MQL5 Wizard techniques you should know (Part 05): Markov Chains
MQL5 Wizard techniques you should know (Part 05): Markov Chains
Markov chains are a powerful mathematical tool that can be used to model and forecast time series data in various fields, including finance. In financial time series modelling and forecasting, Markov chains are often used to model the evolution of financial assets over time, such as stock prices or exchange rates. One of the main advantages of Markov chain models is their simplicity and ease of use.
Learn how to design a trading system by Gator Oscillator
Learn how to design a trading system by Gator Oscillator
A new article in our series about learning how to design a trading system based on popular technical indicators will be about the Gator Oscillator technical indicator and how to create a trading system through simple strategies.
Neural networks made easy (Part 32): Distributed Q-Learning
Neural networks made easy (Part 32): Distributed Q-Learning
We got acquainted with the Q-learning method in one of the earlier articles within this series. This method averages rewards for each action. Two works were presented in 2017, which show greater success when studying the reward distribution function. Let's consider the possibility of using such technology to solve our problems.
Mountain or Iceberg charts
Mountain or Iceberg charts
How do you like the idea of adding a new chart type to the MetaTrader 5 platform? Some people say it lacks a few things that other platforms offer. But the truth is, MetaTrader 5 is a very practical platform as it allows you to do things that can't be done (or at least can't be done easily) in many other platforms.
DoEasy. Controls (Part 25): Tooltip WinForms object
DoEasy. Controls (Part 25): Tooltip WinForms object
In this article, I will start developing the Tooltip control, as well as new graphical primitives for the library. Naturally, not every element has a tooltip, but every graphical object has the ability to set it.
Category Theory in MQL5 (Part 1)
Category Theory in MQL5 (Part 1)
Category Theory is a diverse and expanding branch of Mathematics which as of yet is relatively uncovered in the MQL community. These series of articles look to introduce and examine some of its concepts with the overall goal of establishing an open library that attracts comments and discussion while hopefully furthering the use of this remarkable field in Traders' strategy development.
Magic of time trading intervals with Frames Analyzer tool
Magic of time trading intervals with Frames Analyzer tool
What is Frames Analyzer? This is a plug-in module for any Expert Advisor for analyzing optimization frames during parameter optimization in the strategy tester, as well as outside the tester, by reading an MQD file or a database that is created immediately after parameter optimization. You will be able to share these optimization results with other users who have the Frames Analyzer tool to discuss the results together.
DoEasy. Controls (Part 24): Hint auxiliary WinForms object
DoEasy. Controls (Part 24): Hint auxiliary WinForms object
In this article, I will revise the logic of specifying the base and main objects for all WinForms library objects, develop a new Hint base object and several of its derived classes to indicate the possible direction of moving the separator.
Adaptive indicators
Adaptive indicators
In this article, I will consider several possible approaches to creating adaptive indicators. Adaptive indicators are distinguished by the presence of feedback between the values of the input and output signals. This feedback allows the indicator to independently adjust to the optimal processing of financial time series values.
Neural networks made easy (Part 30): Genetic algorithms
Neural networks made easy (Part 30): Genetic algorithms
Today I want to introduce you to a slightly different learning method. We can say that it is borrowed from Darwin's theory of evolution. It is probably less controllable than the previously discussed methods but it allows training non-differentiable models.
Neural networks made easy (Part 29): Advantage Actor-Critic algorithm
Neural networks made easy (Part 29): Advantage Actor-Critic algorithm
In the previous articles of this series, we have seen two reinforced learning algorithms. Each of them has its own advantages and disadvantages. As often happens in such cases, next comes the idea to combine both methods into an algorithm, using the best of the two. This would compensate for the shortcomings of each of them. One of such methods will be discussed in this article.
Population optimization algorithms: Particle swarm (PSO)
Population optimization algorithms: Particle swarm (PSO)
In this article, I will consider the popular Particle Swarm Optimization (PSO) algorithm. Previously, we discussed such important characteristics of optimization algorithms as convergence, convergence rate, stability, scalability, as well as developed a test stand and considered the simplest RNG algorithm.
Neural networks made easy (Part 28): Policy gradient algorithm
Neural networks made easy (Part 28): Policy gradient algorithm
We continue to study reinforcement learning methods. In the previous article, we got acquainted with the Deep Q-Learning method. In this method, the model is trained to predict the upcoming reward depending on the action taken in a particular situation. Then, an action is performed in accordance with the policy and the expected reward. But it is not always possible to approximate the Q-function. Sometimes its approximation does not generate the desired result. In such cases, approximation methods are applied not to utility functions, but to a direct policy (strategy) of actions. One of such methods is Policy Gradient.
Developing a trading Expert Advisor from scratch (Part 29): The talking platform
Developing a trading Expert Advisor from scratch (Part 29): The talking platform
In this article, we will learn how to make the MetaTrader 5 platform talk. What if we make the EA more fun? Financial market trading is often too boring and monotonous, but we can make this job less tiring. Please note that this project can be dangerous for those who experience problems such as addiction. However, in a general case, it just makes things less boring.
Neural networks made easy (Part 27): Deep Q-Learning (DQN)
Neural networks made easy (Part 27): Deep Q-Learning (DQN)
We continue to study reinforcement learning. In this article, we will get acquainted with the Deep Q-Learning method. The use of this method has enabled the DeepMind team to create a model that can outperform a human when playing Atari computer games. I think it will be useful to evaluate the possibilities of the technology for solving trading problems.
Neural networks made easy (Part 26): Reinforcement Learning
Neural networks made easy (Part 26): Reinforcement Learning
We continue to study machine learning methods. With this article, we begin another big topic, Reinforcement Learning. This approach allows the models to set up certain strategies for solving the problems. We can expect that this property of reinforcement learning will open up new horizons for building trading strategies.
Neural networks made easy (Part 24): Improving the tool for Transfer Learning
Neural networks made easy (Part 24): Improving the tool for Transfer Learning
In the previous article, we created a tool for creating and editing the architecture of neural networks. Today we will continue working on this tool. We will try to make it more user friendly. This may see, top be a step away form our topic. But don't you think that a well organized workspace plays an important role in achieving the result.
Learn how to design a trading system by Fractals
Learn how to design a trading system by Fractals
This article is a new one from our series about how to design a trading system based on the most popular technical indicators. We will learn a new indicator which Fractals indicator and we will learn how to design a trading system based on it to be executed in the MetaTrader 5 terminal.
DIY technical indicator
DIY technical indicator
In this article, I will consider the algorithms allowing you to create your own technical indicator. You will learn how to obtain pretty complex and interesting results with very simple initial assumptions.
Market math: profit, loss and costs
Market math: profit, loss and costs
In this article, I will show you how to calculate the total profit or loss of any trade, including commission and swap. I will provide the most accurate mathematical model and use it to write the code and compare it with the standard. Besides, I will also try to get on the inside of the main MQL5 function to calculate profit and get to the bottom of all the necessary values from the specification.