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AAdevloper/
market-regime-classifier

Name: market-regime-classifier
Availability: InStock

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🚀 Market Regime Classifier - XGBoost Model

This is a production-ready XGBoost model for predicting market regimes (RISK_ON/RISK_OFF) in Indian financial markets (NIFTY 50). Part of a complete MLOps pipeline with experiment tracking, data versioning, and automated deployment.

🎯 Model Description

Task: Binary Classification
Algorithm: XGBoost (Extreme Gradient Boosting)
Target: Market Regime Prediction (RISK_ON vs RISK_OFF)
Framework: MLflow for experiment tracking and model registry

What are Market Regimes?

🟢 RISK_ON: Favorable market conditions - lower volatility, bullish momentum, suitable for aggressive trading
🔴 RISK_OFF: Cautious conditions - higher volatility, defensive positioning advised

📊 Model Performance

ML Metrics

Finance Metrics (Backtesting)

Production Thresholds

✅ F1 Score ≥ 0.65
✅ Sharpe Ratio ≥ 0.5

🔧 Features Used

The model uses four key technical indicators:

India VIX - Volatility index (market fear/greed indicator)
RSI (14-day) - Relative Strength Index (momentum)
50-Day MA - Short-term moving average (trend)
200-Day MA - Long-term moving average (trend)

💻 Usage

Using with MLflow

python

import mlflow
import pandas as pd

# Load the model
model_uri = "models:/market_regime_classifier/Production"
model = mlflow.xgboost.load_model(model_uri)

# Prepare features
features = pd.DataFrame([{
    'india_vix': 15.5,
    'rsi_14': 55.3,
    'ma_50': 18500.25,
    'ma_200': 18200.75
}])

# Predict
prediction = model.predict(features)[0]
proba = model.predict_proba(features)[0]

regime = "RISK_ON" if prediction == 1 else "RISK_OFF"
confidence = proba[prediction]

print(f"Regime: {regime} (confidence: {confidence:.2%})")

Using via REST API

The model is deployed on Hugging Face Spaces with a FastAPI endpoint:

bash

curl -X POST "https://AAdevloper-mlops-finance-pipeline.hf.space/predict_regime" \
  -H "Content-Type: application/json" \
  -d '{
    "india_vix": 15.5,
    "rsi_14": 55.3,
    "ma_50": 18500.25,
    "ma_200": 18200.75
  }'

🏗️ MLOps Pipeline

This model is part of a complete MLOps system:

Components

✅ MLflow: Experiment tracking and model registry
✅ DVC: Data version control
✅ GitHub Actions: Automated CI/CD pipeline
✅ FastAPI: REST API for model serving
✅ Docker: Containerized deployment
✅ Hugging Face Spaces: Cloud deployment

Training Pipeline

Data preprocessing and feature engineering
Train/test split with stratification
XGBoost training with hyperparameters
Model evaluation (ML + Finance metrics)
Backtesting on historical data
Model registration in MLflow
Promotion to production if thresholds met

Automated Retraining

Scheduled weekly retraining (GitHub Actions)
Automatic model promotion based on performance
Version control for models and data

📈 Training Configuration

yaml

model:
  type: xgboost
  params:
    max_depth: 6
    learning_rate: 0.1
    n_estimators: 100
    objective: binary:logistic
    eval_metric: logloss

📊 Dataset

Source: Synthetic market data for NIFTY 50
Features: India VIX, RSI-14, MA-50, MA-200
Target: Market regime (binary classification)
Split: 80/20 train/test

🎓 Skills Demonstrated

MLOps pipeline architecture
Experiment tracking (MLflow)
Model registry management
Data versioning (DVC)
CI/CD automation (GitHub Actions)
Model serving (FastAPI)
Financial metrics & backtesting
Docker containerization
Cloud deployment (Hugging Face)

📚 Project Links

GitHub Repository: mlops-finance-pipeline
Live API Demo: Hugging Face Space
Documentation: Full README with setup instructions

🔄 Model Versioning

This model is version-controlled using MLflow Model Registry:

Model Name: market_regime_classifier
Version: 1
Stage: Production
Run ID: 43544e1ed8eb4c2a9268948b6795bdf5

🤝 Contributing

This is a portfolio project demonstrating MLOps best practices. Feel free to:

Fork and experiment
Submit issues or suggestions
Use as reference for your own MLOps projects

📄 License

MIT License - free to use for learning and projects

Built with ❤️ to showcase MLOps skills in Finance

For questions or collaboration, visit the GitHub repository

AAdevloper/market-regime-classifier