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 Awesome Quant
 Awesome Quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
@@ -29,8 +29,7 @@
- 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.
- 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.
@@ -49,12 +48,11 @@
- ffn (https://github.com/pmorissette/ffn) - A financial function library for Python.
- pynance (https://github.com/GriffinAustin/pynance) - Lightweight Python library for assembling and analysing financial data.
- 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-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.
- FinancePy (https://github.com/domokane/FinancePy) - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and
credit derivatives.
- FinancePy (https://github.com/domokane/FinancePy) - A Python Finance Library that focuses on the pricing and risk-management of Financial Derivatives, including fixed-income, equity, FX and credit derivatives.
- gs-quant (https://github.com/goldmansachs/gs-quant) - Python toolkit for quantitative finance
- willowtree (https://github.com/federicomariamassari/willowtree) - Robust and flexible Python implementation of the willow tree lattice for derivatives pricing.
- financial-engineering (https://github.com/federicomariamassari/financial-engineering) - Applications of Monte Carlo methods to financial engineering projects, in Python.
@@ -74,14 +72,12 @@
- pandas_talib (https://github.com/femtotrader/pandas_talib) - A Python Pandas implementation of technical analysis indicators.
- finta (https://github.com/peerchemist/finta) - Common financial technical analysis indicators implemented in Pandas.
- Tulipy (https://github.com/cirla/tulipy) - Financial Technical Analysis Indicator Library (Python bindings for tulipindicators (https://github.com/TulipCharts/tulipindicators))
- lppls (https://github.com/Boulder-Investment-Technologies/lppls) - A Python module for fitting the Log-Periodic Power Law Singularity (LPPLS) 
(https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.
- lppls (https://github.com/Boulder-Investment-Technologies/lppls) - A Python module for fitting the Log-Periodic Power Law Singularity (LPPLS) (https://en.wikipedia.org/wiki/Didier_Sornette#The_JLS_and_LPPLS_models) model.
- talipp (https://github.com/nardew/talipp) - Incremental technical analysis library for Python.
- streaming_indicators (https://github.com/mr-easy/streaming_indicators) - A python library for computing technical analysis indicators on streaming data.
Trading & Backtesting
- skfolio (https://github.com/skfolio/skfolio) - Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build,
tune and cross-validate portfolio models.
- skfolio (https://github.com/skfolio/skfolio) - Python library for portfolio optimization built on top of scikit-learn. It provides a unified interface and sklearn compatible tools to build, tune and cross-validate portfolio models.
- Investing algorithm framework (https://github.com/coding-kitties/investing-algorithm-framework) - Framework for developing, backtesting, and deploying automated trading algorithms.
- QSTrader (https://github.com/mhallsmoore/qstrader) - QSTrader backtesting simulation engine.
- Blankly (https://github.com/Blankly-Finance/Blankly) - Fully integrated backtesting, paper trading, and live deployment.
@@ -92,7 +88,7 @@
- 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.
- pythalesians (https://github.com/thalesians/pythalesians) - Python library to backtest trading strategies, plot charts, seamlessly download market data, analyze 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.
- basana (https://github.com/gbeced/basana) - A Python async and event driven framework for algorithmic trading, with a focus on crypto currencies.
@@ -104,18 +100,17 @@
- 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.
- zvt (https://github.com/zvtvz/zvt) - the project using sql, pandas to provide an uniform and extendable way to record data, computing factors, select securities, 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.
- Eiten (https://github.com/tradytics/eiten) - Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, 
Minimum Variance Portfolios, Maximum Sharpe Ratio Portfolios, and Genetic Algorithms based Portfolios.
- PyPortfolioOpt (https://github.com/robertmartin8/PyPortfolioOpt) - Financial portfolio optimization in python, including classical efficient frontier and advanced methods.
- Eiten (https://github.com/tradytics/eiten) - Eiten is an open source toolkit by Tradytics that implements various statistical and algorithmic investing strategies such as Eigen Portfolios, Minimum Variance Portfolios, Maximum Sharpe 
Ratio Portfolios, and Genetic Algorithms based Portfolios.
- 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)
- 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.
@@ -126,11 +121,10 @@
- qtpylib (https://github.com/ranaroussi/qtpylib) - QTPyLib, Pythonic Algorithmic Trading 
- 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.
- 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
- Qlib (https://github.com/microsoft/qlib) - An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the 
entire chain of quantitative investment: alpha seeking, risk modeling, portfolio optimization, and order execution.
- Qlib (https://github.com/microsoft/qlib) - An AI-oriented Quantitative Investment Platform by Microsoft. Full ML pipeline of data processing, model training, back-testing; and covers the entire chain of quantitative investment: alpha 
seeking, risk modeling, portfolio optimization, and order execution.
- machine-learning-for-trading (https://github.com/stefan-jansen/machine-learning-for-trading) - Code and resources for Machine Learning for Algorithmic Trading
- AlphaPy (https://github.com/ScottfreeLLC/AlphaPy) - Automated Machine Learning AutoML with Python, scikit-learn, Keras, XGBoost, LightGBM, and CatBoost
- jesse (https://github.com/jesse-ai/jesse) - An advanced crypto trading bot written in Python
@@ -140,8 +134,7 @@
- ib_nope (https://github.com/ajhpark/ib_nope) - Automated trading system for NOPE strategy over IBKR TWS.
- OctoBot (https://github.com/Drakkar-Software/OctoBot) - Open source cryptocurrency trading bot for high frequency, arbitrage, TA and social trading with an advanced web interface.
- bta-lib (https://github.com/mementum/bta-lib) - Technical Analysis library in pandas for backtesting algotrading and quantitative analysis.
- Stock-Prediction-Models (https://github.com/huseinzol05/Stock-Prediction-Models) - Gathers machine learning and deep learning models for Stock forecasting including trading bots and 
simulations.
- Stock-Prediction-Models (https://github.com/huseinzol05/Stock-Prediction-Models) - Gathers machine learning and deep learning models for Stock forecasting including trading bots and simulations.
- TuneTA (https://github.com/jmrichardson/tuneta) - TuneTA optimizes technical indicators using a distance correlation measure to a user defined target feature such as next day return.
- AutoTrader (https://github.com/kieran-mackle/AutoTrader) - A Python-based development platform for automated trading systems - from backtesting to optimisation to livetrading.
- fast-trade (https://github.com/jrmeier/fast-trade) - A library built with backtest portability and performance in mind for backtest trading strategies.
@@ -150,14 +143,13 @@
- vectorbt (https://github.com/polakowo/vectorbt) - Find your trading edge, using a powerful toolkit for backtesting, algorithmic trading, and research.
- Lean (https://github.com/QuantConnect/Lean) - Lean Algorithmic Trading Engine by QuantConnect (Python, C#).
- fast-trade (https://github.com/jrmeier/fast-trade) - Low code backtesting library utilizing pandas and technical analysis indicators.
- pysystemtrade (https://github.com/robcarver17/pysystemtrade) - pysystemtrade is the open source version of Robert Carver's backtesting and trading engine that implements systems according 
to the framework outlined in his book "Systematic Trading", which is further developed on his blog (https://qoppac.blogspot.com/).
- pysystemtrade (https://github.com/robcarver17/pysystemtrade) - pysystemtrade is the open source version of Robert Carver's backtesting and trading engine that implements systems according to the framework outlined in his book 
"Systematic Trading", which is further developed on his blog (https://qoppac.blogspot.com/).
- pytrendseries (https://github.com/rafa-rod/pytrendseries) - Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum drawdown, time underwater.
- PyLOB (https://github.com/DrAshBooth/PyLOB) - Fully functioning fast Limit Order Book written in Python.
- PyBroker (https://github.com/edtechre/pybroker) - Algorithmic Trading with Machine Learning.
- OctoBot Script (https://github.com/Drakkar-Software/OctoBot-Script) - A quant framework to create cryptocurrencies strategies - from backtesting to optimisation to livetrading.
- hftbacktest (https://github.com/nkaz001/hftbacktest) - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full 
tick data for trades and order books.
- hftbacktest (https://github.com/nkaz001/hftbacktest) - A high-frequency trading and market-making backtesting tool accounts for limit orders, queue positions, and latencies, utilizing full tick data for trades and order books.
- vnpy (https://github.com/vnpy/vnpy) - VeighNa is a Python-based open source quantitative trading system development framework.
- Intelligent Trading Bot (https://github.com/asavinov/intelligent-trading-bot) - Automatically generating signals and trading based on machine learning and feature engineering
- fastquant (https://github.com/enzoampil/fastquant) - fastquant allows you to easily backtest investment strategies with as few as 3 lines of python code.
@@ -188,8 +180,7 @@
Quant Research Environment
- Jupyter Quant (https://github.com/gnzsnz/jupyter-quant) - A dockerized Jupyter quant research environment with preloaded tools for quant analysis, statsmodels, pymc, arch, py_vollib, 
zipline-reloaded, PyPortfolioOpt, etc.
- Jupyter Quant (https://github.com/gnzsnz/jupyter-quant) - A dockerized Jupyter quant research environment with preloaded tools for quant analysis, statsmodels, pymc, arch, py_vollib, zipline-reloaded, PyPortfolioOpt, etc.
Time Series
@@ -201,8 +192,7 @@
- hasura/quandl-metabase (https://platform.hasura.io/hub/projects/anirudhm/quandl-metabase-time-series) - Hasura quickstart to visualize Quandl's timeseries datasets with Metabase.
- Facebook Prophet (https://github.com/facebook/prophet) - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
- tsmoothie (https://github.com/cerlymarco/tsmoothie) - A python library for time-series smoothing and outlier detection in a vectorized way.
- pmdarima (https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's 
auto.arima function.
- pmdarima (https://github.com/alkaline-ml/pmdarima) - A statistical library designed to fill the void in Python's time series analysis capabilities, including the equivalent of R's auto.arima function.
- gluon-ts (https://github.com/awslabs/gluon-ts) - vProbabilistic time series modeling in Python.
Calendars
@@ -217,13 +207,12 @@
- 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-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).
- 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.
@@ -247,8 +236,7 @@
- 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.
- iexfinance (https://github.com/addisonlynch/iexfinance) - Python Interface for retrieving real-time and historical prices and equities data from The Investor's Exchange.
- pyEX (https://github.com/timkpaine/pyEX) - Python interface to IEX with emphasis on pandas, support for streaming data, premium data, points data (economic, rates, commodities), and 
technical indicators.
- pyEX (https://github.com/timkpaine/pyEX) - Python interface to IEX with emphasis on pandas, support for streaming data, premium data, points data (economic, rates, commodities), and technical indicators.
- 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! 
@@ -260,19 +248,16 @@
- FinanceDataReader (https://github.com/FinanceData/FinanceDataReader) - Open Source Financial data reader for U.S, Korean, Japanese, Chinese, Vietnamese Stocks
- pystlouisfed (https://github.com/TomasKoutek/pystlouisfed) - Python client for Federal Reserve Bank of St. Louis API - FRED, ALFRED, GeoFRED and FRASER.
- python-bcb (https://github.com/wilsonfreitas/python-bcb) - Python interface to Brazilian Central Bank web services.
- market-prices (https://github.com/maread99/market_prices) - Create meaningful OHLCV datasets from knowledge of exchange-calendars (https://github.com/gerrymanoim/exchange_calendars) (works 
out-the-box with data from Yahoo Finance).
- market-prices (https://github.com/maread99/market_prices) - Create meaningful OHLCV datasets from knowledge of exchange-calendars (https://github.com/gerrymanoim/exchange_calendars) (works out-the-box with data from Yahoo Finance).
- tardis-python (https://github.com/tardis-dev/tardis-python) - Python interface for Tardis.dev high frequency crypto market data
- lake-api (https://github.com/crypto-lake/lake-api) - Python interface for Crypto Lake high frequency crypto market data
- tessa (https://github.com/ymyke/tessa) - simple, hassle-free access to price information of financial assets (currently based on yfinance and pycoingecko), including search and a symbol 
class.
- pandaSDMX (https://github.com/dr-leo/pandaSDMX) - Python package that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national 
statistical agencies, central banks, and international organisations.
- tessa (https://github.com/ymyke/tessa) - simple, hassle-free access to price information of financial assets (currently based on yfinance and pycoingecko), including search and a symbol class.
- pandaSDMX (https://github.com/dr-leo/pandaSDMX) - Python package that implements SDMX 2.1 (ISO 17369:2013), a format for exchange of statistical data and metadata used by national statistical agencies, central banks, and international
organisations.
- cif (https://github.com/LenkaV/CIF) - Python package that include few composite indicators, which summarize multidimensional relationships between individual economic indicators.
- finagg (https://github.com/theOGognf/finagg) - finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those 
APIs into SQL databases, and tools for transforming aggregated data into features useful for analysis and AI/ML.
- FinanceDatabase (https://github.com/JerBouma/FinanceDatabase) - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money 
Markets.
- finagg (https://github.com/theOGognf/finagg) - finagg is a Python package that provides implementations of popular and free financial APIs, tools for aggregating historical data from those APIs into SQL databases, and tools for 
transforming aggregated data into features useful for analysis and AI/ML.
- FinanceDatabase (https://github.com/JerBouma/FinanceDatabase) - This is a database of 300.000+ symbols containing Equities, ETFs, Funds, Indices, Currencies, Cryptocurrencies and Money Markets.
Excel Integration
@@ -283,8 +268,7 @@
- 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.
- 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
@@ -299,18 +283,17 @@
Numerical Libraries & Data Structures
- xts (https://github.com/joshuaulrich/xts) - 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://github.com/Rdatatable/data.table) - 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.
- xts (https://github.com/joshuaulrich/xts) - 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://github.com/Rdatatable/data.table) - 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.
- 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
@@ -346,8 +329,7 @@
- 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.
- 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.
@@ -369,13 +351,13 @@
- pa (https://cran.r-project.org/web/packages/pa/index.html) - Performance Attribution for Equity Portfolios.
- TTR (https://github.com/joshuaulrich/TTR) - Technical Trading Rules.
- QuantTools (https://quanttools.bitbucket.io/_site/index.html) - Enhanced Quantitative Trading Modelling.
- blotter (https://github.com/braverock/blotter) - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides 
portfolio support for multi-asset class and multi-currency portfolios. Actively maintained and developed.
- blotter (https://github.com/braverock/blotter) - Transaction infrastructure for defining instruments, transactions, portfolios and accounts for trading systems and simulation. Provides portfolio support for multi-asset class and 
multi-currency portfolios. Actively maintained and developed.
Backtesting
- quantstrat (https://github.com/braverock/quantstrat) - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and 
multi-currency portfolios for backtesting and other financial research.
- quantstrat (https://github.com/braverock/quantstrat) - Transaction-oriented infrastructure for constructing trading systems and simulation. Provides support for multi-asset class and multi-currency portfolios for backtesting and other
financial research.
Risk Analysis
@@ -383,10 +365,10 @@
Factor Analysis
- FactorAnalytics (https://github.com/braverock/FactorAnalytics) - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in 
conjunction with portfolio construction, optimization and risk management, namely fundamental factor models, time series factor models and statistical factor models.
- Expected Returns (https://github.com/JustinMShea/ExpectedReturns) - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed 
in one of the best investment references of the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.
- FactorAnalytics (https://github.com/braverock/FactorAnalytics) - The FactorAnalytics package contains fitting and analysis methods for the three main types of factor models used in conjunction with portfolio construction, optimization
and risk management, namely fundamental factor models, time series factor models and statistical factor models.
- Expected Returns (https://github.com/JustinMShea/ExpectedReturns) - Solutions for enhancing portfolio diversification and replications of seminal papers with R, most of which are discussed in one of the best investment references of 
the recent decade, Expected Returns: An Investors Guide to Harvesting Market Rewards by Antti Ilmanen.
Time Series
@@ -398,8 +380,7 @@
- tidypredict (https://github.com/edgararuiz/tidypredict) - Run predictions inside the database .
- 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.
- 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.
- matrixprofile (https://github.com/matrix-profile-foundation/matrixprofile) - Time series data mining library built on top of the novel Matrix Profile data structure and algorithms.
- garchmodels (https://github.com/AlbertoAlmuinha/garchmodels) - A parsnip backend for GARCH models.
@@ -444,12 +425,10 @@
JavaScript
- finance.js (https://github.com/ebradyjobory/finance.js) - A JavaScript library for common financial calculations.
- portfolio-allocation (https://github.com/lequant40/portfolio_allocation_js) - PortfolioAllocation is a JavaScript library designed to help constructing financial portfolios made of several 
assets: bonds, commodities, cryptocurrencies, currencies, exchange traded funds (ETFs), mutual funds, stocks...
- Ghostfolio (https://github.com/ghostfolio/ghostfolio) - Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven 
investment decisions.
- IndicatorTS (https://github.com/cinar/indicatorts) - Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for 
trading.
- portfolio-allocation (https://github.com/lequant40/portfolio_allocation_js) - PortfolioAllocation is a JavaScript library designed to help constructing financial portfolios made of several assets: bonds, commodities, cryptocurrencies,
currencies, exchange traded funds (ETFs), mutual funds, stocks...
- Ghostfolio (https://github.com/ghostfolio/ghostfolio) - Wealth management software to keep track of financial assets like stocks, ETFs or cryptocurrencies and make solid, data-driven investment decisions.
- IndicatorTS (https://github.com/cinar/indicatorts) - Indicator is a TypeScript module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
- ccxt (https://github.com/ccxt/ccxt) - A JavaScript / Python / PHP cryptocurrency trading API with support for more than 100 bitcoin/altcoin exchanges.
- PENDAX (https://github.com/CompendiumFi/PENDAX-SDK) - Javascript SDK for Trading/Data API and Websockets for FTX, FTXUS, OKX, Bybit, & More.
@@ -476,22 +455,19 @@
- 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
- Prop (https://github.com/fremantle-industries/prop) - An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution 
and operation.
- Prop (https://github.com/fremantle-industries/prop) - An open and opinionated trading platform using productive & familiar open source libraries and tools for strategy research, execution and operation.
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).
- 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).
- marketstore (https://github.com/alpacahq/marketstore) - DataFrame Server for Financial Timeseries Data.
- IndicatorGo (https://github.com/cinar/indicator) - IndicatorGo is a Golang module providing various stock technical analysis indicators, strategies, and a backtest framework for trading.
CPP
- QuantLib (https://github.com/lballabio/QuantLib) - The QuantLib project is aimed at providing a comprehensive software framework for quantitative finance.
- TradeFrame (https://github.com/rburkholder/trade-frame) - C++ 17 based framework/library (with sample applications) for testing options based automated trading ideas using DTN IQ real time 
data feed and Interactive Brokers (TWS API) for trade execution. Comes with built-in Option Greeks/IV (https://github.com/rburkholder/trade-frame/tree/master/lib/TFOptions) calculation 
library.
- TradeFrame (https://github.com/rburkholder/trade-frame) - C++ 17 based framework/library (with sample applications) for testing options based automated trading ideas using DTN IQ real time data feed and Interactive Brokers (TWS API) 
for trade execution. Comes with built-in Option Greeks/IV (https://github.com/rburkholder/trade-frame/tree/master/lib/TFOptions) calculation library.
Frameworks
@@ -514,10 +490,8 @@
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.
- StockSharp (https://github.com/StockSharp/StockSharp) - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, 
and options).
- TDAmeritrade.DotNetCore (https://github.com/NVentimiglia/TDAmeritrade.DotNetCore) - Free, open-source .NET Client for the TD Ameritrade Trading Platform. Helps developers integrate TD 
Ameritrade API into custom trading solutions.
- StockSharp (https://github.com/StockSharp/StockSharp) - Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
- TDAmeritrade.DotNetCore (https://github.com/NVentimiglia/TDAmeritrade.DotNetCore) - Free, open-source .NET Client for the TD Ameritrade Trading Platform. Helps developers integrate TD Ameritrade API into custom trading solutions.
Rust
@@ -536,27 +510,24 @@
- ML-Quant (https://www.ml-quant.com/) - Top Quant resources like ArXiv (sanity), SSRN, RePec, Journals, Podcasts, Videos, and Blogs.
- 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.
- 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
- QuantEcon (https://quantecon.org/) - Lecture series on economics, finance, econometrics and data science; QuantEcon.py, QuantEcon.jl, notebooks
- FinanceHub (https://github.com/Finance-Hub/FinanceHub) - Resources for Quantitative Finance
- Python_Option_Pricing (https://github.com/dedwards25/Python_Option_Pricing) - An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, 
American, European, Asian, Spread Options.
- Python_Option_Pricing (https://github.com/dedwards25/Python_Option_Pricing) - An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options.
- python-training (https://github.com/jpmorganchase/python-training) - J.P. Morgan's Python training for business analysts and traders.
- Stock_Analysis_For_Quant (https://github.com/LastAncientOne/Stock_Analysis_For_Quant) - Different Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau.
- algorithmic-trading-with-python (https://github.com/chrisconlan/algorithmic-trading-with-python) - Source code for Algorithmic Trading with Python (2020) by Chris Conlan.
- MEDIUM_NoteBook (https://github.com/cerlymarco/MEDIUM_NoteBook) - Repository containing notebooks of cerlymarco (https://github.com/cerlymarco)'s posts on Medium.
- QuantFinance (https://github.com/PythonCharmers/QuantFinance) - Training materials in quantitative finance.
- IPythonScripts (https://github.com/mgroncki/IPythonScripts) - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning 
and Deep Learning.
- IPythonScripts (https://github.com/mgroncki/IPythonScripts) - Tutorials about Quantitative Finance in Python and QuantLib: Pricing, xVAs, Hedging, Portfolio Optimisation, Machine Learning and Deep Learning.
- Computational-Finance-Course (https://github.com/LechGrzelak/Computational-Finance-Course) - Materials for the course of Computational Finance.
- Machine-Learning-for-Asset-Managers (https://github.com/emoen/Machine-Learning-for-Asset-Managers) - Implementation of code snippets, exercises and application to live data from Machine 
Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.
- Machine-Learning-for-Asset-Managers (https://github.com/emoen/Machine-Learning-for-Asset-Managers) - Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in 
Quantitative Finance) written by Prof. Marcos López de Prado.
- Python-for-Finance-Cookbook (https://github.com/PacktPublishing/Python-for-Finance-Cookbook) - Python for Finance Cookbook, published by Packt.
- modelos_vol_derivativos (https://github.com/ysaporito/modelos_vol_derivativos) - "Modelos de Volatilidade para Derivativos" book's Jupyter notebooks
- NMOF (https://github.com/enricoschumann/NMOF) - Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. 
Maringer and E. Schumann (2019, ISBN:978-0128150658).
- NMOF (https://github.com/enricoschumann/NMOF) - Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, 
ISBN:978-0128150658).
- py4fi2nd (https://github.com/yhilpisch/py4fi2nd) - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.
- aiif (https://github.com/yhilpisch/aiif) - Jupyter Notebooks and code for the book Artificial Intelligence in Finance (O'Reilly) by Yves Hilpisch.
- py4at (https://github.com/yhilpisch/py4at) - Jupyter Notebooks and code for the book Python for Algorithmic Trading (O'Reilly) by Yves Hilpisch.
@@ -565,31 +536,25 @@
- QuantFinanceBook (https://github.com/LechGrzelak/QuantFinanceBook) - Quantitative Finance book.
- rough_bergomi (https://github.com/ryanmccrickerd/rough_bergomi) - A Python implementation of the rough Bergomi model.
- frh-fx (https://github.com/ryanmccrickerd/frh-fx) - A python implementation of the fast-reversion Heston model of Mechkov for FX purposes.
- Value Investing Studies (https://github.com/euclidjda/value-investing-studies) - A collection of data analysis studies that examine the performance and characteristics of value investing 
over long periods of time.
- Value Investing Studies (https://github.com/euclidjda/value-investing-studies) - A collection of data analysis studies that examine the performance and characteristics of value investing over long periods of time.
- Machine Learning Asset Management (https://github.com/firmai/machine-learning-asset-management) - Machine Learning in Asset Management (by @firmai).
- Deep Learning Machine Learning Stock (https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock) - Deep Learning and Machine Learning stocks represent a promising long-term or 
short-term opportunity for investors and traders.
- Technical Analysis and Feature Engineering (https://github.com/jo-cho/Technical_Analysis_and_Feature_Engineering) - Feature Engineering and Feature Importance of Machine Learning in 
Financial Market.
- Differential Machine Learning and Axes that matter by Brian Huge and Antoine Savine (https://github.com/differential-machine-learning/notebooks) - Implement, demonstrate, reproduce and 
extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the 
papers.
- systematictradingexamples (https://github.com/robcarver17/systematictradingexamples) - Examples of code related to book Systematic Trading (www.systematictrading.org) and blog 
(http://qoppac.blogspot.com)
- Deep Learning Machine Learning Stock (https://github.com/LastAncientOne/Deep-Learning-Machine-Learning-Stock) - Deep Learning and Machine Learning stocks represent a promising long-term or short-term opportunity for investors and 
traders.
- Technical Analysis and Feature Engineering (https://github.com/jo-cho/Technical_Analysis_and_Feature_Engineering) - Feature Engineering and Feature Importance of Machine Learning in Financial Market.
- Differential Machine Learning and Axes that matter by Brian Huge and Antoine Savine (https://github.com/differential-machine-learning/notebooks) - Implement, demonstrate, reproduce and extend the results of the Risk articles 
'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
- systematictradingexamples (https://github.com/robcarver17/systematictradingexamples) - Examples of code related to book Systematic Trading (www.systematictrading.org) and blog (http://qoppac.blogspot.com)
- pysystemtrade_examples (https://github.com/robcarver17/pysystemtrade_examples) - Examples using pysystemtrade for Robert Carver's blog (http://qoppac.blogspot.com).
- ML_Finance_Codes (https://github.com/mfrdixon/ML_Finance_Codes) - Machine Learning in Finance: From Theory to Practice Book
- Hands-On Machine Learning for Algorithmic Trading (https://github.com/packtpublishing/hands-on-machine-learning-for-algorithmic-trading) - Hands-On Machine Learning for Algorithmic Trading,
published by Packt
- Hands-On Machine Learning for Algorithmic Trading (https://github.com/packtpublishing/hands-on-machine-learning-for-algorithmic-trading) - Hands-On Machine Learning for Algorithmic Trading, published by Packt
- financialnoob-misc (https://github.com/financialnoob/misc) - Codes from @financialnoob's posts
- MesoSim Options Trading Strategy Library (https://github.com/deltaray-io/strategy-library) - Free and public Options Trading strategy library for MesoSim. 
- Quant-Finance-With-Python-Code (https://github.com/lingyixu/Quant-Finance-With-Python-Code) - Repo for code examples in Quantitative Finance with Python by Chris Kelliher
- QuantFinanceTraining (https://github.com/JoaoJungblut/QuantFinanceTraining) - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative 
Finance). The codes are organized by class, facilitating navigation and reference.
- Statistical-Learning-based-Portfolio-Optimization (https://github.com/YannickKae/Statistical-Learning-based-Portfolio-Optimization) - This R Shiny App utilizes the Hierarchical Equal Risk 
Contribution (HERC) approach, a modern portfolio optimization method developed by Raffinot (2018).
- QuantFinanceTraining (https://github.com/JoaoJungblut/QuantFinanceTraining) - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, 
facilitating navigation and reference.
- Statistical-Learning-based-Portfolio-Optimization (https://github.com/YannickKae/Statistical-Learning-based-Portfolio-Optimization) - This R Shiny App utilizes the Hierarchical Equal Risk Contribution (HERC) approach, a modern 
portfolio optimization method developed by Raffinot (2018).
- book_irds3 (https://github.com/attack68/book_irds3) - Code repository for Pricing and Trading Interest Rate Derivatives.
- Autoencoder-Asset-Pricing-Models (https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019 
(https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3335536)).
- Autoencoder-Asset-Pricing-Models (https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models (GKX, 2019 (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3335536)).
- Finance (https://github.com/shashankvemuri/Finance) - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data.
- 101_formulaic_alphas (https://github.com/ram-ki/101_formulaic_alphas) - Implemention of 101 formulaic alphas (https://arxiv.org/ftp/arxiv/papers/1601/1601.00991.pdf) using qstrader.