We continue developing the Connexus library. In this chapter, we explore the concept of headers in the HTTP protocol, explaining what they are, what they are for, and how to use them in requests. We cover the main headers used in communications with APIs, and show practical examples of how to configure them in the library.
In this article, we create buttons for currency pair filters, importance levels, time filters, and a cancel option to improve dashboard control. These buttons are programmed to respond dynamically to user actions, allowing seamless interaction. We also automate their behavior to reflect real-time changes on the dashboard. This enhances the overall functionality, mobility, and responsiveness of the panel.
In this article, we implement filters in the MQL5 Economic Calendar dashboard to refine news event displays by currency, importance, and time. We first establish filter criteria for each category and then integrate these into the dashboard to display only relevant events. Finally, we ensure each filter dynamically updates to provide traders with focused, real-time economic insights.
When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.
This article explores the fundamentals of the HTTP protocol, covering the main methods (GET, POST, PUT, DELETE), status codes and the structure of URLs. In addition, it presents the beginning of the construction of the Conexus library with the CQueryParam and CURL classes, which facilitate the manipulation of URLs and query parameters in HTTP requests.
Proximal Policy Optimization is another algorithm in reinforcement learning that updates the policy, often in network form, in very small incremental steps to ensure the model stability. We examine how this could be of use, as we have with previous articles, in a wizard assembled Expert Advisor.
In this article, we continue the implementation of the approaches of the ATFNet model, which adaptively combines the results of 2 blocks (frequency and time) within time series forecasting.
In this article, we examine the Profitunity System by Bill Williams, breaking down its core components and unique approach to trading within market chaos. We guide readers through implementing the system in MQL5, focusing on automating key indicators and entry/exit signals. Finally, we test and optimize the strategy, providing insights into its performance across various market scenarios.
In this final installment of our Connexus library series, we explored the implementation of the Observer pattern, as well as essential refactorings to file paths and method names. This series covered the entire development of Connexus, designed to simplify HTTP communication in complex applications.
The authors of the FreDF method experimentally confirmed the advantage of combined forecasting in the frequency and time domains. However, the use of the weight hyperparameter is not optimal for non-stationary time series. In this article, we will get acquainted with the method of adaptive combination of forecasts in frequency and time domains.
We have already implemented the first stage of the automated optimization. We perform optimization for different symbols and timeframes according to several criteria and store information about the results of each pass in the database. Now we are going to select the best groups of parameter sets from those found at the first stage.
Temporal Difference is another algorithm in reinforcement learning that updates Q-Values basing on the difference between predicted and actual rewards during agent training. It specifically dwells on updating Q-Values without minding their state-action pairing. We therefore look to see how to apply this, as we have with previous articles, in a wizard assembled Expert Advisor.
This article is the beginning of a series of developments for a library called “Connexus” to facilitate HTTP requests with MQL5. The goal of this project is to provide the end user with this opportunity and show how to use this helper library. I intended to make it as simple as possible to facilitate study and to provide the possibility for future developments.
This Expert Advisor, named SMOC (likely standing for Stochastic Model Optimal Control), is a simple example of an advanced algorithmic trading system for MetaTrader 5. It uses a combination of technical indicators, model predictive control, and dynamic risk management to make trading decisions. The EA incorporates adaptive parameters, volatility-based position sizing, and trend analysis to optimize its performance across varying market conditions.
The Alligator Indicator, which was the brain child of Bill Williams, is a versatile trend identification indicator that yields clear signals and is often combined with other indicators. The MQL5 wizard classes and assembly allow us to test a variety of signals on a pattern basis, and so we consider this indicator as well.
In this article, we create a practical news dashboard panel using the MQL5 Economic Calendar to enhance our trading strategy. We begin by designing the layout, focusing on key elements like event names, importance, and timing, before moving into the setup within MQL5. Finally, we implement a filtering system to display only the most relevant news, giving traders quick access to impactful economic events.
By studying the FEDformer method, we opened the door to the frequency domain of time series representation. In this new article, we will continue the topic we started. We will consider a method with which we can not only conduct an analysis, but also predict subsequent states in a particular area.
The Ichimuko Kinko Hyo is a renown Japanese indicator that serves as a trend identification system. We examine this, on a pattern by pattern basis, as has been the case in previous similar articles, and also assess its strategies & test reports with the help of the MQL5 wizard library classes and assembly.
In this sixth article of the Connexus library series, we will focus on a complete HTTP request, covering each component that makes up a request. We will create a class that represents the request as a whole, which will help us bring together the previously created classes.
All the models we have considered so far analyze the state of the environment as a time sequence. However, the time series can also be represented in the form of frequency features. In this article, I introduce you to an algorithm that uses frequency components of a time sequence to predict future states.
Reinforcement Learning is one of the three main tenets in machine learning, alongside supervised learning and unsupervised learning. It is therefore concerned with optimal control, or learning the best long-term policy that will best suit the objective function. It is with this back-drop, that we explore its possible role in informing the learning-process to an MLP of a wizard assembled Expert Advisor.
We continue to explore the analysis and forecasting of time series in the frequency domain. In this article, we will get acquainted with a new method to forecast data in the frequency domain, which can be added to many of the algorithms we have studied previously.
Monte-Carlo is the fourth different algorithm in reinforcement learning that we are considering with the aim of exploring its implementation in wizard assembled Expert Advisors. Though anchored in random sampling, it does present vast ways of simulation which we can look to exploit.
The number of strategies that can be integrated into an Expert Advisor is virtually limitless. However, each additional strategy increases the complexity of the algorithm. By incorporating multiple strategies, an Expert Advisor can better adapt to varying market conditions, potentially enhancing its profitability. Today, we will explore how to implement MQL5 for one of the prominent strategies developed by Richard Donchian, as we continue to enhance the functionality of our Trend Constraint Expert.
In this article, we explore how to use the MQL5 Economic Calendar for trading by first understanding its core functionalities. We then implement key functions of the Economic Calendar in MQL5 to extract relevant news data for trading decisions. Finally, we conclude by showcasing how to utilize this information to enhance trading strategies effectively.
This article will dive into methods to improve the expert's runtime in the strategy tester, the code will be written to divide news event times into hourly categories. These news event times will be accessed within their specified hour. This ensures that the EA can efficiently manage event-driven trades in both high and low-volatility environments.
In this article, we create an MQL5 Expert Advisor that encodes chart screenshots as image data and sends them to a Telegram chat via HTTP requests. By integrating photo encoding and transmission, we enhance the existing MQL5-Telegram system with visual trading insights directly within Telegram.
In this article, we will understand HTTP methods and status codes, two very important pieces of communication between client and server on the web. Understanding what each method does gives you the control to make requests more precisely, informing the server what action you want to perform and making it more efficient.
In this article, we explored the concept of body in HTTP requests, which is essential for sending data such as JSON and plain text. We discussed and explained how to use it correctly with the appropriate headers. We also introduced the ChttpBody class, part of the Connexus library, which will simplify working with the body of requests.
In this article, we will automate the trading strategies with Parabolic SAR Strategy in MQL5: Crafting an Effective Expert Advisor. The EA will make trades based on trends identified by the Parabolic SAR indicator.
The ATR oscillator is a very popular indicator for acting as a volatility proxy, especially in the forex markets where volume data is scarce. We examine this, on a pattern basis as we have with prior indicators, and share strategies & test reports thanks to the MQL5 wizard library classes and assembly.
In this fourth part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Grid EA through mathematical analysis and a brute force approach, aiming for optimal strategy usage. This article delves deep into the mathematical optimization of the strategy, setting the stage for future exploration of coding-based optimization in later installments.
Today, we will discuss enhancing security for the Trading Administrator Panel currently under development. We will explore how to implement MQL5 in a new security strategy, integrating the Telegram API for two-factor authentication (2FA). This discussion will provide valuable insights into the application of MQL5 in reinforcing security measures. Additionally, we will examine the MathRand function, focusing on its functionality and how it can be effectively utilized within our security framework. Continue reading to discover more!
Dynamic multi pair Expert Advisor leverages both on correlation and inverse correlation strategies to optimize trading performance. By analyzing real-time market data, it identifies and exploits the relationship between currency pairs.
Think about an independent Expert Advisor. Previously, we discussed an indicator-based Expert Advisor that also partnered with an independent script for drawing risk and reward geometry. Today, we will discuss the architecture of an MQL5 Expert Advisor, that integrates, all the features in one program.
Gaussian Process Kernels are the covariance function of the Normal Distribution that could play a role in forecasting. We explore this unique algorithm in a custom signal class of MQL5 to see if it could be put to use as a prime entry and exit signal.
In this article, we focus on transforming our static MQL5 dashboard panel into an interactive tool by enabling button responsiveness. We explore how to automate the functionality of the GUI components, ensuring they react appropriately to user clicks. By the end of the article, we establish a dynamic interface that enhances user engagement and trading experience.
The SMC (Order Block) are key areas where institutional traders initiate significant buying or selling. After a significant price move, fibonacci helps to identify potential retracement from a recent swing high to a swing low to identify optimal trade entry.
Python is a well-known and popular programming language with many features, especially in the fields of finance, data science, Artificial Intelligence, and Machine Learning. Python is a powerful tool that can be useful in trading as well. MQL5 allows us to use this powerful language as an integration to get our objectives done effectively. In this article, we will share how we can use Python as an integration in MQL5 after learning some basic information about Python.
SARSA, which is an abbreviation for State-Action-Reward-State-Action is another algorithm that can be used when implementing reinforcement learning. So, as we saw with Q-Learning and DQN, we look into how this could be explored and implemented as an independent model rather than just a training mechanism, in wizard assembled Expert Advisors.
