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financial-bubbles

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chirindaopensource / identifying_quantifying_financial_bubbles_hyped_log_period_power_law

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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.

deep-learning sentiment-analysis pytorch transformer quantitative-finance algorithmic-trading bert asset-pricing risk-management backtesting time-series-forecasting econophysics financial-modeling finbert market-prediction nlp-finance behavioral-finance research-replication financial-bubbles lppl-model
  • Updated Oct 16, 2025
  • Jupyter Notebook

chirindaopensource / universal_dynamics_financial_bubbles_isolated_markets

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End-to-End Python implementation of LPPLS (Log-Periodic Power Law Singularity) framework for detecting financial bubbles and critical transitions. Features Filimonov-Sornette calibration, Lagrange regularization, Lomb-Scargle spectral validation, and Monte Carlo significance testing. Complete computational replication of Hosseinzadeh (2025).

python numpy pandas scientific-computing scipy monte-carlo-simulation quantitative-finance complex-systems time-series-analysis financial-mathematics bubble-detection econophysics systemic-risk emerging-markets crash-prediction lppls financial-bubbles grid-search-optimization isolated-markets lomb-scargle
  • Updated Dec 20, 2025
  • Jupyter Notebook

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