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Quant AI

Deep Machine-Learning Algos

The Quant AI strategy is a sophisticated blend of proprietary AI and machine learning capabilities, designed to navigate the complexities of the market with a level of precision beyond that of human traders. This approach allows us to stay ahead of 99% of the marketplace, giving us a significant edge in the industry.

Our algorithms leverage advanced deep learning and quantum mechanics to calculate probable market scenarios. By analyzing historical data and live market feeds, they identify correlations that can predict future trends and breakout events. The quant-engines operate on fully independent and secure servers, with the ability to self-optimize and improve upon old versions of code, based on current market conditions and newly discovered patterns.

Our proprietary algorithms and natural language processing models represent the next step in the evolution of AI-driven trading technology. By utilizing advanced deep neural networks for data processing, our algorithms can anticipate market shifts with remarkable precision. Continuously learning from users, trades, and datasets, our system refines its predictions to deliver ever-more accurate results.

Strategy Statistics

Quant AI

Quantitative AI Algorithms

Quant AI

Trading Months: 22
Cumulative Return: 164.12%
Monthly Average: 7.46%

FAQs

What asset classes does Matrix AI trade?

The Matrix AI algorithms can be traded on any asset class and still be profitable. But our deep machine learning algorithms have discovered the top performing pairs and have optimized trading for specific Foreign Exchange pairs and Metal Commodities such as XAU/USD, EUR/USD, and USD/JPY.

What are the benefits of using machine learning trading algorithms?

Machine learning algorithms significantly enhance trading by processing vast amounts of data quickly, identifying complex patterns, and improving market predictions. They enable automated trading, which operates 24/7, and offers superior risk management through anomaly detection and portfolio optimization. Additionally, machine learning reduces operational costs by minimizing human errors and increasing efficiency. Its scalability allows for handling large datasets and adapting to various strategies, giving traders a competitive edge by enabling faster and more informed decision-making.

What is the maximum risk?

The maximum risk on the Matrix AI strategy was a drawdown incident of 15.24% which occurred in September 2020. A hard equity stop loss (SL) of 20% is also employed, but has never been triggered since the algorithms creation in 2019.

How much did the technology cost to develop?

The initial cost to build our proprietary trading technology and set up our servers exceeded $1 million dollars, with ongoing maintenance expenses and server upgrades costing us six figures every year.

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