PyPortfolioOpt:python中的金融投资组合优化,包括经典有效前沿,Black-Litterman,分层风险平价-源码

上传者: 42118770 | 上传时间: 2021-08-17 17:04:12 | 文件大小: 3.92MB | 文件类型: ZIP
PyPortfolioOpt是一个实现投资组合优化方法的库,其中包括经典的均值方差优化技术和Black-Litterman分配,以及该领域的最新发展,例如收缩和分层风险奇偶校验,以及一些新颖的实验功能,例如指数加权协方差矩阵。 它既广泛又易于扩展,对于临时投资者和认真的从业者都可能有用。 无论您是发现了一些被低估的精选期权的基础知识型投资者,还是拥有一篮子策略的算法交易者,PyPortfolioOpt都可以帮助您以风险有效的方式组合Alpha来源。 请上的以深入了解该项目,或查看以查看一些示例,这些示例显示了从下载数据到构建投资组合的完整过程。 目录 入门 如果您想在浏览器中交互使用PyP

文件下载

资源详情

[{"title":"( 90 个子文件 3.92MB ) PyPortfolioOpt:python中的金融投资组合优化,包括经典有效前沿,Black-Litterman,分层风险平价-源码","children":[{"title":"PyPortfolioOpt-master","children":[{"title":"readthedocs.yml <span style='color:#111;'> 149B </span>","children":null,"spread":false},{"title":".dockerignore <span style='color:#111;'> 233B </span>","children":null,"spread":false},{"title":"examples.py <span style='color:#111;'> 4.22KB </span>","children":null,"spread":false},{"title":".env <span style='color:#111;'> 33B </span>","children":null,"spread":false},{"title":"pypfopt","children":[{"title":"exceptions.py <span style='color:#111;'> 494B </span>","children":null,"spread":false},{"title":"discrete_allocation.py <span style='color:#111;'> 13.20KB </span>","children":null,"spread":false},{"title":"risk_models.py <span style='color:#111;'> 20.82KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 674B </span>","children":null,"spread":false},{"title":"expected_returns.py <span style='color:#111;'> 9.87KB </span>","children":null,"spread":false},{"title":"plotting.py <span style='color:#111;'> 7.51KB </span>","children":null,"spread":false},{"title":"objective_functions.py <span style='color:#111;'> 8.28KB </span>","children":null,"spread":false},{"title":"base_optimizer.py <span style='color:#111;'> 19.46KB </span>","children":null,"spread":false},{"title":"black_litterman.py <span style='color:#111;'> 18.99KB </span>","children":null,"spread":false},{"title":"efficient_frontier.py <span style='color:#111;'> 28.91KB </span>","children":null,"spread":false},{"title":"hierarchical_portfolio.py <span style='color:#111;'> 7.58KB </span>","children":null,"spread":false},{"title":"cla.py <span style='color:#111;'> 16.60KB </span>","children":null,"spread":false}],"spread":false},{"title":"pipfile <span style='color:#111;'> 228B </span>","children":null,"spread":false},{"title":".github","children":[{"title":"ISSUE_TEMPLATE","children":[{"title":"bug.md <span style='color:#111;'> 510B </span>","children":null,"spread":false},{"title":"feature_request.md <span style='color:#111;'> 444B </span>","children":null,"spread":false},{"title":"installation-error.md <span style='color:#111;'> 387B </span>","children":null,"spread":false},{"title":"workflows","children":[{"title":"main.yml <span style='color:#111;'> 1.39KB </span>","children":null,"spread":false}],"spread":true},{"title":"help-needed.md <span style='color:#111;'> 390B </span>","children":null,"spread":false}],"spread":true},{"title":"workflows","children":[{"title":"main.yml <span style='color:#111;'> 1.39KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"binder","children":[{"title":"Dockerfile <span style='color:#111;'> 710B </span>","children":null,"spread":false},{"title":"jupyter_notebook_config.py <span style='color:#111;'> 208B </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE.txt <span style='color:#111;'> 1.05KB </span>","children":null,"spread":false},{"title":"poetry.lock <span style='color:#111;'> 119.72KB </span>","children":null,"spread":false},{"title":"docker-compose.test.yml <span style='color:#111;'> 225B </span>","children":null,"spread":false},{"title":"Dockerfile <span style='color:#111;'> 1.02KB </span>","children":null,"spread":false},{"title":"requirements.txt <span style='color:#111;'> 94B </span>","children":null,"spread":false},{"title":"media","children":[{"title":"corrplot_white.png <span style='color:#111;'> 130.46KB </span>","children":null,"spread":false},{"title":"weight_plot.png <span style='color:#111;'> 44.66KB </span>","children":null,"spread":false},{"title":"conceptual_flowchart_v1.png <span style='color:#111;'> 199.90KB </span>","children":null,"spread":false},{"title":"conceptual_flowchart_v2-grey.png <span style='color:#111;'> 176.24KB </span>","children":null,"spread":false},{"title":"conceptual_flowchart_v2.png <span style='color:#111;'> 188.45KB </span>","children":null,"spread":false},{"title":"efficient_frontier.png <span style='color:#111;'> 351.87KB </span>","children":null,"spread":false},{"title":"efficient_frontier_white.png <span style='color:#111;'> 341.01KB </span>","children":null,"spread":false},{"title":"logo_v1.png <span style='color:#111;'> 218.10KB </span>","children":null,"spread":false},{"title":"corrplot.png <span style='color:#111;'> 136.07KB </span>","children":null,"spread":false},{"title":"cla_plot.png <span style='color:#111;'> 113.42KB </span>","children":null,"spread":false},{"title":"ef_plot.png <span style='color:#111;'> 52.45KB </span>","children":null,"spread":false},{"title":"conceptual_flowchart_v1-grey.png <span style='color:#111;'> 200.86KB </span>","children":null,"spread":false},{"title":"dendrogram.png <span style='color:#111;'> 78.18KB </span>","children":null,"spread":false},{"title":"logo_v0.png <span style='color:#111;'> 235.49KB </span>","children":null,"spread":false},{"title":"logo_v1-grey.png <span style='color:#111;'> 210.81KB </span>","children":null,"spread":false}],"spread":false},{"title":"CONTRIBUTING.md <span style='color:#111;'> 3.28KB </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 1.73KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 19.64KB </span>","children":null,"spread":false},{"title":"Makefile <span style='color:#111;'> 1.16KB </span>","children":null,"spread":false},{"title":"docs","children":[{"title":"ExpectedReturns.rst <span style='color:#111;'> 2.47KB </span>","children":null,"spread":false},{"title":"Postprocessing.rst <span style='color:#111;'> 5.26KB </span>","children":null,"spread":false},{"title":"Plotting.rst <span style='color:#111;'> 2.52KB </span>","children":null,"spread":false},{"title":"UserGuide.rst <span style='color:#111;'> 12.32KB </span>","children":null,"spread":false},{"title":"EfficientFrontier.rst <span style='color:#111;'> 13.85KB </span>","children":null,"spread":false},{"title":"Roadmap.rst <span style='color:#111;'> 8.79KB </span>","children":null,"spread":false},{"title":"BlackLitterman.rst <span style='color:#111;'> 11.34KB </span>","children":null,"spread":false},{"title":"conf.py <span style='color:#111;'> 9.35KB </span>","children":null,"spread":false},{"title":"OtherOptimisers.rst <span style='color:#111;'> 6.32KB </span>","children":null,"spread":false},{"title":"Contributing.rst <span style='color:#111;'> 3.46KB </span>","children":null,"spread":false},{"title":"index.rst <span style='color:#111;'> 7.10KB </span>","children":null,"spread":false},{"title":"Makefile <span style='color:#111;'> 7.52KB </span>","children":null,"spread":false},{"title":"RiskModels.rst <span style='color:#111;'> 8.63KB </span>","children":null,"spread":false},{"title":"About.rst <span style='color:#111;'> 1002B </span>","children":null,"spread":false}],"spread":false},{"title":"docker-compose.yml <span style='color:#111;'> 264B </span>","children":null,"spread":false},{"title":"tests","children":[{"title":"test_base_optimizer.py <span style='color:#111;'> 8.37KB </span>","children":null,"spread":false},{"title":"test_discrete_allocation.py <span style='color:#111;'> 12.31KB </span>","children":null,"spread":false},{"title":"test_objective_functions.py <span style='color:#111;'> 4.25KB </span>","children":null,"spread":false},{"title":"test_efficient_semivariance.py <span style='color:#111;'> 16.14KB </span>","children":null,"spread":false},{"title":"test_expected_returns.py <span style='color:#111;'> 8.95KB </span>","children":null,"spread":false},{"title":"test_plotting.py <span style='color:#111;'> 3.71KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"test_risk_models.py <span style='color:#111;'> 10.13KB </span>","children":null,"spread":false},{"title":"test_black_litterman.py <span style='color:#111;'> 19.29KB </span>","children":null,"spread":false},{"title":"test_efficient_frontier.py <span style='color:#111;'> 39.08KB </span>","children":null,"spread":false},{"title":"resources","children":[{"title":"weights_hrp.csv <span style='color:#111;'> 493B </span>","children":null,"spread":false},{"title":"stock_prices.csv <span style='color:#111;'> 1.06MB </span>","children":null,"spread":false},{"title":"spy_prices.csv <span style='color:#111;'> 155.32KB </span>","children":null,"spread":false}],"spread":false},{"title":"test_cla.py <span style='color:#111;'> 4.31KB </span>","children":null,"spread":false},{"title":"utilities_for_tests.py <span style='color:#111;'> 2.23KB </span>","children":null,"spread":false},{"title":"test_hrp.py <span style='color:#111;'> 2.04KB </span>","children":null,"spread":false},{"title":"test_custom_objectives.py <span style='color:#111;'> 9.42KB </span>","children":null,"spread":false}],"spread":false},{"title":".gitignore <span style='color:#111;'> 282B </span>","children":null,"spread":false},{"title":"pyproject.toml <span style='color:#111;'> 1.79KB </span>","children":null,"spread":false},{"title":"cookbook","children":[{"title":"1-RiskReturnModels.ipynb <span style='color:#111;'> 131.18KB </span>","children":null,"spread":false},{"title":"4-Black-Litterman-Allocation.ipynb <span style='color:#111;'> 120.91KB </span>","children":null,"spread":false},{"title":"3-Advanced-Mean-Variance-Optimisation.ipynb <span style='color:#111;'> 106.88KB </span>","children":null,"spread":false},{"title":"2-Mean-Variance-Optimisation.ipynb <span style='color:#111;'> 365.08KB </span>","children":null,"spread":false},{"title":"5-Hierarchical-Risk-Parity.ipynb <span style='color:#111;'> 81.79KB </span>","children":null,"spread":false},{"title":"data","children":[{"title":"stock_prices.csv <span style='color:#111;'> 1.06MB </span>","children":null,"spread":false},{"title":"spy_prices.csv <span style='color:#111;'> 155.32KB </span>","children":null,"spread":false}],"spread":false}],"spread":false}],"spread":false}],"spread":true}]

评论信息

  • weixin_55983616 :
    用户下载后在一定时间内未进行评价,系统默认好评。
    2021-08-12

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明