IFE Infare Cryptocurrency Exchange

IEO strategy

A realistic business model Infare

Cutting-edge technologies Infare

A minimal viable product (MVP) Infare

A Token Infare

A strong team backing the project Infare

A reasonable hard cap Infare

Initial investors’ buzz Infare

A project website for marketing purpose Infare

DLT AI in price prediction

Infare Token Cryptocurrency Exchange is based on five pillars: Infare community, IFE DLT eBank and IoT devices that are integrated in cryptocurrency exchange. Relations between them are characterized by the flows and interactions within the Infare Community.

Many machine learning and deep learning algorithms such as Gated Recurrent Unit (GRU), Deep Neural Networks (DNNs), deep residual network, Long short-term memory (LSTM), Support Vector Machine (SVM), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and their combinations have been used by the researchers to predict and analyze the factors affecting the cryptocurrency prices. 

Financial derivatives

Besides standard pair swaps, there are various forms or financial derivatives, such as Futures Contracts and Equity Options, are used as advanced trading methods. 

Price prediction based on image analysis

Most of traditinally used machine learning price prediction algorithms rely of on numeric alanysic of stocks prices and other their derivatives. We propose a machine learning image recognition based alanysis when price charts and price combinations diagrams are used as  training datasets and methods such as Extended Kalman Filter are used to predict prices.