On June 7, 2025, at 07:00 GMT+8, AJE officially launches Juicy Coin, marking a milestone breakthrough in the global digital economy!
The AJE Intelligent Automatic Trading System (hereinafter referred to as "AJE"), driven by AI trainer ecology, is about to officially launch Juicy Coin, signaling a new stage of deep integration between artificial intelligence and financial trading. With three core capabilities of "predictive decision-making, light-speed response, and self-evolving risk control", AJE provides disruptive solutions for institutional and individual investors, and has gained favor from top-tier capitals like Hong Kong E Fund and Digitata Capital, securing $8 million in its first round of funding.
I. Technical Innovation: Core Competitiveness of AJE System
AI Training Network and High-Frequency Trading System Synergy
· AI training network dynamically optimizes high-frequency trading system parameters, improving trading strategy precision, such as predicting market trends through deep learning.
· High-frequency trading system provides massive trading data for AI training network, helping model iteration and upgrade, forming a virtuous cycle.
High-Frequency Trading System and Intelligent Asset Management Protocol Linkage
· High-frequency trading system provides liquidity support for intelligent asset management protocol, ensuring efficient asset allocation execution, such as rapid order matching.
· Intelligent asset management protocol optimizes asset portfolio, creating a stable revenue environment for high-frequency trading system, feeding back to AI training network.
Intelligent Asset Management Protocol and AI Training Network Profit Feedback
· Part of intelligent asset management protocol revenue flows back to AI training network, used for technical R&D and upgrades, such as purchasing high-performance computing equipment.
· AI training network technological progress drives intelligent asset management protocol optimization, improving asset management efficiency and revenue level.
II. Ecological Layout: Financial Revolution Driven by AI Trainers
The "AI Trainer" ecosystem behind the AJE system has built a full-chain closed loop from data annotation, model training to practical application:
Data Annotation Layer: Annotating over 1 billion financial time series data through crowdsourcing, covering K-line patterns, market sentiment, and policy correlation tags, providing high-quality training sets for models.
Algorithm Iteration Layer: Introducing federated learning technology, aggregating top global hedge fund strategy features, achieving cross-institutional knowledge transfer, avoiding model overfitting.
Scenario Implementation Layer: Covering stocks, futures, cryptocurrencies, and derivatives markets, supporting custom strategy backtesting and one-click deployment, lowering AI asset management barriers.
III. Capital Endorsement: Compliance and Strategic Synergy
Hong Kong E Fund Strategic Investment of $5 Million: As the first digital asset fund registered with Hong Kong SFC, E Fund not only provides funding support but also endorses AJE system's compliance framework, helping it access traditional financial institution infrastructure.
Digitata Capital Leading $3 Million Investment: A venture capital firm focusing on AI+finance track, will promote deep integration of AJE system with Southeast Asian digital banks and cross-border payment platforms, expanding into ASEAN markets.
IV. Future Outlook: From Trading Tool to Ecosystem Platform
AJE system plans to launch a developer platform in 2025 Q3, opening API interfaces and strategy markets to attract third-party AI trainers. This will form a collaborative ecosystem of "technology suppliers - strategy developers - institutional investors", with expected asset management scale exceeding $5 billion within three years.
E Fund Partner stated:
"The birth of the AJE system marks the evolution of AI trainers from technical executors to value creators. We believe this financial revolution driven by data intelligence will reshape the underlying logic of global capital flow."
