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Quantitative investments in decentralized economy

Concept

Quantor is an ecosystem that integrates the marketplace of investment solutions and an online learning platform for crypto-currency markets, knowledge and skills of investment industry experts and developers of investment algorithms.

Computer technologies increase reliability and efficiency in the investment industry, reducing the risk of a human factor in making investment decisions.

The main objective of the platform is to create a know-how conveyor for the implementation of profitable and reliable investment algorithms in a decentralized economy.

Technology

Quantor is an ecosystem that integrates the marketplace of investment solutions and an online learning platform for crypto-currency markets, knowledge and skills of investment industry experts and developers of investment algorithms.

Ecosystem Quantor

The Role of Smart Contracts and Blockchain Technology in Trading Algorithms

Blockchain technology makes it possible to consider a trading algorithm in conjunction with smart contract, which allows you to automate the financial processes on the platform.

The Mechanism for using smart contracts

The executable algorithm file is launched in the Docker container on the platform hosting;

The algorithm analyses the data and sends control signals to the stock exchange;

The actual information is reflected in the smart contract, tied to the algorithm;

Issue and Allocation of tokens QNT

Issue of tokens

Sale on the market

Team and founders

Experts, consultants and partners

Bounty campaign

Allocation of collected funds

Platform development

Marketing

Team and founders

Payroll fund

Unexpected expenses

Quantor team

Sergei Bolshakov Serge Bolshakov

Entrepreneur and manager with 20 years of business experience and work in the field of investment and finance, including asset management experience of more than $ 50 mil.

Vladislav Buchnev Vlad Buchnev

Founder of Consentus Capital Management, Inc. The company was registered with the Commodity Futures Trading Commission (CFTC) as a Commodity Trading Adviser (CTA) in the US.

Aden Aliakberov Aden Aliakberov

Aden is a student at the Faculty of International & European Studies at King's College London. He has an extensive experience in PR and marketing, including participation in successful political campaigns at the University and the Westminster constituency.

Klim Yadrintsev Klim Yadrintsev

Graduate of Brighton College (UK). Certified trader in the stock and derivatives markets. Student at University of London Economics. An expert enthusiast with a great understanding of marketing and human behavior.

Advisors

Ernest Chan Ernest Chan

Dr. Ernest P. Chan is the Managing Member of QTS Capital Management, LLC. His career since 1994 has been focusing on the development of statistical models and advanced computer algorithms to find patterns and trends in large quantities of data. He has applied his expertise in statistical pattern recognition to projects ranging from textual retrieval at IBM Research, mining customer relationship data at Morgan Stanley, and statistical arbitrage trading strategy research at Credit Suisse, Mapleridge Capital Management, and other hedge funds.

Haksun Li Haksun Li

Dr. Haksun Li is a founder and the CEO of NM LTD., an algorithmic trading research and mathematical modeling consulting company. He was a quantitative trader/quantitative analyst with multiple investment banks. Haksun has worked in New York, London, Tokyo, Singapore and Hong Kong. Dr. Haksun Li is the Vice Dean of the Big Data Finance and Investment Institute of Fudan University, China. He has a B.S. and M.S. in Pure and Financial Mathematics from the University of Chicago, an M.S. and a Ph.D. in Computer Science & Engineering from the University of Michigan, Ann Arbor.

Joseph Wang Joseph Wang

Chief Science Officer at Bitquant Research Laboratories. Almost a decade of experience in developing C++ quantitative finance libraries. A decade and half of working experience in developing a wide variety of commercial software. Over two decades of working experience developing scientific and academic software. Experience includes high performance grid computing, complex numerical supercomputing code, database programming, machine learning, and other systems. Developing quantitative finance models to model Chinese securities markets and the global bitcoin markets.

Anastasia Everskova Anastasia Everskova

Private practice (IP, IT and LegalTech). She has an economic and law backgrounds, strong experience in supporting international clients' services (GroupM, Dentsu/Aegis, IPG, a global media investment management groups); worked with offshore/onshore jurisdictions (Cyprus, Singapore, BVI, Gibraltar, Isle of Man) and supported some other ICO projects. Education: International Law Institute, Moscow (2009),

Haksun Li Haksun Li

Dr. Haksun Li is a founder and the CEO of NM LTD., an algorithmic trading research and mathematical modeling consulting company. He was a quantitative trader/quantitative analyst with multiple investment banks. Haksun has worked in New York, London, Tokyo, Singapore and Hong Kong. Dr. Haksun Li is the Vice Dean of the Big Data Finance and Investment Institute of Fudan University, China. He has a B.S. and M.S. in Pure and Financial Mathematics from the University of Chicago, an M.S. and a Ph.D. in Computer Science & Engineering from the University of Michigan, Ann Arbor.

Joseph Wang Joseph Wang

Chief Science Officer at Bitquant Research Laboratories. Almost a decade of experience in developing C++ quantitative finance libraries. A decade and half of working experience in developing a wide variety of commercial software. Over two decades of working experience developing scientific and academic software. Experience includes high performance grid computing, complex numerical supercomputing code, database programming, machine learning, and other systems. Developing quantitative finance models to model Chinese securities markets and the global bitcoin markets.

Experts

Kirill Ilinski Kirill Ilinski

Russian born British businessman and scientist. He is the founder and Chief Investment Officer of Fusion Asset Management and the author of “Physics of Finance: Gauge Modelling in Non-Equilibrium Pricing”.

Maxim Bouev Maxim Bouev

Maxim is a Doctor of Philosophy in Economics, and a holder of the funded chair in applied finance supported by the JTI group. He has worked in an investment banking division for N.M.Rothschild & Sons, and on the FX quantitative analytics teams for ABN AMRO Bank and the Royal Bank of Scotland in the City of London.

Alexander Klimenko Alexander Klimenko

Major works in the field of systems programming and cybernetics. Professor at the systems integration and management department of the MIPT. He was involved in FX market research and trading strategies development since 1995. Alexander graduated from Moscow Institute of Physics and Technology.

Oksana Malysheva Oksana Malysheva

Managing Partner at Linden Venture Fund, Founder and President of Linden Education, Oksana champions the development of innovative, sustainable, world-changing companies. Committed to igniting and fueling social innovation solutions, her mission is creating positive social change through education, technology and innovation.

Vasiliy Pimkin Vasiliy Pimkin

Expert and manager with vast experience in blockchain and web product development, business media and publishing.

Publications

The list of most available online courses to learn Algorithmic trading & Quantitative Finance.

There are many courses offered online on algorithmic trading and quantitative finance. I'd advise to start learning from well-known experts and practitioners in this field. Unfortunately, it’s difficult to find good instructors who can teach algorithmic trading and successfully...

Blockchain in Algorithmic Trading

By mid-2016, the total amount of assets under management of algorithmic hedge-funds was $880 billion. According to the forecasts of McKinsey & Co, the total amount of assets under management of funds using automated trading systems by 2020 could reach $2.2 trillion...

Why Algorithmic Trading?

In today’s world everyone is stimulated to earn money as the whole society is structured around ways of becoming rich and many if not most success stories are linked to trading of which the fundamental idea is to capitalize on the purchases of stocks. Modern technology allows you to easily buy and sell with only a couple of clicks. However, many people are put off by the idea because...

The Rise of the DIY Algorithmic Traders

Every time I talk to someone about how excited I am about algorithmic trading everyone asks me the same questions. Why would I do that instead of any other investment method? Who is writing these algorithms? How can I be sure about it? Do I need to know any programming language or spend the entire day in front of my computer?

Video

Roadmap

Formed the first version of the training program from a set of open courses

2016 January

Launch of the beta version of Quantor website

2016 April

Quantor team was extended by experts from the US, UK, Russia

2016 August

Quantor became the finalist of the EdCrunch 2016 Pitch Competition

2016 September

A participant of the First International Conference of Sberbank Corporate University

2016 October

A participant of the pre-accelerated program of the Internet Initiatives Development Fund

2017 April

Now, we are here!

Concept development of converging blockchain technology with algorithmic trading

2017 July

Issuing platform tokens

Q1 - Q2, 2018

Applying for a license in the selected jurisdiction

Q2, 2018

Creating a platform demo version

Q3, 2018

Debugging and setting up the platform during the demo version work

Q4, 2018

The first contest of developing trading algorithms among quants

Q1, 2019

Launch of the first working version of the platform

Q1 - Q2, 2019

Start collecting statistics and data of the first trading algorithms on live trading accounts

Q2 - Q3, 2019

Launch the marketplace of trading algorithms

Q2 - Q3, 2019

First investments in trading algorithms on marketplace

Q3, 2019

Implementing the function of forming portfolios of algorithms

Q3 - Q4, 2019

Applying for licenses in the US and other jurisdictions as required

Q4, 2019

Quantor crypto fund formation

Q4, 2019

Creating a bridge between the investor's fiat capital and the platform

Q1 - Q2, 2020