Welcome to the world of AI-driven trading! In this article, we will guide you through setting up and utilizing TradzQAI, an innovative environment designed for reinforcement learning (RL) agents. Whether you are interested in backtesting or training, we aim to simplify the process so you can focus on what truly matters: making smarter trading decisions.

Getting Started

To kickstart your journey, you will need to have a few dependencies installed. This project relies heavily on several Python libraries:

• TensorFlow
• Tensorforce
• coinbasepro-python
• Pandas
• Numpy
• tqdm
• h5py

Installation

To install the required packages, open your terminal and run:

pip install -r requirements.txt

Running the Project

TradzQAI offers several commands to choose from based on your preferences:

• python run.py -h to show usage options.
• python run.py -b agent_name to manually build a config file (the default is PPO).
• python run.py -s live for a live trading session.
• python run.py -m eval for evaluation mode.
• python run.py -c config_dir to load a config from a specific directory.
• python run.py to run with default settings.

Working with Sessions and Agents

Once you start the project, a configuration directory will be created. Feel free to modify this directory according to your desired environment settings and agent configurations. All changes will be saved automatically, allowing for easy management of your setups.

TradzQAI supports a variety of agents including DDPG, DQFD, DQN, and more! Each offers unique algorithms tailored for different trading strategies.

An Analogy for Understanding the Code

Think of setting up TradzQAI as preparing a kitchen to cook your favorite meal:

• The dependencies are your utensils and ingredients, ensuring you have everything required to create culinary delights.
• The installation process is like shopping for groceries, where you gather all the essentials in one basket.
• Running commands is akin to following a recipe step-by-step. Each step (or command) leads you closer to that delectable dish you crave — in this case, effective trading strategies!

Network Configuration

Setting up your neural network configurations allows you to tailor the input based on your dataset’s requirements. You can define your input in JSON format, specifying the names and types of inputs (e.g., Price, Volume), output, and layers.

Additional features allow you to employ complex networks or even utilize pre-trained Keras models for better performance!

Troubleshooting Ideas

While you’re embarking on this exciting journey, it’s normal to encounter a few bumps on the road. Here are some troubleshooting tips:

• If you face issues related to dependencies, ensure you installed everything in the requirements.txt file without any errors.
• Double-check your JSON configuration for any syntax errors, as this can hinder your network setup.
• If the live sessions do not start as expected, verify that your API keys and product IDs are configured accurately.

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.