A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
- Numerical Libraries & Data Structures
- Financial Instruments & Pricing
- Technical Indicators
- Trading & Backtesting
- Portfolio Optimization & Risk Analysis
- Factor Analysis
- Sentiment Analysis & Alternative Data
- Time Series Analysis
- Market Data & Data Sources
- Prediction Markets
- Calendars & Market Hours
- Visualization
- Excel & Spreadsheet Integration
- Quant Research Environments
- Cross-Language Frameworks
- Reproducing Works, Training & Books
- Commercial & Proprietary Services
- Historical & Archived Projects
- Related Lists
- numpy -
Python- NumPy is the fundamental package for scientific computing with Python. GitHub - scipy -
Python- SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering. GitHub - pandas -
Python- pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. GitHub - polars -
Python- Polars is a blazingly fast DataFrame library for manipulating structured data. GitHub - quantdsl -
Python- Domain specific language for quantitative analytics in finance and trading. - statistics -
Python- Builtin Python library for all basic statistical calculations. - sympy -
Python- SymPy is a Python library for symbolic mathematics. GitHub - pymc3 -
Python- Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano. GitHub - modelx -
Python- Python reimagination of spreadsheets as formula-centric objects that are interoperable with pandas. GitHub - ArcticDB -
Python- High performance datastore for time series and tick data. - CRNG -
Python- Contingency Random Number Generator that produces random numbers with real financial market statistical signatures (fat tails, volatility clustering, kurtosis). Matches 86% of real market metrics vs 14% for NumPy.