CV
You can download my full CV here:
Contact Information
Email: theo.verdelhan@dauphine.eu
Phone: +33 7 81 79 04 21
LinkedIn: linkedin.com/in/theoverdelhan
GitHub: github.com/theov07
Education
Paris Dauphine University – PSL | Paris, France
MSc in Financial Engineering – Quantitative Finance Track (Program 272)
Sep 2025 – Jun 2026
Relevant Coursework: Stochastic Calculus, Derivatives Pricing, Volatility Modeling, Interest Rate Products, Quantitative Portfolio Management, Algorithmic Trading (Python/C++), Machine Learning, Time Series.
EPF Graduate School of Engineering | Paris, France
Master in Computer Science – Data & AI Track
Sep 2020 – Jun 2025
Rank: 8/157
Relevant Coursework: Probability & Statistics, Linear Algebra, Numerical Optimization, Algorithms & Data Structures, Time Series Analysis, Databases, Machine Learning.
Professional Experience
MYR - Private Investment Fund | Montpellier, France
Quantitative Researcher
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 to optimize execution probability and spread capture across 12 liquid crypto order books.
- Developed microstructure-driven alpha signals from depth imbalance, order flow autocorrelation and spread dynamics, supported by event-time backtesting on 5+ years of tick-level data.
- Implemented inventory risk control (Avellaneda–Stoikov inspired) and low-latency execution systems (<100ms), optimizing quote refresh, queue positioning and mitigating adverse selection.
- Modeled limit order book event arrivals using stochastic intensity frameworks (Poisson-type limit/market/cancel flows) to estimate short-term price pressure and fill probabilities while analyzing competing algorithmic trader behavior.
La Valériane - Investment Branch | Montpellier, France
Quantitative Developer
Sep 2023 – Jan 2024
Designed and built an end-to-end delta-neutral statistical arbitrage strategy between Binance (CEX) and dYdX (DeFi DEX), targeting cross-venue microstructure inefficiencies.
- Reconstructed full L2 order books and built 2-year tick-level datasets to analyze cross-exchange price formation, latency asymmetries and liquidity distribution.
- Developed arbitrage signals based on microprice deviations and depth-adjusted fair value estimators, incorporating slippage modeling and execution constraints.
- Implemented, backtested and deployed the trading bot under realistic latency and partial-fill assumptions, achieving ~8% annualized returns with $800k–$1M daily volume while maintaining strict market neutrality.
Projects
GDP Forecasting with MIDAS Regressions | Paris Dauphine University
Nov 2025 – Jan 2026
- Replicated and extended MIDAS regressions with separate lag/lead dynamics for GDP nowcasting using daily financial data.
- Research-grade Python implementation with out-of-sample evaluation.
Trinomial Tree Option Pricer | Paris Dauphine University
Sep 2025 – Dec 2025
- Implemented a trinomial-tree pricing engine in Python for European & American options with early-exercise handling and Greeks computation.
- Validated against Black–Scholes convergence benchmarks and exposed via lightweight APIs for pricing and diagnostics.
Algorithmic Trading Systems | EPF Capstone Project
Jan 2024 – Jun 2024
- Led a team of 6 to develop an Avellaneda–Stoikov market-making framework with volatility-adaptive spreads and risk controls.
- Backtested 10M+ trades on Hyperliquid (DeFi DEXC), reducing adverse selection by ~9% and stabilizing PnL variance by 7%.
Multi-Asset Basket Option Pricing Engine | Personal Project
C#
- Production-grade pricing engine for multi-asset derivatives combining analytical moment-matching (Brigo et al.) and Monte Carlo simulation with control variate variance reduction.
- Supports term structure modeling, full correlation matrices, and real market data integration (ECB €STR, Bloomberg volatility surfaces).
| LSTM Stock Price Forecaster | Machine Learning Project |
- Built a deep learning model for short-horizon financial time-series forecasting using LSTM architectures and feature engineering techniques.
Skills
Programming: Python, C++, SQL, C#
Libraries & Frameworks: NumPy, Pandas, SciPy, Statsmodels, Scikit-learn, PyTorch
Quantitative Finance:
- Derivatives pricing & option Greeks
- Monte Carlo simulation & variance reduction techniques
- Term structure modeling
- Market microstructure analysis (L1/L2/L3 order books)
- Microprice signals & order flow analysis
- Inventory-aware market making (Avellaneda–Stoikov framework)
Data & Infrastructure:
- Event-time backtesting
- High-frequency data processing
- Low-latency execution systems
- Git, Docker, Linux
Languages:
- French (Native)
- English (Fluent)
- Spanish (Intermediate)
Entrepreneurship
Founder - MASSEEO
White-label electrostimulation brand
- Founded and built a successful e-commerce brand with several thousand euros in revenue
- Managed product development, marketing, and sales operations