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

Hybrid Wasserstein + HMM Regime Detection

A high-performance, physics-informed Genetic Programming engine for regime-aware quantitative research. Evolving robust trading strategies with sub-millisecond latency.

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.

Financial market regime detection using Hidden Markov Models for adaptive trading strategies

Regime-aware quant risk and market stability monitoring framework.

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)

Regime detection using Hidden Markov Models with Swan Beta features to identify tail-risk market states.

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

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.

Quantitative finance research framework for evaluating momentum signals across volatility and macroeconomic regimes

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.

📉 Model market regimes and liquidity stress using advanced analytics to enhance financial stability and assess systemic risks effectively.