Unsupervised regime detection for financial time series using embeddings and clustering.

Hybrid Wasserstein + HMM Regime Detection

RBI-grade market regime detection & liquidity stress modelling using volatility, yield curves, and ensemble HMMs.

LSTM-driven market regime detection with rule-based signal generation for systematic trading.

Regime-based evaluation framework for financial NLP stability. Implements chronological cross-validation, semantic drift quantification via Jensen-Shannon divergence, and multi-faceted robustness profiling. Replicates Sun et al.'s (2025) methodology with modular, auditable Python codebase.

This project applies unsupervised learning to detect latent financial market regimes from macro time series data. It emphasizes stability-based model selection across preprocessing, dimensionality reduction, and clustering methods.

Hull Tactical v7.1: A regime-aware "grey box" strategy for S&P 500 prediction. Combines Econophysics (Chaos/Entropy) with LightGBM and "Smart Noise" logic to challenge the EMH. (Mean Adj. Sharpe: 0.806)

Results-only repository publishing structural risk regime outputs from a private blackbox engine.

A research-grade, regime-aware decision intelligence prototype for portfolio allocation, integrating market state detection, adaptive alpha models, and fund-grade evaluation metrics.

End-to-End Python implementation of the research methodology, from "Geometric Dynamics of Consumer Credit Cycles", by Sudjianto & Setiawan (2025). Implements Clifford Algebra embeddings and Linear Attention for explanatory macroeconomic analysis; i.e. economic regime analysis.

A research project exploring machine learning methods to predict market regimes using a combination of: Macroeconomic indicators from FRED.gov Foreign Currencies data from Yahoo Finance (yfinance) Historical data for the S&P 500 from Polygon.io