Time to ditch Yahoo Finance as the go-to source for stock price data?

I am a UK based, private investor. I like to do my own analysis of potential stock purchases and R is my tool of choice for much of this. One of the biggest problems as a private individual is obtaining high quality data – I just can’t justify the cost of a commercial data supplier. The normal approach is to use Yahoo Finance to download the data you need and this can easily be automated into R – either manually or using the quantmod package. However I’m increasingly frustrated by Yahoo finance. In fairness I’m only concerned with shares and ETFs that trade on the London Stock Exchange and it’s entirely possible that Yahoo doesn’t have these problems for US quoted shares. My main concerns are as follows.

1. Data Quality

I’ve written about this before. Here is another example, this time for iShares Core FTSE 100 Dist (ISF.L). I’m assuming the currency keeps changing between  GBX, GBP and either USD or EUR.
Image

2. Dividends

Dividends quoted on Yahoo are sometimes in the wrong currency. (e.g VUSA).

3. Adjusted Close Prices

This is a bit hit and miss. As you can see from the screen shot above the price has been adjusted. Frequently there is no adjustment and no dividends listed when they have been paid.

4. Questions about the long term future of the service

People are frequently predicting the imminent demise of the service. This may or may not be true, but doesn’t fill me with confidence given the other more serious problems listed.

The solution

Enter Quandl and specifically the data provided by the London Stock exchange. R has a specific package (Quandl) which makes downloading data incredibly easy. You can download data for a number of different securities in one go, select what data (columns and date ranges) you actually want and download directly as an xts object. You will need to register to obtain a free api key if you are going to make more than 50 calls per day on the api. (Replace your XXXXXX in my code with your key). The documentation is here and the cran page is here.
library(Quandl)
## Download all available data for ticker ISF
mydata <- Quandl("LSE/ISF", type = "xts", api_key= XXXXXXX)
tail(mydata)

                 Price   High    Low  Volume Last Close Change  Var%
2017-06-19 743.8 744.80 740.45 1788670      743.8    5.9  0.80
2017-06-20 738.4 761.45 738.20 2543313      738.4   -5.4 -0.73
2017-06-21 735.7 742.45 732.80 3275787      735.7   -2.7 -0.37
2017-06-22 734.9 735.90 730.95 2692453      734.9   -0.8 -0.11
2017-06-23 733.0 744.80 685.95 4659435      733.0   -1.9 -0.26
2017-06-26 735.2 738.85 735.10 2919664      735.2    2.2  0.30

## Download the first column (close) for ISF and VMID from 2016-12-31 until the end of the data
mydata2 <- Quandl(c("LSE/ISF.1", "LSE/VMID.1"), type = "xts", start_date="2016-12-31", 
                api_key= XXXXXXX)
head(mydata2)

                 LSE.ISF - Price LSE.VMID - Price
2017-01-03           708.9            29.17
2017-01-04           709.3            29.19
2017-01-05           710.0            29.46
2017-01-06           711.3            29.50
2017-01-09           714.0            29.61
2017-01-10           717.4            29.66

Obtaining the meta data which goes with each data set is a little more tricky as I can’t get the metaData function within the Quandl package to work for these data sets. The approach is therefore to download the xml file that contains the meta data, convert that into a data frame within R and then do some further processing to get the data I actually want.
library(XML)
library(RCurl)
xml.url <- "https://www.quandl.com/api/v3/datasets/LSE/ISF/metadata.xml?api_key=XXXXXXX"
xmldata <- getURL(xml.url)
xmldf <- xmlToDataFrame(xmldata, stringsAsFactors = F)
stockinfo <- xmldf[, c(2, 4, 8, 7)]
colnames(stockinfo) <- c("Code", "Name", "From", "To")
stockinfo$Currency <- substr(stockinfo$Name, nchar(stockinfo$Name)-2, nchar(stockinfo$Name))
stockinfo$Name <-  substr(stockinfo$Name,1,nchar(stockinfo$Name)-14)
stockinfo

Code  Name                               From          To              Currency
ISF   ISH COREFTSE100 ETF price (ISF)    2006-03-16    2017-06-26      GBX

Issues with Quandl Data

The solution is not perfect
  • You do not get full OHLC data if that is important to you.  However there is high, low, close and volume so you could use the previous close as open
  • There is no index data. Because data for total return indexes are really difficult to find I have always used accumulating ETFs if possible (e.g. CUKX for FTSE100). Personally if I’m using an index for comparison purposes then I would prefer to use something I could actually invest in ie an ETF, rather than a theoretical uninvestible index.
  • No dividends or adjusted close – more on this in a later blog post
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