Gold-i helps MetaTrader brokers implement commission-based model with Swap Free plug-in

Rick Steves

“Our Swap Free plug-in is available as a standalone product and is accessible to all MetaTrader brokers, with no need for them to change their existing infrastructure. It is simple to use and brokers can easily target who to apply the swap free logic to by using common fields such as an account or group.”

Gold-i, the leading provider of MetaTrader tools, has expanded its product offerings with the introduction of a Swap Free plug-in for MT4 and MT5.

Released in response to client demand, this plug-in allows brokers to provide trading accounts without interest payments.

Swap Free plug-in prevents the creation of swaps on open positions

The Swap Free plug-in prevents the creation of swaps on open positions, enabling brokers to implement a commission-based model instead. Brokers can choose from multiple methods to calculate the commission charge, offering flexibility and control. The plug-in is competitively priced and is available on a fixed fee basis.

Tom Higgins, CEO of Gold-i, highlighted the accessibility of the Swap Free plug-in. “Our Swap Free plug-in is available as a standalone product and is accessible to all MetaTrader brokers, with no need for them to change their existing infrastructure. It is simple to use and brokers can easily target who to apply the swap free logic to by using common fields such as an account or group.”

Gold-i, known for developing fast and reliable MT4 and MT5 plug-ins, has a portfolio that includes IB Profit Share, Price Monitor, Margin Caller, Swap Loader, and Balance Monitor. Specializing in Multi-Asset Liquidity Management, MetaTrader tools, and Risk Reporting, Gold-i aims to assist brokers in maximizing profits, reducing risk, and standing out in a competitive market.

A recent move by Gold-i was the partnership with DL Consulting in Q4 2023 to expand its reach in the Cyprus financial market. The partnership will focus on promoting Gold-i’s enhanced liquidity platform, MatrixNET, as well as its MAM and business intelligence tool, Visual Edge.

DL Consulting’s in-depth product knowledge and proximity to Cyprus-based brokers will enable personalized client interactions and tailored technology adoption strategies.

Gold-i’s Tom Higgins discussed rise of AI in retail trading

In a recent feature article by FinanceFeeds, Gold-i’s Tom Higgins drew attention to the crucial distinction between different types of AI used in trading. He clarified that while AI has become a widely used term, it’s essential to specify the type being referred to. He downplayed the relevance of natural language-based AI, like ChatGPT, in the context of trading due to the limited natural language involved in trading activities.

“Well it really depends on what you mean by “AI”. Unfortunately, the term has become so abused and distorted over many years of incorrect use, that it is important to define which type of AI we are referring to. The current buzz in AI has come from tools like ChatGPT, which is a type of AI that is trained with vast amounts of data from the Internet and is then coupled with natural language engines to understand the original question and to generate an answer in a language that us mere humans can understand. This type of AI is not very useful in the context of traders and brokers as there is little natural language to deal with and learning half the knowledge of the Internet is irrelevant.

GOLD i 2014

Higgins recalled the importance of machine learning (ML) from structured datasets, particularly in risk management and business intelligence tools like the Gold-i Visual Edge platform. He explains how ML helps in identifying known and unknown patterns of trading abuse by analyzing input data. When known patterns fail to identify undesired outputs, ML-based AI steps in, recognizing new patterns that might cause losses.

“AI (the ML type) comes in handy when you know there has been some undesired output (like excess broker losses), but none of the known “input” patterns were able to identify it. The AI system will analyse all the input data, when it sees this undesired output, and will find patterns that the broker did not even know they should look for. The “learning” bit (comes into play now as the AI system remembers this rogue input pattern and will now also search for this pattern with the existing fixed patterns. The system will continually add to, and adjust, the patterns it is searching for as it learns the input patterns that cause losses and those that do not.”

“The first stage of deployment of this type of AI is to alert the broker of these new patterns it has found, but not to take any action. The second stage is for it to take some action, like A-booking the client in question if they are B-booked, or even rejecting their orders if necessary.”

That said, Higgins acknowledges potential risks during the initial phase of AI implementation, such as false positives, where AI may identify undesired patterns inaccurately. However, he highlights that these errors tend to decrease over time as the AI analyzes more data and learns complex rules. He stresses the importance of having a clear audit trail to explain AI-driven decisions, cautioning against situations where decisions lack transparency, merely attributed to “Computer says no,” as this lack of explanation could lead to problematic outcomes.

“Initially there may be too many false positives, where the AI identifies an undesired input pattern, but this is perfectly OK. This will improve over time as it analyses more data and learns more complex rules.”

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