Articles on machine learning in trading

In this article, we will attempt to predict the market with a decent model for time series forecasting named DeepAR. A model that is a combination of deep neural networks and autoregressive properties found in models like ARIMA and Vector Autoregressive (VAR).

The article focuses on creating a trading system with a real quantum simulator instead of mathematical analogies. The system uses 3 virtual qubits, quantum gates and superposition principles to analyze markets. It is implemented as a trading EA for MetaTrader 5 in MQL5. The main achievement is the transition from simulation to real quantum principles of financial information processing.

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.

In the context of reinforcement learning, model procrastination can be caused by several reasons. The article considers some of the possible causes of model procrastination and methods for overcoming them.

In the last piece, we concluded our look at the pairing of the gator oscillator and the accumulation/distribution oscillator when used in their typical setting of the raw signals they generate. These two indicators are complimentary as trend and volume indicators, respectively. We now follow up that piece, by examining the effect that supervised learning can have on enhancing some of the feature patterns we had reviewed. Our supervised learning approach is a CNN that engages with kernel regression and dot product similarity to size its kernels and channels. As always, we do this in a custom signal class file that works with the MQL5 wizard to assemble an Expert Advisor.

We continue to build the algorithms that form the basis of the DADA framework, which is an advanced tool for detecting anomalies in time series. This approach enables effective distinguishing random fluctuations from significant deviations. Unlike classical methods, DADA dynamically adapts to different data types, choosing the optimal compression level in each specific case.

Eagle Strategy is an algorithm that mimics the eagle's two-phase hunting strategy: global search via Levy flights using Mantegna method, alternating with intense local exploitation using the firefly algorithm, a mathematically sound approach to balancing exploration and exploitation, and a bioinspired concept that combines two natural phenomena into a single computational method.

In this article, we discuss Directional Diffusion Models that exploit data-dependent anisotropic and directed noise in a forward diffusion process to capture meaningful graph representations.

In today's discussion, we examine the strategy of multiple time-frame analysis to learn on which time frame our AI model performs best. Our analysis leads us to conclude that the Monthly and Hourly time-frames produce models with relatively low error rates on the EURUSD pair. We used this to our advantage and created a trading algorithm that makes AI predictions on the Monthly time frame, and executes its trades on the Hourly time frame.

We will analyze alternative data curated by the Chicago Board Of Options Exchange (CBOE) to improve the accuracy of our deep neural networks when forecasting the XAUEUR symbol.

Adaptation of the classical CAPM model for the Forex currency market in MQL5. The indicator calculates expected return and risk premium based on historical volatility. The indicators rise at peaks and bottoms, reflecting the fundamental principles of pricing. Practical application for counter-trend and trend-following strategies, taking into account the dynamics of the risk-reward ratio in real time. The article includes mathematical apparatus and technical implementation.

In this article we will build a basic neuron. And although it looks simple, and many may consider this code completely trivial and meaningless, I want you to have fun studying this simple sketch of a neuron. Don't be afraid to modify the code, understanding it fully is the goal.

Here I will consider the fairly new Stochastic Marginal Actor-Critic (SMAC) algorithm, which allows building latent variable policies within the framework of entropy maximization.

Economic Calendar Data is not available for testing with Expert Advisors within Strategy Tester, by default. We look at how Databases could help in providing a work around this limitation. So, for this article we explore how SQLite databases can be used to archive Economic Calendar news such that wizard assembled Expert Advisors can use this to generate trade signals.

We invite you to get acquainted with the DADA framework, which is an innovative method for detecting anomalies in time series. It helps distinguish random fluctuations from suspicious deviations. Unlike traditional methods, DADA is flexible and adapts to different data. Instead of a fixed compression level, it uses several options and chooses the most appropriate one for each case.

The article considers the algorithm of the MEC family called the simple mind evolutionary computation algorithm (Simple MEC, SMEC). The algorithm is distinguished by the beauty of its idea and ease of implementation.

The Learning Rate, is a step size towards a training target in many machine learning algorithms’ training processes. We examine the impact its many schedules and formats can have on the performance of a Generative Adversarial Network, a type of neural network that we had examined in an earlier article.

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.

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.

In this article. we will discuss a method of complex multimodal interaction analysis and feature understanding.

I present to you my new population optimization algorithm - Blood Inheritance Optimization (BIO), inspired by the human blood group inheritance system. In this algorithm, each solution has its own "blood type" that determines the way it evolves. Just as in nature where a child's blood type is inherited according to specific rules, in BIO new solutions acquire their characteristics through a system of inheritance and mutations.

The article considers a group of optimization algorithms known as Evolution Strategies (ES). They are among the very first population algorithms to use evolutionary principles for finding optimal solutions. We will implement changes to the conventional ES variants and revise the test function and test stand methodology for the algorithms.

We continue to integrate methods proposed by the authors of the Attraos framework into trading models. Let me remind you that this framework uses concepts of chaos theory to solve time series forecasting problems, interpreting them as projections of multidimensional chaotic dynamic systems.

The article describes hierarchical training models that offer an effective approach to solving complex machine learning problems. Hierarchical models consist of several levels, each of which is responsible for different aspects of the task.

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.

The article presents the Big Bang - Big Crunch method, which has two key phases: cyclic generation of random points and their compression to the optimal solution. This approach combines exploration and refinement, allowing us to gradually find better solutions and open up new optimization opportunities.

All the models we have considered so far analyze the state of the environment as a time sequence. However, the time series can also be represented in the form of frequency features. In this article, I introduce you to an algorithm that uses frequency components of a time sequence to predict future states.

​This article integrates the Optuna hyperparameter optimization (HPO) backend into a unified ModelDevelopmentPipeline. It adds joint tuning of model hyperparameters and sample-weight schemes, early pruning with Hyperband, and crash-resistant SQLite study storage. The pipeline auto-detects primary vs. secondary models, prepends a fitted column-dropping preprocessor for safe inference, supports sequential bootstrapping, generates an Optuna report, and includes bid/ask and LearnedStrategy links. Readers get faster, resumable runs and deployable, self-contained models.

NumPy library is powering almost all the machine learning algorithms to the core in Python programming language, In this article we are going to implement a similar module which has a collection of all the complex code to aid us in building sophisticated models and algorithms of any kind.

The quality of future state predictions plays an important role in the Goal-Conditioned Predictive Coding method, which we discussed in the previous article. In this article I want to introduce you to an algorithm that can significantly improve the prediction quality in stochastic environments, such as financial markets.

News drives the financial markets, especially major releases like Non-Farm Payrolls (NFPs). We've all witnessed how a single headline can trigger sharp price movements. In this article, we dive into the powerful intersection of news data and Artificial Intelligence.

When we use models to analyze the market situation, we mainly focus on the candlestick. However, it has long been known that candlestick patterns can help in predicting future price movements. In this article, we will get acquainted with a method that allows us to integrate both of these approaches.

The article explores the bird swarm-based algorithm (BSA) inspired by the collective flocking interactions of birds in nature. The different search strategies of individuals in BSA, including switching between flight, vigilance and foraging behavior, make this algorithm multifaceted. It uses the principles of bird flocking, communication, adaptability, leading and following to efficiently find optimal solutions.

The Simulated Annealing algorithm is a metaheuristic inspired by the metal annealing process. In the article, we will conduct a thorough analysis of the algorithm and debunk a number of common beliefs and myths surrounding this widely known optimization method. The second part of the article will consider the custom Simulated Isotropic Annealing (SIA) algorithm.

In this article, we introduce a method for segmenting 3D objects based on Superpoint Transformer (SPFormer), which eliminates the need for intermediate data aggregation. This speeds up the segmentation process and improves the performance of the model.

As a result of tests performed in previous articles, we came to the conclusion that the optimality of the trained strategy largely depends on the training set used. In this article, we will get acquainted with a fairly simple yet effective method for selecting trajectories to train models.

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.

In recent articles, we have seen several options for using the Decision Transformer method. The method allows analyzing not only the current state, but also the trajectory of previous states and actions performed in them. In this article, we will focus on using this method in hierarchical models.