{"id":21870687,"url":"https://github.com/coderixc/banknifty_algo_strategy","last_synced_at":"2025-04-14T23:54:04.177Z","repository":{"id":189155693,"uuid":"677493199","full_name":"Coderixc/BankNifty_Algo_Strategy","owner":"Coderixc","description":"Python Trading Strategy Analyzer: Backtesting and Metrics Framework","archived":false,"fork":false,"pushed_at":"2024-03-12T04:40:54.000Z","size":2962,"stargazers_count":6,"open_issues_count":1,"forks_count":3,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-14T23:53:47.140Z","etag":null,"topics":["algotrading","backtesting-engine","python","python-script","quant-dev","quanttrading","stock-market"],"latest_commit_sha":null,"homepage":"","language":"Python","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/Coderixc.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}},"created_at":"2023-08-11T17:59:42.000Z","updated_at":"2024-10-10T11:42:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"3dc3b0a8-3377-47ce-b92c-30df5617c339","html_url":"https://github.com/Coderixc/BankNifty_Algo_Strategy","commit_stats":null,"previous_names":["coderixc/banknifty_algo_strategy"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Coderixc%2FBankNifty_Algo_Strategy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Coderixc%2FBankNifty_Algo_Strategy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Coderixc%2FBankNifty_Algo_Strategy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Coderixc%2FBankNifty_Algo_Strategy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Coderixc","download_url":"https://codeload.github.com/Coderixc/BankNifty_Algo_Strategy/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248981261,"owners_count":21193144,"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":["algotrading","backtesting-engine","python","python-script","quant-dev","quanttrading","stock-market"],"created_at":"2024-11-28T06:12:13.946Z","updated_at":"2025-04-14T23:54:04.161Z","avatar_url":"https://github.com/Coderixc.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Python Trading Strategy Analyzer\n\nThis Python project is designed to facilitate the backtesting of trading strategies and the analysis of their performance. The project is built entirely from scratch using Python as the primary programming language. \nThe framework enables users to analyze various metrics to generate comprehensive reports on the performance of their trading strategies.\n\n## Key Features\nStep 1: Import Dependencies\n\nThe project imports necessary libraries including pandas for data manipulation, datetime for time-related operations, and plotly.graph_objects for visualization.\n# Step 2: Load Data\n\nThe project loads CSV data into memory, allowing users to access and analyze trading data.\n# Step 3: Extract Future Data\n\nIt extracts future trading data based on predefined logic.\n# Step 4: Extract Option Data\n\nThe project extracts option trading data, distinguishing it from other types of trades.\n# Calculate Moving Averages\n\nVarious moving average calculations are performed to aid in strategy analysis.\n#Generate Trades\n\nThe framework generates trades based on predefined conditions and moving average crossovers.\n# Exit Strategies\n\nExit strategies are implemented based on stop-loss, target points, and predefined timeframes.\n\n## Learning Objectives\nGain hands-on experience in Python programming for algorithmic trading.\nUnderstand the importance of preprocessing and analyzing trading data.\nLearn how to implement common trading strategies and indicators.\nExplore techniques for managing trades and defining exit strategies.\nDevelop skills in statistical analysis and performance evaluation of trading strategies.rate meaningful insights.\nThe project assumes certain fixed values for stop-loss and target points, which users may need to adjust based on market conditions and individual preferences.\nStatistical Analysis\nThe framework includes statistical analysis tools to evaluate the performance of trading strategies.\nMetrics such as profit trades, loss trades, profit points, loss points, and profit-to-loss ratio are calculated to assess strategy effectiveness.\n\n\nIf you are interested in collaborating or need assistance with algorithmic trading strategies, feel free to reach out.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoderixc%2Fbanknifty_algo_strategy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcoderixc%2Fbanknifty_algo_strategy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcoderixc%2Fbanknifty_algo_strategy/lists"}