Algorithmic Trading Articles for Experts

Using PSAR, Heiken Ashi, and Deep Learning Together for Trading

This project explores the fusion of deep learning and technical analysis to test trading strategies in forex. A Python script is used for rapid experimentation, employing an ONNX model alongside traditional indicators like PSAR, SMA, and RSI to predict EUR/USD movements. A MetaTrader 5 script then brings this strategy into a live environment, using historical data and technical analysis to make informed trading decisions. The backtesting results indicate a cautious yet consistent approach, with a focus on risk management and steady growth rather than aggressive profit-seeking.

Neural Networks Made Easy (Part 87): Time Series Patching

Forecasting plays an important role in time series analysis. In the new article, we will talk about the benefits of time series patching.

Neural Networks Made Easy (Part 84): Reversible Normalization (RevIN)

We already know that pre-processing of the input data plays a major role in the stability of model training. To process "raw" input data online, we often use a batch normalization layer. But sometimes we need a reverse procedure. In this article, we discuss one of the possible approaches to solving this problem.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 6): Adding Responsive Inline Buttons

In this article, we integrate interactive inline buttons into an MQL5 Expert Advisor, allowing real-time control via Telegram. Each button press triggers specific actions and sends responses back to the user. We also modularize functions for handling Telegram messages and callback queries efficiently.

MQL5 Wizard Techniques you should know (Part 17): Multicurrency Trading

Trading across multiple currencies is not available by default when an expert advisor is assembled via the wizard. We examine 2 possible hacks traders can make when looking to test their ideas off more than one symbol at a time.

How to build and optimize a volatility-based trading system (Chaikin Volatility - CHV)

In this article, we will provide another volatility-based indicator named Chaikin Volatility. We will understand how to build a custom indicator after identifying how it can be used and constructed. We will share some simple strategies that can be used and then test them to understand which one can be better.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 5): Sending Commands from Telegram to MQL5 and Receiving Real-Time Responses

In this article, we create several classes to facilitate real-time communication between MQL5 and Telegram. We focus on retrieving commands from Telegram, decoding and interpreting them, and sending appropriate responses back. By the end, we ensure that these interactions are effectively tested and operational within the trading environment

Developing an Expert Advisor (EA) based on the Consolidation Range Breakout strategy in MQL5

This article outlines the steps to create an Expert Advisor (EA) that capitalizes on price breakouts after consolidation periods. By identifying consolidation ranges and setting breakout levels, traders can automate their trading decisions based on this strategy. The Expert Advisor aims to provide clear entry and exit points while avoiding false breakouts

Neural Networks Made Easy (Part 86): U-Shaped Transformer

We continue to study timeseries forecasting algorithms. In this article, we will discuss another method: the U-shaped Transformer.

Creating an Interactive Graphical User Interface in MQL5 (Part 2): Adding Controls and Responsiveness

Enhancing the MQL5 GUI panel with dynamic features can significantly improve the trading experience for users. By incorporating interactive elements, hover effects, and real-time data updates, the panel becomes a powerful tool for modern traders.

Neural Networks Made Easy (Part 85): Multivariate Time Series Forecasting

In this article, I would like to introduce you to a new complex timeseries forecasting method, which harmoniously combines the advantages of linear models and transformers.

Neural Networks Made Easy (Part 83): The "Conformer" Spatio-Temporal Continuous Attention Transformer Algorithm

This article introduces the Conformer algorithm originally developed for the purpose of weather forecasting, which in terms of variability and capriciousness can be compared to financial markets. Conformer is a complex method. It combines the advantages of attention models and ordinary differential equations.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 4): Modularizing Code Functions for Enhanced Reusability

In this article, we refactor the existing code used for sending messages and screenshots from MQL5 to Telegram by organizing it into reusable, modular functions. This will streamline the process, allowing for more efficient execution and easier code management across multiple instances.

Example of Causality Network Analysis (CNA) and Vector Auto-Regression Model for Market Event Prediction

This article presents a comprehensive guide to implementing a sophisticated trading system using Causality Network Analysis (CNA) and Vector Autoregression (VAR) in MQL5. It covers the theoretical background of these methods, provides detailed explanations of key functions in the trading algorithm, and includes example code for implementation.

Sentiment Analysis and Deep Learning for Trading with EA and Backtesting with Python

In this article, we will introduce Sentiment Analysis and ONNX Models with Python to be used in an EA. One script runs a trained ONNX model from TensorFlow for deep learning predictions, while another fetches news headlines and quantifies sentiment using AI.

Reimagining Classic Strategies (Part VI): Multiple Time-Frame Analysis

In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.

Neural networks made easy (Part 80): Graph Transformer Generative Adversarial Model (GTGAN)

In this article, I will get acquainted with the GTGAN algorithm, which was introduced in January 2024 to solve complex problems of generation architectural layouts with graph constraints.

Introduction to MQL5 (Part 6): A Beginner's Guide to Array Functions in MQL5 (II)

Embark on the next phase of our MQL5 journey. In this insightful and beginner-friendly article, we'll look into the remaining array functions, demystifying complex concepts to empower you to craft efficient trading strategies. We’ll be discussing ArrayPrint, ArrayInsert, ArraySize, ArrayRange, ArrarRemove, ArraySwap, ArrayReverse, and ArraySort. Elevate your algorithmic trading expertise with these essential array functions. Join us on the path to MQL5 mastery!

Neural Networks Made Easy (Part 81): Context-Guided Motion Analysis (CCMR)

In previous works, we always assessed the current state of the environment. At the same time, the dynamics of changes in indicators always remained "behind the scenes". In this article I want to introduce you to an algorithm that allows you to evaluate the direct change in data between 2 successive environmental states.

In this article we will discuss in detail how to write a risk manager class for manual trading from scratch. This class can also be used as a base class for inheritance by algorithmic traders who use automated programs.

News Trading Made Easy (Part 1): Creating a Database

News trading can be complicated and overwhelming, in this article we will go through steps to obtain news data. Additionally we will learn about the MQL5 Economic Calendar and what it has to offer.

Neural networks made easy (Part 79): Feature Aggregated Queries (FAQ) in the context of state

In the previous article, we got acquainted with one of the methods for detecting objects in an image. However, processing a static image is somewhat different from working with dynamic time series, such as the dynamics of the prices we analyze. In this article, we will consider the method of detecting objects in video, which is somewhat closer to the problem we are solving.

Neural networks made easy (Part 77): Cross-Covariance Transformer (XCiT)

In our models, we often use various attention algorithms. And, probably, most often we use Transformers. Their main disadvantage is the resource requirement. In this article, we will consider a new algorithm that can help reduce computing costs without losing quality.

Neural networks made easy (Part 78): Decoder-free Object Detector with Transformer (DFFT)

In this article, I propose to look at the issue of building a trading strategy from a different angle. We will not predict future price movements, but will try to build a trading system based on the analysis of historical data.

Creating an MQL5-Telegram Integrated Expert Advisor (Part 2): Sending Signals from MQL5 to Telegram

In this article, we create an MQL5-Telegram integrated Expert Advisor that sends moving average crossover signals to Telegram. We detail the process of generating trading signals from moving average crossovers, implementing the necessary code in MQL5, and ensuring the integration works seamlessly. The result is a system that provides real-time trading alerts directly to your Telegram group chat.

Reimagining Classic Strategies (Part III): Forecasting Higher Highs And Lower Lows

In this series article, we will empirically analyze classic trading strategies to see if we can improve them using AI. In today's discussion, we tried to predict higher highs and lower lows using the Linear Discriminant Analysis model.

Developing a multi-currency Expert Advisor (Part 4): Pending virtual orders and saving status

Having started developing a multi-currency EA, we have already achieved some results and managed to carry out several code improvement iterations. However, our EA was unable to work with pending orders and resume operation after the terminal restart. Let's add these features.

Example of Auto Optimized Take Profits and Indicator Parameters with SMA and EMA

This article presents a sophisticated Expert Advisor for forex trading, combining machine learning with technical analysis. It focuses on trading Apple stock, featuring adaptive optimization, risk management, and multiple strategies. Backtesting shows promising results with high profitability but also significant drawdowns, indicating potential for further refinement.

Neural networks made easy (Part 82): Ordinary Differential Equation models (NeuralODE)

In this article, we will discuss another type of models that are aimed at studying the dynamics of the environmental state.

Mastering Market Dynamics: Creating a Support and Resistance Strategy Expert Advisor (EA)

A comprehensive guide to developing an automated trading algorithm based on the Support and Resistance strategy. Detailed information on all aspects of creating an expert advisor in MQL5 and testing it in MetaTrader 5 – from analyzing price range behaviors to risk management.

Developing Zone Recovery Martingale strategy in MQL5

The article discusses, in a detailed perspective, the steps that need to be implemented towards the creation of an expert advisor based on the Zone Recovery trading algorithm. This helps aotomate the system saving time for algotraders.

Developing a multi-currency Expert Advisor (Part 5): Variable position sizes

In the previous parts, the Expert Advisor (EA) under development was able to use only a fixed position size for trading. This is acceptable for testing, but is not advisable when trading on a real account. Let's make it possible to trade using variable position sizes.

Creating a Dynamic Multi-Symbol, Multi-Period Relative Strength Indicator (RSI) Indicator Dashboard in MQL5

In this article, we develop a dynamic multi-symbol, multi-period RSI indicator dashboard in MQL5, providing traders real-time RSI values across various symbols and timeframes. The dashboard features interactive buttons, real-time updates, and color-coded indicators to help traders make informed decisions.

Practicing the development of trading strategies

In this article, we will make an attempt to develop our own trading strategy. Any trading strategy must be based on some kind of statistical advantage. Moreover, this advantage should exist for a long time.

Seasonality Filtering and time period for Deep Learning ONNX models with python for EA

Can we benefit from seasonality when creating models for Deep Learning with Python? Does filtering data for the ONNX models help to get better results? What time period should we use? We will cover all of this over this article.

Introduction to MQL5 (Part 5): A Beginner's Guide to Array Functions in MQL5

Explore the world of MQL5 arrays in Part 5, designed for absolute beginners. Simplifying complex coding concepts, this article focuses on clarity and inclusivity. Join our community of learners, where questions are embraced, and knowledge is shared!

Multibot in MetaTrader (Part II): Improved dynamic template

Developing the theme of the previous article, I decided to create a more flexible and functional template that has greater capabilities and can be effectively used both in freelancing and as a base for developing multi-currency and multi-period EAs with the ability to integrate with external solutions.

Reimagining Classic Strategies (Part II): Bollinger Bands Breakouts

This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.

Combine Fundamental And Technical Analysis Strategies in MQL5 For Beginners

In this article, we will discuss how to integrate trend following and fundamental principles seamlessly into one Expert Advisors to build a strategy that is more robust. This article will demonstrate how easy it is for anyone to get up and running building customized trading algorithms using MQL5.

Neural networks made easy (Part 76): Exploring diverse interaction patterns with Multi-future Transformer

This article continues the topic of predicting the upcoming price movement. I invite you to get acquainted with the Multi-future Transformer architecture. Its main idea is to decompose the multimodal distribution of the future into several unimodal distributions, which allows you to effectively simulate various models of interaction between agents on the scene.