Alphacat Report (November 1–15)
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.
1. ACAT Store
1) In the first half of November, the ACAT Store listed 18 new third party applications, increasing the total number of listed applications to 24. These applications are distributed in the following 5 channel segments: Market Forecasting (6 new Apps), Technical Analysis (3 new Apps), Multi Data (5 new Apps), Asset Allocation (2 new Apps), and Trading Tools (8 new Apps).
All ACAT Store applications will be developed by either: Alphacat’s official development team, third party teams integrated with the ACAT platform, or applications fully developed by third parties. In the early days of our platform, applications were mainly developed via integrated cooperation with the Alphacat Team and third parties. Cooperative development refers to the use of our Alphacat Engine to develop and deploy applications. The 24 applications listed in the ACAT Store are ones the Alphacat team has jointly developed with third parties, and ones that third parties fully developed. We welcome more developers and projects to be included in the ACAT Store.
For developers, we will 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 the different types of developers.
2) At the same time, the promotion page of each channel is launched, which is used to publish the application and promotions for the channel.
2. AI Forecasting Engine
Real-time Forecasting System of Cryptocurrency
Our team continues to study the following issues:
1) Based on the previous version of the price series neural network prediction system application, the neural network is used to comprehensively predict the time series of multiple variables for a currency, and is committed to improving the ability of the neural networks to solve training problems in complex market environments
2) Continued research on how to train the neural network to be able to ignore some of the false market conditions, and capture the correct short-term conditions based on new rules set by the market in a JIT (Just In Time) fashion so it may intelligently capture new trading opportunities when the market environment changes significantly.
As of the middle of November, Alphacat’s global community continued to grow steadily. The number of our Facebook users continued to rise, from 22,120 to 24,340, an increase of 10.0%. The number of Twitter users increased from 16,183 to 16,709, a growth rate of 3.3%.