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.

⭐ - My favourites

Financial Machine Learning

Books

⭐ Marcos López de Prado - Advances in Financial Machine Learning [Link].
⭐ Dr Howard B Bandy - Quantitative Technical Analysis: An integrated approach to trading system development and trading management [Link]
Tony Guida - Big Data and Machine Learning in Quantitative Investment [Link]
⭐ Michael Halls-Moore - Advanced Algorithmic Trading [Link]
Jannes Klaas - Machine Learning for Finance: Data algorithms for the markets and deep learning from the ground up for financial experts and economics [Link]
Stefan Jansen - Hands-On Machine Learning for Algorithmic Trading: Design and implement smart investment strategies to analyze market behavior using the Python ecosystem [Link]
Ali N. Akansu et al. - Financial Signal Processing and Machine Learning [Link]
David Aronson - Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading [Link]
David Aronson - Statistically Sound Machine Learning for Algorithmic Trading of Financial Instruments [Link]
Ernest P. Chan - Machine Trading: Deploying Computer Algorithms to Conquer the Markets [Link]

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]
Coursera, NYU - Machine Learning and Reinforcement Learning in Finance Specialization (Weakly related to trading)
- Coursera, NYU - Guided Tour of Machine Learning in Finance [Link]
- Coursera, NYU - Fundamentals of Machine Learning in Finance [Link]
- Coursera, NYU - Reinforcement Learning in Finance [Link]
- Coursera, NYU - Overview of Advanced Methods for Reinforcement Learning in Finance [Link]

Youtube videos

⭐ Siraj Raval - Videos about stock market prediction using Deep Learning [Link]
QuantInsti Youtube - webinars about Machine Learning for trading [Link]
⭐ Quantopian - Webinars about Machine Learning for trading [Link]
Sentdex - Machine Learning for Forex and Stock analysis and algorithmic trading [Link]
Sentdex - Python programming for Finance (a few videos including Machine Learning) [Link]
QuantNews - Machine Learning for Algorithmic Trading 3 part series [Link]
⭐ Howard Bandy - Machine Learning Trading System Development Webinar [Link]
Ernie Chan - Machine Learning for Quantitative Trading Webinar [Link]
Hitoshi Harada, CTO at Alpaca - Deep Learning in Finance Talk [Link]
Prediction Machines - Deep Learning with Python in Finance Talk [Link]
Master Thesis presentation, Uni of Essex - Analyzing the Limit Order Book, A Deep Learning Approach [Link]
Tucker Balch - Applying Deep Reinforcement Learning to Trading [Link]

Blogs and content websites

⭐ Quantstart - Machine Learning for Trading articles [Link]
⭐ Quantopian - Lecture notebooks on ML-related statistics [Link]
⭐ Quantopian - Tutorials and notebooks tagged with Machine Learning [Link]
AAA Quants, Tom Starke Blog [Link]
RobotWealth, Kris Longmore Blog [Link]
Blackarbs blog [Link]
Hardikp, Hardik Patel blog [Link]

Interviews

⭐ Chat with Traders EP042 - Machine learning for algorithmic trading with Bert Mouler [Link]
⭐ Chat with Traders EP142 - Algo trader using automation to bypass human flaws with Bert Mouler [Link]
Chat with Traders EP147 - Detective work leading to viable trading strategies with Tom Starke [Link]
⭐ Chat with Traders Quantopian 5 - Good Uses of Machine Learning in Finance with Max Margenot [Link]
Chat With Traders EP131 - Trading strategies, powered by machine learning with Morgan Slade [Link]
Better System Trader EP023 - Portfolio manager Michael Himmel talks AI and machine learning in trading [Link]
⭐ Better System Trader EP028 - David Aronson shares research into indicators that identify Bull and Bear markets. [Link]
Better System Trader EP082 - Machine Learning With Kris Longmore [Link]
⭐ Better System Trader EP064 - Cryptocurrencies and Machine Learning with Bert Mouler [Link]
Better System Trader EP090 - This quants’ approach to designing algo strategies with Michael Halls-Moore [Link]

Papers

⭐ James Cumming - An Investigation into the Use of Reinforcement Learning Techniques within the Algorithmic Trading Domain [Link]
⭐ Marcos López de Prado - The 10 reasons most Machine Learning Funds fails [Link]
Zhuoran Xiong et al. - Practical Deep Reinforcement Learning Approach for Stock Trading [Link]
Gordon Ritter - Machine Learning for Trading [Link]
J.B. Heaton et al. - Deep Learning for Finance: Deep Portfolios [Link]
Justin Sirignano et al. - Universal Features of Price Formation in Financial Markets: Perspectives From Deep Learning [Link]
Marcial Messmer - Deep Learning and the Cross-Section of Expected Returns [Link]
⭐ Marcos Lopez de Prado - Ten Financial Applications of Machine Learning (Presentation Slides) [Link]
⭐ Marcos Lopez de Prado - The Myth and Reality of Financial Machine Learning (Presentation Slides) [Link]
Artur Sepp - Machine Learning for Volatility Trading (Presentation Slides) [Link]
Marcos Lopez de Prado - Market Microstructure in the Age of Machine Learning [Link]
Jonathan Brogaard - Machine Learning and the Stock Market [Link]
Xinyao Qian - Financial Series Prediction: Comparison Between Precision of Time Series Models and Machine Learning Methods [Link]
Milan Fičura - Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks [Link]
Samuel Edet - Recurrent Neural Networks in Forecasting S&P 500 Index [Link] Amin Hedayati et al. - Stock Market Index Prediction Using Artificial Neural Network [Link]
Jaydip Sen et al. - A Robust Predictive Model for Stock Price Forecasting [Link]
O.B. Sezer et al. - An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework [Link]
Ritika Singh et al. - Stock prediction using deep learning [Link]
Thomas Fischera et al. - Deep learning with long short-term memory networks for financial market predictions [Link]
R.C.Cavalcante et al. - Computational Intelligence and Financial Markets: A Survey and Future Directions [Link]
E. Chong et al. - Deep Learning Networks for Stock Market Analysis and Prediction: Methodology, Data Representations, and Case Studies [Link]
Chien Yi Huang - Financial Trading as a Game: A Deep Reinforcement Learning Approach [Link]
W. Bao et al. - A deep learning framework for financial time series using stacked autoencoders and longshort term memory [Link]
Xingyu Zhou et al. - Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets [Link]
Fuli Feng et al. - Improving Stock Movement Prediction with Adversarial Training [Link]
Z. Zhao et al. - Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction [Link]
Arthur le Calvez, Dave Cliff - Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market [Link]
Dang Lien Minh et al. - Deep Learning Approach for Short-Term Stock Trends Prediction Based on Two-Stream Gated Recurrent Unit Network [Link]
Yue Deng et al. - Deep Direct Reinforcement Learning for Financial Signal Representation and Trading [Link]
Xiao Zhong - A comprehensive cluster and classification mining procedure for daily stock market return forecasting [Link]
J. Zhang et al. - A novel data-driven stock price trend prediction system [Link]
Ehsan Hoseinzade et al. - CNNPred: CNN-based stock market prediction using several data sources [Link]
Hyejung Chung et al. - Genetic Algorithm-Optimized Long Short-Term Memory Network for Stock Market Prediction [Link]
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]
Rajashree Dash et al. - A hybrid stock trading framework integrating technical analysis with machine learning techniques [Link]
E.A. Gerlein et al. - Evaluating machine learning classification for financial trading: an empirical approach [Link]
Justin Sirignano - Deep Learning for Limit Order Books [Link]

Events & Sentiment trading

Frank Z. Xing et al. - Natural language based financial forecasting: a survey [Link]
Ziniu Hu et al. - Listening to Chaotic Whispers: A Deep Learning Framework for News-oriented Stock Trend Prediction [Link]
J.W. Leung, Master Thesis, MIT - Application of Machine Learning: Automated Trading Informed by Event Driven Data [Link]
Xiao Ding et al. - Deep Learning for Event-Driven Stock Prediction [Link]

Reinforcement Learning environments

⭐ TradingGym [Link]
Trading-Gym [Link]
btym [Link]
TradzQAI [Link]

Code

marketneutral - pairs trading with ML [Link]
BlackArbsCEO - Advances in Financial Machine Learning Exercises [Link]
MachineLearningStocks - Using python and scikit-learn to make stock predictions [Link]
AlphaAI - Use unsupervised and supervised learning to predict stocks [Link]
SGX-Full-OrderBook-Tick-Data-Trading-Strategy - Providing the solutions for high-frequency trading (HFT) strategies using ML [Link]
NeuralNetworkStocks - Using Python and keras to make stock predictions [Link]
Stock-Price-Prediction-LSTM - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [Link]
SravB - Algorithmic trading using machine learning [Link]
Flow - High frequency AI based algorithmic trading module [Link]
timestocome - Test-stock-prediction-algorithms [Link]
deepstock - Technical experimentations to beat the stock market using deep learning [Link]
qtrader - Reinforcement Learning for Portfolio Management [Link]
stockPredictor - Predict stock movement with Machine Learning and Deep Learning algorithms [Link]
stock_market_reinforcement_learning - Stock market environment using OpenGym with Deep Q-learning and Policy Gradient [Link]
deep-algotrading - deep learning techniques from regression to LSTM using financial data [Link]
deep_trader - Use reinforcement learning on stock market and agent tries to learn trading [Link]
Deep-Trading - Algorithmic trading with deep learning experiments [Link]
Deep-Trading - Algorithmic Trading using RNN [Link]
Multidimensional-LSTM-BitCoin-Time-Series - Using multidimensional LSTM neural networks to create a forecast for Bitcoin price [Link]
QLearning_Trading - Learning to trade under the reinforcement learning framework [Link]
Day-Trading-Application - Use deep learning to make accurate future stock return predictions [Link]
bulbea - Deep Learning based Python Library for Stock Market Prediction and Modelling [Link]
PGPortfolio - source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem" [Link]
Thesis - Reinforcement Learning for Automated Trading [Link]
DQN - Reinforcement Learning for finance [Link]
Deep-Trading-Agent - Deep Reinforcement Learning based Trading Agent for Bitcoin [Link]
deep_portfolio - Use Reinforcement Learning and Supervised learning to Optimize portfolio allocation [Link]
Deep-Reinforcement-Learning-in-Stock-Trading - Using deep actor-critic model to learn best strategies in pair trading [Link]
Stock-Price-Prediction-LSTM - OHLC Average Prediction of Apple Inc. Using LSTM Recurrent Neural Network [Link]

Kismuz / Awesome-Quant-Machine-Learning-Trading