Alphacat & The ACAT Token

[Alphacat-Support]

Alphacat - Quantification of the Decentralized Future

Alphacat is the most advanced AI robot advisor & marketplace for simple-to-use yet powerful trading tools focused on cryptocurrencies. Developed by a truly stellar team and built with the NEO platform, Alphacat provides tools which gives its users a competitive edge by leveraging proven & patented trading algorithms, combined with advance Artificial Intelligence and Big Data technologies. The goal of Alphacat is to make investing in cryptocurrency as easy and accessible for everyone regardless of their background or experience.

Alphacat Team
The team consists of some of the best minds in the world, with more than 30 experts with advance degrees in the field of Artificial Intelligence (AI), Machine-learning, Big Data and Blockchain. Our experts have real world experience working on Wall Street and in Fortune-500 companies such as Google DeepMind, UBS and Merrill Lynch, to name a few. Bringing years of award-winning knowledge, our experts average working experience is a staggering 10+ years in their respective fields, now pulled together to develop the AlphaCAT algorithms and platform.

The founder, Dr. Li Bin holds a Ph.D. in physics from New York University and has 21 years of experience in the financial industry and quantitative investment on Wall Street. He has successfully served as Vice President at Merrill Lynch, Executive Director/Head of Quantitative Trading Strategies Group at UBS, and Chairman and CEO of Tradetrek.com. In 2000, Dr. Bin and his partners co-founded AAStocks.com, which is currently the most successful financial website in Hong Kong. He has experience in managing funds and assets of more than 10 billion dollars.

Alphacat Platform & Advantage
Alphacat is a decentralized platform built on NEO and is designed to be as simple as possible, allowing even entry level investors to benefit from a powerful set of tools that utilize AI, blockchain, Big Data analysis, and other technologies not yet commercially available in the world.

The ACAT-Store is a marketplace offering a range of advance AI-powered trading robot application that offer advisory services to its users. You can browse the marketplace for your desired application and use its services with a simple click-of-a-button. Some applications work in a pay-per-use model, some offered with a subscription model, and some offered for free.

The marketplace includes applications that were initially developed by the Alphacat team and will later include applications developed by third party development teams using the Alphacat platform and engine.

Over the years Alphacat team had developed a series of successful and proven algorithms in the areas of finance, real estate, Ecommerce, and exports. Alphacat holds international patents in Fintech, with the most outstanding ones include:

· Market neutral (stock) pairing trading model

· Automatic analysis for financial assets

· Financial product prediction system

· Investment graphic automatic search system for technical analysis

The Alphacat team intellectual & technological capabilities puts Alphacat at the forefront of the quantitative investment in Fintech and the Blockchain digital assets field.

Alphacat Partners and Adviors
Since its launch, Alphacat have partnered and received investments from more than 20 highly regarded institutional and individual with some top names include:

Node Capital

ChainFunder

PreAngel Capital

ChainLinker Fund

WaterDrip Capital

Aware Capital

PROBING VC

HQ Finance

Roots Capital

ALPHA CONNET

NEXT BLOCKCHAIN Fund

CFun

Block VC Fund

Influence Chain

Kucoin

Bo Shen — the Founder of FenBuShi Venture Capital

Richard — the Partner of DFJ Dragon Fund, etc.

Please visit us @ www.Alphacat.io
Please join our Alphacat Global Community on Telegram @https://t.me/alphacatglobal
Please visit our other social media platforms:
Medium：https://medium.com/@AlphacatGlobal
Twitter：https://twitter.com/Alphacat_io
Facebook：https://www.facebook.com/Alphacat.io/

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[Alphacat-Support]

Alphacat — The future of Trading

Thinking about investing, yet scared of volatility in the market? Don’t have the necessary tools to study the market? Maybe you have researched and created an algorithm for market analysis but do not want to share it with big banks and financial institutions. The solution is now available — Alphacat, the world’s 1st robot adviser marketplace focused on cryptocurrencies. It is also the first platform to provide AI trading robots for users that offer advice to invest in crypto. The goal of Alphacat is making investing as easy as buying a bottle of Coke.

Alpha+CAT = Alphacat!

‘Alpha’ in financial sector means excess returns, and CAT is the short form of Computer Aided Trading. We use robots in an exchange that advise you about investments instead of human labor in a 24/7 environment.

Immediately after the launch of the product, Alphacat received more than 20 investment partners including but not limited to: Node Capital, ChainFunder, Preangel Capital, Chainliker Fund, Water Drop Capital, Prophet Capital, Probe Angel Investment, Fireball Finance, and Origin Capital.

About the Founder

The founder of Alphacat, Dr. Bin Li, served as a Vice President at Merrill Lynch, he was Executive Director/Head of Quantitative Trading Strategies Group at UBS, and Chairman and CEO of Tradetrek.com.

As if this was not enough, he also holds a Ph.D. in physics from New York University. The star founder did not stop there, after founding one of the most successful financial websites in Hong Kong, he moved on to managing funds and assets worth more than $6 Billion.

The Rockstar Team

Alphacat has 30+ experts with degrees in AI, machine learning, finance technology and big data. Apart from Dr. Bin Li, about whom we discussed earlier, the team consists of the below notable members:

Mary Ma, Co-Founder & COO

Previously a part of Fuson Group and Wanke.

Dr. Alex Hou, Partner, Director of Risk Management

He holds a Ph.D. in Astrophysics from University of Science and Technology of China and he is also an artificial neural network machine learning expert.

Dr. Yue Wang, Chief R & D Engineer

He holds a Ph.D. in Artificial Intelligence from the University of Greenwich, UK; he has two invention patents in Blockchain technology and face recognition technology; R & D Engineer at Deep Mind (the company that originally created the AlphaGO system that defeated the human champion in the game of GO, later acquired by Google), and former vice president and co-founder of Lightening & Sharp Inc.

Peter Yan, Partner, and Technical Director

He holds a MSc in Systems Engineering from the Chinese University of Hong Kong, he is also a Berkeley Exchange Scholar, University of California, USA.

Yaqi Huang, Partner, and Director of Quantitative Trading

Big Data Analyst; Masters in Electrical Engineering from China Science and Technology University.

Bo Gao, Partner, and Marketing Director

10 years of financial industry experience; Ex-Financial analyst/speaker at China National Radio and China Business News.

Achievements

Alphacat has acquired 4 U.S. financial technology patents to date and 10 international patents. To confirm the technological capabilities of project, Alphacat has won a number of international patents in Fintech, including ‘Market neutral (stock) pairing trading model’, ‘Automatic analysis for financial assets’, ‘Financial product prediction system’, ‘Investment graphic automatic search system for technical analysis’ and many more.

These facts give us the impression that the team has solid technological knowledge while at the same time reinstating confidence in the fact that they are changing the face of future trading.

Market — Then and now

With the development of Fintech in last 10 years, financial markets have been churning out more data or rather analyzing big data. The share of quantitative transactions in the market value of investments held in the last few years alone rose from 8% in 2011 to 13% by 2016.

Let’s face it, humans cannot work at a stretch like robots, and thus robots are considered very efficient at processing a large amount of data without taking a break. Not to forget, the massive amount of money they save.

The Problems

Ordinary investors cannot directly benefit from Fintech in their day to day routine investments. The costs, resources, expertise, and information associated with the financial technologies are usually only available at the disposal of large companies. This is a setback to millions of investors across the globe that cannot use Fintech to their advantage and strategize their financial goals.

Secondly, creators do not get their due while working with large banking or financial firms. For e.g., a creator/inventor develops a trading algorithm which is then used by the firm that he/she is working for. Though he is credited for his/her work, he cannot directly profit from it and will be paid according to his/her contract. Hence this roadblock of not being rewarded directly and working under contracts erodes the possibility of many researchers working freely.

Until now, no financial institution is looking to apply Fintech to the crazily evolving field of digital currency. Pegged to grow to nearly one trillion U.S dollars very soon, this is a mammoth growth field where Fintech can be implemented effortlessly for the realization of new strategies.

Solution — The Development of Alphacat

Keeping in scope the above limitations, Alphacat is being developed utilizing a mix of AI, Blockchain, Big Data analysis and other technologies in which one could have thought impossible to work with a few years back, let alone combining them to make an ecosystem.

Investors will be able to use Alphacat’s trading and forecasting models as well as the third-party developers. The end-user (read as Investor) will be then able to use Alphacat’s rating and review system to give their feedback on their platform.

While doing all this Alphacat will necessarily be creating an open system where developers/creators can submit their own professional investment strategies using the keystone tools on the Alphacat platform. The platform also gives freedom to the inventors to develop, test and deploy the algorithms that they created.

Alphacat Platform

The Alphacat ecosystem is built over NEO, that ensures the privacy and information of investors remains secure and safe while at the same time being fast and providing stability of information transfer.

The platform will allow researchers to create technology papers and share them in the community. The papers will be maintained and protected on the NEO Blockchain and could be shared with the community only by the permission of the creator.

How does it work?

Alphacat will allow the user to use the robot-based AI trading which will make the market insights readily available to a normal investor.

The ecosystem is built around 3 types of users:

1. Architect/Engineers (Financial Experts/Programmers) — People who generate the basis for algorithms after scanning through complicated financial information which is otherwise difficult to understand by a normal person.

2. Surveyors or Professional Investors — Investors who use algorithms to analyze the market trends and invest accordingly.

3. Ordinary Investors — They want to participate in token investment with little or no prior investment experience or knowledge.

The Alphacat Token aka ACAT Token

1. It could be used by the Architects/Engineers to publish their findings/research/algorithms on the Alphacat platform. The publishers will be rewarded with the ACAT token every time their published research results are accessed by the users. Architects can also use the tokens to access the development tools available on the Alphacat platform.

2. Surveyors (Professional Investors) can use ACAT tokens for subscriptions to Architect/Engineers inventions.

3. Ordinary Investors can use the ACAT tokens for using the different services on the Alphacat platform.

The Road Ahead

Phase I:

• Continue development of quantitative trading algorithm engines for major Cryptocurrencies such as Bitcoin and NEO.

• Develop guiding standards and development guidelines for architects and engineers on the Alphacat platform.

• The latest quantitative trading tools and algorithms will be open to early testers.

• Development of forecasting tools of Alphacat.

Phase II:

• Begin development of the Architect and Engineer tools and platform on Alphacat.

• Selection of the first predictor user in the Alphacat community.

• Implement token functionality.

Phase III:

• Architect and Engineer roles are publicly opened to all users.

• Create API to allow Engineers to connect algorithms to exchanges to execute trades without users having to manually interpret insights.

Future

• Research into algorithms and quantitative tools.

• Open quantitative tools and algorithms of the platform to non-financial institutions.

There are several applications currently in development and will be released soon. Some examples are:

1. Alpha Cat 1 — AI trading engine for Chinese A-share market

2. BitCat — Quantitative trading engine for Bitcoin

3. ArbCAT — Arbitrage trader engine

4. FutureCAT — short-term price forecast engine for Cryptos

5. EtherCAT — Quantitative algorithmic trading engine for Ethereum

Important Note: Alphacat 1 Quantitative trading engine completed its Beta release in 2016. Also, the Cryptocurrency market forecast engine Beta has been launched and it predicts the price moment of ETH and BTC for next few days.

Allocation:

• 40% for token crowdfunding

• 15% for Alphacat team

• 10% for Alphacat early investors

• 35% in reserve for future supply to the Alphacat platform

Thoughts:

If they execute the roadmap as promised, they have a huge market to conquer and capture. The idea of bringing AI to trading is unique, plus building the platform over the NEO Blockchain gives them sustainability. Offering the rewards for research-based work and introducing the feedback system and platform user reviews will benefit everyone. Welcome to Alphacat!

[Alphacat-Support]

What patents does Alphacat use for digital asset management?

Core Patent 1: Automatic analysis of financial assets.

United States Patent Link:

http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=%2Fnetahtml%2FPTO%2Fsearch-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN%2F6453303

United States Patent

6,453,303

Li

September 17, 2002

Automated analysis for financial assets

Abstract

A system is provided for automatically generating and displaying market analysis related to financial assets whereby the analysis is provided for substantially all financial assets. The system includes a computer, database accessible by the computer and having stored thereon historical and real time data relating to a financial asset, and software executing on the computer for generating and displaying market analysis. The market analysis may, but not necessarily, include historical and real time data, a measure of liquidity and volatility of a financial asset, a measure of a financial asset’s historical performance, an analysis of a financial asset’s return in relation to its risk, and computed correlation coefficients and analysis of relationships between a financial asset and its market or market sectors.

Inventors:

Li; Bin (Westport, CT)

Assignee:

Westport Financial LLC (Westport, CT)

Family ID:

22528665

Appl. No.:

09/640,107

Filed:

August 15, 2000

Current U.S. Class:

705/36R

Current International Class:

G06Q 40/00 (20060101); G06F 017/60 ()

Current CPC Class:

G06Q 40/02 (20130101); G06Q 40/06 (20130101); G06Q 40/08 (20130101)

Field of Search:

705/1,35,36,37,10 707/10,100

Core Patent 2: Financial product prediction system.

United States Patent Link:

http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6735580

United States Patent

6,735,580

Li , et al.

May 11, 2004

Artificial neural network based universal time series

Abstract

A neural network based universal time series prediction system for financial securities includes a pipelined recurrent ANN architecutre having a plurality of identical modules to first adjust internal weights and biases in response to a first training set representing a nonlinear financial time series of samples of a financial quantity and a target value, and then determine and store an estimated prediction error of the ANN in order to adjust short time stock price predictions in accordance with the stored prediction error. The prediction system is also designed to output upper and lower prediction bounds within a confidence region.

Inventors:

Li; Liang (Mission Viejo, CA), Tang; Yi (Kendall Park, NJ), Li; Bin (Westport, CT), Wu; Xiaohua (Mission Viejo, CA)

Assignee:

Westport Financial LLC (Westport, CT)

Family ID:

32232966

Appl. No.:

09/648,878

Filed:

August 25, 2000

Current U.S. Class:

706/21 ; 706/30; 706/925

Current International Class:

G06N 3/00 (20060101); G06N 3/04 (20060101); G06F 015/18 ()

Current CPC Class:

G06N 3/049 (20130101)

Field of Search:

706/30,21,925,15,19 705/10,35

Core Patent 3: Investment graphic automatic search system for technical.

United States Patent Link:

http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6832210

United States Patent

6,832,210

Li

December 14, 2004

Market neutral pairtrade model

Abstract

A process for implementing a market neutral relative value strategy to provide up-to-the-minute equity trading recommendations includes selecting pairtrades, each having both high correlation coefficient and high de-trended correlation coefficient. The de-trended coefficient is determined for a time interval selected such that a starting price of one of the stocks of a pairtrade is substantially equal its ending price. The process further includes calculating an optimal financial hedge ratio by determining regression slopes of the stocks in each selected pairtrade with respect to one another. The selected pairs are further compared with a plurality of templates to select only valid pairtrades whose amplitude and number of crosses through and deviations from the regression line match at least one of the templates. Applying a modified LaGrange-Urenbeck process to determine the optimal cut-loss and profit taking boundaries further optimizes each of the valid pairtrades.

Inventors:

Li; Bin (Westport, CT)

Assignee:

Westport Financial LLC (Westport, CT)

Family ID:

33492659

Appl. No.:

09/639,325

Filed:

August 16, 2000

Current U.S. Class:

705/36R ; 705/37

Current International Class:

G06F 017/60 ()

Current CPC Class:

G06Q 40/04 (20130101); G06Q 40/06 (20130101)

Field of Search:

705/36

Core Patent 4: Market neutral pairing trading model.

United States Patent Link:

http://patft.uspto.gov/netacgi/nph-Parser?Sect2=PTO1&Sect2=HITOFF&p=1&u=/netahtml/PTO/search-bool.html&r=1&f=G&l=50&d=PALL&RefSrch=yes&Query=PN/6907404

United States Patent

6,907,404

Li

June 14, 2005

**Please see images for: ( Certificate of Correction ) **

Automated investment chart pattern search system for technical analysis

Abstract

An automated investment chart pattern search system is provided. The system includes a computer, a historical information database accessible by the computer having historical information for a plurality of investments stored thereon, a connection to a supply of real-time data, the real time data comprising real-time data relating to a plurality of investments, and a templates database accessible by the computer having a plurality of templates stored thereon. Software executing on the computer generates an investment chart for the investment to be examined based upon the historical information and the real-time data relating to the investment to be examined. Software executing on the computer then retrieves at least one template from the templates database, and performs geometric projection analysis on the retrieved template and the investment chart to determine if a pattern exists in the investment chart. Next, software executing on the computer retrieves at least one template from the templates database, and performs template matching analysis on the retrieved template and the investment chart to determine if a pattern exists in the investment chart. Software executing on the computer then performs projection line analysis on the investment chart to determine if a pattern exists in the investment chart.

Inventors:

Li; Bin (Westport, CT)

Assignee:

Westport Financial LLC. (Westport, CT)

Family ID:

34635990

Appl. No.:

09/640,030

Filed:

August 16, 2000

Current U.S. Class:

705/36R ; 705/35; 705/38; 705/39

Current International Class:

G06F 017/60 ()

Current CPC Class:

G06Q 20/10 (20130101); G06Q 40/00 (20130101); G06Q 40/025 (20130101); G06Q 40/04 (20130101); G06Q 40/06 (20130101)

Field of Search:

705/35,36,37

Please visit us @ www.alphacat.io
Please join our Alphacat Global Community on Telegram: https://t.me/alphacatglobal
Please visit our other social media platforms:
Medium：https://medium.com/@AlphacatGlobal
Twitter：https://twitter.com/Alphacat_io/
Facebook：https://www.facebook.com/Alphacat.io/

[torant]

Revisiting this thread...can’t seem to find many others on ACAT unless I missed it...

Great project here and fairly active twitter account from dev team. This is a sleeper considering how hot AI and technology is right now. Volume starting to pickup on KuCoin.

Dev team has good credentials and patents show good foundation. I’m looking forward to seeing how this updated roadmap plays out throughout 2018!

https://medium.com/@AlphacatGlobal/alphacat-new-roadmap-46bf13aed6db

Q1 2018
Database & API
· Coinmarketcap API: Real-time data API
· Exchange API: 4 Exchanges（completed）
Alphacat AI Forecasting Engine
· BTC Daily Forecasting (email version): Closed beta
ACAT Store
· Underlying architecture development

Q2 2018
Database & API
· Coinmarketcap API: Historical API
· Exchange API: 8 Exchanges
· Alphacat Database: Server architecture & DB structure; Accumulatively recording 50 million data sets
Alphacat AI Forecasting Engine
· BTC Daily Forecasting (email version): Open beta
· ETH Daily Forecasting (email version): Closed beta
· NEO Daily Forecasting (email version): Closed beta
· EOS Daily Forecasting (email version): Closed beta
ACAT Store
· H5 development
· English testing version online
· Basic account system

Q3 2018
Database & API
· Alphacat Database: External API development (Terminal); Accumulatively recording 200 million data sets
Alphacat AI Forecasting Engine
· ETH Daily Forecasting (email version): Open beta
· NEO Daily Forecasting (email version): Open beta
· EOS Daily Forecasting (email version): Open beta
· BTC Real-time Forecasting: Closed beta
· ETH Real-time Forecasting: Closed beta
Alphacat Index Engine
· Top 10 index daily forecasting
· Weight Index: Top 10 index closed beta; Top 10 index open beta
Alphacat Analysis Tools
· Market Status Analysis: Closed beta; Open beta
· Digital Currency Asset Allocation Analysis: Closed beta; Open beta
ACAT Store
· Application review
· Statistical system
· Offline developer development
· Comment system
· Application management system
· Chinese testing version online
· Secondary authentication system

Q4 2018
Database & API
· Exchange API: 16 Exchanges
· Alphacat Database: Accumulatively recording 400 million data sets
Alphacat AI Forecasting Engine
· BTC Real-time Forecasting: Open beta; Online
· ETH Real-time Forecasting: Open beta; Online
· NEO Real-time Forecasting: Closed beta
· EOS Real-time Forecasting: Closed beta
· Top 10 index real-time forecasting
Alphacat Index Engine
· Weight Index: Top10 index online; Top20 index closed beta; Top20 index open beta
· Industry Index: 5 industry indexes Closed beta; 5 industry indexes open beta
· Representative Public Blockchain Ecological Index: Neo ecological index closed beta; Neo ecological index open beta
Alphacat Analysis Tools
· Market Status Analysis: Online
· Digital Currency Asset Allocation Analysis: Online
ACAT Store
· Order system
· Transaction history
· Online developer community
· Payment system

Q1 2019
Database & API
· Exchange API: 20 Exchanges
· Alphacat Database: Accumulatively recording 800 million data sets
Alphacat AI Forecasting Engine
· NEO Real-time Forecasting: Open beta
· EOS Real-time Forecasting: Open beta
· Top 10 constituent coin daily forecasting and real-time forecasting
Alphacat Index Engine
· Weight Index: Top20 index online
ACAT Store
· Copy-trading system
· Ranking list system

[cointopper]

Hello Alphacat believers,

Our aim is to add value to the whole crypto community by giving the best user experience to analyse cryptocurrencies.

It would be really great help from your side if you can spend 60 seconds of your time and give us your valuable feedback for our two recent updates 1) Snapshot and 2) Colourful interface of Alphacat at: https://cointopper.com/coin/alphacat

Constructive criticisms are always welcome at CoinTopper.  Thank you!!

[BitMaver]

Alphacat has been invited to attend the 12th Shanghai Financial Services Fair.
Alphacat Founder, Dr. Bin, will deliver a keynote address at the fair.
Exhibition dates: June 27 — June 29, 2018

Learn more: https://medium.com/@AlphacatGlobal/alphacat-has-been-invited-to-attend-the-12th-shanghai-financial-services-fair-955e8b190123.

[BitMaver]

Alphacat announced that it has entered into a deep, strategic agreement and partnership with Israeli blockchain cybersecurity company,
SecureForest.

https://medium.com/@AlphacatGlobal/alphacat-and-israeli-blockchain-cybersecurity-company-secureforest-have-entered-into-a-strategic-6a9a22c6335b

[icohunter1024]

Alphacat plans to disrupt and revolutionize the FinTech industry by offering a complete eco-system for digital assets based financial applications and services. It will serve as a platform for all levels of investors to make informed investment decisions, using its robo-advisor services, and also serve as a full-scale development platform for future service creators who seek to utilize these powerful tools and datasets, typically reserved for large financial institutions – until now.