Dive into the intricate world of decision trees in the latest installment of our Data Science and Machine Learning series. Tailored for traders seeking strategic insights, this article serves as a comprehensive recap, shedding light on the powerful role decision trees play in the analysis of market trends. Explore the roots and branches of these algorithmic trees, unlocking their potential to enhance your trading decisions. Join us for a refreshing perspective on decision trees and discover how they can be your allies in navigating the complexities of financial markets.
Here we will prepare the ground so that if we need to add new functions to the code, this will happen smoothly and easily. The current code cannot yet cover or handle some of the things that will be necessary to make meaningful progress. We need everything to be structured in order to enable the implementation of certain things with the minimal effort. If we do everything correctly, we can get a truly universal system that can very easily adapt to any situation that needs to be handled.
We will be creating a simple hedge EA as a base for our more advanced Grid-Hedge EA, which will be a mixture of classic grid and classic hedge strategies. By the end of this article, you will know how to create a simple hedge strategy, and you will also get to know what people say about whether this strategy is truly 100% profitable.
Obviously the current metrics are very far from the ideal time for creating a 1-minute bar. That's the first thing we are going to fix. Fixing the synchronization problem is not difficult. This may seem hard, but it's actually quite simple. We did not make the required correction in the previous article since its purpose was to explain how to transfer the tick data that was used to create the 1-minute bars on the chart into the Market Watch window.
In this article, we will analyse the impact of dividend announcements on stock market returns and see how investors can earn more returns than those offered by the market when they expect a company to announce dividends. In doing so, we will also check the validity of the Efficient Market Hypothesis in the context of the Indian Stock Market.
We consider XLY, SPDR’s consumer discretionary spending ETF and see if with tools in MetaTrader’s IDE we can sift through an array of data sets in selecting what could work with a forecasting model with a forward outlook of not more than a year.
In this article we will complete the development of a simulator for our system. The main goal here will be to configure the algorithm discussed in the previous article. This algorithm aims to create a RANDOM WALK movement. Therefore, to understand today's material, it is necessary to understand the content of previous articles. If you have not followed the development of the simulator, I advise you to read this sequence from the very beginning. Otherwise, you may get confused about what will be explained here.
In this article we will continue the simulator development stage. this time we will see how to effectively create a RANDOM WALK type movement. This type of movement is very intriguing because it forms the basis of everything that happens in the capital market. In addition, we will begin to understand some concepts that are fundamental to those conducting market analysis.
In this article, we will see how to develop a quality score that your Expert Advisor can display in the strategy tester. We will look at two well-known calculation methods – Van Tharp and Sunny Harris.
Here we will simplify a few elements related to the work in the next article. I'll also explain how you can visualize what the simulator generates in terms of randomness.
In this article, we will look at how to lock the indicator while simply using the MQL5 language, and we will do it in a very interesting and amazing way.
In this article we present the implementation of Combinatorially Symmetric Cross Validation in pure MQL5, to measure the degree to which a overfitting may occure after optimizing a strategy using the slow complete algorithm of the Strategy Tester.
XLV is SPDR healthcare ETF and in an age where it is common to be bombarded by a wide array of traditional news items plus social media feeds, it can be pressing to select a data set for use with a model. We try to tackle this problem for this ETF by sizing up some of its critical data sets in MQL5.
In this article, I will show a completely different approach to algorithmic trading I ended up with after quite a long time. Of course, all this has to do with my brute force program, which has undergone a number of changes that allow it to solve several problems simultaneously. Nevertheless, the article has turned out to be more general and as simple as possible, which is why it is also suitable for those who know nothing about brute force.
Within the framework of the engineering approach developed by the author based on the probability theory, the conditions for opening a profitable position are found and the optimal (profit-maximizing) take profit and stop loss values are calculated.
Developing a simulator can be much more interesting than it seems. Today we'll take a few more steps in this direction because things are getting more interesting.
In this article, we will explore the application of regression models from the Scikit-learn package, attempt to convert them into ONNX format, and use the resultant models within MQL5 programs. Additionally, we will compare the accuracy of the original models with their ONNX versions for both float and double precision. Furthermore, we will examine the ONNX representation of regression models, aiming to provide a better understanding of their internal structure and operational principles.
In this article we present an algorithm for permuting price bars and detail how permutation tests can be used to recognize instances where strategy performance has been fabricated to deceive potential buyers of Expert Advisors.
The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.
This article discusses the use of the Go-Explore algorithm over a long training period, since the random action selection strategy may not lead to a profitable pass as training time increases.
In this article we present the implementation of several risk return metrics billed as alternatives to the Sharpe ratio and examine hypothetical equity curves to analyze their characteristics.
In this article, we will consider one of the methods of spectral analysis and signal processing - the discrete Hartley transform. It allows filtering signals, analyzing their spectrum and much more. The capabilities of DHT are no less than those of the discrete Fourier transform. However, unlike DFT, DHT uses only real numbers, which makes it more convenient for implementation in practice, and the results of its application are more visual.
Dive into the world of ONNX, the powerful open-standard format for exchanging machine learning models. Discover how leveraging ONNX can revolutionize algorithmic trading in MQL5, allowing traders to seamlessly integrate cutting-edge AI models and elevate their strategies to new heights. Uncover the secrets to cross-platform compatibility and learn how to unlock the full potential of ONNX in your MQL5 trading endeavors. Elevate your trading game with this comprehensive guide to Mastering ONNX
This article continues our series into category theory by introducing natural transformations, a key pillar within the subject. We look at the seemingly complex definition, then delve into examples and applications with this series’ ‘bread and butter’; volatility forecasting.
We continue studying the environment in reinforcement learning models. And in this article we will look at another algorithm – Go-Explore, which allows you to effectively explore the environment at the model training stage.
In the previous article, we made some fixes and added tests to our replication system to ensure the best possible stability. We also started creating and using a configuration file for this system.
One of the key problems within reinforcement learning is environmental exploration. Previously, we have already seen the research method based on Intrinsic Curiosity. Today I propose to look at another algorithm: Exploration via Disagreement.
This article, the 21st in our series, continues with a look at Natural Transformations and how they can be implemented using linear discriminant analysis. We present applications of this in a signal class format, like in the previous article.
There is an essential need for MQL5 programmers or developers to master important and valuable tools. One of these tools is the Strategy Tester, this article is a practical guide to understanding and using the strategy tester of MQL5.
This series of articles introduces several time series labeling methods, which can create data that meets most artificial intelligence models, and targeted data labeling according to needs can make the trained artificial intelligence model more in line with the expected design, improve the accuracy of our model, and even help the model make a qualitative leap!
We continue our look at natural transformations by considering naturality square induction. Slight restraints on multicurrency implementation for experts assembled with the MQL5 wizard mean we are showcasing our data classification abilities with a script. Principle applications considered are price change classification and thus its forecasting.
In this article, we will fiddle around ChatGPT from OpenAI in order to understand its capabilities in terms of reducing the time and labor intensity of developing Expert Advisors, indicators and scripts. I will quickly navigate you through this technology and try to show you how to use it correctly for programming in MQL4 and MQL5.
We have managed to develop a way to implement the market replay system in a realistic and accessible way. Now let's continue our project and add data to improve the replay behavior.
This article on Category Theory implementation in MQL5, continues the series by looking at Functors but this time as a bridge between Graphs and a set. We revisit calendar data, and despite its limitations in Strategy Tester use, make the case using functors in forecasting volatility with the help of correlation.
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
