A research project on macro regime clustering using a compact monthly feature space and Jump Models, with stability analysis, external validation, and asset mapping across equities, bonds, oil, and gold.

An end-to-end machine learning trading system: ensemble of transformer models, hybrid RNN model and LightGBM with temperature calibration, and a live trading bot with Kelly Criterion position sizing.

active investing

Identify regimes in financial markets based on multivariate time series data using multiple methodologies, including CNN, AutoEncoder, Siamese Model, Correlation Matrices, K-means++, and Hierarchical Clustering

Institutional-grade market regime decoding via Savitzky-Golay Kinematics and Hidden Markov Models (HMM). Engineered for zero-lag signal demodulation and structural alpha detection.

📊 Detect market regimes by clustering probability distributions using Wasserstein K-means for more accurate financial analysis and insights.

Deterministic market regime detection agent with persistent state and transparent signal-based classification.

Multi-layer market regime classifier — BTC trend (BULL/BEAR/CHOPPY/TRANSITION), GOLD macro (RISK_ON/OFF), contrarian Fear & Greed

Market regime checklist: MA position and slope

Multi-model framework for market regime detection and dynamic asset allocation using HMM, XGBoost, LSTM, backtesting, and RL.

Regime-aware quant risk and market stability monitoring framework.

Python trading bot that adjusts position sizing based on real-time crypto market regime (bull/bear/chop) from getregime.com API

Mobile-first MRI-based market regime interpretation engine with risk-adjusted confidence modeling.

Detects financial market regimes using semantic geometry of news embeddings (FinBERT) with clustering and statistical modeling to predict volatility and regime shifts ahead of price action.