{ "id": 287528, "title": "[Papers List] Deep Learning & Trading - Reading List", "url": "/competitions/g-research-crypto-forecasting/discussion/287528", "author_name": "", "post_date": "2021-11-14T13:23:11.612000", "votes": 99, "comment_count": 11, "views": 0, "content": "

Papers: Deep Learning & Trading

\n\n

Convolutional Neural Networks (CNNs)

\n\n

Long Short-Term Memory (LSTMs)

\n\n

Reinforcement Learning

\n\n
\n

Credit: Everything in this list came from here: https://github.com/cbailes/awesome-deep-trading

", "messages": [ { "id": 1582028, "postDate": "2021-11-14T13:23:11.613Z", "content": "

Papers: Deep Learning & Trading

\n\n

Convolutional Neural Networks (CNNs)

\n\n

Long Short-Term Memory (LSTMs)

\n\n

Reinforcement Learning

\n\n
\n

Credit: Everything in this list came from here: https://github.com/cbailes/awesome-deep-trading

", "rawMarkdown": "\n# Papers: Deep Learning & Trading\n\n* [Classification-based Financial Markets Prediction using Deep Neural Networks](https://arxiv.org/pdf/1603.08604) - Matthew Dixon, Diego Klabjan, Jin Hoon Bang (2016)\n* [Deep Learning for Limit Order Books](https://arxiv.org/pdf/1601.01987) - Justin Sirignano (2016)\n* [High-Frequency Trading Strategy Based on Deep Neural Networks](https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_40) - Andrés Arévalo, Jaime Niño, German Hernández, Javier Sandoval (2016)\n* [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://arxiv.org/pdf/1706.10059) - Zhengyao Jiang, Dixing Xu, Jinjun Liang (2017)\n* [Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks](https://arxiv.org/pdf/1707.07338.pdf) - David W. Lu (2017)\n* [Deep Hedging](https://arxiv.org/pdf/1802.03042) - Hans Bühler, Lukas Gonon, Josef Teichmann, Ben Wood (2018)\n* [Stock Trading Bot Using Deep Reinforcement Learning](https://link.springer.com/chapter/10.1007/978-981-10-8201-6_5) - Akhil Raj Azhikodan, Anvitha G. K. Bhat, Mamatha V. Jadhav (2018)\n* [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1807.02787) - Chien Yi Huang (2018)\n* [Practical Deep Reinforcement Learning Approach for Stock Trading](https://arxiv.org/pdf/1811.07522) - Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang Yang, Anwar Walid (2018)\n* [Algorithmic Trading and Machine Learning Based on GPU](http://ceur-ws.org/Vol-2147/p09.pdf) - Mantas Vaitonis, Saulius Masteika, Konstantinas Korovkinas (2018)\n* [A quantitative trading method using deep convolution neural network ](https://iopscience.iop.org/article/10.1088/1757-899X/490/4/042018/pdf) - HaiBo Chen, DaoLei Liang, LL Zhao (2019)\n* [Deep learning in exchange markets](https://www.sciencedirect.com/science/article/pii/S0167624518300702) - Rui Gonçalves, Vitor Miguel Ribeiro, Fernando Lobo Pereira, Ana Paula Rocha (2019)\n* [Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks](https://arxiv.org/abs/1903.04610) - Omer Berat Sezer, Ahmet Murat Ozbayoglu (2019)\n* [Deep Reinforcement Learning for Financial Trading Using Price Trailing](https://ieeexplore.ieee.org/document/8683161) - Konstantinos Saitas Zarkias, Nikolaos Passalis, Avraam Tsantekidis, Anastasios Tefas (2019)\n* [Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading](https://arxiv.org/abs/1904.00441) - Uk Jo, Taehyun Jo, Wanjun Kim, Iljoo Yoon, Dongseok Lee, Seungho Lee (2019)\n* [Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning](https://www.sciencedirect.com/science/article/pii/S0957417418306134) - Gyeeun Jeong, Ha Young Kim (2019)\n* [Deep Execution - Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3374766) - Kevin Dabérius, Elvin Granat, Patrik Karlsson (2019)\n* [An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy](https://www.hindawi.com/journals/mpe/2019/7816154/ref/) - Dongdong Lv, Shuhan Yuan, Meizi Li, Yang Xiang (2019)\n* [Recipe for Quantitative Trading with Machine Learning](http://dx.doi.org/10.2139/ssrn.3232143) - Daniel Alexandre Bloch (2019)\n* [Exploring Possible Improvements to Momentum Strategies with Deep Learning](http://hdl.handle.net/2105/49940) - Adam Takács, X. Xiao (2019)\n* [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://arxiv.org/abs/1904.04912) - Bryan Lim, Stefan Zohren, Stephen Roberts (2019)\n* [Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis](https://arxiv.org/abs/1906.11046) - Wenhang Bao, Xiao-yang Liu (2019)\n* [Deep learning-based feature engineering for stock price movement prediction](https://www.sciencedirect.com/science/article/abs/pii/S0950705118305264) - Wen Long, Zhichen Lu, Lingxiao Cui (2019)\n* [Review on Stock Market Forecasting & Analysis](https://www.researchgate.net/publication/340583328_Review_on_Stock_Market_Forecasting_Analysis-LSTM_Long-Short_Term_Memory_Holt's_Seasonal_MethodANN_Artificial_Neural_Network_ARIMA_Auto_Regressive_Integrated_Minimum_Average_PCA_MLP_Multi_Layers_Percep) - Anirban Bal, Debayan Ganguly, Kingshuk Chatterjee (2019)\n* [Neural Networks as a Forecasting Tool in the Context of the Russian Financial Market Digitalization](https://www.researchgate.net/publication/340474330_Neural_Networks_as_a_Forecasting_Tool_in_the_Context_of_the_Russian_Financial_Market_Digitalization) - Valery Aleshin, Oleg Sviridov, Inna Nekrasova, Dmitry Shevchenko (2020)\n* [Deep Hierarchical Strategy Model for Multi-Source Driven Quantitative Investment](https://ieeexplore.ieee.org/abstract/document/8743385) - Chunming Tang, Wenyan Zhu, Xiang Yu (2019)\n* [Finding Efficient Stocks in BSE100: Implementation of Buffet Approach INTRODUCTION](https://www.researchgate.net/publication/340501895_Asian_Journal_of_Management_Finding_Efficient_Stocks_in_BSE100_Implementation_of_Buffet_Approach_INTRODUCTION) - Sherin Varghese, Sandeep Thakur, Medha Dhingra (2020)\n* [Deep Learning in Asset Pricing](https://arxiv.org/abs/1904.00745) - Luyang Chen, Markus Pelger, Jason Zhu (2020)\n\n\n\n## Convolutional Neural Networks (CNNs)\n* [A deep learning based stock trading model with 2-D CNN trend detection](https://www.researchgate.net/publication/323131323_A_deep_learning_based_stock_trading_model_with_2-D_CNN_trend_detection) - Ugur Gudelek, S. Arda Boluk, Murat Ozbayoglu, Murat Ozbayoglu (2017)\n* [Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach](https://www.researchgate.net/publication/324802031_Algorithmic_Financial_Trading_with_Deep_Convolutional_Neural_Networks_Time_Series_to_Image_Conversion_Approach) - Omer Berat Sezar, Murat Ozbayoglu (2018)\n* [DeepLOB: Deep Convolutional Neural Networks for Limit Order Books](https://ieeexplore.ieee.org/abstract/document/8673598) - Zihao Zhang, Stefan Zohren, Stephen Roberts (2019)\n\n## Long Short-Term Memory (LSTMs)\n* [Application of Deep Learning to Algorithmic Trading, Stanford CS229](http://cs229.stanford.edu/proj2017/final-reports/5241098.pdf) - Guanting Chen, Yatong Chen, Takahiro Fushimi (2017)\n* [Stock Prices Prediction using Deep Learning Models](https://arxiv.org/abs/1909.12227) - Jialin Liu, Fei Chao, Yu-Chen Lin, Chih-Min Lin (2019)\n* [Deep Learning for Stock Market Trading: A Superior Trading Strategy?](https://doi.org/10.14311/NNW.2019.29.011) - D. Fister, J. C. Mun, V. Jagrič, T. Jagrič, (2019)\n* [Performance Evaluation of Recurrent Neural Networks for Short-Term Investment Decision in Stock Market](https://www.researchgate.net/publication/339751012_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)\n* [Research on financial assets transaction prediction model based on LSTM neural network](https://doi.org/10.1007/s00521-020-04992-7) - Xue Yan, Wang Weihan & Miao Chang (2020)\n* [Prediction Of Stock Trend For Swing Trades Using Long Short-Term Memory Neural Network Model](https://www.researchgate.net/publication/340789607_Prediction_Of_Stock_Trend_For_Swing_Trades_Using_Long_Short-Term_Memory_Neural_Network_Model) - Varun Totakura, V. Devasekhar, Madhu Sake (2020)\n* [A novel Deep Learning Framework: Prediction and Analysis of Financial Time Series using CEEMD and LSTM](https://www.sciencedirect.com/science/article/abs/pii/S0957417420304334) - Yong'an Zhang, Binbin Yan, Memon Aasma (2020)\n* [Deep Stock Predictions](https://arxiv.org/abs/2006.04992) - Akash Doshi, Alexander Issa, Puneet Sachdeva, Sina Rafati, Somnath Rakshit (2020)\n\n\n\n## Reinforcement Learning\n* [Reinforcement learning in financial markets - a survey](http://hdl.handle.net/10419/183139) - Thomas G. Fischer (2018)\n* [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://arxiv.org/abs/1908.02646) - Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong\n* [Capturing Financial markets to apply Deep Reinforcement Learning](https://arxiv.org/abs/1907.04373) - Souradeep Chakraborty (2019)\n* [Reinforcement Learning for FX trading](http://stanford.edu/class/msande448/2019/Final_reports/gr2.pdf) - Yuqin Dai, Chris Wang, Iris Wang, Yilun Xu (2019)\n* [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://arxiv.org/abs/2004.06627) - Thibaut Théate, Damien Ernst (2020)\n* [Single asset trading: a recurrent reinforcement learning approach](http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-47505) - Marko Nikolic (2020)\n* [Beat China’s stock market by using Deep reinforcement learning](https://www.researchgate.net/profile/Huang_Gang9/publication/340438304_Beat_China's_stock_market_by_using_Deep_reinforcement_learning/links/5e88e007299bf130797c7a68/Beat-Chinas-stock-market-by-using-Deep-reinforcement-learning.pdf) - Gang Huang, Xiaohua Zhou, Qingyang Song (2020)\n* [An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features](https://doi.org/10.1109/ACCESS.2020.2982662) - Ding Fengqian, Luo Chao (2020)\n* [Application of Deep Q-Network in Portfolio Management](https://arxiv.org/abs/2003.06365) - Ziming Gao, Yuan Gao, Yi Hu, Zhengyong Jiang, Jionglong Su (2020)\n* [Deep Reinforcement Learning Pairs Trading with a Double Deep Q-Network](https://ieeexplore.ieee.org/abstract/document/9031159) - Andrew Brim (2020)\n* [A reinforcement learning model based on reward correction for quantitative stock selection](https://doi.org/10.1088/1757-899X/768/7/072036) - Haibo Chen, Chenyu Zhang, Yunke Li (2020)\n* [AAMDRL: Augmented Asset Management with Deep Reinforcement Learning](https://arxiv.org/abs/2010.08497) - Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif (2020)\n\n\n____\n**Credit:** Everything in this list came from here: https://github.com/cbailes/awesome-deep-trading\n", "votes": 99 }, { "id": 1595703, "postDate": "2021-11-25T23:18:00.283Z", "content": "

Don't waste time for LSTM, I tried before all possible combinations, the results are very bad. Those articles ,about LSTM in trading, is theoretical , with a lot of mistakes

", "rawMarkdown": "Don't waste time for LSTM, I tried before all possible combinations, the results are very bad. Those articles ,about LSTM in trading, is theoretical , with a lot of mistakes ", "votes": 7, "replies": [ { "id": 2194567, "postDate": "2023-03-24T03:33:07.350Z", "rawMarkdown": "", "isDeleted": true }, { "id": 3234510, "postDate": "2025-06-28T00:27:17.307Z", "content": "

Hey Vladimir, if I may ask, what were the mistakes?

", "rawMarkdown": "Hey Vladimir, if I may ask, what were the mistakes?" } ] }, { "id": 1746997, "postDate": "2022-04-06T08:56:11.453Z", "content": "

@Vladimir Shebunyaev how about Reinforcement Learning in predicting stock?

", "rawMarkdown": "@Vladimir Shebunyaev how about Reinforcement Learning in predicting stock?", "votes": 1 }, { "id": 1590022, "postDate": "2021-11-20T20:20:04.697Z", "content": "

Hello @yamqwe, thanks for all the information shared in this discussion and the previous ones; keep the amazing attitude towards helping all of us.

", "rawMarkdown": "Hello @yamqwe, thanks for all the information shared in this discussion and the previous ones; keep the amazing attitude towards helping all of us. ", "votes": 1 }, { "id": 1671407, "postDate": "2022-02-01T12:43:21.457Z", "content": "

Thank you for sharing, it seems need long time to digest all above.

", "rawMarkdown": "Thank you for sharing, it seems need long time to digest all above." }, { "id": 1591238, "postDate": "2021-11-22T05:37:42.610Z", "content": "

Thank you @yamqwe for sharing this content !

", "rawMarkdown": "Thank you @yamqwe for sharing this content ! " }, { "id": 1591081, "postDate": "2021-11-21T23:44:01.283Z", "content": "

Thanks a lot for the list - very useful regardless of the competition actually :)

", "rawMarkdown": "Thanks a lot for the list - very useful regardless of the competition actually :) " }, { "id": 1584052, "postDate": "2021-11-16T08:31:38.947Z", "content": "

Thank you Yam for sharing, very very useful 👌

", "rawMarkdown": "Thank you Yam for sharing, very very useful 👌" }, { "id": 1591090, "postDate": "2021-11-22T00:18:30.653Z", "content": "

Thank you

", "rawMarkdown": "Thank you " }, { "id": 1589455, "postDate": "2021-11-20T09:50:59.183Z", "content": "

Thanks a lot, Yam for that treasure.

", "rawMarkdown": "Thanks a lot, Yam for that treasure. " } ], "comments": [ { "id": 1595703, "author_name": "Vladimir Shebunyaev", "author_url": "", "post_date": "2021-11-25T23:18:00.283000", "content": "

Don't waste time for LSTM, I tried before all possible combinations, the results are very bad. Those articles ,about LSTM in trading, is theoretical , with a lot of mistakes

", "votes": 7, "replies": [ { "id": 2194567, "author_name": "", "author_url": "", "post_date": "2023-03-24T03:33:07.350000", "content": "", "votes": 0, "replies": [] }, { "id": 3234510, "author_name": "StainlessSteelChair", "author_url": "", "post_date": "2025-06-28T00:27:17.307000", "content": "

Hey Vladimir, if I may ask, what were the mistakes?

", "votes": 0, "replies": [] } ] }, { "id": 1746997, "author_name": "calf34", "author_url": "", "post_date": "2022-04-06T08:56:11.453000", "content": "

@Vladimir Shebunyaev how about Reinforcement Learning in predicting stock?

", "votes": 1, "replies": [] }, { "id": 1590022, "author_name": "C4rl05/V", "author_url": "", "post_date": "2021-11-20T20:20:04.697000", "content": "

Hello @yamqwe, thanks for all the information shared in this discussion and the previous ones; keep the amazing attitude towards helping all of us.

", "votes": 1, "replies": [] }, { "id": 1671407, "author_name": "水彩不好画", "author_url": "", "post_date": "2022-02-01T12:43:21.457000", "content": "

Thank you for sharing, it seems need long time to digest all above.

", "votes": 0, "replies": [] }, { "id": 1591238, "author_name": "ashishtop", "author_url": "", "post_date": "2021-11-22T05:37:42.610000", "content": "

Thank you @yamqwe for sharing this content !

", "votes": 0, "replies": [] }, { "id": 1591081, "author_name": "pAckmAn4000", "author_url": "", "post_date": "2021-11-21T23:44:01.283000", "content": "

Thanks a lot for the list - very useful regardless of the competition actually :)

", "votes": 0, "replies": [] }, { "id": 1584052, "author_name": "Yassine Mesbahi", "author_url": "", "post_date": "2021-11-16T08:31:38.947000", "content": "

Thank you Yam for sharing, very very useful 👌

", "votes": 0, "replies": [] }, { "id": 1591090, "author_name": "Dr. Asimov", "author_url": "", "post_date": "2021-11-22T00:18:30.653000", "content": "

Thank you

", "votes": 0, "replies": [] }, { "id": 1589455, "author_name": "Mohamed Rida Sahib", "author_url": "", "post_date": "2021-11-20T09:50:59.183000", "content": "

Thanks a lot, Yam for that treasure.

", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "1582028": "\n# Papers: Deep Learning & Trading\n\n* [Classification-based Financial Markets Prediction using Deep Neural Networks](https://arxiv.org/pdf/1603.08604) - Matthew Dixon, Diego Klabjan, Jin Hoon Bang (2016)\n* [Deep Learning for Limit Order Books](https://arxiv.org/pdf/1601.01987) - Justin Sirignano (2016)\n* [High-Frequency Trading Strategy Based on Deep Neural Networks](https://link.springer.com/chapter/10.1007%2F978-3-319-42297-8_40) - Andrés Arévalo, Jaime Niño, German Hernández, Javier Sandoval (2016)\n* [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://arxiv.org/pdf/1706.10059) - Zhengyao Jiang, Dixing Xu, Jinjun Liang (2017)\n* [Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks](https://arxiv.org/pdf/1707.07338.pdf) - David W. Lu (2017)\n* [Deep Hedging](https://arxiv.org/pdf/1802.03042) - Hans Bühler, Lukas Gonon, Josef Teichmann, Ben Wood (2018)\n* [Stock Trading Bot Using Deep Reinforcement Learning](https://link.springer.com/chapter/10.1007/978-981-10-8201-6_5) - Akhil Raj Azhikodan, Anvitha G. K. Bhat, Mamatha V. Jadhav (2018)\n* [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1807.02787) - Chien Yi Huang (2018)\n* [Practical Deep Reinforcement Learning Approach for Stock Trading](https://arxiv.org/pdf/1811.07522) - Zhuoran Xiong, Xiao-Yang Liu, Shan Zhong, Hongyang Yang, Anwar Walid (2018)\n* [Algorithmic Trading and Machine Learning Based on GPU](http://ceur-ws.org/Vol-2147/p09.pdf) - Mantas Vaitonis, Saulius Masteika, Konstantinas Korovkinas (2018)\n* [A quantitative trading method using deep convolution neural network ](https://iopscience.iop.org/article/10.1088/1757-899X/490/4/042018/pdf) - HaiBo Chen, DaoLei Liang, LL Zhao (2019)\n* [Deep learning in exchange markets](https://www.sciencedirect.com/science/article/pii/S0167624518300702) - Rui Gonçalves, Vitor Miguel Ribeiro, Fernando Lobo Pereira, Ana Paula Rocha (2019)\n* [Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks](https://arxiv.org/abs/1903.04610) - Omer Berat Sezer, Ahmet Murat Ozbayoglu (2019)\n* [Deep Reinforcement Learning for Financial Trading Using Price Trailing](https://ieeexplore.ieee.org/document/8683161) - Konstantinos Saitas Zarkias, Nikolaos Passalis, Avraam Tsantekidis, Anastasios Tefas (2019)\n* [Cooperative Multi-Agent Reinforcement Learning Framework for Scalping Trading](https://arxiv.org/abs/1904.00441) - Uk Jo, Taehyun Jo, Wanjun Kim, Iljoo Yoon, Dongseok Lee, Seungho Lee (2019)\n* [Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning](https://www.sciencedirect.com/science/article/pii/S0957417418306134) - Gyeeun Jeong, Ha Young Kim (2019)\n* [Deep Execution - Value and Policy Based Reinforcement Learning for Trading and Beating Market Benchmarks](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3374766) - Kevin Dabérius, Elvin Granat, Patrik Karlsson (2019)\n* [An Empirical Study of Machine Learning Algorithms for Stock Daily Trading Strategy](https://www.hindawi.com/journals/mpe/2019/7816154/ref/) - Dongdong Lv, Shuhan Yuan, Meizi Li, Yang Xiang (2019)\n* [Recipe for Quantitative Trading with Machine Learning](http://dx.doi.org/10.2139/ssrn.3232143) - Daniel Alexandre Bloch (2019)\n* [Exploring Possible Improvements to Momentum Strategies with Deep Learning](http://hdl.handle.net/2105/49940) - Adam Takács, X. Xiao (2019)\n* [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://arxiv.org/abs/1904.04912) - Bryan Lim, Stefan Zohren, Stephen Roberts (2019)\n* [Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis](https://arxiv.org/abs/1906.11046) - Wenhang Bao, Xiao-yang Liu (2019)\n* [Deep learning-based feature engineering for stock price movement prediction](https://www.sciencedirect.com/science/article/abs/pii/S0950705118305264) - Wen Long, Zhichen Lu, Lingxiao Cui (2019)\n* [Review on Stock Market Forecasting & Analysis](https://www.researchgate.net/publication/340583328_Review_on_Stock_Market_Forecasting_Analysis-LSTM_Long-Short_Term_Memory_Holt's_Seasonal_MethodANN_Artificial_Neural_Network_ARIMA_Auto_Regressive_Integrated_Minimum_Average_PCA_MLP_Multi_Layers_Percep) - Anirban Bal, Debayan Ganguly, Kingshuk Chatterjee (2019)\n* [Neural Networks as a Forecasting Tool in the Context of the Russian Financial Market Digitalization](https://www.researchgate.net/publication/340474330_Neural_Networks_as_a_Forecasting_Tool_in_the_Context_of_the_Russian_Financial_Market_Digitalization) - Valery Aleshin, Oleg Sviridov, Inna Nekrasova, Dmitry Shevchenko (2020)\n* [Deep Hierarchical Strategy Model for Multi-Source Driven Quantitative Investment](https://ieeexplore.ieee.org/abstract/document/8743385) - Chunming Tang, Wenyan Zhu, Xiang Yu (2019)\n* [Finding Efficient Stocks in BSE100: Implementation of Buffet Approach INTRODUCTION](https://www.researchgate.net/publication/340501895_Asian_Journal_of_Management_Finding_Efficient_Stocks_in_BSE100_Implementation_of_Buffet_Approach_INTRODUCTION) - Sherin Varghese, Sandeep Thakur, Medha Dhingra (2020)\n* [Deep Learning in Asset Pricing](https://arxiv.org/abs/1904.00745) - Luyang Chen, Markus Pelger, Jason Zhu (2020)\n\n\n\n## Convolutional Neural Networks (CNNs)\n* [A deep learning based stock trading model with 2-D CNN trend detection](https://www.researchgate.net/publication/323131323_A_deep_learning_based_stock_trading_model_with_2-D_CNN_trend_detection) - Ugur Gudelek, S. Arda Boluk, Murat Ozbayoglu, Murat Ozbayoglu (2017)\n* [Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach](https://www.researchgate.net/publication/324802031_Algorithmic_Financial_Trading_with_Deep_Convolutional_Neural_Networks_Time_Series_to_Image_Conversion_Approach) - Omer Berat Sezar, Murat Ozbayoglu (2018)\n* [DeepLOB: Deep Convolutional Neural Networks for Limit Order Books](https://ieeexplore.ieee.org/abstract/document/8673598) - Zihao Zhang, Stefan Zohren, Stephen Roberts (2019)\n\n## Long Short-Term Memory (LSTMs)\n* [Application of Deep Learning to Algorithmic Trading, Stanford CS229](http://cs229.stanford.edu/proj2017/final-reports/5241098.pdf) - Guanting Chen, Yatong Chen, Takahiro Fushimi (2017)\n* [Stock Prices Prediction using Deep Learning Models](https://arxiv.org/abs/1909.12227) - Jialin Liu, Fei Chao, Yu-Chen Lin, Chih-Min Lin (2019)\n* [Deep Learning for Stock Market Trading: A Superior Trading Strategy?](https://doi.org/10.14311/NNW.2019.29.011) - D. Fister, J. C. Mun, V. Jagrič, T. Jagrič, (2019)\n* [Performance Evaluation of Recurrent Neural Networks for Short-Term Investment Decision in Stock Market](https://www.researchgate.net/publication/339751012_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)\n* [Research on financial assets transaction prediction model based on LSTM neural network](https://doi.org/10.1007/s00521-020-04992-7) - Xue Yan, Wang Weihan & Miao Chang (2020)\n* [Prediction Of Stock Trend For Swing Trades Using Long Short-Term Memory Neural Network Model](https://www.researchgate.net/publication/340789607_Prediction_Of_Stock_Trend_For_Swing_Trades_Using_Long_Short-Term_Memory_Neural_Network_Model) - Varun Totakura, V. Devasekhar, Madhu Sake (2020)\n* [A novel Deep Learning Framework: Prediction and Analysis of Financial Time Series using CEEMD and LSTM](https://www.sciencedirect.com/science/article/abs/pii/S0957417420304334) - Yong'an Zhang, Binbin Yan, Memon Aasma (2020)\n* [Deep Stock Predictions](https://arxiv.org/abs/2006.04992) - Akash Doshi, Alexander Issa, Puneet Sachdeva, Sina Rafati, Somnath Rakshit (2020)\n\n\n\n## Reinforcement Learning\n* [Reinforcement learning in financial markets - a survey](http://hdl.handle.net/10419/183139) - Thomas G. Fischer (2018)\n* [AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks](https://arxiv.org/abs/1908.02646) - Jingyuan Wang, Yang Zhang, Ke Tang, Junjie Wu, Zhang Xiong\n* [Capturing Financial markets to apply Deep Reinforcement Learning](https://arxiv.org/abs/1907.04373) - Souradeep Chakraborty (2019)\n* [Reinforcement Learning for FX trading](http://stanford.edu/class/msande448/2019/Final_reports/gr2.pdf) - Yuqin Dai, Chris Wang, Iris Wang, Yilun Xu (2019)\n* [An Application of Deep Reinforcement Learning to Algorithmic Trading](https://arxiv.org/abs/2004.06627) - Thibaut Théate, Damien Ernst (2020)\n* [Single asset trading: a recurrent reinforcement learning approach](http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-47505) - Marko Nikolic (2020)\n* [Beat China’s stock market by using Deep reinforcement learning](https://www.researchgate.net/profile/Huang_Gang9/publication/340438304_Beat_China's_stock_market_by_using_Deep_reinforcement_learning/links/5e88e007299bf130797c7a68/Beat-Chinas-stock-market-by-using-Deep-reinforcement-learning.pdf) - Gang Huang, Xiaohua Zhou, Qingyang Song (2020)\n* [An Adaptive Financial Trading System Using Deep Reinforcement Learning With Candlestick Decomposing Features](https://doi.org/10.1109/ACCESS.2020.2982662) - Ding Fengqian, Luo Chao (2020)\n* [Application of Deep Q-Network in Portfolio Management](https://arxiv.org/abs/2003.06365) - Ziming Gao, Yuan Gao, Yi Hu, Zhengyong Jiang, Jionglong Su (2020)\n* [Deep Reinforcement Learning Pairs Trading with a Double Deep Q-Network](https://ieeexplore.ieee.org/abstract/document/9031159) - Andrew Brim (2020)\n* [A reinforcement learning model based on reward correction for quantitative stock selection](https://doi.org/10.1088/1757-899X/768/7/072036) - Haibo Chen, Chenyu Zhang, Yunke Li (2020)\n* [AAMDRL: Augmented Asset Management with Deep Reinforcement Learning](https://arxiv.org/abs/2010.08497) - Eric Benhamou, David Saltiel, Sandrine Ungari, Abhishek Mukhopadhyay, Jamal Atif (2020)\n\n\n____\n**Credit:** Everything in this list came from here: https://github.com/cbailes/awesome-deep-trading\n", "1595703": "Don't waste time for LSTM, I tried before all possible combinations, the results are very bad. Those articles ,about LSTM in trading, is theoretical , with a lot of mistakes ", "1746997": "@Vladimir Shebunyaev how about Reinforcement Learning in predicting stock?", "1590022": "Hello @yamqwe, thanks for all the information shared in this discussion and the previous ones; keep the amazing attitude towards helping all of us. ", "1671407": "Thank you for sharing, it seems need long time to digest all above.", "1591238": "Thank you @yamqwe for sharing this content ! ", "1591081": "Thanks a lot for the list - very useful regardless of the competition actually :) ", "1584052": "Thank you Yam for sharing, very very useful 👌", "1591090": "Thank you ", "1589455": "Thanks a lot, Yam for that treasure. " } }