edge-research-pipeline 0.1.2

Modular pipeline for quantitative signal discovery and validation

🧠 Edge Research Pipeline

The Edge Research Pipeline is a modular, privacy-first research toolkit designed for discovering, validating, and analyzing patterns in tabular datasets. Originally built for quantitative finance, its techniques are broadly applicable to any domain involving structured data and statistical rule discovery.

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🚀 Key Features

A flexible, modular Python library enabling you to:

• Clean, normalize, and transform tabular datasets
• Engineer features relevant to finance, statistics, and other structured-data domains
• Generate and label custom targets for supervised tasks
• Discover signals using rule mining and pattern search methods
• Perform robust validation tests (e.g., train/test splits, bootstrap, walk-forward analysis, false discovery rate)
• Reproduce results with complete configuration export and local-only processing
• Efficiently execute parameter grids via function calls or a CLI

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🔒 Privacy by Design

All computations run locally—no data ever leaves your environment. Designed explicitly for regulated industries, confidential research, and reproducible workflows.

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📦 Installation

Install required dependencies using:

pip install -r ./requirements.txt

Note: Dependencies were generated via pipreqs and may need further validation.

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🧩 Quick Start Example

Run a full pipeline example via the command line:

python edge_research/pipeline/main.py params/grid_params.yaml

Or check the ready-to-run examples in the examples/ directory.

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📁 Project Structure

edge-research-pipeline
├── data/                  # Sample datasets (sandbox only)
├── docs/                  # Documentation per module
├── edge_research/         # Core logic modules
│   ├── logger/
│   ├── pipeline/
│   ├── preprocessing/
│   ├── rules_mining/
│   ├── statistics/
│   ├── utils/
│   └── validation_tests/
├── examples/              # Copy-pasteable usage examples
├── params/                # Configuration files
├── tests/                 # Unit tests for major functions
├── LICENSE
├── README.md
└── requirements.txt

Detailed explanations for each subfolder are available within their respective READMEs.

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⚙️ Configuration Philosophy

Configuration files are managed via YAML files within ./params/:

• default_params.yaml: Base configuration with mandatory default values (do not modify)
• custom_params.yaml: Override specific parameters from defaults
• grid_params.yaml: Parameters specifically for orchestrating grid pipeline runs

Precedence hierarchy:

• For pipeline runs (pipeline.py or CLI): grid_params > custom_params > default_params
• For direct function calls: custom_params > default_params

Parameters can also be directly overridden by passing a Python dictionary at runtime.

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🧪 Testing

Unit tests cover all major logical functions, ensuring correctness and robustness. Tests are written using pytest. Short utility functions, simple wrappers, and internal helpers are generally not included.

Run tests via:

pytest tests/

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🤝 Contributing

We welcome contributions! Follow these guidelines:

• Keep your commits focused and atomic
• Always provide clear, descriptive commit messages
• Add or update tests for any new feature or bug fix
• Follow existing code style (e.g., use black and flake8 for Python formatting)
• Document new functionality thoroughly within the relevant .md file in docs/
• Respect privacy-by-design principles—no logging or external data exposure

Feel free to open issues for discussions or submit pull requests directly.

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📄 License

This project is licensed under the Edge Research Personal Use License (ERPUL).

• ✅ Free for personal, student, and academic use (with citation)
• 💼 Commercial use requires approval (temporarily waived)
• 🔒 No redistribution without permission

See LICENSE for full terms.