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Before Dr Van Tharp s career as a psychologist, author, trading coach, and Market Wizard, he was a neurolinguistic programmer (NLP) modeler For 25 years, he has been applying his research about NLP modeling to help traders think differently about the way they approach the market Each month, he monitors market volatility and direction I measure the volatility of the S&P 500 defined by a rolling 20-day average true range (ATR) as a percentage of the close, which I call ATR%, he says The mean of all of these 20-day windows over the last 60 years is about 131 percent, and the standard deviation is 072 percent When the ATR% is more than three standard deviations above the mean, he considers the market to be very volatile This extreme

Click on the Evaluate Expression button to display the output of the expression in the Evaluated Value text box. (At this point it may be useful to copy and paste this value into a SQL Server Management Studio query window to ensure that the expression was constructed correctly.)

First, it declares the myToDos variable as containing every todo item in the calendar called Work Next, it starts a repeat loop with a counter variable (myCounter) to run from 1 to the count of myToDos in other words, once for each of the todo items found in the calendar

Click on the OK buttons to close the Expression Builder and Property Expressions Editor windows and save all changes. Execute the package to ensure that the functionality added through the expression behaves as required.

volatility only occurs in bear markets, he explains When the ATR% is between +05 and +3 standard deviations, the market is volatile When the ATR% is between +05 and 05 standard deviations, the market has normal volatility When the ATR% is less than 05 standard deviations, then the market is in quiet mode As a rule, he says, bull markets tend to range from quiet to volatile, and bear markets tend to be normal to very volatile

Several important techniques are demonstrated in these steps: We started with a valid static value before we added the expression. Instead of starting off with a dynamic SQL statement, we started with a static statement which we tested to ensure that we had a known good starting point. We added a single piece of dynamic functionality at a time. Because our example was simple, we only added a single piece of dynamic functionality in total; but if we were adding both a dynamic WHERE clause and a dynamic table name, we would ve added each dynamic expression element to the static SQL statement individually. We tested the expression after each change. This basic technique is often overlooked, but it s a vital timesaver. The Expression Editor has limited debugging capabilities, and locating errors in a complex expression can be painfully difficult. By testing the expression after each change, the scope of debugging can be significantly reduced. With this example setting the stage, let s dive deeper into SSIS expressions by illustrating how they can be used to add dynamic functionality to our packages, and solve realworld problems.

The third line assigns to a variable named myToDo the item myCounter of myToDos the first todo on the first iteration of the loop, the second todo on the second iteration, and so on If the summary of myToDo matches the test string, the delete myToDo statement deletes the todo item, and the return statement kicks AppleScript out of the loop so that it doesn t run again If the summary of myToDo doesn t match the test string, the loop keeps running until either it finds a match or it reaches the last item in myToDos: