I don’t have any code to share this week; I’m still deciding on what I would like to post about next and from there I’ll have to get some code written (and working) before I’ll be able to post it here. I’m currently trying to decide between three topics: setting up a Monte Carlo simulator, the ‘nuts and bolts’ portion of an automated trading system, or exploring a mean reversion pairs trading strategy that uses a Kalman filter.
Right now, I’m leaning toward the pairs trading strategy, but let me know if you’d rather see either of the other two (or really any suggestions for that matter). At any rate, regardless of the choice of topic, it will probably be a few weeks before I have any posts that are substantial (read: that have code). In the meantime, I do have a neat Poker Infographic I made a few months back that I can show off. This was sort of the end product of a Data Science class I completed, where I did a somewhat in-depth analysis of betting patterns in Texas Hold’em. There is an extensive database of poker hands here that I used to make this. Basically what I did was analyze the frequency of when betting happens from a few different perspectives, stage of the game, how many chips the player had, etc., then created a logistic model from this data using scikit-learn’s logistic regression functionality.
Without further ado, here it is.
Thanks for reading! Please feel free to post any questions/comments you may have!