Built by Quantitative Practitioners
Engineers who trade and traders who code. Our team combines deep infrastructure engineering with real trading experience to deliver production-grade quant tools.
I'm a software architect and quant engineer building FlashAlpha — a proprietary full-stack platform for options analytics, designed to uncover edge in volatility, pricing, and strategy risk.
I've engineered a Monte Carlo simulator, a real-time EV scanner for options, an SVI-based IV surface fitter, a distributed backtest engine and IBKR execution engine — all integrated via a scalable .NET Core / Azure / Kubernetes / RabbitMQ / SignalR stack.
My strength is rare: combining deep infrastructure engineering with trading intuition, enabling me to deliver production-grade quant tools with the speed and flexibility of a solo builder.
Built a scalable, production-grade Azure Machine Learning workspace architecture supporting end-to-end MLOps — including vectorized data pipelines, embedding-based models, feature engineering flows, and CI/CD for continuous training and deployment. The solution integrates automated data ingestion, retraining triggers, model registry, and real-time inference endpoints, forming a robust full-stack ML system for continuous learning at scale.
Pascal Letourneau is an expert in numerical methods and American option pricing who believes that models must be tested against market reality. While he serves as an Associate Professor (soon Full Professor) of Finance teaching Investments and Derivatives to graduate and undergraduate students, his primary passion lies in the practical application of forecasting models.
For over six years, Pascal has been an active options trader. He uses this hands-on experience to stress-test theoretical concepts, using personal capital to validate risk management strategies and refine volatility forecasts. This active participation in the market ensures his work remains grounded in execution rather than just theory.
Currently, Pascal focuses on applying Machine Learning techniques to forecast the implied volatility surface. His research seeks to identify data-driven edges in pricing, a pursuit that has led to a new curiosity regarding behavioral finance in option markets. By analyzing the intersection of quantitative data and investor behavior, Pascal provides a sharper, more comprehensive view of derivative pricing.
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