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.

• 💼 Exploring opportunities as a Quantitative Researcher or Systematic Trading Analyst.
• 📚 Strong background in Python, C#, Monte Carlo methods, time-series analysis, and financial modeling.
• 🚀 I work on projects involving order book modeling, backtesting frameworks, option pricing engines, and execution-aware strategy design.

• Engineering Degree (EPF, France) — Computer Science & Machine Learning (Top 3%)
• MSc in Financial Engineering (Paris Dauphine – PSL) — Quantitative Finance
• Relevant coursework: Probability & Statistics, Stochastic Processes, Financial Mathematics, Machine Learning, Time Series Analysis, Optimization

• Programming: Python, C#, SQL
• Quantitative Modeling: Derivatives pricing, Monte Carlo simulation, variance reduction techniques, term structure modeling
• Market Microstructure: L1/L2/L3 order book modeling, microprice signals, order flow analysis, inventory-aware market making
• Data & Infrastructure: Event-time backtesting, high-frequency data processing, low-latency execution systems

• Multi-Asset Basket Option Pricing Engine (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).
• Trinomial Tree Option Pricer (Python):
  Implemented a trinomial-tree engine for European and American options with early-exercise handling and Greeks computation, validated against Black–Scholes convergence benchmarks.
• GDP Nowcasting with MIDAS Regressions:
  Implemented and extended MIDAS models to forecast GDP using mixed-frequency financial data, with out-of-sample evaluation and lag/lead structure optimization in Python.
• Algorithmic Trading & Backtesting Framework:
  Developed an event-time backtesting framework for systematic strategies (momentum, mean-reversion, market making), incorporating execution modeling and risk controls.
• LSTM Stock Price Forecaster:
  Built a deep learning model for short-horizon financial time-series forecasting using LSTM architectures and feature engineering techniques.

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.

• LinkedIn: Théo Verdelhan
• Email: theo.verdelhan@dauphine.eu

• 🎓 Reading quantitative finance research papers
• 🧠 Solving algorithmic and mathematical challenges
• ⚽ Playing and watching sports, exploring new technologies
• 🌍 Passionate about crypto and DeFi ecosystems

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!