I have recently discovered Quandl, which, per their website description:
[…] has indexed millions of time-series data from over 400 sources. All of Quandl’s datasets are open and free. You can download any Quandl dataset in any format that you want. You can also visualize, save, share, authenticate, validate, upload, index, merge and transform data. Our long-term goal is to make all the numerical data on the internet easy to find & easy to use.
Quandl offers an impressive amount of data (not only stocks but economic data, social, health, etc.) that is scrubbed, indexed and setup in neat time series one can search and integrate into any apps (downloads for Excel, CSV, R or JSON) or query using their API (web service).
I tested with simple sets in Python and it is very easy to use and very quick to produce results. First test was pulling the GOOG closing prices:
import Quandl goog = Quandl.get("GOOG/NYSE_IBM", collapse="weekly") top = plt.subplot2grid((4,4), (0, 0), rowspan=3, colspan=4) top.plot(goog.index, goog["Close"]) plt.title('Google Stock Price from 1998 - 2012') bottom = plt.subplot2grid((4,4), (3,0), rowspan=1, colspan=4) bottom.bar(goog.index, goog['Volume']) plt.title('Google Trading Volume in Millions') plt.tight_layout() plt.gcf().set_size_inches(12,8)
For the next test, I built a quick superset from the market cap data for a few instruments. In one statement, I can load the set in a frame:
import Quandl marketCap = Quandl.get("USER_230/231.csv?", \ trim_start="2000-12-31", \ trim_end="2012-12-31", \ collapse="annual") marketCap.plot(figsize=(12,8))
Overall, I was very impressed with the amount of data available and with the ease of use. I don’t know what the long term plans are for Quandl but for now, it should come in very handy to anyone looking to analyse data, be it the closing price of a stock or the percentage of population using improved sanitation facilities in Argentina.
Good work Quandl!