{"id":25211446,"url":"https://github.com/aeyage/intraday_prices","last_synced_at":"2025-04-05T06:27:04.977Z","repository":{"id":276603709,"uuid":"929656052","full_name":"aeyage/intraday_prices","owner":"aeyage","description":"GPU-accelerated portfolio optimisation","archived":false,"fork":false,"pushed_at":"2025-02-09T10:26:42.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-09T11:25:33.840Z","etag":null,"topics":["cuda","cupy","nvidia-gpu"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aeyage.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-09T03:56:11.000Z","updated_at":"2025-02-09T10:26:45.000Z","dependencies_parsed_at":"2025-02-09T11:36:04.455Z","dependency_job_id":null,"html_url":"https://github.com/aeyage/intraday_prices","commit_stats":null,"previous_names":["aeyage/intraday_prices"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aeyage%2Fintraday_prices","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aeyage%2Fintraday_prices/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aeyage%2Fintraday_prices/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aeyage%2Fintraday_prices/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aeyage","download_url":"https://codeload.github.com/aeyage/intraday_prices/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247298240,"owners_count":20915993,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cuda","cupy","nvidia-gpu"],"created_at":"2025-02-10T14:15:34.214Z","updated_at":"2025-04-05T06:27:04.937Z","avatar_url":"https://github.com/aeyage.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Portfolio Optimisation on GPU\n\nDemonstration on how to perform financial computations i.e. portfolio optimisation using both CPU (with `pandas` and `NumPy`) and GPU (with `cuDF` and `CuPy`). The goal is to compare the performance of CPU and GPU for these computations.\n\n---\n\nRAPIDS is a suite of GPU-accelerated data science and AI libraries from Nvidia. It is built on NVIDIA CUDA-X AI™ and includes libraries that integrate with popular data science software.\n\nPart of the problem with pandas is that it can be slow with large datasets. That is where `cuDF-pandas comes` in. `cuDF-pandas` accelerates pandas with zero code changes and brings great speed improvements.\n\n`cuDF-pandas` is available as an extension that requires no code changes at all. To use it, just add the following code before you import pandas.\n\n```sh\n%load_ext cudf.pandas\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faeyage%2Fintraday_prices","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faeyage%2Fintraday_prices","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faeyage%2Fintraday_prices/lists"}