{"repo":"evgeniavolkova/kagglejanestreet","free":true,"listed":false,"github":"https://github.com/evgeniavolkova/kagglejanestreet","clone":"git clone https://github.com/evgeniavolkova/kagglejanestreet.git","description":"Solution for the Jane Street 2024 Kaggle competition.","language":"Python","stars":245,"topics":["deep-learning","finance","kaggle","machine-learning"],"license":null,"category":"machine-learning","readme_excerpt":"Kaggle Jane Street Real-Time Market Data Forecasting Solution for the Jane Street 2024 Kaggle competition. A detailed description can be found in solution.md and in Kaggle discussion. Requirements - 100GB RAM - 12GB GPU RAM Usage 1. Install requirements from pyproject.toml . 2. Download the dataset from Kaggle. 3. Set paths and other config variables in janestreet/config.py . Scripts - run cv.py - Estimate model on cross-validation. - run full.py - Estimate model for the final submission (on the whole sample). - run ensemble.py - Evaluate ensemble of models on CV. - run test gap.py - Test model on a sample of the last 200 dates with a gap of 200 dates. Additional scripts - monitor kaggle.py - Monitor kaggle submissions and send notifications when completed. - update kaggle.py - Push code and models to Kaggle datasets to be used in submission.","default_branch":null,"files":null,"tree":[],"storefront":"/r/evgeniavolkova","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/evgeniavolkova/kagglejanestreet/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}