Commodity Channel Index(CCI)

Introduction:

Developed by Donald Lambert, the Commodity Channel Index (CCI) was designed to identify cyclical turns in commodities. The assumption behind the indicator is that commodities (or stocks or bonds) move in cycles, with highs and lows coming at periodic intervals. Lambert recommended using 1/3 of a complete cycle (low to low or high to high) as a time frame for the CCI. (Note: Determination of the cycle's length is independent of the CCI.) If the cycle runs 60 days (a low about every 60 days), then a 20-day CCI would be recommended. For the purpose of this example, a 20-day CCI is used.
Calculation :

There are 4 steps involved in the calculation of the CCI:

Calculate the last period's Typical Price (TP) = (H+L+C)/3 where H = high, L = low, and C = close. Calculate the 20-period Simple Moving Average of the Typical Price (SMATP). Calculate the Mean Deviation. First, calculate the absolute value of the difference between the last period's SMATP and the typical price for each of the past 20 periods. Add all of these absolute values together and divide by 20 to find the Mean Deviation.
The final step is to apply the Typical Price (TP), the Simple Moving Average of the Typical Price (SMATP), the Mean Deviation and a Constant (.015) to the following formula:

CCI = ( Typical Price - SMATP ) / ( .015 X Mean Deviation )




For scaling purposes, Lambert set the constant at .015 to ensure that approximately 70 to 80 percent of CCI values would fall between -100 and +100. The CCI fluctuates above and below zero. The percentage of CCI values that fall between +100 and -100 will depend on the number of periods used. A shorter CCI will be more volatile with a smaller percentage of values between +100 and -100. Conversely, the more periods used to calculate the CCI, the higher the percentage of values between +100 and -100.

Lambert's trading guidelines for the CCI focused on movements above +100 and below -100 to generate buy and sell signals. Because about 70 to 80 percent of the CCI values are between +100 and -100, a buy or sell signal will be in force only 20 to 30 percent of the time. When the CCI moves above +100, a security is considered to be entering into a strong uptrend and a buy signal is given. The position should be closed when the CCI moves back below +100. When the CCI moves below -100, the security is considered to be in a strong downtrend and a sell signal is given. The position should be closed when the CCI moves back above -10

Since Lambert's original guidelines, traders have also found the CCI valuable for identifying reversals. The CCI is a versatile indicator capable of producing a wide array of buy and sell signals.
CCI can be used to identify overbought and oversold levels. A security would be deemed oversold when the CCI dips below -100 and overbought when it exceeds +100. From oversold levels, a buy signal might be given when the CCI moves back above -100. From overbought levels, a sell signal might be given when the CCI moved back below +100.
As with most oscillators, divergences can also be applied to increase the robustness of signals. A positive divergence below -100 would increase the robustness of a signal based on a move back above -100. A negative divergence above +100 would increase the robustness of a signal based on a move back below +100.
Trend line breaks can be used to generate signals. Trend lines can be drawn connecting the peaks and troughs. From oversold levels, an advance above -100 and trend line breakout could be considered bullish. From overbought levels, a decline below +100 and a trend line break could be considered bearish.
Traders and investors use the CCI to help identify price reversals, price extremes and trend strength. As with most indicators, the CCI should be used in conjunction with other aspects of technical analysis. CCI fits into the momentum category of oscillators. In addition to momentum, volume indicators and the price chart may also influence a technical assessment.



The 20-day CCI for Brooktrout (BRKT) provides an example using Lambert's guidelines. Even though a few signals are good, using crosses above and below +100/-100 resulted in plenty of whipsaws. In January, the stock broke resistance at 20, and proceeded to double in the next few weeks. The CCI moved above and below +100 several times, but the stock remained in a strong uptrend. The CCI did manage to remain above +50 for about 7 weeks (blue oval), but the whipsaws below +100 could have caused an early exit. Whipsaws do not make an indicator bad. However, traders and investors should learn to use the CCI in conjunction with other indicators and chart analysis. In addition, various time frames for the CCI should be tested, and you should test buy and sell points, as well. What works for one stock may not necessarily work for another stock. For Brooktrout, a buy point on a cross above and below +50 may have worked better.

CCI and SharpCharts :




Using SharpCharts, the CCI can be set as an indicator above or below a security's price plot. The Parameters text box to the right sets the number of periods to calculate the indicator. The default setting is 20 periods. Horizontal lines have been set at -100, 0 and +100 to help identify extremes and centerline crossovers. When the indicator moves above +100 or below -100, the portion above or below will be shaded. A number of CCI windows can be opened on any chart, and users are invited to compare different settings.

Bollinger Bands

Bollinger Bands:

Introduction :

Developed by John Bollinger, Bollinger Bands are an indicator that allows users to compare volatility and relative price levels over a period time. The indicator consists of three bands designed to encompass the majority of a security's price action.

1. A simple moving average in the middle
2. An upper band (SMA plus 2 standard deviations)
3. A lower band (SMA minus 2 standard deviations)

Standard deviation is a statistical unit of measure that provides a good assessment of a price plot's volatility. Using the standard deviation ensures that the bands will react quickly to price movements and reflect periods of high and low volatility. Sharp price increases (or decreases), and hence volatility, will lead to a widening of the bands.

Calculation :





The center band is the 20-day simple moving average. The upper band is the 20-day simple moving average plus 2 standard deviations. The lower band is the 20-day simple moving average less 2 standard deviations.

Settings :

Closing prices are most often used to compute Bollinger Bands. Other variations, including typical and weighted prices, can also be used.

Typical Price = (high + low + close)/3
Weighted Price = (high + low + close + close)/4

Bollinger recommends using a 20-day simple moving average for the center band and 2 standard deviations for the outer bands. The length of the moving average and number of deviations can be adjusted to better suit individual preferences and specific characteristics of a security.

Trial and error is one method to determine an appropriate moving average length. A simple visual assessment can be used to determine the appropriate number of periods. Bollinger Bands should encompass the majority of price action, but not all. After sharp moves, penetration of the bands is normal. If prices appear to penetrate the outer bands too often, then a longer moving average may be required. If prices rarely touch the outer bands, then a shorter moving average may be required.

A more exact method to determine moving average length is by matching it with a reaction low after a bottom. For a bottom to form and a downtrend to reverse, a security needs to form a low that is higher than the previous low. Properly set Bollinger Bands should hold support established by the second (higher) low. If the second low penetrates the lower band, then the moving average is too short. If the second low remains above the lower band, then the moving average is too long. The same logic can be applied to peaks and reaction rallies. The upper band should mark resistance for the first reaction rally after a peak.



For Wal-Mart (WMT), a 20-period simple moving average proved to be a bit too long for the Bollinger Bands. Notice the wide gap between the lower band and the higher low in March (see top SharpChart). Through trial and error, a 12-period simple moving average appears to offer a better fit (see bottom SharpChart).

For general time frames, Bollinger recommends a 10-day simple moving average for the short term, a 20-day simple moving average for the intermediate term and 50-day simple moving average for the long term.

Use :

In addition to identifying relative price levels and volatility, Bollinger Bands can be combined with price action and other indicators to generate signals and foreshadow significant moves.

Double bottom Buy:

A Double Bottom Buy signal is given when prices penetrate the lower band and remain above the lower band after a subsequent low forms. Either low can be higher or lower than the other. The important thing is that the second low remains above the lower band. The bullish setup is confirmed when the price moves above the middle band, or simple moving average.



AT&T (T) provides an example of a Double Bottom Buy signal. The stock penetrated the lower band in late September (red arrow) and then held above on the subsequent test in October. The October breakout above the middle band (green circle) provided the bullish confirmation.

Double Top Sell: A Double Top Sell signal is given when prices peak above the upper band and a subsequent peak fails to break above the upper band. The bearish setup is confirmed when prices decline below the middle band.

Sharp price changes can occur after the bands have tightened and volatility is low. In this instance, Bollinger Bands do not give any hint as to the future direction of prices. Direction must be determined using other indicators and aspects of technical analysis. Many securities go through periods of high volatility followed by periods of low volatility. Using Bollinger Bands, these periods can be easily identified with a visual assessment. Tight bands indicate low volatility and wide bands indicate high volatility. Volatility can be important for options players because options prices will be cheaper when volatility is low.



Starbucks (SBUX) provides an example of the bands tightening before a big move. In November, the bands were relatively wide and began to tighten over the next 2 months. By early January, the bands were the tightest in over 4 months (red circle). A little over a week later, the stock exploded for a 10+ point gain in less than 2 weeks.

Bollinger Bands and SharpCharts :



As a SharpCharts indicator, Bollinger Bands can be found in the Overlays section of the page. The first number in the Parameter text box sets the number of days for the simple moving average, which is the middle band. The second number sets the number of standard deviations for the upper and lower bands. The default setting is a 20-day simple moving average with the upper and lower bands set 2 standard deviations above and below. Both settings can be changed and users are encouraged to experiment.

Note: Sometimes when using the log scale, the lower band will exceed the price scale and become cut off. To alleviate this, change the scale setting from "log" to "linear."
Conclusions :

Even though Bollinger Bands can help generate buy and sell signals, they are not designed to determine the future direction of a security. The bands were designed to augment other analysis techniques and indicators. By themselves, Bollinger Bands serve two primary functions:

* To identify periods of high and low volatility.
* To identify periods when prices are at extreme, and possibly unsustainable, levels.

As stated above, securities can fluctuate between periods of high volatility and low volatility. Being able to identify a period of low volatility can serve as an alert to monitor the price action of a security. Other aspects of technical analysis, such as momentum, moving averages and retracements, can then be employed to help determine the direction of the potential breakout.

Remember that buy and sell signals are not given when prices reach the upper or lower bands. Such levels merely indicate that prices are high or low on a relative basis. A security can become overbought or oversold for an extended period of time. Knowing whether or not prices are high or low on a relative basis can enhance our interpretation of other indicators, and it can assist with timing issues in trading.

Average True Range (ATR)

Introduction :

Developed by J. Welles Wilder and introduced in his book, New Concepts in Technical Trading Systems (1978), the Average True Range (ATR) indicator measures a security's volatility. As such, the indicator does not provide an indication of price direction or duration, simply the degree of price movement or volatility.

As with most of his indicators, Wilder designed ATR with commodities and daily prices in mind. In 1978, commodities were frequently more volatile than stocks. They were (and still are) often subject to gaps and limit moves. (A limit move occurs when a commodity opens up or down its maximum allowed move and does not trade again until the next session. The resulting bar or candlestick would simply be a small dash.) In order to accurately reflect the volatility associated with commodities, Wilder sought to account for gaps, limit moves, and small high-low ranges in his calculations. A volatility formula based on only the high-low range would fail to capture the actual volatility created by the gap or limit move.

Wilder started with a concept called True Range (TR) which is defined as the greatest of the following:

The current High less the current Low.
The absolute value of the current High less the previous Close.
The absolute value of the current Low less the previous Close.
If the current high-low range is large, chances are it will be used as the True Range. If the current high-low range is small, it is likely that one of the other two methods would be used to calculate the True Range. The last two possibilities usually arise when the previous close is greater than the current high (signaling a potential gap down or limit move) or the previous close is lower than the current low (signaling a potential gap up or limit move).
To ensure positive numbers, absolute values were applied to differences.



The example above shows three potential situations when the TR would not be based on the current high/low range. Notice that all three examples have small high/low ranges and two examples show a significant gap.

A small high/low range formed after a gap up. The TR was found by calculating the absolute value of the difference between the current high and the previous close.
A small high/low range formed after a gap down. The TR was found by calculating the absolute value of the difference between the current low and the previous close.

Even though the current close is within the previous high/low range, the current high/low range is quite small. In fact, it is smaller than the absolute value of the difference between the current high and the previous close, which is used to value the TR.
Note:

Because the ATR shows volatility as an absolute level, low price stocks will have lower ATR levels than high price stocks. For example, a $10 security would have a much lower ATR reading than a $200 stock. Because of this, ATR readings can be difficult to compare across a range of securities. Even for a single security, large price movements, such as a decline from 70 to 20, can make long-term ATR comparisons difficult.
Calculation :

Typically, the Average True Range (ATR) is based on 14 periods and can be calculated on an intraday, daily, weekly or monthly basis. For this example, the ATR will be based on daily data. Because there must be a beginning, the first TR value in a series is simply the High minus the Low, and the first 14-day ATR is the average of the daily ATR values for the last 14 days. After that, Wilder sought to smooth the data set, by incorporating the previous period's ATR value. The second and subsequent 14-day ATR value would be calculated with the following steps:

1. Multiply the previous 14-day ATR by 13.
2. Add the most recent day's TR value.
3. Divide by 14.



In the Excel spread sheet example above, the first True Range value (1.9688) equals the High minus the Low. The first 14-day ATR value (3.6646) was calculated by finding the average of first 14 True Range values. The second ATR value was smoothed by using the previous value.



For those trying this at home, here are a few caveats: There is always a beginning, and the first calculations may not conform exactly with the formula.

The first True Range value is simply the High minus the Low, and the first ATR is a simple average of the first 14 True Range values.

Many indicators involve a smoothing process. In this example, the current ATR calculation uses the previous period's ATR.

The size of the data set will affect the final outcome. This example only contains a small portion of the available historical price data.

Although the difference is not likely to be huge, a data set of 33 days will produce a different ATR value than a data set of 500 days.

Due to rounding issues and decimal places, an exact match may not be possible.
(If you want to create an ATR from your own data, first try to duplicate the above example using the provided Open-High-Low-Close data. Once your calculations match the example's, you can then plug in your own Open-High-Low-Close data.)



The IBM chart above provides an example of the 14-day ATR in action. Extreme levels (both high and low) can mark turning points or the beginning of a move. As a volatility-based indicator like Bollinger Bands, the ATR cannot predict direction or duration, simply activity levels. Low levels indicate quiet trading (small ranges), and high levels indicate violent trading (large ranges). A prolonged period of low ATR readings might indicate consolidation and the beginning of a continuation move or reversal. High ATR readings usually result from a sharp advance or decline and are unlikely to be sustained for extended periods.



The ATR is on the Indicators drop-down menu, listed as "Average True Range." The Parameters box to the right of the indicator contains the default value, 14, for the number of periods used to smooth the data. To adjust the period setting, highlight the default value, and enter a new period setting. SharpCharts also allows you to position the indicator above, below, or behind the price plot.

Accumulation/Distribution Line

Introduction - Volume and the Flow of Money There are many indicators available to measure volume and the flow of money for a particular stock, index or security. One of the most popular volume indicators over the years has been the Accumulation/Distribution Line. The basic premise behind volume indicators, including the Accumulation/Distribution Line, is that volume precedes price. Volume reflects the amount of shares traded in a particular stock, and is a direct reflection of the money flowing into and out of a stock. Many times before a stock advances, there will be period of increased volume just prior to the move. Most volume or money flow indicators are designed to identify early increases in positive or negative volume flow to gain an edge before the price moves. (Note: the terms "money flow" and "volume flow" are essentially interchangeable.)

Methodology :

The Accumulation/Distribution Line was developed by Marc Chaikin to assess the cumulative flow of money into and out of a security. In order to fully appreciate the methodology behind the Accumulation/Distribution Line, it may be helpful to examine one of the earliest volume indicators and see how it compares. In 1963, Joe Granville developed On Balance Volume (OBV), which was one of the earliest and most popular indicators to measure positive and negative volume flow. OBV is a relatively simple indicator that adds the corresponding period's volume when the close is up and subtracts it when the close is down. A cumulative total of the positive and negative volume flow (additions and subtractions) forms the OBV line. This line can then be compared with the price chart of the underlying security to look for divergences or confirmation. In developing the Accumulation/Distribution Line, Chaikin took a different approach. OBV uses the change in closing price from one period to the next to value the volume as positive or negative. Even if a stock opened on the low and closed on the high, the period's OBV value would be negative as long as the close was lower than the previous period's close. Chaikin chose to ignore the change from one period to the next and instead focused on the price action for a given period (day, week, month). He derived a formula to calculate a value based on the location of the close, relative to the range for the period. We will call this value the "Close Location Value" or CLV. The CLV ranges from plus one to minus one with the center point at zero. There are basically five combinations:

( ( (C - L) - (H - C) ) / (H - L) ) = CLV

1. If the stock closes on the high, the top of the range, then the value would be plus one.
2. If the stock closes above the midpoint of the high-low range, but below the high, then the value would be between zero and one.
3. If the stock closes exactly halfway between the high and the low, then the value would be zero.
4. If the stock closes below the midpoint of the high-low range, but above the low, then the value would be negative.
5. If the stock closes on the low, the absolute bottom of the range, then the value would be minus one.
The CLV is then multiplied by the corresponding period's volume, and the cumulative total forms the Accumulation/Distribution Line.

The daily chart of Ciena (CIEN) gives a breakdown of the Accumulation/Distribution Line, and shows how different closing levels affect the value. The top
section shows the price chart for CIEN. The closing level relative to the high-low range is clearly visible. The second section with a black histogram is
the Closing Location Value (CLV). The CLV is multiplied by volume, and the result appears in the green histogram. Finally, at the bottom, is the
Accumulation/Distribution Line.


1. The close is on the low and the CLV = -1. Volume, however, was relatively light, so the Accumulation/Distribution Value for that period is only moderately
negative.

2. The close is very near the high and the CLV = +.9273. Volume is relatively high, so the resulting Accumulation/Distribution Value is high.

3. The close is near the low and the CLV = -.75. Volume is moderately high, so the resulting Accumulation/Distribution Value is moderately high as well.

4. The close is about half way between the mid-point of the high-low range and the high, and the CLV = +.51. Volume is very heavy, so the Accumulation/Distribution Value is also very high.



Accumulation/Distribution Line Signals :

The signals for the Accumulation/Distribution Line are fairly straightforward and center around the concepts of divergence and confirmation.
Bullish Signals :

A bullish signal is given when the Accumulation/Distribution Line forms a positive divergence. Be wary of weak positive divergences that fail to make
higher reaction highs or those that are relatively young. The main issue is to identify the general trend of the Accumulation/Distribution Line.
A two-week positive divergence may be a bit suspect. However, a multi-month positive divergence deserves serious attention.





On the chart for Alcoa, Inc. (AA), the Accumulation/Distribution Line formed a huge positive divergence that was over 4 months in the making. Even though
the stock fell from above 35 to below 30, the Accumulation/Distribution Line continued on a relentless march north. If one did not know better, it would
seem that the two plots did not belong together. However, the stock finally caught up with the Accumulation/Distribution Line when it broke resistance in
November.



Another means of using the Accumulation/Distribution Line is to confirm the strength or sustainability behind an advance. In a healthy advance, the
Accumulation/Distribution Line should keep up or, at the very least, move in an uptrend. If the stock is moving up at a rapid clip, but the
Accumulation/Distribution Line has trouble making higher highs or trades sideways, it should serve as an indication that buying pressure is relatively weak.






Wal-Mart Stores (WMT) began a sharp advance in August that was accompanied by an equally strong move in the Accumulation/Distribution Line. In fact, the
Accumulation/Distribution Line was stronger than the stock in early September. After a bit of a consolidation, both again started higher and recorded new
reaction highs in early October. Volume flows were behind this advance from the very beginning and continued throughout. The stock ended up advancing from
40 to 60 in about 3 months. Interestingly, as of this writing (December 1999) the Accumulation/Distribution Line has started to move sideways and is
indicating that buying pressure is beginning to wane.

Bearish Signals :

The same principles that apply to positive divergences apply to negative divergences. The key issue is to identify the main trend in the
Accumulation/Distribution Line and compare it to the underlying security. Young negative divergences, or those that are relatively flat, should be looked
upon with a healthy dose of skepticism.


The Wal-Mart chart shows a relatively flat negative divergence that is just over a month old. This negative divergence has yet to make a lower low, and
should probably be given a little more time to mature. The relative weakness in the Accumulation/Distribution Line should serve as a sign that buying
pressure is diminishing while the stock remains at lofty levels.


The Delta Air Lines (DAL) chart shows a negative divergence that developed within the confines of a clear downtrend. The stock had clearly broken down, a
nd the Accumulation/Distribution Line was declining in line with the stock. A deteriorating Accumulation/Distribution Line confirmed weakness in the stock.
During the June-July rally, the stock recorded a new reaction high, but the Accumulation/Distribution Line failed, thus setting up the negative divergence.



Accumulation/Distribution Line and SharpCharts :



With SharpCharts, the Accumulation/Distribution Line can be set as an indicator above or below a security's price plot, using the Position drop-down menu.
You can also add a simple moving average (SMA) to the indicator panel by entering the number of periods for the SMA into the Parameters text box.


Conclusions :


The Accumulation/Distribution Line is good means to measure the volume force behind a move.


1. As a volume indicator, the Accumulation/Distribution Line will help to determine if the volume in a security is increasing on the advances or declines.

2. The Accumulation/Distribution Line can be used to gauge the general flow of money. An uptrend indicates that buying pressure is prevailing, and a downtrend indicates that selling pressure is prevailing.

3. The Accumulation/Distribution Line can be used to spot divergences, both positive and negative.

4. The Accumulation/Distribution Line can be used to confirm the strength and sustainability behind a move.

There are some drawbacks to the Accumulation/Distribution Line, though.


1. The indicator does not take gaps into consideration. A stock that gaps up and closes midway between the high and the low will not receive any credit for
the advance off of the gap. A series of gaps could go largely undetected.

2. Because the Accumulation/Distribution Line is clearly tied to price movement, specifically the close, it will sometimes move in step with the underlying
security, and yield few divergences.

3. It sometimes difficult to detect subtle changes in volume flows. The rate of change in a downtrend could be slowing, but it may be impossible to detect
until the Accumulation/Distribution Line turns up. This drawback has been addressed in the form of the Chaikin Oscillator or Chaikin Money Flow, which are
next in the education series.