Research toolkit for tail risk, bubble detection, and market regimes. Rust core, Python bindings, and a terminal dashboard.
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Nov 3, 2026 - Rust
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lppls
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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
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Dec 20, 2025 - Jupyter Notebook
Early-warning system for the AI bubble: weekly web scraping, a DuckDB SQL feature warehouse and 7 models (survival ML, PSY bubble test, LPPLS in Go, HMM) that estimate the odds and timing of an AI-stock crash. Backtested on 67 assets since 1927.
python golang data-science machine-learning sql time-series web-scraping stock-market lightgbm survival-analysis quantitative-finance bubble-detection github-actions duckdb lppls ai-bubble
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Nov 3, 2026 - Python
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