I will start this series with the question **"what is a neuron?".**

Let's take this picture of a simple circle:

I will use this symbol to refer to a "neuron". In it's basic form, a neuron is nothing else but a fancy word for a number. Yes, something as simple as 0.2,-0.15, 0.4 ... etc. Now that wasn't too hard.

Of course we need (1) some rules how this number is calculated and (2) give this neuron some coordinates indicating the position within a network of other neurons / "numbers" that it is connected with.

In a programming language like Mql5 we cannot use a circle like in this visual representation of course, so imagine a neuron to be nothing else but a variable or element of an array (with type "double").

A very simple network could look like this:

Let's say the flow of information goes from left to right. We have a first "layer" with 3 neurons on the left. These are all connected with the neuron in the middle, which is the only neuron in this layer. The neuron in this middle layer receives information from all three neurons of the previous layer, whereas the neuron in the last/right layer receives information from the neuron in the middle layer.

Now let's take a closer look at the neuron in the middle and how exactly it receives information:

I gave this neuron the name j_1 here, because it is the first (and only) neuron in this layer/column and because the letter j is usually used to indicate the layer that we currently refer to with our calculations. According to the alphabete, the preceding layer gets index numbers "i", whereas the following layer gets index numbers "k" in this example. You can see that I also gave some names to the connecting lines. For simplicity I just called them w1,w2,w3 here. These connecting lines, the so-called "weights" also represent nothing else but simple numbers.

If we want to calculate the value for j_1, we first have to calculate the sum of it's "weighted inputs", i.e. inputs times weights:

i_1*w1 + i_2*w2 + i_3*w3

To the result we add another number, the so-called "bias" (which is not shown in this picture). Then we apply some function (f(x)=...) to the result - and that's it: we have the new value for neuron j_1.

The just mentioned function is also called the "activation function", which is just some formula with the purpose of allowing non-linear behaviour of the network. One often used example of an activation function is the hyperbolic tangent function, which is already built in in Metatrader with the function name "tanh()". Some other examples of activation functions are sigmoid, ReLU, leaky_ReLU or softmax. It is irrelevant at this point to exactly understand these formulas and how they are chosen. All you need to understand here is that we obtain the value of a neuron with the simple rule "sum of weighted inputs, add a bias, activate".

to be continued....

This thread won't be about a question or problem, but rather about the anouncement of the presentation and documentation of an exciting trading concept. I plan to do a series of postings here in order to keep you guys updated.

Anybody who has an opinion on the topic, please don't hesitate to comment, even if you don't have profound machine learning knowledge (I'm still learning, too - which never ends).

To those of you who are more familiar with machine learning, the particular topic of this series will be about

Forex price FORECASTING with AUTO-ENCODERs combined with MULTIVARIATE FULLY-CONNECTED STACKED LSTM-networks. To those who are already intimidated by these fancy words: don't worry, it's not so complicated after all and I'm pretty sure that you will grasp the concept after a few introductory explanations. In order to make it easily understandable, I won't go into any calculus details. This is more about the idea. I still remember how it was when I first encountered neural network and how abstract and complicated it all seemed. Believe me: it's not - not after you are familiar with some basic terms.I know that there are many EA's in the market that work with neural networks. Most of them work with "multilayer perceptrons" in their simplest form - which is nothing bad per se, but none of them is the holy grail and they usually suffer from the "garbage in / garbage out" problem and any good results often consist in the same overfitting as many other "less intelligent" EA's. If you feed the network with data from lagging indicators, don't expect any real magic to happen. Some of you who remember me from earlier posts might remember that I have a strong opinion about the limitations of predicting the future, when it comes to trading. As of today, I think that there is more money to be made by reacting to the status quo, i.e. statistical anomalies as they happen, instead of forecasting tomorrow's anomalies. This particularly comprises personally much preferred various break-out and mean reversion techniques. When it comes to forecasting, the task can statistically speaking be broken down to a time series analysis problem, just like we know them in many other fields, like weather forecasting or forecasting of future sales, flights, etc.. Fore those time series that have some kind of repetetive pattern, methods like the so called ARIMA-model or Fast-Fourier-Transformation can very well do the job, or also some kinds of special neural networks (recurrent networks like GRU and LSTM). However, the problem with stock prices or currency pairs is the immense amount of noise and randomness, that makes valid predictions so difficult. In my earlier experiences with time series forecasting in trading (also with LSTM networks) my final conclusion was, that the method does in fact work, but there is not much money left after substracting spreads/commissions and that the method is not superior to other trading methods like e.g. polynomial regression chanel break-outs, that I have good practical experience with. This is why I left the idea of price forecasting for some time. However, it's never a bad idea to put one's opinion to a validity retest. In this project, I want to test if I can make better predictions by making some adjustments to the classic LSTM forecasting concept. The combination of autoencoders with stacked LSTMs is nothing new and therefore not my invention, but I don't know of any realisation in a dedicated trading environment like Metatrader. I don't know what the outcome will be and I might stop the project at any time if I should realize that it doesn't work, so please understand this project is more like a fun "scientific" investigation that stands apart form my real trading and not (yet?) a readily made expert advisor.

I am very well aware that the programming language "Python" is the go-to language when it comes to machine learning, especially with it's powerful "Keras" library. I have some Python knowledge, which is why I could also do the same thing purely in Python, so it's more of a conscious personal choice to realize it all on Metatrader only. I will also do it this way because I already have my own libraries for MLP and LSTM networks complete and working from earlier projects, so it won't be that much additional work.

Okay... having these words gotten out of the way, let's start with a few topics that I plan to write about in the next posts, so that anybody, even without any previous machine learning knowledge, will understand what it is about:

1. What is a "neuron" and what is it good for?

2. What is a "multilayer perceptron"?

3. What is "backpropagation" and how do neural networks learn?

4. What is an "autoencoder" and how can it be used in trading and time series analysis?

5. What is a recurrent neural network (LSTM,GRU...) and what are the benefits?

6. Putting it all together

Next steps:

- practical realisation, debugging and making the networks "learn"

- hyperparameter optimization

- implementation of the networks in a trading system

- backtesting and forward-testing on unseen data

Have fun following me on the journey with the upcoming postings ... and please excuse any mistakes with my mediocre "Netflix English" (german is my main language, but the german part of the forum is less active, which is why I decided to post it here).

Chris.