{"id":26529298,"url":"https://github.com/markcheno/dataprod-project","last_synced_at":"2025-03-21T16:29:41.269Z","repository":{"id":26263929,"uuid":"29711195","full_name":"markcheno/dataprod-project","owner":"markcheno","description":"Course project for Developing Data Products","archived":false,"fork":false,"pushed_at":"2015-01-25T14:10:02.000Z","size":140,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":3,"default_branch":"master","last_synced_at":"2024-04-15T03:56:00.729Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","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/markcheno.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}},"created_at":"2015-01-23T01:53:18.000Z","updated_at":"2024-04-15T03:56:00.730Z","dependencies_parsed_at":"2022-08-26T17:30:20.817Z","dependency_job_id":null,"html_url":"https://github.com/markcheno/dataprod-project","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/markcheno%2Fdataprod-project","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markcheno%2Fdataprod-project/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markcheno%2Fdataprod-project/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/markcheno%2Fdataprod-project/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/markcheno","download_url":"https://codeload.github.com/markcheno/dataprod-project/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244829100,"owners_count":20517246,"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":[],"created_at":"2025-03-21T16:29:40.751Z","updated_at":"2025-03-21T16:29:41.246Z","avatar_url":"https://github.com/markcheno.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\nThis Shiny application implements a Monte Carlo trading system simulator. You select various inputs\ndescribing a trading system and it then simulates many potential outcomes. Finally, it calculates and displays a number of useful statistics about the outcomes. It is useful to get a feel for how a particular trading system may perform in the future.\n\nInputs:\n\n- How many simulations to run - more simulations will give more accurate statistics\n- Number of plots to display - number of plots to sample and display from the entire simulation\n- Percentage of winning trades - how often your system expects to make a winning trade\n- Average winning trade in dollars\n- Average losing trade in dollars\n- Number of trades to simulate\n- Starting equity - the amount of cash the account starts with\n\nOutputs:\n\n- Line plot of simulated equity curves - since we can't display thousands of plots at once, we only sample and display a subset from the full simulation\n- Histogram of probable system ending value in dollars - the average ending value is plotted in blue\n- Histogram of probable maximum drawdown in dollars - the average maxdd is plotted in blue\n- Various other useful statistics - minimum,maximum,average,standard deviation,confidence intervales,expectancy\n\nInteresting things to try:\n\n- Set the average win and loss the same and then see how the winning percentage effects the equity\n- Set the average win to three times the average loss and then vary the winning percentage\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkcheno%2Fdataprod-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmarkcheno%2Fdataprod-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmarkcheno%2Fdataprod-project/lists"}