If you want to contribute to this list (please do), send me a pull request or contact me [@dereknow](https://twitter.com/dereknow) or on [linkedin](https://www.linkedin.com/in/snowderek/). Also, a listed repository should be deprecated if:
- [Deep Learning III](https://github.com/Rachnog/Deep-Trading) - Algorithmic trading with deep learning experiments.
- [Deep Learning IV](https://github.com/achillesrasquinha/bulbea) - Bulbea: Deep Learning based Python Library.
- [LTSM GRU](https://github.com/RajatHanda/Finance-Forecasting) - Stock Market Forecasting using LSTM\GRU.
- [LTSM Recurrent](https://github.com/VivekPa/AIAlpha) - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network.
- [ARIMA-LTSM Hybrid](https://github.com/imhgchoi/Corr_Prediction_ARIMA_LSTM_Hybrid) - Hybrid model to predict future price correlation coefficients of two assets.
- [Neural Network](https://github.com/VivekPa/IntroNeuralNetworks) - Neural networks to predict stock prices.
- [AI Trading](https://github.com/borisbanushev/stockpredictionai/blob/master/readme2.md) - AI to predict stock market movements.
- [RL Trading](https://colab.research.google.com/drive/1FzLCI0AO3c7A4bp9Fi01UwXeoc7BN8sW) - A collection of 25+ Reinforcement Learning Trading Strategies - Google Colab.
- [RL](https://github.com/kh-kim/stock_market_reinforcement_learning) - OpenGym with Deep Q-learning and Policy Gradient.
- [RL II](https://github.com/deependersingla/deep_trader) - reinforcement learning on stock market and agent tries to learn trading.
- [RL III](https://github.com/samre12/deep-trading-agent) - Github - Deep Reinforcement Learning based Trading Agent for Bitcoin.
- [RL IV](https://github.com/jjakimoto/DQN) - Reinforcement Learning for finance.
- [RL V](https://github.com/gstenger98/rl-finance) - Building an Agent to Trade with Reinforcement Learning.
- [Pair Trading RL](https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading) - Using deep actor-critic model to learn best strategies in pair trading.
- [Mixture Models II](https://github.com/BlackArbsCEO/mixture_model_trading_public) - Mixture models and stock trading.
- [Scikit-learn Stock Prediction](https://github.com/robertmartin8/MachineLearningStocks) - Using python and scikit-learn to make stock predictions.
- [Fundamental LT Forecasts](https://github.com/Hvass-Labs/FinanceOps) - Research in investment finance for long term forecasts.
- [Short-Term Movement Cues](https://github.com/anfederico/Clairvoyant) - Identify social/historical cues for short term stock movement.
- [Trend Following](http://inseaddataanalytics.github.io/INSEADAnalytics/ExerciseSet2.html) - A futures trend following portfolio investment strategy.
## Data Processing Techniques and Transformations
- [Advanced ML](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises) - Exercises too Financial Machine Learning (De Prado).
- [Advanced ML II](https://github.com/hudson-and-thames/research) - More implementations of Financial Machine Learning (De Prado).
# Portfolio Management
## 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.
- [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.
- [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.
- [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:
- [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:
- [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.
- [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.
- [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.
- [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:
- [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.
- [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.
- [Critical Transitions](https://github.com/ryanholbrook/critical-transitions) - Detecting critical transitions in financial networks with topological data analysis.
- [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
- [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.
