Articles on data analysis and statistics in MQL5

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Articles on mathematical models and laws of probability are interesting for many traders. Mathematics is the basis of technical indicators, and statistics is required to analyze trading results and develop strategies.

Read about the fuzzy logic, digital filters, market profile, Kohonen maps, neural gas and many other tools that can be used for trading.

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Neural networks made easy (Part 34): Fully Parameterized Quantile Function

We continue studying distributed Q-learning algorithms. In previous articles, we have considered distributed and quantile Q-learning algorithms. In the first algorithm, we trained the probabilities of given ranges of values. In the second algorithm, we trained ranges with a given probability. In both of them, we used a priori knowledge of one distribution and trained another one. In this article, we will consider an algorithm which allows the model to train for both distributions.
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Population optimization algorithms: Bat algorithm (BA)

In this article, I will consider the Bat Algorithm (BA), which shows good convergence on smooth functions.
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News Trading Made Easy (Part 6): Performing Trades (III)

In this article news filtration for individual news events based on their IDs will be implemented. In addition, previous SQL queries will be improved to provide additional information or reduce the query's runtime. Furthermore, the code built in the previous articles will be made functional.
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Price Action Analysis Toolkit Development (Part 2): Analytical Comment Script

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.
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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.
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Integrating Hidden Markov Models in MetaTrader 5

In this article we demonstrate how Hidden Markov Models trained using Python can be integrated into MetaTrader 5 applications. Hidden Markov Models are a powerful statistical tool used for modeling time series data, where the system being modeled is characterized by unobservable (hidden) states. A fundamental premise of HMMs is that the probability of being in a given state at a particular time depends on the process's state at the previous time slot.
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Data Science and Machine Learning (Part 23): Why LightGBM and XGBoost outperform a lot of AI models?

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.
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MQL5 Wizard Techniques you should know (Part 19): Bayesian Inference

Bayesian inference is the adoption of Bayes Theorem to update probability hypothesis as new information is made available. This intuitively leans to adaptation in time series analysis, and so we have a look at how we could use this in building custom classes not just for the signal but also money-management and trailing-stops.
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Price Action Analysis Toolkit Development (Part 12): External Flow (III) TrendMap

The flow of the market is determined by the forces between bulls and bears. There are specific levels that the market respects due to the forces acting on them. Fibonacci and VWAP levels are especially powerful in influencing market behavior. Join me in this article as we explore a strategy based on VWAP and Fibonacci levels for signal generation.
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Developing a Replay System (Part 32): Order System (I)

Of all the things that we have developed so far, this system, as you will probably notice and eventually agree, is the most complex. Now we need to do something very simple: make our system simulate the operation of a trading server. This need to accurately implement the way the trading server operates seems like a no-brainer. At least in words. But we need to do this so that the everything is seamless and transparent for the user of the replay/simulation system.
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Population optimization algorithms: Nelder–Mead, or simplex search (NM) method

The article presents a complete exploration of the Nelder-Mead method, explaining how the simplex (function parameter space) is modified and rearranged at each iteration to achieve an optimal solution, and describes how the method can be improved.
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Developing a Replay System (Part 26): Expert Advisor project — C_Terminal class

We can now start creating an Expert Advisor for use in the replay/simulation system. However, we need something improved, not a random solution. Despite this, we should not be intimidated by the initial complexity. It's important to start somewhere, otherwise we end up ruminating about the difficulty of a task without even trying to overcome it. That's what programming is all about: overcoming obstacles through learning, testing, and extensive research.
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Category Theory in MQL5 (Part 7): Multi, Relative and Indexed Domains

Category Theory is a diverse and expanding branch of Mathematics which is only recently getting some coverage in the MQL5 community. These series of articles look to explore and examine some of its concepts & axioms with the overall goal of establishing an open library that provides insight while also hopefully furthering the use of this remarkable field in Traders' strategy development.
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Developing a Replay System (Part 48): Understanding the concept of a service

How about learning something new? In this article, you will learn how to convert scripts into services and why it is useful to do so.
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Price Action Analysis Toolkit Development (Part 11): Heikin Ashi Signal EA

MQL5 offers endless opportunities to develop automated trading systems tailored to your preferences. Did you know it can even perform complex mathematical calculations? In this article, we introduce the Japanese Heikin-Ashi technique as an automated trading strategy.
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Developing a Replay System (Part 59): A New Future

Developing a Replay System (Part 59): A New Future

Having a proper understanding of different ideas allows us to do more with less effort. In this article, we'll look at why it's necessary to configure a template before the service can interact with the chart. Also, what if we improve the mouse pointer so we can do more things with it?
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Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

Modified Grid-Hedge EA in MQL5 (Part III): Optimizing Simple Hedge Strategy (I)

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.
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MQL5 Wizard Techniques you should know (Part 08): Perceptrons

MQL5 Wizard Techniques you should know (Part 08): Perceptrons

Perceptrons, single hidden layer networks, can be a good segue for anyone familiar with basic automated trading and is looking to dip into neural networks. We take a step by step look at how this could be realized in a signal class assembly that is part of the MQL5 Wizard classes for expert advisors.
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William Gann methods (Part III): Does Astrology Work?

William Gann methods (Part III): Does Astrology Work?

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.
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Feature Engineering With Python And MQL5 (Part I): Forecasting Moving Averages For Long-Range AI Models

Feature Engineering With Python And MQL5 (Part I): Forecasting Moving Averages For Long-Range AI Models

The moving averages are by far the best indicators for our AI models to predict. However, we can improve our accuracy even further by carefully transforming our data. This article will demonstrate, how you can build AI Models capable of forecasting further into the future than you may currently be practicing without significant drops to your accuracy levels. It is truly remarkable, how useful the moving averages are.
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Neural networks made easy (Part 39): Go-Explore, a different approach to exploration

Neural networks made easy (Part 39): Go-Explore, a different approach to exploration

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.
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Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

Data Science and Machine Learning (Part 25): Forex Timeseries Forecasting Using a Recurrent Neural Network (RNN)

Recurrent neural networks (RNNs) excel at leveraging past information to predict future events. Their remarkable predictive capabilities have been applied across various domains with great success. In this article, we will deploy RNN models to predict trends in the forex market, demonstrating their potential to enhance forecasting accuracy in forex trading.
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Developing a Replay System — Market simulation (Part 17): Ticks and more ticks (I)

Developing a Replay System — Market simulation (Part 17): Ticks and more ticks (I)

Here we will see how to implement something really interesting, but at the same time very difficult due to certain points that can be very confusing. The worst thing that can happen is that some traders who consider themselves professionals do not know anything about the importance of these concepts in the capital market. Well, although we focus here on programming, understanding some of the issues involved in market trading is paramount to what we are going to implement.
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Price Action Analysis Toolkit Development (Part 10): External Flow (II) VWAP

Price Action Analysis Toolkit Development (Part 10): External Flow (II) VWAP

Master the power of VWAP with our comprehensive guide! Learn how to integrate VWAP analysis into your trading strategy using MQL5 and Python. Maximize your market insights and improve your trading decisions today.
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Turtle Shell Evolution Algorithm (TSEA)

Turtle Shell Evolution Algorithm (TSEA)

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.
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MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class

MQL5 Wizard Techniques you should know (Part 13): DBSCAN for Expert Signal Class

Density Based Spatial Clustering for Applications with Noise is an unsupervised form of grouping data that hardly requires any input parameters, save for just 2, which when compared to other approaches like k-means, is a boon. We delve into how this could be constructive for testing and eventually trading with Wizard assembled Expert Advisers
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A New Approach to Custom Criteria in Optimizations (Part 1): Examples of Activation Functions

A New Approach to Custom Criteria in Optimizations (Part 1): Examples of Activation Functions

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.
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Developing a Replay System — Market simulation (Part 14): Birth of the SIMULATOR (IV)

Developing a Replay System — Market simulation (Part 14): Birth of the SIMULATOR (IV)

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.
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Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading

Market Reactions and Trading Strategies in Response to Dividend Announcements: Evaluating the Efficient Market Hypothesis in Stock Trading

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.
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Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market

Data Science and ML (Part 37): Using Candlestick patterns and AI to beat the market

Candlestick patterns help traders understand market psychology and identify trends in financial markets, they enable more informed trading decisions that can lead to better outcomes. In this article, we will explore how to use candlestick patterns with AI models to achieve optimal trading performance.
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Estimate future performance with confidence intervals

Estimate future performance with confidence intervals

In this article we delve into the application of boostrapping techniques as a means to estimate the future performance of an automated strategy.
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Data Science and Machine Learning (Part 20): Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5

Data Science and Machine Learning (Part 20): Algorithmic Trading Insights, A Faceoff Between LDA and PCA in MQL5

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.
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Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

Data Science and ML (Part 42): Forex Time series Forecasting using ARIMA in Python, Everything you need to Know

ARIMA, short for Auto Regressive Integrated Moving Average, is a powerful traditional time series forecasting model. With the ability to detect spikes and fluctuations in a time series data, this model can make accurate predictions on the next values. In this article, we are going to understand what is it, how it operates, what you can do with it when it comes to predicting the next prices in the market with high accuracy and much more.
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Category Theory in MQL5 (Part 13): Calendar Events with Database Schemas

Category Theory in MQL5 (Part 13): Calendar Events with Database Schemas

This article, that follows Category Theory implementation of Orders in MQL5, considers how database schemas can be incorporated for classification in MQL5. We take an introductory look at how database schema concepts could be married with category theory when identifying trade relevant text(string) information. Calendar events are the focus.
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Price Action Analysis Toolkit Development (Part 21): Market Structure Flip Detector Tool

Price Action Analysis Toolkit Development (Part 21): Market Structure Flip Detector Tool

The Market Structure Flip Detector Expert Advisor (EA) acts as your vigilant partner, constantly observing shifts in market sentiment. By utilizing Average True Range (ATR)-based thresholds, it effectively detects structure flips and labels each Higher Low and Lower High with clear indicators. Thanks to MQL5’s swift execution and flexible API, this tool offers real-time analysis that adjusts the display for optimal readability and provides a live dashboard to monitor flip counts and timings. Furthermore, customizable sound and push notifications guarantee that you stay informed of critical signals, allowing you to see how straightforward inputs and helper routines can transform price movements into actionable strategies.
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GIT: What is it?

GIT: What is it?

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.
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Artificial Electric Field Algorithm (AEFA)

Artificial Electric Field Algorithm (AEFA)

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.
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Trading Insights Through Volume: Moving Beyond OHLC Charts

Trading Insights Through Volume: Moving Beyond OHLC Charts

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.
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Developing a Replay System — Market simulation (Part 08): Locking the indicator

Developing a Replay System — Market simulation (Part 08): Locking the indicator

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.
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Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes

Data Science and ML (Part 29): Essential Tips for Selecting the Best Forex Data for AI Training Purposes

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.