Description
During my career as a quantitative developer, I have researched and implemented many advanced mathematical models. This repository showcases some of my most used algorithms, including artificial intelligence, machine learning, and tools from stochastic calculus. You can view the full project on my GitHub repository
Note: This repo contains only files related to this specific objective and may lack the higher-level engine, as these resources were part of a larger software system I created. The purpose of this repo is not to provide a ready-to-use solution but to demonstrate my ability to build software relevant to its title.
Resources used
Artificial Intelligence
- Custom genetic optimization algorithm
- Custom Bayesian optimization algorithm
- Custom models of Recurrent Neural Networks (RNNs)
- Custom models of Long Short-Term Memory (LSTM) Neural Networks
Machine Learning
- Different hyperparameter generators for optimization algorithms
Statistics
- Various model validation techniques for robust outcomes
- Hypothesis testing
- Data analysis
- Predictive modeling
Stochastic Calculus
- Merton Jump Diffusion and other related simulation tools
Other Tools
- REST APIs
- Docker
- Pandas
- NumPy
- Keras
- TensorFlow
- SciPy
- Quantstats
- Custom tools