Quantitative Finance · Stochastic Modeling · Machine Learning

Grenoble INP – Ensimag · KTH Royal Institute of Technology

LinkedIn · arXiv · Email · LeetCode – Reda Hmioui

I am a Financial Engineering student at Grenoble INP – Ensimag, specializing in advanced quantitative methods, with an academic exchange at KTH Royal Institute of Technology.

My main interests are quantitative research, stochastic processes, statistical arbitrage, machine learning, derivatives modeling, and numerical methods for stochastic systems.

I enjoy working at the intersection of mathematical modeling and implementation: from stochastic analysis and Monte Carlo simulation to machine-learning models and systematic trading strategies.

Research conducted at the Department of Mathematics, KTH Royal Institute of Technology.

The project studies neural approximation of conditional laws of McKean–Vlasov equations with common noise using:

• Fourier representations of probability measures
• Rough paths and path signatures
• Mixture Density Networks
• Cylindrical neural networks
• Wasserstein approximation
• Numerical SDE methods and Monte Carlo particle systems

The resulting work establishes an L² universal approximation theorem and evaluates the architecture on six stochastic benchmarks, including nonlinear, non-Gaussian, multiplicative-noise, and multidimensional settings.

Preprint:A Cylindrical Neural Approximation Theorem for Conditional Laws of McKean–Vlasov Equations with Common Noise

Co-authored with Nacira Agram and Jan Rems. Currently under review at Neural Networks (Manuscript: NEUNET-D-26-07060) .

Code:cylindrical-mckean-vlasov

An end-to-end quantitative research and backtesting framework for statistical arbitrage in crypto perpetual futures.

Core components:

• Engle–Granger cointegration and ADF testing
• Ornstein–Uhlenbeck half-life filtering
• Walk-forward pair selection
• Kalman-filter dynamic hedge ratios
• Market-neutral pairs trading
• Transaction fees, slippage, and perpetual-futures funding
• FinBERT-based news-turbulence risk overlay

On the 2023 out-of-sample period, the risk overlay improved the strategy's Sharpe ratio from 0.74 to 1.99 and reduced maximum drawdown from −61.99% to −28.34%.

Repository:Quantamental-StatArb-on-Crypto-Perpetual-Futures

Engineering Degree in Applied Mathematics, Computer Science & Quantitative Finance Financial Engineering track · Advanced Quantitative Methods GPA: 3.98 / 4.00

Exchange Semester — Financial Mathematics & Stochastic Processes · 30 ECTS

Selected coursework:

• Martingales & Stochastic Integrals — A
• Computer Intensive Methods in Mathematical Statistics — A
• Financial Mathematics — B
• Computational Methods for SDEs & Machine Learning

Programming Python · C · SQL · Java · R

Quantitative Stochastic Calculus · Monte Carlo · Numerical SDEs · Statistical Arbitrage · Time Series · Machine Learning

Libraries & Tools PyTorch · NumPy · Pandas · Git · Linux · Docker

Quantitative Research · Systematic Trading · Stochastic Modeling · Derivatives · Statistical Arbitrage · Machine Learning · Rough Paths · Path Signatures

I am seeking a 20+ week PFE / off-cycle internship from April 2027 in:

Quantitative Research · Quantitative Development · Strats · Quantitative Analysis

Open to opportunities in France, London, and across Europe.

Reda Hmioui Grenoble INP – Ensimag LinkedIn · GitHub · arXivreda.hmioui@grenoble-inp.org