Financial Data Analysis Development Environment

A Docker-based development environment for financial data analysis using Python, Poetry, and PostgreSQL with TimescaleDB (for time series data).

Features

• Python 3.11 with Poetry for dependency management
• PostgreSQL with TimescaleDB for time series data storage
• SSH access for remote development with Windsurf IDE
• Jupyter Lab for interactive analysis
• pgAdmin for database management
• Pre-configured with popular data science and financial analysis packages

Prerequisites

• Docker and Docker Compose
• SSH key pair for authentication

Setup Instructions

1. Clone this repository:

git clone <repository-url>
cd dev_env

2. Create a .env file from the example:

cp .env.example .env

3. Edit the .env file and add your API credentials.

4. Ensure you have an SSH key:

# Check if you have an existing key
ls -la ~/.ssh/id_rsa.pub

# If not, generate a new SSH key
ssh-keygen -t rsa -b 4096

5. Start the development environment:

docker-compose up -d

Connecting with Windsurf IDE

1. Open Windsurf IDE
2. Go to Settings > SSH Configurations
3. Add a new SSH configuration:
   • Host: localhost
   • Port: 2222
   • Username: developer
   • Authentication: SSH Key
   • SSH Key Path: Path to your private key file (usually ~/.ssh/id_rsa)
4. Connect to the development environment

Services

• Development Container: SSH accessible on port 2222
• PostgreSQL/TimescaleDB: Available on port 5432
• pgAdmin: Available at http://localhost:5050
  • Email: [email protected]
  • Password: pgadmin
• Jupyter Lab: Available at http://localhost:8888 (token provided in container logs)

Working with the Environment

SSH Access

ssh developer@localhost -p 2222

Viewing Jupyter Lab Token

docker logs python_dev_env | grep token

Database Connection Details

• Host: localhost
• Port: 5432
• Database: devdb
• Username: postgres
• Password: postgres

Installed Python Packages

The environment comes with a comprehensive set of data science and financial analysis packages:

• Data Processing: pandas, numpy
• Visualization: matplotlib, seaborn, plotly, dash
• Machine Learning: scikit-learn, statsmodels, prophet
• Time Series: pandas-ta (Note: Using pandas-ta for technical analysis)
• API Integration: requests, httpx, aiohttp, websockets, ccxt
• Development Tools: pytest, black, isort, mypy, pylint

Security Considerations

• SSH password authentication is enabled but not recommended. Use SSH key authentication.
• Default database credentials should be changed in production environments.
• API keys and secrets should never be committed to the repository.

Troubleshooting

• SSH Connection Issues: Ensure your SSH key is properly mounted in the container.
• Database Connection Issues: Check if the database container is running with docker ps.
• Package Installation Issues: Update the pyproject.toml file and rebuild the container.

License

MIT License