Predicting Price Changes in Ethereum (2017) [pdf]

Post by anjc » Sat, Jun 09, 2018, 4:02 PM UTC

adjkant wrote: post #23

anjc wrote: post #21

Earlier quoted context omitted.

Very interesting. Are you buying and selling, or are you shorting too? Does this 60% accuracy remain if you change the training/prediction period? I'd guess that it would be more accurate if you train based on days versus seconds.

Just buying and selling, all GDAX maker to avoid any fees which would more or less cancel out even the best model I have. Lack of fees is another unique feature of the space. The training period I keep to 3 months and haven't really moved much since initially trying things. A month or less and the model is overfit and useless, too long and it's not working with current data. The prediction/"hold time" changes absolut…

Post by anjc » Sat, Jun 09, 2018, 4:10 PM UTC

fenwick67 wrote: post #30

mrwebmaster wrote: post #25

Maybe there are a lot of other trading algorithms that worked for some time but ended with a loss so big that exceeded all the profits, and we will never see those stats. Publisher bias.

That's what I think is quaint about all these people who came up with crypto buying strategies. The naive "just buy as much as as you can" strategy was very effective until January.

Post by AznHisoka » Sat, Jun 09, 2018, 5:16 PM UTC

adjkant wrote: post #17

I've actually run a trading algorithm off a very similar approach for the past 8-10 months, which yielded about 87% return in that time. Using regression I can still to this day hit about 55-60% accuracy. What's not mentioned in the paper is that accuracy is only a small part of the story. If you're accurate 60% of the time but in the 40% accuracy range you're very wrong, acting on the information is useless. As a re…

Post by heptathorp » Sat, Jun 09, 2018, 6:19 PM UTC

anjc wrote: post #32

heptathorp wrote: post #29

> A logical explanation for the high volatility of Ether, relative to Bitcoin and common stock, is that Ether is transferred or traded in a way in which Bitcoin and stock are not. More specifically, Ethereum has contract accounts which can cause Ether to be transferred between accounts in an unpredictable manner. (While Bitcoin and common stock are also traded by algorithms, the distinction is that anyone - even some…

Presumably they're talking about the consumption of Ether as gas, to run contracts. Perhaps they're seeing that algorithmic trading of currency is more predictable than gas consumption by distributed apps, which may be highly and suddenly viral for some period - e.g. Cryptokitties. Viral use of the DApp in theory would increase the price of Ether due to increased demand.

Post by nanis » Sat, Jun 09, 2018, 7:11 PM UTC

tyrex2017 wrote: post #26

nanis wrote: post #14

Earlier quoted context omitted.

As a class project where kids are learning how to use software, clean data, type equations etc, it is acceptable. It is unreasonable to expect them to know better when they are just getting a feel for the tools. If this is supposed to give insight to people on how to allocate their money, the critiques would have to be much harsher.

i have read papers in the best journals. not better, not more insightful, not deeper, than this one. in fact, they usually would have included only half the models. the only question i have: such worse performance by nn is strange, it should be similar to logistic regression..