Live data from Hacker News

MIT computer scientists can predict the price of Bitcoin

newsoffice.mit.edu

21–30 of 86 posts

Re: MIT computer scientists can predict the price of Bitcoin

#21
post #13
post #9

Earlier quoted context omitted.

"Any model, if given enough parameters, can be made to match historical data to an arbitrary degree." I've known this forever but for some reason haven't heard this precise statement of it. Thanks. Reductio ad absurdium: imagine a model where the number of parameters equals the number of data points. Obviously that model will have perfect fit. Predicting the future is hard. Predicting the future without a causal unde…

This picture shows quite nicely what might happen when having too many parameters (or too little data): http://machinelearningac.files.wordpress.com/2011/10/polynom...

In red is your model whereas in green is the real one, M being the number of parameters.

The technical term for the last one is "overfitting" if I remember correctly. But in the case you have an enormous amount of data, it is unlikely to happen.

It reminds me of this awesome course: https://www.coursera.org/course/ml

edit: The parent's parent's parent mention overfit for the MIT work, I don't think it'd be the case if you have that amount of data in hands

Re: MIT computer scientists can predict the price of Bitcoin

#22
post #8

This kind of innovation is cool, but it's a zero sum game. The bitcoin markets are already driven by competing bots. Their profits will be reduced as other bots iterate on their algorithms.

Pleas don't spout out 'zero sum' like you think it means something insightful here. Any marketplace is 'zero sum' if you think about it. There's a buyer and seller. So what?

You're assuming the exchanges don't take a cut...

Re: MIT computer scientists can predict the price of Bitcoin

#23

Earlier quoted context omitted.

Pleas don't spout out 'zero sum' like you think it means something insightful here. Any marketplace is 'zero sum' if you think about it. There's a buyer and seller. So what?

You're assuming the exchanges don't take a cut...

oh, I'm not arguing that it is zero-sum, just that whether it is or not has absolutely no relevance here.

Re: MIT computer scientists can predict the price of Bitcoin

#24
post #8

This kind of innovation is cool, but it's a zero sum game. The bitcoin markets are already driven by competing bots. Their profits will be reduced as other bots iterate on their algorithms.

Pleas don't spout out 'zero sum' like you think it means something insightful here. Any marketplace is 'zero sum' if you think about it. There's a buyer and seller. So what?

Most marketplaces are not zero-sum.

For example, futures and options are zero-sum because every dollar of profit that one trader makes is offset by a dollar of loss from another trader.

But stock markets are not zero-sum. Prices can be bid up without a single share changing hands, creating new wealth out of thin air, and everyone wins. Conversely, prices can go to zero, destroying wealth and making everyone a loser.

Same thing for most other markets you can think of, from real estate to used cars to your local flea market. As with bitcoin exchanges, none of these are zero-sum since there doesn't have to be a loser for every winner.

Re: MIT computer scientists can predict the price of Bitcoin

#25
post #13
post #9

Earlier quoted context omitted.

"Any model, if given enough parameters, can be made to match historical data to an arbitrary degree." I've known this forever but for some reason haven't heard this precise statement of it. Thanks. Reductio ad absurdium: imagine a model where the number of parameters equals the number of data points. Obviously that model will have perfect fit. Predicting the future is hard. Predicting the future without a causal unde…

This picture shows quite nicely what might happen when having too many parameters (or too little data): http://machinelearningac.files.wordpress.com/2011/10/polynom...

"With four parameters I can fit an elephant, and with five I can make him wiggle his trunk." -- John von Neumann

The green "real" signal in your picture is amusing when juxtaposed with the red "zero-noise-assumption" signal in the last frame. TBH this accounts for most of my distrust of e.g. climate modeling.

Re: MIT computer scientists can predict the price of Bitcoin

#26
post #24

Earlier quoted context omitted.

Pleas don't spout out 'zero sum' like you think it means something insightful here. Any marketplace is 'zero sum' if you think about it. There's a buyer and seller. So what?

Most marketplaces are not zero-sum. For example, futures and options are zero-sum because every dollar of profit that one trader makes is offset by a dollar of loss from another trader. But stock markets are not zero-sum. Prices can be bid up without a single share changing hands, creating new wealth out of thin air, and everyone wins. Conversely, prices can go to zero, destroying wealth and making everyone a loser.…

You're mixing the concepts of realized and un-realized profit. A market price change marks your open position and gives you an unrealized (loss)gain. But to realize that you have to trade, plain and simple. And someone must take the other side of that.

A more compelling argument against zero-sum is that traders and investors have different time frames and objectives. And, indeed, the classic argument in the futures market is that a farmer who buys a hedge from a speculator represents a potential positive outcome for both.

Re: MIT computer scientists can predict the price of Bitcoin

#27
post #21
post #13

Earlier quoted context omitted.

This picture shows quite nicely what might happen when having too many parameters (or too little data): http://machinelearningac.files.wordpress.com/2011/10/polynom...

In red is your model whereas in green is the real one, M being the number of parameters. The technical term for the last one is "overfitting" if I remember correctly. But in the case you have an enormous amount of data, it is unlikely to happen. It reminds me of this awesome course: https://www.coursera.org/course/ml edit: The parent's parent's parent mention overfit for the MIT work, I don't think it'd be the case i…

Here's another way to think of it.

If the parameter space for my model includes, let's say 10 binary decisions (which is very conservative), that's 1024 possible states of my model. If I tested all 1024 states against historical data, it is likely that some of them might do very well (depending on the general architecture of the model of course). What if I then selected the successful minority and held them up as clever strategies? Their success would very likely have been arbitrary. By basically brute-forcing enough strategies, I will inevitably come across some that were historically successful. But these same historically successful strategies are unlikely to outperform another random strategy in the future. It's not impossible you'll find a nugget of wisdom hidden from everyone else, just much less likely than the more simple explanation I'm offering.

So to your point, it's not just the size of the parameter space versus the data set that matters. Brute-forcing the former alone will likely produce a deceptive minority of winners.

Re: MIT computer scientists can predict the price of Bitcoin

#28
post #8

This kind of innovation is cool, but it's a zero sum game. The bitcoin markets are already driven by competing bots. Their profits will be reduced as other bots iterate on their algorithms.

Pleas don't spout out 'zero sum' like you think it means something insightful here. Any marketplace is 'zero sum' if you think about it. There's a buyer and seller. So what?

I agree that his usage of zero sum was inaccurate, but so is yours.

Marketplaces are not at all inherently zero sum games. Wikipedia's definition (which is a fine one) is: "a participant's gain (or loss) of utility is exactly balanced by the losses (or gains) of the utility of the other participant(s). If the total gains of the participants are added up and the total losses are subtracted, they will sum to zero."

The key thing here that is is measured in utility (not in the price of the good). Of course every trade or transaction is flat in that I sold it to you for the price you bought it at (that is obvious and non-what a zero sum game means). The issue is whether we are both made better off or not.

If I don't want to bear the risk of holding bitcoins and am happier mitigating that risk by selling them for dollars, my utility increased. Similarly, if you purchased them from me because you want to bear such risk, your utility increased. Even removing the risk/uncertainty from the equation, my willingness to pay for a good is not the same as the market price. Thus, if I sell bitcoins bc my value of them is less than the current market price, then I am improved. Similarly, if you value bitcoins above the current market price and buy them from me, then your willingness to pay was higher than the price you paid. Thus, you have significant consumer surplus from that trade.

There are called pareto efficient transactions. Most transactions are actually pareto efficient where at least one of the individuals increased their utility and no one involved in the trade decreased their utility.

This excerpt does a good job articulating this: "Specifically, all trade is by definition positive sum, because when two parties agree to an exchange each party must consider the goods it is receiving to be more valuable than the goods it is delivering. In fact, all economic exchanges must benefit both parties to the point that each party can overcome its transaction costs, or the transaction would simply not take place."

Re: MIT computer scientists can predict the price of Bitcoin

#29
The problem with the paper is not overfit. They claim to have run their simulation with out of band ("live") data. The actual problem with the paper is that we have no idea if their simulator is any good, which means that their result (89% return in 50 days) could be totally bogus. In other words, we don't know if the actual bitcoin exchange would fill their orders at the same prices (if at all) as their simulator does. A decent simulator for a high-frequecy strategy like this is not trivial because you have to incorporate all the exchange behavior (documented or not) into your simulator, and then you have to validate your results by comparing your simulated results to the results of some actual trading. The fact that they spent none of the paper on the details of the simulator makes me extremely skeptical.

Re: MIT computer scientists can predict the price of Bitcoin

#30

Earlier quoted context omitted.

You're assuming the exchanges don't take a cut...

oh, I'm not arguing that it is zero-sum, just that whether it is or not has absolutely no relevance here.

>Any marketplace is 'zero sum' if you think about it.

vs

>I'm not arguing that it is zero-sum

So, what are you saying there then.

Post reply on HN