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1 tablespoon canola oil 2 medium onions, chopped fine 1 2 teaspoon sea salt 1 pound carrots, peeled and chopped 6 cups filtered water 1 teaspoon roasted sesame tahini In a medium skillet heat the oil and then saut the onions for about four minutes. Add half the salt and saut another minute. Add the carrots and water and bring to a boil. Add the remaining salt. Reduce the heat and simmer for about twenty-five to thirty minutes. Pour the soup into a blender, add the tahini, and puree until smooth. Transfer the contents back to the skillet and reheat. Serve with chopped fresh herbs or leeks.

Figure 14-9

it did not always produce the greatest returns. Stay away from popular volatility breakouts unless they implement some special twist that enables them to hold up, despite wide use. . Choose trendy markets to trade when using such trend-following models as breakouts. In the world of commodities, the currencies traditionally are good for trend-following systems. The tests suggest that the oils and Coffee are also amenable to breakout trading. Do not rely on indicators like the ADX for trendiness determination. . Use something better than the standard exit to close open positions. Better exit strategies are available, as will be demonstrated in Part III. A good exit can go a long way toward making a trading system profitable.

2. My organization keeps at least three copies of our data and always backs up to the oldest version. 3. My end users travel safely by carrying a disk copy of all their important data. 4. My end users always carry a disk copy with them when they re on the road so that they can share large amounts of data. 5. My end users back up all their data at least once a week. 6. My end users all have a continuous backup product to protect data between backups. 7. My end users employ a le version retention program to track le changes. 8. My end users have actually practiced restoring data from backup disks just in case. 9. We use disk passwords to keep private data secure. 10. All our data disks are accurately labeled so we know what s on them and when they were created.

Moving averages are included in many technical analysis software packages and written about in many publications. So popular arc moving averages that in 1998, 5 of the 12 issues of Technical Analysis of Stocks and Commodities contained artcles about them. Newspapers often show a 50-day moving average on stock charts, and a 20.day moving average on commodities charts.

When the last yes or no has been checked, the user hits the Submit button and sees the score (see Figure 14-10). Clicking on the Needs button brings up a further analysis of the user s needs for Iomega products, as shown in Figure 14-11. After a series of questions, another screen provides a Here s What I m Struggling With button that brings the user to a customized list of solutions that involve Iomega products.

To help understand moving averages, it is first necessary to discuss time series, i.e., series of data points that are chronologically ordered. The daily closing prices for a commodity are one example: They form a string of data points or bars that follow one another in time. In a given series, a sample of consecutive data points may be referred to as a time window. If the data points (e.g., closing prices) in a given time window were added together, and the sum divided by the number of data points in the sample, an average would result. A moving average is when this averaging process is repeated over and over as the sampling period is advanced, one data point at a time, through the series. The averages themselves form a new time series, a set of values ordered by time. The new series is referred to as the moving average of the original or underlying time series (in this case, the moving average of the close). The type of moving average just described is known as a simple moving average, since the average was computed by simply summing the data points in the time window, giving each point equal weight, and then dividing by the number of data points summed.

So far, the users are having fun on your Web site. You have their name and their email name, which is wonderful because you can use them to follow up. To get complete information on the users, however, Iomega wanted their company data. It gave people an incentive to provide this by making an offer. Bob McKim advised Iomega to offer respondents a drawing for an Iomega HipDip Digital Audio Player. To enter the drawing, users had to provide their address, their position in their company, information about the number of PCs in the organization, and their authority over purchases.