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What is TradeRiser | Advantages of TradeRiser Smart Trading

TradeRiser Review

Trading and managing stock have always been lucrative to people across the globe, especially with technological advances assisting in analyzing and the trends in the stock market in real time. Blockchain technology has advanced the future of the stock market and the role played by its analysts.

The major issue faced is that this powerful financial analytics are concentrated in select hands and not available to all. Researching rates and trends and how events affect the market is a slow and tedious process where fake news cannot always be differentiated from the truth. The need of the hour is a one-stop solution to all financial needs where instantaneous answers can be provided.



It is this niche in the industry that TradeRiser targets. TradeRiser is an AI-based tool that meets your every requirement with incentives based on the blockchain platform and data collected by a large team of analysts and researchers. TradeRiser suggests one token-based economy called XTI for rewards and a second for quant model developers and content developers to interact with consumers.

Problem

  • The growth of the web has resulted in more opportunities along with the unstoppable growth of big data but these tools are not accessible to the large multitude and most trading companies are racing to democratize the process.

  • Analysis requires a large amount of human investment. The rapid expansion of cryptocurrencies has made the process even more tedious.

  • The technical knowledge required in this process is quite vast and most managers have to rely on quant model developers to implement models. Due to the bottleneck at most institutions fewer ideas to make profits are executed.

  • The existing process is extremely time-consuming due to the need for multiple steps to be completed as well as inefficient because of the multiple data gathering and analyzing steps executed and reports to be created.

  • Data collected today is often unstructured and the chances for data overload are quite high.


Proposed Solution

TradeRiser proposes the use of an AI-based assistant to solve these issues. The AI can answer any queries posed by the consumer in real time allowing them to make better profits.TradeRiser’s token mechanism keeps track of and compensates analysts for their datasets of questions, data validation, accuracy checking, suggestions and example report creation. This way analysts can contribute towards training the machine learning Research Assistant, and are compensated accordingly. XTI ie the token mechanism used on this platform is the underlying mechanism used to facilitate this ecosystem and provides XTI holders with direct participation. XTI is also used as an incentive and to reward loyal consumers.

Traditional approach vs TradeRiser

Traditional:

Gather data from multiple sources--->Clean data--->Load data into a spreadsheet--->Analyse--->Develop reports

TradeRiser:

Ask Traderiser--->Obtain answer from the AI assistant--->Create a report

As you can see from the above metric is much more efficient and powerful than traditional methods of financial analysis.

With the advent of cryptocurrencies, algorithmic trading and machine learning trading has become popular but still requires technical know-how which is not accessible to all. TradeRiser proposes the development of a natural language based tool for trading to cater to the market.

Technology behind TradeRiser

  • Blockchain


This is a software platform where data is stored across a connected network of computers making it a decentralized system that is not controlled by any central authority. Storage and handling of information is based on a set of predetermined rules called smart contracts based on which peer-peer infrastructure is built on the Ethereum platform. XTL is used to provide the necessary infrastructure and incentives.

  • Natural Language Processing


A multidisciplinary linguistic science related to the automatic or semi-automatic processing of human languages. This software is used in the training of the AI assistant.

  • Artificial intelligence (AI)


AI uses data to build models that are used to perform certain functions. The aim of an AI is to simulate the working of a human mind ie to respond to situations automatically without being told what to do by a human controller. Today AI is everywhere, in entertainment, home assistance and on our smartphones too.

XTI Ecosystem

XTI ecosystem is the environment in which TradeRiser works. The entities that make up this ecosystem are:

  • Data Providers

  • Virtual assistants

  • Research assistant technology stack

  • XTI holders / TradeRiser users

  • TradeRiser team

  • Brokers & Exchanges

  • Quants & Analytics Firms

  • Hedge funds/Wealth management firms/ Robo advisory apps


The Challenge

TradeRiser faces one major challenge at the moment ie “reaching critical mass” or acquiring the data set of questions that prospective users may ask the research assistant, which will allow it to be prepared to answer all queries. To do this knowledge will be acquired from a large sample space after compensating the participants in XTI. Using this gathered data machine learning algorithms will build the required models. On achieving “critical mass”, consumers will be attracted to the network through incentives and rewards creating a chain effect, with more consumers joining the platform each day. TradeRiser intends to release three versions of the software to meet all requirements.

  1. Community Edition

  2. Research Marketplace

  3. Enterprise Edition


Team & Advisors

The TradeRiser team is headed by Dennis Owusu-Ansah, CEO & Founder. Other members include Poly Apraku Chief Technology Officer & Co-founder, Rocky Asante Chief Engineer & Co-founder,  Daniel Jiang Head of Blockchain Technologies, and Sunil Kumar Full Stack Developer.

TradeRiser Team

Advisors to the TradeRiser family include eclectic individuals like Luca Zaccagnino - CFA, Investor, Former Business Consultant at RBS Bank, Thomas Wicka - Managing Director at Lloyds Banking Group, Thomas Howell - Growth Strategy at Google, Jude Addo - Director of Private Banking at Standard Chartered Bank and Former Analyst at JP Morgan, Professor Tatiana Kalganova - Reader In Intelligent Systems at Brunel University and Kirill Klinberg - Associate at JP Morgan David Sheppard Former Commodities Trader at Morgan Stanley.

TradeRiser Partners

For More Details of TradeRiser:
Official Website: https://www.traderiser.com
Whitepaper: https://www.traderiser.com/sites/default/files/TradeRiser_WhitePaper.pdf
Telegram: https://t.me/traderiser
Twitter: https://twitter.com/traderiser

 

 

Post By Anton De Mel
Bitcointalk Profile: https://bitcointalk.org/index.php?action=profile;u=1770640

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