Hybrid Wasserstein + HMM Market Regime Detection

This project reimagines the classical Merton portfolio optimization problem using Deep Reinforcement Learning (DRL). Instead of static, closed-form allocation rules, we design an intelligent agent that dynamically adjusts exposures to risky and risk-free assets under changing market regimes.

Sistema de negociação algorítmica para os pares USD/BRL, BTC/BRL, ETH/BRL e SOL/BRL com análise em tempo real do regime de mercado, 4 mecanismos de estratégia, classificador RandomForest e dashboard web em flask.

Regime-aware, sentiment-driven decision support system for risk-controlled equity trading. Research code and experiments

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

This project is a Python-based system that automates the detection and visualization of market regimes—Bull, Bear, and Sideways—using historical stock data and technical indicators. It leverages machine learning clustering to classify different market environments, providing tr

Machine learning project for predicting financial time-series returns using Random Forest with clustering-based market regime detection. Includes feature engineering with lag and rolling statistics, regime identification using K-Means and GMM, and PCA visualisation for analysing market states.

Machine learning approaches to market crash detection and early warning systems.

🚀 Optimize your portfolio with deep reinforcement learning, achieving superior returns and risk management in dynamic asset allocation.