Please Note: The associated research paper that underpins this data is currently under a Revise & Resubmit (R&R). The data might be subject to change over the next few months. Any changes will be explicitly documented in the Changelog on GitHub. If you have suggestions for the data / spot any issues, please reach out to alexander.dickerson1@unsw.edu.au.
TRACE Data Pipeline
💻GitHub: Code for processing Enhanced, Standard, and 144A TRACE corporate bond data. Implements new error correction algorithms (decimal-shift, bounce-back filters) and comprehensive data cleaning procedures to produce high-quality daily and monthly bond panels. Stage 0 and Stage 1 now in public beta. Full code package arriving end December 2025.
Features:
– Process all three TRACE datasets (Enhanced, Standard, 144A) – Novel error correction algorithms (Dickerson, Robotti & Rossetti 2025) – Automated parallel processing on WRDS Cloud – Comprehensive documentation and quality reports – Stage 0 and Stage 1 available now | Stage 2 coming December 2025 – Contributions welcome via GitHub issues or email to alexander.dickerson1@unsw.edu.au.
PyBondLab – Bond Factors
Easily construct hundreds of corporate bond factors. Comes pre-built to compute portfolio turnover, portfolio characteristics, handles longer holding periods and transaction cost mitigation methods.
🔗 GitHub: https://github.com/GiulioRossetti94/PyBondLab
🐍 Install: pip install PyBondLab
📖PyPi: https://pypi.org/project/PyBondLab
Data Uncertainty Code
Please see the PyBondLab PyPi documentation. For additional examples please see Giulio Rossetti’s GitHub Repository.
- Please contact
Giulio.Rossetti.1@wbs.ac.uk(primary maintainer) oralexander.dickerson1@unsw.edu.aufor questions, feedback or contributions.