```fits SIMPLE = T / this page is also data OBJECT = 'quasareum.com/' / one document, two readers LLMSTXT = '/llms.txt' / start here: the machine surface, indexed MANIFEST= '/api/manifest' / everything as JSON, Ed25519-signed MCP = '/mcp ' / MCP server, JSON-RPC 2.0, 17 tools AGNTCARD= '/.well-known/agent-card.json' / A2A agent: the MCP tools as skills AGENTSMD= '/AGENTS.md' / operator guide ACCEPT = 'text/markdown' / markdown: same words; JSON: the manifest END ``` Quasareum · Paris · a studio since 2025 # Quasareum — Quantum finance¹ × AI-native engineering A one-person studio building production systems for financial markets, engineered AI-native. This page has two readers: you, and the machines it was written for first. - PerpLog — live, alpha - Research — a preprint on SSRN, a working paper - Sophria — launching on iOS and Android - Amav Sophro — live, client work Founded by Thomas Erhel — self-taught engineer and quantitative trader. ¹ Quantum computing for finance is studied here, not claimed. The published research is classical and empirical. ![A gravitational lens on a dark sky. A faint galaxy sits at the centre; the light of one quasar behind it arrives twice, as a bright arc on one side and a smaller, fainter image on the other, with a blue jet running toward the lower right. Inside the lens, faint lines of this page's text.](https://quasareum.com/_q/img/lens-1600.webp?v=b7c32569) **Fig. 1** (modelled, stylised) **One source, two images.** A quasar behind a compact galaxy, computed per pixel in your browser with the thin-lens equation: a point mass with a small external shear. Its light arrives twice, outside the Einstein ring and inside it, where the second image is drawn upright as this page's own words, by design; quasar and jet are stylised. Lined up, the light closes into a ring and the jet crosses it: the Q. The first lensed twin quasar was found in 1979 (Walsh, Carswell and Weymann). ## At a glance - **Company** — Quasareum SASU, Paris · RCS Paris 105 826 861. Registered 3 June 2026; a studio since January 2025. - **Founder** — Thomas Erhel — founder, engineer, quantitative trader. - **Built with** — Rust on Cloudflare Workers, native Swift and Kotlin, Claude Code as a peer engineer. - **Contact** — [contact@quasareum.com](mailto:contact@quasareum.com) ## Work Systems in production, and one about to ship. ### PerpLog **Live · alpha** The non-custodial orderflow terminal for Hyperliquid — journal built in. “Read the tape. Size the risk.” - Orderflow on the GPU: footprint, DOM, CVD, TPO, a liquidation map and a liquidity heatmap across five order books on four venues. - Size from your invalidation: risk-based, fee-aware, segmented quarter-Kelly. - Execution through a Hyperliquid builder code: at most 1 bp per filled order. No subscription, no token. - Non-custodial: trades are signed in your browser; the server never sees a private key. - Every trade executed on PerpLog journals itself, with the order book it executed into. Rust end to end: an egui/wgpu terminal, native and WebAssembly, and Leptos on Cloudflare Workers. [perplog.com](https://perplog.com) · [record (JSON)](https://quasareum.com/api/projects/perplog) ![PerpLog's liquidity heatmap for BTC on Hyperliquid over about six hours: resting liquidity as glowing bands, trades as bubbles sized by notional, the order book ladder and a stop map on the right.](https://quasareum.com/_q/img/perplog-heatmap-1512.webp?v=b7c32569) - Trades: each bubble is a print, sized by notional. - Resting liquidity: the order book over the session, as bands. - Stop map: resting take-profit and stop orders, by price. - Node status: PerpLog's own Hyperliquid node, 0.4 s behind the chain at capture. **Fig. 2** (screenshot) PerpLog: six hours of BTC liquidity, from the product. ### Sophria **Launching · iOS and Android** Guided sophrology audio, AI-generated with a certified sophrologist. “Breathe, unwind, fall asleep.” - 140+ guided sessions: a 21-day discovery journey, a 36-session four-degree progression, programs for sleep and stress. - Every script is shaped by co-founder Amandine Avril, certified sophrologist; the audio comes from an ElevenLabs pipeline driven by Claude-designed session specs. The voice is synthetic, and the app says so. - Native on both platforms: Swift on iOS, Kotlin on Android, with a Cloudflare backend (Hono, D1, R2). [sophria.app](https://sophria.app) · [record (JSON)](https://quasareum.com/api/projects/sophria) ![Sophria in dark mode, the screen titled “Your vivance tree”: a tree that grows with each session, above counters for sessions and leaves.](https://quasareum.com/_q/img/sophria-tree-660.webp?v=b7c32569) **Fig. 3** (screenshot) Sophria, dark mode: your practice grows into a tree. ### Amav Sophro **Live · client work** A care practice with an LLM-ready surface. - The booking platform of Amandine Avril, sophrologist in Saint-Mard, near Paris. Amandine is also Sophria's co-founder. - Stripe Payment Element, custom Google OAuth with JWTs at the edge, and a reminder pipeline from the day before to a week after each session. - WCAG 2.1 AAA, Lighthouse 100 on its packs page, and every page served as markdown to agents, like this one. [amavsophro.com](https://amavsophro.com) · [record (JSON)](https://quasareum.com/api/projects/amav-sophro) ### Next - Sophria on the App Store and Google Play. - Research findings, published as they land. - Strategy research stays private. For machines: [`GET /api/projects`](https://quasareum.com/api/projects) · MCP `list_projects`, `get_project` · JSON-LD `#perplog`, `#sophria`, `#amav-sophro` ## Research Hyperliquid publishes its derivatives market in the open, down to a public archive of fills, liquidations included. The work asks narrow questions of that record and publishes the answers, withdrawn claims included. - Pre-registered from experiment 17 onward: hypothesis and falsification criterion committed before the run. - Estimators validated against known answers before use. - Withdrawn claims stay public, with the reasons. - Adversarial review by two independent model reviewers; their reports and the response are public. ### Counting fills misrepresents liquidations: a reconstruction unit that has not been priced **Preprint** SSRN 7189219 · version 2, 11 August 2026 · CC BY 4.0 **2.86×** Counting fills instead of liquidations inflates the event count 2.86 times (95% CI 2.72–3.00), over a year of Hyperliquid's public fill archive, sampled four fixed hours a day: 351,648 liquidation episodes rebuilt from 1,005,157 fills. The distortion sits in the largest events. Below the 90th percentile the median liquidation is a single fill (the mean is 1.49, and that part still carries 46.8% of all fills); in the top 1% the median is 36 fills, and that percentile carries 67.3% of the liquidated notional. Excluding BTC, the factor is 3.17. Median fills per episode: 1 up to about $10k, then 2 near $24k, 4 near $57k, 9 near $136k, 20 near $327k, 34 near $786k, 65 near $1.9M, 117 near $4.5M and 216 near $10.9M. The bars count the episodes in each size bin. **Fig. 4** (published) Median fills per liquidation episode, by size: a median of one fill up to about $10k, 216 at about $10.9M. Bars show how many episodes fall in each size bin. Data: [fills-by-size.json](https://quasareum.com/_q/data/fills-by-size.json), CC BY 4.0. - **Records** — 52,755 fills - **Notional · light on plate** — $563.0M - **Markets** — 181 - **UTC** — 20:00–21:00 **Fig. 5** (replayed from published data) **The same ledger, read twice.** 10 October 2025, 20:00–21:00 UTC: one of four sampled hours on the day of the largest crypto liquidation event on record, and the brightest of the 365 sampled days ($567.9M across its four hours, against $200.2M for the next). Hyperliquid's archive writes this hour as 52,755 fills. Read as liquidations, it is 13,237 episodes across 181 markets. The money does not change: $563.0M either way, so the light on the plate stays constant. The largest, an ETH position at 20:52:54 UTC, is 1,495 fills and one liquidation of $63.9M. Per-fill times and sizes are not in this dataset: each fill is drawn at its episode's time, as an equal share of its notional. The axis compresses 20:00–20:52 (36 episodes); the cascade continued after 21:00. No accounts are shown. Data: [ledger-2025-10-10.json](https://quasareum.com/_q/data/ledger-2025-10-10.json), CC BY 4.0. [SSRN 7189219, version 2](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7189219) · [doi:10.2139/ssrn.7189219](https://doi.org/10.2139/ssrn.7189219) (the DOI record may still show version 1) · [revision log, with the corrected claim](https://github.com/QuasareumGroup/hyperliquid-microstructure/blob/main/paper/CHANGELOG-liquidation-overcounting.md) · [data and code](https://github.com/QuasareumGroup/hyperliquid-microstructure) ### An on-chain venue trails the centralised market by half a second, in every hour measured **Working paper** draft, public in the repository **575 ms** On trade prints, Binance leads Hyperliquid by a median 575 ms on BTC, in every one of 191 hours measured. Across 758 asset-hours of BTC, ETH, SOL and HYPE, not one falls below zero. The same estimator puts OKX within 25 ms of Binance. Median lag behind Binance, Binance first: BTC 575 ms, ETH 550 ms, SOL 650 ms, HYPE 550 ms; OKX vs Binance, the control, 25 ms. **Fig. 6** (measured, draft) One hairline per measured hour, 18–26 July 2026, Hayashi–Yoshida estimator. Open question: how much is block cadence and network, how much is price discovery. A timing measurement, not a trading claim. Data: [lead-lag-hours.json](https://quasareum.com/_q/data/lead-lag-hours.json), CC BY 4.0. Strategy research stays private. For machines: [`GET /api/research`](https://quasareum.com/api/research) · figure data under [`/_q/data/`](https://quasareum.com/_q/data/ledger-2025-10-10.json) · JSON-LD `#paper-liquidations`, `#dataset` ## Studied, not claimed Quantum computing for finance, with IBM Quantum Learning: Qiskit, gates and circuits. The question that pulls is pricing by sampling. Root-mean-square error, measured: 0.35 at N = 2 samples, 0.0011 at N = 262,144, along ∝ 1/√N. The theory line for amplitude estimation is π·σ/N, with σ = 0.50: it needs fault-tolerant hardware and is not measured. **Fig. 7** (computed classically; theory dashed) **Two ways to price by sampling.** A digital option pays 1 if the price ends above the strike. Classical Monte Carlo estimates that probability with an error that shrinks like 1/√N: the dots are measured, the root-mean-square error of 64 seeded runs. Quantum amplitude estimation promises 1/N for N oracle calls, a quadratic speedup (Montanaro, 2015; Stamatopoulos et al., 2020). The dashed line is that theory, not a measurement. Data: [sampling.json](https://quasareum.com/_q/data/sampling.json). The catch: the speedup needs fault-tolerant hardware. A 2021 resource estimate for pricing derivatives put a useful advantage at about 8,000 logical qubits and a T-depth of 54 million (Chakrabarti et al., Quantum 5, 463). No such machine exists yet. Nothing on this site runs on a quantum computer. **Second track: Claude Code, with Anthropic.** Skills, the Agent SDK and prompt engineering for production: the peer engineer behind this page. For machines: [`GET /api/practice`](https://quasareum.com/api/practice) · MCP `list_practice` ## The studio Quasareum is the practice of Thomas Erhel, a self-taught engineer and quantitative trader in Paris. A practice, on purpose: an eternal beginner keeps learning. - **Certifications** — **196** from 38 institutions, since 2015 - **On GitHub** — **2016** joined in January, still shipping - **Ethernaut** — **#46** of 7,853 players, top 0.6% * **2015** — 1 * **2016** — 0 * **2017** — 0 * **2018** — 47 * **2019** — 27 * **2020** — 17 * **2021** — 46 * **2022** — 22 * **2023** — 17 * **2024** — 7 * **2025** — 4 * **2026** — 2 * **undated** — 6 **Fig. 8** (from the record) **A learning light curve.** Certifications per year, 2015–2026, shown, not curated: 71 of the 196 are SymfonyCasts course certificates, the small ones too. The full list is at [/api/certifications](https://quasareum.com/api/certifications). Formation includes Harvard CS50, Yale Financial Markets with Honors, Stanford Cryptography I and II, NYU Machine Learning and Reinforcement Learning in Finance, the NYIF Machine Learning for Trading specialization, and IBM Quantum Business Foundations. ### Convictions - Eternal beginner — Socratic posture - Paradigm shift, anticipated - Agents-first, on purpose - Found by machines, cited by machines - Marketing matters as much as the product - Production or it did not happen - Lifelong learner — never stop studying - No custodial anything - Cloudflare as the substrate - Animated by creation and growth **The name:** quasar + Ethereum. The mark is 3C 273, the first object identified as a quasar, drawn as a letter: its jet is the tail of the Q. Read as a sky image, north up and east left, the tail points at position angle ≈224°; the jet of 3C 273 points at about 222° (Courvoisier, 1998). For machines: [`GET /api/about`](https://quasareum.com/api/about) · [`/api/certifications`](https://quasareum.com/api/certifications) · MCP `get_about`, `list_certifications` · JSON-LD `#person` ## For agents 3C 273 was entry 273 of the Third Cambridge Catalogue of Radio Sources: listed before anyone knew what it was, and read correctly in 1963. This site is built the same way round: agents first, light added for people. This page, the [legal notice](https://quasareum.com/legal) and one easter egg also render HTML for browsers; every other URL returns markdown, JSON, plain text or XML. This page is served as markdown with the same words at [/index.md](https://quasareum.com/index.md), and as JSON, the full manifest, with `Accept: application/json`. - [`/llms.txt`](https://quasareum.com/llms.txt) index of the machine surface - [`/llms-full.txt`](https://quasareum.com/llms-full.txt) the whole corpus as one markdown file - [`/index.md`](https://quasareum.com/index.md) this page as markdown - [`/api/manifest`](https://quasareum.com/api/manifest) everything as JSON, Ed25519-signed at /api/manifest.sig - [`/mcp`](https://quasareum.com/mcp) MCP server, JSON-RPC 2.0, 17 tools - [`/api/graphql`](https://quasareum.com/api/graphql) GraphQL with introspection - [`/.well-known/agent.json`](https://quasareum.com/.well-known/agent.json) agent descriptor - [`/.well-known/api-catalog`](https://quasareum.com/.well-known/api-catalog) RFC 9727 API catalog - [`/.well-known/ard.json`](https://quasareum.com/.well-known/ard.json) the agent resources, typed, with the questions each answers (ARD) - [`/AGENTS.md`](https://quasareum.com/AGENTS.md) operator guide ```sh curl -H 'Accept: text/markdown' https://quasareum.com/ curl -s https://quasareum.com/mcp -H 'content-type: application/json' \ -d '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' ``` In a browser, this page also offers its read tools through WebMCP. To reach the human: `POST /api/contact` with name, email, message and an `agent` field, or `tools/call send_contact` over MCP. [contact@quasareum.com](mailto:contact@quasareum.com) [GitHub](https://github.com/ThomasErhel) · [X](https://x.com/ThomasErhel) · [LinkedIn](https://www.linkedin.com/in/thomaserhel/) · [ORCID](https://orcid.org/0009-0007-1772-9892) · [quasareum.eth](https://app.ens.domains/quasareum.eth) Quasareum SASU · capital €1,337 · RCS Paris 105 826 861 · 47 rue Vivienne, 75002 Paris, France · Publication director: Thomas Erhel · Host: Cloudflare, Inc., 101 Townsend Street, San Francisco, CA 94107, USA · [Legal notice](https://quasareum.com/legal) One Rust → WebAssembly Worker on Cloudflare; Fig. 1 is WebGL2, written for this page. [First light →](https://quasareum.com/3c273)