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Neural networks made easy (Part 72): Trajectory prediction in noisy environments
Neural networks made easy (Part 72): Trajectory prediction in noisy environments
The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.
Statistical Arbitrage with predictions
Statistical Arbitrage with predictions
We will walk around statistical arbitrage, we will search with python for correlation and cointegration symbols, we will make an indicator for Pearson's coefficient and we will make an EA for trading statistical arbitrage with predictions done with python and ONNX models.
Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel
Creating an Interactive Graphical User Interface in MQL5 (Part 1): Making the Panel
This article explores the fundamental steps in crafting and implementing a Graphical User Interface (GUI) panel using MetaQuotes Language 5 (MQL5). Custom utility panels enhance user interaction in trading by simplifying common tasks and visualizing essential trading information. By creating custom panels, traders can streamline their workflow and save time during trading operations.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part III)
This part of the article series is dedicated to integrating WhatsApp with MetaTrader 5 for notifications. We have included a flow chart to simplify understanding and will discuss the importance of security measures in integration. The primary purpose of indicators is to simplify analysis through automation, and they should include notification methods for alerting users when specific conditions are met. Discover more in this article.
Developing a Replay System (Part 41): Starting the second phase (II)
Developing a Replay System (Part 41): Starting the second phase (II)
If everything seemed right to you up to this point, it means you're not really thinking about the long term, when you start developing applications. Over time you will no longer need to program new applications, you will just have to make them work together. So let's see how to finish assembling the mouse indicator.
Triangular arbitrage with predictions
Triangular arbitrage with predictions
This article simplifies triangular arbitrage, showing you how to use predictions and specialized software to trade currencies smarter, even if you're new to the market. Ready to trade with expertise?
MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data
MQL5 Wizard Techniques you should know (Part 21): Testing with Economic Calendar Data
Economic Calendar Data is not available for testing with Expert Advisors within Strategy Tester, by default. We look at how Databases could help in providing a work around this limitation. So, for this article we explore how SQLite databases can be used to archive Economic Calendar news such that wizard assembled Expert Advisors can use this to generate trade signals.
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
MQL5 Trading Toolkit (Part 1): Developing A Positions Management EX5 Library
Learn how to create a developer's toolkit for managing various position operations with MQL5. In this article, I will demonstrate how to create a library of functions (ex5) that will perform simple to advanced position management operations, including automatic handling and reporting of the different errors that arise when dealing with position management tasks with MQL5.
Developing a Replay System (Part 39): Paving the Path (III)
Developing a Replay System (Part 39): Paving the Path (III)
Before we proceed to the second stage of development, we need to revise some ideas. Do you know how to make MQL5 do what you need? Have you ever tried to go beyond what is contained in the documentation? If not, then get ready. Because we will be doing something that most people don't normally do.
MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression
MQL5 Wizard Techniques you should know (Part 20): Symbolic Regression
Symbolic Regression is a form of regression that starts with minimal to no assumptions on what the underlying model that maps the sets of data under study would look like. Even though it can be implemented by Bayesian Methods or Neural Networks, we look at how an implementation with Genetic Algorithms can help customize an expert signal class usable in the MQL5 wizard.
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)
Building A Candlestick Trend Constraint Model (Part 5): Notification System (Part I)
We will breakdown the main MQL5 code into specified code snippets to illustrate the integration of Telegram and WhatsApp for receiving signal notifications from the Trend Constraint indicator we are creating in this article series. This will help traders, both novices and experienced developers, grasp the concept easily. First, we will cover the setup of MetaTrader 5 for notifications and its significance to the user. This will help developers in advance to take notes to further apply in their systems.
Developing a Replay System (Part 38): Paving the Path (II)
Developing a Replay System (Part 38): Paving the Path (II)
Many people who consider themselves MQL5 programmers do not have the basic knowledge that I will outline in this article. Many people consider MQL5 to be a limited tool, but the actual reason is that they do not have the required knowledge. So, if you don't know something, don't be ashamed of it. It's better to feel ashamed for not asking. Simply forcing MetaTrader 5 to disable indicator duplication in no way ensures two-way communication between the indicator and the Expert Advisor. We are still very far from this, but the fact that the indicator is not duplicated on the chart gives us some confidence.
MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial
MQL5 Wizard Techniques you should know (Part 12): Newton Polynomial
Newton’s polynomial, which creates quadratic equations from a set of a few points, is an archaic but interesting approach at looking at a time series. In this article we try to explore what aspects could be of use to traders from this approach as well as address its limitations.
A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy
A Step-by-Step Guide on Trading the Break of Structure (BoS) Strategy
A comprehensive guide to developing an automated trading algorithm based on the Break of Structure (BoS) strategy. Detailed information on all aspects of creating an advisor in MQL5 and testing it in MetaTrader 5 — from analyzing price support and resistance to risk management
Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)
Neural networks made easy (Part 71): Goal-Conditioned Predictive Coding (GCPC)
In previous articles, we discussed the Decision Transformer method and several algorithms derived from it. We experimented with different goal setting methods. During the experiments, we worked with various ways of setting goals. However, the model's study of the earlier passed trajectory always remained outside our attention. In this article. I want to introduce you to a method that fills this gap.
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
MQL5 Wizard Techniques you should know (Part 22): Conditional GANs
Generative Adversarial Networks are a pairing of Neural Networks that train off of each other for more accurate results. We adopt the conditional type of these networks as we look to possible application in forecasting Financial time series within an Expert Signal Class.
News Trading Made Easy (Part 2): Risk Management
News Trading Made Easy (Part 2): Risk Management
In this article, inheritance will be introduced into our previous and new code. A new database design will be implemented to provide efficiency. Additionally, a risk management class will be created to tackle volume calculations.
MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial
MQL5 Wizard Techniques You Should Know (Part 15): Support Vector Machines with Newton's Polynomial
Support Vector Machines classify data based on predefined classes by exploring the effects of increasing its dimensionality. It is a supervised learning method that is fairly complex given its potential to deal with multi-dimensioned data. For this article we consider how it’s very basic implementation of 2-dimensioned data can be done more efficiently with Newton’s Polynomial when classifying price-action.
Causal inference in time series classification problems
Causal inference in time series classification problems
In this article, we will look at the theory of causal inference using machine learning, as well as the custom approach implementation in Python. Causal inference and causal thinking have their roots in philosophy and psychology and play an important role in our understanding of reality.
MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class
MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class
Density Based Spatial Clustering for Applications with Noise is an unsupervised form of grouping data that hardly requires any input parameters, save for just 2, which when compared to other approaches like k-means, is a boon. We delve into how this could be constructive for testing and eventually trading with Wizard assembled Expert Advisers
Building Your First Glass-box Model Using Python And MQL5
Building Your First Glass-box Model Using Python And MQL5
Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.
Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)
Neural networks made easy (Part 69): Density-based support constraint for the behavioral policy (SPOT)
In offline learning, we use a fixed dataset, which limits the coverage of environmental diversity. During the learning process, our Agent can generate actions beyond this dataset. If there is no feedback from the environment, how can we be sure that the assessments of such actions are correct? Maintaining the Agent's policy within the training dataset becomes an important aspect to ensure the reliability of training. This is what we will talk about in this article.
Advanced Variables and Data Types in MQL5
Advanced Variables and Data Types in MQL5
Variables and data types are very important topics not only in MQL5 programming but also in any programming language. MQL5 variables and data types can be categorized as simple and advanced ones. In this article, we will identify and learn about advanced ones because we already mentioned simple ones in a previous article.
MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference
MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference
Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.
MQL5 Wizard Techniques you should know (Part 08): Perceptrons
MQL5 Wizard Techniques you should know (Part 08): Perceptrons
Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.
Algorithmic Trading With MetaTrader 5 And R For Beginners
Algorithmic Trading With MetaTrader 5 And R For Beginners
Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.
Developing a Replay System (Part 37): Paving the Path (I)
Developing a Replay System (Part 37): Paving the Path (I)
In this article, we will finally begin to do what we wanted to do much earlier. However, due to the lack of "solid ground", I did not feel confident to present this part publicly. Now I have the basis to do this. I suggest that you focus as much as possible on understanding the content of this article. I mean not simply reading it. I want to emphasize that if you do not understand this article, you can completely give up hope of understanding the content of the following ones.
Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization
Neural networks made easy (Part 68): Offline Preference-guided Policy Optimization
Since the first articles devoted to reinforcement learning, we have in one way or another touched upon 2 problems: exploring the environment and determining the reward function. Recent articles have been devoted to the problem of exploration in offline learning. In this article, I would like to introduce you to an algorithm whose authors completely eliminated the reward function.