CryptoMarket Regime Classifier is a machine learning framework that detects market regimes (Trend, Range, Squeeze, etc.) in crypto markets using multi-timeframe data and Hidden Markov Models. The project provides plug-and-play labeled datasets and trained models (HMM + LSTM) for downstream strategy development, position sizing, and risk management.

Hidden Markov Model (HMM) for detecting regime shifts in crypto markets.

A Crypto trading algorithm using a Hidden Markov Model to identify market regimes, execute trades, and backtest against a HODL baseline, complete with an interactive performance dashboard.

The principal goal of this investigation is to examine the use of stochastic processes and Markov chains in the context of the RSA attack, how and why the probabilistic methods used in the attack work and the results using a small and controlled RSA implementation example.

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Hidden Markov Model Trading algorithm for Crypto with a dashboard for tracking

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A program that predicts the price of a stock/crypto for that day using Hidden Markov Models

Paper trading simulation using Hidden Markov Models (HMM) to detect market regimes and generate trade signals across crypto and stock assets.

ML-based crypto trading strategy using Hidden Markov Models for market regime detection. On-chain data analysis with backtesting. UM Hackathon 2025.

Volatility Regime Detection in Crypto Markets Hidden Markov Model on ETH-USD Hourly Returns AND Adverse Selection in DeFi Lending Markets Rothschild–Stiglitz Screening Model with Real On-Chain Data

Regime-Based Trading App uses a 7-component Hidden Markov Model to detect Bull/Bear/Sideways market regimes in crypto. Combines HMM with an 8-point confirmation system (RSI, MACD, ADX, EMAs, etc.) for trade entries. Features trailing stops, multiple risk modes, and fully customizable parameters via Streamlit dashboard.