Xaindex, a new cryptocurrency trading platform, was recently launched with a focus on the development of neural networks and synthetic intelligence for cryptocurrency trading. The platform operates without any restrictions in different regions and has its physical office located in Sydney.
THE Australian Securities and Investments Commission regulates society. The main focus of Xaindex is crypto trading, and the company has accumulated significant successful experience in this field, with seven years of crypto trading and high-tech developments in neural networks.
Xaindex has developed its own version of artificial intelligence called synthetic intelligence (SI), based on these developments in the field of neural networks. This version includes complex human functions and the use of multi-level arrays of dynamic and static data, which provides tangible benefits.
Synthetic intelligence also helps Xaindex to significantly improve liquidity management results in cryptocurrency markets, and also manages liquidity according to risk policy, following new patterns and fractals. As a result, the list of crypto-assets used in liquidity management is smartly adjusted.
The entire financial system of the platform is based on an advanced algorithmic stablecoin model, known as SUT, or Synthetic Utility Token. The difference with the standard model is that the whole system works to provide the token with collateral value.
The platform plans to transfer all transactions to one of the popular blockchain platforms later this year, which will ensure full transparency of transactions, as well as the influx of additional liquidity.
Investors and analysts often use AI in high-frequency trading strategies because it can simulate human intelligence. Algorithms based on mathematical calculation data, predictive analytics and forecasting methodologies can also analyze the markets and buy or sell cryptocurrencies in seconds.
Data scientists can offer accurate trading insights to non-tech-savvy traders or investors through an intuitive dashboard or interface. This is possible through the use of Natural Language Processing (NLP) techniques, which classify data and extract entities by specific characteristics, including currency name, document type, founder currency, etc.