Description
As businesses search for diversification by trading new financial products, it is easy for market data infrastructure to become fragmented and inconsistent. We describe how we have successfully used Python, Pandas and MongoDB to build a market data system that stores a variety of Timeseries-based financial data for research and live trading at a large systematic hedge fund. Our system has a simple, high-performance schema, a consistent API for all data access, and built-in support for data versioning and deduplication. We support fast interactive access to the data by quants, as well as clustered batch processing by running a dynamic data flow graph on a cluster.