4 ms·

Yes, in a toy space ML replicates simplistic measures, there's only so much information to be had in such a space to start with. The power of these techniques i

by PLenz 8y ago

Yes, in a toy space ML replicates simplistic measures, there's only so much information to be had in such a space to start with. The power of these techniques is when we apply them to high-complexity or even unbounded-complexity areas.

candiodari 8y ago
And that you apply them to domains where the knowledge of the domain is basic -at best. It enables people who know one subject well -machine learning- to do speech recognition and weather forecasting well. Plus this article has a number of problems. It compares machine learning to MA(3). That it works pretty well totally ignores the selection of MA(3) vs MA(5). MA(100) is "common" indicator often talked up (why I do not know, except perhaps it protects you from really big errors, but other than that it's not very helpful). If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not.
- afro88 8y ago
  > If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not. Judging from the graph in the article, it looks like it doesn't. The model didn't reinvent the moving average, it invented it without the inherent delay. Though it does seem to diverge from real price over time.
- longerthoughts 8y ago
  >If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not. Why does the sensitivity matter when we know that the accuracy was so similar? The point on sensitivity does make me wonder how an exponential moving average would've performed though.
  - candiodari 8y ago
    Because there are "chaotic cases" in statistics. Cases where there are so many possible patterns that stating which pattern the data follows doesn't have any less information than the data itself (or only has a little less information than the data itself). Financial market data is quite notorious for being one such case (if not there wouldn't be any poor mathematicians). So MA(3) is not a general indicator. It's not universally useful, and it isn't the only one this article considers. It was chosen from a great many possible indicators (go to tradingview.com, find a graph, expand the indicators tab, and look at the list. And keep in mind that ALL MA's are just one entry). So there's tens of thousands of indicators that MA(3) was chosen from. So the predictive value of MA(3) as an indicator in the general case is really only 1/10000th of what this person claims it to be (actually infinitely less, but let's assume you use, say, MA(200) as the limit of what you're willing to consider, which makes it a finite number). Predictive information is this: let's say I tell you I know what the outcome is of a football match. And at the end of the match, I pull the outcome out of a stack of papers. The size of that stack of papers is inversely related to how informative my prediction was. If I just put down one paper before the match, that's great. If I put down 1000. Not so great. For this guy, the stack of papers was pretty thick. So the article is saying "I can, out of a great many indicators, find one that performs similar to this machine learning algorithm". Unless the article also gives a clear and definitive reasoning for why THAT indicator was chosen for that dataset, it doesn't contain much information at all.
    - longerthoughts 8y ago
      You're presenting this from the perspective of the author's selection of MA(3), but I think what you're really getting at is yes, MA(3) happened to be comparable to the ML model in this case, but the beauty of the ML model was that it knew to nearly replicate MA(3) out of a great many common indicators. Is that essentially what you're saying? If so, I'm curious how things like longer period moving averages or an exponential moving average perform relative to both MA(3) and the ML model in this case.

by PLenz 8y ago

candiodari 8y ago

And that you apply them to domains where the knowledge of the domain is basic -at best. It enables people who know one subject well -machine learning- to do speech recognition and weather forecasting well. Plus this article has a number of problems. It compares machine learning to MA(3). That it works pretty well totally ignores the selection of MA(3) vs MA(5). MA(100) is "common" indicator often talked up (why I do not know, except perhaps it protects you from really big errors, but other than that it's not very helpful). If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not.

afro88 8y ago
> If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not. Judging from the graph in the article, it looks like it doesn't. The model didn't reinvent the moving average, it invented it without the inherent delay. Though it does seem to diverge from real price over time.
longerthoughts 8y ago
>If you actually try to use the prediction of MA(3) you will find it is often very hard to use, because it's always just too late. I wonder if the machine learning version had the same problem, or not. Why does the sensitivity matter when we know that the accuracy was so similar? The point on sensitivity does make me wonder how an exponential moving average would've performed though.
- candiodari 8y ago
  Because there are "chaotic cases" in statistics. Cases where there are so many possible patterns that stating which pattern the data follows doesn't have any less information than the data itself (or only has a little less information than the data itself). Financial market data is quite notorious for being one such case (if not there wouldn't be any poor mathematicians). So MA(3) is not a general indicator. It's not universally useful, and it isn't the only one this article considers. It was chosen from a great many possible indicators (go to tradingview.com, find a graph, expand the indicators tab, and look at the list. And keep in mind that ALL MA's are just one entry). So there's tens of thousands of indicators that MA(3) was chosen from. So the predictive value of MA(3) as an indicator in the general case is really only 1/10000th of what this person claims it to be (actually infinitely less, but let's assume you use, say, MA(200) as the limit of what you're willing to consider, which makes it a finite number). Predictive information is this: let's say I tell you I know what the outcome is of a football match. And at the end of the match, I pull the outcome out of a stack of papers. The size of that stack of papers is inversely related to how informative my prediction was. If I just put down one paper before the match, that's great. If I put down 1000. Not so great. For this guy, the stack of papers was pretty thick. So the article is saying "I can, out of a great many indicators, find one that performs similar to this machine learning algorithm". Unless the article also gives a clear and definitive reasoning for why THAT indicator was chosen for that dataset, it doesn't contain much information at all.
  - longerthoughts 8y ago
    You're presenting this from the perspective of the author's selection of MA(3), but I think what you're really getting at is yes, MA(3) happened to be comparable to the ML model in this case, but the beauty of the ML model was that it knew to nearly replicate MA(3) out of a great many common indicators. Is that essentially what you're saying? If so, I'm curious how things like longer period moving averages or an exponential moving average perform relative to both MA(3) and the ML model in this case.