{"id":20821244,"url":"https://github.com/mrtkp9993/quantitavefinanceexamplespy","last_synced_at":"2025-07-25T09:02:18.486Z","repository":{"id":52488942,"uuid":"401028804","full_name":"mrtkp9993/QuantitaveFinanceExamplesPy","owner":"mrtkp9993","description":"Financial analysis, algorithmic trading, portfolio optimization examples with Python (DISCLAIMER - No Investment Advice Provided, YASAL UYARI - Yatırım tavsiyesi değildir).","archived":false,"fork":false,"pushed_at":"2022-11-25T15:14:02.000Z","size":10594,"stargazers_count":45,"open_issues_count":0,"forks_count":9,"subscribers_count":4,"default_branch":"main","last_synced_at":"2025-05-07T16:45:00.609Z","etag":null,"topics":["market-data","portfolio-optimization","python","quant","quantitative-finance","quantitative-trading","stock-analysis","stock-market"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/mrtkp9993.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null},"funding":{"github":"mrtkp9993"}},"created_at":"2021-08-29T11:51:38.000Z","updated_at":"2025-03-08T06:55:07.000Z","dependencies_parsed_at":"2023-01-23T14:00:26.460Z","dependency_job_id":null,"html_url":"https://github.com/mrtkp9993/QuantitaveFinanceExamplesPy","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/mrtkp9993/QuantitaveFinanceExamplesPy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrtkp9993%2FQuantitaveFinanceExamplesPy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrtkp9993%2FQuantitaveFinanceExamplesPy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrtkp9993%2FQuantitaveFinanceExamplesPy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrtkp9993%2FQuantitaveFinanceExamplesPy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/mrtkp9993","download_url":"https://codeload.github.com/mrtkp9993/QuantitaveFinanceExamplesPy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/mrtkp9993%2FQuantitaveFinanceExamplesPy/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266983419,"owners_count":24016550,"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":"2025-07-25T02:00:09.625Z","response_time":70,"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":["market-data","portfolio-optimization","python","quant","quantitative-finance","quantitative-trading","stock-analysis","stock-market"],"created_at":"2024-11-17T22:11:44.977Z","updated_at":"2025-07-25T09:02:18.355Z","avatar_url":"https://github.com/mrtkp9993.png","language":"Jupyter Notebook","funding_links":["https://github.com/sponsors/mrtkp9993"],"categories":[],"sub_categories":[],"readme":"# QuantitaveFinanceExamplesPy\n\nFinancial analysis, algorithmic trading, portfolio optimization examples with Python\n\nDISCLAIMER - No Investment Advice Provided\n\nYASAL UYARI - Burada yer alan yatırım bilgi, yorum ve tavsiyeleri yatırım danışmanlığı kapsamında değildir.\n\n## Requirements\n\nPlease install requirements from `requirements.txt`.\n\n## References (for both methods and some code fragments)\n\n* Hilpisch, Y. J. (2021). Python for algorithmic trading: From idea to cloud deployment. O'Reilly.\n* Jansen, S. (2020). Machine learning for algorithmic trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with python. Packt Publishing.\n* Pik, J., \u0026amp; Ghosh, S. (2021). Hands-on financial trading with python. Packt Publishing.\n* Velu, R. P., Hardy, M., \u0026amp; Nehren, D. (2020). Algorithmic trading and quantitative strategies. CRC Press, Taylor \u0026amp; Francis Group.\n* Brugiere, P. (2021). Quantitative portfolio management: With applications in python. Springer Nature. \n* Dowd, K. (2007). Measuring market risk. John Wiley \u0026 Sons.\n* Hilpisch, Y. J. (2020). Artificial Intelligence in Finance. O'Reilly.\n\n## Contact\n\nMurat Koptur, [LinkedIn](https://www.linkedin.com/in/muratkoptur/)\n\nEmail: [muratkoptur@yandex.com](mailto:muratkoptur@yandex.com?subject=QuantitativeFinanceGithub)\n\n## Examples\n\n**Note**: In all examples, assumed the risk-free rate is zero.\n\n### Calculation Alpha and Beta factors\n\n![01_01](imgs/01_01.png)\n\n### Cointegration\n\n```text\nARCLK.IS and TOASO.IS has cointegration, p-value: 0.04903369798110527\nAYGAZ.IS and KCHOL.IS has cointegration, p-value: 0.007029900251131765\nFROTO.IS and MAALT.IS has cointegration, p-value: 0.015757028038897322\nFROTO.IS and OTKAR.IS has cointegration, p-value: 0.004399007493986555\nKCHOL.IS and AYGAZ.IS has cointegration, p-value: 0.007101145930953294\nMAALT.IS and FROTO.IS has cointegration, p-value: 0.00783799297255268\nOTKAR.IS and FROTO.IS has cointegration, p-value: 0.003094678911810982\nOTKAR.IS and TTRAK.IS has cointegration, p-value: 0.04185601871282213\nOTKAR.IS and YKGYO.IS has cointegration, p-value: 0.00282083357242191\nTTRAK.IS and OTKAR.IS has cointegration, p-value: 0.03639137062922606\nTTRAK.IS and YKGYO.IS has cointegration, p-value: 0.03834839887528665\nYKGYO.IS and OTKAR.IS has cointegration, p-value: 0.0017665073676291331\nYKGYO.IS and TOASO.IS has cointegration, p-value: 0.046004150077470406\nYKGYO.IS and TTRAK.IS has cointegration, p-value: 0.027200620035757236\n```\n\n### PCA on Returns\n\n![03_01](imgs/03_01.png)\n\n### Volatility calculations\n\n```text\nStd.Dev. Estimator:          0.16988244687319595\nClassical Estimator:         0.0013349197336295028\nRogers - Satchell Estimator: 0.0009643228704150725\nYang - Zang estimator:       0.0016329397449278639\n```\n\n### Volatility-Volume Relationship\n\n![05_01](imgs/05_01.png)\n\n### AR-ARCH models for volatility\n\n![06_01](imgs/06_01.png)\n\n### VWAP\n\n![07_01](imgs/07_01.svg)\n\n### Technical Indicators\n\n![08_01](imgs/08_01.png)\n\n### Denoising Data\n\n![09_01](imgs/09_01.png)\n\n### Trading Signals\n\n![10_01](imgs/10_01.png)\n![10_02](imgs/10_02.png)\n\n### Backtesting\n\n![11_01](imgs/11_01.png)\n\n### Pairs Trading\n\n![12_02](imgs/12_02.png)\n\n### Modern Portfolio Theory - Efficient Frontier\n\n![13_01](imgs/13_01.png)\n\n### Value-At-Risk - Expected Shortfall\n\n![14_01](imgs/14_01.png)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrtkp9993%2Fquantitavefinanceexamplespy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmrtkp9993%2Fquantitavefinanceexamplespy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmrtkp9993%2Fquantitavefinanceexamplespy/lists"}