{"id":17154258,"url":"https://github.com/jaydu1/cvar-portfolio","last_synced_at":"2025-07-27T01:03:21.067Z","repository":{"id":136145917,"uuid":"492056033","full_name":"jaydu1/CVaR-Portfolio","owner":"jaydu1","description":"CVaR Portfolio Optimization in High Dimensions","archived":false,"fork":false,"pushed_at":"2022-05-14T17:03:02.000Z","size":458,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-25T11:50:39.406Z","etag":null,"topics":["cvar-optimization","high-dimensionality","portfolio-selection"],"latest_commit_sha":null,"homepage":"","language":"Python","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/jaydu1.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":"2022-05-13T22:29:32.000Z","updated_at":"2023-03-02T05:12:28.000Z","dependencies_parsed_at":null,"dependency_job_id":"936ae84c-4e9b-4358-8875-f93579be9fe1","html_url":"https://github.com/jaydu1/CVaR-Portfolio","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/jaydu1/CVaR-Portfolio","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FCVaR-Portfolio","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FCVaR-Portfolio/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FCVaR-Portfolio/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FCVaR-Portfolio/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jaydu1","download_url":"https://codeload.github.com/jaydu1/CVaR-Portfolio/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jaydu1%2FCVaR-Portfolio/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":267278631,"owners_count":24063252,"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","status":"online","status_checked_at":"2025-07-26T02:00:08.937Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["cvar-optimization","high-dimensionality","portfolio-selection"],"created_at":"2024-10-14T21:48:44.116Z","updated_at":"2025-07-27T01:03:21.007Z","avatar_url":"https://github.com/jaydu1.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CVaR-Portfolio\nConditional value-at-risk (CVaR) Portfolio Optimization in High Dimensions.\n\n\n\n\r\n\n\n\n## Scripts\n\nOur algothim is implemented in `opt_algo.py`; `utils.py` contains utils functions.\n\n- Estimation error bound: `estimation_err.py`\n- Simulation: `run_simu.py` which requires a sample covariance matrix `Sigma.npy`.\n- Real data: `run_real.py`, which requires the S\\\u0026P stock data. See the subsection at the end for more details about the data.\n\nThe script `summary.py` outputs the results and reproduces the figures.\n\n\n## Dependencies\n\nThis code is delivered via the files described above.\n\nPython (version 3.6 or later) is required to run the files, and it has only been tested on the Linux (6 Xeon(R) CPU E5-2690 @ 2.90GHz and 128 GB memory) and the MacOS platforms.\n\r\n\r\n\r\nPython packages to run reproducible code:\r\n\r\n- cvxopt=1.2.7\n- cvxpy=1.2.0\r\n- joblib=1.1.0\n- nonlinshrink=0.7\r\n- numba=0.51.1\r\n- numpy=1.21.2\r\n- pandas=1.3.4\n- statsmodels=0.13.2\r\n- scikit-learn=1.0.2\r\n- scipy=1.7.1\r\n- tqdm=4.62.3\n\n\n## S\\\u0026P 500 Data\n\nThe S\\\u0026P dataset and the constituent information are proprietary, purchased through WRDS and Siblis Research, Inc. \nThe contract heavily restricts even characteristics of the data (for example, information on stock prices that appear in the datasets). \nPlease refer to [repo](https://github.com/jaydu1/SparsePortfolio/tree/supplement) for obtaining the S\\\u0026P 500 dataset.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaydu1%2Fcvar-portfolio","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjaydu1%2Fcvar-portfolio","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjaydu1%2Fcvar-portfolio/lists"}