Scaling a trading bot with a time-series database

QuestDB is highlighted as a next-generation, open-source database optimized for handling large volumes of market data, particularly suited for tick data, offering high ingestion throughput and efficient SQL analytics. The text explores the potential of scaling trading bots using QuestDB, inspired by Marc van Duyn's article on building a trading bot with the investing-algorithm-framework, an open-source Python library. The framework, which integrates order execution, broker connection, and backtesting, typically uses SQLite or in-memory databases, but the text advocates for QuestDB's use due to its ability to handle complex, high-frequency, multi-market scenarios. By leveraging QuestDB, trading bots can efficiently manage data ingestion and storage, offering advantages like proprietary dataset handling, fault-tolerant architectures, and seamless data visualization for diverse use cases. The piece delves into practical implementations, such as using ccxt for data extraction and Cryptofeed for real-time updates, and demonstrates how QuestDB can be integrated into a trading bot system, ultimately supporting a scalable, modular, and optimized architecture for modern trading needs.