Articles on machine learning in trading

How best can we combine multiple strategies to create a powerful ensemble strategy? Join us in this discussion as we look to fit together three different strategies into our trading application. Traders often employ specialized strategies for opening and closing positions, and we want to know if our machines can perform this task better. For our opening discussion, we will get familiar with the faculties of the strategy tester and the principles of OOP we will need for this task.

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.

The Contrastive Transformer is designed to analyze markets both at the level of individual candlesticks and based on entire patterns. This helps improve the quality of market trend modeling. Moreover, the use of contrastive learning to align representations of candlesticks and patterns fosters self-regulation and improves the accuracy of forecasts.

Self-supervised learning can be an effective way to analyze large amounts of unlabeled data. The efficiency is provided by the adaptation of models to the specific features of financial markets, which helps improve the effectiveness of traditional methods. This article introduces an alternative attention mechanism that takes into account the relative dependencies and relationships between inputs.

In the article, an attempt is made to build a trading EA for predicting exchange rate quotes. The algorithm is based on classical classification models - logistic and probit regression. The likelihood ratio criterion is used as a filter for trading signals.

The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We continue where we left off in the last article by examining how in-use training, and updating of our developed model, can be made thanks to reinforcement-learning. We are using an algorithm we are yet to cover in these series, known as Trusted Region Policy Optimization. And, as always, Expert Advisor assembly by the MQL5 Wizard allows us to set up our model(s) for testing much quicker and also in a way where it can be distributed and tested with different signal types.

The ADX Oscillator and CCI oscillator are trend following and momentum indicators that can be paired when developing an Expert Advisor. We look at how this can be systemized by using all the 3 main training modes of Machine Learning. Wizard Assembled Expert Advisors allow us to evaluate the patterns presented by these two indicators, and we start by looking at how Supervised-Learning can be applied with these Patterns.

In this article, we will continue diving into the implementation of the ACMO (Atmospheric Cloud Model Optimization) algorithm. In particular, we will discuss two key aspects: the movement of clouds into low-pressure regions and the rain simulation, including the initialization of droplets and their distribution among clouds. We will also look at other methods that play an important role in managing the state of clouds and ensuring their interaction with the environment.

We wrap our look into the complementary pairing of the MA & Stochastic oscillator by examining what role inference-learning can play in a post supervised-learning & reinforcement-learning situation. There are clearly a multitude of ways one can choose to go about inference learning in this case, our approach, however, is to use variational auto encoders. We explore this in python before exporting our trained model by ONNX for use in a wizard assembled Expert Advisor in MetaTrader.

We invite you to get acquainted with a new approach to detecting objects using hypernetworks. A hypernetwork generates weights for the main model, which allows taking into account the specifics of the current market situation. This approach allows us to improve forecasting accuracy by adapting the model to different trading conditions.