5 thoughts on “The diggers – “In God we trust, all others must bring data””
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Hi team :), Good work!!!
I like your idea to use NN. How you selected the NN structure? If you have described this step – sory, I have missed it.
May about missings there are better ways to handle, espacially when the missing period is long…
We have to admit that we, in fact, guessed. We tried different depths as well as drop layers.
Moreover, the guessing process was actually prepared in advance, because we have experimented previously with other financial data and especially data for Bitcoin movements from BitcoinDesk.
We have tried one, two, three layers of LSTM, intermixed with drop layers. For the neuron we also tried different numbers in each layer. We are pretty convinced that all this is an overkill due the way financial market moves. There is not that many useful information for the current price of any financial asset that many periods back EXCEPT if you try to predict volatility.
This concept was not about volatility but about predicting the direction of the market, which is, in our opinion, not very wise. On the other side, if we try to predict volatility (scale of change, not the direction), we cannot use that information for any real trades, because, as far as we know, nobody sells futures on Bitcoin. Not “legitimate ones” that is.
I think the exposition can be improved. The team may elaborate some points and it could do better job at putting everything together, including the data and source files. I can help edit this ….
5 thoughts on “The diggers – “In God we trust, all others must bring data””
Hi team :), Good work!!!
I like your idea to use NN. How you selected the NN structure? If you have described this step – sory, I have missed it.
May about missings there are better ways to handle, espacially when the missing period is long…
We have to admit that we, in fact, guessed. We tried different depths as well as drop layers.
Moreover, the guessing process was actually prepared in advance, because we have experimented previously with other financial data and especially data for Bitcoin movements from BitcoinDesk.
We have tried one, two, three layers of LSTM, intermixed with drop layers. For the neuron we also tried different numbers in each layer. We are pretty convinced that all this is an overkill due the way financial market moves. There is not that many useful information for the current price of any financial asset that many periods back EXCEPT if you try to predict volatility.
This concept was not about volatility but about predicting the direction of the market, which is, in our opinion, not very wise. On the other side, if we try to predict volatility (scale of change, not the direction), we cannot use that information for any real trades, because, as far as we know, nobody sells futures on Bitcoin. Not “legitimate ones” that is.
🙂 Yes, OK, Thanks for the long answer. It is always helpful to have domain expertise.
Good job! You probably needed to explain a bit more about the solution, but this is good
I think the exposition can be improved. The team may elaborate some points and it could do better job at putting everything together, including the data and source files. I can help edit this ….