投资组合优化：从 Markowitz 到遗传算法(python代码)

[{"title":"（ 59 个子文件 18.68MB ） 投资组合优化：从 Markowitz 到遗传算法(python代码)","children":[{"title":"portfolio-optimization-main","children":[{"title":"data","children":[{"title":"prices - Copy.csv <span style='color:#111;'> 2.43MB </span>","children":null,"spread":false},{"title":"prices.csv <span style='color:#111;'> 4.52MB </span>","children":null,"spread":false},{"title":"resources","children":[{"title":"Pr倂isualiser la pi奵e jointe 2012AnimprovedestimationtomakeMarkowitzsportfoliooptimizationtheoryusersfriendlyandestimationac.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"Large-Scale_Portfolio_Optimization_Using_Multiobje.pdf <span style='color:#111;'> 1.17MB </span>","children":null,"spread":false},{"title":"Pr倂isualiser la pi奵e jointe GetFile.pdfGetFile.pdf2 MB.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"drawdown_0.pdf <span style='color:#111;'> 967.06KB </span>","children":null,"spread":false},{"title":"Ren_Sigmundsdottir.pdf <span style='color:#111;'> 772.69KB </span>","children":null,"spread":false},{"title":"IJTheoreticalAppliedFinance.8.1.2005.pdf <span style='color:#111;'> 498.02KB </span>","children":null,"spread":false},{"title":"risks-08-00029-v2.pdf <span style='color:#111;'> 498.73KB </span>","children":null,"spread":false},{"title":"599440_paper.pdf <span style='color:#111;'> 405.07KB </span>","children":null,"spread":false},{"title":"Computing_the_Nondominated_Surface_in_Tr.pdf <span style='color:#111;'> 567.03KB </span>","children":null,"spread":false},{"title":"Pr倂isualiser la pi奵e jointe Performance_of_Portfolios_Optimized_with_Estimatio.pdfPerformance_of_Portfolios_Optimized_with_.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"Reducing Estimation Risk in Mean-Variance Portfolios with Machine Learning.pdf <span style='color:#111;'> 539.97KB </span>","children":null,"spread":false},{"title":"1602.06186.pdf <span style='color:#111;'> 782.27KB </span>","children":null,"spread":false},{"title":"A New Approach in Nonparametric Estimation of Returns in mean-DownSide Risk Portfolio frontier.pdf <span style='color:#111;'> 614.57KB </span>","children":null,"spread":false},{"title":"Pr倂isualiser la pi奵e jointe TheRiskofOut-of-SamplePortfolioPerformance.pdfTheRiskofOut-of-SamplePortfolioPerformance.pdf1.6.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"downsideriskRevise7.pdf <span style='color:#111;'> 417.98KB </span>","children":null,"spread":false},{"title":"Bachelor_Thesis__Philipp_Dubach.pdf <span style='color:#111;'> 1.29MB </span>","children":null,"spread":false},{"title":"ijerph-17-06324.pdf <span style='color:#111;'> 5.32MB </span>","children":null,"spread":false},{"title":"Pr倂isualiser la pi奵e jointe MaximizingtheOut-of-SampleSharpeRatio.pdfMaximizingtheOut-of-SampleSharpeRatio.pdf1.2 MB.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"Effectiveness and efficiency of non-dominated sorting for evolutionary multi- and many-objective optimization.pdf <span style='color:#111;'> 898.32KB </span>","children":null,"spread":false},{"title":"Drawdown_Portfolio_Optimization_Problems_and_Drawdown_Betas.pdf <span style='color:#111;'> 837.00KB </span>","children":null,"spread":false},{"title":"CVaR1_JOR.pdf <span style='color:#111;'> 283.30KB </span>","children":null,"spread":false},{"title":"Pr倂isualiser la pi奵e jointe 1602.06186.pdf1602.06186.pdf782 KB.url <span style='color:#111;'> 162B </span>","children":null,"spread":false},{"title":"Bachelor_Thesis__Philipp_Dubach (1).pdf <span style='color:#111;'> 1.29MB </span>","children":null,"spread":false}],"spread":false},{"title":"prices_olc.csv <span style='color:#111;'> 2.43MB </span>","children":null,"spread":false},{"title":"tickers.csv <span style='color:#111;'> 11.33KB </span>","children":null,"spread":false}],"spread":true},{"title":"LICENSE <span style='color:#111;'> 1.04KB </span>","children":null,"spread":false},{"title":"portfolio_optimization","children":[{"title":"example","children":[{"title":"mean_cvar.py <span style='color:#111;'> 2.80KB </span>","children":null,"spread":false},{"title":"mean_cdar.py <span style='color:#111;'> 5.66KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"mean_semivariance.py <span style='color:#111;'> 2.91KB </span>","children":null,"spread":false},{"title":"mean_variance.py <span style='color:#111;'> 4.73KB </span>","children":null,"spread":false}],"spread":true},{"title":"test","children":[{"title":"test_population.py <span style='color:#111;'> 2.13KB </span>","children":null,"spread":false},{"title":"test_assets.py <span style='color:#111;'> 2.21KB </span>","children":null,"spread":false},{"title":"test_portfolio.py <span style='color:#111;'> 2.95KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"test_pre_selection.py <span style='color:#111;'> 506B </span>","children":null,"spread":false}],"spread":true},{"title":"assets.py <span style='color:#111;'> 5.14KB </span>","children":null,"spread":false},{"title":"optimization","children":[{"title":"mean_cvar.py <span style='color:#111;'> 2.82KB </span>","children":null,"spread":false},{"title":"mean_cdar.py <span style='color:#111;'> 2.97KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"mean_semivariance.py <span style='color:#111;'> 3.18KB </span>","children":null,"spread":false},{"title":"mean_variance.py <span style='color:#111;'> 3.00KB </span>","children":null,"spread":false}],"spread":true},{"title":"meta.py <span style='color:#111;'> 318B </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 299B </span>","children":null,"spread":false},{"title":"portfolio.py <span style='color:#111;'> 7.90KB </span>","children":null,"spread":false},{"title":"population.py <span style='color:#111;'> 5.74KB </span>","children":null,"spread":false},{"title":"utils","children":[{"title":"sorting.py <span style='color:#111;'> 2.18KB </span>","children":null,"spread":false},{"title":"tools.py <span style='color:#111;'> 2.78KB </span>","children":null,"spread":false},{"title":"metrics.py <span style='color:#111;'> 2.45KB </span>","children":null,"spread":false},{"title":"assets.py <span style='color:#111;'> 11.90KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false}],"spread":true},{"title":"bloomberg","children":[{"title":"__init__.py <span style='color:#111;'> 0B </span>","children":null,"spread":false},{"title":"loader.py <span style='color:#111;'> 1.98KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"requirements.txt <span style='color:#111;'> 1.47KB </span>","children":null,"spread":false},{"title":"setup.py <span style='color:#111;'> 600B </span>","children":null,"spread":false},{"title":".gitignore <span style='color:#111;'> 1.76KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 85B </span>","children":null,"spread":false}],"spread":true}],"spread":true}]