Today we will learn how to add multiple indicators running simultaneously on one chart, but without occupying a separate area on it. Many traders feel more confident if they monitor multiple indicators at a time (for example, RSI, STOCASTIC, MACD, ADX and some others), or in some cases even at different assets which an index is made of.
In this article, I will share with you one of the most popular and commonly used indicators in the world of trading which is RSI. You will learn how to design a trading system using this indicator.
The popularity of these two methods grows, so a lot of libraries have been developed in Matlab, R, Python, C++ and others, which receive a training set as input and automatically create an appropriate network for the problem. Let us try to understand how the basic neural network type works (including single-neuron perceptron and multilayer perceptron). We will consider an exciting algorithm which is responsible for network training - gradient descent and backpropagation. Existing complex models are often based on such simple network models.
This article deals with the theory and practical use of the algorithm for forecasting time series, based on the empirical decomposition mode. It proposes the MQL implementation of this method and presents test indicators and Expert Advisors.
We have earlier discussed some types of neural network implementations. In the considered networks, the same operations are repeated for each neuron. A logical further step is to utilize multithreaded computing capabilities provided by modern technology in an effort to speed up the neural network learning process. One of the possible implementations is described in this article.
In this article, we will analyze the step-by-step implementation of a trading system based on the programming of deep neural networks in Python. This will be performed using the TensorFlow machine learning library developed by Google. We will also use the Keras library for describing neural networks.
In the upcoming series of articles, I will demonstrate the development of self-adapting algorithms considering most market factors, as well as show how to systematize these situations, describe them in logic and take them into account in your trading activity. I will start with a very simple algorithm that will gradually acquire theory and evolve into a very complex project.
The article considers all kinds of divergence: simple, hidden, extended, triple, quadruple, convergence, as well as divergences of A, B and C classes. A universal indicator for their search and display on the chart is developed.
There has been a recent rise of interest in the cluster analyses of the FOREX market. MQL5 opens up new possibilities of researching the trends of the movement of currency pairs. A key feature of MQL5, differentiating it from MQL4, is the possibility of using an unlimited amount of indicator buffers. This article describes an example of the creation of a multi-currency indicator.
For a long time multi-currency analysis and multi-currency trading has been of interest to people. The opportunity to implement a full fledged multi-currency regime became possible only with the public release of MetaTrader 5 and the MQL5 programming language. In this article we propose a way to analyze and process all incoming ticks for several symbols. As an illustration, let's consider a multi-currency RSI indicator of the USDx dollar index.