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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.
Machine learning overlay for SPY using ^GSPC regime signals, Jump Model labels, and XGBoost. Focused on downside protection, recovery timing, and risk-adjusted improvement under a unified long-sample backtest.
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
Free REST API for the moneyfeel Macro & Geopolitical Risk Index (MRI) — market regime classifier covering 5 regions across Daily/Weekly/Monthly timeframes. Free for researchers, quants and portfolio managers.
MacroPulse is a real-time macro intelligence platform that transforms fragmented global data into actionable investment insights through a unified decision layer.
Institutional-grade market regime decoding via Savitzky-Golay Kinematics and Hidden Markov Models (HMM). Engineered for zero-lag signal demodulation and structural alpha detection.
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.