AI Stock Trading

AI Stock Trading

AI Stock Trading : Step 1 : Correcting For Over-fitting

AI Stock Trading : For example, we can imagine a naïve way of using these factors to make an investment portfolio. 

One could look at how the factors have performed historically, and come up with the investment style with a variety of free online tools, including through a free stock screener, or program your portfolio if you can code or by hand and see which has performed the best over the past decade. 

Then after identifying that style, simply buy all the stocks which correspond to that style, sit back, and watch the alpha accrue. 

Unfortunately, this approach is almost certainly doomed to failure.  The risk that the single best investment style would just be the style that happened to work well over the past decade, but will not work in the future is simply way too high.  When dealing with so many factors, some are always bound to float to the top, or near the top of the list just by random chance.

Step 2 : Modified Bayesian Learner

So that brings me to the second modification we made.  Which was to use a modified Bayesian Learner.  Now Bayesian learners are well known, but very useful algorithms to use for machine learning.  

Step 3 : Bayesian Learners Update probabilities given new information 

Bayesian learners get their name because the rely on what is known as Bayes’ theorem in probability, which dictates how probabilities of events should be updated given new pieces of knowledge.  What is particularly appealing is that the programmer can specifically model and control the ability of the learning algorithm to actually learn the data.  

Step 4 : Rate of Learning can be controlled

That is the programmer can set the burden of proof the data in the training set needs to overcome, and how precise the eventual predictions can be.  By controlling the precision of the algorithm then, we can make sure that a single strategy does not completely dominate all the others.  

Step 5 : Algorithm does not become too precise

Therefore our investment decisions on a single stock is not dominated by any one particular strategy, but is influenced by about 30-40.  The number of strategies which influence our portfolio as a whole is much larger.  

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