NIFTY 50 Forecasting using Bayesian Models

A comprehensive Python implementation for forecasting NIFTY 50 stock prices using multiple Bayesian modeling approaches. This project leverages PyMC for Bayesian inference and includes various model architectures for robust prediction.

🚀 Features

• Multiple Bayesian model implementations
• Technical indicator generation
• GPU acceleration support
• Model persistence
• Forecast visualization
• Uncertainty quantification

📊 Models Implemented

1. Bayesian Linear Regression

   • Multiple technical indicators
   • Trend component
   • Global parameter estimation

2. AR(1) Model

   • Simple autoregressive structure
   • Single lag dependency
   • Basic time series patterns

3. Hierarchical Model

   • Year-level grouping
   • Volatility and momentum features
   • Temporal clustering support

4. Gaussian Process Regression (GPR)

   • Nonlinear relationships
   • Sparse approximation
   • Matern 3/2 kernel

5. Dynamic Regression

   • Time-varying coefficients
   • Random walk evolution
   • Adaptive trend modeling

🛠️ Technical Indicators

• RSI (Relative Strength Index)
• Volatility (14-day rolling)
• Momentum (5-day difference)
• SMA (Simple Moving Average)
• EMA (Exponential Moving Average)

📈 Features

Data Processing

• Automatic feature engineering
• Data normalization
• Missing value handling
• Time series preprocessing

Model Training

• MCMC sampling with PyMC
• GPU acceleration support
• Proper chain initialization
• Convergence monitoring

Forecasting

• Multi-step ahead predictions
• Uncertainty quantification
• Confidence intervals
• Forecast visualization

🔧 Installation

# Create virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install pymc numpy pandas matplotlib jax

💻 Usage

# Load and process data
df = load_nifty_data("data/nifty_data.csv")

# Train models
results = train_and_evaluate_models(df)

# Save trained models
save_trained_models(results)

📊 Model Persistence

Models are automatically saved with:

• Model parameters
• MCMC traces
• Scaling parameters

📉 Visualization

Each model generates:

• Historical vs predicted plots
• Confidence intervals
• Trend analysis
• Forecast evaluation

🔬 Technical Details

• Framework: PyMC for Bayesian modeling
• Acceleration: JAX for GPU support
• Visualization: Matplotlib
• Data Processing: Pandas, NumPy

📝 License

MIT License

🤝 Contributing

Feel free to open issues and pull requests for improvements and bug fixes.

📚 References

• PyMC Documentation
• Bayesian Methods for Hackers
• Time Series Analysis by State Space Methods