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# Financial Machine Learning and Data Science
A curated list of practical financial machine learning (FinML) tools and applications. This collection is primarily in Python.
A listed repository should be deprecated if:
- Repository's owner explicitly say that "this library is not maintained".
- Not committed for long time (2~3 years).
**This repo is officially under revamp as of 3/29/2021!!**
***TODOs and roadmap is under the github project [here](https://github.com/firmai/financial-machine-learning/projects/1) If you would like to contribute to this repo, please send us a pull request or contact [@dereknow](https://twitter.com/dereknow) or [@bin-yang-algotune](https://twitter.com/b3yang) or join us in the gitter chat [here](https://gitter.im/financial-machine-learning/community)
***
___
Updated Repo Information including the creation date/last update date/number of stars etc can be found [here](https://github.com/firmai/financial-machine-learning/blob/master/raw_data/url_list.csv) <br>
Repo list is updated weekly and status is shown on the badge
___
# Trading
## Deep Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/deep_learning))
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## Reinforcement Learning ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/reinforcement_learning))
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## Other Models ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/other_models))
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## Data Processing Techniques and Transformations ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data_processing_techniques_and_transformations))
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# Portfolio Management
## Portfolio Selection and Optimisation ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/portfolio_selection_and_optimisation))
- [Distribution Characteristic Optimisation](https://github.com/VivekPa/OptimalPortfolio) - Extends classical portfolio optimisation to take the skewness and kurtosis of the distribution of market invariants into account.
- [Reinforcement Learning](https://github.com/filangel/qtrader) - Reinforcement Learning for Portfolio Management.
- [Efficient Frontier](https://github.com/tthustla/efficient_frontier/blob/master/Efficient%20_Frontier_implementation.ipynb) - Modern Portfolio Theory.
- [PyPortfolioOpt](https://github.com/robertmartin8/PyPortfolioOpt) - Financial portfolio optimisation, including classical efficient frontier and advanced methods.
- [Policy Gradient Portfolio](https://github.com/ZhengyaoJiang/PGPortfolio) - A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem.
- [Deep Portfolio Theory](https://github.com/tcloaa/Deep-Portfolio-Theory) - Autoencoder framework for portfolio selection.
- [401K Portfolio Optimisation](https://github.com/otosman/Python-for-Finance/blob/master/Portfolio%20Optimization%20401k.ipynb) - Portfolio analyses and optimisation for 401K.
- [Online Portfolio Selection](https://nbviewer.jupyter.org/github/paulperry/quant/blob/master/OLPS_Comparison.ipynb) - ****Comparing OLPS algorithms on a diversified set of ETFs.
- [OLMAR Algorithm](https://github.com/charlessutton/OLMAR/blob/master/Part3.ipynb) - Relative importance of each component of the OLMAR algorithm.
- [Modern Portfolio Theory](https://nbviewer.jupyter.org/github/Marigold/universal-portfolios/blob/master/modern-portfolio-theory.ipynb) - Universal portfolios; modern portfolio theory.
- [DeepDow](https://github.com/jankrepl/deepdow) - Portfolio optimization with deep learning.
## Factor and Risk Analysis ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/factor_and_risk_analysis))
- [Various Risk Measures](https://github.com/Jorgencr/Alternative-and-Responsible-Investments/blob/master/Final_masterfile.ipynb) - Risk measures and factors for alternative and responsible investments.
- [Pyfolio](https://github.com/quantopian/pyfolio) - Portfolio and risk analytics in Python.
- [Risk Basic](https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%203%2C%20Risk.ipynb) - Active portfolio risk management .
- [CAPM](https://github.com/RJT1990/Active-Portfolio-Management-Notes/blob/master/Chapter%202%2C%20CAPM.ipynb) - Expected returns using CAPM.
- [Factor Analysis](https://github.com/garvit-kudesia91/factor_analysis/blob/master/Factor%20Analysis%20of%20Mutual%20Funds.ipynb) - Factor analysis for mutual funds.
- [VaR GaN](https://github.com/hamaadshah/market_risk_gan_keras) - Estimate Value-at-Risk for market risk management using Keras and TensorFlow.
- [VaR](https://github.com/willb/var-notebook/blob/master/var-notebook/var-pdfs.ipynb) - Value-at-risk calculations.
- [Python for Finance](https://github.com/yhilpisch/py4fi/tree/master/jupyter36) - Various financial notebooks.
- [Performance Analysis](https://github.com/quantopian/alphalens) - Performance analysis of predictive (alpha) stock factors.
- [Quant Finance](https://github.com/mrefermat/quant_finance) - General quant repository.
- [Risk and Return](https://github.com/PyDataBlog/Python-for-Data-Science/tree/master/Tutorials) - Riskiness of portfolios and assets.
- [Convex Optimisation](https://github.com/ssanderson/convex-optimization-for-finance/blob/master/notebooks/Main.ipynb) - Convex Optimization for Finance.
- [Factor Analysis](https://github.com/alpha-miner/alpha-mind/tree/master/notebooks) - Factor strategy notebooks.
- [Statistical Finance](https://github.com/mrefermat/FinancePhD/tree/master/FinancialExperiments) - Various financial experiments.
# Techniques
## Unsupervised ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/unsupervised))
- [PCA Pairs Trading](https://github.com/joelQF/quant-finance/tree/master/Artificial_IntelIigence_for_Trading) - PCA, Factor Returns, and trading strategies.
- [Fund Clusters](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb) - Data exploration of fund clusters.
- [VRA Stock Embedding](https://github.com/ml-hongkong/stock2vec) - Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history.
- [Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries) - Clustering of industries.
- [Pairs Trading](https://github.com/marketneutral/pairs-trading-with-ML/blob/master/Pairs%2BTrading%2Bwith%2BMachine%2BLearning.ipynb) - Finding pairs with cluster analysis.
- [Industry Clustering](https://github.com/SeanMcOwen/FinanceAndPython.com-ClusteringIndustries) - Project to cluster industries according to financial attributes.
## Textual ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/textual))
- [NLP](https://github.com/toamitesh/NLPinFinance) - This project assembles a lot of NLP operations needed for finance domain.
- [Earning call transcripts](https://github.com/lin882/WebAnalyticsProject) - Correlation between mutual fund investment decision and earning call transcripts.
- [Buzzwords](https://github.com/swap9047/Cutting-Edge-Technologies-Effect-on-S-P500-Companies-Performance-and-Mutual-Funds) - Return performance and mutual fund selection.
- [Fund classification](https://github.com/frechfrechfrech/Mutual-Fund-Market-Clusters/blob/master/Initial%20Data%20Exploration.ipynb) - Fund classification using text mining and NLP.
- [NLP Event](https://github.com/yuriak/DLQuant) - Applying Deep Learning and NLP in Quantitative Trading.
- [Financial Sentiment Analysis](https://github.com/EricHe98/Financial-Statements-Text-Analysis) - Sentiment, distance and proportion analysis for trading signals.
- [Financial Statement Sentiment](https://github.com/MAydogdu/TextualAnalysis) - Extracting sentiment from financial statements using neural networks.
- [Extensive NLP](https://github.com/TiesdeKok/Python_NLP_Tutorial/blob/master/NLP_Notebook.ipynb) - Comprehensive NLP techniques for accounting research.
- [Accounting Anomalies](https://github.com/GitiHubi/deepAI/blob/master/GTC_2018_Lab-solutions.ipynb) - Using deep-learning frameworks to identify accounting anomalies.
# Other Assets
## Derivatives and Hedging ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/derivatives_and_hedging))
- [Options](https://github.com/QuantConnect/Tutorials/tree/master/06%20Introduction%20to%20Options%5B%5D) - Introduction to options.
- [Derivative Markets](https://github.com/broughtj/Fin6470/tree/master/Notebooks) - The economics of futures, futures, options, and swaps.
- [Black Scholes](https://github.com/irajwani/numerical_methods_python/blob/master/black_scholes.ipynb) - Options pricing.
- [Computational Derivatives](https://github.com/chenbowen184/Computational_Finance) - Projects focusing on investigating simulations and computational techniques applied in finance.
- [Reinforcement Learning](https://github.com/FinTechies/HedgingRL) - Hedging portfolios with reinforcement learning.
- [Delta Hedging](https://github.com/RobinsonGarcia/delta-hedging) - Advanced derivatives.
- [Options Risk Measures](https://github.com/wanglouis49/risk_estimation) - Efficient financial risk estimation via computer experiment design (regression + variance-reduced sampling).
- [Derivatives Python](https://github.com/yhilpisch/dawp/tree/master/python36) - Derivative analytics with Python.
- [Volatility and Variance Derivatives](https://github.com/yhilpisch/lvvd/tree/master/lvvd) - Volatility derivatives analytics.
- [Options](https://github.com/PHBS/2018.M1.ASP/tree/master/py) - Black Scholes and Copula.
- [Option Strategies](https://github.com/rstreppa/valuation-OptionStrategies) - Valuation of Vanilla and Exotic option strategies (Butterfly, Risk Reversal etc.) with widget animations.
- [Derman](https://github.com/rstreppa/valuation-convertibles-Goldman1994/blob/master/ConvertibleBond_Goldman1994_Derman.ipynb) - Binomial tree for American call.
- [Hull White](https://github.com/rstreppa/valuation-callables-HullWhite/blob/master/CallableBond_HullWhite.ipynb) - Callable Bond, Hull White.
## Fixed Income ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/fixed_income))
- [Vasicek](https://github.com/RobinsonGarcia/fixed-income/blob/master/2.0%20Vasicek%20-%20example.ipynb) - Bootstrapping and interpolation.
- [Binomial Tree](https://github.com/hy-lei/math-finance-exercise) - Utility functions in fixed income securities.
- [Corporate Bonds](https://github.com/ishank011/gs-quantify-bond-prediction) - Predicting the buying and selling volume of the corporate bonds.
## Alternative Finance ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/alternative_finance))
- [Kiva Crowdfunding](https://github.com/CJL89/Kiva-Crowdfunding/blob/master/Kiva%20Crowdfunding.ipynb) - Exploratory data analysis.
- [Venture Capital](https://github.com/julian-chan/etothex) - Insight into a new founder to make data-driven investment decisions.
- [Venture Capital NN](https://github.com/tr7200/National-Culture-and-Venture-Capital-Monitoring) - Cox-PH neural network predictions for VC/innovations finance research.
- [Private Equity](https://github.com/TheVinhLuong102/ChicagoBooth-EntrepreneurialFinancePrivateEquity/blob/master/RightNow%20Technologies/RightNow%20Technologies.ipynb) - Valuation models.
- [VC OLS](https://github.com/fionawhitefield/venture-capital-ols/blob/master/sec_project.ipynb) - VC regression.
- [Watch Valuation](https://github.com/alporter08/Luxury-Watch-Valuation/blob/master/Luxury-Watch-Valuation.ipynb) - Analysis of luxury watch data to classify whether a certain model is likely to be over- or undervalued.
- [Art Valuation](https://github.com/ahmedhosny/theGreenCanvas/blob/gh-pages/ImageProcessing1210.ipynb) - Art evaluation analytics.
- [Blockchain](https://github.com/nud3l/dInvest) - Repository for distributed autonomous investment banking.
# Extended Research ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/extended_research))
- [HFT](https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy) - High frequency trading.
- [Deep Portfolio](https://github.com/DLColumbia/DL_forFinance) - Deep learning for finance Predict volume of bonds.
- [Mathematical Finance](https://github.com/Auquan/Tutorials) - Notebooks for math and financial tutorials.
- [NLP Finance Papers](https://github.com/chenbowen184/Research_Documents_Curation_with_NLP) - Curating quantitative finance papers using machine learning.
- [Simulation](https://github.com/chenbowen184/Computational_Finance) - Investigating simulations as part of computational finance.
- [Market Crash Prediction](https://github.com/sarachmax/MarketCrashes_Prediction/blob/master/LPPL_Comparasion.ipynb) - Predicting market crashes using an LPPL model.
- [Commodity](https://github.com/felipessalvatore/fin2vec/blob/master/src/Commodity2BR.ipynb) - Commodity influence over Brazilian stocks.
- [Finance Graph Theory](https://github.com/AvijitGhosh82/Finance_Graph_Theory) - Modelling Contentedness of Firms in Financial Markets with Heterogeneous Agents.
- [Real Estate Property Fraud](https://github.com/aviroop1/Real_Estate_Property_Fraud) - Unsupervised fraud detection model that can identify likely candidates of fraud.
- [Behavioural Economics](https://github.com/pcmichaud/notebooks) - Behavioural Economics and Finance Python Notebooks.
- [Bayesian Finance](https://github.com/marketneutral/alphatools/blob/master/notebooks/pymc3-minimal.ipynb) - Notebook PyMC3 implementation.
- [Bayesian Finance I](https://github.com/AlexIoannides/pymc-stochastic-process/blob/master/bayes_stoch_proc_calib.ipynb) - Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs.
- [Currency PCA](https://github.com/shanemulqueen/python-finance-pca/blob/master/FX_spots_w_PCA.ipynb) - Forex spots PCA.
- [Backtests](https://github.com/AlgoTraders/stock-analysis-engine) - Trading data and algorithms.
- [High Frequency](https://github.com/cswaney/prickle) - A Python toolkit for high-frequency trade research.
- [Financial Economics](https://github.com/rsvp/fecon235/tree/master/nb) - Financial Economics Models.
- [Critical Transitions](https://github.com/ryanholbrook/critical-transitions) - Detecting critical transitions in financial networks with topological data analysis.
- [Economic Foundations](https://github.com/SeanMcOwen/FinanceAndPython.com-EconomicFoundations) - Basic economic models.
- [Corporate Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-CorporateFinance) - Basic corporate finance.
- [Applied Corporate Finance](https://github.com/chenbowen184/Data_Science_in_Applied_Corporate_Finance) - Studies the empirical behaviours in stock market.
- [M&A](https://github.com/atulram/Finance-and-Stocks) - Mergers and Acquisitions.
- [Life-cycle](https://github.com/atulram/Finance-and-Stocks/blob/master/CompanyLifeCycle.ipynb) - Company life cycle.
- [Computational Finance](https://github.com/lnsongxf/Applied_Computational_Economics_and_Finance) - Applied Computational Economics and Finance.
- [Liquidity and Momentum](https://github.com/mrefermat/quant_finance) - Various factors and portfolio constructions.
# Courses ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/courses))
- [Mathematical Finance](https://github.com/yadongli/nyumath2048) - NYU Math-GA 2048: Scientific Computing in Finance.
- [Algo Trading](https://github.com/JCreeks/Machine-Learning-in-Finance/tree/master/0_Intro_to_Algo_Trading) - Intro to algo trading.
- [Python for Finance](https://github.com/siaen/python_finance_course) - CEU python for finance course material.
- [Handson Python for Finance](https://github.com/PacktPublishing/Hands-on-Python-for-Finance) - Hands-on Python for Finance published by Packt.
- [Machine Learning for Trading](https://github.com/stefan-jansen/machine-learning-for-trading) - Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading.
- [ML Specialisation](https://github.com/Ahmed0028/Machine-Learning-and-Reinforcement-Learning-in-Finance-Specialization) - Machine Learning in Finance.
- [Risk Management](https://github.com/andrey-lukyanov/Risk-Management) - Finance risk engagement course resources.
- [Basic Investments](https://github.com/SeanMcOwen/FinanceAndPython.com-Investments) - Basic investment tools in python.
- [Basic Derivatives](https://github.com/SeanMcOwen/FinanceAndPython.com-Derivatives) - Basic forward contracts and hedging.
- [Basic Finance](https://github.com/SeanMcOwen/FinanceAndPython.com-BasicFinance) - Source code notebooks basic finance applications.
# Data ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/data))
- [Capital Markets Data](https://www.capitalmarketsdata.com/) (and [chartpack](https://1drv.ms/b/s!AjyO2n0JmhakhJIy-jZXLsLGxOw38Q?e=uOuPKY))
- [Employee Count SEC Filings](https://github.com/healthgradient/sec_employee_information_extraction)
- [SEC Parsing](https://github.com/healthgradient/sec-doc-info-extraction/blob/master/classify_sections_containing_relevant_information.ipynb)
- [Open Edgar](https://github.com/LexPredict/openedgar)
- [EDGAR](https://github.com/TiesdeKok/UW_Python_Camp/blob/master/Materials/Session_5/EDGAR_walkthrough.ipynb)
- [IRS](http://social-metrics.org/sox/)
- [Rating Industries](http://www.ratingshistory.info/)
- [Web Scraping (FirmAI)](https://github.com/firmai/business-machine-learning/blob/master/www.firmai.org/data)
- [Financial Corporate](http://raw.rutgers.edu/Corporate%20Financial%20Data.html)
- [Non-financial Corporate](http://raw.rutgers.edu/Non-Financial%20Corporate%20Data.html)
- [http://finance.yahoo.com/](http://finance.yahoo.com/)
- [https://fred.stlouisfed.org/](https://fred.stlouisfed.org/)
- [https://stooq.com](https://stooq.com)
- [https://github.com/timestocome/StockMarketData](https://github.com/timestocome/StockMarketData)
# Colleges, Centers and Departments ([Wiki](https://github.com/firmai/financial-machine-learning/wiki/colleges_centers_and_departments))
- [NYU FRE](https://engineering.nyu.edu/academics/departments/finance-and-risk-engineering) - Finance and Risk Engineering (NYU Tandon)
- [Cornell University](https://www.cornell.edu/)
- [NYU Courant](https://cims.nyu.edu/) - Courant Institute of Mathematical Sciences, New York University
- [Oxford Man](https://www.oxford-man.ox.ac.uk/) - Oxford-Man Institute of Quantitative Finance
- [Stanford Advanced Financial Technologies](https://fintech.stanford.edu/) - Stanford Advanced Financial Technologies Laboratory
- [Berkeley Lab CIFT](https://cs.lbl.gov/news-media/news/news-archive/2010/berkeley-lab-launches-new-center-for-innovative-financial-technology/)
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