Company and use case

Trading Strategy is a cutting-edge platform that provides quantitative trading strategies for the crypto market. By leveraging advanced algorithms and vast datasets, Trading Strategy enables traders to develop, test, and deploy sophisticated trading strategies in the volatile world of cryptocurrencies. Their platform is designed to offer seamless access to a variety of crypto exchanges, providing users with the tools they need to execute high-frequency and algorithmic trading strategies effectively.

Performance problems to solve

Trading Strategy initially faced challenges related to the scalability and performance of their data infrastructure. Handling the immense volume of real-time trading data from multiple crypto exchanges required a robust system capable of processing and storing vast amounts of time-series data efficiently. The legacy systems in place struggled with slow query performance and high latency, which hindered their ability to provide timely insights and execute trades at optimal speeds.

Performance gains unlocked

Implementing TimescaleDB transformed Trading Strategy's data processing capabilities. TimescaleDB's automatic partitioning and compression features significantly improved data storage efficiency, while continuous aggregates and hyperfunctions enhanced query performance. This enabled Trading Strategy to handle real-time data streams with reduced latency, providing their users with faster and more accurate trading insights. The improved performance and scalability of their data infrastructure allowed Trading Strategy to offer a more reliable and responsive trading platform, ultimately supporting the success of their users in the competitive crypto market.