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TradeRiser (XTI) ICO Review

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TradeRiser is a decentralized social networking platform that is putting users privacy and satisfaction as its first priority. It is an innovative approach towards transparent and independent means of user data ownership, reward on ads and free of speech. It is the first get paid to content creation and sharing ecosystem that leveraged OCR token payments for its reward system.

Essential Information

Ico TimeUnknown – Unknown
Token NameTradeRiser
Token SymbolXTI
WhitepaperView Whitepaper
Website LinkHome
Price1 XTI = 0.1 USD
PlatformEthereum
AcceptingBTC, ETH
Hard Cap23,000,000 USD

More about TradeRiser (XTI) ICO:

TradeRiser is solving the enormousness time spent by traders and investing when researching and trading the financial and crypto markets. We are doing this by providing an AI Research Assistant that can instantly answer both simple and complex trading questions. We using the blockchain to build an ecosystem of workers and reviewers that carry out machine learning tasks.

In world of trading and investing, the most powerful financial analytics is normally in the reserve of the few. TradeRiser is looking to disrupt this, by democratizing financial data analytics and making it available to the masses. Researching trading ideas and exploring the financial markets is a slow process. What is required is a single source of truth, that can provide instantaneous answers, to trading questions at a large scale. Specifically how the news and events affect asset prices around the world.

TradeRiser is an artificially intelligent Research Assistant, that can answer simple and complex trading questions. To train the artificial intelligence we will be leveraging the blockchain to build an incentivization system, which will be supported and fed by data from a large network of quantitative analysts and researchers. A token based economy called XTI will be introduced, to incentivize researchers, for their data and contributions to the platform.

Following this a second economy will be created, around a research marketplace, in which quant model developers and content producers will be able to reach consumers within the ecosystem. This participation of the community will help fulfill the goal of democratizing and simplifying financial data analytics.

The Problem

Motivation – S implifying financial data analytics The growth of the world wide web led to innovations in search engine technology. This made the web more accessible and ubiquitous. However financial data analytics, has not enjoyed the same the level of simplicity and accessibility seen in the world wide web. The growth of big data cannot be stopped, financial firms and individuals alike are in a race to find trading opportunities. This task will only get harder as new avenues of data are discovered, human beings will struggle to keep up. This disconnect in accessibility and ubiquity presents a huge opportunity, to systems that seek to democratize financial data analytics.

Disrupting Human Intensive Research

TradeRiser is building an AI based Research Assistant, that can answer both simple and complex trading questions. Financial professionals worldwide spend a lot of time and money in research trying to answer these trading questions. This kind of research is normally time consuming, inefficient, prone to information overload and requires a lot of manpower. These problems are further compounded with the advent of cryptocurrencies and financial professionals wanting to trade them, alongside traditional securities. The rapid explosion of cryptocurrencies has left many other technologies playing catch up, individual traders need an easy way to analysis these asset classes.

Fewer Ideas Are Tested

Current platforms rely on a great degree of technical know how to test trading ideas, and due to the barriers to entry fewer trading ideas are tested. Every day a portfolio manager has an investment idea and has to go to a quant to build the model. That’s a bottleneck within most financial services firms, and as a result far fewer ideas are tested. The same is true of individual traders who want to test ideas but do not have access to sufficient tools.

Time-Consuming

Quantitative research can be an incredibly time consuming process, as it requires multiple steps in order to be completed, sometimes spanning across several days and hours. Other bottlenecks include the computational process due to the amount of data being analysed.

Inefficiency

The research process requires the data gathering, data cleaning and data analyses, and the final step being report creation. This is an incredibly an inefficient process.

Information Overload

With data being the new “oil” or a valuable resource, the job of analysts is all the more difficult in trying to process data. New avenues of data are constantly creeping up which can potentially be exploited in financial research, especially unstructured data.

News and Events – Unstructured Data

It is well known that the news and world events have impact on the financial markets, it is for this reason that tools such as the economic and earnings report calendars were created. These tools allow traders to keep up and monitor impactful events, however there is a whole basket of world events that have not be organised to be included in a calendar, that needs to be structured. As it stands traders struggle to keep up or hedge against data from sources such as twitter, cryptocurrency news, weather data and even satellite data. The whole universe of drug approvals, economic reports, monetary policy changes, and political events and their impact on nearly every kind of financial asset needs to be tamed and structured.

Solution

TradeRiser solves these problems through its Research Assistant that can immediately answer trading questions that a trader or investor has about the financial markets. TradeRiser’s token mechanism will keep track and compensate financial analysts for their datasets of questions, data validation, accuracy checking, suggestions and example report creation. The financial analysts can contribute in these ways to help train our machine learning Research Assistant, and be compensated accordingly. XTI is the underlying mechanism used to facilitate this ecosystem, and provides XTI holders with direct participation in advancing our “single source of truth” questioning and answering system.

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