How to structure your algorithmic trading project

It is borderline impossible trying to work out how to structure an algorithmic trading project.

The internet is filled with useless experiments done in jupyter notebooks, but there are no real well structured algorithmic trading projects out there.

Before we look at the individual folder structure of the project, lets look at some of the key considerations we might have:

Version Control - Utilize version control systems like Git to track changes, revert to previous versions, and collaborate with others. This helps maintain a history of your project and allows for easy rollbacks if needed.

Error Handling - Implement robust error handling mechanisms such as logging and alerting to gracefully handle unexpected situations like data errors, API connectivity issues, or market disruptions. This prevents your script from crashing unexpectedly and protects your capital.

Testability - Beyond unit tests, consider integration tests to ensure different modules interact correctly and system tests to verify the overall functionality of your strategy under various market conditions.

Security - Implement security measures to protect sensitive data like API keys and account credentials. Encrypt sensitive information and enforce secure authentication protocols.

Documentation - Maintain comprehensive documentation throughout your code, explaining algorithms, assumptions, and reasoning behind decisions. This facilitates understanding, maintenance, and future improvements.

With that in mind, let’s take a look at how we might structure our codebase.

Root directory:

config/

: Stores configuration files (e.g., API keys, trading parameters, data sources).

data/

: Contains raw and processed financial data.

logs/

: Holds log files for debugging and monitoring.

models/

: Houses trained machine learning or statistical models (if applicable).

strategies/

: Contains the core strategy logic for each trading strategy.

risk_management/

: Risk management rules and models. Could include stop-loss algorithms, position sizing methods, etc.

utils/

: Stores reusable utility functions and scripts (e.g., data fetching, preprocessing, visualization).

backtesting/

: Stores reusable utility functions and scripts (e.g., data fetching, preprocessing, visualization).

tests/

: Code for unit tests and an integration test.

README.md

: Detailed documentation for the project.

requirements.txt

: Lists required Python libraries.

main.py

: The primary script for executing the trading strategy.