Welcome to EnigMX, a sophisticated Python library designed for quantitative finance, specifically focusing on feature engineering, backtesting, model tuning, and advanced data analysis techniques in financial markets.
- DataBundle: Scripts for data bundling and preprocessing.
- EnigmxMain: The main executable scripts that combine various components.
- FeatureImportance: Tools for evaluating and optimizing the importance of different data features.
- Backtester: A module for backtesting trading strategies.
- Feature Clustering: Scripts for clustering features based on their importance and contributions to model performance.
- ModelTunning: Contains utilities for tuning and optimizing machine learning models.
- utils.py: General utilities that support data handling and manipulation.
- databundle_interface.py: Interface for managing data bundles effectively.
- metrics.py: Metrics for evaluating model and strategy performance.
- tests: Directory containing test scripts ensuring the reliability of the core components.
- alternative_methods.py: Contains alternative approaches and methods for statistical analysis.
- Triple Barrier Method: Implementation of the triple barrier method in financial data analysis.
- SADF: Scripts for performing Sequential Augmented Dickey-Fuller tests on data series.
- Microstructural Features: Addition of microstructural features to the analysis toolkit.
Clone the repository and install the required Python packages:
git clone https://github.com/quantmoon/enigmx.git
cd enigmx
pip install -r requirements.txt