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.
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.
Learn how to design a trading system by Accelerator Oscillator
Learn how to design a trading system by Accelerator Oscillator
A new article from our series about how to create simple trading systems by the most popular technical indicators. We will learn about a new one which is the Accelerator Oscillator indicator and we will learn how to design a trading system using it.
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.
Learn how to design a trading system by VIDYA
Learn how to design a trading system by VIDYA
Welcome to a new article from our series about learning how to design a trading system by the most popular technical indicators, in this article we will learn about a new technical tool and learn how to design a trading system by Variable Index Dynamic Average (VIDYA).
Learn how to design a trading system by Bear's Power
Learn how to design a trading system by Bear's Power
Welcome to a new article in our series about learning how to design a trading system by the most popular technical indicator here is a new article about learning how to design a trading system by Bear's Power technical indicator.
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.
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.
Population optimization algorithms
Population optimization algorithms
This is an introductory article on optimization algorithm (OA) classification. The article attempts to create a test stand (a set of functions), which is to be used for comparing OAs and, perhaps, identifying the most universal algorithm out of all widely known ones.
Data Science and Machine Learning (Part 06): Gradient Descent
Data Science and Machine Learning (Part 06): Gradient Descent
The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.
Neural networks made easy (Part 21): Variational autoencoders (VAE)
Neural networks made easy (Part 21): Variational autoencoders (VAE)
In the last article, we got acquainted with the Autoencoder algorithm. Like any other algorithm, it has its advantages and disadvantages. In its original implementation, the autoenctoder is used to separate the objects from the training sample as much as possible. This time we will talk about how to deal with some of its disadvantages.
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.
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.