Gamuchirai Zororo Ndawana / Profilo

In this article, we revisit a classic crude oil trading strategy with the aim of enhancing it by leveraging supervised machine learning algorithms. We will construct a least-squares model to predict future Brent crude oil prices based on the spread between Brent and WTI crude oil prices. Our goal is to identify a leading indicator of future changes in Brent prices.

Spurious regressions occur when two time series exhibit a high degree of correlation purely by chance, leading to misleading results in regression analysis. In such cases, even though variables may appear to be related, the correlation is coincidental and the model may be unreliable.

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Dive into the heart of Artificial Intelligence's enigma as we navigate the tumultuous waters of explainability. In a realm where models conceal their inner workings, our exploration unveils the "disagreement problem" that echoes through the corridors of machine learning.

Machine Learning is a complex and rewarding field for anyone of any experience. In this article we dive deep into the inner mechanisms powering the models you build, we explore the intricate world of features,predictions and impactful decisions unravelling the complexities and gaining a firm grasp of model interpretation. Learn the art of navigating tradeoffs , enhancing predictions, ranking feature importance all while ensuring robust decision making. This essential read helps you clock more performance from your machine learning models and extract more value for employing machine learning methodologies.

Embark on a compelling exploration where financial analysis meets algorithmic trading as we unravel the art of seamlessly uniting R and MetaTrader 5. This article is your guide to bridging the realms of analytical finesse in R with the formidable trading capabilities of MetaTrader 5.

Machine learning models are difficult to interpret and understanding why our models deviate from our expectations is critical if we want to gain any value from using such advanced techniques. Without comprehensive insight into the inner workings of our model, we might fail to spot bugs that are corrupting our model's performance, we may waste time over engineering features that aren't predictive and in the long run we risk underutilizing the power of these models. Fortunately, there is a sophisticated and well maintained all in one solution that allows us to see exactly what our model is doing underneath the hood.