A couple questions:

1) I see bad tick data on a regular basis, in downloaded tick data, in instruments like the S&P 500 sector ETFs. I assume the same for individual stock data though I don’t gather that. I don’t see the same thing for liquid futures coming from the CME for example. See below for a sample. Do your clients have to write their own algorithms to eliminate these programmatically?

2) I’ve often noted a discrepancy between volume data for tick downloads that I’ve summed myself and the Total Volume reported in the IQFeed tick data. See below for an example from 2/22/19 business day that began 2/21/19 at 6pm ET. Seems like some records were dropped that totaled 7 contracts in this single example. At the end of the day the difference can be quite substantial. I downloaded this data three times over several days and all results were identical. Can you explain what may be happening here?

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Hello, concerning the trades in question. These are valid trades so it will be up to you to filter within your app. However we provide some help on filtering these by sending the trade conditions for each trade along with a classification of last-qualified or non-last-qualified. In your example, all of these trades are non-last-qualifed ODDLOT trades. Additionally, 4 of the 5 are labeled as CASH trades with the 5th being labeled as TTEXEMPT.

You will have to do some experimentation to figure out exactly which types trades you want to exclude from your analysis.

I am still looking into the volume discrepancy however, generally speaking, summation of tick volumes and comparing to total volumes is not a valid data verification technique because frequently these will not match up due to corrections, inserts, deletes, etc by the exchange.