R Code Snippet : Read and Concatenate Prices of Constituents of a Stock Index

This post shows how to read all prices of constituents of a stock index given all symbols as a string. It is a prerequisite of the pairs trading backtest.

Pairs trading aims to select good performing pairs from a set of universe of a stock index. It is, therefore, a starting point of pairs trading backtest to collect daily stock prices of a given stock index.

Sometimes there are symbols for which price information is not available. In this case, we should check the all components of the stock index. In case of Nasdaq 100, recent information can be found at

https://www.slickcharts.com/nasdaq100

R code

The following R code retreives historical daily prices of constituents of the Nasdaq 100 index given all symbols of it as of 2022-07-29.

#========================================================#
# Quantitative ALM, Financial Econometrics & Derivatives 
# ML/DL using R, Python, Tensorflow by Sang-Heon Lee 
#
# https://kiandlee.blogspot.com
#--------------------------------------------------------#
# load prices of constituents of a stock index
#========================================================#
 
graphics.off(); rm(list = ls())
 
library(quantmod)
 
#-------------------------------------------------
# Components of the Nasdaq 100, as of 2022-07-29
#-------------------------------------------------
vstr_nasdaq100 <- 
    "AAPL,MSFT,AMZN,TSLA,GOOG,GOOGL,NVDA,META,PEP,COST,
    AVGO,CMCSA,ADBE,CSCO,TMUS,QCOM,INTC,TXN,AMD,AMGN,
    HON,INTU,NFLX,PYPL,ADP,SBUX,AMAT,MDLZ,ADI,ISRG,
    CHTR,GILD,BKNG,VRTX,CSX,MU,FISV,LRCX,REGN,MNA,
    ATVI,SNPS,KLAC,KDP,MNST,MAR,AEP,CDNS,PANW,NXPI,
    ASML,FTNT,ORLY,PAYX,MRVL,KHC,ADSK,EXC,CTAS,ABNB,
    MELI,AZN,XEL,CRWD,EA,MCHP,CTSH,LULU,DLTR,WBA,
    DXCM,ILMN,SGEN,IDXX,JD,BIIB,ODFL,PCAR,LCID,BIDU,
    WDAY,CPRT,VRSK,TEAM,ROST,FAST,ZM,DDOG,EBAY,SIRI,
    PDD,ANSS,ZS,ALGN,MTCH,VRSN,CEG,NTES,SWKS,SPLK,
    OKTA,DOCU"
 
#-------------------------------------------
# split symbols and make vector
#-------------------------------------------
nasdaq100_symbols <- 
    gsub(" ", "", strsplit(vstr_nasdaq100, 
                           "\\s*,\\s*")[[1]])
nasdaq100_symbols
 
#-------------------------------------------
# read price information of constituents
#-------------------------------------------
sdate <- as.Date("2020-07-01")
edate <- as.Date("2022-06-30")
getSymbols(nasdaq100_symbols,from=sdate,to=edate)
 
#-------------------------------------------
# collect only adjusted prices
#-------------------------------------------
price <- NULL
for(i in 1:length(nasdaq100_symbols)) {
    eval(parse(text=paste0(
        "price <- cbind(price,",
        nasdaq100_symbols[i],"[,6])")))
}
 
# modify column name as only symbol
colnames(price) <- 
    gsub(".Adjusted", "", colnames(price))
 
#-------------------------------------------
# print price time series of Components
#-------------------------------------------
head(price)
tail(price)

Running the above R code displays the status of data reading process as follows.

Finally, we can get the collection of individual stock prices, which are components of the Nasdaq 100 index

Originally posted on SH Fintech Modeling.

Disclosure: Interactive Brokers

Information posted on IBKR Campus that is provided by third-parties and not by Interactive Brokers does NOT constitute a recommendation by Interactive Brokers that you should contract for the services of that third party. Third-party participants who contribute to IBKR Campus are independent of Interactive Brokers and Interactive Brokers does not make any representations or warranties concerning the services offered, their past or future performance, or the accuracy of the information provided by the third party. Past performance is no guarantee of future results.

This material is from SH Fintech Modeling and is being posted with permission from SH Fintech Modeling. The views expressed in this material are solely those of the author and/or SH Fintech Modeling and IBKR is not endorsing or recommending any investment or trading discussed in the material. This material is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.

In accordance with EU regulation: The statements in this document shall not be considered as an objective or independent explanation of the matters. Please note that this document (a) has not been prepared in accordance with legal requirements designed to promote the independence of investment research, and (b) is not subject to any prohibition on dealing ahead of the dissemination or publication of investment research.

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.