1. Saeed Ebrahimijam SPRING 2013-2014 Faculty of Business and Economics Department of Banking and Finance Doğu Akdeniz Üniversitesi FINA417

2.  Trading Band  Bollinger Band  ATR indicator (Average True Range Indicator) 2 Fundamentals of Technical Analysis and Algorithmic Trading

3.  In chapter 11, you learned how moving averages are used to smooth price fluctuations and get a clearer picture of a security’s price trend.  In this lesson, two moving average filters are discussed: trading bands and Bollinger Bands. Fundamental of Technical Analysis and Algorithmic Trading3

4.  Trading bands (also known as envelopes) create a filter around a moving average line.  Two lines, one above and one below, are drawn parallel to the moving average.  The distance between the moving average and the upper and lower trading bands is a certain percentage. Fundamental of Technical Analysis and Algorithmic Trading4

5.  For example, Figure 12-1 shows a 21-day simple moving average of the S&P 500 index closing prices with trading bands at 4 percent above and 4 percent below the moving average.  Note that only the daily closing prices (not the high-low range) are plotted. This makes it easier to spot when prices cross over the moving average and trading band lines. Fundamental of Technical Analysis and Algorithmic Trading5

6. 1. The majority of price movement occurs inside the upper and lower trading bands. 2. Prices tend to move back and forth between the upper and lower trading bands. 3. The moving average (mid-band line) often acts as support or resistance. 4. When prices cross above the upper trading band, great market strength is signaled. On the other hand, when prices move below the lower trading band, great market weakness is signaled. Fundamental of Technical Analysis and Algorithmic Trading6

7.  The same four observations can be made when examining other trading band charts.  They serve as the basis for the variety of trading band systems that have been developed over the years by technicians. Fundamental of Technical Analysis and Algorithmic Trading7

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9.  Trading bands can be successfully applied to various types of investments (individual stocks, the overall stock market, ETFs, commodities, etc.) and investment time horizons ranging from those that are very short term to very long term.  The following three steps can be used to construct valid trading bands and interpret price action relative to such trading bands: Fundamental of Technical Analysis and Algorithmic Trading9

10.  The first step is the easiest of the three steps. You simply select the individual security or market you which to trade and define your investment time horizon (very short term, short term, intermediate term, long term, or very long term). Fundamental of Technical Analysis and Algorithmic Trading10

11.  Select a moving average length.  The type of moving average you use can be simple, weighted, or exponential.  However, the much-easier to calculate simple moving average tends to work just as well as weighted or exponential averages when used for trading band purposes.  As is the case when you use a moving average by itself, you want the length to correspond closely to a dominant cycle in the particular security or market you are trading. Fundamental of Technical Analysis and Algorithmic Trading11

12.  The third step is the most time-consuming.  In it you determine the appropriate percentage distance the upper and lower trading bands should be from the moving average and interpret price action relative to those trading bands. Keep in mind that if trading bands are set too close or too far from the moving average, they will lose their effectiveness.  As a general rule of thumb, trading bands should be set so that 70 to 85 percent of all price movement occurs between the upper and lower trading bands. Fundamental of Technical Analysis and Algorithmic Trading12

13.  Trading bands can be used in a mechanical fashion by generating buy and sell signals when prices move through the upper and lower trading bands.  However, they are best viewed subjectively and used in conjunction with other technical indicators. (i.e., as prices approach the upper trading band, watch for a top; as prices fall to near the lower trading band, look for a bottom) Fundamental of Technical Analysis and Algorithmic Trading13

14.  The technique of Bollinger Bands was developed by John Bollinger (Capital Growth Letter) for the purpose of factoring in price volatility.  Bollinger Bands are effective for virtually any security or market and for any investment time horizon. Fundamental of Technical Analysis and Algorithmic Trading14

15.  Rather than placing bands at a certain percentage distance from a moving average line as is done with trading bands, Bollinger Bands are placed two moving standard deviations above and below a simple moving average line.  More specifically, the bands are placed above and below a simple moving average at a distance of two times the root mean square of the deviations from the average. The amount of data used in the calculation is equal to the number of periods used for the simple moving average.  For example, if you use a 20-day simple moving average, all calculations should be based on 20 days of data. Fundamental of Technical Analysis and Algorithmic Trading15

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17.  There is no set number of periods to use for the simple moving average or number of standard deviations above and below that work best in all markets and for all investment time horizons.  As with many technical indicators, only by experimenting with the particular security or market you are trading can you determine the best combination of simple moving average and standard deviations to use in plotting Bollinger Bands. Fundamental of Technical Analysis and Algorithmic Trading17

18.  The recommendation is made that you use a 10- period simple moving average with bands 1.5 moving standard deviations above and below for short-term analysis as illustrated in Figure 12-2.  For intermediate-term analysis, a 20-period simple moving average with bands 2.0 moving standard deviations above and below can be used (see Figure 12-3 for an example).  Long-term analysis can be accomplished with a 50-period moving average with bands 2.5 moving standard deviations above and below (see Figure 12-4 for an example). Fundamental of Technical Analysis and Algorithmic Trading18

19.  Although calculating Bollinger Bands is complex, interpreting the indicator is one Bollinger Band to the other.  This gives you an opportunity to project price levels to be reached.  Again, as with normal trading bands: - when prices close above the upper Bollinger Band, it is a sign of great market strength and a buying opportunity. - when prices close below the lower Bollinger Band, great market weakness is signaled. Fundamental of Technical Analysis and Algorithmic Trading19

20.  The upper and lower Bollinger Bands move closer to the simple moving average line.  There is a tendency for sharp price moves after such occurrences. Fundamental of Technical Analysis and Algorithmic Trading20

21. Finally, tops and bottoms made outside the bands followed by tops and bottoms made inside the bands indicate a trend reversal:  When price tops form first above the upper Bollinger Band and then below it, a reversal in trend from up to down is signaled.  Conversely, when price bottoms develop first below the lower Bollinger Band and then above it, a trend reversal from down to up is signaled. Fundamental of Technical Analysis and Algorithmic Trading21

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27.  Average true range (ATR) is a technical analysis volatility indicator originally developed by J. Welles Wilder, Jr. for commodities.  The indicator does not provide an indication of price trend, simply the degree of price volatility. (Not price trend direction!)  The average true range is an N-day exponential moving average of the true range values. Wilder recommended a 14-period smoothing. Fundamental of Technical Analysis and Algorithmic Trading27

28. Fundamental of Technical Analysis and Algorithmic Trading28 The true range is the largest of the (why? To consider if there was any Gaps): Most recent period's high minus the most recent period's low Absolute value of the most recent period's high minus the previous close Absolute value of the most recent period's low minus the previous close The ATR at the moment of time t is calculated using the following formula The idea of ranges is that they show the commitment or enthusiasm of traders. - Large or increasing ranges suggest traders prepared to continue to bid up or sell down a stock through the course of the day. - Decreasing range suggests waning interest.

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