在 R DEoptim 中传递优化函数参数

Question

我正在尝试学习 R 中的 DEoptim 库，但我认为我误解了来自

的库文档

https://www.rdocumentation.org/packages/DEoptim/versions/2.2-4/topics/DEoptim

当我尝试下面的代码时，我收到了错误 argument "returns_covar" is missing, with no default

我要优化（最小化）的功能是：

calculate_portfolio_variance <- function(allocations, returns_covar)
{
  # Name: calculate_portfolio_variance
  # Purpose: Computes expected portfolio variance, to be used as the minimization objective function
  # Input: allocations = vector of allocations to be adjusted for optimality; returns_covar = covariance matrix of stock returns
  # Output: Expected portfolio variance

  portfolio_variance <- allocations%*%returns_covar%*%t(allocations)
  return(portfolio_variance)
}

filter_and_sort_symbols <- function(symbols)
{
  # Name: filter_and_sort_symbols
  # Purpose: Convert to uppercase if not
  # and remove any non valid symbols
  # Input: symbols = vector of stock tickers
  # Output: filtered_symbols = filtered symbols

  # convert symbols to uppercase
  symbols <- toupper(symbols)

  # Validate the symbol names
  valid <- regexpr("^[A-Z]{2,4}$", symbols)

  # Return only the valid ones
  return(sort(symbols[valid == 1]))
}

# Create the list of stock tickers and check that they are valid symbols
tickers <- filter_and_sort_symbols(c("XLE", "XLB", "XLI", "XLY", "XLP", "XLV", "XLK", "XLU", "SHY", "TLT"))
# Set the start and end dates
start_date <- "2013-01-01"
end_date <- "2014-01-01"

# Gather the stock data using quantmod library
getSymbols(Symbols=tickers, from=start_date, to=end_date, auto.assign = TRUE)

# Create a matrix of only the adj. prices
price_matrix <- NULL
for(ticker in tickers){price_matrix <- cbind(price_matrix, get(ticker)[,6])}
# Set the column names for the price matrix
colnames(price_matrix) <- tickers

# Compute log returns
returns_matrix <- apply(price_matrix, 2, function(x) diff(log(x)))
returns_covar <- cov(returns_matrix)

# Specify lower and upper bounds for the allocation percentages
lower <- rep(0, ncol(returns_matrix))
upper <- rep(1, ncol(returns_matrix))

# Calculate the optimum allocation; THIS CAUSES AN ERROR
set.seed(1234)
optim_result <- DEoptim(calculate_portfolio_variance, lower, upper, control = list(NP=100, itermax=300, F=0.8, CR=0.9, allocations, returns_covar))

最后一行的错误是缺少returns_covar 参数，但我尝试将其传递给DEoptim() 函数。

我认为上面有括号错误，所以我尝试了以下

optim_result <- DEoptim(calculate_portfolio_variance, lower, upper, control = list(NP=100, itermax=300, F=0.8, CR=0.9), returns_covar)

这会导致以下错误：

Error in allocations %*% returns_covar %*% t(allocations) : non-conformable arguments

当我检查矩阵的维度时，一切似乎都正常

> dim(allocations)
[1]  1 10
> dim(returns_covar)
[1] 10 10

在calculate_portfolio_variance() 函数中添加维度检查

  print(dim(allocations))
  print(dim(returns_covar))

显示分配向量在第二次迭代时变为NULL。我不知道为什么或如何解决它。

[1]  1 10
[1] 10 10
NULL
[1] 10 10
Error in allocations %*% returns_covar %*% t(allocations) : non-conformable arguments

Answer 1

不清楚这是否是您想要的，但如果您将calculate_portfolio_variance 更改为

  portfolio_variance <- t(allocations)%*%returns_covar%*%allocations

它对我有用。我认为这是您的矩阵数学的问题。

编辑完整的工作可重现示例：

library(quantmod)
library(DEoptim)


calculate_portfolio_variance <- function(allocations, returns_covar)
{
  # Name: calculate_portfolio_variance
  # Purpose: Computes expected portfolio variance, to be used as the minimization objective function
  # Input: allocations = vector of allocations to be adjusted for optimality; returns_covar = covariance matrix of stock returns
  # Output: Expected portfolio variance

  ### I CHANGED THIS LINE
  #portfolio_variance <- allocations%*%returns_covar%*%t(allocations)
  portfolio_variance <- t(allocations)%*%returns_covar%*%allocations
  return(portfolio_variance)
}

filter_and_sort_symbols <- function(symbols)
{
  # Name: filter_and_sort_symbols
  # Purpose: Convert to uppercase if not
  # and remove any non valid symbols
  # Input: symbols = vector of stock tickers
  # Output: filtered_symbols = filtered symbols

  # convert symbols to uppercase
  symbols <- toupper(symbols)

  # Validate the symbol names
  valid <- regexpr("^[A-Z]{2,4}$", symbols)

  # Return only the valid ones
  return(sort(symbols[valid == 1]))
}

# Create the list of stock tickers and check that they are valid symbols
tickers <- filter_and_sort_symbols(c("XLE", "XLB", "XLI", "XLY", "XLP", "XLV", "XLK", "XLU", "SHY", "TLT"))
# Set the start and end dates
start_date <- "2013-01-01"
end_date <- "2014-01-01"

# Gather the stock data using quantmod library
getSymbols(Symbols=tickers, from=start_date, to=end_date, auto.assign = TRUE)

# Create a matrix of only the adj. prices
price_matrix <- NULL
for(ticker in tickers){price_matrix <- cbind(price_matrix, get(ticker)[,6])}
# Set the column names for the price matrix
colnames(price_matrix) <- tickers

# Compute log returns
returns_matrix <- apply(price_matrix, 2, function(x) diff(log(x)))
returns_covar <- cov(returns_matrix)

# Specify lower and upper bounds for the allocation percentages
lower <- rep(0, ncol(returns_matrix))
upper <- rep(1, ncol(returns_matrix))

# Calculate the optimum allocation
set.seed(1234)
### USING YOUR CORRECTED CALL
optim_result <- DEoptim(calculate_portfolio_variance, lower, upper, control = list(NP=100, itermax=300, F=0.8, CR=0.9), returns_covar)