How to download intraday stock market data with R

Question

All,

I'm looking to download stock data either from Yahoo or Google on 15 - 60 minute intervals for as much history as I can get. I've come up with a crude solution as follows:

library(RCurl)
tmp <- getURL('https://www.google.com/finance/getprices?i=900&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL')
tmp <- strsplit(tmp,'\n')
tmp <- tmp[[1]]
tmp <- tmp[-c(1:8)]
tmp <- strsplit(tmp,',')
tmp <- do.call('rbind',tmp)
tmp <- apply(tmp,2,as.numeric)
tmp <- tmp[-apply(tmp,1,function(x) any(is.na(x))),]

Given the amount of data I'm looking to import, I worry that this could be computationally expensive. I also don't for the life of me, understand how the time stamps are coded in Yahoo and Google.

So my question is twofold--what's a simple, elegant way to quickly ingest data for a series of stocks into R, and how do I interpret the time stamping on the Google/Yahoo files that I would be using?

Loganloganberry

It gives me authorization failure when trying to use getURL. I have been using it myself for some auction websites, and I use functions of the application Emacs to have code run at a time interval. It can even edit the text for you when you program it. I don't know if the time part is still unresolved? – Hospitaler

Answer 1

I will try to answer timestamp question first. Please note this is my interpretation and I could be wrong.

Using the link in your example https://www.google.com/finance/getprices?i=900&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL I get following data :

EXCHANGE%3DNASDAQ
MARKET_OPEN_MINUTE=570
MARKET_CLOSE_MINUTE=960
INTERVAL=900
COLUMNS=DATE,CLOSE,HIGH,LOW,OPEN,VOLUME
DATA=
TIMEZONE_OFFSET=-300
a1357828200,528.5999,528.62,528.14,528.55,129259
1,522.63,528.72,522,528.6499,2054578
2,523.11,523.69,520.75,522.77,1422586
3,520.48,523.11,519.6501,523.09,1130409
4,518.28,520.579,517.86,520.34,1215466
5,518.8501,519.48,517.33,517.94,832100
6,518.685,520.22,518.63,518.85,565411
7,516.55,519.2,516.55,518.64,617281
...
...

Note the first value of first column a1357828200, my intuition was that this has something to do with POSIXct. Hence a quick check :

> as.POSIXct(1357828200, origin = '1970-01-01', tz='EST')
[1] "2013-01-10 14:30:00 EST"

So my intuition seems to be correct. But the time seems to be off. Now we have one more info in the data. TIMEZONE_OFFSET=-300. So if we offset our timestamps by this amount we should get :

as.POSIXct(1357828200-300*60, origin = '1970-01-01', tz='EST')
[1] "2013-01-10 09:30:00 EST"

Note that I didn't know which day data you had requested. But quick check on google finance reveals, those were indeed price levels on 10th Jan 2013.

Remaining values from first column seem to be some sort of offset from first row value.

Bignonia

Good question. Good answer! Some days ago I realized that this google finance API doesn't work anymore. So when I copy paste the link of your answer/ question I'm forwarded to the standard google search result if I type "AAPL" into the google search field. Does anyone have a solution, or is there a new weblink I just haven't found yet? Thanks. – Forespent

Answer 2

So downloading and standardizing the data ended up being more much of a bear than I figured it would--about 150 lines of code. The problem is that while Google provides the past 50 training days of data for all exchange-traded stocks, the time stamps within the days are not standardized: an index of '1,' for example could either refer to the first of second time increment on the first trading day in the data set. Even worse, stocks that only trade at low volumes only have entries where a transaction is recorded. For a high-volume stock like APPL that's no problem, but for low-volume small caps it means that your series will be missing much if not the majority of the data. This was problematic because I need all the stock series to lie neatly on to of each other for the analysis I'm doing.

Fortunately, there is still a general structure to the data. Using this link:

https://www.google.com/finance/getprices?i=1800&p=1000d&f=d,o,h,l,c,v&df=cpct&q=AAPL 

and changing the stock ticker at the end will give you the past 50 days of trading days on 1/2-hourly increment. POSIX time stamps, very helpfully decoded by @geektrader, appear in the timestamp column at 3-week intervals. Though the timestamp indexes don't invariably correspond in a convenient 1:1 manner (I almost suspect this was intentional on Google's part) there is a pattern. For example, for the half-hourly series that I looked at the first trading day of ever three-week increment uniformly has timestamp indexes running in the 1:15 neighborhood. This could be 1:13, 1:14, 2:15--it all depends on the stock. I'm not sure what the 14th and 15th entries are: I suspect they are either daily summaries or after-hours trading info. The point is that there's no consistent pattern you can bank on.The first stamp in a training day, sadly, does not always contain the opening data. Same thing for the last entry and the closing data. I found that the only way to know what actually represents the trading data is to compare the numbers to the series on Google maps. After days of futiley trying to figure out how to pry a 1:1 mapping patter from the data, I settled on a "ballpark" strategy. I scraped APPL's data (a very high-volume traded stock) and set its timestamp indexes within each trading day as the reference values for the entire market. All days had a minimum of 13 increments, corresponding to the 6.5 hour trading day, but some had 14 or 15. Where this was the case I just truncated by taking the first 13 indexes. From there I used a while loop to essentially progress through the downloaded data of each stock ticker and compare its time stamp indexes within a given training day to the APPL timestamps. I kept the overlap, gap-filled the missing data, and cut out the non-overlapping portions.

Sounds like a simple fix, but for low-volume stocks with sparse transaction data there were literally dozens of special cases that I had to bake in and lots of data to interpolate. I got some pretty bizarre results for some of these that I know are incorrect. For high-volume, mid- and large-cap stocks, however, the solution worked brilliantly: for the most part the series either synced up very neatly with the APPL data and matched their Google Finance profiles perfectly.

There's no way around the fact that this method introduces some error, and I still need to fine-tune the method for spare small-caps. That said, shifting a series by a half hour or gap-filling a single time increment introduces a very minor amount of error relative to the overall movement of the market and the stock. I am confident that this data set I have is "good enough" to allow me to get relevant answers to some questions that I have. Getting this stuff commercially costs literally thousands of dollars.

Thoughts or suggestions?

Loganloganberry

Interactive Brokers does not cost thousands of dollars and you can get intraday data for thousands of stocks, bonds, futures, forex, options, etc. See the IBrokers package and my twsInstrument package. Other thoughts: stat.ethz.ch/pipermail/r-sig-finance/2013q1/011417.html – Mixologist

this looks good. you would need an ibrokers account to implement these packages, correct? at the moment i'm with optionshouse and will need to lean on my solution for scraping Google in the short-run. consistent access to high-resolution data my provide yet another incentive to switch over, though. – Loganloganberry

yes you'd need an IB account. There's a maintenance fee of around $10-$20 per month I think, but that fee is waived if you spend that much in commissions. – Mixologist

got it, thank you. until i set up with IB it seems that scraping Google or Yahoo data is my best bet. those are some good pointers though. the fact that there are R packages out there to allow a user to interact with IB's data streams is super cool... – Loganloganberry

Answer 3

Why not loading the data from Quandl? E.g.

library(Quandl)
Quandl('YAHOO/AAPL')

---

Update: sorry, I have just realized that only daily data is fetched with Quandl - but I leave my answer here as Quandl is really easy to query in similar cases

Proteus

Answer 4

For the timezone offset, try:

as.POSIXct(1357828200, origin = '1970-01-01', tz=Sys.timezone(location = TRUE))

(The tz will automatically adjust according to your location)

Vanessa

This is old question with an accepted answer. Could you add why your answer is better / different? – Aponte

This is a international answer. No need to adjust for time zones within the as.POSIXct function. (By adding tz=Sys.timezone(location = TRUE)) – Vanessa