And in 2019, there were significantly more opportunities there as well. Over the last years I’ve seen more and more traditional investors enter the crypto trading market, so simple things like arbitrage aren’t real opportunities anymore nowadays.
This feels very much like a post that will be followed by a future post titled: "Lessons learned losing 200k in a flash crash". These sorts of systems and models often work until they don't. Often, there is a hidden risk that isn't hedged correctly - a black swan event not predicted by the model, or even just an exchange losing funds after a hack.
> A common misconception is that the market cannot be predicted and that hedge fund managers are no better than dart-throwing monkeys. Many academic research papers back up this claim with data.
That's not exactly what the research says. It says that fund managers ask for more in fees than they earn you back in capital gains.
(Which is obvious: if they couldn't charge their customers more than they earn, they wouldn't offer their services in the first place -- they would just invest on their own.)
> Which is obvious: if they couldn't charge their customers more than they earn, they wouldn't offer their services in the first place -- they would just invest on their own.
This isn't obviously true. Taking 1% yearly of $100,000,000 (compounding) from investors is better than earning 15% yearly on investing your own $1,000,000 (compounding). (Or some similar charging strategy).
That is true, and indeed what happens with some passive index funds, for example.
However, from my experience, when looking for profits, people tend to prefer to arb inefficient markets before they choose to work on larger scales. So if one can choose between charging more than one provides, or charging "fairly" but at giant scales, the first step of the evolution will be the arbitrage.
One way to think about this: as a founder you charge your seed investors, whose capital you deploy, say 85% (preferences notwithstanding). Why wouldn't you do it all on your own and retain 100%? Capital inflow is a multiplier for your efforts, sometimes a gatekeeper too.
Two friends are passing through Vegas and need to stay overnight in a hotel.
At evening, one friend is tired and goes sleep, the other one has 100$ bill in the pocket and wants to try his luck.
He goes to a casino and puts 100$ on red and wins 1,000$.
He then puts these 1,000$ on red and wins 100,000$.
He puts again on red and wins 1,000,000$.
Lastly, he puts all on red and loses all.
Later, when he is back to his hotel room, his other friend asks: "how was at the casino?"
The friend who found a bet with 42% implied probability that paid at the equivalent of 10% implied prob should just go back and try again because they’ll become extremely wealthy in a few more minutes time at that casino!
> For example, in Advances in Financial Machine Learning, the author discusses how to pick sensible thresholds and transform the data to convert the regression into a classification problem.
This is interesting! Standard wisdom says that you get more statistical power from predicting a continuous variable as such, and then applying a threshold on the output, rather than trying to model the classification directly. Modeling the dichotomisation directly is equivalent to throwing a third of the data in the rubbish bin: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972292/#__sec1...
I always find it hard to trust articles like this. Call me cynical but in the opening paragraph the author clearly states that they make their money from other algorithms that lose money.
They therefore have a vested interest in filling the market with more such suboptimal algorithms that they can exploit for profit.
Noone will share an algorithm that can consistently and predictably beat the market online. Ever. It would be stupid, because the moment it goes out it destroys its own possibilities for excess returns. And if an algorithm doesn't outperform the market, you're automatically better off just buying and holding. 100% of these articles are useless if you want to be serious about investing. You might as well go and put all your money on a casino table.
While unlikely in this case, a few ways this can play out is:
1. You’re connected to more venues to your counterparty, and can hedge out profitably even if their trade was good.
2. The loss is conditional on trading with you (maybe you can price a derivative very well). The counter parties might on average make money, except when you’re sitting on the right side of a mispriced derivative.
3. You lose money when trading with other bots, but are good at competing to provide liquidity to other traders who are not toxic on a short term
It’s interesting to see so many people here talking about how this can’t be real, probably lost later, etc.
The early crypto market was absolutely disgustingly inefficient and easy to succeed as a liquidity provider in. Simply understanding how to price a perpetual would have done much better for them than fancy ML trading.
Readit News
Clicked, read, well -- it's about crypto trading.
Putting the word "crypto" in title might have been better.
That's not exactly what the research says. It says that fund managers ask for more in fees than they earn you back in capital gains.
(Which is obvious: if they couldn't charge their customers more than they earn, they wouldn't offer their services in the first place -- they would just invest on their own.)
This isn't obviously true. Taking 1% yearly of $100,000,000 (compounding) from investors is better than earning 15% yearly on investing your own $1,000,000 (compounding). (Or some similar charging strategy).
However, from my experience, when looking for profits, people tend to prefer to arb inefficient markets before they choose to work on larger scales. So if one can choose between charging more than one provides, or charging "fairly" but at giant scales, the first step of the evolution will be the arbitrage.
Imagine you knew, 100%, a 20% bounce coming. You may be able to scraggle together a few hundred thousand, maybe a million, and do OK.
Or you can find people with millions or billions in total assets, skim 5% or even 10% off the top, and make out like a bandit.
All of that said, on aggregate, you're probably right.
At evening, one friend is tired and goes sleep, the other one has 100$ bill in the pocket and wants to try his luck.
He goes to a casino and puts 100$ on red and wins 1,000$. He then puts these 1,000$ on red and wins 100,000$. He puts again on red and wins 1,000,000$. Lastly, he puts all on red and loses all.
Later, when he is back to his hotel room, his other friend asks: "how was at the casino?"
To that he replies: "Oh, I just lost 100$".
This is interesting! Standard wisdom says that you get more statistical power from predicting a continuous variable as such, and then applying a threshold on the output, rather than trying to model the classification directly. Modeling the dichotomisation directly is equivalent to throwing a third of the data in the rubbish bin: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972292/#__sec1...
They therefore have a vested interest in filling the market with more such suboptimal algorithms that they can exploit for profit.
1. You’re connected to more venues to your counterparty, and can hedge out profitably even if their trade was good.
2. The loss is conditional on trading with you (maybe you can price a derivative very well). The counter parties might on average make money, except when you’re sitting on the right side of a mispriced derivative.
3. You lose money when trading with other bots, but are good at competing to provide liquidity to other traders who are not toxic on a short term
The early crypto market was absolutely disgustingly inefficient and easy to succeed as a liquidity provider in. Simply understanding how to price a perpetual would have done much better for them than fancy ML trading.