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Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
Reimagining Classic Strategies (Part IV): SP500 and US Treasury Notes
In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.
News Trading Made Easy (Part 3): Performing Trades
News Trading Made Easy (Part 3): Performing Trades
In this article, our news trading expert will begin opening trades based on the economic calendar stored in our database. In addition, we will improve the expert's graphics to display more relevant information about upcoming economic calendar events.
MQL5 Integration: Python
MQL5 Integration: Python
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.
MQL5 Wizard Techniques you should know (Part 43): Reinforcement Learning with SARSA
MQL5 Wizard Techniques you should know (Part 43): Reinforcement Learning with SARSA
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.
Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer
Creating a Trading Administrator Panel in MQL5 (Part IV): Login Security Layer
Imagine a malicious actor infiltrating the Trading Administrator room, gaining access to the computers and the Admin Panel used to communicate valuable insights to millions of traders worldwide. Such an intrusion could lead to disastrous consequences, such as the unauthorized sending of misleading messages or random clicks on buttons that trigger unintended actions. In this discussion, we will explore the security measures in MQL5 and the new security features we have implemented in our Admin Panel to safeguard against these threats. By enhancing our security protocols, we aim to protect our communication channels and maintain the trust of our global trading community. Find more insights in this article discussion.
MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library
MQL5 Trading Toolkit (Part 3): Developing a Pending Orders Management EX5 Library
Learn how to develop and implement a comprehensive pending orders EX5 library in your MQL5 code or projects. This article will show you how to create an extensive pending orders management EX5 library and guide you through importing and implementing it by building a trading panel or graphical user interface (GUI). The expert advisor orders panel will allow users to open, monitor, and delete pending orders associated with a specified magic number directly from the graphical interface on the chart window.
Neural Network in Practice: Least Squares
Neural Network in Practice: Least Squares
In this article, we'll look at a few ideas, including how mathematical formulas are more complex in appearance than when implemented in code. In addition, we will consider how to set up a chart quadrant, as well as one interesting problem that may arise in your MQL5 code. Although, to be honest, I still don't quite understand how to explain it. Anyway, I'll show you how to fix it in code.
Сode Lock Algorithm (CLA)
Сode Lock Algorithm (CLA)
In this article, we will rethink code locks, transforming them from security mechanisms into tools for solving complex optimization problems. Discover the world of code locks viewed not as simple security devices, but as inspiration for a new approach to optimization. We will create a whole population of "locks", where each lock represents a unique solution to the problem. We will then develop an algorithm that will "pick" these locks and find optimal solutions in a variety of areas, from machine learning to trading systems development.
Reimagining Classic Strategies (Part X): Can AI Power The MACD?
Reimagining Classic Strategies (Part X): Can AI Power The MACD?
Join us as we empirically analyzed the MACD indicator, to test if applying AI to a strategy, including the indicator, would yield any improvements in our accuracy on forecasting the EURUSD. We simultaneously assessed if the indicator itself is easier to predict than price, as well as if the indicator's value is predictive of future price levels. We will furnish you with the information you need to decide whether you should consider investing your time into integrating the MACD in your AI trading strategies.
How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 1): Setting Up the Panel
How to Create an Interactive MQL5 Dashboard/Panel Using the Controls Class (Part 1): Setting Up the Panel
In this article, we create an interactive trading dashboard using the Controls class in MQL5, designed to streamline trading operations. The panel features a title, navigation buttons for Trade, Close, and Information, and specialized action buttons for executing trades and managing positions. By the end of the article, you will have a foundational panel ready for further enhancements in future installments.
Developing a Replay System (Part 47): Chart Trade Project (VI)
Developing a Replay System (Part 47): Chart Trade Project (VI)
Finally, our Chart Trade indicator starts interacting with the EA, allowing information to be transferred interactively. Therefore, in this article, we will improve the indicator, making it functional enough to be used together with any EA. This will allow us to access the Chart Trade indicator and work with it as if it were actually connected with an EA. But we will do it in a much more interesting way than before.
Data Science and ML (Part 31): Using CatBoost AI Models for Trading
Data Science and ML (Part 31): Using CatBoost AI Models for Trading
CatBoost AI models have gained massive popularity recently among machine learning communities due to their predictive accuracy, efficiency, and robustness to scattered and difficult datasets. In this article, we are going to discuss in detail how to implement these types of models in an attempt to beat the forex market.
Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(I)-Fine-tuning
Integrate Your Own LLM into EA (Part 5): Develop and Test Trading Strategy with LLMs(I)-Fine-tuning
With the rapid development of artificial intelligence today, language models (LLMs) are an important part of artificial intelligence, so we should think about how to integrate powerful LLMs into our algorithmic trading. For most people, it is difficult to fine-tune these powerful models according to their needs, deploy them locally, and then apply them to algorithmic trading. This series of articles will take a step-by-step approach to achieve this goal.
Matrix Factorization: A more practical modeling
Matrix Factorization: A more practical modeling
You might not have noticed that the matrix modeling was a little strange, since only columns were specified, not rows and columns. This looks very strange when reading the code that performs matrix factorizations. If you were expecting to see the rows and columns listed, you might get confused when trying to factorize. Moreover, this matrix modeling method is not the best. This is because when we model matrices in this way, we encounter some limitations that force us to use other methods or functions that would not be necessary if the modeling were done in a more appropriate way.
Ordinal Encoding for Nominal Variables
Ordinal Encoding for Nominal Variables
In this article, we discuss and demonstrate how to convert nominal predictors into numerical formats that are suitable for machine learning algorithms, using both Python and MQL5.
Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager
Developing a multi-currency Expert Advisor (Part 12): Developing prop trading level risk manager
In the EA being developed, we already have a certain mechanism for controlling drawdown. But it is probabilistic in nature, as it is based on the results of testing on historical price data. Therefore, the drawdown can sometimes exceed the maximum expected values (although with a small probability). Let's try to add a mechanism that ensures guaranteed compliance with the specified drawdown level.
How to create a trading journal with MetaTrader and Google Sheets
How to create a trading journal with MetaTrader and Google Sheets
Create a trading journal using MetaTrader and Google Sheets! You will learn how to sync your trading data via HTTP POST and retrieve it using HTTP requests. In the end, You have a trading journal that will help you keep track of your trades effectively and efficiently.
Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models
Self Optimizing Expert Advisor With MQL5 And Python (Part V): Deep Markov Models
In this discussion, we will apply a simple Markov Chain on an RSI Indicator, to observe how price behaves after the indicator passes through key levels. We concluded that the strongest buy and sell signals on the NZDJPY pair are generated when the RSI is in the 11-20 range and 71-80 range, respectively. We will demonstrate how you can manipulate your data, to create optimal trading strategies that are learned directly from the data you have. Furthermore, we will demonstrate how to train a deep neural network to learn to use the transition matrix optimally.
Reimagining Classic Strategies (Part IX): Multiple Time Frame Analysis (II)
Reimagining Classic Strategies (Part IX): Multiple Time Frame Analysis (II)
In today's discussion, we examine the strategy of multiple time-frame analysis to learn on which time frame our AI model performs best. Our analysis leads us to conclude that the Monthly and Hourly time-frames produce models with relatively low error rates on the EURUSD pair. We used this to our advantage and created a trading algorithm that makes AI predictions on the Monthly time frame, and executes its trades on the Hourly time frame.
From Novice to Expert: Collaborative Debugging in MQL5
From Novice to Expert: Collaborative Debugging in MQL5
Problem-solving can establish a concise routine for mastering complex skills, such as programming in MQL5. This approach allows you to concentrate on solving problems while simultaneously developing your skills. The more problems you tackle, the more advanced expertise is transferred to your brain. Personally, I believe that debugging is the most effective way to master programming. Today, we will walk through the code-cleaning process and discuss the best techniques for transforming a messy program into a clean, functional one. Read through this article and uncover valuable insights.
MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks
MQL5 Wizard Techniques you should know (Part 41): Deep-Q-Networks
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.
Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram
Creating an MQL5-Telegram Integrated Expert Advisor (Part 1): Sending Messages from MQL5 to Telegram
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.
Gain An Edge Over Any Market (Part V): FRED EURUSD Alternative Data
Gain An Edge Over Any Market (Part V): FRED EURUSD Alternative Data
In today’s discussion, we used alternative Daily data from the St. Louis Federal Reserve on the Broad US-Dollar Index and a collection of other macroeconomic indicators to predict the EURUSD future exchange rate. Unfortunately, while the data appears to have almost perfect correlation, we failed to realize any material gains in our model accuracy, possibly suggesting to us that investors may be better off using ordinary market quotes instead.
Risk manager for algorithmic trading
Risk manager for algorithmic trading
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.