This makes sense, too. If the markets were becoming less chaotic then it would slowly become more and more regularized and predictable until prices would become simply a function of x and easy to calculate at any x. This also makes sense because currently markets are grounded in reality which is infinitely chaotic and will imbue unpredictability. It would require unspeakable amounts of order to make chaotic markets gain order even over the short-term - at best they can maintain the level of disorder already present.

What's required:

- Linux,
- one or many GPU cards capable of parallel computing (ideally with more and faster CUDA cores)
- good, clean, reliable forex data
- free Linux software for neural networks - tensorflow, python, etc.
- some understanding of and experience with: programming, artificial recurrent neural networks (like LSTM), statistics and probability, finance, math, forex (for example, if you can implement a five-dimensional matrix in python real quick it will speed things up)
- time
- watchfulness and attention to (and love for) numbers

EDIT: you can supposedly use Google Collab service with access to free GPU and no setup. You can at least learn there. https://colab.research.google.com/?utm_source=scs-index

Steps:

- Prepare the data - clean, fill gaps, check for accuracy - no comparing apples with oranges, consider data source and data's essence, add data to compensate for neural network's shortcomings). This step is very important.
- Load the data in, prep for the neural network (standardize carefully, deal with outliers, decide on input data window, test data window, etc)
- Run the neural network to find a statistical model predicting the next day's close - the key word here is statistical
- Bet against it
- Profit
- Optional - optimize

This link is a good tutorial to get started with neural network programming: https://machinelearningmastery.com/m...g-lstms-keras/ that I found very helpful, the entire site is. But you must think for yourself and specifically about your project's goals when you program.

Because of how statistics works, this will require trading at every data point, that's the only way to capture the probability and the profits - after the backtesting confirms this model. I confirmed it but you should see for yourself.

This can and has been done on an older GPU card. Imagine, if we could have a supercomputer?

All of this in other words - this may be a mathematical way of saying and proving that every single model based on price (and its derivative indicators) will fail in the long-run to be profitable. That is, price has no predictive value.

If you are completely new to neural networks - try to have a neural network predict a simple relationship you feed it. I feel it's a good way to get started. Have it learn and predict that the next value in a sequence is addition of two values in different columns a step before. Then try a more complex relationship. Then try conditional or fuzzy logic.