I am Balthazar Courvoisier, a quantitative researcher at Queensfield AI and a PhD student working on deep learning for finance in collaboration with PSL’s LAMSADE laboratory. My work connects statistical modeling, machine learning, and systematic investment strategies.
Trained at ENSAE and Polytechnique. My experience spans market-neutral strategy development, fixed-income risk controls, and financial forecasting. Alongside my research, I teach Python for Finance in the Master’s in Asset Management at Dauphine PSL.
Experience in quantitative finance
- Quantitative Researcher at Queensfield AI · Since January 2024
- Implemented a market-neutral alpha strategy based on pairs trading.
- Pursuing PhD research on deep learning for finance in collaboration with PSL LAMSADE.
- Quantitative Research Intern at Ostrum Asset Management · 2023, six months
- Investigated Hurst exponents across a range of underlying assets for low-frequency trading research.
- Developed and deployed a stop-loss and take-profit mechanism for fixed-income desk strategies.
- Quantitative Research Intern at Crédit Agricole CIB · 2022, six months
- Modeled and forecast financial performance for long-term planning using econometric methods.
- Implemented OLS, LASSO, and SARIMAX models, with a systematic feature selection methodology for multivariate data.
- Data Science Intern at BNP Paribas Cardif · 2021–2022, six months
- Developed an econometric robustness index to assess smartphone durability.
- Processed large datasets with Python in a cloud environment, using NLP and clustering to clean and enrich the data.
- Data Science Intern at Agence France Locale · 2021, three months
- Used clustering to identify financial trajectories of French municipalities and examined their relationship with social factors.
- Implemented statistical metrics for ongoing reporting.
Teaching
- Python for Finance Instructor at Dauphine PSL · Since 2025
- Teaching in the Master’s in Asset Management. Explore the course materials.
- Mathematics Tutor at Ipesup · Since 2019
- Mathematics instruction and tutoring for students in classes préparatoires aux grandes écoles (CPGE).
Education
- Master 2 in Data Science, Institut Polytechnique de Paris · 2022–2023
- Deep learning, computer vision, missing data and causality, and tail-event analysis.
- Engineering Program, ENSAE Paris · 2019–2023
- Statistics, time series, econometrics, Monte Carlo methods, stochastic calculus, and online learning.
- Classes Préparatoires, Janson de Sailly · 2017–2019
- Physics and engineering track, PCSI–PSI*.
Selected projects
- Bayesian time series clustering · 2023
- Studied model-based clustering and estimation for multiple time series. Read the article or view the code.
- Hidden Markov models and particle filtering · 2022
- Studied filtering for binary time series and benchmarked an optimal particle filter against a bootstrap filter. Read the article.
- ESG Hackathon with Ostrum Asset Management · 2022
- First prize in the ESG topic for work on the relationship between corporate carbon footprints and default risk.
About Quants Quest
Quants Quest is where I share research and practical explanations of quantitative finance, AI, and statistical learning. The articles connect mathematical ideas with intuition and Python implementations, drawing on the questions I encounter in research and teaching.
For questions about my work, research, or teaching, get in touch.