Package for extracting emotions from social media text. Tailored for financial data.
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.
PROFIT (Processing and Reasoning Over Financial Information in Text): A benchmark designed to evaluate language models across five tasks of increasing complexity in numerical reasoning using SEC filings
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