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RL_Finance_Survey

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Gathering material for a survey paper in RL finance

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# RL_Finance_Survey
Gathering material for a survey paper in RL finance

# Table of contents
* [Quote](#Quote)
* [RL](#RL)

# Quote

One should avoid solving more difficult intermediate problems when solving a target problem. Vladimir Vapnik, Statistical Learning Theory, 1998

# RL
* [Reinforcement learning in financial markets - a survey](https://www.econstor.eu/bitstream/10419/183139/1/1032172355.pdf), Fischer (2018).
* [Optimal Asset Allocation Using Adaptive Dynamic Programming](https://papers.nips.cc/paper/1121-optimal-asset-allocation-using-adaptive-dynamic-programming.pdf), Neuneier (1996).
* [Reinforcement Learning for Trading Systems and Portfolios](https://pdfs.semanticscholar.org/10f3/4407d0f7766cfb887334de4ce105d5aa8aae.pdf), Moody et al (1998).
* [PERFORMANCE FUNCTIONS AND REINFORCEMENT LEARNING FOR TRADING SYSTEMS AND PORTFOLIOS](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.87.8437&rep=rep1&type=pdf), Moody et al (1998).
* [Reinforcement Learning for Trading](http://papers.nips.cc/paper/1551-reinforcement-learning-for-trading.pdf), Moody et al. (1999).
* [Learning to trade via direct reinforcement](http://people.idsia.ch/~juergen/rnnaissance2003talks/MoodySaffellTNN01.pdf), Moody (2001).
* [Moody presentation](http://www.cs.cmu.edu/afs/cs/project/link-3/lafferty/www/ml-stat-www/moody.pdf), Moody (2003).
* [Stochastic Direct Reinforcement: Application to Simple Games with Recurrence](https://www.aaai.org/Papers/Symposia/Fall/2004/FS-04-02/FS04-02-004.pdf), Moody et al (2003).
* [Reinforcement Learning for Stochastic Control Problems in Finance](http://web.stanford.edu/class/cme241/), Rao (2018).
* [Deep Hedging](https://arxiv.org/pdf/1802.03042.pdf), BUEHLER et al. (2018).
* [Deep Hedging: Hedging Derivatives Under Generic Market Frictions Using Reinforcement Learning](https://poseidon01.ssrn.com/delivery.php?ID=699064116031077097087112121097105025123011062088031092022073127100022097028123112089013000007026033049005010005121089085004101102026033060067071107028113069028068020022001080013026079071105097107078018096065081067105125014124087106094118127009119065&EXT=pdf), Buehler (2019).
* [Model-Free Option Pricing with Reinforcement Learning](https://cfe.columbia.edu/files/seasieor/industrial-engineering-operations-research/IgorHalperin_Columbia_ML_in_finance_QLBS.pdf), Halperin, (2018).
* [QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds](https://arxiv.org/pdf/1712.04609.pdf), Halperin (2019).
* [Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks](https://arxiv.org/pdf/1707.07338.pdf), Lu (2017).
* [A reinforcement learning approach for pricing derivatives](http://cs229.stanford.edu/proj2010/Grassl-AReinforcementLearningApproachForPricingDerivatives.pdf), Grassl, (2008).
* [Regime-switching recurrent reinforcement learning for investment decision making](http://doc.rero.ch/record/321803/files/10287_2011_Article_131.pdf), Maringer (2009).
* [Automating Transition Functions: A Way To Improve Trading Profits with Recurrent Reinforcement Learning](https://hal.inria.fr/hal-01391291/document), Zhang (2013).
* [Algorithm trading using q-learning and recurrent
reinforcement learning](http://cs229.stanford.edu/proj2009/LvDuZhai.pdf), Du et al. (2009).
* [RL for Portfolio Management - Thesis](https://arxiv.org/pdf/1909.09571.pdf), Filos (2018).
* [A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem](https://arxiv.org/pdf/1706.10059.pdf), Jiang (2017).
* [An Automated fx trading system using adaptive reinforcement learning](https://www.jbs.cam.ac.uk/fileadmin/user_upload/research/workingpapers/wp0418.pdf), Dempster (2004).
* [Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization](https://arxiv.org/pdf/1901.08740.pdf),  Yu et al (2019).
* [Cryptocurrency Portfolio Management with Deep Reinforcement Learning](https://arxiv.org/pdf/1612.01277.pdf), Jiang et al  (2017).
* [An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown](https://www.researchgate.net/profile/Steve_Yang2/publication/317622713_An_adaptive_portfolio_trading_system_A_risk-return_portfolio_optimization_using_recurrent_reinforcement_learning_with_expected_maximum_drawdown/links/59f3401da6fdcc075ec339c5/An-adaptive-portfolio-trading-system-A-risk-return-portfolio-optimization-using-recurrent-reinforcement-learning-with-expected-maximum-drawdown.pdf), Almahdi et al (2017).
* [Portfolio Choices with Orthogonal Bandit Learning](http://yugangjiang.info/publication/ijcai15-OBL.pdf), Shen et al. (2015)
* [Risk-Aware Multi-Armed Bandit Problem with Application to Portfolio Selection](https://arxiv.org/pdf/1709.04415.pdf), Huo (2017)
* [Application of stochastic recurrent reinforcement learning to index trading ](http://discovery.ucl.ac.uk/1339314/1/es2011-60%5B1%5D.pdf), Gorse (2011).
* [Testing different Reinforcement Learning configurations
for financial trading: Introduction and applications](https://pdf.sciencedirectassets.com/282136/1-s2.0-S2212567112X00042/1-s2.0-S2212567112001220/main.pdf?X-Amz-Security-Token=AgoJb3JpZ2luX2VjEHgaCXVzLWVhc3QtMSJHMEUCIFM%2Ft60m0FP5zqqIJYury%2BWGUH2qmQJooNgLQMZsU41sAiEAjcntQWo1Un6bJz32BA0IOgft5%2F8aiYaE29fX9c5HUjgq2gMIQRACGgwwNTkwMDM1NDY4NjUiDAD3VLuf0EW314iCdyq3AyqnwrQi6NpoKkOMjur1OfjVlJX%2BwRsgNP2v8QSiPSaY0wkAdUfOZyYDPj4hyWptbwK5O5icJQbYoGfH3mg5K6aQXGbJXFCgENaKsDi0YapzZLwq55oV0pFGQvgGGFMIdZ6GUQXoM0j54vIRo%2FPC60xDCtJfQj1Wxg4kENPoxLHr%2FsqCdmY7GX9EGW3Nj7657izJ8S9Ou0GnkkBwtcwhmse6QKJStftotgmIpuvgQvE%2FpEkstIsTK0opyfeYXSPGVzUVX1khd4aHAUPyLINvbSslekIEcIzL0BDQ5YMv9Zp%2F1AGjJm3n8sZ0mMkyUEwwKUvRfV9JU8I4%2FWnZlY6yUxFS39KEPEBARjlw1QJgIJ8xsmJVHtIUN6GgRSM1r5hwze8ZbV0L0ONySiO3ZXmMQIoaxV1aVWxw0DthRjl2Ds2P391nZxAPiwRNqYyFHslyMiRNz89SNFRoIA03Se3CNL6%2FvWSo6LmKUVLKCOzZnjzUHKgsj%2FaYRHdMx2t0MduKlhbwD3OkUd86ELGN7IiWN30hbNCmL1b0cr2Ei2QaKkqlNwIgyotWqnMNr%2FEeCMrptGdtWjQtGMww4df36wU6tAF7hsVWtgwWf3xeAomhR4eIL9sIStgT9z01xVY9h4jwQF1qIlBhz1bm53rvBnCLn4e5R2zVw12LGbABb1CSSfz780ZmzFy%2BgZXCfPwb%2FTBaKgJXO2%2BAy6kXTyDKXAJsubd4IuJ9DJrIuRosCOexglX5OcLRkUrvGky3oVhnaP6cCZAY0qr4FFM472WvnZW1aRH1MjHgTiDh8Ilm5cf4e7mQNmQbX7eN2%2BsVRlO5P9uxiIR%2BQsY%3D&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Date=20190915T083038Z&X-Amz-SignedHeaders=host&X-Amz-Expires=300&X-Amz-Credential=ASIAQ3PHCVTYQKNDAQHW%2F20190915%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Signature=56b4003379943f5cd332add3aea57da65cf5b322aa73f3a4b2af686bad26fd8b&hash=61129b7967890d1bd6a8195ec8e12cd6cea96bcedc073fe7d3b8f30a13374bba&host=68042c943591013ac2b2430a89b270f6af2c76d8dfd086a07176afe7c76c2c61&pii=S2212567112001220&tid=spdf-07f94ef6-fa11-4ce2-bcf2-3b1999a259f9&sid=8f72a70b45b6524359780172d34111628b2dgxrqa&type=client), Bertolluzo et al, (2012).
* [An Investigation into the Use of Reinforcement Learning Techniques
within the Algorithmic Trading Domain](https://www.doc.ic.ac.uk/teaching/distinguished-projects/2015/j.cumming.pdf), Cumming (2015).
* [Financial Trading as a Game: A Deep Reinforcement Learning Approach](https://arxiv.org/pdf/1807.02787.pdf), Huang (2018).
* [Deep Reinforcement Learning for Foreign Exchange Trading](https://arxiv.org/pdf/1908.08036.pdf), Wang (2019).
* [Capturing Financial markets to apply Deep Reinforcement Learning](https://arxiv.org/pdf/1907.04373.pdf), Chakraborty (2019).
* [Reinforcement Learning For Automated Trading](http://www1.mate.polimi.it/~forma/Didattica/ProgettiPacs/BrambillaNecchi15-16/PACS_Report_Pierpaolo_Necchi.pdf), Necchi  (2016).
* [Towards Inverse Reinforcement Learning for Limit Order Book Dynamics](https://arxiv.org/pdf/1906.04813.pdf), Roa-Vicens et al (2019).
* [Multi-Period Trading via Convex Optimization](https://arxiv.org/pdf/1705.00109.pdf). Boyd et al. (2017).
* [Optimal Execution of Portfolio Transactions](https://www.math.nyu.edu/faculty/chriss/optliq_f.pdf), Chris (1999).
* [Deep Learning Volatility](https://arxiv.org/pdf/1901.09647.pdf), Horvath (2019).
* [Pricing options and computing implied volatilities using neural networks](https://arxiv.org/pdf/1901.08943.pdf), Liu (2019).
* [Machine Learning and Option Implied Information](https://spiral.imperial.ac.uk/bitstream/10044/1/57953/5/Yu-Z-2018-PhD-Thesis.pdf), Zheng (2018).
* [Hedged Monte-Carlo: low variance derivative pricing with objective probabilities](https://arxiv.org/pdf/cond-mat/0008147.pdf), Potter et al. (2000).
* [Dynamic Replication and Hedging: A Reinforcement Learning Approach](https://www.researchgate.net/publication/329435926_Dynamic_Replication_and_Hedging_A_Reinforcement_Learning_Approach), Ritter (2019).
* [Deep learning approach to hedging Thesis](https://www.natixis.com/natixis/upload/docs/application/pdf/2019-10/michal_kozyra_prix_natixis_2019_du_meilleur_memoire_de_master_en_finance_quantitative.pdf), Kozyra (2018).
* [Deep Hedging - Presentation](https://www.maths.ox.ac.uk/system/files/attachments/2019%2004%2024%20Deep%20Hedging%20Frontiers%20Imperial%202.1.pdf), Beuler (2018).
* [Pricing and Hedging Exotic Options with Monte Carlo Simulations](http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=E50E03046624FEC67933C3D86B01FA71?doi=10.1.1.194.9001&rep=rep1&type=pdf), Perilla Msc (2003).
* [The Four Horsemen of Machine Learning in Finance](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3453564), Halperin (2019).
* [Machine Learning for Trading](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3015609), Ritter (2017).
* [Reinforcement Learning With Continuous States](https://cims.nyu.edu/~ritter/ritter2018sarsa.pdf), Ritter (2018).
* [The Usefulness of Reinforcement Learning in Finance - Pres](https://www.iaqf.org/dev/files/IAQF-2018%20-%20slides.pdf), Ritter (2018).
* [Generative Bayesian neural network model for risk-neutral pricing of American index options](https://www.tandfonline.com/doi/pdf/10.1080/14697688.2018.1490807?needAccess=true), Jang et al. (2018).
* [Reinforcement Learning Applied to Option
Pricing - MSc](https://core.ac.uk/download/pdf/39674013.pdf), Martin (2014).
* [Quant GANs:Deep Generation of Financial Time Series](https://arxiv.org/pdf/1907.06673.pdf), Wiese et al. (2019).
* [Enhancing Time Series Momentum Strategies Using Deep Neural Networks](https://arxiv.org/pdf/1904.04912.pdf), Lim et al. (2019).
* [Deep Learning Approximation for Stochastic Control Problems](https://www.researchgate.net/publication/310672039_Deep_Learning_Approximation_for_Stochastic_Control_Problems/link/5a37c5a3aca272a6ec1e4fd1/download) Han et al. (2016).
* [Deep Reinforcement Learning for Trading](https://arxiv.org/pdf/1911.10107.pdf), Zhang (2019).
* [Model-based Reinforcement Learning for Predictions and Control for Limit Order Books](https://arxiv.org/pdf/1910.03743.pdf), Wei et al. (2019). 
* [Practical Deep Reinforcement Learning Approach for
Stock Trading](https://arxiv.org/pdf/1811.07522.pdf), Xiong et al. (2018).
* [Deep Robust Reinforcement Learning for
Practical Algorithmic Trading](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8786132), Li et al. (2019).

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