In this discussion, we will carefully extend the existing Dialog library to incorporate theme management logic. Furthermore, we will integrate methods for theme switching into the CDialog, CEdit, and CButton classes utilized in our Admin Panel project. Continue reading for more insightful perspectives.
The Deep-Q-Network is a reinforcement learning algorithm that engages neural networks in projecting the next Q-value and ideal action during the training process of a machine learning module. We have already considered an alternative reinforcement learning algorithm, Q-Learning. This article therefore presents another example of how an MLP trained with reinforcement learning, can be used within a custom signal class.
Loss Function is the key metric of machine learning algorithms that provides feedback to the training process by quantifying how well a given set of parameters are performing when compared to their intended target. We explore the various formats of this function in an MQL5 custom wizard class.
In this article, we create an Expert Advisor (EA) in MQL5 to send messages to Telegram using a bot. We set up the necessary parameters, including the bot's API token and chat ID, and then perform an HTTP POST request to deliver the messages. Later, we handle the response to ensure successful delivery and troubleshoot any issues that arise in case of failure. This ensures we send messages from MQL5 to Telegram via the created bot.
The objectives of this article are to prove the necessity of using a risk manager and to implement the principles of controlled risk in algorithmic trading in a separate class, so that everyone can verify the effectiveness of the risk standardization approach in intraday trading and investing in financial markets. In this article, we will create a risk manager class for algorithmic trading. This is a logical continuation of the previous article in which we discussed the creation of a risk manager for manual trading.
To get a good EA, we need to select multiple good sets of parameters of trading strategy instances for it. This can be done manually by running optimization on different symbols and then selecting the best results. But it is better to delegate this work to the program and engage in more productive activities.
We wrap up our look at learning rate sensitivity to the performance of Expert Advisors by primarily examining the Adaptive Learning Rates. These learning rates aim to be customized for each parameter in a layer during the training process and so we assess potential benefits vs the expected performance toll.
Today, we will explore the possibilities of incorporating multiple strategies into an Expert Advisor (EA) using MQL5. Expert Advisors provide broader capabilities than just indicators and scripts, allowing for more sophisticated trading approaches that can adapt to changing market conditions. Find, more in this article discussion.
In this article, we explore how to integrate Telegram commands with MQL5 to automate the addition of indicators on trading charts. We cover the process of parsing user commands, executing them in MQL5, and testing the system to ensure smooth indicator-based trading
Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this discussion, we will use the SciPy library and the MQL5 language to create an optimal and diversified portfolio using all the data we have.
In an attempt to obtain the most accurate forecasts, researchers often complicate forecasting models. Which in turn leads to increased model training and maintenance costs. Is such an increase always justified? This article introduces an algorithm that uses the simplicity and speed of linear models and demonstrates results on par with the best models with a more complex architecture.
The Parabolic Stop-and-Reversal (SAR) is an indicator for trend confirmation and trend termination points. Because it is a laggard in identifying trends its primary purpose has been in positioning trailing stop losses on open positions. We, however, explore if indeed it could be used as an Expert Advisor signal, thanks to custom signal classes of wizard assembled Expert Advisors.
Tired of wasting time searching for that very file that you application needs in order to work? How about including everything in the executable? This way you won't have to search for the things. I know that many people use this form of distribution and storage, but there is a much more suitable way. At least as far as the distribution of executable files and their storage is concerned. The method that will be presented here can be very useful, since you can use MetaTrader 5 itself as an excellent assistant, as well as MQL5. Furthermore, it is not that difficult to understand.
A step-by-step guide to creating and implementing an automated trading algorithm in MQL5 based on the Fair Value Gap (FVG) trading strategy. A detailed tutorial on creating an expert advisor that can be useful for both beginners and experienced traders.
The RSI is a popular momentum oscillator that measures pace and size of a security’s recent price change to evaluate over-and-under valued situations in the security’s price. These insights in speed and magnitude are key in defining reversal points. We put this oscillator to work in another custom signal class and examine the traits of some of its signals. We start, though, by wrapping up what we started previously on Bollinger Bands.
Learn how to create custom indicators using MQL5. This introductory article will guide you through the fundamentals of building simple custom indicators and demonstrate a hands-on approach to coding different custom indicators for any MQL5 programmer new to this interesting topic.
Let's outline the main stages of the EA development. One of the first things to be done will be to optimize a single instance of the developed trading strategy. Let's try to collect all the necessary information about the tester passes during the optimization in one place.
The EA development plan includes several stages with intermediate results being saved in the database. They can only be retrieved from there again as strings or numbers, not objects. So we need a way to recreate the desired objects in the EA from the strings read from the database.
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.
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.
Bollinger Bands are a very common Envelope Indicator used by a lot of traders to manually place and close trades. We examine this indicator by considering as many of the different possible signals it does generate, and see how they could be put to use in a wizard assembled Expert Advisor.
In this article, we will enhance the responsiveness of the Admin Panel that we previously created. Additionally, we will explore the significance of quick messaging in the context of trading signals.
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.
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
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
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.
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.
ONNX is a great tool for integrating complex AI code between different platforms, it is a great tool that comes with some challenges that one must address to get the most out of it, In this article we discuss the common issues you might face and how to mitigate them.
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.
As we progressed, we used more and more simultaneously running instances of trading strategies in one EA. Let's try to figure out how many instances we can get to before we hit resource limitations.
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.
The main purpose of this article is to introduce and explain the C_ChartFloatingRAD class. We have a Chart Trade indicator that works in a rather interesting way. As you may have noticed, we still have a fairly small number of objects on the chart, and yet we get the expected functionality. The values present in the indicator can be edited. The question is, how is this possible? This article will start to make things clearer.
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.
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.
The Hurst Exponent is a measure of how much a time series auto-correlates over the long term. It is understood to be capturing the long-term properties of a time series and therefore carries some weight in time series analysis even outside of economic/ financial time series. We however, focus on its potential benefit to traders by examining how this metric could be paired with moving averages to build a potentially robust signal.
Previously, we evaluated the selection of a group of trading strategy instances, with the aim of improving the results of their joint operation, only on the same time period, in which the optimization of individual instances was carried out. Let's see what happens in the forward period.
Let's create something more interesting. I don't want to spoil the surprise, so follow the article for a better understanding. From the very beginning of this series on developing the replay/simulator system, I was saying that the idea is to use the MetaTrader 5 platform in the same way both in the system we are developing and in the real market. It is important that this is done properly. No one wants to train and learn to fight using one tool while having to use another one during the fight.
Most people who want or dream of learning to program don't actually have a clue what they're doing. Their activity consists of trying to create things in a certain way. However, programming is not about tailoring suitable solutions. Doing it this way can create more problems than solutions. Here we will be doing something more advanced and therefore different.
