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Unleashing the Power of Reinforcement Learning for Trading

AdvancedGuided Project

This Guided Project will dive into the fascinating world of Reinforcement Learning. Learners will be taken on an in-depth journey through Artificial Intelligence (AI) and exploring how AI can be used in trading. We leverage trading indicators such as MACD, EMA, RSI, BB, and OBV to use them for training the reinforcement learning agent. By the end, participants will understand AI principles and have the ability to apply them to create professional trading strategies.

4.4 (59 Reviews)

Language

  • English

Topic

  • Artificial Intelligence

Industries

  • Financial Services

Enrollment Count

  • 586

Skills You Will Learn

  • Artificial Intelligence

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 90 minutes

Platform

  • SkillsNetwork

Last Update

  • May 9, 2025
About this Guided Project
Join us on an exciting journey into the world of stock trading and artificial intelligence! In this project, we explore the power of reinforcement learning to create a cutting-edge system that emulates the strategies used by hedge funds. By leveraging advanced techniques such as Exponential Moving Averages, Relative Strength Index, and Bollinger Bands, we preprocess and analyze stock data to train an intelligent agent. This agent learns to make informed buy and sell decisions, adapting its actions based on current market conditions. Through rigorous training and testing, we aim to develop a robust model capable of maximizing profits and navigating the complex landscape of stock trading. Embark on this fascinating endeavor and unlock the potential of reinforcement learning in revolutionizing your understanding of the financial world.

A Look at the Project Ahead

We are aiming to implement a reinforcement learning system for stock / Forex / Crypto trading using indicators:

Step 1: Data preparation
  • Preprocess the stock dataset by cleaning and adding trading indicators including Exponential moving averages (EMA), Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), On-Balance Volume (OBV), and Bollinger Bands.
Step 2: Define the environment
  • Define the action space, which represents the actions the agent can take. In this case, the agent can either buy or sell the stock.
  • Define the state space, which represents the current state of the stock. In this case, the state space can include the current price and the indicators.
  • Define the reward function, which will be used to evaluate the agent's performance. In this case, the reward function can be based on the profit or loss made by the agent.
Step 3: Define the agent
  • Define the agent, which will take actions based on the current state of the stock and the reward function.
  • Choose a reinforcement learning algorithm such as Q-Learning or Deep Q-Network (DQN) to train the agent.
Step 4: Train the agent
  • Train the agent using the stock data and the defined reward function.
  • The agent should learn the optimal policy for buying and selling the stock based on the current state of the stock and the reward function.
Step 5: Test the agent
  • Test the agent on a separate dataset to evaluate its performance.
Step 6: Refine the model
  • Refine the model by adjusting the parameters of the reinforcement learning algorithm, the reward function, or the state space.

What You'll Need

It is recommended to have a background in programming (especially in Python) and have a familiarity with classes and objects. 

Instructors

Sina Nazeri

Data Scientist at IBM

I am grateful to have had the opportunity to work as a Research Associate, Ph.D., and IBM Data Scientist. Through my work, I have gained experience in unraveling complex data structures to extract insights and provide valuable guidance.

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Contributors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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J.C.(Junxing) Chen

Data scientist at IBM

Data science is easy and helpful! I want to let everyone know data science and help everyone using it for everyday life! Not only being a Data science guide person but also making friends, I want to make friends with peoples like you! As a data scienist, I hope my spread data science could help my friend!

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Sheng-Kai Chen

Data Scientist

Sheng-Kai Chen is a graduate student at the University of Toronto, concentrating on Information Systems & Design. Having several experiences analyzing data for retail stores and designing small software for small businesses. Sheng-Kai was inspired to shift toward answering new challenges with machine learning and new technics.

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Artem Arutyunov

Data Scientist

Hey, Artem here! I am excited about answering new challenges with data science, machine learning and especially Reinforcement Learning. Love helping people to learn, and learn myself. Studying Math and Stats at University of Toronto, hit me up if you are from there as well.

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Justin Correia

IBM UX UI Designer | Website Designer

What do you call a bagel that can fly? A plain bagel. ✈️

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Roodra Kanwar

Data Scientist at IBM

I am a data scientist by day, superhero by night. Psych! I wish I was that cool. Only the former part is true which is still pretty cool! I believe in constant learning and it is an essential part of being a productive data enthusiast. I am also pursuing my masters in computer science from Simon Fraser University specializing in Big Data. Moreover, knowledge is transfer learning (pun intended!) and what I have gained, I plan on reflecting it back to the data community.

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Vicky Kuo

Data Scientist

I believe that success isn't just about individual milestones, but also about uplifting and encouraging others to reach their potential. This is why I'm passionate about combining my technical background with my eagerness to help people overcome technological hurdles and accelerate growth. When I’m not on the job, I love hiking with my two dogs or relaxing in a coffee shop. There's nothing better than having an insightful conversation over coffee, or even better, some volunteer work! Please feel free to reach out to me on LinkedIn.

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