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Reinforcement Learning with TensorFlow

You're reading from  Reinforcement Learning with TensorFlow

Product type Book
Published in Apr 2018
Publisher Packt
ISBN-13 9781788835725
Pages 334 pages
Edition 1st Edition
Languages
Author (1):
Sayon Dutta Sayon Dutta
Profile icon Sayon Dutta
Toc

Table of Contents (21) Chapters close

Title Page
Packt Upsell
Contributors
Preface
1. Deep Learning – Architectures and Frameworks 2. Training Reinforcement Learning Agents Using OpenAI Gym 3. Markov Decision Process 4. Policy Gradients 5. Q-Learning and Deep Q-Networks 6. Asynchronous Methods 7. Robo Everything – Real Strategy Gaming 8. AlphaGo – Reinforcement Learning at Its Best 9. Reinforcement Learning in Autonomous Driving 10. Financial Portfolio Management 11. Reinforcement Learning in Robotics 12. Deep Reinforcement Learning in Ad Tech 13. Reinforcement Learning in Image Processing 14. Deep Reinforcement Learning in NLP 1. Further topics in Reinforcement Learning 2. Other Books You May Enjoy Index

Problem definition


As we already know, portfolio management is the continuous reallocation of funds across different multiple financial products (assets). In this work, the time is divided into equal length periods, where each period T = 30 minutes. At the beginning of each period, the trading agent reallocates the fund across different assets. The price of an asset fluctuates within a period, but four important price metrics are taken into consideration, which are good enough to characterize the price movement of an asset in the period. These price metrics are as follows:

  • Opening price
  • Highest price
  • Lowest price
  • Closing price

For a continuous market (such as our test case), the opening price of an asset in a period t is its closing price in the previous period t-1. The portfolio consists of m assets. For a time period t, the closing prices of all the m assets create the price vector 

. Thus, 

 element of 

 that is 

 is the closing price of the

 asset in that 

 time period.

Similarly, we have vector...

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