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Data quality varies from excellent to awful. Since bad data can wreak havoc with all forms of analysis, lead to misleading results, and waste precious time, only use the best data that can be found when running tests and trading simulations. Some
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forecasting models, including those based on neural networks, can be exceedingly sensitive to a few errant data points; in such cases, the need for clean, error-free data is extremely important. Time spent finding good data, and then giving it a final scrubbing, is time well spent. Data errors take many forms, some more innocuous than others. In real-time trading, for example, ticks are occasionally received that have extremely deviant, if not obviously impossible, prices. The S&P 500 may appear to be trading at 952.00 one moment and at 250.50 the next! Is this the ultimate market crash No-a few seconds later, another tick will come along, indicating the S&P 500 is again trading at 952.00 or thereabouts. What happened A bad tick, a noise spike, occurred in the data. This kind of data error, if not detected and eliminated, can skew the results produced by almost any mechanical trading model. Although anything but innocuous, such errors are obvious, are easy to detect (even automatically), and are readily corrected or otherwise handled. More innocuous, albeit less obvious and harder to find, are the common, small errors in the settling price, and other numbers reported by the exchanges, that are frequently passed on to the consumer by the data vendor. Better data vendors repeatedly check their data and post corrections as such errors are detected. For example, on an almost daily basis, Pinnacle Data posts error corrections that are handled automatically by its software. Many of these common, small errors are not seriously damaging to software-based trading simulations, but one never knows for sure. Depending on the sensitivity of the trading or forecasting model being analyzed, and on such other factors as the availability of data-checking software, it may be worthwhile to run miscellaneous statistical scans to highlight suspicious data points. There are many ways to flag these data points, or ourlieru, as they are sometimes referred to by statisticians. Missing, extra, and logically inconsistent data points are also occasionally seen; they should be noted and corrected. As an example of data checking, two data sets were run through a utility program that scans for missing data points, outliers, and logical inconsistencies. The results appear in Tables I-1 and 1-2, respectively. Table I 1 shows the output produced by the data-checking program when it was used on Pinnacle Data Corporation s (800-724-4903) end-of-day, continuous-contract data for the S&P 500 futures. The utility found no illogical prices or volumes in this data set; there were no observed instances of a high that wan less than the close, a low that was greater than the open, a volume that was less than zero, or of any cognate data faux pas. Rvo data points (bars) with suspiciously high ranges, however, were noted by the software: One bar with unusual range occurred on 1 O/l 9/87 (or 871019 in the report). The other was dated 10/13/89. The abnormal range observed on 10/19/87 does not reflect an error, just tbe normal volatility associated with a major crash like that of Black Monday; nor is a data error responsible for the aberrant range seen on 10/13/89, which appeared due to the so-called anniversary effect. Since these statistically aberrant data points were not errors, corrections were unnecessary.
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Nonetheless, the presence of such data points should emphasize the fact that market events involving exceptional ranges do occur and must be managed adequately by a trading system. All ranges shown in Table l-l are standardized ranges, computed by dividing a bar s range by the average range over the last 20 bars. As is common with market data, the distribution of the standardized range had a longer tail than would be
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Output from Data-Checking Utility for End-of-Day S&P 500 Continuous-Contract Futures Data from Pinnacle
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