Curious, self-driven and obsessive in the pursuit of knowledge; currently, of how machines learn and how markets work.

Lives in New York City

I build applied AI for financial markets: the systems that make agents work in production. I like to innovate and rethink workflows from first principles, with agent systems in April 2023 and an agentic workspace in December 2023.

I built an open-source financial terminal as a side project over the 2020 holidays; it went viral in February 2021 and became OpenBB, one of the most popular finance projects on GitHub and an AI-native research platform used by institutional asset managers. I raised close to $10M from OSS Capital, Naval Ravikant, Ram Shriram and Elad Gil. What started as a CLI became an agentic workspace used by institutions, deployable on-prem or in a VPC (Workspace demo · AI capabilities · agentic workflows).

OpenBB closed in September 2026, but we open sourced it to the world.

Before OpenBB, I was a Sensor Fusion Engineer at NURVV, working on smart running insoles. I built and cleaned the running dataset the team developed its algorithms on, redesigned altitude estimation around a Kalman filter, added GPS outlier filtering, and shipped footstrike detection and inertial navigation that improved the distance and speed reported to runners. Along the way I published Step Detection using SVM on NURVV Trackers at IEEE ICMLA 2021.

In 2020 I also wrote the code behind my former university maths professor's PhD thesis, Data Science in the Modeling and Forecasting of Financial Time Series: from Classic Methodologies to Deep Learning, which is where I got hands-on with LSTM neural networks. It is open sourced as UnivariateTimeSeriesForecast.

My background is in Control Systems, machine learning and AI. MSc with Distinction, Imperial College London. Top 1 student in the BSc in Electrical and Computer Engineering at the Faculty of Sciences and Technology, New University of Lisbon (Portugal).