[stfwn]

> The primary dataset consists of the price of Ether sampled at approximately one-hour intervals between August 30, 2015 and December 2, 2017.

As far as I am aware, automated trading often works with much shorter intervals, in the milliseconds range. The traditional stock trading industry also does not only look at the price history, but also at planned buy and sell orders in the books and even news and social media sentiment analysis. Perhaps prediction methods that also use these strategies would produce better results, especially given that the crypto market has been in such a chaotic flux between 2015 and 2017.

[varjag]

There doesn't seem to be enough frequency in crypto trading for meaningful info at millisecond resolution.

[sgwae]

minutes or seconds would be more useful than one hour intervals.

[anjc]

This is assuming no trading fees and highly liquid exchanges with low spreads, otherwise the profit from these micro-movements could be eaten up. I see no problem with automatic trading over periods of hours if you're getting >60% accuracy.

[nanis]

> no problem with automatic trading over periods of hours if you're getting >60% accuracy.

They a look at the sampling distribution of your projected P/L assuming each trade is an independent Bernoulli trial with success probability 60%.