Getting Historical Data from Yahoo Finance in R - DataScienceCentral.com (2024)

t1 <- ISOdate(2020,6,15) as.integer(t1)

## [1] 1592222400

Not quite so. What happened is that ISOdate function is defined as

ISOdate(year, month, day, hour = 12, min = 0, sec = 0, tz = “GMT”)

That is it gives us the number of seconds at noon. So let’s set it to the beginning of 15/06:

t1 <- ISOdate(2020,6,15,hour=0) as.integer(t1)

## [1] 1592179200

andvoilà. What about 19/06/2020? Well, it turns out that both dates are codified as

t1 <- ISOdate(2020,6,15,hour=0)
 t2 <- ISOdate(2020,6,19,hour=0)
 as.integer(t1)

## [1] 1592179200

as.integer(t2)

## [1] 1592524800

Now we see both 1592179200 and 1592524800 from the URL. This also explains why we asked for data up to 19/06 and got only up to 18/06: markets were closed by 00:00 19/06/2020. This is something you must correct for, if you should stay faithful to your original query. Fortunately, this correction is easy: all you have to do is to ask for data from the next day, or from midnight. So both

as.integer(ISOdate(2020,6,19,hour=24))

## [1] 1592611200

as.integer(ISOdate(2020,6,20,hour=0))

## [1] 1592611200

will return the desired number. Or you might just forget about it, as I did in this example.

So, the URL in our example may finally be assembled with

stock <- "VALE"
url <- paste("https://query1.finance.yahoo.com/v7/finance/download/",
 stock,
 "?period1=",
 as.integer(t1),
 "&period2=",
 as.integer(t2),
 "&interval=1d&events=history",
 sep="")
 url

## [1] "https://query1.finance.yahoo.com/v7/finance/download/VALE?period1=1592179200&period2=1592524800&interval=1d&events=history"

(here I kept the 1d interval, but obviously you can change it at will).

Downloading the .csv File

Now that we have the URL, all we have to do is to download the file. To do this, an alternative would be


dataset <- read.csv(url)

and you’ll have your data frame with Yahoo Finance’s data.


str(dataset)

## 'data.frame': 4 obs. of 7 variables:
## $ Date : Factor w/ 4 levels "2020-06-15","2020-06-16",..: 1 2 3 4
 ## $ Open : num 10.1 10.8 10.6 10.5
 ## $ High : num 10.6 10.9 10.8 10.6
 ## $ Low : num 10.1 10.4 10.5 10.4
 ## $ Close : num 10.6 10.7 10.7 10.6
 ## $ Adj.Close: num 10.6 10.7 10.7 10.6
 ## $ Volume : int 33837300 43970200 34886400 34436100

But that comes for a price

• Every time we run our code, we download the very same dataset; and
• We must savedatasetif we wish to use it offline in the future

Alternatively, we could download a local copy of the data

fileName <- "my_dataset.csv"
 download.file(url, fileName)

and read it whenever we feel like

dataset2 <- read.csv(fileName)

str(dataset2)

## 'data.frame': 4 obs. of 7 variables:
## $ Date : Factor w/ 4 levels "2020-06-15","2020-06-16",..: 1 2 3 4
 ## $ Open : num 10.1 10.8 10.6 10.5
 ## $ High : num 10.6 10.9 10.8 10.6
 ## $ Low : num 10.1 10.4 10.5 10.4
 ## $ Close : num 10.6 10.7 10.7 10.6
 ## $ Adj.Close: num 10.6 10.7 10.7 10.6
 ## $ Volume : int 33837300 43970200 34886400 34436100

There you are. Your dataset is ready for action.

And that’s all for the moment. Hope you find this small contribution useful.