chirindaopensource/identifying_quantifying_financial_bubbles_hyped_log_period_power_law
An end-to-end Python implementation of Cao et al.'s (2025) HLPPL methodology for the identification of financial (asset price) bubbles. Implements 7-parameter Log-Periodic Power Law model fitting, confidence-weighted sentiment analysis, regime-dependent 'BubbleScore' fusion, and Transformer-based forecasting with a backtesting framework.
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Jupyter Notebook
License
MIT
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Last pushed
Oct 16, 2025
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