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Financial Data Science (FinDS) API Reference
============================================

Designed to support financial data science workflows,
the `FinDS Python package <https://github.com/terence-lim/financial-data-science>`_
demonstrates how to use database engines
such as SQL, Redis, and MongoDB to manage and access large datasets, including:

- Core financial databases such as CRSP, Compustat, IBES, and TAQ

- Public economic data APIs from sources like FRED and the Bureau of Economic Analysis (BEA)

- Structured and unstructured data from academic and research websites

Its companion `Financial Data Science Python Notebooks <https://terence-lim.github.io/docs/financial-data-science-notebooks/>`_
provides practical examples and templates for applying:

- Financial econometrics and time series modeling

- Graph analytics, event studies, and backtesting strategies

- Machine learning for predictive analytics

- Natural language processing (NLP) to extract insights from financial text

- Neural networks and large language models (LLMs) for advanced decision-making


Documentation:
--------------

- `Financial Data Science Notebooks <https://terence-lim.github.io/docs/financial-data-science-notebooks/>`_

- `Download PDF <https://terence-lim.github.io/docs/financial-data-science-notebooks.pdf>`_

- `FinDS API reference <https://terence-lim.github.io/docs/financial-data-science/>`_


Github repos:
-------------

- `FinDS package <https://github.com/terence-lim/financial-data-science>`_

- `Jupyter notebooks <https://github.com/terence-lim/financial-data-science-notebooks>`_


.. toctree::
   :maxdepth: 1
   :caption: Modules:

   finds.database
   finds.structured
   finds.backtesting
   finds.unstructured
   finds.readers
   finds.recipes
   finds.utils