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ARIMA Forecasting Indicator in MQL5
ARIMA Forecasting Indicator in MQL5
In this article we are implementing ARIMA forecasting indicator in MQL5. It examines how the ARIMA model generates forecasts, its applicability to the Forex market and the stock market in general. It also explains what AR autoregression is, how autoregressive models are used for forecasting, and how the autoregression mechanism works.
Angular Analysis of Price Movements: A Hybrid Model for Predicting Financial Markets
Angular Analysis of Price Movements: A Hybrid Model for Predicting Financial Markets
What is angular analysis of financial markets? How to use price action angles and machine learning to make accurate forecasts with 67% accuracy? How to combine a regression and classification model with angular features and obtain a working algorithm? What does Gann have to do with it? Why are price movement angles a good indicator for machine learning?
Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester
Python-MetaTrader 5 Strategy Tester (Part 05): Multi-Symbols and Timeframes Strategy Tester
This article presents a MetaTrader 5–compatible backtesting workflow that scales across symbols and timeframes. We use HistoryManager to parallelize data collection, synchronize bars and ticks from all timeframes, and run symbol‑isolated OnTick handlers in threads. You will learn how modelling modes affect speed/accuracy, when to rely on terminal data, how to reduce I/O with event‑driven updates, and how to assemble a complete multicurrency trading robot.
Analyzing Overbought and Oversold Trends Via Chaos Theory Approaches
Analyzing Overbought and Oversold Trends Via Chaos Theory Approaches
We determine the overbought and oversold condition of the market according to chaos theory: integrating the principles of chaos theory, fractal geometry and neural networks to forecast financial markets. The study demonstrates the use of the Lyapunov exponent as a measure of market randomness and the dynamic adaptation of trading signals. The methodology includes an algorithm for generating fractal noise, hyperbolic tangent activation, and moment optimization.
Integrating Computer Vision into Trading in MQL5 (Part 1): Creating Basic Functions
Integrating Computer Vision into Trading in MQL5 (Part 1): Creating Basic Functions
The EURUSD forecasting system with the use of computer vision and deep learning. Learn how convolutional neural networks can recognize complex price patterns in the foreign exchange market and predict exchange rate movements with up to 54% accuracy. The article shares the methodology for creating an algorithm that uses artificial intelligence technologies for visual analysis of charts instead of traditional technical indicators. The author demonstrates the process of transforming price data into "images", their processing by a neural network, and a unique opportunity to peer into the "consciousness" of AI through activation maps and attention heatmaps. Practical Python code using the MetaTrader 5 library allows readers to reproduce the system and apply it in their own trading.
Market Simulation (Part 10): Sockets (IV)
Market Simulation (Part 10): Sockets (IV)
In this article, we'll look at what you need to do to start using Excel to manage MetaTrader 5, but in a very interesting way. To do this, we will use an Excel add-in to avoid using built-in VBA. If you don't know what add-in is meant, read this article and learn how to program in Python directly in Excel.
Implementation of a table model in MQL5: Applying the MVC concept
Implementation of a table model in MQL5: Applying the MVC concept
In this article, we look at the process of developing a table model in MQL5 using the MVC (Model-View-Controller) architectural pattern to separate data logic, presentation, and control, enabling structured, flexible, and scalable code. We consider implementation of classes for building a table model, including the use of linked lists for storing data.
Forex Arbitrage Trading: Relationship Assessment Panel
Forex Arbitrage Trading: Relationship Assessment Panel
This article presents the development of an arbitrage analysis panel in MQL5. How to get fair exchange rates on Forex in different ways? Create an indicator to obtain deviations of market prices from fair exchange rates, as well as to assess the benefits of arbitrage ways of exchanging one currency for another (as in triangular arbitrage).
Market Simulation (Part 05): Creating the C_Orders Class (II)
Market Simulation (Part 05): Creating the C_Orders Class (II)
In this article, I will explain how Chart Trade, together with the Expert Advisor, will process a request to close all of the users' open positions. This may sound simple, but there are a few complications that you need to know how to manage.
Building a Trading System (Part 5): Managing Gains Through Structured Trade Exits
Building a Trading System (Part 5): Managing Gains Through Structured Trade Exits
For many traders, it's a familiar pain point: watching a trade come within a whisker of your profit target, only to reverse and hit your stop-loss. Or worse, seeing a trailing stop close you out at breakeven before the market surges toward your original target. This article focuses on using multiple entries at different Reward-to-Risk Ratios to systematically secure gains and reduce overall risk exposure.
Biological neuron for forecasting financial time series
Biological neuron for forecasting financial time series
We will build a biologically correct system of neurons for time series forecasting. The introduction of a plasma-like environment into the neural network architecture creates a kind of "collective intelligence," where each neuron influences the system's operation not only through direct connections, but also through long-range electromagnetic interactions. Let's see how the neural brain modeling system will perform in the market.
Price Action Analysis Toolkit Development (Part 45): Creating a Dynamic Level-Analysis Panel in MQL5
Price Action Analysis Toolkit Development (Part 45): Creating a Dynamic Level-Analysis Panel in MQL5
In this article, we explore a powerful MQL5 tool that let's you test any price level you desire with just one click. Simply enter your chosen level and press analyze, the EA instantly scans historical data, highlights every touch and breakout on the chart, and displays statistics in a clean, organized dashboard. You'll see exactly how often price respected or broke through your level, and whether it behaved more like support or resistance. Continue reading to explore the detailed procedure.
Market Simulation (Part 03): A Matter of Performance
Market Simulation (Part 03): A Matter of Performance
Often we have to take a step back and then move forward. In this article, we will show all the changes necessary to ensure that the Mouse and Chart Trade indicators do not break. As a bonus, we'll also cover other changes that have occurred in other header files that will be widely used in the future.
From Novice to Expert: Market Periods Synchronizer
From Novice to Expert: Market Periods Synchronizer
In this discussion, we introduce a Higher-to-Lower Timeframe Synchronizer tool designed to solve the problem of analyzing market patterns that span across higher timeframe periods. The built-in period markers in MetaTrader 5 are often limited, rigid, and not easily customizable for non-standard timeframes. Our solution leverages the MQL5 language to develop an indicator that provides a dynamic and visual way to align higher timeframe structures within lower timeframe charts. This tool can be highly valuable for detailed market analysis. To learn more about its features and implementation, I invite you to join the discussion.
Price Action Analysis Toolkit Development (Part 44): Building a VWMA Crossover Signal EA in MQL5
Price Action Analysis Toolkit Development (Part 44): Building a VWMA Crossover Signal EA in MQL5
This article introduces a VWMA crossover signal tool for MetaTrader 5, designed to help traders identify potential bullish and bearish reversals by combining price action with trading volume. The EA generates clear buy and sell signals directly on the chart, features an informative panel, and allows for full user customization, making it a practical addition to your trading strategy.
Time Evolution Travel Algorithm (TETA)
Time Evolution Travel Algorithm (TETA)
This is my own algorithm. The article presents the Time Evolution Travel Algorithm (TETA) inspired by the concept of parallel universes and time streams. The basic idea of the algorithm is that, although time travel in the conventional sense is impossible, we can choose a sequence of events that lead to different realities.
Market Simulation (Part 02): Cross Orders (II)
Market Simulation (Part 02): Cross Orders (II)
Unlike what was done in the previous article, here we will test the selection option using an Expert Advisor. Although this is not a final solution yet, it will be enough for now. With the help of this article, you will be able to understand how to implement one of the possible solutions.
From Novice to Expert: Demystifying Hidden Fibonacci Retracement Levels
From Novice to Expert: Demystifying Hidden Fibonacci Retracement Levels
In this article, we explore a data-driven approach to discovering and validating non-standard Fibonacci retracement levels that markets may respect. We present a complete workflow tailored for implementation in MQL5, beginning with data collection and bar or swing detection, and extending through clustering, statistical hypothesis testing, backtesting, and integration into an MetaTrader 5 Fibonacci tool. The goal is to create a reproducible pipeline that transforms anecdotal observations into statistically defensible trading signals.
MetaTrader 5 Machine Learning Blueprint (Part 3): Trend-Scanning Labeling Method
MetaTrader 5 Machine Learning Blueprint (Part 3): Trend-Scanning Labeling Method
We have built a robust feature engineering pipeline using proper tick-based bars to eliminate data leakage and solved the critical problem of labeling with meta-labeled triple-barrier signals. This installment covers the advanced labeling technique, trend-scanning, for adaptive horizons. After covering the theory, an example shows how trend-scanning labels can be used with meta-labeling to improve on the classic moving average crossover strategy.
Price Action Analysis Toolkit Development (Part 43): Candlestick Probability and Breakouts
Price Action Analysis Toolkit Development (Part 43): Candlestick Probability and Breakouts
Enhance your market analysis with the MQL5-native Candlestick Probability EA, a lightweight tool that transforms raw price bars into real-time, instrument-specific probability insights. It classifies Pinbars, Engulfing, and Doji patterns at bar close, uses ATR-aware filtering, and optional breakout confirmation. The EA calculates raw and volume-weighted follow-through percentages, helping you understand each pattern's typical outcome on specific symbols and timeframes. On-chart markers, a compact dashboard, and interactive toggles allow easy validation and focus. Export detailed CSV logs for offline testing. Use it to develop probability profiles, optimize strategies, and turn pattern recognition into a measurable edge.
Price movement discretization methods in Python
Price movement discretization methods in Python
We will look at price discretization methods using Python + MQL5. In this article, I will share my practical experience developing a Python library that implements a wide range of approaches to bar formation — from classic Volume and Range bars to more exotic methods like Renko and Kagi. We will consider three-line breakout candles and range bars analyzing their statistics and trying to define how else the prices can be represented discretely.
From Novice to Expert: Animated News Headline Using MQL5 (III) — Indicator Insights
From Novice to Expert: Animated News Headline Using MQL5 (III) — Indicator Insights
In this article, we’ll advance the News Headline EA by introducing a dedicated indicator insights lane—a compact, on-chart display of key technical signals generated from popular indicators such as RSI, MACD, Stochastic, and CCI. This approach eliminates the need for multiple indicator subwindows on the MetaTrader 5 terminal, keeping your workspace clean and efficient. By leveraging the MQL5 API to access indicator data in the background, we can process and visualize market insights in real-time using custom logic. Join us as we explore how to manipulate indicator data in MQL5 to create an intelligent and space-saving scrolling insights system, all within a single horizontal lane on your trading chart.
Price Action Analysis Toolkit Development (Part 42): Interactive Chart Testing with Button Logic and Statistical Levels
Price Action Analysis Toolkit Development (Part 42): Interactive Chart Testing with Button Logic and Statistical Levels
In a world where speed and precision matter, analysis tools need to be as smart as the markets we trade. This article presents an EA built on button logic—an interactive system that instantly transforms raw price data into meaningful statistical levels. With a single click, it calculates and displays mean, deviation, percentiles, and more, turning advanced analytics into clear on-chart signals. It highlights the zones where price is most likely to bounce, retrace, or break, making analysis both faster and more practical.
Cyclic Parthenogenesis Algorithm (CPA)
Cyclic Parthenogenesis Algorithm (CPA)
The article considers a new population optimization algorithm - Cyclic Parthenogenesis Algorithm (CPA), inspired by the unique reproductive strategy of aphids. The algorithm combines two reproduction mechanisms — parthenogenesis and sexual reproduction — and also utilizes the colonial structure of the population with the possibility of migration between colonies. The key features of the algorithm are adaptive switching between different reproductive strategies and a system of information exchange between colonies through the flight mechanism.
Price Action Analysis Toolkit Development (Part 41): Building a Statistical Price-Level EA in MQL5
Price Action Analysis Toolkit Development (Part 41): Building a Statistical Price-Level EA in MQL5
Statistics has always been at the heart of financial analysis. By definition, statistics is the discipline that collects, analyzes, interprets, and presents data in meaningful ways. Now imagine applying that same framework to candlesticks—compressing raw price action into measurable insights. How helpful would it be to know, for a specific period of time, the central tendency, spread, and distribution of market behavior? In this article, we introduce exactly that approach, showing how statistical methods can transform candlestick data into clear, actionable signals.
Functions for activating neurons during training: The key to fast convergence?
Functions for activating neurons during training: The key to fast convergence?
This article presents a study of the interaction of different activation functions with optimization algorithms in the context of neural network training. Particular attention is paid to the comparison of the classical ADAM and its population version when working with a wide range of activation functions, including the oscillating ACON and Snake functions. Using a minimalistic MLP (1-1-1) architecture and a single training example, the influence of activation functions on the optimization is isolated from other factors. The article proposes an approach to manage network weights through the boundaries of activation functions and a weight reflection mechanism, which allows avoiding problems with saturation and stagnation in training.
Quantum computing and trading: A fresh approach to price forecasts
Quantum computing and trading: A fresh approach to price forecasts
The article describes an innovative approach to forecasting price movements in financial markets using quantum computing. The main focus is on the application of the Quantum Phase Estimation (QPE) algorithm to find prototypes of price patterns allowing traders to significantly speed up the market data analysis.
Pipelines in MQL5
Pipelines in MQL5
In this piece, we look at a key data preparation step for machine learning that is gaining rapid significance. Data Preprocessing Pipelines. These in essence are a streamlined sequence of data transformation steps that prepare raw data before it is fed to a model. As uninteresting as this may initially seem to the uninducted, this ‘data standardization’ not only saves on training time and execution costs, but it goes a long way in ensuring better generalization. In this article we are focusing on some SCIKIT-LEARN preprocessing functions, and while we are not exploiting the MQL5 Wizard, we will return to it in coming articles.
Building MQL5-Like Trade Classes in Python for MetaTrader 5
Building MQL5-Like Trade Classes in Python for MetaTrader 5
MetaTrader 5 python package provides an easy way to build trading applications for the MetaTrader 5 platform in the Python language, while being a powerful and useful tool, this module isn't as easy as MQL5 programming language when it comes to making an algorithmic trading solution. In this article, we are going to build trade classes similar to the one offered in MQL5 to create a similar syntax and make it easier to make trading robots in Python as in MQL5.
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.
Price Action Analysis Toolkit Development (Part 24): Price Action Quantification Analysis Tool
Price Action Analysis Toolkit Development (Part 24): Price Action Quantification Analysis Tool
Candlestick patterns offer valuable insights into potential market moves. Some single candles signal continuation of the current trend, while others foreshadow reversals, depending on their position within the price action. This article introduces an EA that automatically identifies four key candlestick formations. Explore the following sections to learn how this tool can enhance your price-action analysis.
Price Action Analysis Toolkit Development (Part 40): Market DNA Passport
Price Action Analysis Toolkit Development (Part 40): Market DNA Passport
This article explores the unique identity of each currency pair through the lens of its historical price action. Inspired by the concept of genetic DNA, which encodes the distinct blueprint of every living being, we apply a similar framework to the markets, treating price action as the “DNA” of each pair. By breaking down structural behaviors such as volatility, swings, retracements, spikes, and session characteristics, the tool reveals the underlying profile that distinguishes one pair from another. This approach provides more profound insight into market behavior and equips traders with a structured way to align strategies with the natural tendencies of each instrument.