fbpx

Deep Dive into Neural Network AI

Deep Dive into Neural Network AI

When it comes to modern forex trading it can take a lot more than a second to wrap your head around the concepts, technological advancements, and algorithms.  We’re here to help by breaking things down with a deep dive into everything you need to know about the Neural Network when it comes to AI and forex trading.

Neural networks are defined as a set of intelligent algorithms designed to recognise patterns by organising raw data. Real-world information like images, sound, text, etc, are translated into numerical data that is then contained in vectors. Pattern recognition, just like in the human brain, allows for huge amounts of data to be used in artificial intelligence driven learning in the form of algorithms, which makes AI  work more like the human brain.

How, you ask?

Neural networks cluster, organise and recognise patterns rather than simply storing that information, they also manage and group unlabelled data according to similarities. This allows the networks to extract the features and trends that underpin all the data, similarly to the way we humans would process data, except on a much larger, highly efficient scale.

A smart trader will analyse financial markets to predict curves and trends. However, humans are limited in the amount of data we can work with at any one time. Deep learning algorithms have the capacity to map inputs to outputs using thousands upon thousands of data points. This results in establishing correlations that may not be obvious to the human eye. 

Exposed to enough data, artificial intelligence can establish relationships between past, present and future events which in turn has the ability to clear the way for predictive tools with previously unthinkable precision. To the algorithm, the future event is just like another label or category, transforming 3D reality into 2D data sets that can be broken down for analysis. The benefit of this process is accurate pathfinding through deep data sets that can pre-empt and reduce error.

The goal in using artificial intelligence is to arrive at as little error as possible. Deep-learning networks differ from more commonplace single-layer networks by their depth; as explained by Pathmind, that is the number of node layers which data can pass through to arrive at an analysis with precision. The greater the number of node layers the higher the accuracy of the out-put. Facial recognition models are a good way of understanding the phenomenon. Successive layers allow sensitive comparison between images to arrive at facial recognition. 

Forex is a fast growing industry that constantly changes, currency is rarely ever stagnant and successfully tracking trades comes with incredible amounts of data analysis. Neural networks draw upon the best of both worlds, making computer learning technology work a little more like the human brain. This means you can apply all the increased volume and efficiency of AI with technology that works more like the human brain. It’s like the best of both worlds, brain like learning powered by AI technology. This means it is more flexible and responsive than ever, something that is crucial when trading in Forex.

If you think your brain is a reliable enough tool to take on this task, think again. AI technology gives you the reliability and scale to do the job while Neural networks introduce a human edge to enhance the computer learning technology even further. 

Halcyon’s deep-learning algorithm analyses more than 37,000 data points to optimise on trading opportunities. Our algorithm is automated and unlike traditional machine-learning, our neural networks can perform without human intervention. Given that data extraction and analysis is a task that can take experts years to complete, Halcyon’s deep learning algorithm is a pathway through human limitations.

Talk to the team at Halcyon Global to find out more.