Articles on data analysis and statistics in MQL5

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.

This is a unique optimization algorithm inspired by the evolution of the turtle shell. The TSEA algorithm emulates the gradual formation of keratinized skin areas, which represent optimal solutions to a problem. The best solutions become "harder" and are located closer to the outer surface, while the less successful solutions remain "softer" and are located inside. The algorithm uses clustering of solutions by quality and distance, allowing to preserve less successful options and providing flexibility and adaptability.

As a price action observer and trader, I've noticed that when a trend is confirmed by multiple timeframes, it usually continues in that direction. What may vary is how long the trend lasts, and this depends on the type of trader you are, whether you hold positions for the long term or engage in scalping. The timeframes you choose for confirmation play a crucial role. Check out this article for a quick, automated system that helps you analyze the overall trend across different timeframes with just a button click or regular updates.

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.

In the previous article, we discussed a simple RNN which despite its inability to understand long-term dependencies in the data, was able to make a profitable strategy. In this article, we are discussing both the Long-Short Term Memory(LSTM) and the Gated Recurrent Unit(GRU). These two were introduced to overcome the shortcomings of a simple RNN and to outsmart it.

In this third part, we revisit the Simple Hedge and Simple Grid Expert Advisors (EAs) developed earlier. Our focus shifts to refining the Simple Hedge 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.

Uncover the secrets behind these powerful dimensionality reduction techniques as we dissect their applications within the MQL5 trading environment. Delve into the nuances of Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), gaining a profound understanding of their impact on strategy development and market analysis.

The first of a series of articles looking at the mathematics of Custom Criteria with a specific focus on non-linear functions used in Neural Networks, MQL5 code for implementation and the use of targeted and correctional offsets.

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.

Aligned with our vision of simplifying price action, we are pleased to introduce another tool that can significantly enhance your market analysis and help you make well-informed decisions. This tool displays key technical indicators such as previous day's prices, significant support and resistance levels, and trading volume, while automatically generating visual cues on the chart.

Principal Component Analysis, a dimensionality reducing technique in data analysis, is looked at in this article, with how it could be implemented with Eigen values and vectors. As always, we aim to develop a prototype expert-signal-class usable in the MQL5 wizard.

Do the positions of planets and stars affect financial markets? Let's arm ourselves with statistics and big data, and embark on an exciting journey into the world where stars and stock charts intersect.

The Kalman filter is a recursive algorithm used in algorithmic trading to estimate the true state of a financial time series by filtering out noise from price movements. It dynamically updates predictions based on new market data, making it valuable for adaptive strategies like mean reversion. This article first introduces the Kalman filter, covering its calculation and implementation. Next, we apply the filter to a classic mean-reversion forex strategy as an example. Finally, we conduct various statistical analyses by comparing the filter with a moving average across different forex pairs.

The article presents an artificial electric field algorithm (AEFA) inspired by Coulomb's law of electrostatic force. The algorithm simulates electrical phenomena to solve complex optimization problems using charged particles and their interactions. AEFA exhibits unique properties in the context of other algorithms related to laws of nature.

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.

Here we'll see how custom events are triggered and how the indicator reports the state of the replay/simulation service.

Correlation Pathfinder offers a fresh approach to understanding currency pair dynamics as part of the Price Action Analysis Toolkit Development Series. This tool automates data collection and analysis, providing insight into how pairs like EUR/USD and GBP/USD interact. Enhance your trading strategy with practical, real-time information that helps you manage risk and spot opportunities more effectively.

Here we are going to develop a script from scratch that simplifies unloading print screens of deals for analyzing trading entries. All the necessary information on a single deal is to be conveniently displayed on one chart with the ability to draw different timeframes.

These advanced gradient-boosted decision tree techniques offer superior performance and flexibility, making them ideal for financial modeling and algorithmic trading. Learn how to leverage these tools to optimize your trading strategies, improve predictive accuracy, and gain a competitive edge in the financial markets.

The article observes classical tools of correlation analysis. An emphasis is made on brief theoretical background, as well as on the practical implementation of the Pearson chi-square test of independence and the correlation ratio.

In this article, we dive deep into the crucial aspects of choosing the most relevant and high-quality Forex data to enhance the performance of AI models.

Today we will remove a limitation that has been preventing simulations based on the Last price and will introduce a new entry point specifically for this type of simulation. The entire operating mechanism will be based on the principles of the forex market. The main difference in this procedure is the separation of Bid and Last simulations. However, it is important to note that the methodology used to randomize the time and adjust it to be compatible with the C_Replay class remains identical in both simulations. This is good because changes in one mode lead to automatic improvements in the other, especially when it comes to handling time between ticks.

Discover the secrets of algorithmic alchemy as we guide you through the blend of artistry and precision in decoding financial landscapes. Unearth how Random Forests transform data into predictive prowess, offering a unique perspective on navigating the complex terrain of stock markets. Join us on this journey into the heart of financial wizardry, where we demystify the role of Random Forests in shaping market destiny and unlocking the doors to lucrative opportunities

Now the creation occurs at the same point where we converted ticks into bars. This way, if something goes wrong during the conversion process, we will immediately notice the error. This is because the same code that places 1-minute bars on the chart during fast forwarding is also used for the positioning system to place bars during normal performance. In other words, the code that is responsible for this task is not duplicated anywhere else. This way we get a much better system for both maintenance and improvement.

Linear Kernels are the simplest matrix of its kind used in machine learning for linear regression and support vector machines. The Matérn kernel on the other hand is a more versatile version of the Radial Basis Function we looked at in an earlier article, and it is adept at mapping functions that are not as smooth as the RBF would assume. We build a custom signal class that utilizes both kernels in forecasting long and short conditions.

Algorithmic trading system that combines volume analysis with machine learning techniques, specifically LSTM neural networks. Unlike traditional trading approaches that primarily focus on price movements, this system emphasizes volume patterns and their derivatives to predict market movements. The methodology incorporates three main components: volume derivatives analysis (first and second derivatives), LSTM predictions for volume patterns, and traditional technical indicators.

Although this is the third article on this topic, I must explain for those who have not yet understood the difference between the stock market and the foreign exchange market: the big difference is that in the Forex there is no, or rather, we are not given information about some points that actually occurred during the course of trading.

In this article, I will introduce a very important tool for developers. If you are not familiar with GIT, read this article to get an idea of what it is and how to use it with MQL5.

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.

Long Short-Term Memory (LSTM) is a type of recurrent neural network (RNN) designed to model sequential data by effectively capturing long-term dependencies and addressing the vanishing gradient problem. In this article, we will explore how to utilize LSTM to predict future trends, enhancing the performance of trend-following strategies. The article will cover the introduction of key concepts and the motivation behind development, fetching data from MetaTrader 5, using that data to train the model in Python, integrating the machine learning model into MQL5, and reflecting on the results and future aspirations based on statistical backtesting.

Moving Averages are a very common indicator that are used and understood by most Traders. We explore possible use cases that may not be so common within MQL5 Wizard assembled Expert Advisors.

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.