Théo Verdelhan

Théo Verdelhan

Quantitative Researcher | MSc Financial Engineering (Dauphine-PSL) | Top 3% Computer Science & AI (EPF) | Market microstructure, systematic trading, and derivatives pricing in crypto/DeFi markets

Théo Verdelhan

Quantitative Researcher | Aspiring Quant Researcher | ML Engineer

I am a highly motivated quantitative researcher in training, focused on market microstructure, systematic trading, and derivatives pricing.
I enjoy bridging mathematics, stochastic modeling, and programming to design and implement data-driven quantitative strategies.

Background in Financial Engineering (MSc, Paris Dauphine–PSL) and Computer Science & Machine Learning (EPF, Top 3%), with hands-on production experience in crypto markets and DeFi.

🎯 Professional Ambition:
I aim to work as a Quantitative Researcher in a cryptocurrency hedge fund with exposure to DeFi. I am passionate about combining trading strategy research, mathematics, and programming to develop innovative systematic strategies and explore alternative data-driven models.

💼 Exploring opportunities as: Quantitative Researcher Systematic Trading Analyst Quant Engineer

Links:

About Me

I am passionate about quantitative finance and algorithmic trading, with a strong focus on:

  • Market Microstructure: L1/L2/L3 order book modeling, microprice signals, order flow analysis, inventory-aware market making
  • Quantitative Modeling: Derivatives pricing, Monte Carlo simulation, variance reduction techniques, term structure modeling
  • Systematic Trading: Event-time backtesting, high-frequency data processing, low-latency execution systems
  • Machine Learning: Time series forecasting, feature engineering, deep learning for financial applications

I have hands-on experience developing and deploying market-making strategies on DeFi derivatives venues (Hyperliquid), building statistical arbitrage systems between centralized and decentralized exchanges, and creating production-grade pricing engines for multi-asset derivatives.

Highlights

  • Active Market Maker on Hyperliquid (DeFi): Designed and deployed short-horizon market-making strategies using order book data, microprice signals, and inventory risk control across 12 liquid crypto order books.

  • Microstructure-Driven Alpha: Developed alpha signals from depth imbalance, order flow autocorrelation, and spread dynamics, supported by event-time backtesting on 5+ years of tick-level data.

  • Low-Latency Execution Systems: Implemented inventory risk control (Avellaneda–Stoikov inspired) and execution systems with <100ms latency, optimizing quote refresh and queue positioning.

  • Statistical Arbitrage: Built end-to-end delta-neutral stat-arb strategy between Binance (CEX) and dYdX (DeFi DEX), achieving ~8% annualized returns with $800k–$1M daily volume.

  • Quantitative Research: Developed GDP forecasting models using MIDAS regressions, implemented trinomial tree option pricers, and built multi-asset basket option pricing engines with variance reduction techniques.

Selected Experience

Quantitative Researcher — MYR (Private Investment Fund)
Montpellier, France | Aug 2024 – Jul 2025

Active market maker on Hyperliquid (DeFi LBO), developing proprietary quantitative strategies in digital asset markets.

  • Designed and deployed short-horizon market-making strategies using L1/L2/L3 order book data, microprice signals, order book imbalance and FIFO queue dynamics
  • Developed microstructure-driven alpha signals from depth imbalance, order flow autocorrelation and spread dynamics
  • Implemented inventory risk control (Avellaneda–Stoikov inspired) and low-latency execution systems (<100ms)
  • Modeled limit order book event arrivals using stochastic intensity frameworks to estimate short-term price pressure and fill probabilities

Quantitative Developer — La Valériane (Investment Branch)
Montpellier, France | Sep 2023 – Jan 2024

Designed and built an end-to-end delta-neutral statistical arbitrage strategy between Binance (CEX) and dYdX (DeFi DEX).

  • Reconstructed full L2 order books and built 2-year tick-level datasets to analyze cross-exchange price formation
  • Developed arbitrage signals based on microprice deviations and depth-adjusted fair value estimators
  • Achieved ~8% annualized returns with $800k–$1M daily volume while maintaining strict market neutrality

Education

Paris Dauphine University – PSL
MSc in Financial Engineering – Quantitative Finance Track (Program 272)
Sep 2025 – Jun 2026

EPF Graduate School of Engineering
Master in Computer Science – Data & AI Track
Rank: 8/157
Sep 2020 – Jun 2025

What You’ll Find on This Site

  • Projects: Research notes, trading systems, pricing models, and quantitative experiments
  • CV: Detailed background, experience, education, and skills
  • Portfolio: Showcase of selected quantitative projects

Interests & Activities

  • 🎓 Reading quantitative finance research papers
  • 🧠 Solving algorithmic and mathematical challenges
  • ⚽ Playing and watching sports, exploring new technologies
  • 🌍 Passionate about crypto and DeFi ecosystems
  • 🚀 Entrepreneurship: Founded MASSEEO, a white-label electrostimulation brand with several thousand euros in revenue

Thank you for visiting! I’m always open to conversations about quant research, internships, or collaboration on open-source quant projects. Feel free to reach out!