Dr. Thomas Starke – Deep Reinforcement Learning in Trading

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Description

Description

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Dr. Thomas Starke – Deep Reinforcement Learning in Trading

What you will get in 25 sessions:

  • Introduction
  • Need for Reinforcement Learning
  • State, Actions and Rewards
  • Q Learning
  • State Construction
  • Policies in Reinforcement Learning
  • Challenges in Reinforcement Learning
  • Initialise Game Class
  • Positions and Rewards
  • Input Features
  • Construct and Assemble State
  • Game Class
  • Experience Replay
  • Artificial Neural Network Concepts
  • Artificial Neural Network Implementation
  • Backtesting Logic
  • Backtesting Implementation
  • Performance Analysis: Synthetic Data
  • Performance Analysis: Real World Price Data
  • Automated Trading Strategy
  • Paper and Live Trading
  • Capstone Project
  • Future Enhancements
  • Python Installation
  • Course Summary
Refund Policy

I will refund your money in case of:

  • Firstly, Item is not as described.
  • Secondly, Items do not work the way it should.
  • Thirdly, and most importantly, Item support extension can not be used.

However, I do not refund after you get the link and download the course.

How do you receive download link?
  • After you pay me. Then, please follow your Email to get download link.
  • I will send you P cloud, Mega, or Google Drive LINKS OF COURSES through email in 5 minutes – 8 hours
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