Underlying Technology Logic of M1 AI

Multi-Signature Key Management

Multi-signature key management is a security technique for managing private keys. When multiple parties jointly manage an account, each participant holds a share of the private key. To restore the full key, a predefined number of key shares must be combined. This technology enables cross-chain locking of account keys, where multiple blockchain-based nodes work together to manage and maintain the locked account. This ensures the security and trustworthiness of the account while reducing the risk of key loss.

By distributing the responsibility of key management across multiple participants, multi-signature management significantly enhances security, preventing any single party from having full control of the account, thus mitigating the risk of unauthorized access or loss of keys.

Distributed Cross-Chain Storage of Digital Assets

Security is the cornerstone of M1's operations. As the volume of digital assets stored on digital asset trading platforms continues to grow, these platforms increasingly become targets for malicious actors. As a result, platforms must dedicate substantial human, material, and financial resources to defend against such attacks.

M1 aims to build a distributed financial infrastructure that connects various blockchain networks, enabling seamless asset transfers, bookkeeping, and storage between them. By integrating different cryptocurrencies and digital assets on the trading platform, assets can be transferred in and out, completing transactions in a blockchain-based manner. Additionally, financial products and contracts based on digital currencies and assets can be created and executed, while ensuring effective privacy protection for all related transactions.

Through the combination of distributed cross-chain asset storage and multi-signature key management, M1 will break the traditional funding pool model seen in "classic" trading platforms. This allows M1 to maintain the high-speed matching capabilities of centralized platforms, while offering the security, transparency, and public accountability of decentralized trading platforms' asset custody functions.

Intelligent Quantitative Technology Implementation

Create Quantitative Grid Object: Determine the grid density (width) for both upward and downward movements based on historical transaction data, and define the buy and sell prices and quantities for each grid level.

• Cancel All Orders: Cancel all currently pending orders that have not been completed.

  Place Buy Orders: Create buy orders based on the grid, establishing levels at which the system is prepared to purchase digital assets.

  Place Sell Orders: Create sell orders based on the grid, establishing levels at which the system is prepared to sell digital assets.

  Check Order Status: Monitor whether the digital asset price reaches any of the established grid levels. If the price touches a grid level and a transaction is executed, the system will automatically re-create the grid object at the appropriate levels. This process is repeated continuously, allowing the grid to adapt to market movements.

  This process enables continuous, automated execution of trades within the established grid strategy, ensuring that buy and sell orders are constantly adjusted according to market conditions.

System Risk Control Technology

1) Database Read-Write Separation Mechanism In the early stages, M1's system risk control typically ensures data synchronization and separation of read and write operations through mechanisms such as database master-slave replication, read-write separation, and sharding. The risk control system generally only has read access to necessary customer/account data and transaction data, ensuring the security and reliability of account data. This separation helps reduce the risk of unauthorized write access and ensures that critical transaction and account data are handled securely.

2) Cache/Memory Database Mechanism An efficient caching system is an effective way to enhance performance. Typically, this mechanism stores frequently accessed data in caching systems like Redis. Examples of such data include risk control rules, risk case libraries, intermediate result sets, blacklists and whitelists, preprocessing results, transaction parameters, billing templates, settlement rules, profit-sharing rules, and more. For high-frequency trading, memory databases are used for global distributed node storage, considering performance needs. This allows the system to handle large volumes of data with low latency and high speed, improving overall system efficiency.

3) RPC / SOA Architecture To reduce the coupling between the trading system and the risk control system, initially, when the number of system services is relatively small, middleware like RabbitMQ or ActiveMQ is typically used for message-based communication or Remote Procedure Call (RPC) to facilitate service calls between systems. As the number of system services increases and service governance issues arise, Service-Oriented Architecture (SOA) middleware, such as Dubbo, is adopted to manage and streamline service calls between systems more effectively. This approach ensures better scalability, flexibility, and maintainability of the overall system architecture as it grows in complexity.

4)Complex Event Processing (CEP) For real-time or near-real-time trading risk control, the Complex Event Processing (CEP) model is used instead of relying purely on rule-based processing. This model offers better performance and scalability.