Algorithmic Trading Articles for Expert advisors

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.

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

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 Awesome Oscillator

In this new article in our series, we will learn about a new technical tool that may be useful in our trading. It is the Awesome Oscillator (AO) indicator. We will learn how to design a trading system by this indicator.

Neural networks made easy (Part 25): Practicing Transfer Learning

In the last two articles, we developed a tool for creating and editing neural network models. Now it is time to evaluate the potential use of Transfer Learning technology using practical examples.

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

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.

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)

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.

Developing a trading Expert Advisor from scratch (Part 10): Accessing custom indicators

How to access custom indicators directly in an Expert Advisor? A trading EA can be truly useful only if it can use custom indicators; otherwise, it is just a set of codes and instructions.

Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design

There are minor things to cover on the feed-forward neural network before we are through, the design being one of them. Let's see how we can build and design a flexible neural network to our inputs, the number of hidden layers, and the nodes for each of the network.

Neural networks made easy (Part 22): Unsupervised learning of recurrent models

We continue to study unsupervised learning algorithms. This time I suggest that we discuss the features of autoencoders when applied to recurrent model training.

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.

Developing a trading Expert Advisor from scratch (Part 28): Towards the future (III)

There is still one task which our order system is not up to, but we will FINALLY figure it out. The MetaTrader 5 provides a system of tickets which allows creating and correcting order values. The idea is to have an Expert Advisor that would make the same ticket system faster and more efficient.

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.

Developing a trading Expert Advisor from scratch (Part 23): New order system (VI)

We will make the order system more flexible. Here we will consider changes to the code that will make it more flexible, which will allow us to change position stop levels much faster.

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.

Developing a trading Expert Advisor from scratch (Part 27): Towards the future (II)

Let's move on to a more complete order system directly on the chart. In this article, I will show a way to fix the order system, or rather, to make it more intuitive.

Developing a trading Expert Advisor from scratch (Part 26): Towards the future (I)

Today we will take our order system to the next level. But before that, we need to solve a few problems. Now we have some questions that are related to how we want to work and what things we do during the trading day.

Developing a trading Expert Advisor from scratch (Part 25): Providing system robustness (II)

In this article, we will make the final step towards the EA's performance. So, be prepared for a long read. To make our Expert Advisor reliable, we will first remove everything from the code that is not part of the trading system.

Developing a trading Expert Advisor from scratch (Part 24): Providing system robustness (I)

In this article, we will make the system more reliable to ensure a robust and secure use. One of the ways to achieve the desired robustness is to try to re-use the code as much as possible so that it is constantly tested in different cases. But this is only one of the ways. Another one is to use OOP.

Neural networks made easy (Part 23): Building a tool for Transfer Learning

In this series of articles, we have already mentioned Transfer Learning more than once. However, this was only mentioning. in this article, I suggest filling this gap and taking a closer look at Transfer Learning.

Neural networks made easy (Part 12): Dropout

As the next step in studying neural networks, I suggest considering the methods of increasing convergence during neural network training. There are several such methods. In this article we will consider one of them entitled Dropout.

Neural networks made easy (Part 13): Batch Normalization

In the previous article, we started considering methods aimed at improving neural network training quality. In this article, we will continue this topic and will consider another approach — batch data normalization.

Neural networks made easy (Part 14): Data clustering

It has been more than a year since I published my last article. This is quite a lot time to revise ideas and to develop new approaches. In the new article, I would like to divert from the previously used supervised learning method. This time we will dip into unsupervised learning algorithms. In particular, we will consider one of the clustering algorithms—k-means.

Neural networks made easy (Part 16): Practical use of clustering

In the previous article, we have created a class for data clustering. In this article, I want to share variants of the possible application of obtained results in solving practical trading tasks.

Neural networks made easy (Part 17): Dimensionality reduction

In this part we continue discussing Artificial Intelligence models. Namely, we study unsupervised learning algorithms. We have already discussed one of the clustering algorithms. In this article, I am sharing a variant of solving problems related to dimensionality reduction.

Neural networks made easy (Part 20): Autoencoders

We continue to study unsupervised learning algorithms. Some readers might have questions regarding the relevance of recent publications to the topic of neural networks. In this new article, we get back to studying neural networks.

Experiments with neural networks (Part 2): Smart neural network optimization

In this article, I will use experimentation and non-standard approaches to develop a profitable trading system and check whether neural networks can be of any help for traders. MetaTrader 5 as a self-sufficient tool for using neural networks in trading.

Developing a trading Expert Advisor from scratch (Part 22): New order system (V)

Today we will continue to develop the new order system. It is not that easy to implement a new system as we often encounter problems which greatly complicate the process. When these problems appear, we have to stop and re-analyze the direction in which we are moving.

Learn how to design a trading system by Parabolic SAR

In this article, we will continue our series about how to design a trading system using the most popular indicators. In this article, we will learn about the Parabolic SAR indicator in detail and how we can design a trading system to be used in MetaTrader 5 using some simple strategies.

Fix PriceAction Stoploss or Fixed RSI (Smart StopLoss)

Stop-loss is a major tool when it comes to money management in trading. Effective use of stop-loss, take profit and lot size can make a trader more consistent in trading and overall more profitable. Although stop-loss is a great tool, there are challenges that are encountered when being used. The major one being stop-loss hunt. This article looks on how to reduce stop-loss hunt in trade and compare with the classical stop-loss usage to determine its profitability.

MQL5 Wizard techniques you should know (Part 02): Kohonen Maps

These series of articles will proposition that the MQL5 Wizard should be a mainstay for traders. Why? Because not only does the trader save time by assembling his new ideas with the MQL5 Wizard, and greatly reduce mistakes from duplicate coding; he is ultimately set-up to channel his energy on the few critical areas of his trading philosophy.

MQL5 Wizard techniques you should know (Part 01): Regression Analysis

Todays trader is a philomath who is almost always (either consciously or not...) looking up new ideas, trying them out, choosing to modify them or discard them; an exploratory process that should cost a fair amount of diligence. This clearly places a premium on the trader's time and the need to avoid mistakes. These series of articles will proposition that the MQL5 wizard should be a mainstay for traders. Why? Because not only does the trader save time by assembling his new ideas with the MQL5 wizard, and greatly reduce mistakes from duplicate coding; he is ultimately set-up to channel his energy on the few critical areas of his trading philosophy.

Data Science and Machine Learning (Part 05): Decision Trees

Decision trees imitate the way humans think to classify data. Let's see how to build trees and use them to classify and predict some data. The main goal of the decision trees algorithm is to separate the data with impurity and into pure or close to nodes.

Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash

In this article I am going to attempt to use our logistic model to predict the stock market crash based upon the fundamentals of the US economy, the NETFLIX and APPLE are the stocks we are going to focus on, Using the previous market crashes of 2019 and 2020 let's see how our model will perform in the current dooms and glooms.

Data Science and Machine Learning (Part 03): Matrix Regressions

This time our models are being made by matrices, which allows flexibility while it allows us to make powerful models that can handle not only five independent variables but also many variables as long as we stay within the calculations limits of a computer, this article is going to be an interesting read, that's for sure.

Data Science and Machine Learning (Part 02): Logistic Regression

Data Classification is a crucial thing for an algo trader and a programmer. In this article, we are going to focus on one of classification logistic algorithms that can probability help us identify the Yes's or No's, the Ups and Downs, Buys and Sells.

Learn Why and How to Design Your Algorithmic Trading System

This article shows the basics of MQL for beginners to design their Algorithmic trading system (Expert Advisor) through designing a simple algorithmic trading system after mentioning some basics of MQL5

Learn how to design a trading system by Williams PR

A new article in our series about learning how to design a trading system by the most popular technical indicators by MQL5 to be used in the MetaTrader 5. In this article, we will learn how to design a trading system by the Williams' %R indicator.