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Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
Feature Engineering With Python And MQL5 (Part IV): Candlestick Pattern Recognition With UMAP Regression
Dimension reduction techniques are widely used to improve the performance of machine learning models. Let us discuss a relatively new technique known as Uniform Manifold Approximation and Projection (UMAP). This new technique has been developed to explicitly overcome the limitations of legacy methods that create artifacts and distortions in the data. UMAP is a powerful dimension reduction technique, and it helps us group similar candle sticks in a novel and effective way that reduces our error rates on out of sample data and improves our trading performance.
Statistical Arbitrage Through Mean Reversion in Pairs Trading: Beating the Market by Math
Statistical Arbitrage Through Mean Reversion in Pairs Trading: Beating the Market by Math
This article describes the fundamentals of portfolio-level statistical arbitrage. Its goal is to facilitate the understanding of the principles of statistical arbitrage to readers without deep math knowledge and propose a starting point conceptual framework. The article includes a working Expert Advisor, some notes about its one-year backtest, and the respective backtest configuration settings (.ini file) for the reproduction of the experiment.
MQL5 Wizard Techniques you should know (Part 58): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
MQL5 Wizard Techniques you should know (Part 58): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns
Moving Average and Stochastic Oscillator are very common indicators whose collective patterns we explored in the prior article, via a supervised learning network, to see which “patterns-would-stick”. We take our analyses from that article, a step further by considering the effects' reinforcement learning, when used with this trained network, would have on performance. Readers should note our testing is over a very limited time window. Nonetheless, we continue to harness the minimal coding requirements afforded by the MQL5 wizard in showcasing this.
Day Trading Larry Connors RSI2 Mean-Reversion Strategies
Day Trading Larry Connors RSI2 Mean-Reversion Strategies
Larry Connors is a renowned trader and author, best known for his work in quantitative trading and strategies like the 2-period RSI (RSI2), which helps identify short-term overbought and oversold market conditions. In this article, we’ll first explain the motivation behind our research, then recreate three of Connors’ most famous strategies in MQL5 and apply them to intraday trading of the S&P 500 index CFD.
Advanced Memory Management and Optimization Techniques in MQL5
Advanced Memory Management and Optimization Techniques in MQL5
Discover practical techniques to optimize memory usage in MQL5 trading systems. Learn to build efficient, stable, and fast-performing Expert Advisors and indicators. We’ll explore how memory really works in MQL5, the common traps that slow your systems down or cause them to fail, and — most importantly — how to fix them.
Non-linear regression models on the stock exchange
Non-linear regression models on the stock exchange
Non-linear regression models on the stock exchange: Is it possible to predict financial markets? Let's consider creating a model for forecasting prices for EURUSD, and make two robots based on it - in Python and MQL5.
Price Action Analysis Toolkit Development (Part 31): Python Candlestick Recognition Engine (I) — Manual Detection
Price Action Analysis Toolkit Development (Part 31): Python Candlestick Recognition Engine (I) — Manual Detection
Candlestick patterns are fundamental to price-action trading, offering valuable insights into potential market reversals or continuations. Envision a reliable tool that continuously monitors each new price bar, identifies key formations such as engulfing patterns, hammers, dojis, and stars, and promptly notifies you when a significant trading setup is detected. This is precisely the functionality we have developed. Whether you are new to trading or an experienced professional, this system provides real-time alerts for candlestick patterns, enabling you to focus on executing trades with greater confidence and efficiency. Continue reading to learn how it operates and how it can enhance your trading strategy.
Master MQL5 from Beginner to Pro (Part VI): Basics of Developing Expert Advisors
Master MQL5 from Beginner to Pro (Part VI): Basics of Developing Expert Advisors
This article continues the series for beginners. Here we will discuss the basic principles of developing Expert Advisors (EAs). We will create two EAs: the first one will trade without indicators, using pending orders, and the second one will be based on the standard MA indicator, opening deals at the current price. Here I assume that you are no longer a complete beginner and have a relatively good command of the material from the previous articles.
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (2)
Join us for our follow-up discussion, where we will merge our first two trading strategies into an ensemble trading strategy. We shall demonstrate the different schemes possible for combining multiple strategies and also how to exercise control over the parameter space, to ensure that effective optimization remains possible even as our parameter size grows.
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (3) — Weighted Voting Policy
Self Optimizing Expert Advisors in MQL5 (Part 8): Multiple Strategy Analysis (3) — Weighted Voting Policy
This article explores how determining the optimal number of strategies in an ensemble can be a complex task that is easier to solve through the use of the MetaTrader 5 genetic optimizer. The MQL5 Cloud is also employed as a key resource for accelerating backtesting and optimization. All in all, our discussion here sets the stage for developing statistical models to evaluate and improve trading strategies based on our initial ensemble results.
Singular Spectrum Analysis in MQL5
Singular Spectrum Analysis in MQL5
This article is meant as a guide for those unfamiliar with the concept of Singular Spectrum Analysis and who wish to gain enough understanding to be able to apply the built-in tools available in MQL5.
Developing a Replay System (Part 74): New Chart Trade (I)
Developing a Replay System (Part 74): New Chart Trade (I)
In this article, we will modify the last code shown in this series about Chart Trade. These changes are necessary to adapt the code to the current replay/simulation system model. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
Automating Trading Strategies in MQL5 (Part 23): Zone Recovery with Trailing and Basket Logic
Automating Trading Strategies in MQL5 (Part 23): Zone Recovery with Trailing and Basket Logic
In this article, we enhance our Zone Recovery System by introducing trailing stops and multi-basket trading capabilities. We explore how the improved architecture uses dynamic trailing stops to lock in profits and a basket management system to handle multiple trade signals efficiently. Through implementation and backtesting, we demonstrate a more robust trading system tailored for adaptive market performance.
MQL5 Wizard Techniques you should know (Part 74): Using Patterns of Ichimoku and the ADX-Wilder with Supervised Learning
MQL5 Wizard Techniques you should know (Part 74): Using Patterns of Ichimoku and the ADX-Wilder with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the Ichimoku and the ADX, by looking at how this duo could be improved with Supervised Learning. Ichimoku and ADX are a support/resistance plus trend complimentary pairing. Our supervised learning approach uses a neural network that engages the Deep Spectral Mixture Kernel to fine tune the forecasts of this indicator pairing. As per usual, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Neural Networks in Trading: Hyperbolic Latent Diffusion Model (Final Part)
Neural Networks in Trading: Hyperbolic Latent Diffusion Model (Final Part)
The use of anisotropic diffusion processes for encoding the initial data in a hyperbolic latent space, as proposed in the HypDIff framework, assists in preserving the topological features of the current market situation and improves the quality of its analysis. In the previous article, we started implementing the proposed approaches using MQL5. Today we will continue the work we started and will bring it to its logical conclusion.
Graph Theory: Dijkstra's Algorithm Applied in Trading
Graph Theory: Dijkstra's Algorithm Applied in Trading
Dijkstra's algorithm, a classic shortest-path solution in graph theory, can optimize trading strategies by modeling market networks. Traders can use it to find the most efficient routes in the candlestick chart data.
Formulating Dynamic Multi-Pair EA (Part 3): Mean Reversion and Momentum Strategies
Formulating Dynamic Multi-Pair EA (Part 3): Mean Reversion and Momentum Strategies
In this article, we will explore the third part of our journey in formulating a Dynamic Multi-Pair Expert Advisor (EA), focusing specifically on integrating Mean Reversion and Momentum trading strategies. We will break down how to detect and act on price deviations from the mean (Z-score), and how to measure momentum across multiple forex pairs to determine trade direction.
From Basic to Intermediate: Union (I)
From Basic to Intermediate: Union (I)
In this article we will look at what a union is. Here, through experiments, we will analyze the first constructions in which union can be used. However, what will be shown here is only a core part of a set of concepts and information that will be covered in subsequent articles. The content presented here is intended solely for educational purposes. Under no circumstances should the application be viewed for any purpose other than to learn and master the concepts presented.
From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights
From Novice to Expert: Animated News Headline Using MQL5 (IV) — Locally hosted AI model market insights
In today's discussion, we explore how to self-host open-source AI models and use them to generate market insights. This forms part of our ongoing effort to expand the News Headline EA, introducing an AI Insights Lane that transforms it into a multi-integration assistive tool. The upgraded EA aims to keep traders informed through calendar events, financial breaking news, technical indicators, and now AI-generated market perspectives—offering timely, diverse, and intelligent support to trading decisions. Join the conversation as we explore practical integration strategies and how MQL5 can collaborate with external resources to build a powerful and intelligent trading work terminal.
Using association rules in Forex data analysis
Using association rules in Forex data analysis
How to apply predictive rules of supermarket retail analytics to the real Forex market? How are purchases of cookies, milk and bread related to stock exchange transactions? The article discusses an innovative approach to algorithmic trading based on the use of association rules.
MQL5 Wizard Techniques you should know (Part 73): Using Patterns of Ichimoku and the ADX-Wilder
MQL5 Wizard Techniques you should know (Part 73): Using Patterns of Ichimoku and the ADX-Wilder
The Ichimoku-Kinko-Hyo Indicator and the ADX-Wilder oscillator are a pairing that could be used in complimentarily within an MQL5 Expert Advisor. The Ichimoku is multi-faceted, however for this article, we are relying on it primarily for its ability to define support and resistance levels. Meanwhile, we also use the ADX to define our trend. As usual, we use the MQL5 wizard to build and test any potential these two may possess.
Statistical Arbitrage Through Cointegrated Stocks (Part 1): Engle-Granger and Johansen Cointegration Tests
Statistical Arbitrage Through Cointegrated Stocks (Part 1): Engle-Granger and Johansen Cointegration Tests
This article aims to provide a trader-friendly, gentle introduction to the most common cointegration tests, along with a simple guide to understanding their results. The Engle-Granger and Johansen cointegration tests can reveal statistically significant pairs or groups of assets that share long-term dynamics. The Johansen test is especially useful for portfolios with three or more assets, as it calculates the strength of cointegrating vectors all at once.
MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning
MQL5 Wizard Techniques you should know (Part 72): Using Patterns of MACD and the OBV with Supervised Learning
We follow up on our last article, where we introduced the indicator pair of the MACD and the OBV, by looking at how this pairing could be enhanced with Machine Learning. MACD and OBV are a trend and volume complimentary pairing. Our machine learning approach uses a convolution neural network that engages the Exponential kernel in sizing its kernels and channels, when fine-tuning the forecasts of this indicator pairing. As always, this is done in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.
Atomic Orbital Search (AOS) algorithm: Modification
Atomic Orbital Search (AOS) algorithm: Modification
In the second part of the article, we will continue developing a modified version of the AOS (Atomic Orbital Search) algorithm focusing on specific operators to improve its efficiency and adaptability. After analyzing the fundamentals and mechanics of the algorithm, we will discuss ideas for improving its performance and the ability to analyze complex solution spaces, proposing new approaches to extend its functionality as an optimization tool.
Price Action Analysis Toolkit Development (Part 29): Boom and Crash Interceptor EA
Price Action Analysis Toolkit Development (Part 29): Boom and Crash Interceptor EA
Discover how the Boom & Crash Interceptor EA transforms your charts into a proactive alert system-spotting explosive moves with lightning-fast velocity scans, volatility surge checks, trend confirmation, and pivot-zone filters. With crisp green “Boom” and red “Crash” arrows guiding your every decision, this tool cuts through the noise and lets you capitalize on market spikes like never before. Dive in to see how it works and why it can become your next essential edge.
Volumetric neural network analysis as a key to future trends
Volumetric neural network analysis as a key to future trends
The article explores the possibility of improving price forecasting based on trading volume analysis by integrating technical analysis principles with LSTM neural network architecture. Particular attention is paid to the detection and interpretation of anomalous volumes, the use of clustering and the creation of features based on volumes and their definition in the context of machine learning.