# awesome-quant
[![Awesome](https://awesome.re/badge.svg)](https://awesome.re)

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

## Languages

- [Python](#python)
- [R](#r)
- [Matlab](#matlab)
- [Julia](#julia)
- [Java](#java)
- [JavaScript](#javascript)
- [Haskell](#haskell)
- [Scala](#scala)
- [Ruby](#ruby)
- [Elixir/Erlang](#elixirerlang)
- [Golang](#golang)
- [CSharp](#csharp)
- [Frameworks](#frameworks) - frameworks that support different languages
- [Reproducing Works](#reproducing-works) - repositories that reproduce books and papers results or implement examples

## Python

### Numerical Libraries & Data Structures

- [numpy](https://www.numpy.org) - NumPy is the fundamental package for scientific computing with Python.
- [scipy](https://www.scipy.org) - SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
- [pandas](https://pandas.pydata.org) - 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.
- [quantdsl](https://github.com/johnbywater/quantdsl) - Domain specific language for quantitative analytics in finance and trading.
- [statistics](https://docs.python.org/3/library/statistics.html) - Builtin Python library for all basic statistical calculations.
- [sympy](https://www.sympy.org/) - SymPy is a Python library for symbolic mathematics.
- [pymc3](https://docs.pymc.io/) - Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano.

### Financial Instruments and Pricing

- [PyQL](https://github.com/enthought/pyql) - QuantLib's Python port.
- [pyfin](https://github.com/opendoor-labs/pyfin) - Basic options pricing in Python. [ARCHIVED]
- [vollib](https://github.com/vollib/vollib) - vollib is a python library for calculating option prices, implied volatility and greeks.
- [QuantPy](https://github.com/jsmidt/QuantPy) - A framework for quantitative finance In python.
- [Finance-Python](https://github.com/alpha-miner/Finance-Python) - Python tools for Finance.
- [ffn](https://github.com/pmorissette/ffn) - A financial function library for Python.
- [pynance](https://pynance.net) - PyNance is open-source software for retrieving, analysing and visualizing data from stock and derivatives markets.
- [tia](https://github.com/bpsmith/tia) - Toolkit for integration and analysis.
- [hasura/base-python-dash](https://platform.hasura.io/hub/projects/hasura/base-python-dash) - Hasura quickstart to deploy Dash framework. Written on top of Flask, Plotly.js, and React.js, Dash is ideal for building data visualization apps with highly custom user interfaces in pure Python.
- [hasura/base-python-bokeh](https://platform.hasura.io/hub/projects/hasura/base-python-bokeh) - Hasura quickstart to visualize data with bokeh library.
- [pysabr](https://github.com/ynouri/pysabr) - SABR model Python implementation.

### Indicators
- [pandas_talib](https://github.com/femtotrader/pandas_talib) - A Python Pandas implementation of technical analysis indicators.
- [Tulipy](https://github.com/cirla/tulipy) - Financial Technical Analysis Indicator Library (Python bindings for [tulipindicators]( https://github.com/TulipCharts/tulipindicators))

### Trading & Backtesting

- [TA-Lib](https://ta-lib.org) - perform technical analysis of financial market data.
- [trade](https://github.com/rochars/trade) - trade is a Python framework for the development of financial applications.
- [zipline](https://www.zipline.io) - Pythonic algorithmic trading library.
- [QuantSoftware Toolkit](https://github.com/QuantSoftware/QuantSoftwareToolkit) - Python-based open source software framework designed to support portfolio construction and management.
- [quantitative](https://github.com/jeffrey-liang/quantitative) - Quantitative finance, and backtesting library.
- [analyzer](https://github.com/llazzaro/analyzer) - Python framework for real-time financial and backtesting trading strategies.
- [bt](https://github.com/pmorissette/bt) - Flexible Backtesting for Python.
- [backtrader](https://github.com/backtrader/backtrader) - Python Backtesting library for trading strategies.
- [pythalesians](https://github.com/thalesians/pythalesians) - Python library to backtest trading strategies, plot charts, seamlessly download market data, analyse market patterns etc.
- [pybacktest](https://github.com/ematvey/pybacktest) - Vectorized backtesting framework in Python / pandas, designed to make your backtesting easier.
- [pyalgotrade](https://github.com/gbeced/pyalgotrade) - Python Algorithmic Trading Library.
- [tradingWithPython](https://pypi.org/project/tradingWithPython/) - A collection of functions and classes for Quantitative trading.
- [pandas-ta](https://github.com/twopirllc/pandas-ta) - An easy to use Python 3 Pandas Extension with 80+Technical Analysis Indicators
- [ta](https://github.com/bukosabino/ta) - Technical Analysis Library using Pandas (Python)
- [algobroker](https://github.com/joequant/algobroker) - This is an execution engine for algo trading.
- [pysentosa](https://pypi.org/project/pysentosa/) - Python API for sentosa trading system.
- [finmarketpy](https://github.com/cuemacro/finmarketpy) - Python library for backtesting trading strategies and analyzing financial markets.
- [binary-martingale](https://github.com/metaperl/binary-martingale) - Computer program to automatically trade binary options martingale style.
- [fooltrader](https://github.com/foolcage/fooltrader) - the project using big-data technology to provide an uniform way to analyze the whole market.
- [zvt](https://github.com/zvtvz/zvt) - the project using sql,pandas to provide an uniform and extendable way to record data,computing factors,select securites, backtesting,realtime trading and it could show all of them in clearly charts in realtime.
- [pylivetrader](https://github.com/alpacahq/pylivetrader) - zipline-compatible live trading library.
- [pipeline-live](https://github.com/alpacahq/pipeline-live) - zipline's pipeline capability with IEX for live trading.
- [zipline-extensions](https://github.com/quantrocket-llc/zipline-extensions) - Zipline extensions and adapters for QuantRocket.
- [moonshot](https://github.com/quantrocket-llc/moonshot) - Vectorized backtester and trading engine for QuantRocket based on Pandas.
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) - Financial portfolio optimisation in python, including classical efficient frontier and advanced methods.
- [riskparity.py](https://github.com/dppalomar/riskparity.py) - fast and scalable design of risk parity portfolios with TensorFlow 2.0
- [mlfinlab](https://github.com/hudson-and-thames/mlfinlab) - Implementations regarding "Advances in Financial Machine Learning" by Marcos Lopez de Prado. (Feature Engineering, Financial Data Structures, Meta-Labeling)
- [pyqstrat](https://github.com/abbass2/pyqstrat) - A fast, extensible, transparent python library for backtesting quantitative strategies.
- [NowTrade](https://github.com/edouardpoitras/NowTrade) - Python library for backtesting technical/mechanical strategies in the stock and currency markets.
- [pinkfish](https://github.com/fja05680/pinkfish) - A backtester and spreadsheet library for security analysis.
- [aat](https://github.com/timkpaine/aat) - Async Algorithmic Trading Engine
- [Backtesting.py](https://kernc.github.io/backtesting.py/) - Backtest trading strategies in Python
- [catalyst](https://github.com/enigmampc/catalyst) - An Algorithmic Trading Library for Crypto-Assets in Python
- [quantstats](https://github.com/ranaroussi/quantstats) - Portfolio analytics for quants, written in Python
- [qtpylib](https://github.com/ranaroussi/qtpylib) - QTPyLib, Pythonic Algorithmic Trading <http://qtpylib.io> 
- [Quantdom](https://github.com/constverum/Quantdom) - Python-based framework for backtesting trading strategies & analyzing financial markets [GUI :neckbeard:]
- [freqtrade](https://github.com/freqtrade/freqtrade) - Free, open source crypto trading bot
- [algorithmic-trading-with-python](https://github.com/chrisconlan/algorithmic-trading-with-python) - Free `pandas` and `scikit-learn` resources for trading simulation, backtesting, and machine learning on financial data.
- [DeepDow](https://github.com/jankrepl/deepdow) - Portfolio optimization with deep learning

### Risk Analysis

- [pyfolio](https://github.com/quantopian/pyfolio) - Portfolio and risk analytics in Python.
- [empyrical](https://github.com/quantopian/empyrical) - Common financial risk and performance metrics.
- [fecon235](https://github.com/rsvp/fecon235) - Computational tools for financial economics include: Gaussian Mixture model of leptokurtotic risk, adaptive Boltzmann portfolios.
- [finance](https://pypi.org/project/finance/) - Financial Risk Calculations. Optimized for ease of use through class construction and operator overload.
- [qfrm](https://pypi.org/project/qfrm/) - Quantitative Financial Risk Management: awesome OOP tools for measuring, managing and visualizing risk of financial instruments and portfolios.
- [visualize-wealth](https://github.com/benjaminmgross/visualize-wealth) - Portfolio construction and quantitative analysis.
- [VisualPortfolio](https://github.com/wegamekinglc/VisualPortfolio) - This tool is used to visualize the perfomance of a portfolio.

### Factor Analysis

- [alphalens](https://github.com/quantopian/alphalens) - Performance analysis of predictive alpha factors.

### Time Series

- [ARCH](https://github.com/bashtage/arch) - ARCH models in Python.
- [statsmodels](http://statsmodels.sourceforge.net) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests.
- [dynts](https://github.com/quantmind/dynts) - Python package for timeseries analysis and manipulation.
- [PyFlux](https://github.com/RJT1990/pyflux) - Python library for timeseries modelling and inference (frequentist and Bayesian) on models.
- [tsfresh](https://github.com/blue-yonder/tsfresh) - Automatic extraction of relevant features from time series.
- [hasura/quandl-metabase](https://platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series) - Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.

### Calendars

- [trading_calendars](https://github.com/quantopian/trading_calendars) - Stock Exchange Trading Calendars.
- [bizdays](https://github.com/wilsonfreitas/python-bizdays) - Business days calculations and utilities.
- [pandas_market_calendars](https://github.com/rsheftel/pandas_market_calendars) - Exchange calendars to use with pandas for trading applications.

### Data Sources

- [findatapy](https://github.com/cuemacro/findatapy) - Python library to download market data via Bloomberg, Quandl, Yahoo etc.
- [googlefinance](https://github.com/hongtaocai/googlefinance) - Python module to get real-time stock data from Google Finance API.
- [yahoo-finance](https://github.com/lukaszbanasiak/yahoo-finance) - Python module to get stock data from Yahoo! Finance.
- [pandas-datareader](https://github.com/pydata/pandas-datareader) - Python module to get data from various sources (Google Finance, Yahoo Finance, FRED, OECD, Fama/French, World Bank, Eurostat...) into Pandas datastructures such as DataFrame, Panel with a caching mechanism.
- [pandas-finance](https://github.com/davidastephens/pandas-finance) - High level API for access to and analysis of financial data.
- [pyhoofinance](https://github.com/innes213/pyhoofinance) - Rapidly queries Yahoo Finance for multiple tickers and returns typed data for analysis.
- [yfinanceapi](https://github.com/Karthik005/yfinanceapi) - Finance API for Python.
- [yql-finance](https://github.com/slawek87/yql-finance) - yql-finance is simple and fast. API returns stock closing prices for current period of time and current stock ticker (i.e. APPL, GOOGL).
- [ystockquote](https://github.com/cgoldberg/ystockquote) - Retrieve stock quote data from Yahoo Finance.
- [wallstreet](https://github.com/mcdallas/wallstreet) - Real time stock and option data.
- [stock_extractor](https://github.com/ZachLiuGIS/stock_extractor) - General Purpose Stock Extractors from Online Resources.
- [Stockex](https://github.com/cttn/Stockex) - Python wrapper for Yahoo! Finance API.
- [finsymbols](https://github.com/skillachie/finsymbols) - Obtains stock symbols and relating information for SP500, AMEX, NYSE, and NASDAQ.
- [FRB](https://github.com/avelkoski/FRB) - Python Client for FRED® API.
- [inquisitor](https://github.com/econdb/inquisitor) - Python Interface to Econdb.com API.
- [yfi](https://github.com/nickelkr/yfi) - Yahoo! YQL library.
- [chinesestockapi](https://pypi.org/project/chinesestockapi/) - Python API to get Chinese stock price.
- [exchange](https://github.com/akarat/exchange) - Get current exchange rate.
- [ticks](https://github.com/jamescnowell/ticks) - Simple command line tool to get stock ticker data.
- [pybbg](https://github.com/bpsmith/pybbg) - Python interface to Bloomberg COM APIs.
- [ccy](https://github.com/lsbardel/ccy) - Python module for currencies.
- [tushare](https://pypi.org/project/tushare/) - A utility for crawling historical and Real-time Quotes data of China stocks.
- [jsm](https://pypi.org/project/jsm/) - Get the japanese stock market data.
- [cn_stock_src](https://github.com/jealous/cn_stock_src) - Utility for retrieving basic China stock data from different sources.
- [coinmarketcap](https://github.com/barnumbirr/coinmarketcap) - Python API for coinmarketcap.
- [after-hours](https://github.com/datawrestler/after-hours) - Obtain pre market and after hours stock prices for a given symbol.
- [bronto-python](https://pypi.org/project/bronto-python/) - Bronto API Integration for Python.
- [pytdx](https://github.com/rainx/pytdx) - Python Interface for retrieving chinese stock realtime quote data from TongDaXin Nodes.
- [pdblp](https://github.com/matthewgilbert/pdblp) - A simple interface to integrate pandas and the Bloomberg Open API.
- [tiingo](https://github.com/hydrosquall/tiingo-python) - Python interface for daily composite prices/OHLC/Volume + Real-time News Feeds, powered by the Tiingo Data Platform.
- [IEX](https://github.com/addisonlynch/iexfinance) - Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.
- [alpaca-trade-api](https://github.com/alpacahq/alpaca-trade-api-python) - Python interface for retrieving real-time and historical prices from Alpaca API as well as trade execution.
- [metatrader5](https://pypi.org/project/MetaTrader5/) - API Connector to MetaTrader 5 Terminal
- [akshare](https://github.com/jindaxiang/akshare) - AkShare is an elegant and simple financial data interface library for Python, built for human beings! <https://akshare.readthedocs.io>
- [yahooquery](https://github.com/dpguthrie/yahooquery) - Python interface for retrieving data through unofficial Yahoo Finance API.
- [investpy](https://github.com/alvarobartt/investpy) - Financial Data Extraction from Investing.com with Python! <https://investpy.readthedocs.io/>

### Excel Integration

- [xlwings](https://www.xlwings.org/) - Make Excel fly with Python.
- [openpyxl](https://openpyxl.readthedocs.io/en/latest/) - Read/Write Excel 2007 xlsx/xlsm files.
- [xlrd](https://github.com/python-excel/xlrd) - Library for developers to extract data from Microsoft Excel spreadsheet files.
- [xlsxwriter](https://xlsxwriter.readthedocs.io/) - Write files in the Excel 2007+ XLSX file format.
- [xlwt](https://github.com/python-excel/xlwt) - Library to create spreadsheet files compatible with MS Excel 97/2000/XP/2003 XLS files, on any platform.
- [DataNitro](https://datanitro.com/) - DataNitro also offers full-featured Python-Excel integration, including UDFs. Trial downloads are available, but users must purchase a license.
- [xlloop](http://xlloop.sourceforge.net) - XLLoop is an open source framework for implementing Excel user-defined functions (UDFs) on a centralised server (a function server).
- [expy](http://www.bnikolic.co.uk/expy/expy.html) - The ExPy add-in allows easy use of Python directly from within an Microsoft Excel spreadsheet, both to execute arbitrary code and to define new Excel functions.
- [pyxll](https://www.pyxll.com) - PyXLL is an Excel add-in that enables you to extend Excel using nothing but Python code.

### Visualization

- [D-Tale](https://github.com/man-group/dtale) - Visualizer for pandas dataframes and xarray datasets.

## R

### Numerical Libraries & Data Structures

- [xts](https://cran.r-project.org/web/packages/xts/index.html) - 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](https://cran.r-project.org/web/packages/data.table/index.html) - 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](https://github.com/dppalomar/sparseEigen) - Sparse pricipal component analysis.
- [TSdbi](http://tsdbi.r-forge.r-project.org/) - Provides a common interface to time series databases.
- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [zoo](https://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
- [tis](https://cran.r-project.org/web/packages/tis/index.html) - Functions and S3 classes for time indexes and time indexed series, which are compatible with FAME frequencies.
- [tfplot](https://cran.r-project.org/web/packages/tfplot/index.html) - Utilities for simple manipulation and quick plotting of time series data.
- [tframe](https://cran.r-project.org/web/packages/tframe/index.html) - A kernel of functions for programming time series methods in a way that is relatively independently of the representation of time.

### Data Sources

- [IBrokers](https://cran.r-project.org/web/packages/IBrokers/index.html) - Provides native R access to Interactive Brokers Trader Workstation API.
- [Rblpapi](https://cran.r-project.org/web/packages/Rblpapi/index.html) - An R Interface to 'Bloomberg' is provided via the 'Blp API'.
- [Quandl](https://www.quandl.com/tools/r) - Get Financial Data Directly Into R.
- [Rbitcoin](https://cran.r-project.org/web/packages/Rbitcoin/index.html) - Unified markets API interface (bitstamp, kraken, btce, bitmarket).
- [GetTDData](https://cran.r-project.org/web/packages/GetTDData/index.html) - Downloads and aggregates data for Brazilian government issued bonds directly from the website of Tesouro Direto.
- [GetHFData](https://cran.r-project.org/web/packages/GetHFData/index.html) - Downloads and aggregates high frequency trading data for Brazilian instruments directly from Bovespa ftp site.

### Financial Instruments and Pricing

- [RQuantLib](http://dirk.eddelbuettel.com/code/rquantlib.html) - RQuantLib connects GNU R with QuantLib.
- [quantmod](https://cran.r-project.org/web/packages/quantmod/index.html) - Quantitative Financial Modelling Framework.
- [Rmetrics](https://www.rmetrics.org) - The premier open source software solution for teaching and training quantitative finance.
	- [fAsianOptions](https://cran.r-project.org/web/packages/fAsianOptions/index.html) - EBM and Asian Option Valuation.
	- [fAssets](https://cran.r-project.org/web/packages/fAssets/index.html) - Analysing and Modelling Financial Assets.
	- [fBasics](https://cran.r-project.org/web/packages/fBasics/index.html) - Markets and Basic Statistics.
	- [fBonds](https://cran.r-project.org/web/packages/fBonds/index.html) - Bonds and Interest Rate Models.
	- [fExoticOptions](https://cran.r-project.org/web/packages/fExoticOptions/index.html) - Exotic Option Valuation.
	- [fOptions](https://cran.r-project.org/web/packages/fOptions/index.html) - Pricing and Evaluating Basic Options.
	- [fPortfolio](https://cran.r-project.org/web/packages/fPortfolio/index.html) - Portfolio Selection and Optimization.
- [portfolio](https://cran.r-project.org/web/packages/portfolio/index.html) - Analysing equity portfolios.
- [portfolioSim](https://cran.r-project.org/web/packages/portfolioSim/index.html) - Framework for simulating equity portfolio strategies.
- [sparseIndexTracking](https://github.com/dppalomar/sparseIndexTracking) - Portfolio design to track an index.
- [covFactorModel](https://github.com/dppalomar/covFactorModel) - Covariance matrix estimation via factor models.
- [riskParityPortfolio](https://github.com/dppalomar/riskParityPortfolio) - Blazingly fast design of risk parity portfolios.
- [sde](https://cran.r-project.org/web/packages/sde/index.html) - Simulation and Inference for Stochastic Differential Equations.
- [YieldCurve](https://cran.r-project.org/web/packages/YieldCurve/index.html) - Modelling and estimation of the yield curve.
- [SmithWilsonYieldCurve](https://cran.r-project.org/web/packages/SmithWilsonYieldCurve/index.html) - Constructs a yield curve by the Smith-Wilson method from a table of LIBOR and SWAP rates.
- [ycinterextra](https://cran.r-project.org/web/packages/ycinterextra/index.html) - Yield curve or zero-coupon prices interpolation and extrapolation.
- [AmericanCallOpt](https://cran.r-project.org/web/packages/AmericanCallOpt/index.html) - This package includes pricing function for selected American call options with underlying assets that generate payouts.
- [VarSwapPrice](https://cran.r-project.org/web/packages/VarSwapPrice/index.html) - Pricing a variance swap on an equity index.
- [RND](https://cran.r-project.org/web/packages/RND/index.html) - Risk Neutral Density Extraction Package.
- [LSMonteCarlo](https://cran.r-project.org/web/packages/LSMonteCarlo/index.html) - American options pricing with Least Squares Monte Carlo method.
- [OptHedging](https://cran.r-project.org/web/packages/OptHedging/index.html) - Estimation of value and hedging strategy of call and put options.
- [tvm](https://cran.r-project.org/web/packages/tvm/index.html) - Time Value of Money Functions.
- [OptionPricing](https://cran.r-project.org/web/packages/OptionPricing/index.html) - Option Pricing with Efficient Simulation Algorithms.
- [credule](https://cran.r-project.org/web/packages/credule/index.html) - Credit Default Swap Functions.
- [derivmkts](https://cran.r-project.org/web/packages/derivmkts/index.html) - Functions and R Code to Accompany Derivatives Markets.
- [FinCal](https://github.com/felixfan/FinCal) - Package for time value of money calculation, time series analysis and computational finance.
- [r-quant](https://github.com/artyyouth/r-quant) - R code for quantitative analysis in finance.
- [options.studies](https://github.com/taylorizing/options.studies) - options trading studies functions for use with options.data package and shiny.

### Trading

- [TA-Lib](https://ta-lib.org) - perform technical analysis of financial market data.
- [backtest](https://cran.r-project.org/web/packages/backtest/index.html) - Exploring Portfolio-Based Conjectures About Financial Instruments.
- [pa](https://cran.r-project.org/web/packages/pa/index.html) - Performance Attribution for Equity Portfolios.
- [TTR](https://cran.r-project.org/web/packages/TTR/index.html) - Technical Trading Rules.
- [QuantTools](https://quanttools.bitbucket.io/_site/index.html) - Enhanced Quantitative Trading Modelling.

### Risk Analysis

- [PerformanceAnalytics](https://cran.r-project.org/web/packages/PerformanceAnalytics/index.html) - Econometric tools for performance and risk analysis.

### Time Series

- [tseries](https://cran.r-project.org/web/packages/tseries/index.html) - Time Series Analysis and Computational Finance.
- [zoo](https://cran.r-project.org/web/packages/zoo/index.html) - S3 Infrastructure for Regular and Irregular Time Series (Z's Ordered Observations).
- [xts](https://cran.r-project.org/web/packages/xts/index.html) - eXtensible Time Series.
- [fGarch](https://cran.r-project.org/web/packages/fGarch/index.html) - Rmetrics - Autoregressive Conditional Heteroskedastic Modelling.
- [timeSeries](https://cran.r-project.org/web/packages/timeSeries/index.html) - Rmetrics - Financial Time Series Objects.
- [rugarch](https://cran.r-project.org/web/packages/rugarch/index.html) - Univariate GARCH Models.
- [rmgarch](https://cran.r-project.org/web/packages/rmgarch/index.html) - Multivariate GARCH Models.
- [tidypredict](https://github.com/edgararuiz/tidypredict) - Run predictions inside the database <https://tidypredict.netlify.com/>.
- [tidyquant](https://github.com/business-science/tidyquant) - Bringing financial analysis to the tidyverse.
- [timetk](https://github.com/business-science/timetk) - A toolkit for working with time series in R.
- [tibbletime](https://github.com/business-science/tibbletime) - Built on top of the tidyverse, tibbletime is an extension that allows for the creation of time aware tibbles through the setting of a time index.

### Calendars

- [timeDate](https://cran.r-project.org/web/packages/timeDate/index.html) - Chronological and Calendar Objects
- [bizdays](https://cran.r-project.org/web/packages/bizdays/index.html) - Business days calculations and utilities

## Matlab

### FrameWorks

- [QUANTAXIS](https://github.com/yutiansut/quantaxis) - Integrated Quantitative Toolbox with Matlab.


## Julia

- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Quantlib implementation in pure Julia.
- [FinancialMarkets.jl](https://github.com/imanuelcostigan/FinancialMarkets.jl) - Describe and model financial markets objects using Julia.
- [Ito.jl](https://github.com/aviks/Ito.jl) - A Julia package for quantitative finance.
- [TALib.jl](https://github.com/femtotrader/TALib.jl) - A Julia wrapper for TA-Lib.
- [Miletus.jl](https://juliacomputing.com/docs/miletus/index.html) - A financial contract definition, modeling language, and valuation framework.
- [Temporal.jl](https://github.com/dysonance/Temporal.jl) - Flexible and efficient time series class & methods.
- [Indicators.jl](https://github.com/dysonance/Indicators.jl) - Financial market technical analysis & indicators on top of Temporal.
- [Strategems.jl](https://github.com/dysonance/Strategems.jl) - Quantitative systematic trading strategy development and backtesting.
- [TimeSeries.jl](https://github.com/JuliaStats/TimeSeries.jl) - Time series toolkit for Julia.
- [MarketTechnicals.jl](https://github.com/JuliaQuant/MarketTechnicals.jl) - Technical analysis of financial time series on top of TimeSeries.
- [MarketData.jl](https://github.com/JuliaQuant/MarketData.jl) - Time series market data.
- [TimeFrames.jl](https://github.com/femtotrader/TimeFrames.jl) - A Julia library that defines TimeFrame (essentially for resampling TimeSeries).


## Java

- [Strata](http://strata.opengamma.io/) - Modern open-source analytics and market risk library designed and written in Java.
- [JQuantLib](http://www.jquantlib.org) - JQuantLib is a free, open-source, comprehensive framework for quantitative finance, written in 100% Java.
- [finmath.net](http://finmath.net) - Java library with algorithms and methodologies related to mathematical finance.
- [quantcomponents](https://github.com/lsgro/quantcomponents) - Free Java components for Quantitative Finance and Algorithmic Trading.
- [DRIP](https://lakshmidrip.github.io/DRIP) - Fixed Income, Asset Allocation, Transaction Cost Analysis, XVA Metrics Libraries.

## JavaScript

### Data Visualization
- [QUANTAXIS_Webkit](https://github.com/yutiansut/QUANTAXIS_Webkit) an awesome visualization center based on quantaxis.

## Haskell

- [quantfin](https://github.com/boundedvariation/quantfin) - quant finance in pure haskell.
- [hqfl](https://github.com/co-category/hqfl) - Haskell Quantitative Finance Library.

## Scala

- [QuantScale](https://github.com/choucrifahed/quantscale) - Scala Quantitative Finance Library.
- [Scala Quant](https://github.com/frankcash/Scala-Quant) Scala library for working with stock data from IFTTT recipes or Google Finance.

## Ruby

- [Jiji](https://github.com/unageanu/jiji2) - Open Source Forex algorithmic trading framework using OANDA REST API.
-
## Elixir/Erlang

- [Tai](https://github.com/fremantle-capital/tai) - Open Source composable, real time, market data and trade execution toolkit.
- [Workbench](https://github.com/fremantle-industries/workbench) - From Idea to Execution - Manage your trading operation across a globally distributed cluster

## Golang

- [Kelp](https://github.com/stellar/kelp) - Kelp is an open-source Golang algorithmic cryptocurrency trading bot that runs on centralized exchanges and Stellar DEX (command-line usage and desktop GUI).

## Frameworks

- [QuantLib](https://www.quantlib.org) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
	- [JQuantLib](http://www.jquantlib.org) - Java port.
	- [RQuantLib](http://dirk.eddelbuettel.com/code/rquantlib.html) - R port.
	- [QuantLibAddin](https://www.quantlib.org/quantlibaddin/) - Excel support.
	- [QuantLibXL](https://www.quantlib.org/quantlibxl/) - Excel support.
	- [QLNet](https://github.com/amaggiulli/qlnet) - .Net port.
	- [PyQL](https://github.com/enthought/pyql) - Python port.
	- [QuantLib.jl](https://github.com/pazzo83/QuantLib.jl) - Julia port.
- [TA-Lib](https://ta-lib.org) - perform technical analysis of financial market data.

## CSharp

- [QuantConnect](https://github.com/QuantConnect/Lean) - Lean Engine is an open-source fully managed C# algorithmic trading engine built for desktop and cloud usage.

## Reproducing Works

- [Derman Papers](https://github.com/MarcosCarreira/DermanPapers) - Notebooks that replicate original quantitative finance papers from Emanuel Derman.
- [volatility-trading](https://github.com/jasonstrimpel/volatility-trading) - A complete set of volatility estimators based on Euan Sinclair's Volatility Trading.
- [quant](https://github.com/paulperry/quant) - Quantitative Finance and Algorithmic Trading exhaust; mostly ipython notebooks based on Quantopian, Zipline, or Pandas.
- [fecon235](https://github.com/rsvp/fecon235) - Open source project for software tools in financial economics. Many jupyter notebook to verify theoretical ideas and practical methods interactively.
- [Quantitative-Notebooks](https://github.com/LongOnly/Quantitative-Notebooks) - Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy