akash-kumar5/CryptoMarket_Regime_Classifier
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.
This project helps crypto traders and quantitative analysts understand the current market environment by identifying underlying market "regimes" like strong trends, ranges, or squeezes. It takes in multi-timeframe cryptocurrency price data and outputs the detected market regime, along with predictions for future regime probabilities. This intelligence is crucial for adapting trading strategies, managing risk, and sizing positions effectively.
Use this if you need to dynamically adapt your crypto trading strategies and risk management based on the current market conditions, rather than relying on static rules.
Not ideal if you are looking for direct price predictions or buy/sell signals, as this tool focuses on market conditions rather than price movement itself.
Stars
28
Forks
10
Language
Python
License
—
Category
Last pushed
Dec 13, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/akash-kumar5/CryptoMarket_Regime_Classifier"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
skfolio/skfolio
Python library for portfolio optimization built on top of scikit-learn
emoen/Machine-Learning-for-Asset-Managers
Implementation of code snippets, exercises and application to live data from Machine Learning...
WLM1ke/poptimizer
Оптимизация долгосрочного портфеля акций
jankrepl/deepdow
Portfolio optimization with deep learning.
baobach/mlfinpy
Mlfin.py is an advance Machine Learning toolbox for financial applications in Python.