About the Author¶
Janus B. Advincula — physicist by training, quantitative researcher and algorithmic trading engineer by profession: designing, backtesting, and deploying systematic trading strategies across cryptocurrency, forex, and equities, with the research infrastructure, backtesting engines, and live deployment pipelines built from the ground up. This course distills that work — the research methodology, the statistics, and the engineering — into a curriculum for people who want to do quantitative trading properly.
The path here ran through university physics instruction, an MIT MicroMasters in Statistics and Data Science, and years of professional quantitative research and trading-infrastructure work — which is why this course insists on both mathematical rigor and production-quality software, and refuses to treat either as optional.
Education¶
| Degree | Institution | Year |
|---|---|---|
| MicroMasters Program in Statistics and Data Science | MIT (edX) | March 2020 |
| Master of Science, Physics | University of the Philippines Diliman | June 2016 |
| Bachelor of Science, Psychology | University of the Philippines Diliman | April 2010 |
Experience¶
| Role | Organization | Period |
|---|---|---|
| Algorithmic Trading Engineer | Algoforce Phils., Inc. | Jun 2023 – Apr 2026 |
| Financial Analyst / Python Developer | Alpha OpenSource | May 2022 – Jun 2023 |
| Physics Instructor | National Institute of Physics, UP Diliman | Aug 2016 – Jul 2018 |
| Tutor | Freelance | Feb 2006 – Dec 2013 |
Skills¶
- Python — NumPy, Pandas, Matplotlib, TensorFlow
- R — tidyverse
- Machine learning, statistical analysis, and data visualization
- Research infrastructure — MySQL, Airflow, MLflow
Selected projects¶
- Django Backtesting Platform — full-stack system for launching, tracking, and analyzing backtests: Django, Celery, Redis, PostgreSQL, and Django Channels, with real-time progress updates, portfolio-level analytics, and report exports
- 2020 SIIM-ISIC Melanoma Classification (Kaggle) — deep learning model classifying lesion images as benign or malignant
- Collaborative Filtering via Gaussian Mixture Model — recommendation model learning latent user-preference distributions with NumPy and SciPy
Affiliations¶
- MIT Alumni Association — Affiliate Member (May 2020 – present)
Interests¶
Algorithmic trading · Deep learning · Reinforcement learning · Statistical analysis