wilsonfreitas / awesome-quant

# Awesome Quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
## Contents
- 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
## Numerical Libraries & Data Structures
- 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.
- xts - `R` - eXtensible Time Series: Provide for uniform handling of R's different time-based data classes by extending zoo, maximizing native format information preservation and allowing for user level customization and extension, while simplifying cross-class interoperability.
- data.table - `R` - Extension of data.frame: Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns and a fast file reader (fread). Offers a natural and flexible syntax, for faster development.
- sparseEigen - `R` - Sparse principal component analysis.
- TSdbi - `R` - Provides a common interface to time series databases.
- tseries - `R` - Time Series Analysis and Computational Finance.
- zoo - `R` - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
- tis - `R`