ProjectBeta
Market Agents using FinRL
Medium
8 completions
~ 14 hours
3.5 This content is new. Please help us improve it by reporting bugs if you encounter them.
- Master the use of FinRL library for financial reinforcement learning tasks.
- Gain hands-on experience in preprocessing financial data.
- Learn how to train, backtest, and optimize trading strategies using reinforcement learning techniques.
Provided by
JetBrains Academy
About
This project leverages the FinRL library to conduct market analysis and optimize trading strategies using reinforcement learning techniques. We will follow a structured approach covering data collection, preprocessing, model training, backtesting, and hyperparameter tuning. The aim is to build robust trading strategies that can adapt to dynamic market changes and effectively utilize historical stock data. By the end of this project, you will have a solid understanding of how to apply reinforcement learning to financial markets, from data collection to strategy optimization.
Training project
This project allows you to practice and strengthen your coding skills, helping you get ready for more advanced tasks ahead.
What you'll learn
Once you choose a project, we'll provide you with a study plan that includes all the necessary topics from your course to get it built. Here’s what awaits you:
Gather and preprocess historical stock data to prepare it for model training. This involves installing necessary dependencies, downloading data from Yahoo Finance, normalizing the data, and splitting it into training and testing sets.
Train a reinforcement learning model using the preprocessed data by setting up the training environment, configuring the model parameters, and using DRLAgent from the FinRL library to train the model.
Evaluate the trained model using backtesting techniques to ensure its effectiveness. Create a testing environment and validate the model's performance on unseen data to ensure robustness.
Optimize the model by tuning hyperparameters using Optuna. Define the objective function for optimization and use Optuna to find the best hyperparameters to improve the model's performance.
Reviews
8 months ago
I like applied field of this project, but think its not suits for Hyperskill and their test.There is a lot of troubles with installing required libraries and a lot of error with executing time in internal tests. Also you need check their documentation, and probably use chat GPT to end this project.
3.5
Learners who completed this project within the Introduction to Data Science course rated it as follows: