TradeRiser

In the world of trade and investment, the most powerful financial analyzers are usually some that are protected by law. TradeRiser seeks to disrupt this, by democratizing analytical financial data and making it available to the masses. Researching ideas and exploring financial market trading is a slow process. What is needed is the source of truth, it can give instant answers, to trade questions on a large scale. Specifically how news and events affect assets around the world.

TradeRiser is a smart, artificial Research Assistant, who can answer simple questions and complex trade questions. To train artificial intelligence, we will use blockchain to build incentive systems, which will be supported and fed by data from a large network of analysts and quantitative researchers. The Token-based economy called XTI will be introduced, to provide incentives to researchers, for their data and contributions to the platform.

After this second economy will be created, around the research market, where quant model developers and content producers will be able to reach consumers in the ecosystem. Participation of this community will help meet the objectives of democratization and simplify the analytics of financial data.

How does it work?

SPEED
Find investment opportunities and trade quickly.

QUESTION
Have a question, just ask. TradeRiser handles natural language questions

STATISTICS
Using statistics to create and test optimal trading strategies without relying on software engineers and quants

NEWS
Smartly analyzes world news and events data and their effects on cryptocurrency and traditional assets

ECOSYSTEM
Uses blockchain to create decentralized ecosystems from

NOTIFICATION financial analysts
Get signal and give a trade mark.

VIDEO:
Below is the video clip of Expluang

PROBLEM
1. THE PROBLEM

1.1 Motivation – Simplifies financial data analysis

The growth of the world wide web led to innovations in search engine technology.
This makes the web more accessible and scattered everywhere. But the analytics of financial data, have not enjoyed the same level of simplicity and accessibility across the web world. The growth of large data cannot be stopped, financial companies and individuals are equally in the race to find trading opportunities. This task will only become more difficult when new data is discovered, humans will struggle to follow it. Determine this accessibility and wherever it presents great opportunities, for systems that seek to democratize the analysis of financial data.

1.2 Annoying Human Intensive Research

TradeRiser builds an AI-based Research Assistant, which can answer simple questions and complex trade questions. Financial professionals around the world spend a lot of time and money in research to answer this trading question. This type of research is usually time consuming, inefficient, vulnerable to information overload and requires a lot of manpower. These problems are exacerbated by the emergence of cryptocurrency and financial professionals who want to trade them, in addition to traditional securities. The rapid burst of cryptocurrency has left many other technologies that follow, individual traders need an easy way to analyze these asset classes.

1.3 Fewer Ideas Tested

The current platform relies on excellent technical knowledge to test trading ideas, and because fewer ideas enter barriers to trading are tested. Every day a portfolio manager has an investment idea and must go to quant to build a model. That’s the congestion in most financial services firms, and as a result, far fewer ideas are being tested. The same applies to any merchant who wants to test ideas but does not have access to enough tools.

1.4 Timing

Quantitative research can be a very time-consuming process, because it requires many steps to complete, sometimes covering several days and hours. Other congestion includes the calculation process because the amount of data analyzed.

1.5 Inefficiency

The research process requires data collection, data cleaning and data analysis, and the final step is report generation. This is a very inefficient process.

1.6 Excess Information

With data being a new “oil” or valuable resource, the work of analysts is even more difficult in trying to process the data. New paths of data continue to creep can potentially be exploited in financial research, especially unstructured data.

1.7 News and Events – Unstructured Data

It is well known that news and world events have an impact on financial markets, for this reason tools like economic reporting calendars and earnings are made. These tools allow traders to follow and monitor events that are affected, but there is a world event basket that is not set to be included in the calendar, which needs to be structured. Because traders stand struggling to maintain or protect data from sources such as Twitter, cryptocurrency news, weather data and even satellite data. The entire universe of drug approvals, economic reports, changes in monetary policy, and political events and their impact on almost all types of financial assets need to be tamed and structured.

1.8 Solutions

TradeRiser solves this problem through its Research Assistant who can immediately answer trade questions that traders or investors have about financial markets.
The TradeRiser token mechanism will continue to track and compensate financial analysts for their data question sets, data validation, accuracy checks, suggestions, and sample reporting. Financial analysts can contribute in these ways to help us train our Engine Engine Assistant, and are compensated accordingly. XTI is the basic mechanism used to facilitate this ecosystem, and provides XTI holders with direct participation in advancing our “single source of truth” to question and answer the system.

TradeRiser XTI Distribution Name 250 million Tokens