In spare time, I have been working on a Fintech project, which is done in conjunction with a thick book I have been reading called Machine Learning for Algorithmic Trading by Stefan Jansen.
I am mostly finished with this book, and have coded - from scratch - my own implementations of the concepts introduced in this book.
What have I learned thus far?
It is ALL ABOUT THE DATA. Most of my time has been scrutinizing the data. Disqualifying data, throwing away of imputing data that has no values, and Winsorizing/capping data values so that they don't skew into outliers.
Dates. Dates have always been a problem. Dropping timestamps off of dates properly so that date comparisons and date math work properly.
So far, a lot of what I have done is data clustering, using algorithms like DBSCAN, K-Means, Agglomerative, etc to find useful cluster patterns. Regression techniques to find correlations. The models and scoring so far are my own "secret sauce" Deterministic models. But I do plan to snap in some AI to do automatic weight adjustment soon.
Right now, I am using my own Deterministic scoring model - so it can be used as a comparative baseline. But eventually I will enhance this to be more dynamic through self-learning.
No comments:
Post a Comment