Designed to support financial data science workflows, the FinDS Python package demonstrates how to use database engines such as SQL, Redis, and MongoDB to manage and access large datasets, including:
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Core financial databases such as CRSP, Compustat, IBES, and TAQ
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Public economic data APIs from sources like FRED and the Bureau of Economic Analysis (BEA)
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Structured and unstructured data from academic and research websites
Its companion Financial Data Science Python Notebooks provides practical examples and templates for applying:
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Financial econometrics and time series modeling
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Graph analytics, event studies, and backtesting strategies
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Machine learning for predictive analytics
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Natural language processing (NLP) to extract insights from financial text
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Neural networks and large language models (LLMs) for advanced decision-making
March 2025: Updated with data through early 2025 and incorporated the latest LLMs -- Microsoft Phi-4-multimodal (released Feb 2025), Google Gemma-3-12B (March 2025), DeepSeek-R1-14B (January 2025), Meta Llama-3.1-8B (July 2024), GPT-4o-mini (July 2024).
