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I am obtaining bid/ask price and volume market data from two different sources for the same ticker and for the same day and checking to see that at time intervals X they are "roughly the same". The timestamps from the two different sources are not exactly the same, though, and so what I am doing is dumping the price every time the timestamps span a different second of the day. Sometimes the price varies in between that second, sometimes it doesn't, but regardless I dump the way it looks at the beginning of that second (or millisecond). After doing this for both sources, I did a simple plot of the results and things are looking consistent graphically:

All the ask observations throughout the day for source 1

All the ask observations throughout the day for source 2

The time stamps do not always align, though when I intersect the time stamp columns and get a subset of the observations, and cross correlate them I get relatively poor values. Worse at higher granularities, probably due to time stamp lag-like discrepancies. I'm not sure if I should be looking at some other metric to convince myself that both sources are providing me with similar information about the ask/bid throughout the day, or what the appropiate methodology is to compare these two time series with each other. Is it cointegration between them that I am looking for? What I care to confirm is that, say, assuming the first source I know to be accurate data from which I build my view of the bid/ask throughout the day - that the second source is not too off.

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mean absolute error is a start –  pyCthon Oct 30 '12 at 20:26
    
It would be interesting to see a scatterplot of the data. Even though you have poor cross correlations, you might be able to detect some level shifts in the plot and formulate if there is an easy calibration for the shifts. –  pat Oct 31 '12 at 0:25
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