Exploring Financial Tick Data with Jupyter Notebook and Pandas
Blog post from QuestDB
QuestDB, an open-source database designed for market data, excels in high ingestion throughput and SQL analytics, making it ideal for analyzing tick data. Paired with Pandas, a popular Python library for data manipulation, it provides a robust tech stack for handling and analyzing time-series data. This combination allows users to leverage Pandas' in-memory data structures for preliminary exploration and QuestDB's efficient storage for large datasets, mitigating Pandas' memory limitations. The article demonstrates this synergy through a tutorial on analyzing historical crypto prices, utilizing various methods to ingest data into Python or QuestDB, and performing analyses with Pandas or SQL to visualize results in Jupyter Notebook. The workflow's effectiveness depends on data size, format, and team structure, offering flexibility as data or team dynamics evolve.