{"repo":"chirindaopensource/identifying_quantifying_financial_bubbles_hyped_log_period_power_law","free":true,"listed":false,"github":"https://github.com/chirindaopensource/identifying_quantifying_financial_bubbles_hyped_log_period_power_law","clone":"git clone https://github.com/chirindaopensource/identifying_quantifying_financial_bubbles_hyped_log_period_power_law.git","description":"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.","language":"Jupyter Notebook","stars":10,"topics":["algorithmic-trading","asset-pricing","backtesting","behavioral-finance","bert","deep-learning","econophysics","financial-modeling","finbert","market-prediction"],"license":"MIT","category":"trading","readme_excerpt":"README.md Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model --- Repository: https://github.com/chirindaopensource/identifying quantifying financial bubbles hyped log period power law Owner: 2025 Craig Chirinda (Open Source Projects) This repository contains an independent , professional-grade Python implementation of the research methodology from the 2025 paper entitled \"Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model\" by: Zheng Cao Xingran Shao Yuheng Yan Helyette Geman The project provides a complete, end-to-end computational framework for replicating the paper's findings. It delivers a modular, auditable, and extensible pipeline that executes the entire research workflow: from rigorous data validation and NLP feature engineering to LPPL model fitting, deep learning, and backtesting. Table of Contents - Introduction - Theoretical Background - Features - Methodology Implemented - Core Components (Notebook Structure) - Key Callable: execute full study - Prerequisites - Installation - Input Data Structure - Usage - Output Structure - Project Structure - Customization - Contributing - Recommended Extensions - License - Citation - Acknowledgments Introduction This project provides a Python implementation of the methodologies presented in the 2025 paper \"Identifying and Quantifying Financial Bubbles with the Hyped Log-Periodic Power Law Model.\" The core of this repository is the iPython Notebook ide","default_branch":null,"files":null,"tree":[],"storefront":"/r/chirindaopensource","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/chirindaopensource/identifying_quantifying_financial_bubbles_hyped_log_period_power_law/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}