我正在对一系列股票进行投资组合优化，我正在尝试提取重新平衡的投资组合的权重。
我遇到的问题：我没有得到重新平衡的投资组合的权重，而是得到了3个日期。该项目的代码在下面。
library(ROI)
install.packages("DEoptim")
library(ggplot2)
install.packages("quantmod")
library(quantmod)
library(quantmod)
install.packages("PerfomanceAnalytics")
library(PerformanceAnalytics)
library(PortfolioAnalytics)
library(random)
install.packages("random")
library(random)
library(DEoptim)
install.packages("fPortfolio")
library(fPortfolio)
install.packages("foreach")
install.packages("doParallel")
library(PortfolioAnalytics)
#vector of stocks in my portfolio of
tickers <- c("FB", "AAPL", "AMZN", "GM", "GOOGL", "SQ", "NVDA","RYAM", "AMAT", "IMMR","SOI","PETS")
#bind porfolio prices
portfolioPrices <- NULL
for(ticker in tickers) {
portfolioPrices <- cbind(portfolioPrices,
getSymbols.yahoo(ticker, from='2003-01-03', periodicity = 'daily', auto.assign=FALSE)[,4])
}
#portfolio returns
portfolioReturns <- na.omit(ROC(portfolioPrices))
print(portfolioReturns)
portf <- portfolio.spec(colnames(portfolioReturns))
portf <- add.constraint(portf, type="weight_sum", min_sum=.99, max_sum=1,01)
portf <- add.constraint(portf, type="box", min=.02, max=.60)
portf<-add.constraint(portf,type="transation_cost", ptc=.001)
portf <- add.objective(portf, type="return", name="mean")
portf <- add.objective(portf, type="risk", name="StdDev",target=.005)
rp<-random_portfolios(portf, 10000, "sample")
#optimize portfolio using the "DEoptim solver"
optPort <- optimize.portfolio(portfolioReturns, portf, optimize_method = "DEoptim", trace=TRUE)
#chart weights of optimized portfolio
chart.Weights(optPort)
summary(optPort)
chart.RiskReward(optPort, risk.col = "StDev", return.col = "mean", chart.assets = TRUE)
rp<-random_portfolios(portf, 10000, "sample")
#rebalance portfolo
opt_rebal <- optimize.portfolio.rebalancing(portfolioReturns,
portf,
optimize_method="ROI",
rp=rp,
rebalance_on="years",
training_period=60,
rolling_window=60)
extractWeights(optPort)
chart.Weights(optPort)
#extract weights of rebalanced portfolio
extractWeights(opt_rebal))
我该如何解决？
我们将非常感谢您的帮助。
谢谢。
答案 0 :(得分：0)
首先，您的代码非常混乱！
因此，在为您提供解决方案的同时，我也对其进行了清洁。
以下是涵盖问题所有方面的要点：
由于您不仅使用DEoptim求解器，而且还使用ROI，因此需要下载ROI的推荐支持插件：
install.packages(c("fGarch",
"Rglpk",
"ROI.plugin.glpk",
"ROI.plugin.quadprog",
"ROI.plugin.symphony",
"pso",
"GenSA",
"corpcor",
"testthat",
"nloptr",
"MASS",
"robustbase")
)
您应该一次并以正确的顺序加载库，因为某些库可以相互掩盖某些功能。这是推荐的顺序：
library(ROI)
library(ggplot2)
library(quantmod)
library(PerformanceAnalytics)
library(random)
library(DEoptim)
library(fPortfolio)
library(PortfolioAnalytics)
library(dplyr)
请注意，还要加载另外的dplyr库，这是管道（%>%）所需要的，即使您的代码更高效，更易读！：
#vector of stocks in my portfolio of
tickers <- c("FB", "AAPL", "AMZN", "GM", "GOOGL", "SQ", "NVDA","RYAM", "AMAT", "IMMR","SOI","PETS")
#bind porfolio prices
portfolioPrices <- NULL
for(ticker in tickers) {
portfolioPrices <- cbind(portfolioPrices,
getSymbols.yahoo(ticker, from='2003-01-03', periodicity = 'daily', auto.assign=FALSE)[,4])
}
#portfolio returns
portfolioReturns <- na.omit(ROC(portfolioPrices))
print(portfolioReturns)
portf <- portfolio.spec(colnames(portfolioReturns)) %>%
add.constraint(type="weight_sum", min_sum=1, max_sum=1) %>%
add.constraint(type="box", min=.02, max=.60) %>%
add.constraint(type="transation_cost", ptc=.001) %>%
add.objective(type="return", name="mean") %>%
add.objective(type="risk", name="StdDev",target=.005)
不确定为什么不能使用先前输入到rp的随机投资组合optPort作为opt_rebal的输入。
rp<-random_portfolios(portf, 10000, "sample")
#optimize portfolio using the "DEoptim solver"
optPort <- optimize.portfolio(portfolioReturns, portf, optimize_method = "DEoptim", trace=TRUE,
rp=rp)
#chart weights of optimized portfolio
chart.Weights(optPort)
summary(optPort)
# chart.RiskReward(optPort, risk.col = "StDev", return.col = "mean", chart.assets = TRUE)
#not sure why you cannot use the previous random portfolio!!
rp<-random_portfolios(portf, 10000, "sample")
此函数调用中有一个错误，我认为这是由于您的portf中的双重目标所致，因为它可能会阻止您获得有效的边界。对此不确定。这不是必需的，但是是您探索的任务：-）
# chart.RiskReward(optPort, risk.col = "StDev", return.col = "mean", chart.assets = TRUE)
ROI与其他后端不同，因此需要单独的投资组合规范portfolio.spec和投资组合优化optimize.portfolio或optimize.portfolio.rebalancing。
这里是实现它的一种方法（请注意里面没有add.objective的投资组合规范）：
portf2 <- portfolio.spec(colnames(portfolioReturns)) %>%
add.constraint(type="weight_sum", min_sum=1, max_sum=1) %>%
add.constraint(type="box", min=.02, max=.60) %>%
add.constraint(type="transation_cost", ptc=.001)
#this optimises based on Sharpe Ratio
optPort2 <- optimize.portfolio(portfolioReturns, portf2, optimize_method = "ROI", trace=TRUE,
maxSR=TRUE)
#rebalance portfolo
opt_rebal <- optimize.portfolio.rebalancing(portfolioReturns,
portf2,
optimize_method="ROI",
rp=rp,
rebalance_on="years",
training_period=60,
rolling_window=60)
extractWeights(optPort)
chart.Weights(optPort)
#extract weights of rebalanced portfolio
extractWeights(opt_rebal)
输出：
> extractWeights(opt_rebal)
FB.Close AAPL.Close AMZN.Close GM.Close GOOGL.Close SQ.Close NVDA.Close RYAM.Close AMAT.Close
2017-12-29 0.6 0.2 0.02 0.02 0.02 0.02 0.02 0.02 0.02
2018-12-31 0.6 0.2 0.02 0.02 0.02 0.02 0.02 0.02 0.02
2019-09-20 0.6 0.2 0.02 0.02 0.02 0.02 0.02 0.02 0.02
IMMR.Close SOI.Close PETS.Close
2017-12-29 0.02 0.02 0.02
2018-12-31 0.02 0.02 0.02
2019-09-20 0.02 0.02 0.02
您可以阅读有关optimize.portfolio的文档，以了解它可以解决哪些有限类型的凸优化问题。