CryptoMarket Regime Classifier

Adaptive Market Intelligence for Crypto Strategies

Most 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

and is intended to power the regime-awareness module in Dazai.

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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)

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Key Ideas (Why this is different)

• Regime-aware, not signal-based
  The model does not predict price — it predicts market conditions.

• Unsupervised → Supervised learning
  HMM discovers latent regimes first.
  LSTM then learns temporal structure to predict them.

• Multi-timeframe context
  Combines short-term and slightly higher-timeframe behavior (5m, 15m).

• Designed for integration
  Models and scalers are exported for downstream systems (bots, dashboards, APIs).

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Key Features

• Multi-timeframe OHLCV data (5m, 15m) from Binance
• Technical indicators covering:
  • momentum
  • volatility
  • trend
• Hidden Markov Models (HMM) for unsupervised regime discovery
• LSTM trained on HMM-labeled sequences
• 6 discovered regimes, including:
  • Strong Trend
  • Weak Trend
  • Range
  • Choppy High-Volatility
  • Volatility Spike
  • Squeeze
• Evaluation metrics:
  • Precision / Recall / F1
  • Confusion Matrix

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Project Structure

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├── 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

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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

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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

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Installation

git clone https://github.com/akash-kumar5/CryptoMarket_Regime_Classifier.git
cd CryptoMarket_Regime_Classifier
pip install -r requirements.txt

Usage

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.