Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

MIT computer scientists can predict the price of Bitcoin

newsoffice.mit.edu

Post reply on HN
Print view View original

41–50 of 86 posts

Re: MIT computer scientists can predict the price of Bitcoin

#41

Post by lrm242 »

ISL wrote: post #12

Can a HFT-knowledgeable commenter chime in on the viability of the Sharpe ratio here? From a physics perspective, it appears that the Sharpe ratio of 4.1 is roughly equivalent to a 4.1-sigma claim that their algorithm is better than random trading. I can't check easily, but I'd guess that the movement of Bitcoin prices isn't normally-distributed (looking at the paper's time series suggests that there's more low-frequ…

A sharpe of 4 is ok. Most "HFT" type strategies have sharpes so high they don't talk about sharpe anymore as it is meaningless. It is simply a different type of trading. See Virtu's pnl distribution in their S1. As a point of reference, Blair Hull is on record saying they are interested in nothing below a sharpe of 10 (r-finance talk, should be googleable).

Re: MIT computer scientists can predict the price of Bitcoin

#44

Post by Enzolangellotti »

pbhjpbhj wrote: post #30
joosters wrote: post #23

Earlier quoted context omitted.

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.

How can the stock market be zero sum. What about dividends?

Re: MIT computer scientists can predict the price of Bitcoin

#45

Post by joosters »

pbhjpbhj wrote: post #30
joosters wrote: post #23

Earlier quoted context omitted.

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.

Sorry, very badly phrased by me. I'm trying to say 'zero sum - so what?' and 'not zero sum - so what?' I regret writing the second part, I was trying to say (badly) that any simple model of a market could be zero sum. You can add in more things to the model (commission, whatever) and say now it's not zero sum. And you can add in opportunity costs and claim it's zero sum again. But who cares? It means nothing.

Now I've unintentionally started an argument about what is / is not zero sum and what bits of a market you take your definitions from. That was the exact opposite of what I was trying to say. If it is zero-sum, why does that even change anything or make the trading (from the original paper) worthwhile or not? I'm trying to say that 'zero sum' or not, it changes nothing and gives no additional insight.

Re: MIT computer scientists can predict the price of Bitcoin

#46

Post by dnautics »

jacobwcarlson wrote: post #2

The paper states that the strategy was simulated with live data and makes no mention of slippage. I've never traded bitcoin so I'm not sure how difficult it is to get fills, but that along with spreads are non-trivial components of real trading.

it would have been better if they set up an wallet and put bitcoin in it and demonstrated the trades that were automatically executed by this wallet. Since bitcoin is a ledger history, we could be 100% certain that SOME algorithm 'did the right thing', although we couldn't be certain that they spun up more than one algorithm and then just showed us the best one.

Re: MIT computer scientists can predict the price of Bitcoin

#47

Post by [deleted] »

minimax wrote: post #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 do…

[deleted]

Re: MIT computer scientists can predict the price of Bitcoin

#48

Post by Houshalter »

Sometime in 2013, before the bitcoin prices exploded, I downloaded some bitcoin historical price data and ran symbolic regression on it with Eureqa. It came up with a formula that fit the observed data fairly well, and wasn't very complicated.

But when I extrapolated it forward a few months, it predicted the price would explode to unreasonable levels. I was disappointed and threw it away, assuming that it must be wrong.

Re: MIT computer scientists can predict the price of Bitcoin

#49

Post by gaze »

chollida1 wrote: post #14
Animats wrote: post #10

This is short-term trading. "Every two seconds they predicted the average price movement (on OKcoin) over the following 10 seconds. If the price movement was higher than a certain threshold, they bought a Bitcoin; if it was lower than the opposite threshold, they sold one; and if it was in-between, they did nothing." I don't see them allowing for commissions and fees. OKcoin, at peak, had a trading volume so high tha…

> What this group at MIT may have done is reverse-engineered the fake trade generation algorithm. Just to be clear, there is nothing wrong with this. Infact, sitting around and reverse engineering what other traders are doing is what many funds do. I'm in this group so I"m happy to answer questions if anyone has any. > Every two seconds they predicted the average price movement (on OKcoin) over the following 10 secon…

Where can I read about the actual strategies used? Perhaps some that aren't used any more. I've read an absolute ton about how to set up a trading system with hadoop and cassandra and blah blah but that's all pretty trivial. I'd be more interested in the strategies... this decay factor is interesting. Where did you find out about this?

Re: MIT computer scientists can predict the price of Bitcoin

#50

Post by Houshalter »

vmarsy wrote: post #21
arno_v wrote: 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…

It's entirely possible to overfit with enormous amounts of data. As people are now creating models with enormous numbers of parameters.
Post reply on HN