DESIGN AND IMPLEMENTATION OF AN AUTOMATED FOREX TRADING WITH MICROMAX BOT
Abstract
The rapid volatility of digital asset markets necessitates trading systems capable of sub-second execution, dynamic data retrieval, and resilient risk mitigation. Existing systems frequently suffer from high API overhead and hard-coded dependencies on specific exchanges, which severely limits scalability. This research presents Micromax, an exchange-agnostic trading engine designed to decouple strategy execution from specific exchange APIs using the Adapter Design Pattern. To solve the problem of data retrieval latency, the system implements an intelligent batch price caching strategy, effectively reducing API overhead by processing data in 250-coin batches and utilizing persistent local storage. Furthermore, the engine integrates an automated risk management logic layer featuring real-time WebSocket price tracking, automated break-even triggers, and secure webhook validation. Developed using Node.js (TypeScript) for logic execution and Redis for asynchronous queuing, the results indicate a signal processing latency of less than 50ms and an 89% reduction in API rate-limit bottlenecks. The study concludes that adopting decoupled architectural patterns alongside batch caching significantly enhances the performance and reliability of automated trading systems.
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