{"id":19358943,"url":"https://github.com/quinta0/montecarlo","last_synced_at":"2026-05-16T04:10:18.337Z","repository":{"id":246549366,"uuid":"821453517","full_name":"Quinta0/MonteCarlo","owner":"Quinta0","description":"A statistical overview of the computation of Pi and an application of the Monte Carlo principle to a portfolio","archived":false,"fork":false,"pushed_at":"2024-06-28T15:57:06.000Z","size":10644,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-06T18:43:16.642Z","etag":null,"topics":["monte-carlo-simulation","python","simulation","statistics"],"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/Quinta0.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":"2024-06-28T15:15:25.000Z","updated_at":"2024-06-30T00:19:37.000Z","dependencies_parsed_at":"2024-06-28T16:43:02.451Z","dependency_job_id":"0a155399-cf3c-4742-af2e-818dff99e7a6","html_url":"https://github.com/Quinta0/MonteCarlo","commit_stats":null,"previous_names":["quinta0/montecarlo"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quinta0%2FMonteCarlo","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quinta0%2FMonteCarlo/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quinta0%2FMonteCarlo/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Quinta0%2FMonteCarlo/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Quinta0","download_url":"https://codeload.github.com/Quinta0/MonteCarlo/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240475233,"owners_count":19807292,"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":["monte-carlo-simulation","python","simulation","statistics"],"created_at":"2024-11-10T07:13:35.259Z","updated_at":"2026-05-16T04:10:13.283Z","avatar_url":"https://github.com/Quinta0.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MonteCarlo\n## Monte Carlo Simulation to Estimate Pi\n\nThis program uses a Monte Carlo simulation to estimate the value of Pi. The program generates random points within a square of side length 2, centered at the origin, and calculates how many of those points fall inside a unit circle. The ratio of the points inside the circle to the total number of points is used to estimate Pi.\n\n### How to Run\n\n1. Make sure you have `numpy` and `matplotlib` installed, if not you can install them using the following command:\n    ```bash\n    pip install requirements.txt\n    ```\n2. Save the script as `pi.py`.\n3. Run the script using the command `python pi.py` or `py pi.py` if you have installed python from the microsoft store.\n\n### Output\nThe output is an animation of the Monte Carlo simulation and a GIF file named PI.gif saved in the same directory.\n\n\n## Portfolio Analysis with Real Stock Data\nThis program performs a Monte Carlo simulation to analyze the potential future returns of a portfolio composed of real stocks. Historical data is fetched from Yahoo Finance.\n\n### How to Run\n1. Make sure you have `numpy`, `pandas`, `matplotlib`, and `yfinance` installed, just as before if dont have them installed you can install them with the following command:\n```bash\npip install requirements.txt\n```\n2. Save the script as `portfolio.py`.\n3. Run the script using the command `python portfolio.py` or `py pi.py` if you have installed python from the microsoft store.\n\n### Output\nThe output is a histogram of the simulated portfolio returns and summary statistics such as mean return, standard deviation, and Value at Risk (VaR).","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquinta0%2Fmontecarlo","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquinta0%2Fmontecarlo","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquinta0%2Fmontecarlo/lists"}