dan7geo/awesome-deep-trading

List of awesome resources for machine learning-based algorithmic trading

awesome-deep-trading

List of code, papers, and resources for AI/deep learning/machine learning/neural networks applied to algorithmic trading.

Open access: all rights granted for use and re-use of any kind, by anyone, at no cost, under your choice of either the free MIT License or Creative Commons CC-BY International Public License.

© 2020 Craig Bailes (@cbailes | Patreon | contact@craigbailes.com)

Contents

• Papers
  • Meta Analyses & Systematic Reviews
  • Convolutional Neural Networks (CNNs)
  • Long Short-Term Memory (LSTMs)
  • Generative Adversarial Networks (GANs)
  • High Frequency
  • Portfolio
  • Reinforcement Learning
  • Cryptocurrency
  • Social Processing
    • Behavioral Analysis
    • Sentiment Analysis
• Repositories
  • Generative Adversarial Networks (GANs)
  • Cryptocurrency
  • Datasets
    • Simulation
• Resources
  • Presentations
  • Courses
  • Guides & Further Reading

Papers

• Classification-based Financial Markets Prediction using Deep Neural Networks - Matthew Dixon, Diego Klabjan, Jin Hoon Bang (2016)
• Deep Learning for Limit Order Books - Justin Sirignano (2016)
• High-Frequency Trading Strategy Based on Deep Neural Networks - Andrés Arévalo, Jaime Niño, German Hernández, Javier Sandoval (2016)
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem - Zhengyao Jiang, Dixing Xu, Jinjun Liang (2017)
• Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks - David W. Lu (2017)
• Deep Hedging - Hans Bühler, Lukas Gonon, Josef Teichmann, Ben Wood (2018)
• Stock Trading Bot Using Deep Reinforcement Learning - Akhil Raj Azhikodan, Anvitha G. K. Bhat, Mamatha V. Jadhav (2018)
• Financial Trading as a Game: A Deep Reinforcement Learning Approach - Chien Yi Huang (2018)
• Practical Deep Reinforcement Learning Approach for Stock Trading - Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang Yang, Anwar Walid (2018)
• Algorithmic Trading and Machine Learning Based on GPU - Mantas Vaitonis, Saulius Masteika, Konstantinas Korovkinas (2018)
• A quantitative trading method using deep convolution neural network - HaiBo Chen, DaoLei Liang, LL Zhao (2019)
• Deep learning in exchange markets - Rui Gonçalves, Vitor Miguel Ribeiro, Fernando Lobo Pereira, Ana Paula Rocha (2019)
• Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks - Omer Berat Sezer, Ahmet Murat Ozbayoglu (2019)
• Deep Reinforcement Learning for Financial Trading Using Price Trailing - Konstantinos Saitas Zarkias, Nikolaos Passalis, Avraam Tsantekidis, Anastasios Tefas (2019)
• Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading - Uk Jo, Taehyun Jo, Wanjun Kim, Iljoo Yoon, Dongseok Lee, Seungho Lee (2019)
• Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning - Gyeeun Jeong, Ha Young Kim (2019)
• Deep Execution - Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks - Kevin Dabérius, Elvin Granat, Patrik Karlsson (2019)
• An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy - Dongdong Lv, Shuhan Yuan, Meizi Li, Yang Xiang (2019)
• Recipe for Quantitative Trading with Machine Learning - Daniel Alexandre Bloch (2019)
• Exploring Possible Improvements to Momentum Strategies with Deep Learning - Adam Takács, X. Xiao (2019)
• Enhancing Time Series Momentum Strategies Using Deep Neural Networks - Bryan Lim, Stefan Zohren, Stephen Roberts (2019)
• Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis - Wenhang Bao, Xiao-yang Liu (2019)
• Deep learning-based feature engineering for stock price movement prediction - Wen Long, Zhichen Lu, Lingxiao Cui (2019)
• Review on Stock Market Forecasting & Analysis - Anirban Bal, Debayan Ganguly, Kingshuk Chatterjee (2019)
• Neural Networks as a Forecasting Tool in the Context of the Russian Financial Market Digitalization - Valery Aleshin, Oleg Sviridov, Inna Nekrasova, Dmitry Shevchenko (2020)
• Deep Hierarchical Strategy Model for Multi-Source Driven Quantitative Investment - Chunming Tang, Wenyan Zhu, Xiang Yu (2019)
• Finding Efficient Stocks in BSE100: Implementation of Buffet Approach INTRODUCTION - Sherin Varghese, Sandeep Thakur, Medha Dhingra (2020)

Meta Analyses & Systematic Reviews

• Application of machine learning in stock trading: a review - Kok Sheng Tan, Rajasvaran Logeswaran (2018)
• Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey - Lukas Ryll, Sebastian Seidens (2019)
• Reinforcement Learning in Financial Markets - Terry Lingze Meng, Matloob Khushi (2019)
• Financial Time Series Forecasting with Deep Learning: A Systematic Literature Review: 2005-2019 - Omer Berat Sezer, Mehmet Ugur Gudelek, Ahmet Murat Ozbayoglu (2019)
• A systematic review of fundamental and technical analysis of stock market predictions - Isaac kofi Nti, Adebayo Adekoya, Benjamin Asubam Weyori (2019)

Convolutional Neural Networks (CNNs)

• A deep learning based stock trading model with 2-D CNN trend detection - Ugur Gudelek, S. Arda Boluk, Murat Ozbayoglu, Murat Ozbayoglu (2017)
• Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach - Omer Berat Sezar, Murat Ozbayoglu (2018)
• DeepLOB: Deep Convolutional Neural Networks for Limit Order Books - Zihao Zhang, Stefan Zohren, Stephen Roberts (2019)

Long Short-Term Memory (LSTMs)

• Application of Deep Learning to Algorithmic Trading, Stanford CS229 - Guanting Chen, Yatong Chen, Takahiro Fushimi (2017)
• Deep Learning for Stock Market Trading: A Superior Trading Strategy? - D. Fister, J. C. Mun, V. Jagrič, T. Jagrič, (2019)
• Performance Evaluation of Recurrent Neural Networks for Short-Term Investment Decision in Stock Market - Alexandre P. da Silva, Silas S. L. Pereira, Mário W. L. Moreira, Joel J. P. C. Rodrigues, Ricardo A. L. Rabêlo, Kashif Saleem (2020)
• Prediction Of Stock Trend For Swing Trades Using Long Short-Term Memory Neural Network Model - Varun Totakura, V. Devasekhar, Madhu Sake (2020)

Generative Adversarial Networks (GANs)

• Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination - Adriano Koshiyama (2019)
• Stock Market Prediction Based on Generative Adversarial Network - Kang Zhang, Guoqiang Zhong, Junyu Dong, Shengke Wang, Yong Wang (2019)
• Generative Adversarial Network for Stock Market price Prediction - Ricardo Alberto Carrillo Romero (2019)
• Generative Adversarial Network for Market Hourly Discrimination - Luca Grilli, Domenico Santoro (2020)

High Frequency

• Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
• Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets - Xingyu Zhou, Zhisong Pan, Guyu Hu, Siqi Tang, Cheng Zhao (2018)
• Deep Neural Networks in High Frequency Trading - Prakhar Ganesh, Puneet Rakheja (2018)
• Application of Machine Learning in High Frequency Trading of Stocks - Obi Bertrand Obi (2019)

Portfolio

• Multi Scenario Financial Planning via Deep Reinforcement Learning AI - Gordon Irlam (2020)
• G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning - Matthew Dixon, Igor Halperin (2020)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States - Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Jun Xiao, Bo Li (2020)

Reinforcement Learning

• Reinforcement learning in financial markets - a survey - Thomas G. Fischer (2018)
• AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks - Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong
• Capturing Financial markets to apply Deep Reinforcement Learning - Souradeep Chakraborty (2019)
• Reinforcement Learning for FX trading - Yuqin Dai, Chris Wang, Iris Wang, Yilun Xu (2019)
• An Application of Deep Reinforcement Learning to Algorithmic Trading - Thibaut Théate, Damien Ernst (2020)
• Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
• Beat China’s stock market by using Deep reinforcement learning - Gang Huang, Xiaohua Zhou, Qingyang Song (2020)
• An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features - Ding Fengqian, Luo Chao (2020)
• Application of Deep Q-Network in Portfolio Management - Ziming Gao, Yuan Gao, Yi Hu, Zhengyong Jiang, Jionglong Su (2020)
• Deep Reinforcement Learning Pairs Trading with a Double Deep Q-Network - Andrew Brim (2020)
• A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)

Cryptocurrency

• Recommending Cryptocurrency Trading Points with Deep Reinforcement Learning Approach - Otabek Sattarov, Azamjon Muminov, Cheol Won Lee, Hyun Kyu Kang, Ryumduck Oh, Junho Ahn, Hyung Jun Oh, Heung Seok Jeon (2020)

Social Processing

Behavioral Analysis

• Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting - Yaodong Yang, Alisa Kolesnikova, Stefan Lessmann, Tiejun Ma, Ming-Chien Sung, Johnnie E.V. Johnson (2019)
• Investor behaviour monitoring based on deep learning - Song Wang, Xiaoguang Wang, Fanglin Meng, Rongjun Yang, Yuanjun Zhao (2020)

Sentiment Analysis

• Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures - Stefan Feuerriegel, Ralph Fehrer (2015)
• Big Data: Deep Learning for financial sentiment analysis - Sahar Sohangir, Dingding Wang, Anna Pomeranets, Taghi M. Khoshgoftaar (2018)
• Using Machine Learning to Predict Stock Prices - Vivek Palaniappan (2018)
• Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
• Sentiment and Knowledge Based Algorithmic Trading with Deep Reinforcement Learning - Abhishek Nan, Anandh Perumal, Osmar R. Zaiane (2020)

Repositories

• Yvictor/TradingGym - Trading and Backtesting environment for training reinforcement learning agent or simple rule base algo
• Rachnog/Deep-Trading - Experimental time series forecasting
• jobvisser03/deep-trading-advisor - Deep Trading Advisor uses MLP, CNN, and RNN+LSTM with Keras, zipline, Dash and Plotly
• rosdyana/CNN-Financial-Data - Deep Trading using a Convolutional Neural Network
• iamSTone/Deep-trader-CNN-kospi200futures - Kospi200 index futures Prediction using CNN
• ha2emnomer/Deep-Trading - Keras-based LSTM RNN
• gujiuxiang/Deep_Trader.pytorch - This project uses Reinforcement learning on stock market and agent tries to learn trading. PyTorch based.
• ZhengyaoJiang/PGPortfolio - PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"
• yuriak/RLQuant - Applying Reinforcement Learning in Quantitative Trading (Policy Gradient, Direct RL)
• ucaiado/QLearning_Trading - Trading Using Q-Learning
• laikasinjason/deep-q-learning-trading-system-on-hk-stocks-market - Deep Q learning implementation on the Hong Kong Stock Exchange
• golsun/deep-RL-trading - Codebase for paper "Deep reinforcement learning for time series: playing idealized trading games" by Xiang Gao
• huseinzol05/Stock-Prediction-Models - Gathers machine learning and deep learning models for Stock forecasting, included trading bots and simulations
• jiewwantan/StarTrader - Trains an agent to trade like a human using a deep reinforcement learning algorithm: deep deterministic policy gradient (DDPG) learning algorithm
• notadamking/RLTrader - A cryptocurrency trading environment using deep reinforcement learning and OpenAI's gym

Generative Adversarial Networks (GANs)

• borisbanushev/stockpredictionai - A notebook for stock price movement prediction using an LSTM generator and CNN discriminator
• kah-ve/MarketGAN - Implementing a Generative Adversarial Network on the Stock Market

Cryptocurrency

• samre12/deep-trading-agent - Deep Reinforcement Learning-based trading agent for Bitcoin using DeepSense Network for Q function approximation.
• ThirstyScholar/trading-bitcoin-with-reinforcement-learning - Trading Bitcoin with Reinforcement Learning
• lefnire/tforce_btc_trader - A TensorForce-based Bitcoin trading bot (algo-trader). Uses deep reinforcement learning to automatically buy/sell/hold BTC based on price history.

Datasets

• kaggle/Huge Stock Market Dataset - Historical daily prices and volumes of all U.S. stocks and ETFs
• Alpha Vantage - Free APIs in JSON and CSV formats, realtime and historical stock data, FX and cryptocurrency feeds, 50+ technical indicators
• Quandl

Simulation

• Generating Realistic Stock Market Order Streams - Anonymous Authors (2018)
• Deep Hedging: Learning to Simulate Equity Option Markets - Magnus Wiese, Lianjun Bai, Ben Wood, Hans Buehler (2019)

Resources

Presentations

• BigDataFinance Neural Networks Intro - Anastasios Tefas, Assistant Professor at Aristotle University of Thessaloniki (2016)
• Trading Using Deep Learning: Motivation, Challenges, Solutions - Yam Peleg, GPU Technology Conference (2017)
• FinTech, AI, Machine Learning in Finance - Sanjiv Das (2018)
• Deep Residual Learning for Portfolio Optimization:With Attention and Switching Modules - Jeff Wang, Ph.D., NYU

Courses

• Artificial Intelligence for Trading (ND880) nanodegree at Udacity (+GitHub code repo)
• Neural Networks in Trading course by Dr. Ernest P. Chan at Quantra
• Machine Learning and Reinforcement Learning in Finance Specialization by NYU at Coursera

Meetups

• Artificial Intelligence in Finance & Algorithmic Trading on Meetup (New York City)

Guides & Further Reading

• Neural networks for algorithmic trading. Simple time series forecasting - Alex Rachnog (2016)
• Predicting Cryptocurrency Prices With Deep Learning - David Sheehan (2017)
• Introduction to Learning to Trade with Reinforcement Learning - Denny Britz (2018)
• Webinar: How to Forecast Stock Prices Using Deep Neural Networks - Erez Katz, Lucena Research (2018)
• Creating Bitcoin trading bots that don’t lose money - Adam King (2019)
• Why Deep Reinforcement Learning Can Help Improve Trading Efficiency - Viktor Tachev (2019)
• Optimizing deep learning trading bots using state-of-the-art techniques - Adam King (2019)
• Using the latest advancements in deep learning to predict stock price movements - Boris Banushev (2019)
• Introduction to Deep Learning Trading in Hedge Funds - Neven Pičuljan