Quandl has indexed millions of time-series datasets from over 400 sources. All of Quandl’s datasets are open and free. This is great news but before performing any backtest using Quandl data, I want to compare it with a trusted source: Bloomberg for the purpose of this post. I will focus only on daily Futures data here (front month contract).

In the table below, the first few columns are self explanatory, the last two columns are:

• Intersec. the number of common dates between Quandl and Bloomberg as of November 14, 2013
• Mismatch the number of different closing prices among those common dates (Intersec. ≥ Mismatch).

Main findings

• I managed to find Bloomberg tickers for 60 contracts out of 68 available on Quandl
• At first glance differences seem scary but digging a bit deeper reveals a different picture.
• For some contracts Bloomberg and Quandl don’t use the same number of decimals which inflate artificially the mismatch numbers
• Some contracts are not expressed in the same unit between Bloomberg and Quandl. I had to apply a multiplier to make an “apple to apple” comparison.
• Differences seem to cluster in time. It seems like there are different rules for building front month contract (or data source changes?)
• Differences are often more pronounced at the beginning of the sample.
• I used PX_LAST field in Bloomberg which might not be the settlement price for all contracts (I have to check this). Quandl uses a settlement price. This might explain some differences as well.

Conclusion

This post isn’t a fully-fledged analysis but it highlights potential issues and it’s up to the interested reader to solve them. I don’t think there are major issues with Quandl data but I wouldn’t use it for serious analysis until I understand better the differences with Bloomberg. This means going through each contract individually and trying to understand the source of the differences. Finally I put below my Quandl wish list:

• Add  European Equity Indices Futures coverage (I’m not sure a free source even exists)
• Add an ETF category to Quandl list of instruments
• Provide more accurate availability schedule for all instruments

For anyone keen on reproducing this analysis, I saved the code used in this post on a Gist/Github repository.

Any comments welcome