The article presents a progressive implementation of trading systems using three reinforcement learning approaches: basic RL with Q-tables, Deep RL with neural networks, and Agentic AI combining RL wi
th language models. Each level adds complexity and capabilities, culminating in an explainable AI system that considers multiple factors for trading decisions.
Reasons to Read -- Learn:
how to implement a complete trading system using reinforcement learning, starting from basic Q-learning and progressing to advanced neural networks with practical Python code examples.
how to combine multiple AI approaches (RL, neural networks, and LLMs) into an integrated system that makes explainable trading decisions while considering portfolio constraints and market news.
how to build an Agentic AI system that uses multiple specialized agents (risk, news, and supervisor) to create a more robust and explainable trading strategy with concrete PyTorch implementations.
publisher: @anixlynch
0
What is ReadRelevant.ai?
We scan thousands of websites regularly and create a feed for you that is:
directly relevant to your current or aspired job roles, and
free from repetitive or redundant information.
Why Choose ReadRelevant.ai?
Discover best practices, out-of-box ideas for your role
Introduce new tools at work, decrease costs & complexity
Become the go-to person for cutting-edge solutions
Increase your productivity & problem-solving skills
Spark creativity and drive innovation in your work