Discussing the article: "MQL5 Wizard Techniques you should know (Part 59): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns"

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Check out the new article: MQL5 Wizard Techniques you should know (Part 59): Reinforcement Learning (DDPG) with Moving Average and Stochastic Oscillator Patterns.

We continue our last article on DDPG with MA and stochastic indicators by examining other key Reinforcement Learning classes crucial for implementing DDPG. Though we are mostly coding in python, the final product, of a trained network will be exported to as an ONNX to MQL5 where we integrate it as a resource in a wizard assembled Expert Advisor.

Rather than ask we posed in supervised learning, what is price going to do next?, we ask the question given this incoming price changes, what actions should the trader take. We thus perform simulation trainings as outlined above for the year 2023 and then do a forward walk for the year 2024 where our entry conditions get modified slightly.

Rather than solely basing our long or short positions on what price is going to do next, we also consider what actions we really need to take in light of what price will do next. We also factor in whether the rewards will be profitable. Of the 7 patterns that walked forward in article 7, only 3 walk forward meaningfully when reinforcement-learning is used. Using our indexing of the 10 that runs from 0 to 9, these patterns are 1, 2, and 5. Their reports are presented below:

For pattern 1:

Author: Stephen Njuki