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https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading

Quant/Algorithm trading resources with an emphasis on Machine Learning
https://github.com/grananqvist/Awesome-Quant-Machine-Learning-Trading

List: Awesome-Quant-Machine-Learning-Trading

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Quant/Algorithm trading resources with an emphasis on Machine Learning

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# Awesome-Quant-Machine-Learning-Trading

Quant/Algorithm trading resources with an emphasis on Machine Learning. 



I have excluded any kind of resources that I consider to be of low quality.  



:star: - My favourites



# Financial Machine Learning

## Books



* :star: Marcos López de Prado - Advances in Financial Machine Learning [[Link]](https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos-ebook/dp/B079KLDW21/ref=sr_1_1?s=books&ie=UTF8&qid=1541717436&sr=1-1).

* :star: Dr Howard B Bandy - Quantitative Technical Analysis: An integrated approach to trading system development and trading management [[Link]](https://www.amazon.com/Quantitative-Technical-Analysis-integrated-development/dp/0979183855/ref=sr_1_1?s=books&ie=UTF8&qid=1541718134&sr=1-1)

* Tony Guida - Big Data and Machine Learning in Quantitative Investment [[Link]](https://www.amazon.com/Machine-Learning-Quantitative-Investment-Finance/dp/1119522196/ref=sr_1_1?s=books&ie=UTF8&qid=1541717791&sr=1-1)

* :star: Michael Halls-Moore - Advanced Algorithmic Trading [[Link]](https://www.quantstart.com/advanced-algorithmic-trading-ebook)

* Jannes Klaas - Machine Learning for Finance: Data algorithms for the markets and deep learning from the ground up for financial experts and economics [[Link]](https://www.amazon.com/Machine-Learning-Finance-algorithms-financial-ebook/dp/B07BDK6LF9/ref=sr_1_1?s=digital-text&ie=UTF8&qid=1541717605&sr=1-1)

* Stefan Jansen - Hands-On Machine Learning for Algorithmic Trading: Design and implement smart investment strategies to analyze market behavior using the Python ecosystem [[Link]](https://www.amazon.com/Hands-Machine-Learning-Algorithmic-Trading-ebook/dp/B07JLFH7C5/ref=sr_1_1?s=digital-text&ie=UTF8&qid=1541717705&sr=1-1)

* Ali N. Akansu et al. - Financial Signal Processing and Machine Learning [[Link]](https://www.amazon.com/Financial-Signal-Processing-Machine-Learning/dp/1118745671/ref=sr_1_1?s=books&ie=UTF8&qid=1541718070&sr=1-1)

* David Aronson - Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading [[Link]](https://www.amazon.com/Evidence-Based-Technical-Analysis-Scientific-Statistical/dp/0470008741/ref=sr_1_1?s=books&ie=UTF8&qid=1541974508&sr=1-1&keywords=david+aronson)

* David Aronson - Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments [[Link]](https://www.amazon.com/Statistically-Learning-Algorithmic-Financial-Instruments/dp/148950771X/ref=sr_1_3?s=books&ie=UTF8&qid=1541718293&sr=1-3)

* Ernest P. Chan - Machine Trading: Deploying Computer Algorithms to Conquer the Markets [[Link]](https://www.amazon.co.uk/gp/product/1119219604/ref=as_li_qf_asin_il_tl?ie=UTF8&tag=startupanalyt-21&creative=24630&linkCode=as2&creativeASIN=1119219604&linkId=ce2ca9a67128675e3fcdc9ec9696e2c7)



## Online series and courses

The selection of online courses for ML for trading is very poor in my opinion.  



* Udacity, Georgia Tech - Machine Learning for Trading [[Link]](https://eu.udacity.com/course/machine-learning-for-trading--ud501)

* Udacity, WorldQuant - Artificial Intelligence for Trading [[Link]](https://eu.udacity.com/course/ai-for-trading--nd880)



* Coursera, NYU - Machine Learning and Reinforcement Learning in Finance Specialization (Weakly related to trading)

  * Coursera, NYU - Guided Tour of Machine Learning in Finance [[Link]](https://www.coursera.org/learn/guided-tour-machine-learning-finance)

  * Coursera, NYU - Fundamentals of Machine Learning in Finance [[Link]](https://www.coursera.org/learn/fundamentals-machine-learning-in-finance)

  * Coursera, NYU - Reinforcement Learning in Finance [[Link]](https://www.coursera.org/learn/reinforcement-learning-in-finance)

  * Coursera, NYU - Overview of Advanced Methods for Reinforcement Learning in Finance [[Link]](https://www.coursera.org/learn/advanced-methods-reinforcement-learning-finance)



## Youtube videos

* :star: Siraj Raval - Videos about stock market prediction using Deep Learning [[Link]](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/search?query=trading)

* QuantInsti Youtube - webinars about Machine Learning for trading [[Link]](https://www.youtube.com/user/quantinsti/search?query=machine+learning)

* :star: Quantopian - Webinars about Machine Learning for trading [[Link]](https://www.youtube.com/channel/UC606MUq45P3zFLa4VGKbxsg/search?query=machine+learning)

* Sentdex - Machine Learning for Forex and Stock analysis and algorithmic trading [[Link]](https://www.youtube.com/watch?v=v_L9jR8P-54&list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO)

* Sentdex - Python programming for Finance (a few videos including Machine Learning) [[Link]](https://www.youtube.com/watch?v=Z-5wNWgRJpk&index=9&list=PLQVvvaa0QuDcOdF96TBtRtuQksErCEBYZ)

* QuantNews - Machine Learning for Algorithmic Trading 3 part series [[Link]](https://www.youtube.com/playlist?list=PLHJACfjILJ-91qkw5YC83S6COKGscctzz)

* :star: Howard Bandy - Machine Learning Trading System Development Webinar [[Link]](https://www.youtube.com/watch?v=v729evhMpYk&t=1s)

* Ernie Chan - Machine Learning for Quantitative Trading Webinar [[Link]](https://www.youtube.com/watch?v=72aEDjwGMr8&t=1023s)

* Hitoshi Harada, CTO at Alpaca - Deep Learning in Finance Talk [[Link]](https://www.youtube.com/watch?v=FoQKCeDuPiY)

* Prediction Machines - Deep Learning with Python in Finance Talk [[Link]](https://www.youtube.com/watch?v=xvm-M-R2fZY)

* Master Thesis presentation, Uni of Essex - Analyzing the Limit Order Book, A Deep Learning Approach [[Link]](https://www.youtube.com/watch?v=qxSh2VFmRGw)

* Tucker Balch - Applying Deep Reinforcement Learning to Trading [[Link]](https://www.youtube.com/watch?v=Pka0DC_P17k)

* Krish Naik - Machine learning tutorials and their Application in Stock Prediction [[Link]](https://www.youtube.com/watch?v=H6du_pfuznE)

## Blogs and content websites

* :star: Quantstart - Machine Learning for Trading articles [[Link]](https://www.quantstart.com/articles)

* :star: Quantopian - Lecture notebooks on ML-related statistics [[Link]](https://www.quantopian.com/lectures)

* :star: Quantopian - Tutorials and notebooks tagged with Machine Learning [[Link]](https://www.quantopian.com/posts/tag/machine-learning/newest?attachment=notebooks)

* AAA Quants, Tom Starke Blog [[Link]](http://aaaquants.com/category/blog/)

* RobotWealth, Kris Longmore Blog [[Link]](https://robotwealth.com/blog/)

* Quantsportal, Jacques Joubert's Blog [[Link]](http://www.quantsportal.com/blog-page/)

* Blackarbs blog [[Link]](http://www.blackarbs.com/blog/)

* Hardikp, Hardik Patel blog [[Link]](https://www.hardikp.com/)



## Interviews

* :star: Chat with Traders EP042 - Machine learning for algorithmic trading with Bert Mouler [[Link]](https://www.youtube.com/watch?v=i8FNO8r7PaE)

* :star: Chat with Traders EP142 - Algo trader using automation to bypass human flaws with Bert Mouler [[Link]](https://www.youtube.com/watch?v=ofL66mh6Tw0)

* Chat with Traders EP147 - Detective work leading to viable trading strategies with Tom Starke [[Link]](https://www.youtube.com/watch?v=JjXw9Mda7eY)

* :star: Chat with Traders Quantopian 5 - Good Uses of Machine Learning in Finance with Max Margenot [[Link]](https://www.youtube.com/watch?v=Zj5sXWv9SDM)

* Chat With Traders EP131 - Trading strategies, powered by machine learning with Morgan Slade [[Link]](https://www.youtube.com/watch?v=EbWbeYu8zwg)

* Better System Trader EP023 - Portfolio manager Michael Himmel talks AI and machine learning in trading [[Link]](https://www.youtube.com/watch?v=9tZjeyhfG0g)

* :star: Better System Trader EP028 - David Aronson shares research into indicators that identify Bull and Bear markets. [[Link]](https://www.youtube.com/watch?v=Q4rV0Y9NokI)

* Better System Trader EP082 - Machine Learning With Kris Longmore [[Link]](https://www.youtube.com/watch?v=0syNgsd635M)

* :star: Better System Trader EP064 - Cryptocurrencies and Machine Learning with Bert Mouler [[Link]](https://www.youtube.com/watch?v=YgRTd4nLJoU)

* Better System Trader EP090 - This quants’ approach to designing algo strategies with Michael Halls-Moore [[Link]](https://chatwithtraders.com/ep-090-michael-halls-moore/)



## Papers

* :star: James Cumming - An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain [[Link]](http://www.doc.ic.ac.uk/teaching/distinguished-projects/2015/j.cumming.pdf)

* :star: Marcos López de Prado - The 10 reasons most Machine Learning Funds fails [[Link]](http://www.smallake.kr/wp-content/uploads/2018/07/SSRN-id3104816.pdf)

* Zhuoran Xiong et al. - Practical Deep Reinforcement Learning Approach for Stock Trading [[Link]](https://arxiv.org/abs/1811.07522)

* Gordon Ritter - Machine Learning for Trading [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015609)

* J.B. Heaton et al. - Deep Learning for Finance: Deep Portfolios [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2838013)

* Justin Sirignano et al. - Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3141294)

* Marcial Messmer - Deep Learning and the Cross-Section of Expected Returns [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3081555)

* :star: Marcos Lopez de Prado - Ten Financial Applications of Machine Learning (Presentation Slides) [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3197726)

* :star: Marcos Lopez de Prado - The Myth and Reality of Financial Machine Learning (Presentation Slides) [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3120557)

* Artur Sepp - Machine Learning for Volatility Trading (Presentation Slides) [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3186401)

* Marcos Lopez de Prado - Market Microstructure in the Age of Machine Learning [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3193702)

* Jonathan Brogaard - Machine Learning and the Stock Market [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3233119)

* Xinyao Qian - Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods [[Link]](https://arxiv.org/pdf/1706.00948.pdf)

* Milan Fičura - Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2903547)

* Samuel Edet - Recurrent Neural Networks in Forecasting S&P 500 Index [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3001046)

Amin Hedayati et al. - Stock Market Index Prediction Using Artificial Neural Network [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3004032)

* Jaydip Sen et al. - A Robust Predictive Model for Stock Price Forecasting [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3068204)

*  O.B. Sezer et al. - An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework [[Link]](https://dl.acm.org/citation.cfm?id=3077294)

* Ritika Singh et al. - Stock prediction using deep learning [[Link]](https://link.springer.com/article/10.1007/s11042-016-4159-7)

* Thomas Fischera et al. - Deep learning with long short-term memory networks for financial market predictions [[Link]](https://www.econstor.eu/bitstream/10419/157808/1/886576210.pdf)

* R.C.Cavalcante et al. - Computational Intelligence and Financial Markets: A Survey and Future Directions [[Link]](https://www.sciencedirect.com/science/article/pii/S095741741630029X)

* E. Chong et al. - Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies [[Link]](http://dro.dur.ac.uk/21533/1/21533.pdf)

* Chien Yi Huang - Financial Trading as a Game: A Deep Reinforcement Learning Approach [[Link]](https://arxiv.org/pdf/1807.02787.pdf)

* W. Bao et al. - A deep learning framework for financial time series using stacked autoencoders and longshort term memory [[Link]](https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0180944&type=printable)

* Xingyu Zhou et al. - Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets [[Link]](http://downloads.hindawi.com/journals/mpe/2018/4907423.pdf)

* Fuli Feng et al. - Improving Stock Movement Prediction with Adversarial Training [[Link]](https://arxiv.org/pdf/1810.09936.pdf)

* Z. Zhao et al. - Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction [[Link]](https://ieeexplore.ieee.org/abstract/document/8372087)

* Arthur le Calvez, Dave Cliff - Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market [[Link]](https://arxiv.org/abs/1811.02880)

* Dang Lien Minh et al. - Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network [[Link]](https://ieeexplore.ieee.org/abstract/document/8456512)

* Yue Deng et al. - Deep Direct Reinforcement Learning for Financial Signal Representation and Trading [[Link]](http://cslt.riit.tsinghua.edu.cn/mediawiki/images/a/aa/07407387.pdf)

* Xiao Zhong - A comprehensive cluster and classification mining procedure for daily stock market return forecasting [[Link]](https://www.sciencedirect.com/science/article/pii/S0925231217310652)

* J. Zhang et al. - A novel data-driven stock price trend prediction system [[Link]](https://www.sciencedirect.com/science/article/pii/S0957417417308485)

* Ehsan Hoseinzade et al. - CNNPred: CNN-based stock market prediction using several data sources [[Link]](https://arxiv.org/pdf/1810.08923.pdf)

* Hyejung Chung et al. - Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction [[Link]](https://www.mdpi.com/2071-1050/10/10/3765/pdf)

* Yujin Baek et al. - ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module [[Link]](https://www.sciencedirect.com/science/article/pii/S0957417418304342)

* Rajashree Dash et al. - A hybrid stock trading framework integrating technical analysis with machine learning techniques [[Link]](https://www.sciencedirect.com/science/article/pii/S2405918815300179)

* E.A. Gerlein et al. - Evaluating machine learning classification for financial trading: an empirical approach [[Link]](http://nrl.northumbria.ac.uk/34544/1/Evaluating%20machine%20learning.pdf)

* Justin Sirignano - Deep Learning for Limit Order Books [[Link]](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2710331)



### Events & Sentiment trading

* Frank Z. Xing et al. - Natural language based financial forecasting: a survey [[Link]](http://sentic.net/natural-language-based-financial-forecasting.pdf)

* Ziniu Hu et al. - Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction [[Link]](https://arxiv.org/abs/1712.02136v1)

* J.W. Leung, Master Thesis, MIT - Application of Machine Learning: Automated Trading Informed by Event Driven Data [[Link]](https://dspace.mit.edu/bitstream/handle/1721.1/105982/965785890-MIT.pdf?sequence=1)

* Xiao Ding et al. - Deep Learning for Event-Driven Stock Prediction [[Link]](http://www.aaai.org/ocs/index.php/IJCAI/IJCAI15/paper/download/11031/10986)



## Reinforcement Learning environments

* :star: TradingGym [[Link]](https://github.com/Yvictor/TradingGym)

* Trading-Gym [[Link]](https://github.com/thedimlebowski/Trading-Gym)

* btym [[Link]](https://github.com/Kismuz/btgym)

* TradzQAI [[Link]](https://github.com/kkuette/TradzQAI)



## Code

* marketneutral - pairs trading with ML [[Link]](https://github.com/marketneutral/pairs-trading-with-ML)

* BlackArbsCEO - Advances in Financial Machine Learning Exercises [[Link]](https://github.com/BlackArbsCEO/Adv_Fin_ML_Exercises)

* mlfinlab - Package for Advances in Financial Machine Learning [[Link]](https://github.com/hudson-and-thames)

* MachineLearningStocks - Using python and scikit-learn to make stock predictions [[Link]](https://github.com/robertmartin8/MachineLearningStocks)

* AlphaAI - Use unsupervised and supervised learning to predict stocks [[Link]](https://github.com/VivekPa/AlphaAI)

* SGX-Full-OrderBook-Tick-Data-Trading-Strategy - Providing the solutions for high-frequency trading (HFT) strategies using ML [[Link]](https://github.com/rorysroes/SGX-Full-OrderBook-Tick-Data-Trading-Strategy)

* NeuralNetworkStocks - Using Python and keras to make stock predictions [[Link]](https://github.com/VivekPa/NeuralNetworkStocks)

* Stock-Price-Prediction-LSTM - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [[Link]](https://github.com/NourozR/Stock-Price-Prediction-LSTM)

* SravB - Algorithmic trading using machine learning [[Link]](https://github.com/SravB/Algorithmic-Trading)

* Flow - High frequency AI based algorithmic trading module [[Link]](https://github.com/yazanobeidi/flow)

* timestocome - Test-stock-prediction-algorithms [[Link]](https://github.com/timestocome/Test-stock-prediction-algorithms)

* deepstock - Technical experimentations to beat the stock market using deep learning [[Link]](https://github.com/keon/deepstock)

* qtrader - Reinforcement Learning for Portfolio Management [[Link]](https://github.com/filangel/qtrader)

* stockPredictor - Predict stock movement with Machine Learning and Deep Learning algorithms [[Link]](https://github.com/Nazanin1369/stockPredictor)

* stock_market_reinforcement_learning - Stock market environment using OpenGym with Deep Q-learning and Policy Gradient [[Link]](https://github.com/kh-kim/stock_market_reinforcement_learning)

* deep-algotrading - deep learning techniques from regression to LSTM using financial data [[Link]](https://github.com/LiamConnell/deep-algotrading)

* deep_trader - Use reinforcement learning on stock market and agent tries to learn trading [[Link]](https://github.com/deependersingla/deep_trader)

* Deep-Trading - Algorithmic trading with deep learning experiments [[Link]](https://github.com/Rachnog/Deep-Trading)

* Deep-Trading - Algorithmic Trading using RNN [[Link]](https://github.com/ha2emnomer/Deep-Trading)

* Multidimensional-LSTM-BitCoin-Time-Series - Using multidimensional LSTM neural networks to create a forecast for Bitcoin price [[Link]](https://github.com/jaungiers/Multidimensional-LSTM-BitCoin-Time-Series)

* QLearning_Trading - Learning to trade under the reinforcement learning framework [[Link]](https://github.com/ucaiado/QLearning_Trading)

* Day-Trading-Application - Use deep learning to make accurate future stock return predictions [[Link]](https://github.com/jbboltz123/Day-Trading-Application)

* bulbea - Deep Learning based Python Library for Stock Market Prediction and Modelling [[Link]](https://github.com/achillesrasquinha/bulbea)

* PGPortfolio - source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [[Link]](https://github.com/ZhengyaoJiang/PGPortfolio)

* Thesis - Reinforcement Learning for Automated Trading [[Link]](https://github.com/pnecchi/Thesis)

* DQN - Reinforcement Learning for finance [[Link]](https://github.com/jjakimoto/DQN)

* Deep-Trading-Agent - Deep Reinforcement Learning based Trading Agent for Bitcoin [[Link]](https://github.com/samre12/deep-trading-agent)

* deep_portfolio - Use Reinforcement Learning and Supervised learning to Optimize portfolio allocation [[Link]](https://github.com/deependersingla/deep_portfolio)

* Deep-Reinforcement-Learning-in-Stock-Trading - Using deep actor-critic model to learn best strategies in pair trading [[Link]](https://github.com/shenyichen105/Deep-Reinforcement-Learning-in-Stock-Trading)

* Stock-Price-Prediction-LSTM - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [[Link]](https://github.com/NourozR/Stock-Price-Prediction-LSTM)