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expected given a normally distributed underlying process. Nevertheless, the events of 10/19/87 and 10/13/89 appear to be statistically exceptional: The distribution of all other range data declined, in an orderly fashion, to zero at a standardized value of 7, well below the range of 10 seen for the critical bars. The data-checking utility also flagged 5 bars as having exceptionally deviant closing prices. As with range, deviance has been defined in terms of a distribution, using a standardized close-to-close price measure. In this instance, the standardized measure was computed by dividing the absolute value of the difference between each closing price and its predecessor by the average of the preceding 20 such absolute values. When the 5 flagged (and most deviant) bars were omitted, the same distributional behavior that characterized the range was observed: a longtailed distribution of close-to-close price change that fell off, in an orderly fasbion, to zero at 7 standardized units. Standardized close-to-close deviance scores (DEV) of 8 were noted for 3 of the aberrant bars, and scores of 10 were observed for the remaining 2 bars. Examination of the flagged data points again suggests that unusual market activity, rather than data error, was responsible for their statistical deviance. It is not surprising that the 2 most deviant data points were the same ones noted earlier for their abnormally high range. Finally, the data-checking software did not find any missing bars, bars falling on weekends, or bars with duplicate or out-of-order dates. The only outliers detected appear to be the result of bizarre market conditions, not cormpted data. Overall, the S&P 500 data series appears to be squeaky-clean. This was expected: In our experience, Pinnacle Data Corporation (the source of the data) supplies data of very high quality. As an example of how bad data quality can get, and the kinds of errors that can be expected when dealing with low-quality data, another data set was analyzed with the same data-checking utility. This data, obtained from an acquaintance, was for Apple Computer (AAPL). The data-checking results appear in Table l-2. In this data set, unlike in the previous one, 2 bars were flagged for having outright logical inconsistencies. One logically invalid data point had an opening price of zero, which was also lower than the low, while the other bar had a high price that was lower than the closing price. Another data point was detected as having an excessive range, which may or may not be a data error, In addition, several bars evidenced extreme closing price deviance, perhaps reflecting uncorrected stock splits. There were no duplicate or out-of-order dates, but quite a few data points were missing. In this instance, the missing data points were holidays and, therefore, only reflect differences in data handling: for a variety of reasons, we usually fill holidays with data from previous bars. Considering that the data series extended only from l/2/97 through 1 l/6/98 (in contrast to the S&P 500, which ran from l/3/83 to 5/21/98), it is distressing that several serious errors, including logical violations, were detected by a rather simple scan. The implication of this exercise is that data should be purchased only from a
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Output from Data-Checking Utility for Apple Computer, Symbol AAPL
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reputable vendor who takes data quality seriously; this will save time and ensure reliable, error-free data for system development, testing, and trading, In addition, all data should be scanned for errors to avoid disturbing surprises. For an in-depth discussion of data quality, which includes coverage of how data is produced, transmitted, received, and stored, see Jurik (1999).
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Today there are a great many sowces from which data may be acquired. Data may be purchased from value-added vendors, downloaded from any of several exchanges, and extracted from a wide variety of databases accessible over the Internet and on compact discs. Value-added vendors, such as Tick Data and Pinnacle, whose data have been used extensively in this work, can supply the trader with relatively clean data in easy-to-use form. They also provide convenient update services and, at least in the case of Pinnacle, error corrections that are handled automatically by the downloading software, which makes the task of maintaining a reliable, up-to-date database very straightforward. Popular suppliers of end-of-day commodities data include Pinnacle Data Corporation (800-724-4903), Prophet Financial Systems (650-322-4183). Commodities Systems Incorporated (CSI, 800.274.4727), and Technical Tools (800-231-8005). Intraday historical data, which are needed for testing short time frame systems, may be purchased from Tick Data (SOO-8228425) and Genesis Financial Data Services (800-62 l-2628). Day traders should also look into Data Transmission Network (DTN, SOO-485-4000), Data Broadcasting Corporation (DBC, 800.367.4670), Bonneville Market Information (BMI, 800-532-3400), and FutureSource-Bridge (X00-621 -2628); these data distributors can provide the fast, real-time data feeds necessary for successful day trading. For additional information on data sources, consult Marder (1999). For a comparative review of end-of-day data, see Knight (1999). Data need not always be acquired from a commercial vendor. Sometimes it can be obtained directly from the originator. For instance, various exchanges occasionally furnish data directly to the public. Options data can currently be downloaded over the Internet from the Chicago Board of Trade (CBOT). When a new contract is introduced and the exchange wants to encourage traders, it will often release a kit containing data and other information of interest. Sometimes this is the only way to acquire certain kinds of data cheaply and easily. FiiaIly, a vast, mind-boggling array of databases may be accessed using an Internet web browser or ftp client. These days almost everything is on-line. For example, the Federal Reserve maintains files containing all kinds of economic time series and business cycle indicators. NASA is a great source for solar and astronomical data. Climate and geophysical data may be downloaded from the National Climatic Data Center (NCDC) and the National Geophysical Data Center (NGDC), respectively.
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For the ardent net-surfer, there is an overwh&lming abundance of data in a staggering variety of formats. Therein, however, lies another problem: A certain level of skill is required in the art of the search, as is perhaps some basic programming or scripting experience, as well as the time and effort to find, tidy up, and reformat the data. Since time is money, it is generally best to rely on a reputable, value-added data vendor for basic pricing data, and to employ the Internet and other sources for data that is more specialized or difficult to acquire. Additional sources of data also include databases available through libraries and on compact discs. ProQuest and other periodical databases offer full text retrieval capabilities and can frequently be found at the public library. Bring a floppy disk along and copy any data of interest. Finally, do not forget newspapers such as Investor s Business Daily, Barron s, and the Wall Street Journal; these can be excellent sources for certain kinds of information and are available on microfilm from many libraries. In general, it is best to maintain data in a standard text-based (ASCII) format. Such a format has the virtue of being simple, portable across most operating systems and hardware platforms, and easily read by all types of software, from text editors to charting packages.
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