No. of Recommendations: 3
Lizodal:
There's quite a bit of interest in Machine Learning over at Portfolio 123. This book sounds interesting:
As a P123 subscriber I've been following the flow but so far, their existing system is limited. The data vendor doesn't allow direct access to the raw data except with a very large premium. You must rely on their factor ranks for machine learning. Some of the posts that make it seem a piece of cake gloss over many of the details.
The only alternate relatively low-cost access to the required data is still Stock Investor Pro. I've slowly been trying to rebuild a historical backtesting data set for Machine Learning. Turns out it isn't as easy as it first seemed. The difficulty is compared to a backtester like GTR1 where each run you end up with a small number of ranked stocks, each of the current members have a known purchase and sales dates. Easy to accounts for slippage, splits and dividends. ML evaluation data sets assume a fixed holding period, most use either 1 month or 1 quarter, an assumed 1 month hold eliminates almost 2/3's of the dividends. I'm ending up ignoring the dividends for ML and then reevaluating the selected portfolio's performance the conventional way with friction, splits and dividends.
My favorite book for ML is 'Hands on Machine Learning with Sciket-Learn, Keras & Tensor Flow' by Aurelien Geron.
Seems like over the last 25 years for MI to remain profitable it keeps getting more and more complex. Complicated by the fact that I seem to have less drive & compulsion to squeeze a few extra percent out of the investments.
RAM
