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awesome-deep-trading

List of awesome resources for machine learning-based algorithmic trading
https://github.com/cbailes/awesome-deep-trading

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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)
- Stock Prices Prediction using Deep Learning Models - Jialin Liu, Fei Chao, Yu-Chen Lin, Chih-Min Lin (2019)
- Deep Learning for Stock Market Trading: A Superior Trading Strategy? - D. Fister, J. C. Mun, V. Jagrič, T. Jagrič, (2019)
- Research on financial assets transaction prediction model based on LSTM neural network - Xue Yan, Wang Weihan & Miao Chang (2020)
- Prediction Of Stock Trend For Swing Trades Using Long Short-Term Memory Neural Network Model - Varun Totakura, V. Devasekhar, Madhu Sake (2020)
- A novel Deep Learning Framework: Prediction and Analysis of Financial Time Series using CEEMD and LSTM - Yong'an Zhang, Binbin Yan, Memon Aasma (2020)
- Deep Stock Predictions - Akash Doshi, Alexander Issa, Puneet Sachdeva, Sina Rafati, Somnath Rakshit (2020)
- 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)
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)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- 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)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
- Algorithmic Trading Using Deep Neural Networks on High Frequency Data - Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón (2017)
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)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (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)
- AAMDRL: Augmented Asset Management with Deep Reinforcement Learning - Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- An Application of Deep Reinforcement Learning to Algorithmic Trading - Thibaut Théate, Damien Ernst (2020)
- Beat China’s stock market by using Deep reinforcement learning - Gang Huang, Xiaohua Zhou, Qingyang Song (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features - Ding Fengqian, Luo Chao (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
- Single asset trading: a recurrent reinforcement learning approach - Marko Nikolic (2020)
- A reinforcement learning model based on reward correction for quantitative stock selection - Haibo Chen, Chenyu Zhang, Yunke Li (2020)
Guides
- Stock Price Prediction And Forecasting Using Stacked LSTM- Deep Learning - Krish Naik (2020)
- Comparing Arima Model and LSTM RNN Model in Time-Series Forecasting - Vaibhav Kumar (2020)
- LSTM to predict Dow Jones Industrial Average: A Time Series forecasting model - Sarit Maitra (2020)
Vulnerabilities
- Adversarial Attacks on Deep Algorithmic Trading Policies - Yaser Faghan, Nancirose Piazza, Vahid Behzadan, Ali Fathi (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)
  - jobvisser03/deep-trading-advisor - Deep Trading Advisor uses MLP, CNN, and RNN+LSTM with Keras, zipline, Dash and Plotly
  - gujiuxiang/Deep_Trader.pytorch - This project uses Reinforcement learning on stock market and agent tries to learn trading. PyTorch based.
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Improving Decision Analytics with Deep Learning: The Case of Financial Disclosures - Stefan Feuerriegel, Ralph Fehrer (2015)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
  - Stock Prediction Using Twitter - Khan Saad Bin Hasan (2019)
Datasets
- Sentiment Analysis
  - 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
- 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)
Presentations
- Simulation
  - BigDataFinance Neural Networks Intro - Anastasios Tefas, Assistant Professor at Aristotle University of Thessaloniki (2016)
  - 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
- Simulation
Meetups
- Simulation
  - Artificial Intelligence in Finance & Algorithmic Trading on Meetup
Further Reading
- Simulation
  - 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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - RNN and LSTM — The Neural Networks with Memory - Nagesh Singh Chauhan (2020)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Neural networks for algorithmic trading. Simple time series forecasting - Alex Rachnog (2016)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)
  - Creating Bitcoin trading bots that don’t lose money - Adam King (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)

Categories

Further Reading 100 Reinforcement Learning 66 High Frequency 41 Social Processing 41 Long Short-Term Memory (LSTMs) 8 Meta Analyses & Systematic Reviews 5 Datasets 4 Generative Adversarial Networks (GANs) 4 Courses 3 Guides 3 Portfolio 3 Presentations 3 Convolutional Neural Networks (CNNs) 3 Cryptocurrency 1 Meetups 1 Vulnerabilities 1

Sub Categories

Simulation 109 Sentiment Analysis 41 Behavioral Analysis 2