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used to calculate the probabilities that can then be acted upon They create places where there is a high probability of success When you find one of these places, you act at that time What they don t do, he cautions, is provide continuous advice: Can you look at any given point in time at Bollinger Bands and know what to do The answer, at least in my practice, is no He says that Bollinger Bands are not forecasting tools They can t predict, for example, what the Dow will do on a certain date Nevertheless, he acknowledges that within the Bollinger Band setups there are obvious targets and obvious projections that can be made It s especially useful to see whether a setup is working or not But I wouldn t call that market forecasting One phenomenon that is unique to Bollinger Bands is that it tends to stay overbought or oversold for long time periods We call that a walk up the bands, he explains, or a walk down the bands It s perfectly normal In fact, that is the sort of activity that is expected in a trending market You may have to go for a long time, or a very short time, before you find a usable setup If someone gets impatient because there s no immediate buy or sell signal, he suggests it s a failure of the person, not the tool He adds: The biggest mistake that people make with my indicator is they assume that any tag of the upper band is an automatic sell, and that any tag of the lower band is an automatic buy That s completely wrong Another problem is that people get fixated on certain signals even though the market is going against them As a result, they go through a lot of pain The key, Bollinger says, is to be flexible Think of these trading bands as a component of a system analysis that also includes trend and sentiment information It s more useful to think of my trading bands as part of a whole trading system rather than try to be too simplistic.

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Figure 14-5

Aggregations don t have to exactly match the queried dimension attributes If userdefined hierarchies exist that relate higher levels to lower levels in each dimension, queries at higher levels can leverage aggregations at the lower level Let s assume the three user-defined hierarchies represented in table 1 and the same aggregation that was previously mentioned (summing values of sales by state, month, and product category) What happens if a follow-up query is issued to ask, What is the percentage of total sales for nonfiction books in the state of Texas, compared to the total sales across all states in the US This answer is also easy for Analysis Services to answer Because the values for total sales by state per product category are precalculated, only 50 statelevel aggregations are affected by this query.

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This compares to summing values down to the lowest level of granularity, which could include anywhere from thousands to millions, or even billions, of values! If you come from a relational database background, you can loosely compare aggregations to indexes In this comparison, you know that indexes provide an expeditious way of retrieving query results Unlike indexes, though, aggregations provide the best benefit to queries that select data at higher levels, which would require accessing a very large number of cells Just as with indexes, the savings at query time come at a cost, and the goal is to design the right indexes and aggregations to match your query load With aggregations, this results in an OLAP solution that responds well to a wide variety of queries with the minimum cube-processing overhead.

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In Bollinger s opinion, the best way to use his indicator is by experimenting with the defaults Fool around with the defaults as widely and creatively as possible, he suggests What could happen is you might stumble across something that works really well, a combination of rules or bands that fits your trading style

Analysis Services makes no assumptions when it comes to aggregations no aggregations will be created unless you tell it to Performance may even be acceptable on smaller cubes without them, but larger cubes will likely benefit greatly from a good set of aggregations..

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The easiest way to design aggregations is to go into the Aggregation Design Wizard (ADW), shown in figure 1, through SQL Server Management Studio (SSMS) or Business Intelligence Development Studio (BIDS). Aggregations are designed at the partition level, so you can access the wizard by right-clicking on a partition in either of those tools (in SSMS, drill down from the server to the database, to a cube, to a measure group, then to a partition). The ADW lets you design aggregations based on the target size, the desired performance benefit, or until you tell it to stop. By default, the ADW in Analysis Services knows nothing about your expected query patterns and gives equal weight when considering possible aggregations across dimension attribute candidates. Using the ADW, with no additional input, is like rolling the dice. Hopefully, some of the resulting aggregations will be useful in satisfying the queries that will be issued against the cube, but wouldn t it be better to give Analysis Services some hints as to which dimensions attributes will be most queried by users of the cube When you create indexes in SQL Server, don t you use your knowledge of the queries that will be issued

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