The broad category of “artificial intelligence” comprises of multiple sub-categories with 2 standing out among the rest; Machine Learning and Deep Learning.
Example: A popular application of A.I. that is a good example of this is Netflix. When it creates viewing suggestions tailored to each subscriber’s preferences it’s using A.I. But it applies machine learning to update those recommendations after learning the subscriber’s interests and habits, what they watch, how long they watch it for, and what they don’t watch.
Machine Learning constructs mathematical algorithms that are able to parse data, learn from that data and make data-related predictions based on what’s been learned to date.
Deep learning is a more sophisticated refinement, of machine learning. The key difference is that it can learn on its own, independently, through iterative learning or training process similar to how the human brain, processes information and arrive at intelligent decisions.
Deep learning models must organize its processes into structured layers that form an ‘artificial neural network’ that can learn from the data it was presented with and can make intelligent decisions and informed predictions based on what it has learned.
Artificial Neural Networks
Artificial neural networks (ANNs) are modeled after human brains to best mimic this process. A major reason why deep learning with artificial neural networks has become so popular recently is that they are powered by huge amounts of data which increases their ability to learn from that data and make highly accurate forecasts.
There have been considerable advancements made in computing hardware related to how ANNs crunch its data which has sparked renewed interest in artificial intelligence and its applications in numerous industries such as medicine, More recently, Internet-connected cloud computing technology emerged, in which neural network software code and big data can be stored and run on powerful physical or virtual servers that make up the ‘Cloud’. This development made the application of deep learning with neural networks more practical and far more cost-effective. Now, large-scale projects involving big data can utilize cloud computing to store its data.
Automation VS AI
A key feature
Fast forward to today, AI is the next-generation technology that is quickly changing industries and has now arrived in retail forex trading.
A key feature of automated AI trading, compared to merely automating something, is the ability to understand which are the best and worst decisions to be made, for a given task and data set. AI can be smart and intelligent, learning from success and past mistakes. In other words, AI can adapt itself in order to find the best trading strategies for specific scenarios in the forex market.
Automation alone is no longer enough to build a forex trading system, and the use of AI, such as machine learning and neural networks is the way forward.
Automation in forex trading
Over the two last decades, the foreign exchange (forex) markets were saturated with automated trading systems, from retail to institutional traders, thousands of forex robots across the world were touted as the next-generation technology that everyone wanted to have.
Unfortunately, many trading systems of the past decade were short-lived and eventually could not adapt to changing market conditions. As a result, many forex trading systems were overpriced and did not meet performance expectations.
What is the Loyal Infinity?
Loyal Infinity uses a proprietary neural network that analyses market’s depth and looks for patterns of pre‐ set mathematical models (such as fractals, chaos and waves) that allows it to understand and forecast market’s trends on a real‐time basis (in fact it is a trending program). By employing human knowledge, discretionary trading, artificial intelligence and oversight, our system helps members cut risk, increase profits and simplify investment decisions.
It’s intelligent and self-learning, with the ability to analyse and recognise patterns of accumulated historic data over a multitude of data points to better predict and react to future outcomes. The neural network is supervised, multi-layered and composed by variable nodes. Meaning:
- By feeding the network with pre-set data, the system creates its own real time strategies to achieve the established objectives.
- In only milliseconds, our system can choose the most appropriate strategy among more than 30,000 options in every single market condition.
Pattern recognition, Process optimization, Signal validation and information processing.Usage of analysed market intelligence and troubleshooting are the network’s main task.
If the system identifies a trend which is not followed by the market, our / software applies a counter strategy to solve the situation on a real‐time basis, adapting to the new scenario. During this non‐stop analysis, research and optimization process, all new movements and patterns are stored to be used in up‐coming scenarios, helping solve future market situations.
The A.I implements trades at a high frequency, low volume (HFLV) approach. This means that although there are potentially thousands of trades over the terms of the investment, only a tiny percentage of the account is utilized per trade. The A.I system is constantly monitored, updated and overseen by a team of experienced traders and coders. This ensures that human intuition and foresight is not removed from the product offering, rather only the inherent emotional risk with discretionary trading. In order to assure the quality of the AI trading system, our multi-layer platform is integrated with AWS, IBM Watson and Google AI.
Our Loyal Infinity is the result of years of research and development by specialists in Artificial Intelligence, Finance and Mathematics, who combined their skills specifically to design the best tools for helping traders invest automated or manually with AI forex signals in the forex market, regardless the experience level.