https://github.com/karelze/thesis
My thesis 🏅
https://github.com/karelze/thesis
classification karlsruhe-institute-of-technology kit ml quantitive-finance
Last synced: 4 months ago
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My thesis 🏅
- Host: GitHub
- URL: https://github.com/karelze/thesis
- Owner: KarelZe
- License: bsd-3-clause
- Created: 2022-08-12T08:16:18.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2025-02-03T21:35:14.000Z (4 months ago)
- Last Synced: 2025-02-03T22:29:36.463Z (4 months ago)
- Topics: classification, karlsruhe-institute-of-technology, kit, ml, quantitive-finance
- Language: TeX
- Homepage:
- Size: 74.9 MB
- Stars: 5
- Watchers: 3
- Forks: 1
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
Awesome Lists containing this project
README
# thesis
## Overview
This repository contains all the resources for my thesis on option trade classification at Karlsruhe Institute of Technology.
| notes 📜 | schedule ⌚ | experiments 🧪 | computing resources ☄️ | document 🎓 |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------- |
| See [`references`](https://github.com/KarelZe/thesis/tree/main/references) folder. Download obsidian from [obsidian.md](https://obsidian.md/) to easily browse the notes. | Link to [tasks and mile stones](https://github.com/KarelZe/thesis/milestones?direction=asc&sort=due_date&state=open). | Link to [weights & biases](https://wandb.ai/fbv/thesis) (requires login). | Link to [gcp](https://console.cloud.google.com/welcome?project=flowing-mantis-239216) (requires login), and to [bwHPC](https://bwhpc.de/) (requires login). | see [`releases`](https://github.com/KarelZe/thesis/releases/). |
## Development
### Set up git pre-commit hooks 🐙
Pre-commit hooks are pre-checks to avoid committing error-prone code. The tests are defined in the [`.pre-commit-config.yaml`](https://github.com/KarelZe/thesis/blob/main/.pre-commit-config.yaml). Install them using:
```shell
pip install .[dev]
pre-commit install
pre-commit run --all-files
```
### Run tests🧯
Tests can be run using [`tox`](https://tox.wiki/en/latest/). Just type:
```shell
tox
```
## Acknowledgement
The authors acknowledge support by the state of Baden-Württemberg through [bwHPC](https://bwhpc.de/).
Our implementation is based on:
Gorishniy, Y., Rubachev, I., Khrulkov, V., & Babenko, A. (2021). Revisiting Deep Learning Models for Tabular Data. Advances in Neural Information Processing Systems, 34, 18932–18943.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 32, 6639–6649.
Rubachev, I., Alekberov, A., Gorishniy, Y., & Babenko, A. (2022). Revisiting pretraining objectives for tabular deep learning (arXiv:2207.03208). arXiv. http://arxiv.org/abs/2207.03208