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.
CryptoMarket Regime Classifier
Adaptive Market Intelligence for Crypto StrategiesMost trading strategies fail not because the logic is wrong, but because they are applied in the wrong market regime.
A breakout strategy thrives in trends and bleeds in chop. Mean-reversion works in ranges and dies in momentum.
CryptoMarket Regime Classifier is a machine learning pipeline that detects and predicts crypto market regimes using multi-timeframe OHLCV data, technical indicators, and a two-stage ML approach (HMM → LSTM).
It is built as a foundational intelligence layer for:
- strategy selection
- position sizing
- risk management
High-Level Pipeline
OHLCV (5m, 15m) ↓ Feature Engineering (momentum, trend, volatility) ↓ PCA Reduction ↓ Hidden Markov Model (Regime Discovery) ↓ LSTM (Regime Prediction) ↓ Current Regime (+ future probabilistic output)Key Ideas (Why this is different)
- Regime-aware, not signal-based
- Unsupervised → Supervised learning
- Multi-timeframe context
- Designed for integration
Key Features
- Multi-timeframe OHLCV data (5m, 15m) from Binance
- Technical indicators covering:
- Hidden Markov Models (HMM) for unsupervised regime discovery
- LSTM trained on HMM-labeled sequences
- 6 discovered regimes, including:
- Evaluation metrics:
Project Structure
├── dashboard/ # Visualizations, regime plots ├── models/ # Trained models & scalers ├── src/ # Feature engineering + training scripts ├── main.py # End-to-end pipeline execution ├── requirements.txt # Dependencies └── README.md
Workflow Details
1. Data Fetching
- Periodically fetches OHLCV data from Binance
- Currently optimized for 5m data, with support for higher TF context
2. Feature Engineering
- Computes momentum, trend, and volatility indicators
- Aligns and scales features for ML stability
3. Regime Discovery (HMM)
- PCA-reduced feature space
- 6-state HMM selected using lowest BIC
- Produces regime labels without human bias
4. Regime Prediction (LSTM)
- Sequence model trained on HMM labels
- Captures temporal transitions between regimes
- Hyperparameters tuned using Keras Tuner
- Planned upgrade: probabilistic regime distributions
5. Model Export & Usage
- Trained LSTM + scalers saved to
/models - Designed for reuse in live systems
Results (High-Level)
- Strong separation between trend vs non-trend regimes
- Transitional regimes (range ↔ weak trend, spike ↔ chop) are naturally harder — and informative
- Confusion matrix reflects realistic regime overlap instead of artificial sharp boundaries
Installation
git clone https://github.com/akash-kumar5/CryptoMarketRegimeClassifier.git
cd CryptoMarketRegimeClassifier
pip install -r requirements.txtUsage
Run the full pipeline: streamlit run dashboard/app.py
Models & scalers will be saved in /models for reuse.
Notes
- Data range: ~2 years (to prioritize recent regime behavior and avoid stale market patterns).
- Designed as a research + foundational tool for live trading systems.
- Future versions will connect directly into Dazai as a core regime intelligence component.
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Disclaimer
This project is for research and educational purposes only. It does not constitute financial advice.
