Evolution of a Personal Trading Bot Project

Introduction

In the realm of software engineering, personal projects not only reflect a passion for technology but also serve as a playground for exploring new concepts and honing skills. This blog post delves into the evolution of my personal project: developing a trading bot using Python, generative AI, and trading algorithms. It's a journey through the intricacies of development processes and methodologies.

Project Genesis

The project began with an ambitious goal: to create a trading bot capable of growing the balance of BTC in a user's Coinbase account. Python was chosen for its simplicity and robust ecosystem, particularly suitable for integrating with generative AI and algorithmic trading.

Development Methodology

Adopting an iterative and modular approach, the project was broken down into manageable components, each encapsulating a specific functionality. This method ensured a clear focus and allowed for incremental improvements and testing.

Technical Stack

• Language: Python 3.6+
• API: Coinbase Cloud Python API
• Libraries: pandas for data manipulation, requests for API calls, logging for tracking events and errors.

File Structure

A well-organized file structure was established to maintain clarity and scalability:

          trading_bot/
          │
          ├── bot/
          │   ├── main.py            # Orchestrates the trading operations
          │   ├── strategy.py        # Defines the trading logic
          │   ├── indicators.py      # Calculates technical indicators
          │   └── api.py             # Handles API interactions
          │
          ├── utils/
          │   ├── logging.py         # Configures logging and error handling
          │
          ├── data/
          │   ├── historical_data/   # Stores historical market data
          │
          └── logs/
              ├── error_logs.txt     # Logs errors and exceptions
              

Core Components

- API Integration (api.py): A custom class was created to interact with the Coinbase API, handling authentication, data retrieval, and order execution.

- Indicator Calculation (indicators.py): The Donchian Channel, a key technical indicator for the trading strategy, was implemented using pandas.

- Trading Strategy (strategy.py): Logic to determine buy, sell, or hold signals based on the Donchian Channel and current market price.

- Logging and Error Handling (utils.py and errors.py): Robust logging mechanisms were set up to record events and errors, facilitating debugging and accountability.

Challenges and Learnings

Throughout the project, challenges such as API rate limits, error handling, and performance considerations were addressed. These hurdles underscored the importance of thorough testing and the need for a responsive design capable of adapting to real-time market conditions.

Conclusion

The journey of building a trading bot has been enlightening, blending the technical aspects of software development with the analytical rigor of trading algorithms. It stands as a testament to the power of personal projects in fostering growth and innovation in the field of software engineering.

Next Steps

Moving forward, the project will undergo rigorous backtesting, interface enhancements, and possibly integration with other languages or platforms for performance gains. The exploration continues, with each iteration bringing new insights and opportunities for improvement.

Stay tuned for more updates as this personal project evolves, and feel free to share your thoughts or embark on your own journey of discovery in the fascinating world of trading bots and algorithmic trading.