Houssem Ben Salem, MSc

2021 — present

Lumin is a systematic directional model for crypto perpetual futures, driven by a cohort of reasoning agents.

+123% on BTCUSDT — February 2025 → May 27, 2026, net of all exchange costs, vs buy-and-hold −12%.
Live through the Lumin terminal since March 2026 — on BTC, ETH, SOL.

The result extends a 6.5-year chained backtest — seven independent cohorts, Sharpe 1.98 net of costs, 7 of 7 years profitable — through the COVID crash, the FTX/LUNA capitulation, the Bitcoin ETF rally, and the Trump tariff cycles.

The architecture is a multi-agent LLM cohort: reasoning specialists that read the market bar by bar against a structured curriculum — a practitioner's mind made portable, compounding with every resolved trade and inheriting the gains of every next-generation reasoning model without retraining. The approach generalises anywhere skilled experts produce value.

Lumin advances alongside my main career — fourteen years of Machine Vision engineering at Swiss precision metrology houses. Reading the physics behind a metrology measurement uses the same logic as reading the psychology behind a market price: identify the generator, model its influence, engineer for it. The reading reflexes, modelling tools, and post-mortem discipline that resolve an optical measurement are directly reusable in Lumin. Since 2021 the two trajectories have advanced in parallel, each breakthrough on one side feeding the other.

On the Lumin side, the work began from a personal need: cut the emotions out of trades and let an algorithm decide. Years of iteration distilled price action into a structural vocabulary of recurring multi-bar configurations — the signatures — which became the language the current architecture speaks. The production cohort runs on frontier substrates in parallel — Claude Opus and DeepSeek V4 Pro, with Claude Fable, the newest frontier generation, validated on the bench — reading the same curriculum and reaching consensus across model families. The system extracts edge in choppy markets where deterministic logic breaks down, and captures the asymmetric tail when a clean trend opens.

2015 — present

At SYLVAC SA — Swiss precision instruments, Yverdon — I joined in 2015 as an applied optics engineer building vision metrology systems. In 2018 I moved to project management and delivered a generation of optical measurement machines end-to-end: specifications, industrialisation, production. Since 2021 I lead the R&D machines team — still building alongside, pushing the domain into embedded agents, adaptive workflows, machines that interact with humans more naturally.

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Machine vision · optical metrology · quality control · robotic guidance · automation

2012 — 2015

Before SYLVAC, I was an applied optics engineer at Hexagon Manufacturing Intelligence. I worked on metrological machines, optical sensor development, and image processing for precision measurement systems.

2022 — 2024

Applied Data Science: Machine Learning program at the EPFL Extension School — the foundation for the data and Machine Learning work that became Lumin.

2010 — 2012

MSc in Microengineering — Photonics and Applied Optics at École Polytechnique Fédérale de Lausanne (EPFL). Photonics (light guides and lasers), advanced image processing, optical system design.

2007 — 2010

BSc in Microengineering at École Polytechnique Fédérale de Lausanne (EPFL). Optics, vision systems, mechanics, electronics, software engineering.

I work at the intersection of quantitative systems, AI architectures, and precision engineering. My strength is not staying inside one domain but connecting several until they become one solution. I'm an engineer first: every problem reduces to understanding the physics, building the math, writing the code, and delivering it personally — and staying on it longer than most people think is reasonable. I think outside the box, inside the path. Multilingual. I used to code in C++ and MATLAB. Now fully in Python, mostly through AI. English is becoming the new language of code.