Alphacat Report (December 16–31)

Dear Alphacats!

As part of our efforts to be transparent and have regular communication with our community, we are pleased to share our half-monthly report which includes our progress during this period and our outlook for the future.

New Products Released

The Cryptocurrency Risk Analysis Bot (CRAB) and ACE Top Index were officially released! These two applications were independently developed by the Alphacat Team.

Cryptocurrency Risk Analysis Bot: Through analyzing the historical transaction data of a given cryptocurrency, a series of risk indicators are calculated, and through the numerical comparison of risk indicators between different currencies, this tool can comprehensively assess the relative difference in risk between two different currencies, thus revealing the possible reasons for the difference in risk suggested.

This application helps ordinary investors pay attention to the inherent market risk of a given cryptocurrency and helps cultivate a scientific and mature approach to investing.

ACE Top Index: An index made from a variety of representative cryptocurrencies which uses scientific calculations to produce the aggregated value for the ACE Top Index in real time. By comparing the recent historical trends of the index with other cryptocurrencies, (such as BTC), the correlation and difference between a cryptocurrency and the index are analyzed. This allows users to get a better and more complete profile of the trending cryptocurrencies, and a deeper understanding of the currency characteristics which have been examined by the user.

Registered users can enjoy three free predictions per day, and new users can enjoy this tool by registering their email address on our platform. If you need more services, you can purchase them by following the instructions found on the platform.

Product Development

  1. ACAT Store

By the end of December, the ACAT Store had listed 11 new applications, increasing the total number of listed applications to 65. These applications are distributed in the following nine channel categories.

Market Forecasting: 11 Apps

ACE Indices: 1 App

Technical Analysis: 6 Apps

Multi Data: 17 Apps

Risk Management: 1 App

Asset Allocation: 2 Apps

Trading Tools: 19 Apps

Derivatives Market: 7 Apps

Others: 1 App

All of the ACAT Store applications are developed by either: Alphacat’s official development team, third party teams integrated with the ACAT platform, or as applications fully developed by third parties. In the early days of our platform, most applications were developed via cooperation with the Alphacat Team and third parties. Cooperative development refers to the use of our Alphacat Engine to create and deploy applications. The 65 applications listed in the ACAT Store are ones that the Alphacat Team has jointly developed with third parties and ones that third parties fully developed on their own.

We welcome more developers and projects to be part of the ACAT Store and its ecosystem.

For developers, we support the following:

i) Developers at the conceptual stages of product design.

ii) Developers who already have products listed in the store.

iii) Developers who already have products and are certified by Alphacat.

iv) Developers who are working in cooperation with the Alphacat Team.

In the future, we will provide different services for different types of developers.

For listing inquiries and business cooperation, please contact: public@alphacat.io.

2. AI Forecasting Engine

Real-time Forecasting System

(1) Our team is making good progress on solving the prediction issue of multiple input variables. Each time, we use the opening price, highest price, lowest price, and closing price, as input variables for the common deep learning process, and then predict the closing price. This solution is proving to be far superior than only using the closing price as the input, and the accuracy had increased by roughly 5% while using the same dataset. Next, various transaction data will be used for the algorithm verification and the transaction backtesting.

(2) To improve the computing performance, we have improved the PRNN-LSTM algorithm, introducing an acceleration mod to the GPU, and switched to a batch calculation process which prepares for large-scale computational verification in the future.