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A Deep Reinforcement Learning Agent Using Multiple Assets Financial Signals for Portfolio Management

  • In this study, we investigated possible applications of reinforcement learning in the area of portfolio management. Aa specific topology of reinforcement learning is choosen to study the feasibility, Deep Deterministic Policy Gradient (DDPG), to train neural networks to perform trade in an environment simulated real world trading. The results show that the DDPG agent is able to learn price pattern and perform profitable trades. In single stock backtest, the DDPG is able to generate an annual return of 7%. While in multiple stocks backtest, DDPG agent can generate an annual return of 12%.

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Metadaten
Author:Francis Liu
Referee:Roland Müller
Advisor:Natalie Packham
Document Type:Master's Thesis
Language:English
Date of first Publication:2019/08/13
Publishing Institution:Hochschulbibliothek HWR Berlin
Granting Institution:Hochschule für Wirtschaft und Recht Berlin
Date of final exam:2019/01/23
Release Date:2019/08/13
Tag:Active Portfolio Management; Algorithmic Trading; Convolutional Neural Networks; Machine learning; Optimization on Continuous action Space; Portfolio Management; Reinforcement Learning
Page Number:58
Institutes:FB I - Wirtschaftswissenschaften / Business Intelligence and Process Management M.Sc.
Licence (German):License LogoUrheberrechtsschutz