Yes to everything Satvik said, plus: it helps if you’ve tested the algorithm across multiple different market conditions. E.g. in this case we’ve looked at 2017 and 2018 and 2019, each having a pretty different market regime. (For other assets you might have 10+ years of data, which makes it easier to be more confident in your findings since there are more crashes + weird market regimes + underlying assumptions changing.)
But you’re also getting at an important point I was hinting at in my homework question:
We’re predicting up bars, but what we ultimately want is returns. What assumptions are we making? What should we consider instead?
Basically, it’s possible that we predict the sign of the bar with a 99% accuracy, but still lose money. This would happen if every time we get the prediction right the price movement is relatively small, but every time we get it wrong, the price moves a lot and we lose money.
Stop losses can help. Another way to mitigate this is to run a lot of uncorrelated strategies. Then even if the market conditions becomes particularly adversarial for one of your algorithms, you won’t lose too much money because other algorithms will continue to perform well: https://www.youtube.com/watch?v=Nu4lHaSh7D4
That sounds equivalent to kelly criterion, that most of your bankroll is in a low variance strategy and some proportion of your bankroll is spread across strategies with varying amounts of higher variance. Is there any existing work on kelly optimization over distributions rather than points?
edit: full kelly allows you to get up to 6 outcomes before you’re in 5th degree polynomial land which is no fun. So I guess you need to choose your points well. http://www.elem.com/~btilly/kelly-criterion/
Yes to everything Satvik said, plus: it helps if you’ve tested the algorithm across multiple different market conditions. E.g. in this case we’ve looked at 2017 and 2018 and 2019, each having a pretty different market regime. (For other assets you might have 10+ years of data, which makes it easier to be more confident in your findings since there are more crashes + weird market regimes + underlying assumptions changing.)
But you’re also getting at an important point I was hinting at in my homework question:
Basically, it’s possible that we predict the sign of the bar with a 99% accuracy, but still lose money. This would happen if every time we get the prediction right the price movement is relatively small, but every time we get it wrong, the price moves a lot and we lose money.
Stop losses can help. Another way to mitigate this is to run a lot of uncorrelated strategies. Then even if the market conditions becomes particularly adversarial for one of your algorithms, you won’t lose too much money because other algorithms will continue to perform well: https://www.youtube.com/watch?v=Nu4lHaSh7D4
That sounds equivalent to kelly criterion, that most of your bankroll is in a low variance strategy and some proportion of your bankroll is spread across strategies with varying amounts of higher variance. Is there any existing work on kelly optimization over distributions rather than points?
edit: full kelly allows you to get up to 6 outcomes before you’re in 5th degree polynomial land which is no fun. So I guess you need to choose your points well. http://www.elem.com/~btilly/kelly-criterion/
Good question. I don’t know.