Welcome to the world of automated trading where cutting-edge frameworks meet the power of Reinforcement Learning (RL). In this guide, we will walk through the setup and usage of the Freqtrade-Gym project, built on the foundational principles of Freqtrade. Although this project is in its early stages, it serves as a promising platform for developing trading strategies using RL. Let’s dive into how you can get started!

Installation Steps

Before we can engage in the thrill of training agents, there are a few installations you’ll need to complete. Here is how to set everything up:

1. Install Freqtrade:
   • Follow the Freqtrade documentation for installation.
   • Initialize the user directory with the command:

     sh freqtrade create-userdir --userdir user_data

2. Install Pandas:

   pip install pandas

3. Install OpenAI Gym:

   pip install gym

4. Copy Freqtrade-Gym Files:

   Ensure you copy the following critical files:

   • IndicatorforRL.py
   • config_rl.json
   • freqtradegym.py
   • deep_rl.py
5. Copy RLlib Files:

   Now, add these files to your project:

   • LoadRLModel.py
   • rllib_example.py

Example Usage for Baseline

Let’s illustrate the basic usage of the Freqtrade-Gym framework with an analogy. Imagine you’re training a well-behaved dog. You need to establish a routine that teaches them tricks, much like how we establish a model training routine for our agent. Now, follow these steps:

• Run the command for training your agent:

  sh python deep_rl.py

• Monitor your progress using Tensorboard:

  htensorboard --logdir logdir

Example Usage for RLlib

Just like a dog’s training can be enhanced with different techniques, our RL agent can also be trained using various libraries. Here’s how to utilize RLlib:

• Run the agent with:

  sh python rllib_example.py

Backtesting and Trading

To ensure your agent is ready for the real world, you’ll need to backtest its performance. Just as you’d review a dog’s tricks before a performative event, backtest your model using these commands:

• Move the LoadRLModel.py to user_datastrategies and tweak necessary components.
• Execute backtesting:

  sh freqtrade backtesting -c config_rl.json -s LoadRLModel

• For a dry run of trading, use:

  sh freqtrade trade --dry-run -c config_rl.json -s LoadRLModel

Troubleshooting

If you run into any issues during installation or execution, here are some troubleshooting tips:

• Verify that all required libraries are correctly installed.
• Ensure that the file paths are accurate after copying necessary scripts.
• Check that your historical data has been downloaded as specified in the configuration files.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Final Thoughts

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.

Happy Trading!