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Machine learning is for closers

December 23, 2017

Put that machine learning tutorial down. Machine learning is for closers only.

As some of you that were around back in the early of this blog may know, I
always held high hopes for the application of machine learning (ml) to
generate trading edges. I think like many people first coming across machine
learning the promises of being able to feed raw data in some algorithm you
don’t really understand to conjure profitable trading ideas seemingly out of
thin air is very appealing. There is only one problem with that; it doesn’t
work like that!

Much to my chagrin now, a lot (and I mean a lot) of what this blog is known
for is exactly this type of silly applications of ml. With this post, I hope
to share some of the mistakes I made and lessons I learned trying to
successfully make ml work for me that haven’t made it on the blog due to my
abysmal posting frequency. Here they are, in no particular order:

Understanding the algorithm you are using is important.

It is almost too easy to use ml these days. Multiple times I would read a
paper forecasting the markets using some obscure algorithm and would be able
to, through proper application of google-fu, find open-sourced code that I
could instantly try out with my data. This is both good and bad; on the one
hand it is nice to be able to use the cutting edge of research easily but of
the other, should you really give something you don’t understand very well
access to your trading account? Many of my dollars were mercilessly lit on
fire walking that path. I don’t think you need to master the algorithm to be
able to apply it successfully but understand it enough to be able to explain
why it might be successful with your specific problem is a good start.

Simple, not easy.

One of my worst flaws as a trader is that I am relentlessly attracted to
complex ideas. I dream about building out complex models able solve the
market puzzle raking in billions of dollars a la RenTec. Meanwhile, back in
the ruthless reality of the trading life, just about all the money I make
trading comes from thoughtful application of simple concepts I understand
well to generate meaningful trading edges. That however, does not mean that
it needs has to be easy. For instance, there are multiple reasons why a
properly applied ml algorithm might outperform say ordinary least-squares
regression in certain cases. The trick is to figure out if the problem you
are currently trying to solve is one of those. Related to the point above,
understanding a ml technique allows you to have a better idea beforehand and
saves you time.

Feature engineering is often more important than the algorithm you choose.

I cannot emphasize this point enough. A lot of the older posts on this blog
are quite bad in that respect. Most of them use the spray-and-pray approach,
that is to say put a bunch of technical indicators as features, cry .fit()!,
and let slip the dogs of war as data-scientist Mark Antony would say. As you
can imagine it is quite difficult to actually make that work and a lot of the
nice equity curves generated by these signals don’t really hold up
out-of-sample. Not a particularly efficient way to go about it. Generating
good features is the trader’s opportunity to leverage their market knowledge
and add value to the process. Think of it as getting the algorithm to augment
a trader’s ability, not replacing it altogether.

Ensembles > single model.

Classical finance theory teaches us that diversification through combining
multiple uncorrelated bets is the only free-lunch left out there. In my
experience, combining models is quite superior to trying to find the one
model to rule them all.

Model predictions can themselves be features.

Model-stacking might seem counter-intuitive at first but there were many
Kaggle competition winners that built very good models based on that concept.
The idea is simple, use the predictions of ml models as features for a
meta-model to generate the final output for the model.

I’ll conclude this non-exhaustive list by saying that the best results I have
had come from using genetic programming to find a lot of simple edge that by
themselves wouldn’t make great systems but when thoughtfully combined create a profitable platform. I will discuss the approach in forthcoming posts.

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3 Comments
  1. Michael Astor permalink
    December 24, 2017 05:38

    Markets are highly non-stationary and the relationships among features ( aka factors) and corresponding returns change frequently, severely limiting the amount of suitable historical data available for parameter estimation. Complex machine learning algos are notorious data hogs, requiring huge amount of data to estimate multiple internal parameters. With few exceptions, that makes machine learning unsuitable for trading.

    • December 24, 2017 08:38

      You make good points, I agree with your premise but I disagree with the conclusion. As I mention in the post, I think a lot of it is misused, I certainly did more than my part on that front. It is not a silver bullet that is going to solve all your trading problems. It is also true that it is likely to perform better in a well-behaved environment (ie. not the markets). That being said, if we only applied techniques where basic assumptions were 100% met, we wouldn’t be using anything. Think about OLS, just about all it’s assumptions are violated when used on market data. Yet, many uses of OLS have been shown to work quite well for extended periods of time. Thorp, Bamberger and Tartaglia would all be a lot less rich if he decided not to use OLS because of that fact. What I am saying is that by understanding what assumptions a particular technique makes, it is easier to apply correctly. Finally, there is no reason why one would have to use ml. It’s not like you can’t be successful without it. The name of the game for me as I said in the post is to find concepts and technique I understand well and apply them thoughtfully.

      QF

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