Hi, my name is Colton Smith and I have a passion for investigating and exploiting financial phenomena from the perspective of a data scientist.
While working at Imbue Capital, I have built out a cutting-edge research pipeline for developing quantitative trading strategies that implements the best practices in financial machine learning including labeling techniques, feature engineering and extraction, model selection, cross-validation methods, model interpretability, and portfolio construction. We have used this pipeline to run systematic commodities strategies and aid global macro discretionary trading.
While working at Social Market Analytics, I assisted clients in successfully extracting alpha from alternative data through natural language processing and sentiment analysis. Whether used as the primary dataset or meant to augment an existing strategy, I am familiar with the development process beginning with the initial ingestion of the data to it being the backbone of a live, production model.
To further my financial knowledge, I explore topics of interest on my blog, Quantoisseur. I am guided by a principle that was well-stated in Euan Sinclair’s option trading book which reads, “it is better to deeply understand a simple concept than to have a superficial grasp of a more complex model.” Taking this further, it is necessary to be able to effectively communicate data and its implications, thus my blog serves as a method for me to solidify my understandings of the interactions between mathematics and finance. Additionally, I enjoy projects which allow me to find novel ways to clearly visualize data principles and findings.
MS Applied Mathematics – Columbia University
BS Industrial Engineering – University of Washington
Academic Interests: Numerical Methods, Optimization, Bayesian Statistics, Compressed Sensing, Scientific Computing
Feel free to email me at firstname.lastname@example.org or connect with me on LinkedIn.
6 thoughts on “Combinatorial Purged Cross-Validation Explained”
Hi Colton. How Are you? Thanks for sharing your knowledge.
I was wondering if you might share your CPCV python code please.
I´m working in a college homework. I´m trying to work this out and implement CPCV in my code but It´s not working.
Sorry for the delayed reply. Send me an email if you are still working on it.
Well done on the video Colton.
Hello there, will the code used on this video be posted? It would really help me!
Hi Colton, great piece on Prado’s CPCV, clear and concise! As someone deeply interested in the financial markets and learning python on my own, I am trying to get this to work but to no avail. Any possibility in gaining access to the code via a github repository or email. Kind regards, Dom.
Very nice Colton! Very clear explanation of the censored cross-validation approach. It’s good to clarify too that it wasn’t De Prado who invented the technique.