reinforcement learning for trading strategies

By attending Reinforcement Learning for Trading Strategies workshop, Participants will:

  • Understand what reinforcement learning is and how trading is an RL problem
  • Build Trading Strategies Using Reinforcement Learning (RL)
  • Understand the benefits of using RL vs. other learning methods
  • Differentiate between actor-based policies and value-based policies 
  • Incorporate RL into a momentum trading strategy

This Reinforcement Learning for Trading Strategies training course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.

To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library.You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

COURSE AGENDA

  • Introduction to Course
  • What is Reinforcement Learning?
  • History Overview2m
  • Value Iteration
  • Policy Iteration
  • TD Learning
  • Q Learning
  • Benefits of Reinforcement Learning in Your Trading Strategy
  • DRL Advantages for Strategy Efficiency and Performance
  • Introduction to Qwiklabs
  • TD-Gammon
  • Deep Q Networks - Loss
  • Deep Q Networks Memory
  • Deep Q Networks - Code
  • Policy Gradients
  • Actor-Critic
  • What is LSTM?
  • More on LSTM
  • Applying LSTM to Time Series Data
  • How to Develop a DRL Trading System
  • Steps Required to Develop a DRL Strategy
  • Final Checks Before Going Live with Your Strategy
  • Investment and Trading Risk Management
  • Trading Strategy Risk Management
  • Portfolio Risk Reduction
  • Why AutoML?
  • AutoML Vision
  • AutoML NLP
  • AutoML Tables

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