{"id":15195661,"url":"https://github.com/monouns/portfolio-allocation-tutorial","last_synced_at":"2026-03-05T19:42:23.136Z","repository":{"id":133884254,"uuid":"495383053","full_name":"monouns/Portfolio-Allocation-tutorial","owner":"monouns","description":"Portfolio allocation tutorial with python","archived":false,"fork":false,"pushed_at":"2022-06-03T11:06:30.000Z","size":3275,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-12T17:09:34.214Z","etag":null,"topics":["colab-notebook","hedging","ipynb-jupyter-notebook","portfolio","portfolio-allocation","python","tutorial"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/monouns.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2022-05-23T11:32:49.000Z","updated_at":"2022-05-23T11:35:06.000Z","dependencies_parsed_at":null,"dependency_job_id":"701e91cf-dfa2-486f-acea-0a768e018445","html_url":"https://github.com/monouns/Portfolio-Allocation-tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monouns%2FPortfolio-Allocation-tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monouns%2FPortfolio-Allocation-tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monouns%2FPortfolio-Allocation-tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monouns%2FPortfolio-Allocation-tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/monouns","download_url":"https://codeload.github.com/monouns/Portfolio-Allocation-tutorial/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241460008,"owners_count":19966511,"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":["colab-notebook","hedging","ipynb-jupyter-notebook","portfolio","portfolio-allocation","python","tutorial"],"created_at":"2024-09-27T23:43:09.170Z","updated_at":"2025-11-26T19:01:45.759Z","avatar_url":"https://github.com/monouns.png","language":"Jupyter Notebook","readme":"# Portfolio-Allocation-tutorial\nThis git repository is for \"Portfolio allocation tutorial with python\"\n\n**My Portfolio is constructed by several model algorithms.**\n\n### * Process for Algorithmic Trading\n\u003cimg src=\"./img/Trading_Process.png\" width=\"700\" height=\"400\"/\u003e\n\n### * Get Data from FinanceDataReader\n\u003cimg src=\"./img/Get_Data.png\" width=\"800\" height=\"400\"/\u003e\n\n### * For low risk, Pre-Investment is done with bond!\n\u003cimg src=\"./img/Pre_investment.png\" width=\"700\" height=\"400\"/\u003e\n\n***You can run the codes with Colab!***\n\u003cbr\u003e\u003c/br\u003e\n\n## 1. Markowitz Portfolio\nMarkowitz Portfolio is \"Modern Portfolio Theory\" which is one of the most popular Portfolio Optimization theory. \n\n\u003cimg src=\"./img/Markowitz_Portfolio.png\" width=\"700\" height=\"400\"/\u003e\n\nWe can get \"minimum volatility portfolio\" or \"maximum return portfolio\" which is in efficient frontier with red line. \n\nAlso, we can calculate expected return and volatility(risk).\n\u003cbr\u003e\u003c/br\u003e\n\n## 2. HRP Portfolio\nHRP is the method that may can make up Markowitz model with hierarchical tree algorithm which is clustering stocks.\n\n\u003cimg src=\"./img/HRP.png\" width=\"700\" height=\"400\"/\u003e\n\n***[Reference URL](https://medium.com/@orenji.eirl/hierarchical-risk-parity-with-python-and-riskfolio-lib-c0e60b94252e)***\n\u003cbr\u003e\u003c/br\u003e\n\n## 3. RL Portfolio\n***[Benchmark Gihub](https://github.com/AI4Finance-Foundation/FinRL)***\n\nRL for automatic portfolio allocation based on \"FinRL: Deep Reinforcement Learning for Quantitative Finance\" which is accepted paper of NeurIPS 2018.\n\n\u003cimg src=\"./img/finrl.png\" width=\"700\" height=\"400\"/\u003e\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonouns%2Fportfolio-allocation-tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmonouns%2Fportfolio-allocation-tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonouns%2Fportfolio-allocation-tutorial/lists"}