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README.md

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+---
+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)*

config.yaml

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+api:
+  host: 0.0.0.0
+  port: 7860
+  reload: false
+backtest:
+  initial_capital: 100000
+  position_size: 1.0
+  risk_free_rate: 0.05
+data:
+  features:
+  - india_vix
+  - rsi_14
+  - ma_50
+  - ma_200
+  path: data/market_regime_data_nifty50.csv
+  random_state: 42
+  target: regime
+  test_size: 0.2
+mlflow:
+  experiment_name: market_regime_prediction
+  registered_model_name: market_regime_classifier
+  tracking_uri: ./mlruns
+model:
+  name: xgboost_market_regime
+  params:
+    eval_metric: logloss
+    learning_rate: 0.1
+    max_depth: 6
+    n_estimators: 100
+    objective: binary:logistic
+    random_state: 42
+  type: xgboost
+thresholds:
+  min_f1_score: 0.65
+  min_sharpe_ratio: 0.5

metrics.json

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+{
+  "backtest_max_drawdown": -0.2939306903526135,
+  "backtest_sharpe_ratio": 1.0079896816731886,
+  "backtest_total_return": 0.6487209877779612,
+  "backtest_win_rate": 0.5486284289276808,
+  "final_portfolio_value": 164872.0987777961,
+  "test_accuracy": 0.5727069351230425,
+  "test_f1_score": 0.6924315619967794,
+  "test_precision": 0.5361596009975063,
+  "test_recall": 0.9772727272727273,
+  "train_accuracy": 0.9737136465324385
+}