About me.

OpTrade is a comprehensive toolkit for quantitative research and development of options trading strategies, designed to bridge the gap between institutional capabilities and individual traders. While large financial institutions maintain sophisticated in-house research frameworks, retail traders and smaller proprietary trading firms often lack access to comparable tools, particularly for options markets where complexity significantly exceeds equity trading. Our framework provides a professional-grade environment that allows traders to conduct alpha research (and eventually backtest) with advanced hypotheses, deploy state-of-the-art machine learning models including modern deep learning architectures, and perform cross-sectional market analysis. By democratizing access to institutional-quality research tools, OpTrade enables quantitative researchers to focus on sophisticated strategy innovation rather than framework development.

Data in the real world is distributed through time. Time series modelling isn’t a new idea, dating back to the era of Galton (1880s), but still remains a significant challenge in deep learning. I enjoy working on unique and impactful problems within time series, where traditional deep learning paradigms fail. For example, many time series within healthcare (e.g., ECG, blood glucose measurements, sleep studies) are variable in length, yet, most of the current literature focuses on finite-context methods, which can only process sequences of fixed length. This need for robust variable-length time series classification (VSTC), is present not only in healthcare, but in many other real-world scenarios where there is no predetermined sequence length (e.g., finance, weather). We recently released a paper addressing this, called Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification—for more info on this, see my projects page.

While traditional deep learning methods provide point estimates—such as a single value or vector—they lack measures of uncertainty, limiting their usefulness in high-risk domains such as healthcare or finance. For example, incorporating uncertainty measures can be highly advantageous in clinical contexts, as it not only enhances patient outcomes but also improves clinician trust and promotes the adoption of models in the clinic. Currently, I am focusing on developing conformal prediction (CP) methods for time series forecasting of electromagnetic radiation to reduce radiation exposure. Conformal prediction is advantageous over other uncertainty quantification methods, as it provides a guarantee that prediction intervals contain the true outcome at a specified confidence level, regardless of data distribution or base model choice.