No what we think are patterns is actually randomness. Thats why you trade probality not patterns. Why are stochastic processes counter to what I say? All I claim is markets can't be predicted.
Anyone who claims they can predict the future is either lucky or delusional
Have you been reading what I wrote at all? Do you still not understand that there is no randomness? This isn't an opinion, it's a fact. NOTHING IS RANDOM. EVER. Not even the random number generators in computers - it's based off of complicated strings and your cpu clock.
Do you realize that if you can predict whether the price of something is going to move up or down correctly more than 50% of the time, you are successfully predicting future prices? Nobody said they can predict prices perfectly 100% of the time.
I don't understand how you don't understand this -_- It just plain is NOT random. All I'm claiming is that I can predict the price within a reasonable margin of error MOST of the time. And it's not like you can argue against this, because it's objectively true. My software does it on the ENTIRE HISTORY OF BITCOIN DATA every hour and predicts within an average of 1.3% error. You're arguing against something that is just clearly objectively true.
Edit: Read the name of the post!!! I said it's a prediction, that that it WILL be $500! ugh please try to understand...
Causes may not be *truly* random but if the effect you're trying to predict is too far from the cause you *can* model, then you run into another problem, that of chaos theory. Incredibly small error ranges in your modelled causes can result in such wide ranges in the predicted effect that the prediction becomes pretty much worthless. Anyway this sounds like your field (and it's not mine to PhD+ level) so I'll butt out
However I *will* chime in on neural network market modelling, because it brings back a VERY interesting memory of mine from when I first started out in finance fresh out of university over 18 years ago. Neural networks were primitive but existed then - I remember doing a module on AI (at uni) and studying backpropagation training of neural nets. I'm presuming the tech has improved significantly in 18 years (exponentially, heh, if you buy into Kurzweil's Singularity theory), but making the assumption that the basic underlying concept you're using is the same, i.e. 'train' the neural net to model cause and effect by reinforcing correct predicted effects via a large batch of historical data.
My idea 18-odd years ago was to try modelling markets this way, but all the old pros told me it was impossible, and the limitations of the tech (not just the fact that significant numbers of days of market data resulted in sheer quantity problems back then - we didn't have 64GB flash memory sticks on our key rings 18 years ago!) caused me to rethink the problem. Modelling movements in the market is, by my previous analogy, modelling an effect several layers removed from the original causes. I'm not expressing this particularly scientifically but I felt that I was asking too much accuracy of the neural net in order to predict the market.
Fundamentally, the market moves due to the choices made by the actors within the market - i.e. the decisions of the traders affect the market. So my idea was to use neural nets to model individual traders. In a small enough market with a small enough quantity of meaningful individual traders (I was looking at LIFFE at the time, since it was in the process of switching from an open-outcry market to an online market), this may have been feasible at the time if I had the resources, skill and expertise with neural nets. Alas, I didn't - just another young gun with a bright idea.
But I kept the idea with me. Looks like you're doing something pretty similar… the bitcoin market may have a small enough (by modern tech standards) absolute number of active traders across the very few major exchanges to make modelling the *traders* technically feasible now. As has been said, modelling human behaviour with neural nets is not only feasible but has already been done (in limited, specific cases, of course), so modelling trading behaviour given the ongoing activity in the market should be possible.
The practical difficulties will be acquiring the data on a timely basis and 'training' the neural net (or nets) in a timely fashion, identifying the individual traders and picking out the major players, and then rolling up the predicted trader behaviour into an expected market pricing outcome. I reckon you'd effectively need access to the exchange's database and I guess this would be seen as insider trading (or at least some other form of market abuse).
And, of course, human trader behaviour isn't solely affected by the price of the instrument they're trading and the movement of said price on the exchange. External events (Taleb's black swans) could always break your model - but given that I'd be modelling *trader* behaviour, who'd all have to react to said black swans themselves, the neural net may be one step behind the first traders to react to the black swan but should then follow what the traders do.
I still think it would work, but the data requirements are pretty extreme and may constitute 'unfair market knowledge' since you'd effectively have to match each public trade on the exchange to an individual trader and this would need a feed straight from each exchange.
Perhaps there's a way to simplify this idea - this was 18 years ago, after all!!!! I'm not sure that simply modelling the overall market (by price) using a neural net would capture the human behaviour element without falling foul of chaos theory - the traders may be modellable (I think they are) but the effect of hundreds / thousands of traders rolled up into a single price movement may be too far for a consistent pattern to emerge.
Apologies for huge post but this thread brought back some cool memories from when I first started out