Free Quantitative Finance Resources for Data, Learning, and Research Tools
Summary
This curated directory surveys free or freely accessible resources for quantitative finance, grouped into market data, financial engineering, algorithmic trading, programming, and software. It highlights data-quality considerations: foreign-exchange tick histories may be accessible for research, while equity data requires corporate-action adjustments; the listed end-of-day feeds vary in reliability and should be checked against other sources. The resource list also covers study materials for options pricing, risk management, portfolio construction, statistics, Bayesian methods, and sentiment analysis.
For strategy research, it points readers toward forums, browser-based environments, backtesting platforms, and programming libraries used for data analysis, machine learning, derivatives pricing, and storage of large datasets. The article is a dated resource roundup, last updated in 2019, rather than a tutorial, comparative evaluation, or systematic guide. Access terms, service status, data coverage, and tool capabilities may have changed since publication, and the author cautions that free market data can be limited by history, instruments, or quality.
Key ideas
- Free market data can support research, but coverage, historical depth, and quality vary by asset and provider.
- Equity backtests need data adjusted for dividends and stock splits, and questionable feeds should be cross-checked.
- The directory gathers learning materials spanning financial engineering, programming, statistics, and trading research.
- It identifies open-source and browser-based tools for backtesting, analysis, data storage, and derivatives work.
- The recommendations reflect a resource list updated in 2019, so current availability should be verified.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.