https://github.com/iffranciscome/genetic_net
Trading System with Genetic Programming for Feature Engineering, Multilayer Perceptron Neural Network, Logistic Regression with Elastic Net Regularization and Support Vector Machines with L1 Regularization for Predictive Models and Genetic Algorithms for Hyperparameter Optimization.
https://github.com/iffranciscome/genetic_net
artificial-intelligence geneticalgorithm geneticprogramming trading
Last synced: 2 months ago
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Trading System with Genetic Programming for Feature Engineering, Multilayer Perceptron Neural Network, Logistic Regression with Elastic Net Regularization and Support Vector Machines with L1 Regularization for Predictive Models and Genetic Algorithms for Hyperparameter Optimization.
- Host: GitHub
- URL: https://github.com/iffranciscome/genetic_net
- Owner: IFFranciscoME
- License: gpl-3.0
- Created: 2020-10-02T14:34:09.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2020-12-06T05:55:58.000Z (over 4 years ago)
- Last Synced: 2024-06-17T22:46:01.767Z (about 1 year ago)
- Topics: artificial-intelligence, geneticalgorithm, geneticprogramming, trading
- Language: Jupyter Notebook
- Homepage:
- Size: 38.5 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Description
This project was created for the class: Design and Analysis of Algorithms, an elective class for the
Masters in Science in Data Science, offered by ITESO university.
## Install dependencies
Install all the dependencies stated in the requirements.txt file, just run the following command in terminal:
pip install -r requirements.txt
Or you can manually install one by one using the name and version in the file.
## Functionalities
- Autoregressive Feature Generation (**autoregressive_features**)
- Hadamard Product for Feature Generation (**hadamard_features**)
- Genetic Programming for Symbolic Operations for Feature Generation (**symbolic_features**)
- Timeseries Block Folds without filtration (**t_folds**)
- Classifier model: Logistic Regression with Elastic Net Regularization (**logistic_net**)
- Classifier model: Least Squares Support Vector Machines (**ls_svm**)
- Classifier model: Artificial Neural Net Multilayer Perceptron (**ann_mlp**)
- Genetic Algorithms Optimization (**genetic_algo_optimization**)
- Plotly visualizations of results (**visualizations.py**)
- Machine Learning Models Performance Metrics (**model_evaluation**)
## Author
B.Eng in Financial Engineering, M.Sc in Data Science candidate, Juan Francisco Muñoz-Elguezabal
## License
**GNU General Public License v3.0**
*Permissions of this strong copyleft license are conditioned on making available
complete source code of licensed works and modifications, which include larger
works using a licensed work, under the same license. Copyright and license notices
must be preserved. Contributors provide an express grant of patent rights.*
## Contact
*For more information in reggards of this project, please contact [email protected]*