestone banner — 4-tier authority, data moat, BYO-AI, backtest returns

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	---
	title: README
	emoji: 🧪
	colorFrom: indigo
	colorTo: purple
	sdk: static
	pinned: false
	---

	# 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_SCOPED` heads can influence a named consumer strategy. `SYNTH_VALIDATED_SHADOW` runs alongside live decisions, logged, never mutating. `VERIFIED_FORECAST_CONTEXT` heads are feature-input only. `OFFLINE_FORECAST_ADVISORY` is research-only. Nothing overrides an order without a signed head→consumer→mutation contract. Full write-up: [Head Architecture Advances](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/head_architecture_advances_20260720.md).
	- 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](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/the_data_moat_20260720.md).
	- 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](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/qovaryx-350m-scratch-base) — 350M parameter mid-size substrate. Fits on a 12 GB card. The deployment-friendly size.
	- [qovaryx-50m-scratch-base](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/vfai-x-3.5-9b-options) — legacy VFAi-X lineage page retained for reproducibility and research.
	- [vfaix-vpa-options-trader](https://huggingface.co/tjarvis91/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](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/sovereign_compact_cognition_deployed.md) 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](https://qovaryx.jehorizon.com/) \|
	\| 📜 Public research devlog \| [github.com/thron-j/qovaryx-ai-research](https://github.com/thron-j/qovaryx-ai-research) \|
	\| 🎫 Community Discord (builders training their own trading/finance models, no signals) \| [discord.gg/PtuHZDv5ju](https://discord.gg/PtuHZDv5ju) \|
	\| 🧪 Deployed Q-Chat router \| type `/qchat ask <question>` in the Discord \|
	\| 🤗 Founder profile \| [huggingface.co/tjarvis91](https://huggingface.co/tjarvis91) \|
	\| 📦 VFAi-X desktop releases \| [github.com/thron-j/vfai-x-releases](https://github.com/thron-j/vfai-x-releases) \|
	\| ☕ Support the next training run \| [ko-fi.com/tjarvis91](https://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.

	---
	title: README
	emoji: 🧪
	colorFrom: indigo
	colorTo: purple
	sdk: static
	pinned: false
	---

	# 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_SCOPED` heads can influence a named consumer strategy. `SYNTH_VALIDATED_SHADOW` runs alongside live decisions, logged, never mutating. `VERIFIED_FORECAST_CONTEXT` heads are feature-input only. `OFFLINE_FORECAST_ADVISORY` is research-only. Nothing overrides an order without a signed head→consumer→mutation contract. Full write-up: [Head Architecture Advances](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/head_architecture_advances_20260720.md).
	- 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](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/the_data_moat_20260720.md).
	- 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](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/qovaryx-350m-scratch-base) — 350M parameter mid-size substrate. Fits on a 12 GB card. The deployment-friendly size.
	- [qovaryx-50m-scratch-base](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/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](https://huggingface.co/tjarvis91/vfai-x-3.5-9b-options) — legacy VFAi-X lineage page retained for reproducibility and research.
	- [vfaix-vpa-options-trader](https://huggingface.co/tjarvis91/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](https://github.com/thron-j/qovaryx-ai-research/blob/main/research/sovereign_compact_cognition_deployed.md) 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](https://qovaryx.jehorizon.com/) \|
	\| 📜 Public research devlog \| [github.com/thron-j/qovaryx-ai-research](https://github.com/thron-j/qovaryx-ai-research) \|
	\| 🎫 Community Discord (builders training their own trading/finance models, no signals) \| [discord.gg/PtuHZDv5ju](https://discord.gg/PtuHZDv5ju) \|
	\| 🧪 Deployed Q-Chat router \| type `/qchat ask <question>` in the Discord \|
	\| 🤗 Founder profile \| [huggingface.co/tjarvis91](https://huggingface.co/tjarvis91) \|
	\| 📦 VFAi-X desktop releases \| [github.com/thron-j/vfai-x-releases](https://github.com/thron-j/vfai-x-releases) \|
	\| ☕ Support the next training run \| [ko-fi.com/tjarvis91](https://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.