https://github.com/iffranciscome/genetic-finance

Implementations of Genetic Methods for Financial Machine Learning Applications
https://github.com/iffranciscome/genetic-finance

evolutionary-computation financial-machine-learning genetic-algorithms genetic-programming hyperparameters-optimization python symbolic-model topology-optimization

Last synced: 4 months ago
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Implementations of Genetic Methods for Financial Machine Learning Applications

• Host: GitHub
• URL: https://github.com/iffranciscome/genetic-finance
• Owner: IFFranciscoME
• License: gpl-3.0
• Created: 2021-03-04T03:15:39.000Z (over 4 years ago)
• Default Branch: main
• Last Pushed: 2021-06-01T03:13:21.000Z (over 4 years ago)
• Last Synced: 2025-04-23T21:41:57.877Z (6 months ago)
• Topics: evolutionary-computation, financial-machine-learning, genetic-algorithms, genetic-programming, hyperparameters-optimization, python, symbolic-model, topology-optimization
• Language: HTML
• Homepage:
• Size: 18.7 MB
• Stars: 10
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Application Cases

## Case 1: Genetic Programming for Feature Engineering.

Repo used the DataDays 2021 talk -> Genetic Programming for Feature Engineering: An application in Trading System with Cryptocurrencies

## Case 2: Genetic Algorithms for Hyperparameter Optimization.

Non-gradient-based optimization method, useful when having cost functions that are potentially non-diferentiable, discontinuous, with "cliffs" or "steps".

## Case 3: Genetic Algorithms for Neural Network Topology Optimization.

To evolve the topology of a neural network in order to explore more variations of architectures. Insted of minization of a cost function, the goal is to maximize a fitness function.