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 Awesome Quant
 Awesome Quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance).
@@ -48,8 +48,8 @@
- 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.
@@ -72,14 +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.
@@ -102,18 +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 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.
- 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.
- 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.
@@ -124,11 +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
@@ -147,14 +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.
@@ -185,8 +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
@@ -213,8 +207,8 @@
- 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.
@@ -254,16 +248,15 @@
- 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.
- 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.
- 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
@@ -275,8 +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
@@ -291,10 +283,10 @@
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.
@@ -359,13 +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
@@ -373,10 +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
@@ -433,8 +425,8 @@
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...
- 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.
@@ -474,8 +466,8 @@
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
@@ -499,8 +491,7 @@
- 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.
- 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
@@ -523,8 +514,7 @@
- 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.
@@ -532,12 +522,12 @@
- 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.
- 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.
@@ -546,14 +536,13 @@
- 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.
- 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.
- 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
@@ -561,12 +550,11 @@
- 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.