QuantForecaster-Modeling is a machine learning project designed to tackle predictive challenges in quantitative finance, particularly in sideways market scenarios. It leverages a custom asymmetric loss function to prioritize domain-specific requirements, emphasizing the penalty of overpredictions.
This project showcases:
- Custom asymmetric loss functions to align with financial objectives.
- Feature selection and engineering tailored to sideways quant data.
- Hyperparameter optimization using GridSearchCV.
- Performance evaluation using Asymmetric MSE, MSE, MAE, and ( R^2 ).
- Custom Loss Function: Optimized for asymmetric penalties to align with domain needs.
- Feature Engineering: Effective feature selection to boost model performance.
- Model Tuning: Robust hyperparameter optimization for Random Forest and XGBoost.
- Comprehensive Metrics: Includes Asymmetric MSE, MSE, MAE, and ( R^2 ).
Clone the repository and install the required dependencies:
git clone https://github.com/yourusername/QuantForecaster.git
cd QuantForecaster
pip install -r requirements.txt