Open research

Open-source tools for quantitative finance and the idea of researching a systematic fund in public.

• Creating a systematic strategy step by step, in the open

  A research workflow for building a systematic trading strategy: forming a hypothesis, specifying rules, sourcing clean data, backtesting with costs, checking factor exposures, testing robustness, holding out data, paper trading and monitoring, with the checks that belong at each stage.

• Getting started with open source for quantitative finance

  A practical starting guide to open-source tools for quantitative finance: choosing between Python, R, Julia and C++, the core libraries for data, statistics, machine learning and pricing, backtesting frameworks, data sources, and good habits for reproducible research.

• Running a research project in the open: a practical guide

  How to organise an open quantitative research project: repository structure, version control and review, licences for code and data, reproducible environments, documenting experiments and negative results, governance, and the legal lines an open project must not cross.

• What is an open-source hedge fund?

  The idea of an open-source hedge fund explained: open research, crowdsourced strategy platforms and data science tournaments, what they achieved and why most stopped short of a fully open fund, and the legal and practical limits of openness in asset management.

• mlfinlab on the PyPI index: packaging financial research code, and what happened next

  How a Python research library reaches users through the Python Package Index, what versioning, licences and tests mean for reproducible quantitative research, and the story of mlfinlab, which moved from an open-source licence to a commercial one.