Hello everyone, I'm asking for your help because I have a programming assignment in progress on R for market finance. the exercise is the following:
< Redacted : >
My biggest problem is to set up the portfolios, I can't find a formula in the PerformanceAnalytics package and in the PortfolioAnalytics package I can't use the different functions.
If someone could help me it would be great.
Thank you very much, have a nice day.
I think we need a see your code and some sample data.
oh yes sorry, this is what I have to do :
< Redacted >
and I quickly get stuck when it comes to building portfolios and calculating returns and standard deviations for each.
for the I have this :
get_stock_data <- function(stock_list, date_debut, date_fin){
stock_list_df <- vector(length = length(stock_list), 'list')
i = 1
for (stock_symbol in stock_list) {
stock_results <- tq_get(stock_symbol, from = date_debut, to = date_fin, get = "stock.prices")
stock_results <- as.numeric(unlist(stock_results[, 6]))
n <- length(stock_results)
stock_results$returns <- ((stock_results[2:n] -stock_results[1:(n-1)])/stock_results[1:(n-1)])
mean_returns <- mean(stock_results$returns, na.rm = TRUE)
sd_returns <- sd(stock_results$returns, na.rm = TRUE)
results_data <- tibble(Stock_symbol = stock_symbol, Mean_returns = mean_returns, SD_returns = sd_returns )
stock_list_df[[i]] <- results_data
i <- i + 1
#return(results_data)
}
return(do.call("rbind", stock_list_df))
}
#creation d'une liste de stocks
stock_list <- c("AAPL", "MSFT","TSLA", "AMZN", "GOOG", "JPM", "PFE", "BLK", "IBM", "NKE")
#appel de la fonction pour les stocks et les donner
results_stocks <- get_stock_data(stock_list, "2022-01-01", "2023-01-01")
# Nombre de portefeuilles à créer
num_portfolios <- 10
# Initialisation d'une matrice pour stocker les poids des portefeuilles
portfolio_weights <- matrix(0, ncol = length(stock_list), nrow = num_portfolios)
# Boucle pour créer chaque portefeuille
for (i in 1:num_portfolios) {
# Génération aléatoire des poids de chaque stock
weights <- runif(length(stock_list))
# Normalisation des poids pour que la somme soit égale à 1
weights <- weights / sum(weights)
# Stockage des poids dans la matrice
portfolio_weights[i, ] <- weights
}
# Conversion de la matrice des poids en un data.frame
portfolio_weights_df <- as.data.frame(portfolio_weights)
# Ajout des noms de colonnes pour chaque stock
colnames(portfolio_weights_df) <- stock_list
I have two databases, one with the stocks and their average yield and average standard deviation
and 1 other with the weights for each asset in each portfolio. and I can't calculate the average return and the deviation of each portfolio according to the weights
thank's
Thanks, this is totally outside my area but it looks like a start for someone who knows a bit about it. However we need to know what R packages you have loaded. For example when I ran your code I get an error could not find function "tq_get". I do not see it in the PerformanceAnalytics package and in the PortfolioAnalytics package that you mention. BTW' are you loading either of them?
I think we, also need some sample data. You say you have two data sets. We probably need data from both.
A handy way to supply some sample data is the dput() function. In the case of a large dataset something like dput(head(mydata, 100)) should supply the data we need. Just do dput(mydata) where mydata is your data. Copy the output and paste it here.
BTW your code is a bit messy here. I think you have a few ``` in the wrong spots.
I think this is what you intended.
get_stock_data <- function(stock_list, date_debut, date_fin){
stock_list_df <- vector(length = length(stock_list), 'list')
i = 1
for (stock_symbol in stock_list) {
stock_results <- tq_get(stock_symbol, from = date_debut, to = date_fin, get = "stock.prices")
stock_results <- as.numeric(unlist(stock_results[, 6]))
n <- length(stock_results)
stock_results$returns <- ((stock_results[2:n] -stock_results[1:(n-1)])/stock_results[1:(n-1)])
mean_returns <- mean(stock_results$returns, na.rm = TRUE)
sd_returns <- sd(stock_results$returns, na.rm = TRUE)
results_data <- tibble(Stock_symbol = stock_symbol, Mean_returns = mean_returns, SD_returns = sd_returns )
stock_list_df[[i]] <- results_data
i <- i + 1
# return(results_data)
}
return(do.call("rbind", stock_list_df))
}
#creation d'une liste de stocks
stock_list <- c("AAPL", "MSFT","TSLA", "AMZN", "GOOG", "JPM", "PFE", "BLK", "IBM", "NKE")
#appel de la fonction pour les stocks et les donner
results_stocks <- get_stock_data(stock_list, "2022-01-01", "2023-01-01")
# Nombre de portefeuilles à créer
num_portfolios <- 10
# Initialisation d'une matrice pour stocker les poids des portefeuilles
portfolio_weights <- matrix(0, ncol = length(stock_list), nrow = num_portfolios)
# Boucle pour créer chaque portefeuille
for (i in 1:num_portfolios) {
# Génération aléatoire des poids de chaque stock
weights <- runif(length(stock_list))
# Normalisation des poids pour que la somme soit égale à 1
weights <- weights / sum(weights)
Stockage des poids dans la matrice
portfolio_weights[i, ] <- weights
}
# Conversion de la matrice des poids en un data.frame
portfolio_weights_df <- as.data.frame(portfolio_weights)
# Ajout des noms de colonnes pour chaque stock
colnames(portfolio_weights_df) <- stock_list
Hi, welcome!
Please have a look to our homework policy, homework inspired questions are welcome but they should not include verbatim instructions from your course.
Thank you for assisting the homeworkee, they key detail is only not to verbatim reproduce the text of the set text. This is a courtesy to insituations that set such work, to limit the degree to which it is distributed, and to encourage the one seeking support to phrase the issue in their own words and compile an example
Ah, a point I had not considered. Accidental plagarism? 
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