Algorithmic Trading Articles for Machine learning

In this article, we will get acquainted with the Anarchic Society Optimization (ASO) algorithm and discuss how an algorithm based on the irrational and adventurous behavior of participants in an anarchic society (an anomalous system of social interaction free from centralized power and various kinds of hierarchies) is able to explore the solution space and avoid the traps of local optimum. The article presents a unified ASO structure applicable to both continuous and discrete problems.

Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR

In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.

Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)

Most modern multimodal time series forecasting methods use the independent channels approach. This ignores the natural dependence of different channels of the same time series. Smart use of two approaches (independent and mixed channels) is the key to improving the performance of the models.

Neural Networks in Trading: Practical Results of the TEMPO Method

We continue our acquaintance with the TEMPO method. In this article we will evaluate the actual effectiveness of the proposed approaches on real historical data.

Animal Migration Optimization (AMO) algorithm

The article is devoted to the AMO algorithm, which models the seasonal migration of animals in search of optimal conditions for life and reproduction. The main features of AMO include the use of topological neighborhood and a probabilistic update mechanism, which makes it easy to implement and flexible for various optimization tasks.

Neural Networks in Trading: Lightweight Models for Time Series Forecasting

Lightweight time series forecasting models achieve high performance using a minimum number of parameters. This, in turn, reduces the consumption of computing resources and speeds up decision-making. Despite being lightweight, such models achieve forecast quality comparable to more complex ones.

Adaptive Social Behavior Optimization (ASBO): Two-phase evolution

We continue dwelling on the topic of social behavior of living organisms and its impact on the development of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will dive into the two-phase evolution, test the algorithm and draw conclusions. Just as in nature a group of living organisms join their efforts to survive, ASBO uses principles of collective behavior to solve complex optimization problems.

Neural Networks in Trading: Reducing Memory Consumption with Adam-mini Optimization

One of the directions for increasing the efficiency of the model training and convergence process is the improvement of optimization methods. Adam-mini is an adaptive optimization method designed to improve on the basic Adam algorithm.

Neural Networks in Trading: Spatio-Temporal Neural Network (STNN)

In this article we will talk about using space-time transformations to effectively predict upcoming price movement. To improve the numerical prediction accuracy in STNN, a continuous attention mechanism is proposed that allows the model to better consider important aspects of the data.

Neural Networks Made Easy (Part 97): Training Models With MSFformer

When exploring various model architecture designs, we often devote insufficient attention to the process of model training. In this article, I aim to address this gap.

Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier

When working with machine learning models, it’s essential to ensure consistency in the data used for training, validation, and testing. In this article, we will create our own version of the Pandas library in MQL5 to ensure a unified approach for handling machine learning data, for ensuring the same data is applied inside and outside MQL5, where most of the training occurs.

Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 2): Creating Synthetic Symbol for Testing

In this article we are creating a synthetic symbol using a Generative Adversarial Network (GAN) involves generating realistic Financial data that mimics the behavior of actual market instruments, such as EURUSD. The GAN model learns patterns and volatility from historical market data and creates synthetic price data with similar characteristics.

Neural Network in Practice: Pseudoinverse (II)

Since these articles are educational in nature and are not intended to show the implementation of specific functionality, we will do things a little differently in this article. Instead of showing how to apply factorization to obtain the inverse of a matrix, we will focus on factorization of the pseudoinverse. The reason is that there is no point in showing how to get the general coefficient if we can do it in a special way. Even better, the reader can gain a deeper understanding of why things happen the way they do. So, let's now figure out why hardware is replacing software over time.

In this article, we continue our exploration of ensemble models by discussing the concept of gates, specifically how they may be useful in combining model outputs to enhance either prediction accuracy or model generalization.

Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified

Many people love them but a few understand the whole operations behind Neural Networks. In this article I will try to explain everything that goes behind closed doors of a feed-forward multi-layer perception in plain English.

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.

Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model

We continue the discussion about the use of piecewise linear representation of time series, which was started in the previous article. Today we will see how to combine this method with other approaches to time series analysis to improve the price trend prediction quality.

Hidden Markov Models for Trend-Following Volatility Prediction

Hidden Markov Models (HMMs) are powerful statistical tools that identify underlying market states by analyzing observable price movements. In trading, HMMs enhance volatility prediction and inform trend-following strategies by modeling and anticipating shifts in market regimes. In this article, we will present the complete procedure for developing a trend-following strategy that utilizes HMMs to predict volatility as a filter.

Neural Networks in Trading: Piecewise Linear Representation of Time Series

This article is somewhat different from my earlier publications. In this article, we will talk about an alternative representation of time series. Piecewise linear representation of time series is a method of approximating a time series using linear functions over small intervals.

Integrating MQL5 with data processing packages (Part 3): Enhanced Data Visualization

In this article, we will perform Enhanced Data Visualization by going beyond basic charts by incorporating features like interactivity, layered data, and dynamic elements, enabling traders to explore trends, patterns, and correlations more effectively.

Adaptive Social Behavior Optimization (ASBO): Schwefel, Box-Muller Method

This article provides a fascinating insight into the world of social behavior in living organisms and its influence on the creation of a new mathematical model - ASBO (Adaptive Social Behavior Optimization). We will examine how the principles of leadership, neighborhood, and cooperation observed in living societies inspire the development of innovative optimization algorithms.

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.

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.

Neural Networks Made Easy (Part 96): Multi-Scale Feature Extraction (MSFformer)

Efficient extraction and integration of long-term dependencies and short-term features remain an important task in time series analysis. Their proper understanding and integration are necessary to create accurate and reliable predictive models.

Reimagining Classic Strategies in Python: MA Crossovers

In this article, we revisit the classic moving average crossover strategy to assess its current effectiveness. Given the amount of time since its inception, we explore the potential enhancements that AI can bring to this traditional trading strategy. By incorporating AI techniques, we aim to leverage advanced predictive capabilities to potentially optimize trade entry and exit points, adapt to varying market conditions, and enhance overall performance compared to conventional approaches.

Chemical reaction optimization (CRO) algorithm (Part II): Assembling and results

In the second part, we will collect chemical operators into a single algorithm and present a detailed analysis of its results. Let's find out how the Chemical reaction optimization (CRO) method copes with solving complex problems on test functions.

Integrating MQL5 with data processing packages (Part 4): Big Data Handling

Exploring advanced techniques to integrate MQL5 with powerful data processing tools, this part focuses on efficient handling of big data to enhance trading analysis and decision-making.

The article reveals the potential of the ANS algorithm as an important step in the development of flexible and intelligent optimization methods that can take into account the specifics of the problem and the dynamics of the environment in the search space.

Ensemble methods to enhance numerical predictions in MQL5

In this article, we present the implementation of several ensemble learning methods in MQL5 and examine their effectiveness across different scenarios.

Neural Network in Practice: Pseudoinverse (I)

Today we will begin to consider how to implement the calculation of pseudo-inverse in pure MQL5 language. The code we are going to look at will be much more complex for beginners than I expected, and I'm still figuring out how to explain it in a simple way. So for now, consider this an opportunity to learn some unusual code. Calmly and attentively. Although it is not aimed at efficient or quick application, its goal is to be as didactic as possible.

Developing a trading robot in Python (Part 3): Implementing a model-based trading algorithm

We continue the series of articles on developing a trading robot in Python and MQL5. In this article, we will create a trading algorithm in Python.

Neural Networks Made Easy (Part 95): Reducing Memory Consumption in Transformer Models

Transformer architecture-based models demonstrate high efficiency, but their use is complicated by high resource costs both at the training stage and during operation. In this article, I propose to get acquainted with algorithms that allow to reduce memory usage of such models.

Trading Insights Through Volume: Trend Confirmation

The Enhanced Trend Confirmation Technique combines price action, volume analysis, and machine learning to identify genuine market movements. It requires both price breakouts and volume surges (50% above average) for trade validation, while using an LSTM neural network for additional confirmation. The system employs ATR-based position sizing and dynamic risk management, making it adaptable to various market conditions while filtering out false signals.

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.

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

In the forex markets It is very challenging to predict the future trend without having an idea of the past. Very few machine learning models are capable of making the future predictions by considering past values. In this article, we are going to discuss how we can use classical(Non-time series) Artificial Intelligence models to beat the market

Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling

This article introduces traders to Generative Adversarial Networks (GANs) for generating Synthetic Financial data, addressing data limitations in model training. It covers GAN basics, python and MQL5 code implementations, and practical applications in finance, empowering traders to enhance model accuracy and robustness through synthetic data.

Neural Networks Made Easy (Part 94): Optimizing the Input Sequence

When working with time series, we always use the source data in their historical sequence. But is this the best option? There is an opinion that changing the sequence of the input data will improve the efficiency of the trained models. In this article I invite you to get acquainted with one of the methods for optimizing the input sequence.

Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models

Today, we will demonstrate how you can build AI-powered trading applications capable of learning from their own mistakes. We will demonstrate a technique known as stacking, whereby we use 2 models to make 1 prediction. The first model is typically a weaker learner, and the second model is typically a more powerful model that learns the residuals of our weaker learner. Our goal is to create an ensemble of models, to hopefully attain higher accuracy.

MQL5 Wizard Techniques you should know (Part 49): Reinforcement Learning with Proximal Policy Optimization

Proximal Policy Optimization is another algorithm in reinforcement learning that updates the policy, often in network form, in very small incremental steps to ensure the model stability. We examine how this could be of use, as we have with previous articles, in a wizard assembled Expert Advisor.

Mutual information as criteria for Stepwise Feature Selection

In this article, we present an MQL5 implementation of Stepwise Feature Selection based on the mutual information between an optimal predictor set and a target variable.

Anarchic Society Optimization (ASO) algorithm

Reimagining Classic Strategies (Part V): Multiple Symbol Analysis on USDZAR

Neural Networks in Trading: Injection of Global Information into Independent Channels (InjectTST)

Neural Networks in Trading: Practical Results of the TEMPO Method

Animal Migration Optimization (AMO) algorithm

Neural Networks in Trading: Lightweight Models for Time Series Forecasting

Adaptive Social Behavior Optimization (ASBO): Two-phase evolution

Neural Networks in Trading: Reducing Memory Consumption with Adam-mini Optimization

Neural Networks in Trading: Spatio-Temporal Neural Network (STNN)

Neural Networks Made Easy (Part 97): Training Models With MSFformer

Data Science and ML (Part 33): Pandas Dataframe in MQL5, Data Collection for ML Usage made easier

Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 2): Creating Synthetic Symbol for Testing

Neural Network in Practice: Pseudoinverse (II)

Gating mechanisms in ensemble learning

Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified

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

Neural Networks in Trading: Dual-Attention-Based Trend Prediction Model

Hidden Markov Models for Trend-Following Volatility Prediction

Neural Networks in Trading: Piecewise Linear Representation of Time Series

Integrating MQL5 with data processing packages (Part 3): Enhanced Data Visualization

Adaptive Social Behavior Optimization (ASBO): Schwefel, Box-Muller Method

Artificial Electric Field Algorithm (AEFA)

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

Neural Networks Made Easy (Part 96): Multi-Scale Feature Extraction (MSFformer)

Reimagining Classic Strategies in Python: MA Crossovers

Chemical reaction optimization (CRO) algorithm (Part II): Assembling and results

Integrating MQL5 with data processing packages (Part 4): Big Data Handling

Across Neighbourhood Search (ANS)

Ensemble methods to enhance numerical predictions in MQL5

Neural Network in Practice: Pseudoinverse (I)

Developing a trading robot in Python (Part 3): Implementing a model-based trading algorithm

Neural Networks Made Easy (Part 95): Reducing Memory Consumption in Transformer Models

Trading Insights Through Volume: Trend Confirmation

Trading Insights Through Volume: Moving Beyond OHLC Charts

Data Science and Machine Learning (Part 24): Forex Time series Forecasting Using Regular AI Models

Generative Adversarial Networks (GANs) for Synthetic Data in Financial Modeling (Part 1): Introduction to GANs and Synthetic Data in Financial Modeling

Neural Networks Made Easy (Part 94): Optimizing the Input Sequence

Self Optimizing Expert Advisor With MQL5 And Python (Part IV): Stacking Models

MQL5 Wizard Techniques you should know (Part 49): Reinforcement Learning with Proximal Policy Optimization

Mutual information as criteria for Stepwise Feature Selection