This is quite interesting. I don’t expect to ever get into quant-trading myself, but still expect to find a bunch of this valuable. I like the hands-on approach and the pace seems roughly good to me so far, though I already have some amount of data-science experience.
The anti-inductive nature of the whole thing makes me really confused about what kinds of strategies I would want to explore. Like, we could use any of the standard AI methods, from bayesian modeling, to a hidden markov model to deep neural nets, but I don’t expect any of them to work.
This comment was written in response to you feeling confused about what strategies to explore. I might write a fuller post about it, but for now here’re the thoughts off the top of my head:
Calling marking anti-inductive is correct, but it’s not helpful when trying to find strategies (as you’ve just noticed). I’d break down the strategy research process steps into:
1) Can you find a strategy (algorithm + data) that historically has performed well?
2) Can you find this strategy in such a way so as not to find a ton of other strategies that worked by random chance?
3) What % of the market has figured out this strategy?
Let’s say you see me flipping a coin. It is not necessarily a fair coin. It’s a biased coin, and you don’t know the bias. I flip the coin nine times, and the coin comes up “heads” each time. I flip the coin a tenth time. What is the probability that it comes up heads?
If you answered “ten-elevenths, by Laplace’s Rule of Succession”, you are a fine scientist in ordinary environments, but you will lose money in finance.
In finance the correct reply is, “Well… if everyone else also saw the coin coming up heads… then by now the odds are probably back to fifty-fifty.”
Right. But if it’s slightly more complicated than just looking at the coin, then suddenly we can have an edge:
1) May be not everyone can write the code to compute which way the coin is facing (algorithm). May be not everyone can see the coin (data).
2) May be other people are looking at the weather and the weather has been sunny nine days in a row.
3) May be not everyone can run their algorithm fast enough to make the trading decision in time. May be others figured out this strategy, but they’re not confident in it enough to deploy a lot of money.
So once you find your strategy, you might be in a pretty small group of people who have discovered it. So you’ll be fine in proportion to how much money is allocated to this strategy vs how much capacity it has.
And the lesson is: aim for a strategy complexity that’s simple enough to pass 2), but complicated enough that most people haven’t found it. And the bar for that is actually not that high (at least in crypto).
This is quite interesting. I don’t expect to ever get into quant-trading myself, but still expect to find a bunch of this valuable. I like the hands-on approach and the pace seems roughly good to me so far, though I already have some amount of data-science experience.
The anti-inductive nature of the whole thing makes me really confused about what kinds of strategies I would want to explore. Like, we could use any of the standard AI methods, from bayesian modeling, to a hidden markov model to deep neural nets, but I don’t expect any of them to work.
This comment was written in response to you feeling confused about what strategies to explore. I might write a fuller post about it, but for now here’re the thoughts off the top of my head:
Calling marking anti-inductive is correct, but it’s not helpful when trying to find strategies (as you’ve just noticed). I’d break down the strategy research process steps into:
1) Can you find a strategy (algorithm + data) that historically has performed well?
2) Can you find this strategy in such a way so as not to find a ton of other strategies that worked by random chance?
3) What % of the market has figured out this strategy?
From Eliezer’s post:
Right. But if it’s slightly more complicated than just looking at the coin, then suddenly we can have an edge:
1) May be not everyone can write the code to compute which way the coin is facing (algorithm). May be not everyone can see the coin (data).
2) May be other people are looking at the weather and the weather has been sunny nine days in a row.
3) May be not everyone can run their algorithm fast enough to make the trading decision in time. May be others figured out this strategy, but they’re not confident in it enough to deploy a lot of money.
So once you find your strategy, you might be in a pretty small group of people who have discovered it. So you’ll be fine in proportion to how much money is allocated to this strategy vs how much capacity it has.
And the lesson is: aim for a strategy complexity that’s simple enough to pass 2), but complicated enough that most people haven’t found it. And the bar for that is actually not that high (at least in crypto).