---
license: mit
tags:
- finance
- machine-learning
- mlops
- xgboost
- market-prediction
- trading
- risk-management
- time-series
library_name: xgboost
pipeline_tag: tabular-classification
---

# 🚀 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:

1. **India VIX** - Volatility index (market fear/greed indicator)
2. **RSI (14-day)** - Relative Strength Index (momentum)
3. **50-Day MA** - Short-term moving average (trend)
4. **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
1. Data preprocessing and feature engineering
2. Train/test split with stratification
3. XGBoost training with hyperparameters
4. Model evaluation (ML + Finance metrics)
5. Backtesting on historical data
6. Model registration in MLflow
7. 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](https://github.com/AAdevloper/mlops-finance-pipeline)
- **Live API Demo**: [Hugging Face Space](https://huggingface.co/spaces/AAdevloper/mlops-finance-pipeline)
- **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](https://github.com/AAdevloper/mlops-finance-pipeline)*