Can a Retail Trader Succeed at Algorithmic Trading? (Part 7)

Today I will conclude with presentation and commentary in an algorithmic trading thread that took place on a popular online forum about 18 months ago.

     > What if you want to make more money; a higher SR? Then you are going
     > to have to move towards (a) the world of HFT and / or (b) the world of
     > weirder, shorter lived alpha-decaying, non linear patterns and/or
     > (c) the world of ‘alternative data’. And away from classical linear
     > statistical methods, towards the wacky world of ML. To play in these
     > worlds you are going to need to make serious investment in automated
     > trading technology, but more importantly you are going to have to be
     > able to use ML properly.
     >
     > The average person using ML in finance does so very badly, and this
     > is based on an observation of ‘professionals’ that doesn’t include the
     > hoards of amateurs who’ve just downloaded a python package and have
     > no idea what they are doing. It’s much easier to overfit with fancy…

Notice the implication here that he has been in position to observe professionals work the craft (of ML). Few can say we have done this. He claims to be an industry professional and I think his writing is very polished. He’s also a book author.

     > ML techniques than with classical ones. Given how much overfitting
     > goes on just using old-fashioned grid searches and regressions, it’s
     > no surprise that overfitting is absolutely endemic within the neural
     > network, AI, non-linear classifying crowd.

This is very consistent with what I learned in Datacamp ML courses.

     > You need a team to do this properly, first because of the alpha
     > decay you are going to spend so much time finding new effects you
     > don’t have time to do anything else like actually implement them.
     > Second, because it’s less likely that a single person will have
     > the full range of skills required to test and implement ML based
     > trading strategies. Such people do exist, but they are rare: after
     > all it’s rare enough to find people with the full set of skills
     > to test and implement classical trading strategies.

Here’s yet another call for a team-based approach (also mentioned in Part 1 and Part 5).

     > What does this mean for the individual trader? Simply put, don’t
     > use ML unless you know exactly what you are doing. And stay
     > away from trading arenas where you need to be able to use ML to
     > discover the edges that exist, plus have access to the technology
     > that will allow you to exploit those edges. There are plenty of
     > areas where you can still compete, but you will have to lower your
     > expectations for SR, and thus increase your bankroll or remain
     > as a part time trader.

Is RC right about these claims? I really don’t know. His credentials look good, but those can be phony. Many details seem consistent with comments I’ve heard elsewhere and for me, convergence usually boosts credibility.