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public class ErrorInfo { public ErrorInfo(string Code, String Message) { this.ErrorCode = Code; this.ErrorMessage = Message; } public string ErrorCode { get; set; } public string ErrorMessage { get; set; } public override string ToString() { return ErrorCode + ": " + ErrorMessage; } } In the region public members (#region public members), a Dictionary object is used to store the validation errors as a property name key and error value pair. Here is the code for it. #region public members public Dictionary<string, List<ErrorInfo>> Errors = new Dictionary<string, List<ErrorInfo>>(); #endregion Now it s time to implement three required members of the INotifyDataErrorInfo interface in the region INotifyDataErrorInfo Members (#region INotifyDataErrorInfo Members). #region INotifyDataErrorInfo Members public event EventHandler<DataErrorsChangedEventArgs> ErrorsChanged; public System.Collections.IEnumerable GetErrors(string propertyName) { if (string.IsNullOrEmpty(propertyName))//retrieve errors for entire entity { return Errors.Values; } else { if (Errors.ContainsKey(propertyName)) return Errors[propertyName]; return null; } } public bool HasErrors { get { if (Errors.Count == 0) return false; return true; }



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Tick volume differs from the volume reported for endof-day data series: For intraday data, it is the number of ticks that occur in the period making up the bar, regardless of the number of contracts involved in the transactions reflected in those ticks Only date, time, and price information are reported for individual ticks: volume is not Intraday tick data is easily converted into data with fixed-interval bars using readily available software Conversion software is frequently provided by the data vendor at no extra cost to the consumer In addition to commodities pricing data, other kinds of data may be of value For instance, long-term historical data on sunspot activity, obtained from the Royal Observatory of Belgium, is used in the chapter on lunar and solar influences Temperature and rainfall data have a bearing on agricultural markets.





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Downloaded from Digital Engineering Library @ McGraw-Hill (www.digitalengineeringlibrary.com) Copyright 2004 The McGraw-Hill Companies. All rights reserved. Any use is subject to the Terms of Use as given at the website.

As long as we are on the subject of Raman Scattering, we should introduce the concept of optical amplification. This may seem like a bit of a non sequitur, but it really isn t; true optical amplification actually uses a form of Raman Scattering to amplify the transmitted signal!

Various economic time series that cover every aspect of the economy, from inflation to housing starts, may improve the odds of trading commodities successfully Do not forget to examine reports and measures that reflect sentiment, such as the Commitment of Traders (COT) releases, bullish and bearish consensus surveys, and put-call ratios Nonquantitative forms of sentiment data, such as news headlines, may also be acquired and quantified for use in systematic tests Nothing should be ignored Mining unusual data often uncovers interesting and profitable discoveries It is often the case that the more esoteric or arcane the data, and the more difficult it is to obtain, the greater its value!.

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In a traditional metallic analog environment, transmitted signals tend to weaken over distance. To overcome this problem, amplifiers are placed in the circuit periodically to raise the power level of the signal. There is a problem with this technique, however: In addition to amplifying the signal, amplifiers also amplify whatever cumulative noise has been picked up by the signal during its trip across the network. Over time, it becomes difficult for a receiver to discriminate between the actual signal and the noise embedded in the signal. Extraordinarily complex recovery mechanisms are required to discriminate between optical wheat and noise chaff. In digital systems, regenerators are used to not only amplify the signal, but to also remove any extraneous noise that has been picked up along the way. Thus, digital regeneration is a far more effective signalrecovery methodology than is simple amplification.

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