Intelligent API Caching for Financial Data: A Scalable and Performance-Optimized Approach

This paper proposes an Intelligent API caching framework specially designed to handle one of the challenges faced by financial data systems.Stock prices and forex rates are highly volatile data that require APIs capable of high-frequency delivery.These APIs should have a balance between fast response times, scalability, and data accuracy.Traditional caching techniques, including fixed Time-to-Live (TTL) strategies, fail to meet the demands in dynamic environments.This results in stale data and performance degradation.To address these issues, we need to take specific actions.This paper proposes a caching framework designed for financial APIs with its core components.The first is dynamic TTL adjustments which respond to data volatility, the second is the event-driven cache invalidation for real-time updates.Finally, the analysis of request patterns to prioritize high-demand data.Built on Redis [1], this framework confirms rapid response times while reducing backend load, making it suitable for handling large-scale requests in real-world applications.The test results confirm the performance gains, including a 50% reduction in response times and a 42% decrease in backend load.These findings emphasize the potential of domain-specific caching solutions.It improves the scalability, reliability, and responsiveness of FinTech systems.