Explore: free data, open-source tools and learning resources for quantitative finance
A working list of public resources we rely on when researching articles. Nothing here is sponsored, and inclusion is not an endorsement of a provider's services; it means the resource is useful, documented and either free or widely used. Check each provider's terms before using its data.
Free data
- Kenneth R. French Data Library (Tuck School of Business): monthly and daily factor returns for the Fama-French models, momentum, and many sorted portfolios, back to 1926 for U.S. data. The standard starting point for factor research.
- FRED (Federal Reserve Bank of St. Louis): hundreds of thousands of economic and financial time series, including Treasury yields, credit spreads and exchange rates, with a free API.
- Robert Shiller's online data: long-run U.S. stock prices, dividends, earnings and the cyclically adjusted P/E ratio from 1871.
- SEC EDGAR: company filings, including quarterly 13F holdings reports from large institutional managers.
- World Bank Open Data and the BIS Data Portal: macro, credit and foreign exchange statistics by country.
Free daily prices for individual stocks are available from several websites and libraries, but they often lack delisted companies, which creates survivorship bias. For serious equity research, academic and commercial databases that include dead stocks are the norm.
Open-source software
- Python: pandas for time series, NumPy and SciPy for numerics, statsmodels for econometrics, scikit-learn for machine learning, arch for volatility models.
- R: the CRAN Empirical Finance task view lists maintained packages such as quantmod, xts and PerformanceAnalytics.
- Pricing and risk: QuantLib, a C++ library with Python bindings for derivatives pricing and yield curves.
- Backtesting: event-driven and vectorised frameworks exist in Python; our guide to open source for quantitative finance discusses how to choose one.
Research archives
- SSRN: working papers in finance and economics, often years before journal publication.
- arXiv quantitative finance: preprints in mathematical finance, portfolio management, statistical finance and trading.
- NBER working papers: economics and asset pricing research.
Communities
- Quantitative Finance Stack Exchange: a moderated question-and-answer site; search before asking, most beginner questions are answered.
- Quantocracy: an aggregator of quantitative trading blogs.
Professional designations
None of these is required to work in quantitative finance, and none replaces programming and statistics skills, but they are widely recognised:
- CFA (CFA Institute): broad investment management curriculum.
- CAIA (CAIA Association): alternative investments, including hedge funds.
- FRM (GARP) and PRM (PRMIA): risk management.
- CQF: an applied quantitative finance programme.
For algorithmic trading courses specifically, see our notes on QuantInsti and how to evaluate a trading course.