TrakInvest wants to empower a new generation of milleninial investors by providing them with the tools that they need in order to compete with large investors.
One of the more interesting features of TrakInvest is it’s use of AI technology in the form of a recommendation engine on the platform. Here we’ll look at how this AI recommendation engine works in more detail.
What is TrakInvest AI?
In 2018 TrakInvest is planning to introduce its next generation crowdsourced tools and models, which will use trading and behavioral data which has been collected over the past three years.
TrakInvest AI recommendation engine will be composed of three key initiatives. These include TI Score, Sentiment Analysis and Network Analysis.
Each user on the platform has a TI Score. This TI Score is a combination of that users trading track score and how they have contributed socially to the platform.
The recommendation engine will analyse and rank traders and stocks based on their performance on the TI platform. This will provide a TI STOCK and TI TRADER SCORE.
Sentiment Analysis will examine the sentiment of the crowds. This will be made up of a both a push and pull mode. For the push mode announcement and news will be pushed to user based on their internet. The pull action will provide users with the ability to to use engine to check on the stats of a stock. The information used in this sentiment analysis will be gained from websites that feature specific news, corporate disclosures, twitter feeds and other sources of data. Thus is possible thanks to Natural Language Processing (NLP).
In order to raise funds for research and development of the AI technology and other costs associated with developing and operating the TrakInvest platform, they are running an Initial Coin Offering (ICO). During this ICO the TRAK Token used on the TrakInvest platform will be distributed to investors.
You can learn more about the TrakInvest ICO here:
TrakInvest Website: https://trakinvest.ai/
TrakInvest Whitepaper: https://trakinvest.ai/files/ti-whitepaper.pdf