The Truth Nobody Wants to Tell You About AI for Trading

1–The answer is 42

Have you ever read Hitchhiker’s Guide to the Galaxy?

So good!

The ‘Answer to the Ultimate Question of Life, the Universe, and Everything’, calculated by an enormous supercomputer named Deep Thought over a period of 7.5 million years is… 42.

Hilarious! :’)

Finally, the one and only truth.

Right, and Deep Thought points out that the answer is meaningless because the beings who instructed it never knew what the Question actually was.

The funny part is that quants do it all the time. They set up GPUs and train complex algorithms for days to ask what the price will be tomorrow.

What’s wrong with that? You can’t feed-in historical prices to predict future ones?

Don’t forget, markets are unpredictable and you’re breaking one of the most essential rules of trading:

Past Performances are not indicative of Future Results.

Not convinced yet?

Here is a great reality-check for you with some price predictions of Bitcoin using an LSTM. The web is full of disillusioned traders attempting ML-based price predictions.

Ouch! Party with backtest, hangover with live trading…

When something looks too good to be true, it probably is.

Now, let’s discuss the second most frequent mistake quants make.

2-The Rube Goldberg Syndrome

Have you heard of the Rube Goldberg Machine?

The machine that uses a complex chain reaction of events to achieve a final, trivial goal?

Correct. Or metaphorically, the tale of an over-engineered useless machine.

Quant: “I think we’re getting really close!”

Come here… Can you read me out loud my notes from that year-long experiment I told you about?

Sure. Let’s see…

“Normalizing data. Check.

Eliminating noise. Done.

Reducing overfitting… I almost lost my sleep over it, but I think we are in a pretty good shape.

Using gradient descent to find global minima. Eh eh! that’s it… I’m a genius.

Wait a minute… Why is the live agent still not behaving like in backtest?

Either I’m going mad, or I’m missing something.”

We dug really deep but never ended up finding the gold mine.

Could you have used more features, run a better data cleaning, or further tweaked hyper-parameters?

Smart suggestions… but you’re now catching the Rube Goldberg syndrome!

Eventually, it became clear that we were approaching it from the wrong angle.

Your machine will deliver on what it is designed to do. You’re not trying to land a rover on the moon, so no need to build a rocket ship.

As often in life, less is more.

Last but not least, let’s unwrap the sneakiest misunderstanding that tricks us all.

3-’Buy low, Sell high’ they say

Aaah, I know that one! Must be the single most famous adage in investing.

Well, this law is misleading at best in algorithmic terms. Lows and highs only become clear in retrospect, and what looks high one day may look low another day.

And we, humans, are good at exercising ‘common sense’. We employ our judgment in universal ways without thinking expansively or requiring large data sets. Machines are in their relative infancy in this field.

Take a market price for example.

Right, it’s just a number.

The number is a shell. But underneath the shell is a complex derivative of the underlying business, its capital structure, its macro-economic fundamentals, as well as human emotions and buyers/sellers’ intentions.

So the only way for a machine to precisely predict the market price, you would need to feed all those elements that could potentially affect the price. Which is practically impossible to obtain and train an algorithm on.

Gotcha! And if you only input price history, there is a whole lot of information your algo is missing about the underlying factors that affect the price.

Price is the consequence of everything that is happening on the market, not the cause.

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