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This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets.
A Survey of Deep Reinforcement Learning in Video Games
Shao, K., Tang, Z., Zhu, Y., Li, N., and Zhao, D. (2019) · 1912
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Technical Note: Q-Learning
Watkins, C. J. C. H. and Dayan, P. (1992) · 1992
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Learning to Trade via Direct Reinforcement
Moody, J. E. and Saffell, M. (2001) · 2001
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A Deep Reinforcement Learning Framework for Continuous Intraday Market Bidding
Boukas, I., Ernst, D., Théate, T., Bolland, A., Huynen, A., Buchwald, M., Wynants, C., and Cornélusse, B. (2020) · 2004
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Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · 2005
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An Automated FX Trading System using Adaptive Reinforcement Learning
Dempster, M. A. H. and Leemans, V. (2006) · 2006
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Quantitative Trading: How to Build Your Own Algorithmic Trading Business
Chan, E. P. (2009) · 2009
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Inside the Black Box
Narang, R. K. (2009) · 2009
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Reinforcement Learning and Dynamic Programming using Function Approximators
Busoniu, L., Babuska, R., De Schutter, B., and Ernst, D. (2010) · 2010
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Algorithms for Reinforcement Learning
Szepesvari, C. (2010) · 2010
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Twitter Mood Predicts the Stock Market
Bollen, J., Mao, H., and jun Zeng, X. (2011) · 2011
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Does Algorithmic Trading Improve Liquidity?
Hendershott, T., Jones, C. M., and Menkveld, A. J. (2011) · 2011
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Event-Driven Trading and the “New News”
Leinweber, D. and Sisk, J. (2011) · 2011
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Algorithmic Trading
Nuti, G., Mirghaemi, M., Treleaven, P. C., and Yingsaeree, C. (2011) · 2011
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Algorithmic Trading: Winning Strategies and Their Rationale
Chan, E. P. (2013) · 2013
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Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. A. (2013) · 2013
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Algorithmic Trading Review
Treleaven, P. C., Galas, M., and Lalchand, V. (2013) · 2013
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Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance
Bailey, D. H., Borwein, J. M., de Prado, M. L., and Zhu, Q. J. (2014) · 2014
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An Automated Framework for Incorporating News into Stock Trading Strategies
Nuij, W., Milea, V., Hogenboom, F., Frasincar, F., and Kaymak, U. (2014) · 2014
Cited alongside, same era.
Deep Learning
Deep Learning
Goodfellow, I., Bengio, Y., and Courville, A. (2016) · 2016
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Schaul, T., Quan, J., Antonoglou, I., and Silver, D. (2016) · 2016
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Mastering the Game of Go with Deep Neural Networks and Tree Search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T. P., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. (2016) · 2016
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A Brief Survey of Deep Reinforcement Learning
Arulkumaran, K., Deisenroth, M. P., Brundage, M., and Bharath, A. A. (2017) · 2017
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A Deep Learning Framework for Financial Time Series using Stacked Autoencoders and Long-Short Term Memory
Bao, W. N., Yue, J., and Rao, Y. (2017) · 2017
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Goodfellow, I. J., Bengio, Y., and Courville, A. C. (2015) · 2015
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Deep Recurrent Q-Learning for Partially Observable MDPs
Hausknecht, M. J. and Stone, P. (2015) · 2015
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C. (2015) · 2015
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Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J. (2015) · 2015
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Deep Learning
LeCun, Y., Bengio, Y., and Hinton, G. (2015) · 2015
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Human-Level Control through Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M. A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., and Hassabis, D. (2015) · 2015
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Deep Reinforcement Learning with Double Q-Learning
van Hasselt, H. P., Guez, A., and Silver, D. (2015) · 2015
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A Distributional Perspective on Reinforcement Learning
Bellemare, M. G., Dabney, W., and Munos, R. (2017) · 2017
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Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
Deng, Y., Bao, F., Kong, Y., Ren, Z., and Dai, Q. (2017) · 2017
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Rainbow: Combining Improvements in Deep Reinforcement Learning
Hessel, M., Modayil, J., van Hasselt, H. P., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M. G., and Silver, D. (2017) · 2017
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Deep Reinforcement Learning: An Overview
Li, Y. (2017) · 2017
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Proximal Policy Optimization Algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017) · 2017
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Reinforcement Learning applied to Forex Trading
Carapuço, J., Neves, R. F., and Horta, N. (2018) · 2018
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Noisy Networks for Exploration
Fortunato, M., Azar, M. G., Piot, B., Menick, J., Hessel, M., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., Pietquin, O., Blundell, C., and Legg, S. (2018) · 2018
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
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A Study on Overfitting in Deep Reinforcement Learning
Zhang, C., Vinyals, O., Munos, R., and Bengio, S. (2018) · 2018
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