Resources.md

• Websites
• Blogs
• Projects
• Data
• Books
• Papers

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Websites

• https://chatwithtraders.com/ -

• Wilmott -

• quant stackexchange -

• QuantNet -

• SmartQuant -

• Traders Laboratory -

Blogs

• Quantocracy - blog aggregator

• Quantpedia - Encyclopedia of strategies; not free access

• Academic Quant News - academic paper aggregator

• Quant News - Quantitative Trading

• PyQuant News - resources and news

• https://alphaarchitect.com/blog/

• https://asmquantmacro.com/

• https://cssanalytics.wordpress.com/

• http://jonathankinlay.com/

• https://www.pythonforfinance.net/

• https://qoppac.blogspot.com/ - Systematic Trading - A unique new method for designing trading and investing systems

• Quantified Strategies -

• https://quantoisseur.com/

• https://robotwealth.com/blog/

• https://systematicedge.wordpress.com/

• https://systematicinvestor.wordpress.com/

• https://www.thertrader.com/

Projects

• financial-machine-learning - A curated list of practical financial machine learning (FinML) tools and applications from FirmAI

• machine-learning-asset-management

• research - financial machine learning from Hudson and Thames, blog

• awesome-quant - Awesome quant is another curated list of quant

• Awesome-Quant-Machine-Learning-Trading - Quant/Algorithm trading resources with an emphasis on Machine Learning

• awesome-deep-trading - List of awesome resources for machine learning-based algorithmic trading

• awesome-ai-in-finance - Machine learning strategies and useful tools use in financial market

Data

• iqfeed

Books

• Algorithmic Trading: Winning Strategies and Their Rationale, 2013

• Machine Learning for Finance: Principles and practice for financial insiders, 2019

• Trading Evolved: Anyone can Build Killer Trading Strategies in Python, 2019 - author of Following the Trend

• Artificial Intelligence in Finance: A Python-Based Guide, 2020 - to be published

• Machine Learning for Algorithmic Trading 2nd Edition, 2020 - more pertinent to trading than the first edition

• Machine Learning in Finance: From Theory to Practice, 2020

Papers

before 2005

• Neuneier, Ralph. "Optimal asset allocation using adaptive dynamic programming." Advances in Neural Information Processing Systems. 1996.

• Moody, John, et al. "Performance functions and reinforcement learning for trading systems and portfolios." Journal of Forecasting 17.5‐6 (1998): 441-470.

• Neuneier, Ralph. "Enhancing Q-learning for optimal asset allocation." Advances in neural information processing systems. 1998.

• Moody, John E., and Matthew Saffell. "Reinforcement learning for trading." Advances in Neural Information Processing Systems. 1999.

• Dempster, Michael AH, et al. "Computational learning techniques for intraday FX trading using popular technical indicators." IEEE Transactions on neural networks 12.4 (2001): 744-754. - cite 188;

• Lee, Jae Won. "Stock price prediction using reinforcement learning." ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No. 01TH8570). Vol. 1. IEEE, 2001.

• Moody, John, and Matthew Saffell. "Learning to trade via direct reinforcement." IEEE transactions on neural Networks 12.4 (2001): 875-889. - recurrent reinforcement learning + Q-learning, Sharpe 2.3

• Dempster, Michael Alan Howarth, and Yazann S. Romahi. "Intraday FX trading: An evolutionary reinforcement learning approach." International Conference on Intelligent Data Engineering and Automated Learning. Springer, Berlin, Heidelberg, 2002.

• Lee, Jae Won, and O. Jangmin. "A multi-agent Q-learning framework for optimizing stock trading systems." International Conference on Database and Expert Systems Applications. Springer, Berlin, Heidelberg, 2002.

• Gold, Carl. "FX trading via recurrent reinforcement learning." 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.. IEEE, 2003.

• Kim, Kyoung-jae. "Financial time series forecasting using support vector machines." Neurocomputing 55.1-2 (2003): 307-319.

• Hryshko*, Andrei, and Tom Downs. "System for foreign exchange trading using genetic algorithms and reinforcement learning." International journal of systems science 35.13-14 (2004): 763-774. - used a genetic algorithm for in-sample trading strategy search to select the optimal trading strategy of entry and exit rules, The data, namely the financial indicators making up the rules were then passed on to the q learning

• Sherstov, Alexander A., and Peter Stone. "Three automated stock-trading agents: A comparative study." International Workshop on Agent-Mediated Electronic Commerce. Springer, Berlin, Heidelberg, 2004. - For the reinforcement learning algorithm, the action space consisted only of a single variable that represented the volume of shares to purchase (if number is positive) or sell (if number is negative) with the price to buy and sell determined by the market at that point in time. static order-book imbalance strategy.

• Huang, Wei, Yoshiteru Nakamori, and Shou-Yang Wang. "Forecasting stock market movement direction with support vector machine." Computers & operations research 32.10 (2005): 2513-2522.

2006-2010

• Dempster, Michael AH, and Vasco Leemans. "An automated FX trading system using adaptive reinforcement learning." Expert Systems with Applications 30.3 (2006): 543-552.

• Jangmin, O., et al. "Adaptive stock trading with dynamic asset allocation using reinforcement learning." Information Sciences 176.15 (2006): 2121-2147. - portfolio management

• Kumar, Manish, and M. Thenmozhi. "Forecasting stock index movement: A comparison of support vector machines and random forest." Indian institute of capital markets 9th capital markets conference paper. 2006.

• Nevmyvaka, Yuriy, Yi Feng, and Michael Kearns. "Reinforcement learning for optimized trade execution." Proceedings of the 23rd international conference on Machine learning. 2006. - milliseconds

• Bertoluzzo, Francesco, and Marco Corazza. "Making financial trading by recurrent reinforcement learning." International Conference on Knowledge-Based and Intelligent Information and Engineering Systems. Springer, Berlin, Heidelberg, 2007.

• Chen, Yan, et al. "Trading rules on stock markets using genetic network programming with sarsa learning." Proceedings of the 9th annual conference on Genetic and evolutionary computation. 2007.

• Lee, Jae Won, et al. "A multiagent approach to $ q $-learning for daily stock trading." IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 37.6 (2007): 864-877.

• Li, Hailin, Cihan H. Dagli, and David Enke. "Short-term stock market timing prediction under reinforcement learning schemes." 2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning. IEEE, 2007. - The 50-day training period was fixed and used to train the system for the next day prediction on a rolling basis

• Atsalakis, George S., and Kimon P. Valavanis. "Surveying stock market forecasting techniques–Part II: Soft computing methods." Expert Systems with applications 36.3 (2009): 5932-5941. - survey

• Teixeira, Lamartine Almeida, and Adriano Lorena Inacio De Oliveira. "A method for automatic stock trading combining technical analysis and nearest neighbor classification." Expert systems with applications 37.10 (2010): 6885-6890.

2011-2015

• Tan, Zhiyong, Chai Quek, and Philip YK Cheng. "Stock trading with cycles: A financial application of ANFIS and reinforcement learning." Expert Systems with Applications 38.5 (2011): 4741-4755.

• Bertoluzzo, Francesco, and Marco Corazza. "Testing different reinforcement learning configurations for financial trading: Introduction and applications." Procedia Economics and Finance 3 (2012): 68-77. - cite 24;

• Shen, Shunrong, Haomiao Jiang, and Tongda Zhang. "Stock market forecasting using machine learning algorithms." Department of Electrical Engineering, Stanford University, Stanford, CA (2012): 1-5.

• Zhang, Jin, and Dietmar Maringer. "Indicator selection for daily equity trading with recurrent reinforcement learning." Proceedings of the 15th annual conference companion on Genetic and evolutionary computation. 2013. - sharpe 1.0

• Booth, Ash, Enrico Gerding, and Frank Mcgroarty. "Automated trading with performance weighted random forests and seasonality." Expert Systems with Applications 41.8 (2014): 3651-3661.

• Corazza, Marco, and Francesco Bertoluzzo. "Q-learning-based financial trading systems with applications." University Ca'Foscari of Venice, Dept. of Economics Working Paper Series No 15 (2014).

• Eilers, Dennis, et al. "Intelligent trading of seasonal effects: A decision support algorithm based on reinforcement learning." Decision support systems 64 (2014): 100-108.

• Zhang, Jin, and Dietmar Maringer. "Two parameter update schemes for recurrent reinforcement learning." 2014 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2014.

• Corazza, Marco, and Andrea Sangalli. "Q-Learning and SARSA: a comparison between two intelligent stochastic control approaches for financial trading." University Ca'Foscari of Venice, Dept. of Economics Research Paper Series No 15 (2015). - on-policy vs off-policy; Q-learning > SARSA

• Cumming, James, Dalal Alrajeh, and Luke Dickens. "An investigation into the use of reinforcement learning techniques within the algorithmic trading domain." Imperial College London: London, UK (2015). - fx, least-squares temporal difference

• Gabrielsson, Patrick, and Ulf Johansson. "High-frequency equity index futures trading using recurrent reinforcement learning with candlesticks." 2015 IEEE Symposium Series on Computational Intelligence. IEEE, 2015.

2016

• D’Eramo, Carlo, Marcello Restelli, and Alessandro Nuara. "Estimating maximum expected value through gaussian approximation." International Conference on Machine Learning. 2016. - FX ε-greedy policy Q-learning, ε-greedy policy double Q-learning and weighted Q-learning as well as weighted policy for weighted Q-learning; weighted Q-learning performs the best.

• Deng, Yue, et al. "Deep direct reinforcement learning for financial signal representation and trading." IEEE transactions on neural networks and learning systems 28.3 (2016): 653-664.

• Jin, Olivier, and Hamza El-Saawy. "Portfolio management using reinforcement learning." Stanford University (2016).

2017

• Almahdi, Saud, and Steve Y. Yang. "An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown." Expert Systems with Applications 87 (2017): 267-279.

• Heaton, James B., Nick G. Polson, and Jan Hendrik Witte. "Deep learning for finance: deep portfolios." Applied Stochastic Models in Business and Industry 33.1 (2017): 3-12

• Jiang, Zhengyao, and Jinjun Liang. "Cryptocurrency portfolio management with deep reinforcement learning." 2017 Intelligent Systems Conference (IntelliSys). IEEE, 2017.

• Jiang, Zhengyao, Dixing Xu, and Jinjun Liang. "A deep reinforcement learning framework for the financial portfolio management problem." arXiv preprint arXiv:1706.10059 (2017).

• Krauss, Christopher, Xuan Anh Do, and Nicolas Huck. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500." European Journal of Operational Research 259.2 (2017): 689-702.

• Lu, David W. "Agent inspired trading using recurrent reinforcement learning and lstm neural networks." arXiv preprint arXiv:1707.07338 (2017).

• Ritter, Gordon. "Machine learning for trading." Available at SSRN 3015609 (2017).

2018

• Carapuço, João, Rui Neves, and Nuno Horta. "Reinforcement learning applied to Forex trading." Applied Soft Computing 73 (2018): 783-794.

• Fischer, Thomas G. Reinforcement learning in financial markets-a survey. No. 12/2018. FAU Discussion Papers in Economics, 2018. - survey

• Gu, Shihao, Bryan Kelly, and Dacheng Xiu. Empirical asset pricing via machine learning. No. w25398. National Bureau of Economic Research, 2018.

• Huang, Chien Yi. "Financial trading as a game: A deep reinforcement learning approach." arXiv preprint arXiv:1807.02787 (2018).

• Lim, Ye-Sheen, and Denise Gorse. "Reinforcement Learning for High-Frequency Market Making." ESANN. 2018.

• Jeong, Gyeeun, and Ha Young Kim. "Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning." Expert Systems with Applications 117 (2019): 125-138.

• Liang, Zhipeng, et al. "Adversarial deep reinforcement learning in portfolio management." arXiv preprint arXiv:1808.09940 (2018).

• Pendharkar, Parag C., and Patrick Cusatis. "Trading financial indices with reinforcement learning agents." Expert Systems with Applications 103 (2018): 1-13. - allocation between stock and bond, rebalance, online learning

• Spooner, Thomas, et al. "Market making via reinforcement learning." arXiv preprint arXiv:1804.04216 (2018).

• Xiong, Zhuoran, et al. "Practical deep reinforcement learning approach for stock trading." arXiv preprint arXiv:1811.07522 (2018).

• Zhou, Xingyu, et al. "Stock market prediction on high-frequency data using generative adversarial nets." Mathematical Problems in Engineering 2018 (2018).

2019

• García-Galicia, Mauricio, Alin A. Carsteanu, and Julio B. Clempner. "Continuous-time reinforcement learning approach for portfolio management with time penalization." Expert Systems with Applications 129 (2019): 27-36. - portfolio management, continuous-time discrete-state Markov chain

• Meng, Terry Lingze, and Matloob Khushi. "Reinforcement learning in financial markets." Data 4.3 (2019): 110. - survey

• Kanwar, Nitin. Deep Reinforcement Learning-based Portfolio Management. Diss. 2019. - uses reinforcement learning to optimise a financial portfolio in order to maximise the return over a long period. The algorithm was model free but the parameters were learned and iteratively improved using Policy Gradient and Actor Critic Methods on real past stock market data. Sharpe 0.486

• Rundo, Francesco. "Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems." Applied Sciences 9.20 (2019): 4460.

• Snow, Derek. "Machine Learning in Asset Management." JFDS: https://jfds. pm-research. com/content/2/1/10 (2019).

• Sornmayura, Sutta. "Robust forex trading with deep q network (dqn)." ABAC Journal 39.1 (2019). - Q-Learning > buy and hold

• Yu, Pengqian, et al. "Model-based deep reinforcement learning for dynamic portfolio optimization." arXiv preprint arXiv:1901.08740 (2019).

• Zarkias, Konstantinos Saitas, et al. "Deep reinforcement learning for financial trading using price trailing." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.

2020

• Gu, Shihao, Bryan Kelly, and Dacheng Xiu. "Autoencoder asset pricing models." Journal of Econometrics (2020).

• Hongyang Yang, Xiao-Yang Liu, Shan Zhong, and Anwar Walid. 2020. Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy. In ICAIF ’20

• Kaur, Simerjot. "Algorithmic Trading using Sentiment Analysis and Reinforcement Learning."

• Ozbayoglu, Ahmet Murat, Mehmet Ugur Gudelek, and Omer Berat Sezer. "Deep learning for financial applications: A survey." Applied Soft Computing (2020): 106384. - survey