Prediction: Breaking $500 within 24 hours

[damnek]

Isn't that just a thesis paper?  The guy describes how to apply ANN to price prediction and concludes that its fairly accurate.  But nowhere in the paper he gives evidence

I looked on your site where you compare 24h results compared to actual price and none of the data points were exact.  Most of your predictions were below the actual price.  Only one or two times you got it right.  How can you claim that you predicted anything?

More importantly how can someone make money trading this info?

Read the paper again, they describe experiments that they ran to predict stock prices on a data set. If this study doesn't satisfy you just choose one of the dozens of others on the same subject.

You really expected the predicted prices to be EXACTLY correct? I never claimed this to be true and, in fact, the average margin of error implies that the exact opposite is the case. I've never claimed that you can make money predicting with the data on my website, nor have I claimed that it is always accurate. I've explicitly said multiple times (and on the website itself) that I don't know whether or not you could make money trading based on this model, and that the predictions are not always right.

I really don't know what point you are trying to argue here (not that this is even an argument). I've explained to you why you are wrong about prices being unpredictable and I've explained to you why we know with certainty that there are patterns in the data. It seems like you are just trying to argue about my site for the sake of arguing yet you don't really have any points, so I don't know what you are doing here.

You haven't shown prices are predictable.  Nobody has.  If somebody could do this theyd win a Nobel Prize

I think the belief whether markets are predictable or not can be seen as a litmus test for traders. It takes experience to understand that you cannot predict liquid markets, sorry K128kevin2. I also trained a neural network when I first started trading. Of course it didn't make me any money at all, all it did was lose because of the commissions paid..

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[RandomPedestrianN9]

I think the belief whether markets are predictable or not can be seen as a litmus test for traders. It takes experience to understand that you cannot predict liquid markets, sorry K128kevin2. I also trained a neural network when I first started trading. Of course it didn't make me any money at all, all it did was lose because of the commissions paid..

Did you write one or you used someone elses? I read some research papers too and certain NN structures combined with other modules had 70%-80% accuracy, one team claimed to achieve 91% but did not disclose any details (because it was either fake or they wrote great system and kept it secret for themselves). Markets are neither random walk or logical is what i understood from materials i have read. If one could combine the right factors and systems, i believe it can at the least increase noobs trading success (say, 6-7 instead of 4-5 successful trades out of 10).

Depends what variables was your NN using, what did it count as important factor, networks structure etc...

[damnek]

I think the belief whether markets are predictable or not can be seen as a litmus test for traders. It takes experience to understand that you cannot predict liquid markets, sorry K128kevin2. I also trained a neural network when I first started trading. Of course it didn't make me any money at all, all it did was lose because of the commissions paid..

Did you write one or you used someone elses? I read some research papers too and certain NN structures combined with other modules had 70%-80% accuracy, one team claimed to achieve 91% but did not disclose any details (because it was either fake or they wrote great system and kept it secret for themselves). Markets are neither random walk or logical is what i understood from materials i have read. If one could combine the right factors and systems, i believe it can at the least increase noobs trading success (say, 6-7 instead of 4-5 successful trades out of 10).

Depends what variables was your NN using, what did it count as important factor, networks structure etc...

It was based on some python library, neural networks are pretty much a generic tool so I don't think it really matters which implementation you choose.
This guy explains the problems with overfitting really well: https://www.tastytrade.com/tt/shows/the-skinny-on-options-math/episodes/11152. Obviously neural networks are the prime candidate for overfit.

[K128kevin2]

I think the belief whether markets are predictable or not can be seen as a litmus test for traders. It takes experience to understand that you cannot predict liquid markets, sorry K128kevin2. I also trained a neural network when I first started trading. Of course it didn't make me any money at all, all it did was lose because of the commissions paid..

This is the fundamentally flawed argument that some people are making. Just because you were unable to do it doesn't mean that I can't - and it certainly doesn't mean that it is impossible. To assume that something is impossible just because you can't do it is pretty arrogant imo. Furthermore, to say that it cannot be predicted is objectively false based on the arguments that I've made earlier.

[blatchcorn]

Why is this thread still going on?

[K128kevin2]

It was based on some python library, neural networks are pretty much a generic tool so I don't think it really matters which implementation you choose.
This guy explains the problems with overfitting really well: https://www.tastytrade.com/tt/shows/the-skinny-on-options-math/episodes/11152. Obviously neural networks are the prime candidate for overfit.

Neural networks are not a generic tool. There are TONS of ways that they can be structured and trained, much of which would require designing and understanding a neural network on your own (like I have done) instead of using a library.

[damnek]

It was based on some python library, neural networks are pretty much a generic tool so I don't think it really matters which implementation you choose.
This guy explains the problems with overfitting really well: https://www.tastytrade.com/tt/shows/the-skinny-on-options-math/episodes/11152. Obviously neural networks are the prime candidate for overfit.

Neural networks are not a generic tool. There are TONS of ways that they can be structured and trained, much of which would require designing and understanding a neural network on your own (like I have done) instead of using a library.

lol you just contradicted yourself

[K128kevin2]

It was based on some python library, neural networks are pretty much a generic tool so I don't think it really matters which implementation you choose.
This guy explains the problems with overfitting really well: https://www.tastytrade.com/tt/shows/the-skinny-on-options-math/episodes/11152. Obviously neural networks are the prime candidate for overfit.

Neural networks are not a generic tool. There are TONS of ways that they can be structured and trained, much of which would require designing and understanding a neural network on your own (like I have done) instead of using a library.

lol you just contradicted yourself

um no? lol nothing here that I wrote that contradicts anything I said

[RandomPedestrianN9]

Well, feedforward and backpropagation delivered different results, then you have Kohenens maps - not many avaible materials on those. You can combine different activation functions, use different input variables (volume and closing + opening prices or volume + trend lines i donno), have another system overseeing your NN and deleting bad outputs, have another NN to predict some of your inputs....i read that S§P500 is using NN so there must be a way of making at least a little bit precise predictions with NN.

I don't think it really matters which implementation you choose.

I agree that different implementations wont probably make BIG differences but you can get 2-5% differences in errors.

[damnek]

i read that S§P500 is using NN so there must be a way of making at least a little bit precise predictions with NN.

The S&P500 is just an index that tracks the valuation of a basket of stocks.

[K128kevin2]

Well, feedforward and backpropagation delivered different results, then you have Kohenens maps - not many avaible materials on those. You can combine different activation functions, use different input variables (volume and closing + opening prices or volume + trend lines i donno), have another system overseeing your NN and deleting bad outputs, have another NN to predict some of your inputs....i read that S§P500 is using NN so there must be a way of making at least a little bit precise predictions with NN.

I don't think it really matters which implementation you choose.

I agree that different implementations wont probably make BIG differences but you can get 2-5% differences in errors.

The way that you represent your inputs can have a HUGE (way more than 2-5%) impact on accuracy. The size and number of hidden layers, of course, has a large effect too. So does the activation function. Another huge factor is the learning rate, and the strategy you use for changing learning rate as training occurs. I spent a good while on this and found that depending on what strategy I used, I would get error rates as low as 1.3% or as high as 10-20% or even higher.

[DustyRah]

um no? lol nothing here that I wrote that contradicts anything I said

You clearly said you are an idiot and that anything you say should be treated as such. Then you go on to say almost anything which seems clever so there you go, you just contradicted yourself.

Peace be with the morons~

[K128kevin2]

You clearly said you are an idiot and that anything you say should be treated as such. Then you go on to say almost anything which seems clever so there you go, you just contradicted yourself.

Peace be with the morons~

LOL

[knightcoin]

the price supposed to go down since I want to buy my btc back ....

[Malin Keshar]

more than 24 later, $20 fall, and we back to 430's. Your neural network just suffered a brain hemmorhage.


But i missed too, was expecting more than a $20 variation

[K128kevin2]

more than 24 later, $20 fall, and we back to 430's. Your neural network just suffered a brain hemmorhage.


But i missed too, was expecting more than a $20 variation

Dude this prediction was made ages ago lol... and as I recall, the result was that the price did rise when it was predicted to rise, but not quite as high as it predicted.

[DustyRah]

the price supposed to go down since I want to buy my btc back ....

Its bottom heavy, you will get it to buy back your BTC ~