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一个笨蛋的股指交易记录-------地狱级炒手

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 楼主| 发表于 2009-3-15 12:24 | 显示全部楼层
[url=]ZigZag[/url]
[url=]Introduction[/url]The ZigZag feature on SharpCharts is not an indicator per se, but rather a means to filter out random noise and compare relative price movements. The ZigZag can be set to acknowledge minimum price changes and ignore those that do not fit the criteria. The minimum price movements are set in percentage terms and can be based on either the close or high/low range.
A ZigZag set at 10% with OHCL bars would yield a line that only reverses after a change from high to low of 10% or greater. All movements less than 10% would be ignored. If a stock traded from a low of 100 to a high of 109, the ZigZag would not draw a line because the move was less than 10%. If the stock advanced from a low of 100 to a high of 110, then the ZigZag would draw a line from 100 to 110. If the stock continued on to a high of 112, this line would be extended to 112 (100 to 112). The ZigZag would not reverse until the stock declined 10% or more from its high. From a high of 112, a stock would have to decline 11.2 points (or to a low of 100.8) for the ZigZag to reverse and display another line.
The ZigZag has zero predictive power and draws lines base on hindsight. Any predictive power will come from applications such as Elliott Wave or Fibonacci retracements and projections.

[url=]Uses[/url]
[url=]Filter:[/url]Volatility and daily price fluctuations can produce erratic movements or noise. The ZigZag can be used to filter this noise. If price movements smaller than 5% are deemed insignificant, then the ZigZag can be set at 5% and all movements less than 5% will be ignored.

[url=]Elliott Wave[/url]The ZigZag can be used to identify waves for Elliott Wave counts. (Note: The object of this article is not Elliott Wave Theory, but simply to illustrate methods of using the ZigZag.)

([url=http://stockcharts.com/h-sc/ui?c=hpq,UU[500,400]WFOLYNMY[D19980901,20001122][PE15][I]]ZigZag Chart for HPQ[/url])
The HPQ example set the ZigZag at 15%. All moves 15% or greater were drawn and those less that 15% ignored. A large advance began in Oct-99 and formed a 5-wave structure that lasted until mid 2000. Within this larger structure, other smaller waver counts can also be deciphered.

[url=]Retracements[/url]The ZigZag can be used to measure retracements. After an advance, it is common for a security to retrace a portion of its advance with a correction. After a decline, it is common for a security to retrace part of its decline with a reaction rally. According to Dow Theory, 1/3, 1/2 and 2/3 retracements are most likely. Based on Fibonacci numbers, 38.2% or 61.8% retracement levels are deemed significant.

([url=http://stockcharts.com/h-sc/ui?c=hal,UU[500,400]DFOLYNMY[D20000201,20001123][PG15][I]]ZigZag Chart for HAL[/url])
During the advance from 34 to 55, HAL corrected twice (waves 2 and 4) and fulfilled two Fibonacci retracement targets: .618 and .786. Perhaps the most important Fibonacci number is .618, which is the golden mean. The square root of .618 is .786 (78.6%), another Fibonacci number used frequently by Scott Carney. In Mar-00, HAL retraced 79.8% of its wave 1 advance (red oval). From the Mar-00 low, the stock advanced 1.70 times its previous decline to form wave 3, which is close to a Fibonacci 1.618. The correction on wave 4 retraced 67.6% of the wave 3 advance. While 67.6% and 79.8% are not exact Fibonacci retracements, they are close enough to 61.8% and 78.6% to warrant attention.

[url=]Projections[/url]The ZigZag can be used to measure primary price movements. As opposed to a correction or reaction rally, a primary price movement is in the direction of the underlying trend. Instead of retracing a portion of the previous move, primary moves extend past the previous reaction high or low. Many analysts that use Elliott Wave and Fibonacci sequences project the length of an advance or decline by multiplying a ratio to the previous retracement. If the previous decline (correction) was 50 points and a Fibonacci specialist was looking for new highs on the subsequent advance, the projection might be 1.618 times the previous move, or 81 points (50 x 1.618 = 81). The 81 points would be added to the beginning of the advance for a price target.

[url=]Examples[/url]
[url=]ZigZag (Basic)[/url]
([url=http://stockcharts.com/def/servlet/SC.web?c=ibm,UU[500,300]DFLLYNMY[DC][PG12][I]]ZigZag Chart for IBM[/url])
The percentage price change for the ZigZag can be changed with the first box to the right. The default setting is 5%. In the example, the indicator was set at 12, or 12 percent. All price movements greater than or equal to 12% will produce a ZigZag line. All price movements less than 12% will be ignored. The ZigZag is plotted as a thick line on top of the price plot.

[url=]ZigZag w/Retracements[/url]
([url=http://stockcharts.com/def/servlet/SC.web?c=ibm,UU[500,300]DFOLYNMY[DC][PG12][I]]ZigZag Chart for IBM[/url])
The ZigZag w/Retracements includes ratios of adjacent price movements. For the IBM example, the ZigZag w/Retracements was set at 12% to filter out all price movements less than 12%. Three pairs of price movements were compared from the Jun-00 to Nov-00. Dotted lines connect the relevant highs or relevant lows and the ratio is labeled in the middle of the dotted line. The first ratio is 1.566, representing an advance that was 156.6% of the previous decline. The formula is calculated in three steps:
    First Price Move - Decline: 122.31 - 100 = 22.31
    Second Price Move - Advance: 134.94 - 100 = 34.94
  • Advance/Decline Ratio: 34.94/22.31 = 1.566
Calculations for the other two ratios (1.374 and .309) are shown on the corresponding chart.

The final line for the ZigZag is subject to change. On the IBM example above, the current ZigZag high is 104.38. Because of the recent decline, the ZigZag continued down from 104.38. However, the current decline is well short of the 12% minimum. Should the current decline fail to exceed 12% and should IBM advance above 104.38, then the line from 86.94 would be extended to the new high and the ratio (.363) would change. The red line in the example above provides an idea of what would happen should IBM turn up from current levels and move to 110. The green lines extending from the October low would be replaced by a line extending straight up to 110.

[url=]ZigZag and SharpCharts[/url]
There are two ZigZag options on SharpCharts: the ZigZag and the ZigZag (Retracements). Both plot the same line, but the ZigZag (Retracements) adds labels and dotted lines for retracement ratios. The parameters box selects the % change necessary for a line to be drawn.
The ZigZag (standard) plots a line based on a minimum percentage change in price. The price change can be based on closing levels or the high/low range. To calculate the ZigZag based on closing prices only, select one of the Line options from the Chart Type dropdown in the Chart Attributes section. To calculate the ZigZag based on the high/low range, select OHCL Bars, HLC Bars or Candlesticks as the Chart Type.
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 楼主| 发表于 2009-3-15 12:25 | 显示全部楼层
[url=]Accumulation/Distribution Line[/url]
[url=]Introduction - Volume and the Flow of Money[/url]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.)
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[url=]Methodology[/url]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

    If the stock closes on the high, the top of the range, then the value would be plus one.
    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.
    If the stock closes exactly halfway between the high and the low, then the value would be zero.
    If the stock closes below the midpoint of the high-low range, but above the low, then the value would be negative.
  • 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.
    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.
    The close is very near the high and the CLV = +.9273. Volume is relatively high, so the resulting Accumulation/Distribution Value is high.
    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.
  • 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.

[url=]Accumulation/Distribution Line Signals[/url]The signals for the Accumulation/Distribution Line are fairly straightforward and center around the concepts of divergence and confirmation.

[url=]Bullish Signals[/url]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.

[url=]Bearish Signals[/url]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, and 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.

[url=]Accumulation/Distribution Line and SharpCharts[/url]
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.
[url=http://stockcharts.com/def/servlet/SC.web?c=INTC,UU[L,A]DACLNNMY[P][IUF]]Click here[/url] to see a live example of the Acc/Dist Line.

[url=]Conclusions[/url]The Accumulation/Distribution Line is good means to measure the volume force behind a move.
    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.
    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.
    The Accumulation/Distribution Line can be used to spot divergences, both positive and negative.
  • 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.
    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.
    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.
  • 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.



[url=]Aroon[/url]
[url=]Introduction[/url]Developed by Tushar Chande in 1995, Aroon is an indicator system that can be used to determine whether a stock is trending or not and how strong the trend is. "Aroon" means "Dawn's Early Light" in Sanskrit and Chande chose that name for this indicator since it is designed to reveal the beginning of a new trend.
The Aroon indicator system consists of two lines, 'Aroon(up)' and 'Aroon(down)'. It takes a single parameter which is the number of time periods to use in the calculation. Aroon(up) is the amount of time (on a percentage basis) that has elapsed between the start of the time period and the point at which the highest price during that time period occurred. If the stock closes at a new high for the given period, Aroon(up) will be +100. For each subsequent period that passes without another new high, Aroon(up) moves down by an amount equal to (1 / # of periods) x 100.
Technically, the formula for Aroon(up) is:
[ [ (# of periods) - (# of periods since highest high during that time) ] / (# of periods) ] x 100

For example, consider plotting a 10-period Aroon(up) line on a daily chart. If the highest price for the past ten days occurred 6 days ago (4 days since the start of the time period), Aroon(up) for today would be equal to ((10-6)/10) x 100 = 40.
Aroon(down) is calculated in just the opposite manner, looking for new lows instead of new highs. When a new low is set, Aroon(down) is equal to +100. For each subsequent period that passes without another new low, Aroon(down) moves down by an amount equal to (1 / # of periods) x 100.
The formula for Aroon(down) is :
[ [ (# of periods) - (# of periods since lowest low during that time) ] / (# of periods) ] x 100

Continuing the example above, if the lowest price in that same ten-day period happened yesterday (i.e. on day 9), Aroon(down) for today would be 90.

[url=]Aroon Oscillator[/url]A separate indicator called the Aroon Oscillator can be constructed by subtracting Aroon(down) from Aroon(up). Since Aroon(up) and Aroon(down) oscillate between 0 and +100, the Aroon Oscillator will oscillate between -100 and +100 with zero as the center crossover line.

[url=]Interpretation Guidelines[/url]Chande states that when Aroon(up) and Aroon(down) are moving lower in close proximity, it signals that a consolidation phase is under way and no strong trend is evident. When Aroon(up) dips below 50, it indicates that the current trend has lost its upward momentum. Similarly, when Aroon(down) dips below 50, the current downtrend has lost its momentum. Values above 70 indicate a strong trend in the same direction as the Aroon (up or down) is under way.
The Aroon Oscillator signals an upward trend is underway when it is above zero and a downward trend is underway when it falls below zero. The farther away the oscillator is from the zero line, the stronger the trend.

In some ways, Aroon is similar to Wilder's DMI system (and the Aroon Oscillator is similar to Wilder's ADX line) however the Aroon is constructed in a completely different manner. Divergences between the two systems may be very instructive.

[url=]Aroon and SharpCharts[/url]
With SharpCharts, you can chart the Aroon and Aroon oscillator indicators using any specified number of periods. The default is 25, but it can be edited using the Parameters text box. The Position drop-down menu determines whether the indicators are placed above, below, or behind the main price plot window.
Click here to see a live example of Aroon.
Click here to see a live example of the Aroon Oscillator.
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 楼主| 发表于 2009-3-15 12:27 | 显示全部楼层
[url=]Average Directional Index (ADX)[/url]
[url=]Introduction[/url]J. Welles Wilder developed the Average Directional Index (ADX) to evaluate the strength of a current trend, be it up or down. It's important to determine whether the market is trending or trading (moving sideways), because certain indicators give more useful results depending on the market doing one or the other.
The ADX is an oscillator that fluctuates between 0 and 100. Even though the scale is from 0 to 100, readings above 60 are relatively rare. Low readings, below 20, indicate a weak trend and high readings, above 40, indicate a strong trend. The indicator does not grade the trend as bullish or bearish, but merely assesses the strength of the current trend. A reading above 40 can indicate a strong downtrend as well as a strong uptrend.
ADX can also be used to identify potential changes in a market from trending to non-trending. When ADX begins to strengthen from below 20 and moves above 20, it is a sign that the trading range is ending and a trend is developing.

When ADX begins to weaken from above 40 and moves below 40, it is a sign that the current trend is losing strength and a trading range could develop.


[url=]Positive/Negative Directional Indicators[/url]
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The ADX is derived from two other indicators, also developed by Wilder, called the Positive Directional Indicator (sometimes written +DI) and the Negative Directional Indicator (-DI).
When the ADX Indicator is selected, SharpCharts plots the Positive Directional Indicator (+DI), Negative Directional Indicator (-DI) and Average Directional Index (ADX). With the Red, White and Green color scheme on SharpCharts, ADX is the thick black line with less fluctuation, +DI is green and -DI is red. +DI measures the force of the up moves and -DI measures the force of the down moves over a set period. The default setting is 14 periods, but users are encouraged to modify these settings according to their personal preferences.
In its most basic form, buy and sell signals can be generated by +DI/-DI crosses. A buy signal occurs when +DI moves above -DI and a sell signal when -DI moves above the +DI. Be careful, though; when a security is in a trading range, this system may produce many whipsaws. As with most technical indicators, +DI/-DI crosses should be used in conjunction with other aspects of technical analysis.
The ADX combines +DI with -DI, and then smooths the data with a moving average to provide a measurement of trend strength. Because it uses both +DI and -DI, ADX does not offer any indication of trend direction, just strength. Generally, readings above 40 indicate a strong trend and readings below 20 a weak trend. To catch a trend in its early stages, you might look for stocks with ADX that advances above 20. Conversely, an ADX decline from above 40 might signal that the current trend is weakening and a trading range is developing.

[url=]The Average Directional Index (ADX) and SharpCharts[/url]
With SharpCharts, you can plot the +DI/-DI using the Wilder's DMI (ADX) indicator above, below, or behind the price plot chart. The Parameters text box controls the number of periods used to calculate the ADX, with the default being 14. The Position drop-down menu controls the positioning of the indicator.
Bear in mind that increasing the number of periods will smooth the ADX line (making it less volatile), and display more significant readings. The readings, however, will present more of a lag. For example, if charting 30 periods, readings over 40 become stronger indicators of a trend. However, the trend may have already started and could have been caught earlier less periods were used.
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 楼主| 发表于 2009-3-15 12:27 | 显示全部楼层
[url=]Average True Range (ATR)[/url]
[url=]Introduction[/url]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.

[url=]Calculation[/url]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:
    Multiply the previous 14-day ATR by 13.
    Add the most recent day's TR value.
  • 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.

[url=]Average True Range (ATR) and SharpCharts[/url]
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.
Click here to see a live example of ATR.
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 楼主| 发表于 2009-3-15 12:28 | 显示全部楼层
[url=]Bollinger Band Width[/url]
[url=]Introduction[/url]Bollinger Bands measure volatility by placing trading bands around a moving average. These bands are charted usually two standard deviations away from the average, so as the average changes, the value of two standard deviations also changes. This value is the Bollinger Band Width, which represents the expanding and contracting of the bands based on recent volatility.
During a period of rising price volatility, the distance between the two bands will widen (BB Width will increase). Conversely, during a period of low market volatility, the distance between the two bands will contract (BB Width will decrease).
There is a tendency for bands to alternate between expansion and contraction. When the bands are unusually far apart, that is often a sign that the current trend may be ending. When the distance between the two bands has narrowed too far, that is often a sign that a market may be about to initiate a new trend.

This chart from Dell shows Bollinger Band Width for a 20-day moving average using 4 standard deviations. It is easily seen how a high BB Width often indicates a slowing trend (red lines), and how a low BB Width often indicates a forming trend (green line).

[url=]Bollinger Band Width and SharpCharts[/url]
With SharpCharts, you can plot the Bollinger Band Width indicator using an n-period simple moving average and any multiple of standard deviations by entering the simple moving average-period setting and standard deviation number into the Parameters text box using the format "SMA-period,Std dev." You can also display it above, below, or behind the price plot window. The Bollinger Band Width's default settings, of a 20-period simple moving average and 2 Standard Deviations, are illustrated above.
Click here to see a live example of Bollinger Band Width.



[url=]Commodity Channel Index (CCI)[/url]
[url=]Introduction[/url]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.
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[url=]Calculation[/url]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 )

(Click here to download an Excel spreadsheet that contains a example of the CCI being calculated.)

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 -100.
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.

[url=]Example[/url]
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.

[url=]CCI and SharpCharts[/url]
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.
[url=http://stockcharts.com/def/servlet/SC.web?c=T,UU[L,A]DACLNIMY[P][VC60][IUD20]]Click here[/url] to see a live example of CCI.
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 楼主| 发表于 2009-3-15 12:29 | 显示全部楼层
[url=]Chaikin Money Flow (CMF)[/url]
[url=]Introduction[/url]Developed by Marc Chaikin, the Chaikin Money Flow oscillator is calculated from the daily readings of the Accumulation/Distribution Line. The basic premise behind the Accumulation Distribution Line is that the degree of buying or selling pressure can be determined by the location of the Close relative to the High and Low for the corresponding period (Closing Location Value). There is buying pressure when a stock closes in the upper half of a period's range and there is selling pressure when a stock closes in the lower half of the period's trading range. The Closing Location Value multiplied by volume forms the Accumulation/Distribution Value for each period. (See our Chart School article for a detailed analysis of the Accumulation/Distribution Line.)

([url=http://stockcharts.com/def/servlet/SC.web?c=DELL,UU[L,A]DACLNIMY[P][VC60][IUC20]]Click here[/url] to see a live example of CMF)

[url=]Methodology[/url]
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The CIENA (CIEN) chart details the breakdown of the daily Accumulation/Distribution Values and how they relate to Chaikin Money Flow. The formula for Chaikin Money Flow is the cumulative total of the Accumulation/Distribution Values for 21 periods divided by the cumulative total of volume for 21 periods.
On the CIENA chart, the purple box encloses 21 days of Accumulation/Distribution Values. The total of these 21 days divided by the total for the 21 days of volume forms the value of Chaikin Money Flow at the end of that day (purple arrow). To calculate the next day, the Accumulation/Distribution Value from the first day is removed and the value for the next day is entered into the equation.
The number of periods can be changed to suit a particular security and time frame. The 21-day Chaikin Money Flow is a good representation of the buying and selling pressure for the past month. A month is long enough to filter out the random noise. By using a longer time frame, the indicator will be less volatile and be less prone to whipsaws. For weekly and monthly charts, a shorter time frame is usually suitable.
Generally speaking, Chaikin Money Flow is bullish when it is positive and bearish when it is negative. The next item to assess is the range, the length of time Chaikin Money Flow has remained positive or negative. Even though divergences are not an intricate part of the strategy behind Chaikin Money Flow, the absolute level and the general direction of the oscillator can be important.

[url=]Accumulation Indications[/url]The Chaikin Money Flow oscillator generates bullish signals by indicating that a security is under accumulation. There are three factors that determine if a security is under accumulation. They also determine the strength of the accumulation.
    The first and most obvious factor is whether the Chaikin Money Flow value is greater than zero? It is an indication of buying pressure and accumulation when the indicator is positive.
    The second factor is the duration of the reading, how long the oscillator has been positive. The longer the oscillator remains above zero, the more evidence there is that the security is under sustained accumulation. Extended periods of accumulation or buying pressure are bullish, and they indicate that sentiment towards the security remains positive.
  • The third factor is the intensity of the oscillator. Not only should the oscillator remain above zero, but it should also be able to increase and attain a certain level. The more positive the reading is, the more evidence of buying pressure and accumulation. This is usually a judgment call, based on prior levels for the oscillator, but a move above .10 would be significant enough to warrant a bullish signal. A reading above .25 would be an indication of strong buying pressure. You should consider prior levels of the indicator to be sure.

On the Alcoa chart (AA), Chaikin Money Flow actually strengthened while the stock continued to decline. For most of October, the stock traded flat while Chaikin Money Flow remained positive and continued to strengthen. The accumulation levels, as evidenced by Chaikin Money Flow, were very strong in October. The stock fell at the end of October, and Chaikin Money Flow declined in November. When the stock fell, distribution levels never surpassed -.10, indicating that selling pressure was not that intense. In late November, the stock managed a comeback, and broke resistance at 31. Chaikin Money Flow formed a higher low and returned to positive territory to confirm the breakout. Selling pressure dried up quickly and Chaikin Money Flow was able to bounce back in strong fashion. The evidence was clearly bullish, but to capitalize, a trader would have had to act fast.

The chart for Time Warner (TWX) is a bit different. The stock formed a Double Bottom in August and September while Chaikin Money Flow formed a rather large positive divergence. This divergence was not a signal, but would have served as an alert that the selling pressure was decreasing. Divergences can be difficult to act on and should be used in conjunction with other aspects of technical analysis. By the time the stock broke resistance at 52, Chaikin Money Flow had moved from a mildly bearish levels just above -.10 to moderately bullish levels just above +.13. The interesting point about Time Warner is the period from 28-Sept to 22-Oct (gray lines). During this period, the stock traded sideways, but Chaikin Money Flow continued to strengthen as buying pressure intensified. The oscillator moved from +.1208 on 28-Sept to +.2377 on 22-Oct. Buying pressure has nearly doubled. This was a clearly bullish indication and the stock soon obliged with an advance from the low fifties to over 90.

[url=]Distribution Indications[/url]The Chaikin Money Flow oscillator generates bearish signals by indicating that a security is experiencing selling pressure, or distribution. As with the bullish signals, there are three items used to determine whether or not a security is experiencing selling pressure and the intensity of that selling pressure.
    The first and most obvious bearish signal is when Chaikin Money Flow is negative. A negative reading indicates that the security in question is under selling pressure or experiencing distribution.
    The second bearish signal is the duration of the negative Chaikin Money Flow reading. The longer the oscillator remains negative, the greater the evidence of sustained selling pressure or distribution. Extended periods below zero indicate that sentiment towards the underlying security is bearish, and there is likely to be downward pressure on the price as well. The length of time can be determined by measuring the percentage of time that the indicator remains below zero. If Chaikin Money Flow is negative to 3 out of 4 weeks, then it would be experiencing selling pressure 75% of the time.
  • The third bearish signal is the intensity of selling pressure or distribution. This can be determined by the oscillator's absolute level. Once the indicator moves below -.10, the degree selling pressure begins to warrant a bearish signal. Any further movement would increase the degree of selling pressure. Marc Chaikin considers a reading below -25 percent (-.25) to be indicative of strong selling pressure. These levels are general guidelines, and determining the significance of a reading's intensity should be based on the characteristics of the individual security and past readings for Chaikin Money Flow.

JC Penny (JCP) is an example of a stock that experienced distribution for many weeks before the price actually fell. Once the price began to fall, the indicator remained in negative territory for an extended period of time. From March to May, Chaikin Money Flow had been positive (green). On 18 May, the stock gapped up on the Open, but the indicator abruptly fell, and turned negative (red arrows). The stock advanced intraday on the 18th, but fell by the Close to end the day near the Lows. Based on the previous Close, the stock advanced. However, from the perspective of Chaikin Money Flow, the stock closed near the Low for the day on heavy volume, which is regarded as selling pressure.

To prove that this abrupt change was not a fluke, the indicator declined further over the next several weeks, and it remained negative for almost 3 months, indicating that selling pressure was strong in the stock. Not only did the selling pressure remain for an extended period, but the intensity of the selling pressure increased, also. Chaikin Money Flow reached a low of -.468 (negative 46.8 percent) while the stock was near its Highs around 50. The stock began to confirm the selling pressure and worked its way down in June and July.
There were a few weeks in August when the indicator turned positive. This might have been seen as bullish, but it lasted a mere 3 weeks, and Chaikin Money Flow only managed to get as high as +.1270. Furthermore, the price action of the stock never confirmed this strength, and it is likely that other price and momentum indicators were bearish, as well. The positive readings did not last long. By early September, Chaikin Money Flow was trading below -.25 and the stock was trading around 36. This was a solid signal that selling pressure in the stock remained heavy, and there would likely be downward pressure on the price before long. The stock subsequently declined below 20, and Chaikin Money Flow has been negative since late August.
All three indications of selling pressure were prevalent in JC Penny (JCP):
    Chaikin Money Flow turned negative before the stock declined.
    The indicator remained negative for 6 out of 7 months (85% of the time).
  • Almost all of the negative readings were below -.10 and many times the indicator dipped below -.25.

IBM provides an excellent example of a reaction rally that had failure written all over it. When the stock peaked in July, Chaikin Money Flow was already well off of its Highs. The indicator was still positive and mildly bullish, but could not surpass +.10 to even partially confirm the High. The indicator formed a Double Top in July with both peaks well below +.10. After the decline in late July, the stock began to find support, and rallied in August, but Chaikin Money Flow would have none of it. The indicator broke below -.10 twice, and remained negative for almost the entire month. When the stock reached its September reaction high, Chaikin Money Flow was still negative.
After the September High in the stock, things began to fall apart. On 17 Sept, the stock declined with heavy volume, and Chaikin Money Flow recorded a new reaction low. Each of these items is marked with a blue arrow on the chart. By this time, selling pressure had been evident for over a month. Chaikin Money Flow had been negative the whole time, and had progressively weakened. The sharp decline in the stock on the heaviest volume in over 4 months indicated something was not right. The final straw came when support at 118.5 was broken, and Chaikin Money Flow was trading below -.20.

[url=]Chaikin Money Flow and Other Indicators[/url]It is best to choose indicators that complement each other. In a recent interview with Technical Analysis of Stocks and Commodities magazine, Marc Chaikin advises against using indicators that have common characteristics. It would be redundant to analyze both Momentum and MACD. These are both momentum oscillators that are based on the closing price and reflect the rate of change. Their signals will not be exactly the same, but it would be a waste of valuable time to analyze both. Chaikin singles out the Stochastic Oscillator, CCI and RSI as similar indicators. All three are banded momentum oscillators that are good for detecting overbought and oversold conditions. Buy and sell signals are also generated in much the same fashion. All three are excellent indicators, but it would be a waste of time to follow all three when one will be sufficient.
Chaikin Money Flow can be used to identify the tradable trend. If Chaikin Money Flow has been above zero for most of the past three months, then prudence would dictate that the tradable trend is up. The oscillator is indicating that buying pressure prevails. It would not be sensible to attempt a short sale if the tradable trend is up. By identifying the tradable trend, traders can ignore bearish signals and only pay attention to signals that concur. If Chaikin Money Flow indicates that buying pressure prevails, then positive divergences, bullish moving average crossovers, bullish centerline crossovers and bullish oversold crossovers would be potential buy signals. (A bullish oversold crossover occurs when an indicator advances above the oversold line. This would be a move from below 30 to above 30 for RSI). All bearish signals would be ignored, at least as long as Chaikin Money Flow indicated that buying pressure reigned.
One possible combination of indicators would be the following:
    Chaikin Money Flow - A non-trend-following volume indicator to identify buying and selling pressure.
    RSI - A momentum indicator used to identify potential overbought and oversold levels.
    Moving Averages - A trend-following indicator to identify the underlying trend in the stock.
  • Price Relative - A comparative indicator to identify the strength of the stock relative to a major index.
These four indicators have little in common and complement each other very well.

[url=]Chaikin Money Flow and SharpCharts[/url]
With SharpCharts, CMF can be charted above, below, or behind the price plot window. The default value for the indicator's duration is 20-periods, but any period can be used. Simply enter the desired duration in the Parameters text box.
Click here to see a live example of CMF.

[url=]Conclusion[/url]Chaikin Money Flow is an indicator that is best used in conjunction with other aspects of technical analysis. This is usually the case with indicators, but probably even more so in this case. The oscillator is unlike a momentum oscillator, and is not influenced by the price change from day to day. Instead, the indicator focuses on the location of the close relative to the range for the period (daily or weekly). This is the strength of Chaikin Money Flow, but can also be its weakness.
Because Chaikin Money Flow does not reflect the change in price from day to day or week to week, large opening gaps are sometimes not reflected in the indicator. Sometimes the indicator moves in the opposite direction of the gap, and creates a misleading picture.

Starbucks (SBUX) formed a large down gap on 1 July with extremely heavy volume. Even though the stock opened more than 10 points lower, it managed to close on the High for the day. Strong Closes indicate accumulation, and the heavy volume amplified this message to cause a large jump in the indicator. The strength was a bit misleading, and the indicator slowly declined over the next 20 days. On the 21st day, the data from 1 July was removed, and the current day's data added. This caused an immediate drop in the indicator. The Chaikin Money Flow was well below zero the next day, reflecting more accurately the selling pressure taking place in the stock.
Even though Chaikin Money Flow can be used on an intraday, daily or weekly basis, it was designed with daily data in mind. One day is an unambiguous time period with measurable volume and a specific Open, High, Low and Close. This preference may lessen in the future, with the proliferation of after-hours trading, but determining the location of the Close relative to the High and Low is still fairly straightforward. When dealing with weekly or monthly data, the beginning and end are less precise. This imprecision can affect the location of the Close relative to the High and Low for the period. Weekly is obviously more definable than monthly, but less definable than daily. This is something to consider when analyzing Chaikin Money Flow with periods other than daily.
Chaikin advocated a 21-day time frame for Chaikin Money Flow. If Chaikin Money Flow is to be used on a weekly chart, a shorter time frame will probably work better. A 21-day period represents about one month of trading, and will allow for some smoothing. A shorter time frame might prove too choppy, but a longer time frame may lag too much. Each security will have its own optimum time frame.
Keep in mind that the short-term trend is not as important as the absolute level. As long as the indicator remains above zero, it is considered bullish. It is also important to gauge the length of time that the indicator remains positive. If the indicator is positive for 7 out of 9 weeks, then buying pressure is the order of the day. The two negative weeks are a blip on the radar, and should not be taken out of context.
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 楼主| 发表于 2009-3-15 12:30 | 显示全部楼层
[url=]Chaikin Oscillator[/url]
[url=]Introduction[/url]The Accumulation/Distribution Line was covered in a previous article; here, we will examine an indicator that stems from the concept behind the Accumulation/Distribution Line: the Chaikin Oscillator – or Chaikin A/D Oscillator, as it is sometimes called – named after its creator, Marc Chaikin. Before reading this article, you may want to become familiar with the concepts behind the Accumulation/Distribution Line.
The basic premise of the Accumulation/Distribution Line is that the degree of buying or selling pressure can be determined by the location of the Close, relative to the High and Low for the corresponding period. There is buying pressure when a stock closes in the upper half of a period's range and there is selling pressure when a stock closes in the lower half of the period's trading range.

The CIENA (CIEN) chart shows the relationship among each period's Accumulation/Distribution Value, Accumulation/Distribution Line, and Chaikin Oscillator. The same four points noted in the Accumulation/Distribution Line article have been noted in this example for reference, as well.

[url=]Methodology[/url]The Chaikin Oscillator is simply the Moving Average Convergence Divergence indicator (MACD) applied to the Accumulation/Distribution Line. The formula is the difference between the 3-day exponential moving average and the 10-day exponential moving average of the Accumulation/Distribution Line. Just as the MACD-Histogram is an indicator to predict moving average crossovers in MACD, the Chaikin Oscillator is an indicator to predict changes in the Accumulation/Distribution Line.
Many of the same signals that apply to MACD are also applicable to the Chaikin Oscillator. Keep in mind though, that these signals relate to the Accumulation/Distribution Line, not directly to the stock itself. Readers may want to refer to our MACD article for more detailed information on various signals such as positive divergences, negative divergences and centerline crossovers.
Just as MACD injects momentum characteristics into moving averages, the Chaikin Oscillator gives momentum characteristics to the Accumulation/Distribution Line, which can be a bit of a laggard sometimes. By adding momentum features, the Chaikin Oscillator will lead the Accumulation/Distribution Line. The CIENA chart (CIEN) confirms that movements in the Accumulation/Distribution Line are usually preceded by corresponding divergences in the Chaikin Oscillator.
    The July negative divergence in the Chaikin Oscillator foreshadowed the impending weakness in the Accumulation/Distribution Line. This was a slant-type divergence that is characterized by its lack of distinctive peaks to form the divergence. The Chaikin Oscillator peaked about a week before the Accumulation/Distribution Line, and formed a bearish centerline crossover 2 weeks later. When the oscillator is negative, it implies that momentum for the Accumulation/Distribution Line is negative or bearish, which would ultimately be a negative reflection on the stock.
  • The August positive divergence in the Chaikin Oscillator foreshadowed a sharp advance in the Accumulation/Distribution Line. This divergence was longer and could have been referred to as a trough divergence. In a trough divergence, there are two noticeable troughs, one higher than the other, that form the divergence. The bullish, or positive, momentum was confirmed when the Chaikin Oscillator formed a bullish centerline crossover in late August.

[url=]Bullish Signals[/url]There are two bullish signals that can be generated from the Chaikin Oscillator: positive divergences and centerline crossovers. Because the Chaikin Oscillator is an indicator of an indicator, it is prudent to look for confirmation of a positive divergence (a bullish moving average crossover, for example) before counting this as a bullish signal. The chart for Coca-Cola (KO) is an excellent example of a positive divergence that has been confirmed by a centerline crossover:

    The positive divergence was sharp and pronounced. (When using an indicator of an indicator, it is preferable to take only strong signals. Note the steepness of the positive divergence.)
    The bullish centerline crossover occurred in the Chaikin Oscillator before the Accumulation/Distribution Line broke to a new reaction high.
  • At the point of the centerline crossover (green dotted line), the stock also broke resistance and the bullish signal was further validated.

[url=]Bearish Signals[/url]There are, also, two bearish signals that can be generated from the Chaikin Oscillator: a negative divergence and a bearish centerline crossover. Allow a negative divergence to be confirmed by a bearish centerline crossover before a bearish signal is rendered. The chart for Merck (MRK) shows a recent bearish signal that coincided with a support break in the stock:

    The negative divergence is not as sharp and pronounced as the positive divergence in Coca-Cola (KO), but it is detectable, nonetheless. Divergences that cover long time spans are sometimes difficult to time for a trade.
    It is easy to see the effects of price action on the Chaikin Oscillator and the Accumulation/Distribution Line in this example. The blue lines mark a period when the stock traded basically flat for 13 days. However, many of the Closes for this period were below the midway point, and some were near the intraday Lows. Note the action of the Chaikin Oscillator and Accumulation/Distribution Line during this period; both declined markedly.
  • The bearish centerline crossover to confirm the divergence occurred just recently and coincided with a break of support in the stock and a trend line break in the Accumulation/Distribution Line.

[url=]Chaikin Oscillator and SharpCharts[/url]
SharpCharts offers flexible use of the Chaikin Oscillator. The standard formula is the difference between the 3-day exponential moving average and the 10-day exponential moving average of the Accumulation/Distribution Line. Enter the durations for the exponential moving averages into the Parameters text box, separating them with a comma, as shown above. The Chaikin Oscillator can be displayed above, below, or behind the price plot window.
Click here for a live example of the Chaikin Oscillator.

[url=]Conclusion[/url]The Chaikin Oscillator is good for adding momentum to the Accumulation/Distribution Line, but can sometimes add a little too much momentum and be difficult to interpret. The moving averages are both relatively short, and will be more sensitive to changes in the Accumulation/Distribution Line, as a consequence. Sensitivity is important, but one must also be able to interpret the indicator. Those with the software and resources might try changing the moving averages on the indicator to further smooth the fluctuations. This indicator should definitely be used in conjunction with other aspects of technical analysis.
Chaikin Money Flow is one answer to the volatility that has been created from the Chaikin Oscillator.




[url=]Force Index[/url]Developed by Alexander Elder, the Force Index is a price-and-volume oscillator that helps technical analysts determine if a stock's trend is strengthening or weakening.

[url=]Calculation[/url]The raw Force Index is calculated as the difference between today's close and yesterday's close times today's volume. The "FORCE(1)" indicator in the chart below is an example of the raw Force Index:

Because the raw Force Index is so choppy, many people use an EMA to smooth it. The 13-period EMA is the most popular choice for smoothing the Force Index. You can see the difference between the raw Force Index and the smoothed Force Index above.

[url=]Interpretation[/url]Like all oscillators, the Force Index generates buy and sell signals when it crosses it's center line. Trendlines can also be used to determine the direction and strength of the indicator's current movement. When the Force Index is setting new highs, the stock's current uptrend is likely to continue. When it is setting new low's, the stock will probably keep sinking. If the Force Index is moving sideways, a trend change is likely.

[url=]Parameters[/url]In SharpCharts, the Force Index can be found in the "Indicators" dropdowns. It takes one parameter which is the number of periods to use when exponentially smoothing the raw Force Index. The default smoothing value is 13. You can see the raw Force Index by using "1" as the parameter.
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 楼主| 发表于 2009-3-15 12:31 | 显示全部楼层
[url=]Moving Average Convergence/Divergence (MACD)[/url]
[url=]Introduction[/url]
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Developed by Gerald Appel, Moving Average Convergence/Divergence (MACD) is one of the simplest and most reliable indicators available. MACD uses moving averages, which are lagging indicators, to include some trend-following characteristics. These lagging indicators are turned into a momentum oscillator by subtracting the longer moving average from the shorter moving average. The resulting plot forms a line that oscillates above and below zero, without any upper or lower limits. MACD is a centered oscillator and the guidelines for using centered oscillators apply.

[url=]MACD Formula[/url]The most popular formula for the "standard" MACD is the difference between a security's 26-day and 12-day Exponential Moving Averages (EMAs). This is the formula that is used in many popular technical analysis programs, including SharpCharts, and quoted in most technical analysis books on the subject. Appel and others have since tinkered with these original settings to come up with a MACD that is better suited for faster or slower securities. Using shorter moving averages will produce a quicker, more responsive indicator, while using longer moving averages will produce a slower indicator, less prone to whipsaws. For our purposes in this article, the traditional 12/26 MACD will be used for explanations. Later in the indicator series, we will address the use of different moving averages in calculating MACD.
Of the two moving averages that make up MACD, the 12-day EMA is the faster and the 26-day EMA is the slower. Closing prices are used to form the moving averages. Usually, a 9-day EMA of MACD is plotted along side to act as a trigger line. A bullish crossover occurs when MACD moves above its 9-day EMA, and a bearish crossover occurs when MACD moves below its 9-day EMA. The Merrill Lynch (MER) chart below shows the 12-day EMA (thin blue line) with the 26-day EMA (thin red line) overlaid the price plot. MACD appears in the box below as the thick black line and its 9-day EMA is the thin blue line. The histogram represents the difference between MACD and its 9-day EMA. The histogram is positive when MACD is above its 9-day EMA and negative when MACD is below its 9-day EMA.


[url=]What Does MACD Do?[/url]MACD measures the difference between two Exponential Moving Averages (EMAs). A positive MACD indicates that the 12-day EMA is trading above the 26-day EMA. A negative MACD indicates that the 12-day EMA is trading below the 26-day EMA. If MACD is positive and rising, then the gap between the 12-day EMA and the 26-day EMA is widening. This indicates that the rate-of-change of the faster moving average is higher than the rate-of-change for the slower moving average. Positive momentum is increasing, indicating a bullish period for the price plot. If MACD is negative and declining further, then the negative gap between the faster moving average (blue) and the slower moving average (red) is expanding. Downward momentum is accelerating, indicating a bearish period of trading. MACD centerline crossovers occur when the faster moving average crosses the slower moving average.

This Merrill Lynch (MER) chart shows MACD as a solid black line, and its 9-day EMA as the thin blue line. Even though moving averages are lagging indicators, notice that MACD moves faster than the moving averages. In this example, MACD provided a few good trading signals as well:
    In March and April, MACD turned down ahead of both moving averages, and formed a negative divergence ahead of the price peak.
    In May and June, MACD began to strengthen and make higher Lows while both moving averages continued to make lower Lows.
  • Finally, MACD formed a positive divergence in October while both moving averages recorded new Lows.

[url=]MACD Bullish Signals[/url]MACD generates bullish signals from three main sources:
    Positive Divergence
    Bullish Moving Average Crossover
  • Bullish Centerline Crossover

[url=]Positive Divergence[/url]
A Positive Divergence occurs when MACD begins to advance and the security is still in a downtrend and makes a lower reaction low. MACD can either form as a series of higher Lows or a second Low that is higher than the previous Low. Positive Divergences are probably the least common of the three signals, but are usually the most reliable, and lead to the biggest moves.

[url=]Bullish Moving Average Crossover[/url]
A Bullish Moving Average Crossover occurs when MACD moves above its 9-day EMA, or trigger line. Bullish Moving Average Crossovers are probably the most common signals and as such are the least reliable. If not used in conjunction with other technical analysis tools, these crossovers can lead to whipsaws and many false signals. Bullish Moving Average Crossovers are used occasionally to confirm a positive divergence. A positive divergence can be considered valid when a Bullish Moving Average Crossover occurs after the MACD Line makes its second "higher Low".
Sometimes it is prudent to apply a price filter to the Bullish Moving Average Crossover to ensure that it will hold. An example of a price filter would be to buy if MACD breaks above the 9-day EMA and remains above for three days. The buy signal would then commence at the end of the third day.

[url=]Bullish Centerline Crossover[/url]
A Bullish Centerline Crossover occurs when MACD moves above the zero line and into positive territory. This is a clear indication that momentum has changed from negative to positive, or from bearish to bullish. After a Positive Divergence and Bullish Centerline Crossover, the Bullish Centerline Crossover can act as a confirmation signal. Of the three signals, moving average crossover are probably the second most common signals.

[url=]Using a Combination of Signals[/url]
Even though some traders may use only one of the above signals to form a buy or a sell signal, using a combination can generate more robust signals. In the Halliburton (HAL) example, all three bullish signals were present and the stock still advanced another 20%. The stock formed a lower Low at the end of February, but MACD formed a higher Low, thus creating a potential Positive Divergence. MACD then formed a Bullish Moving Average Crossover by moving above its 9-day EMA. And finally, MACD traded above zero to form a Bullish Centerline Crossover. At the time of the Bullish Centerline Crossover, the stock was trading at 32 1/4 and went above 40 immediately after that. In August, the stock traded above 50.
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[url=]Bearish Signals[/url]MACD generates bearish signals from three main sources. These signals are mirror reflections of the bullish signals:
    Negative Divergence
    Bearish Moving Average Crossover
  • Bearish Centerline Crossover

[url=]Negative Divergence[/url]A Negative Divergence forms when the security advances or moves sideways, and the MACD declines. The Negative Divergence in MACD can take the form of either a lower High or a straight decline. Negative Divergences are probably the least common of the three signals, but are usually the most reliable, and can warn of an impending peak.

The FedEx (FDX) chart shows a Negative Divergence when MACD formed a lower High in May, and the stock formed a higher High at the same time. This was a rather blatant Negative Divergence, and signaled that momentum was slowing. A few days later, the stock broke the uptrend line, and the MACD formed a lower Low.
There are two possible means of confirming a Negative Divergence. First, the indicator can form a lower Low. This is traditional peak-and-trough analysis applied to an indicator. With the lower High and subsequent lower Low, the uptrend for MACD has changed from bullish to bearish. Second, a Bearish Moving Average Crossover (which is explained below) can act to confirm a negative divergence. As long as MACD is trading above its 9-day EMA, or trigger line, it has not turned down and the lower High is difficult to confirm. When MACD breaks below its 9-day EMA, it signals that the short-term trend for the indicator is weakening, and a possible interim peak has formed.

[url=]Bearish Moving Average Crossover[/url]The most common signal for MACD is the moving average crossover. A Bearish Moving Average Crossover occurs when MACD declines below its 9-day EMA. Not only are these signals the most common, but they also produce the most false signals. As such, moving average crossovers should be confirmed with other signals to avoid whipsaws and false readings.

Sometimes a stock can be in a strong uptrend, and MACD will remain above its trigger line for a sustained period of time. In this case, it is unlikely that a Negative Divergence will develop. A different signal is needed to identify a potential change in momentum. This was the case with Merck (MRK) in February and March. The stock advanced in a strong uptrend, and MACD remained above its 9-day EMA for 7 weeks. When a Bearish Moving Average Crossover occurred, it signaled that upside momentum was slowing. This slowing momentum should have served as an alert to monitor the technical situation for further clues of weakness. Weakness was soon confirmed when the stock broke its uptrend line and MACD continued its decline and moved below zero.

[url=]Bearish Centerline Crossover[/url]A Bearish Centerline Crossover occurs when MACD moves below zero and into negative territory. This is a clear indication that momentum has changed from positive to negative, or from bullish to bearish. The centerline crossover can act as an independent signal, or confirm a prior signal such as a moving average crossover or negative divergence. Once MACD crosses into negative territory, momentum, at least for the short term, has turned bearish.

The significance of the centerline crossover will depend on the previous movements of MACD as well. If MACD is positive for many weeks, begins to trend down, and then crosses into negative territory, it would be bearish. However, if MACD has been negative for a few months, breaks above zero, and then back below, it might be a correction. In order to judge the significance of a centerline crossover, traditional technical analysis can be applied to see if there has been a change in trend, higher High or lower Low.
The Unisys (UIS) chart depicts a Bearish Centerline Crossover that preceded a 25% drop in the stock that occurs just off the right edge of the chart. Although there was little time to act once this signal appeared, there were other warnings signs prior to the dramatic drop:
    After the drop to trend line support, a Bearish Moving Average Crossover formed.
    When the stock rebounded from the drop, MACD did not even break above the trigger line, indicating weak upside momentum.
    The peak of the reaction rally was marked by a shooting star candlestick (blue arrow) and a gap down on increased volume (red arrows).
  • After the gap down, the blue trend line extending up from Apr, 1999, was broken.
In addition to the signals mentioned above, a Bearish Centerline Crossover occurred after MACD had been above zero for almost two months. From 20 Sept on, MACD had been weakening and momentum was slowing. The break below zero acted as the final straw of a long weakening process.

[url=]Combining Signals[/url]As with bullish MACD signals, bearish signals can be combined to create more robust signals. In most cases, stocks fall faster than they rise. This was definitely the case with Unisys (UIS), and only two bearish MACD signals were present. Using momentum indicators like MACD, technical analysis can sometimes provide clues to impending weakness. While it may be impossible to predict the length and duration of the decline, being able to spot weakness can enable traders to take a more defensive position.

In 2002, Intel (INTC) dropped from above 36 to below 28 in a few months. Yet it would seem that smart money began distributing the stock before the actual decline. Looking at the technical picture, we can spot evidence of this distribution and a serious loss of momentum:
    In December, a negative divergence formed in MACD.
    Chaikin Money Flow turned negative on December 21.
    Also in December, a Bearish Moving Average Crossover occurred in MACD (black arrow).
    The trend line extending up from October was broken on 20 December.
    A Bearish Centerline Crossover occurred in MACD on 10 Feb (green arrow).
  • On 15 February, support at 31 1/2 was violated (red arrow).
For those waiting for a recovery in the stock, the continued decline of momentum suggested that selling pressure was increasing, and not about to decrease. Hindsight is 20/20, but with careful study of past situations, we can learn how to better read the present and prepare for the future.

[url=]MACD Benefits[/url]
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One of the primary benefits of MACD is that it incorporates aspects of both momentum and trend in one indicator. As a trend-following indicator, it will not be wrong for very long. The use of moving averages ensures that the indicator will eventually follow the movements of the underlying security. By using Exponential Moving Averages (EMAs), as opposed to Simple Moving Averages (SMAs), some of the lag has been taken out.
As a momentum indicator, MACD has the ability to foreshadow moves in the underlying security. MACD divergences can be key factors in predicting a trend change. A Negative Divergence signals that bullish momentum is waning, and there could be a potential change in trend from bullish to bearish. This can serve as an alert for traders to take some profits in long positions, or for aggressive traders to consider initiating a short position.
MACD can be applied to daily, weekly or monthly charts. MACD represents the convergence and divergence of two moving averages. The standard setting for MACD is the difference between the 12 and 26-period EMA. However, any combination of moving averages can be used. The set of moving averages used in MACD can be tailored for each individual security. For weekly charts, a faster set of moving averages may be appropriate. For volatile stocks, slower moving averages may be needed to help smooth the data. Given that level of flexibility, each individual should adjust the MACD to suit his or her own trading style, objectives and risk tolerance.

[url=]MACD Drawbacks[/url]One of the beneficial aspects of the MACD is also one of its drawbacks. Moving averages, be they simple, exponential or weighted, are lagging indicators. Even though MACD represents the difference between two moving averages, there can still be some lag in the indicator itself. This is more likely to be the case with weekly charts than daily charts. One solution to this problem is the use of the MACD-Histogram.
MACD is not particularly good for identifying overbought and oversold levels. Even though it is possible to identify levels that historically represent overbought and oversold levels, MACD does not have any upper or lower limits to bind its movement. MACD can continue to overextend beyond historical extremes.
MACD calculates the absolute difference between two moving averages and not the percentage difference. MACD is calculated by subtracting one moving average from the other. As a security increases in price, the difference (both positive and negative) between the two moving averages is destined to grow. This makes its difficult to compare MACD levels over a long period of time, especially for stocks that have grown exponentially.

The Amazon (AMZN) chart demonstrates the difficult in comparing MACD levels over a long period of time. Before 1999, Amazon's MACD is barely recognizable, and appears to trade close to the zero line. MACD was indeed quite volatile at the time, but this volatility has been dwarfed since the stock rose from below 20 to almost 100.
An alternative is to use the Price Oscillator, which shows the percentage difference between two moving averages:
(12 day EMA - 26 day EMA) / (26 day EMA)

(20 - 18) / 18 = .11 or +11%

The resulting percentage difference can be compared over a longer period of time. On the Amazon chart, we can see that the Price Oscillator provides a better means for a long-term comparison. For the short term, MACD and the Price Oscillator are basically the same. The shape of the lines, the divergences, moving average crossovers and centerline crossovers for MACD and the Price Oscillator are virtually identical.

[url=]Pros and Cons of the MACD[/url]Since Gerald Appel developed the MACD, there have been hundreds of new indicators introduced to technical analysis. While many indicators have come and gone, the MACD has stood the test of time. The concept behind its use is straightforward, and its construction is simple, yet it remains one of the most reliable indicators around. The effectiveness of the MACD will vary for different securities and markets. The lengths of the moving averages can be adapted for a better fit to a particular security or market. As with all indicators , MACD is not infallible and should be used in conjunction with other technical analysis tools.

[url=]MACD-Histogram[/url]In 1986, Thomas Aspray developed the MACD-Histogram. Some of his findings were presented in a series of articles for Technical Analysis of Stocks and Commodities. Aspray noted that MACD's lag would sometimes miss important moves in a security, especially when applied to weekly charts. He first experimented by changing the moving averages and found that shorter moving averages did indeed speed up the signals. However, he was looking for a means to anticipate MACD crossovers. One of the answers he came up with was the MACD-Histogram.


[url=]Definition and Construction[/url]The MACD-Histogram represents the difference between the MACD and its trigger line, the 9-day EMA of MACD. The plot of this difference is presented as a histogram, making centerline crossovers and divergences easily identifiable. A centerline crossover for the MACD-Histogram is the same as a moving average crossover for MACD. If you will recall, a moving average crossover occurs when MACD moves above or below the trigger line.
If the value of MACD is larger than the value of its 9-day EMA, then the value on the MACD-Histogram will be positive. Conversely, if the value of MACD is less than its 9-day EMA, then the value on the MACD-Histogram will be negative.
Further increases or decreases in the gap between MACD and its trigger line will be reflected in the MACD-Histogram. Sharp increases in the MACD-Histogram indicate that MACD is rising faster than its 9-day EMA and bullish momentum is strengthening. Sharp declines in the MACD-Histogram indicate that MACD is falling faster than its 9-day EMA and bearish momentum is increasing.

On the chart above, we can see that the MACD-Histogram movements are relatively independent of the actual MACD. Sometimes the MACD is rising while the MACD-Histogram is falling. At other times, the MACD is falling while the MACD-Histogram is rising. The MACD-Histogram does not reflect the absolute value of the MACD, but rather the value of the MACD relative to its 9-day EMA. Usually, but not always, a move in the MACD is preceded by a corresponding divergence in the MACD-Histogram.
    The first point shows a sharp positive divergence in the MACD-Histogram that preceded a Bullish Moving Average Crossover.
    On the second point, the MACD continued to new Highs but the MACD-Histogram formed two equal Highs. Although not a textbook case of Positive Divergence, the equal High failed to confirm the strength seen in the MACD.
    A Positive Divergence formed when the MACD-Histogram formed a higher Low and the MACD continued lower.
  • A Negative Divergence formed when the MACD-Histogram formed a lower High and the MACD continued higher.

[url=]Usage[/url]Thomas Aspray designed the MACD-Histogram as a tool to anticipate a moving average crossover in the MACD. Divergences between MACD and the MACD-Histogram are the main tool used to anticipate moving average crossovers. A Positive Divergence in the MACD-Histogram indicates that the MACD is strengthening and could be on the verge of a Bullish Moving Average Crossover. A Negative Divergence in the MACD-Histogram indicates that the MACD is weakening, and it foreshadows a Bearish Moving Average Crossover in the MACD.
In his book, Technical Analysis of the Financial Markets, John Murphy asserts that the best use for the MACD-Histogram is in identifying periods when the gap between the MACD and its 9-day EMA is either widening or shrinking. Broadly speaking, a widening gap indicates strengthening momentum and a shrinking gap indicates weakening momentum. Usually a change in the MACD-Histogram will precede any changes in the MACD.

[url=]Signals[/url]The main signal generated by the MACD-Histogram is a divergence followed by a moving average crossover. A bullish signal is generated when a Positive Divergence forms and there is a Bullish Centerline Crossover. A bearish signal is generated when there is a Negative Divergence and a Bearish Centerline Crossover. Keep in mind that a centerline crossover for the MACD-Histogram represents a moving average crossover for the MACD.
Divergences can take many forms and varying degrees. Generally speaking, two types of divergences have been identified: the slant divergence and the peak-trough divergence.


[url=]Slant Divergence[/url]A Slant Divergence forms when there is a continuous and relatively smooth move in one direction (up or down) to form the divergence. Slant Divergences generally cover a shorter time frame than divergences formed with two peaks or two troughs. A Slant Divergence can contain some small bumps (peaks or troughs) along the way. The world of technical analysis is not perfect and there are exceptions to most rules and hybrids for many signals.


[url=]Peak-Trough Divergence[/url]A peak-trough divergence occurs when at least two peaks or two troughs develop in one direction to form the divergence. A series of two or more rising troughs (higher lows) can form a Positive Divergence and a series of two or more declining peaks (lower highs) can form a Negative Divergence. Peak-trough Divergences usually cover a longer time frame than slant divergences. On a daily chart, a peak-trough divergence can cover a time frame as short as two weeks or as long as several months.
Usually, the longer and sharper the divergence is, the better any ensuing signal will be. Short and shallow divergences can lead to false signals and whipsaws. In addition, it would appear that Peak-trough Divergences are a bit more reliable than Slant Divergences. Peak-trough Divergences tend to be sharper and cover a longer time frame than Slant Divergences.

[url=]MACD-Histogram Benefits[/url]The main benefit of the MACD-Histogram is its ability to anticipate MACD signals. Divergences usually appear in the MACD-Histogram before MACD moving average crossovers do. Armed with this knowledge, traders and investors can better prepare for potential trend changes.
The MACD-Histogram can be applied to daily, weekly or monthly charts. (Note: This may require some tinkering with the number of periods used to form the original MACD; shorter or faster moving averages might be necessary for weekly and monthly charts.) Using weekly charts, the broad underlying trend of a stock can be determined. Once the broad trend has been determined, daily charts can be used to time entry and exit strategies. In Technical Analysis of the Financial Markets, John Murphy advocates this type of two-tiered approach to investing in order to avoid making trades against the major trend. The weekly MACD-Histogram can be used to generate a long-term signal in order to establish the tradable trend. Then only short-term signals that agree with the major trend would be considered.
After the trend has been established, MACD-Histogram divergences can be used to signal impending reversals. If the long-term trend was bullish, a negative divergences with bearish centerline crossovers would signal a possible reversal. If the long-term trend was bearish, traders would watch for a positive divergences with bullish centerline crossovers.

On the IBM weekly chart, the MACD-Histogram generated four signals. Before each moving average crossover in the MACD, a corresponding divergence formed in the MACD-Histogram. To make adjustments for the weekly chart, the moving averages have been shortened to 6 and 12. This MACD is formed by subtracting the 6-week EMA from the 12-week EMA. A 6-week EMA has been used as the trigger. The MACD-Histogram is calculated by taking the difference between MACD (6/12) and the 6-day EMA of MACD (6/12).
    The first signal was a Bearish Moving Average Crossover in January, 1999. From its peak in late November, 1998, the MACD-Histogram formed a Negative Divergence that preceded the Bearish Moving Average Crossover in the MACD.
    The second signal was a Bullish Moving Average Crossover in April. From its low in mid-February, the MACD-Histogram formed a Positive Divergence that preceded the Bullish Moving Average Crossover in the MACD.
    The third signal was a Bearish Moving Average Crossover in late July. From its May peak, the MACD-Histogram formed a Negative Divergence that preceded a Bearish Moving Average Crossover in the MACD.
  • The final signal was a Bullish Moving Average Crossover, which was preceded by a slight Positive Divergence in the MACD-Histogram.
The third signal was based on a Peak-trough Divergence Two readily identifiable and consecutive lower peaks formed to create the divergence. The peaks and troughs on the previous divergences, although identifiable, do not stand out as much.

[url=]MACD-Histogram Drawbacks[/url]The MACD-Histogram is an indicator of an indicator or a derivative of a derivative. The MACD is the first derivative of the price action of a security, and the MACD-Histogram is the second derivative of the price action of a security. As the second derivative, the MACD-Histogram is further removed from the actual price action of the underlying security. The further removed an indicator is from the underlying price action, the greater the chances of false signals. Keep in mind that this is an indicator of an indicator. The MACD-Histogram should not be compared directly with the price action of the underlying security.
Because MACD-Histogram was designed to anticipate MACD signals, there is a temptation to jump the gun. The MACD-Histogram should be used in conjunction with other aspects of technical analysis. This will help to alleviate the temptation for early entry. Another means to guard against early entry is to combine weekly signals with daily signals. Of course, there will be more daily signals than weekly signals. However, by using only the daily signals that agree with the weekly signals, there will be fewer daily signals to act on. By acting only on those daily signals that are in agreement with the weekly signals, you are also assured of trading with the longer trend and not against it.
Be careful of small and shallow divergences. While these may sometimes lead to good signals, they are also more apt to create false signals. One method to avoid small divergences is to look for larger divergences with two or more readily identifiable peaks or troughs. Compare the peaks and troughs from past action to determine significance. Only peaks and troughs that appear to be significant should warrant attention.

[url=]MACD and SharpCharts[/url]
Using SharpCharts, the MACD can be set as an indicator above or below or behind a security's price plot. Once the indicator is chosen from the drop down list, the Parameters text box to the right is used to adjust the settings. The default setting is "12,26,9," which automatically appears. The MACD created would be calculated using a 12-day EMA and 26-day EMA to calculate MACD and a 9-day EMA of MACD as the signal/trigger line. The Position drop-down menu determines where the indicator appears in relation to the price plot chart.
The MACD-Histogram, which measures the difference between the MACD and its signal/trigger line, shows the MACD's settings in the Parameter text box to its right. It, too, can be displayed above, below, or behind the price plot window.
Click here to see a live example of the MACD.
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 楼主| 发表于 2009-3-15 12:32 | 显示全部楼层
[url=]Money Flow Index (MFI)[/url]
[url=]Introduction[/url]The Money Flow Index (MFI) is a momentum indicator that is similar to the Relative Strength Index (RSI) in both interpretation and calculation. However, MFI is a more rigid indicator in that it is volume-weighted, and is therefore a good measure of the strength of money flowing in and out of a security. It compares "positive money flow" to "negative money flow" to create an indicator that can be compared to price in order to identify the strength or weakness of a trend. Like the RSI, the MFI is measured on a 0 - 100 scale and is often calculated using a 14 day period.

[url=]Formula[/url]The "flow" of money is the product of price and volume and shows the demand for a security and a certain price. The money flow is not the same as the Money Flow Index but rather is a component of calculating it. So when calculating the money flow, we first need to find the average price for a period. Since we are often looking at a 14-day period, we will calculate the typical price for a day and use that to create a 14-day average.
Typical Price = ( (Day High + Day Low + Day Close) / 3)

Money Flow = (Typical Price) x (Volume)

The MFI compares the ratio of "positive" money flow and "negative" money flow. If typical price today is greater than yesterday, it is considered positive money. For a 14-day average, the sum of all positive money for those 14 days is the positive money flow. The MFI is based on the ratio of positive/negative money flow (Money Ratio).
Money Ratio = (Positive Money Flow / Negative Money Flow)

Finally, the MFI can be calculated using this ratio:
Money Flow Index = 100 - (100 / (1 + Money Ratio))

The fewer number of days used to calculate the MFI, the more volatile it will be.

[url=]Use[/url]The MFI can be interpreted much like the RSI in that it can signal divergences and overbought/oversold conditions.

[url=]Divergences[/url]Positive and negative divergences between the stock and the MFI can be used as buy and sell signals respectively, for they often indicate the imminent reversal of a trend. If the stock price is falling, but positive money flow tends to be greater than negative money flow, then there is more volume associated with daily price rises than with the price drops. This suggests a weak downtrend that threatens to reverse as money flowing into the security is "stronger" than money flowing out of it.

[url=]Overbought/Oversold[/url]As with the RSI, the MFI can be used to determine if there is too much or too little volume associated with a security. A stock is considered "overbought" if the MFI indicator reaches 80 and above (a bearish reading). On the other end of the spectrum, a bullish reading of 20 and below suggests a stock is "oversold".

[url=]Examples[/url]
The PeopleSoft (PSFT) example shows how the MFI is a useful indicator of market tops and bottoms. Overbought conditions in September and January resulted in reversals of uptrends, and the oversold condition in March resulted in a reversal of the prior downtrend.

The Washington Mutual example shows how the MFI can also be used to anticipate imminent reversals. If prices are trending upwards and the MFI is trending downwards, reversals such as those December and March may occur. However, divergences between price and MFI can exist for long periods of time. Therefore, as with all indicators, the MFI should be used in tandem with other indicators that can provide confirmation of any signals it sends. MFI and SharpCharts2

[url=]MFI and SharpCharts[/url]
MFI is available on our SharpCharts charting tool. In the example box, MFI has been assigned 14 and 30 day periods. A swing trader might prefer 14-day periods, while an investor may prefer 30-day periods. Users are encouraged to test different MFI settings and judge for themselves which ones work best and suit their particular trading/investing style.




[url=]On Balance Volume (OBV)[/url]
[url=]Introduction[/url]Joe Granville introduced the On Balance Volume (OBV) indicator in his 1963 book, Granville's New Key to Stock Market Profits. This was one of the first and most popular indicators to measure positive and negative volume flow. The concept behind the indicator: volume precedes price. OBV is a simple indicator that adds a period's volume when the close is up and subtracts the period's volume when the close is down. A cumulative total of the volume 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.

[url=]Calculation[/url]As stated above, OBV is calculated by adding the day's volume to a running cumulative total when the security's price closes up, and subtracts the volume when it closes down.
For example, if today the closing price is greater than yesterday's closing price, then the new
OBV = Yesterday's OBV + Today's Volume

If today the closing price is less than yesterday's closing price, then the new
OBV = Yesterday's OBV - Today's Volume

If today the closing price is equal to yesterday's closing price, then the new
OBV = Yesterday's OBV


[url=]Use[/url]The idea behind the OBV indicator is that changes in the OBV will precede price changes. A rising volume can indicate the presence of smart money flowing into a security. Then once the public follows suit, the security's price will likewise rise.
Like other indicators, the OBV indicator will take a direction. A rising (bullish) OBV line indicates that the volume is heavier on up days. If the price is likewise rising, then the OBV can serve as a confirmation of the price uptrend. In such a case, the rising price is the result of an increased demand for the security, which is a requirement of a healthy uptrend.
However, if prices are moving higher while the volume line is dropping, a negative divergence is present. This divergence suggests that the uptrend is not healthy and should be taken as a warning signal that the trend will not persist.
The numerical value of OBV is not important, but rather the direction of the line. A user should concentrate on the OBV trend and its relationship with the security's price.

[url=]Example[/url]
This chart shows how the OBV line can be used as confirmation of a price trend. The peak in September was followed by lower price movements that corresponded with volume spikes, thus implying that the downtrend was going to continue.

[url=]OBV and SharpCharts[/url]
Using SharpCharts, the On Balance Volume can be charted as an indicator. The optional parameter edits the amount of periods for the moving average overlay.
(A live example of OBV)
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 楼主| 发表于 2009-3-15 12:32 | 显示全部楼层
[url=]Price Oscillators (PPO)[/url]
[url=]Introduction[/url]The Price Oscillator is an indicator based on the difference between two moving averages, and is expressed as either a percentage or in absolute terms. The number of time periods can vary depending on user preference. For daily data, longer moving averages might be preferred to filter out some of the randomness associated with daily prices. For weekly data, which will have already filtered out some of the randomness, shorter moving averages may be deemed more appropriate. In addition, a moving average of the ensuing plot can be overlaid to act as a trigger line, much like is done with MACD. In our charts and commentary, we will use the abbreviation PPO to refer to the Percentage Price Oscillator and APO to refer to the Absolute Price Oscillator.

[url=]Absolute Price Oscillator (APO)[/url]The Absolute Price Oscillator (APO) is calculated by subtracting the longer moving average from the shorter moving average. For example:

10-period exponential moving average (EMA) minus 30-period EMA
The resulting plot forms an oscillator that fluctuates above and below zero according to the differences in the moving averages. If the shorter moving average is above the longer moving average, then the indicator will be positive. If the shorter moving average is below the longer moving average, then the indicator will be negative.
The Moving Average Convergence Divergence (MACD) indicator calculated as the difference between two exponential moving averages and is essentially equivalent to the APO. StockCharts does not provide an indicator called "APO" in our SharpChart tool - you should use the MACD instead.

[url=]Percentage Price Oscillator (PPO)[/url]The Percentage Price Oscillator is found by subtracting the longer moving average from the shorter moving average and then dividing the result by the longer moving average. For example:

(10-period EMA minus 30-period EMA) divided by the 30-period EMA
This formula displays the difference between the two moving averages as a percentage of the longer moving average.

[url=]Absolute versus Percentage[/url]The Percentage Price Oscillator (PPO) and the Absolute Price Oscillator (APO) generate many of the same signals and have basically the same shape. All centerline crossovers, which represent the shorter moving average crossing above or below the longer moving average, occur at the same time. However, because the PPO is percentage-based, the shape of its lines can differ in subtle but important ways from the shape of the APO's lines. Below is a chart of the NASDAQ Composite that illustrates some of the differences that may crop up.

    The green circle shows that the PPO formed a lower high in December while the APO formed a higher high.
    Later in December, the APO continued higher and the PPO began to flatten out. (red arrows)
  • In early January, the PPO recorded a lower low, which was a day earlier than the APO.
There are two main reasons for using the PPO instead of the APO.
  • With the Percentage Price Oscillator, it is possible to compare Price Oscillator levels from one security to the next. A PPO reading of +5% means that the shorter moving average is 5% higher than the longer moving average. This percentage reading is comparable against another security, regardless of the price of a security. The Percentage Price Oscillator (PPO) for SLB only reached 3% for its highs while that of the NASDAQ Composite rose above 7%.


  • The Percentage Price Oscillator is a better representation of the two moving averages relative to each other. The difference between the two moving averages is shown in relation to the shorter moving average. This allows for comparisons across time periods, regardless of the price of the stock. With the Absolute Price Oscillator, the higher the price of the stock, the greater the extremes of the oscillator. With the Percentage Price Oscillator, a comparison of Amazon over time is possible regardless of whether the stock is at 10 or 100.



[url=]PPO-Histogram[/url]Because the Price Oscillator and MACD are so similar, the concept of the MACD-Histogram has been applied to the PPO. The PPO-Histogram shows the difference between the PPO and the 9-day EMA of the PPO. The plot is presented as a histogram so that centerline crossovers and divergences are easily identifiable. The same principles that apply to the MACD-Histogram are also applicable to the PPO-Histogram.
A centerline crossover for the PPO-Histogram is the same as a moving average crossover for the PPO. If the value of the PPO is larger than the value of its 9-day EMA, then the value on the PPO-Histogram will be positive. Conversely, if the value of the PPO is less than its 9-day EMA, then the value of the PPO-Histogram will be negative.

Further increases or decreases in the gap between the PPO and its 9-day EMA will be reflected in the PPO-Histogram. Sharp increases in the PPO-Histogram indicate that the PPO is rising faster than its 9-day EMA – bullish momentum is strengthening. Sharp declines in the PPO-Histogram indicate that the PPO is falling faster than its moving average – bearish momentum is increasing.

[url=]PPO and SharpCharts[/url]
SharpCharts allows users to chart the Percentage Price Oscillator by selecting "Price Oscillator (PPO)" from any "Indicators" dropdown. The standard settings of "12,26,9" are automatically added to the "Parameters" box and can be changed to any combination of integer numbers. The first parameter is the number of periods to use for the first EMA in the calculation. The second parameter is the number of periods to use for the second (typically longer) EMA. The last parameter is the number of periods to use for the signal line.
For more on the interpretation of this oscillator and its signals, see our articles about oscillators and MACD.
Click here to see a live example of the PPO.








[url=]Percentage Volume Oscillator (PVO)[/url]
[url=]Introduction and Calculation[/url]The Percentage Volume Oscillator (PVO) is the percentage difference between two moving averages of volume. The indicator is calculated with the following formula:
PVO = ((Shorter EMA of Volume - Longer EMA of Volume) / Longer EMA of Volume) x 100

The PVO oscillates around zero.

[url=]Uses[/url]The PVO can be used to identify periods of expanding or contracting volume in three different ways:
    Centerline Crossovers: like the PPO, the PVO oscillates above and below the zero line. When PVO is positive, the shorter EMA of volume is greater than the longer EMA of volume. When PVO is negative, the shorter EMA of volume is less than the longer EMA of volume. A PVO above zero indicates that volume levels are generally above average and relatively heavy. When the PVO is below zero, volume levels are generally below average and light.
    Directional Movement: General directional movement of the PVO can offer a quick visual assessment of volume patterns. A rising PVO signals that volume levels are increasing and a falling PVO signals that volume levels are decreasing.
  • Moving Average Crossovers: The last variable in the PVO forms the signal line. For example: PVO(12,26,9) would include a 9-day EMA of PVO as well as a histogram representing the difference between the PVO and its 9-day EMA. When PVO moves above its signal line, volume levels are generally increasing. When PVO moves below its signal line, volume levels are generally decreasing.
Movements in the PVO are completely separate from price movements. As such, movements in PVO can correlated with price movements to assess the degree of buying or selling pressure. Advances combined with strength in the PVO would be considered strong. Should the PVO decline while a security's price fell, it would indicate decreasing volume on the decline.

[url=]Examples[/url]
In the example above, [url=http://stockcharts.com/h-sc/ui?c=FILE,UU[L,A]DACLYYMY[PB50!B200][VC60][IUS12,26,9]]FILE[/url][url=http://stockcharts.com/h-sc/ui?c=FILE,UU[L,A]DACLYYMY[PB50!B200][VC60][IUS12,26,9]][/url] is shown with two PVO settings: PVO (12,26,9) in the top window and PVO (5,60,1) in the bottom window. When the final variable is set at 1, as with PVO(5,60,1) there is no signal line or histogram. During August and September, the stock traded between 15 and 21, and the PVO remained mostly below zero. There was a small bounce above zero with the late August advance, but the stock remained confined to its trading range. When the stock began to advance off of its low in October, the PVO moved into positive territory with a sharp rise (green line). The advance was confirmed with expanding volume and the stock broke resistance. The breakout with expanding volume signaled exceptionally strong buying pressure.

In the example above, the PVO is set in the top window at the default setting (12,26,9) and in the bottom window at (5,60,1). Even though the line shapes for both PVO settings are almost identical, the scales on the right reflect different ranges and crossover points.
    PVO (12,26,9) surpassed +20 in late October, while PVO (5,60,1) surpassed +50.
    In early October (red line #1), PVO (5,60,1) crossed below zero, but PVO (12,26,9) remained above.
  • At the beginning of December (red line #2), PVO (5,60,1) moved above zero before PVO (12,26,9) did.
Much of this difference can be attributed to the short EMA of volume in both PVO settings. The 5-day EMA of volume is much more sensitive than the 12-day EMA of volume. Shorter moving averages are more volatile and more likely to have centerline crossovers. Above-average volume periods can also be confirmed by watching for volume bars that exceed the 60-day EMA (green oval in October). Notice that both PVO's shot up in the second half of October as volume spiked above 60m shares.

[url=]PVO and SharpCharts[/url]
Using SharpCharts, PVO can be set as an indicator above or below a security's price plot. Once the indicator is chosen from the drop down list, the three boxes to the right are used to adjust the settings. The first box is for the short exponential moving average (EMA) of volume, the second is for the long exponential moving average of volume and the third is for the signal line. The default setting (12,26,9) uses a 12-day EMA of volume and a 26-day EMA of volume to calculate the PVO and a 9-day EMA of PVO as the signal/trigger line. For those who do not wish to have a trigger line or histogram, the third box can be set equal to 1. PVO appears as the thick solid line and the signal/trigger line as the thinner and smoother line.
Click here to see a live example of the PVO.
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 楼主| 发表于 2009-3-15 12:33 | 显示全部楼层
[url=]Price Relative[/url]
[url=]Introduction[/url]The Price Relative compares the performance of one security against that of another. It is often used to compare the performance of a particular stock to a market index, usually the S&P 500. Because the goal of many portfolio managers is to outperform the S&P 500, they are usually interested in the strongest stocks. The price relative offers a straightforward and accurate portrayal of a stock's performance relative to the market.

[url=]Calculation[/url]The price relative is calculated by dividing the security's price by the value of the S&P 500. If WMT were trading at 60 and the S&P 500 were 1400, then the price relative would be 60/1400, which equals .0428. Should WMT advance to 70 and the S&P 500 to 1450, the price relative would be .0482 (70/1450). The advance from .0428 to .0482 shows the WMT is stronger than the S&P 500. This number is then plotted along the Y-axis to form a line chart. The price relative can be calculated on a daily, weekly or monthly basis; closing prices are normally used.
Traditional technical analysis techniques can be used to analyze the plot of the price relative. Support, resistance, trend lines, moving averages and pattern analysis can all be applied. Some analysts even apply indicators to the price relative in an attempt to identify changes.

[url=]Examples[/url]
In the WMT chart, we can see that the price relative peaked on 16-Dec (red line), about two weeks earlier than the stock. A series of lower highs ensued, and short-term support was broken in mid-January. A few days later, the price relative broke its trend line extending up from early August (blue line). The support and trend line breaks in the stock occurred later than those in the price relative. A sharp decline in the stock was foreshadowed by weakness in the price relative.

In the 1998 chart for SUNW, the price relative recorded a higher low in August and a new reaction high in September (black arrow). This created a positive divergence and signaled that SUNW was much stronger than the overall market. When the October-1998 rally kicked in, SUNW was one of the top performers over the next 17 months.
Rotation among sectors and stocks plays a big part in today's market. By applying the price relative to industry groups and stocks, traders and investors can identify pockets of relative strength and relative weakness. As with most indicators and analysis techniques, the price relative is just one tool and should be used in conjunction with other aspects of technical analysis.

[url=]Price Relative and SharpCharts[/url]
The price relative for SharpCharts can displayed above or below the price plot of the underlying security. The Parameters box to the right can be used to change the symbol – any security can be entered here. For example, if the underlying security was the Amex Oil Index ($XOI) and you wanted to plot a price relative against West Texas Intermediate Crude ($WTIC), then "$XOI:$WTIC" would be entered into the "Price" box.
An advancing price relative would indicate that $XOI was outperforming $WTIC and a declining price relative would show under-performance relative to $WTIC.
Click here to see a live example of Price Relative.






[url=]Rabbitt Q-Rank[/url]
[url=]Introduction[/url]The Rabbitt Q-Rank is a combination of nine fundamental and technical models, melded together for ranking stocks. The combined score ranges from 99 (highest) to 1 (lowest); that is, a stock with a Q-Rank of 99 has combined qualities better than 99% of the stocks measured. The Q-Rank system, devised by Paul Rabbitt, combines a Technical Sub-Rank (TSR) and an Earnings Sub-Rank (ESR) which offer further insight into the qualities influencing the total Q-Rank.

[url=]Calculation[/url]The Technical Sub-Rank (TSR) combines four models rewarding price leadership, trading status, long-term trend, and sector attractiveness. It makes up 50% of the Rabbitt Q-Rank and is calculated as follows:

The Earnings Sub-rank (ESR) combines value and growth characteristics divided into four categories. It rewards or penalizes companies based on Wall Street consensus earnings revisions, earnings surprises, earnings consistency, earnings growth/acceleration, and reasonable valuations. It makes up 50% of the Rabbitt Q-Rank and is calculated as follows:


[url=]Example[/url]When charted, the Rabbitt Q-Rank will display a line representing the 1 - 99 score. Significantly high Q-Rank values suggest that a stock is outperforming the rest of the market, while low scores suggest the opposite.


[url=]Use[/url]The Rabbitt Q-Rank can improve timing by delaying purchases or sales that may be just a little too early. Q-Rank stocks ranked above 90 tend to significantly outperform the market; those under 10 tend to significantly under-perform. Investors considering buying a stock with a Q-Rank under 50 should consider delaying purchase unless the stock offers an extremely solid value, new product and/or turnaround story. Investors considering selling a stock with a Q-Rank over 90 should consider holding the stock until its rank declines. The Q-Rank is meant to assist pure fundamental research, not replace it.

[url=]Rabbitt Q-Rank and SharpCharts[/url]
Using SharpCharts, the Rabbitt Q-Rank can be plotted above or below the price. Rabbitt Q-Rank takes no parameters.
Click here for a live example of the Rabbitt Q-Rank.
For a more detailed explanation of the Rabbitt Q-Rank system, see the Rabbitt Analytics website.






[url=]Rate of Change (ROC) and Momentum[/url]
[url=]Introduction and Calculation[/url]The Rate of Change (ROC) indicator is a very simple yet effective momentum oscillator that measures the percent change in price from one period to the next. The ROC calculation compares the current price with the price n periods ago.
ROC = ( (Today's close - Close n periods ago) / (Close n periods ago) ) * 100

The plot forms an oscillator that fluctuates above and below the zero line as the Rate of Change moves from positive to negative. The oscillator can be used as any other momentum oscillator by looking for higher lows, lower highs, positive and negative divergences, and crosses above and below zero for signals.

[url=]Example[/url]
The chart of Lucent shows that a large negative divergence formed in Dec-99 and the ROC moved into negative territory just before the large decline. While this was a superb sell signal, the ROC can produce whipsaws as it moves above and below zero. As with most technical indicators, ROC should be used in conjunction with other aspects or technical analysis as well as other non-momentum based indicators.

[url=]Rate of Change and SharpCharts[/url]
ROC can be plotted using different periods such as 12 days or 30 days by changing the value in the first box. The longer the time span used, the greater the fluctuation in the indicator (in terms of both magnitude and duration). The optional second parameter allows the user to add a moving average line to the ROC indicator.
Rate of Change (ROC) vs the "Momentum" Indicator
There is another popular indicator called "Momentum" that is almost identical to the Rate of Change indicator. Where the ROC indicator displays the rate of change as a percentage, the Momentum indicator displays it as a ratio. Because both indicators give identical signals, StockCharts.com has choosen to only implement the Rate of Change version.
[url=http://stockcharts.com/h-sc/ui?c=ibm,uu[l,a]daclnimy[p][vc60][ium12]&style=]Click here[/url] to see a live example of Rate of Change (ROC).
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 楼主| 发表于 2009-3-15 12:35 | 显示全部楼层
[url=]Relative Strength Index (RSI)[/url]
[url=]Introduction[/url]Developed by J. Welles Wilder and introduced in his 1978 book, New Concepts in Technical Trading Systems, the Relative Strength Index (RSI) is an extremely useful and popular momentum oscillator. The RSI compares the magnitude of a stock's recent gains to the magnitude of its recent losses and turns that information into a number that ranges from 0 to 100. It takes a single parameter, the number of time periods to use in the calculation. In his book, Wilder recommends using 14 periods.
The RSI's full name is actually rather unfortunate as it is easily confused with other forms of Relative Strength analysis such as John Murphy's "Relative Strength" charts and IBD's "Relative Strength" rankings. Most other kinds of "Relative Strength" stuff involve using more than one stock in the calculation. Like most true indicators, the RSI only needs one stock to be computed. In order to avoid confusion, many people avoid using the RSI's full name and just call it "the RSI."

[url=]Calculation[/url]                  100    RSI = 100 - --------                 1 + RS    RS = Average Gain / Average Loss    Average Gain = [(previous Average Gain) x 13 + current Gain] / 14    First Average Gain = Total of Gains during past 14 periods / 14    Average Loss = [(previous Average Loss) x 13 + current Loss] / 14    First Average Loss = Total of Losses during past 14 periods / 14     Note: "Losses" are reported as positive values.

To simplify our explanation of the formula, the RSI has been broken down into its basic components which are the RS, the Average Gain, and the Average Loss.
To calculate RSI values for a given dataset, first find the magnitude of all gains and losses for the 14 periods prior to the time where you wish to start the calculation. (Note: 14 is the standard number of periods used when calculating the RSI. If a different number is specified, just substitute that number in for "14" throughout this discussion.)
It is important to understand that the RSI is a "running" calculation and the accuracy of the calculation depends on how long ago the calculations started. The first RSI value is an estimate - subsequent values improve on that estimate. You should calculate at least 14 values prior to the start of any values that you will rely on - going back 28+ periods is even better.
To start the running calculation, the First Average Gain is calculated as the total of all gains during the past 14 periods divided by 14. Similarly, the First Average Loss is calculated as the total magnitude of all losses during the past 14 periods divided by 14. The next values for the "averages" are calculated by taking the previous value, multiplying it by 13, adding in the next Gain (or Loss), and then dividing by 14. This is Wilder's modified "smoothing" technique in action.
The RS value is simply the Average Gain divided by the Average Loss for each period.
Finally, the RSI is simply the RS converted into an oscillator that goes between zero and 100 using this formula: 100 - (100 / RS + 1).
Here's an Excel Spreadsheet that shows the start of an RSI calculation in action.
When the Average Gain is greater than the Average Loss, the RSI rises because RS will be greater than 1. Conversely, when the Average Loss is greater than the Average Gain, the RSI declines because RS will be less than 1. The last part of the formula ensures that the indicator oscillates between 0 and 100. Note: If the Average Loss ever becomes zero, RSI becomes 100 by definition.
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Important Note: The more data points that are used to calculate the RSI, the more accurate the results. The smoothing factor is a continuous calculation that - in theory - takes into account all of the closing values in the data set. If you start an RSI calculation in the middle of an existing data set, your values will only approximate the true RSI value. SharpCharts uses at least 250 data points prior to the starting date of any chart (assuming that much data exists) when calculating its RSI values. To duplicate our RSI numbers, you'll need to use at least that much data also.

[url=]Use[/url]
[url=]Overbought/Oversold[/url]Wilder recommended using 70 and 30 and overbought and oversold levels respectively. Generally, if the RSI rises above 30 it is considered bullish for the underlying stock. Conversely, if the RSI falls below 70, it is a bearish signal. Some traders identify the long-term trend and then use extreme readings for entry points. If the long-term trend is bullish, then oversold readings could mark potential entry points.

[url=]Divergences[/url]Buy and sell signals can also be generated by looking for positive and negative divergences between the RSI and the underlying stock. For example, consider a falling stock whose RSI rises from a low point of (for example) 15 back up to say, 55. Because of how the RSI is constructed, the underlying stock will often reverse its direction soon after such a divergence. As in that example, divergences that occur after an overbought or oversold reading usually provide more reliable signals.

[url=]Centerline Crossover[/url]The centerline for RSI is 50. Readings above and below can give the indicator a bullish or bearish tilt. On the whole, a reading above 50 indicates that average gains are higher than average losses and a reading below 50 indicates that losses are winning the battle. Some traders look for a move above 50 to confirm bullish signals or a move below 50 to confirm bearish signals.

[url=]Example[/url]
The DELL example shows a number of extreme readings as well as a negative divergence. In Oct-99, RSI reached oversold for a brief moment to mark the low around 38. The next extreme reading (overbought) occurred after a large advance that peaked in Dec-99. RSI reached overbought levels in late Dec-99 and moved below 50 by the second week of Jan-00. The next oversold reading occurred in Feb. for another brief moment and marked the low around 35. By the end of Feb-00, RSI moved back above 50 and into overbought territory in March. A negative divergence formed in March and marked the high in the upper fifties.

[url=]RSI and SharpCharts[/url]
RSI is available on our SharpCharts charting tool. In the example, RSI has been assigned 14, 20 and 30 periods. A swing trader might prefer 14-periods, while an investor may prefer 30-periods. Users are encouraged to test different RSI settings and judge for themselves which ones work best and suit their particular trading/investing style.
[url=http://stockcharts.com/h-sc/ui?c=intc,uu[l,a]daclnimy[p][vc60][iub14]&style=]Click here[/url] to see a live example of RSI.
For more on oscillators, please read our ChartSchool article on how to use and interpret oscillators.





[url=]Standard Deviation (Volatility)[/url]
[url=]Introduction[/url]Standard deviation is a statistical term that provides a good indication of volatility. It measures how widely values (closing prices for instance) are dispersed from the average. Dispersion is the difference between the actual value (closing price) and the average value (mean closing price). The larger the difference between the closing prices and the average price, the higher the standard deviation will be and the higher the volatility. The closer the closing prices are to the average price, the lower the standard deviation and the lower the volatility.

[url=]Calculation[/url]The steps for calculating a 20-period standard deviation are as follows:
    Calculate the simple average (mean) of the closing price. i.e., Sum the last 20 closing prices and divide by 20.
    For each period, subtract the average closing price from the actual closing price. This gives us the deviation for each period.
    Square each period's deviation.
    Sum the squared deviations.
    Divide the sum of the squared deviations by the number of periods (20 in our example below).
  • The standard deviation is then equal to the square root of that number.


The 20-period standard deviation for the data above is 6.787. Note that this is the "full population" version of the Standard Deviation. There is a different kind of Standard Deviation calculation that is used when you are taking a statistical sample of a population, but that version is not used in technical analysis since all of the data points are known.
Because Standard Deviation is used to determine the spread between upper and lower Bollinger Bands, the Bollinger Band Width indicator can be used as a substitute for the Standard Deviation indicator. The general shape and direction of the Bollinger Band Width indicator match that of the underlying Standard Deviation regardless of the parameters used. If you want to see the actual Standard Deviation values, set the second parameter of the Bollinger Band Width indicator to 0.5 as in this example.

[url=]Examples[/url]The chart below shows how the standard deviation (shown via the Bollinger Band Width indicator) can change over time.

After extended periods of consolidation, the standard deviation dropped. Notice that in late December the stock traded in a tight range and volatility dropped again. Later in mid-March, the stock also traded in a tight range and volatility dropped. When the stock took off in the second half of March, volatility also rose.

Amazon (AMZN), which is in a similar price range as IBM, has a higher standard deviation. Until late December, the standard deviation hovered around 7.5. With the decline at the year's end, the standard deviation rose from 7 to above 12.5. Afterwards it plunged to 2.5 over the course of two weeks. Since then it leveled out around 5. This is quite a volatile stock and its options will have more premium than IBM options. The higher the volatility for a particular stock, the higher the option premiums. The lower the volatility is for a particular stock, the lower the option premiums.

[url=]Standard Deviation and SharpCharts[/url]
You can chart Standard Deviation in SharpCharts. The Parameters value is the period multiple to use. The default value is 10, which would be a 10-day Standard Deviation on a daily chart, or a 10-month Standard Deviation on a monthly chart.
Click here to see a live example of Standard Deviation.
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 楼主| 发表于 2009-3-15 12:36 | 显示全部楼层
[url=]Stochastic Oscillator (Fast, Slow, and Full)[/url]
[url=]Introduction[/url]Developed by George C. Lane in the late 1950s, the Stochastic Oscillator is a momentum indicator that shows the location of the current close relative to the high/low range over a set number of periods. Closing levels that are consistently near the top of the range indicate accumulation (buying pressure) and those near the bottom of the range indicate distribution (selling pressure).

[url=]Calculation[/url]
A 14-day %K (14-period Stochastic Oscillator) would use the most recent close, the highest high over the last 14 days and the lowest low over the last 14 days. The number of periods will vary according to the sensitivity and the type of signals desired. As with RSI, 14 is a popular number of periods for calculation.
%K tells us that the close (115.38) was in the 57th percentile of the high/low range, or just above the mid-point. Because %K is a percentage or ratio, it will fluctuate between 0 and 100. A 3-day simple moving average of %K is usually plotted alongside to act as a signal or trigger line, called %D.

[url=]Slow versus Fast versus Full[/url]There are three types of Stochastic Oscillators: Fast, Slow, and Full. The Full Stochastic is discussed later. For now, let's look at Fast versus Slow. As shown above, the Fast Stochastic Oscillator is made up of %K and %D. In order to avoid confusion between the two, I'll use %K (fast) and %D (fast) to refer to those used in the Fast Stochastic Oscillator, and %K (slow) and %D (slow) to refer to those used in the Slow Stochastic Oscillator. The driving force behind both Stochastic Oscillators is %K (fast), which is found using the formula provided above.

In the CSCO example, the Fast Stochastic Oscillator is plotted in the box just below the price plot. The thick black line represents %K (fast) and the thin red line represents %D (fast). Also called the trigger line, %D (fast) is a smoothed version of %K (fast). One method of smoothing data is to apply a moving average. To smooth %K (fast) and create %D (fast), a 3-period simple moving average was applied to %K (fast). Notice how the %K (fast) line pierces the %D (fast) line a number of times during May, June and July. To alleviate some of these false breaks and smooth %K (fast), the Slow Stochastic Oscillator was developed.
The Slow Stochastic Oscillator is plotted in the lower box: the thick black line represents %K (slow) and the thin red line represents %D (slow). To find %K (slow) in the Slow Stochastic Oscillator, a 3-day SMA was applied to %K (fast). This 3-day SMA slowed (or smoothed) the data to form a slower version of %K (fast). A close examination would reveal that %D (Fast), the thin red line in the Fast Stochastic Oscillator, is identical to %K (Slow), the thick black line in the Slow Stochastic Oscillator. To form the trigger line, or %D (slow) in the Slow Stochastic Oscillator, a 3-day SMA was applied to %K (Slow).
The Full Stochastic Oscillator takes three parameters. Just as in the Fast and Slow versions, the first parameter is the number of periods used to create the initial %K line and the last parameter is the number of periods used to create the %D (full) signal line. What's new is the additional parameter, the one in the middle. It is a "smoothing factor" for the initial %K line. The %K (full) line that gets plotted is a n-period SMA of the initial %K line (where n is equal to the middle parameter).
The Full Stochastic Oscillator is more advanced and more flexible than it's Fast and Slow cousins. You can even use it to duplicate the other versions. For example, a (14, 3) Fast Stochastic is equivalent to a (14, 1, 3) Full Stochastic and a (12, 2) Slow Stochastic is equal to a (12, 3, 2) Full Stochastic.

[url=]%K and %D Recap[/url]
    %K (fast) = %K formula presented above using x periods
    %D (fast) = y-day SMA of %K (fast)
    %K (slow) = 3-day SMA of %K (fast)
    %D (slow) = y-day SMA of %K (slow)
    %K (full) = y-day SMA of %K (fast)
  • %D (full) = z-day SMA of %K (full)
Where x is the first parameter, y is the second parameter and (in the case of Full stochastics), z is the third parameter. In the case of Fast and Slow Stochastics, x is typically 14 and y is usually set to 3.

[url=]Use[/url]Readings below 20 are considered oversold and readings above 80 are considered overbought. However, Lane did not believe that a reading above 80 was necessarily bearish or a reading below 20 bullish. A security can continue to rise after the Stochastic Oscillator has reached 80 and continue to fall after the Stochastic Oscillator has reached 20. Lane believed that some of the best signals occurred when the oscillator moved from overbought territory back below 80 and from oversold territory back above 20.
Buy and sell signals can also be given when %K crosses above or below %D. However, crossover signals are quite frequent and can result in a lot of whipsaws.
One of the most reliable signals is to wait for a divergence to develop from overbought or oversold levels. Once the oscillator reaches overbought levels, wait for a negative divergence to develop and then a cross below 80. This usually requires a double dip below 80 and the second dip results in the sell signal. For a buy signal, wait for a positive divergence to develop after the indicator moves below 20. This will usually require a trader to disregard the first break above 20. After the positive divergence forms, the second break above 20 confirms the divergence and a buy signal is given.

[url=]Example[/url]
In the IBM example above, it is clear that acting solely on overbought and oversold crossovers can generate false signals. Using crossovers of %D (slow) by %K (slow) can result in some good signals, but there are still whipsaws. By looking for divergences and overbought/oversold crossovers together, the 14-day Slow Stochastic Oscillator can produce fewer yet more reliable signals. The Slow Stochastic Oscillator produced 2 solid signals in IBM between Aug-99 and Mar-00. In Nov-99, a buy signal was given when the indicator formed a positive divergence and moved above 20 for the second time. Note that the double top in Nov-Dec (gray circle) was not a negative divergence – the stock continued higher after this formed. In Jan-00, a sell signal was given when a negative divergence formed and the indicator dipped below 80 for the second time.

[url=]Stochastic Oscillators and SharpCharts[/url]
In StockCharts.com's SharpCharts tool, the Slow Stochastics oscillator uses %K (slow) and the Fast Stochastics oscillator uses %K (fast). There are two options available for both fast or slow. The first Parameters value represents the number of periods used to calculate %K for each. The second value represents the number of periods used in the moving average to form %D. The defaults are 14 and 3. For the Slow Stochastics oscillator, that would imply a 14-period %K (slow) with a 3-day SMA of %K (slow) to form %D (slow).
The Full Stochastics oscillator uses three parameters: the period for %K (fast), the period for the SMA that smooths %K (fast), and the period of the SMA that forms %D (full). While the tool provides some excellent default values, I encourage you to test different variations to discover what fits with their particular investing style or what works with a particular security.
[url=http://stockcharts.com/h-sc/ui?c=INTC,UU[L,A]DACLNIMY[P][VC60][IUH14,3!UI14,3]]Click here[/url] to see a live example of Fast and Slow Stochastics.
For more, please read our Chart School article on how to use and interpret oscillators.




[url=]StochRSI[/url]
[url=]Introduction[/url]Developed by Tushard Chande and Stanley Kroll, StochRSI is an oscillator that measures the level of RSI relative to its range, over a set period of time. The indicator uses RSI as the foundation and applies to it the formula behind Stochastics. The result is an oscillator that fluctuates between 0 and 1.
In their 1994 book, The New Technical Trader, Chande and Kroll explain that RSI sometimes trades between 80 and 20 for extended periods without reaching overbought and oversold levels. Traders looking to enter a stock based on an overbought or oversold reading in RSI might find themselves continuously on the sidelines. To increase the sensitivity and provide a method for identifying overbought and oversold levels in RSI, Chande and Kroll developed StochRSI.
Developed by Welles Wilder, RSI is a momentum oscillator that compares the magnitude of gains to the magnitude of losses over a period of time. Developed by George Lane, Stochastics is a momentum oscillator that compares the closing level to the high/low range over a given period of time. Calculation



From the formula above, it can be seen that StochRSI is the Stochastics formula applied to RSI; that is, it's an indicator of RSI. StochRSI measures the value of RSI relative to its high/low range over a set number of periods. When RSI records a new low for the period, StochRSI will be at 0. When RSI records a new high for the period, StochRSI will be at 100. A reading of .20 would mean that the current RSI was 20% above the lowest level of the period, or 80% below the highest level. A reading of .80 would mean that the current RSI was 80% above the lowest level of the period, or 20% below the highest level.

[url=]Signals[/url]
    Overbought and Oversold Crossovers: If an uptrend has been identified in the underlying security, then a buy signal would be generated when StochRSI advances from oversold (below .20) to above .20. Conversely, if a downtrend has been identified, then a sell signal would be generated when StochRSI declines from overbought (above .80) to below .80.
    Centerline Crossovers: Some traders look for moves above or below .50 (the centerline) to confirm signals and reduce whipsaws. A move from oversold to above .50 could constitute a buy signal and would remain in place until a decline below .50. Conversely, a move from overbought to below .50 would could act as a sell signal that would remain in place until an advance back above .50.
    Positive and Negative Divergences: A positive divergence followed by a confirming advance above .20 could constitute a buy signal and a negative divergence followed by a decline below .80 could act as a sell signal.
    Failures: Chande and Kroll also note that moves back past the trigger lines would indicate a failed signal. An advance back above .80 would indicate a failed signal and traders would be advised to close positions.
  • Strong Trend: As with many oscillators, StochRSI can become overbought (or oversold) and remain overbought (or oversold) for an extended period. A move above .80 may imply overbought, but it can also indicate a strong up trend and remain above .80 for a prolonged period. Conversely, a quick move below .20 could indicate the beginning of a strong downtrend. Moves to 1 are considered very strong and moves to 0 very weak.

[url=]Example[/url]
In the WorldCom example above, the stock peaked in Jun-99 and was in a well-established downtrend. A series of lower lows and lower highs confirmed the primary trend as bearish. According to Chande and Kroll, these conditions would best suit StochRSI for identifying overbought levels from which to short the stock. Each time StochRSI advances above .80, an overbought situation would occur. When the indicator declined from its overbought level back below .80, a sell signal would have been given.
From March to June, the indicator gave 4 sell signals, or one per month. The July sell signal was not recognized because there was a possible change in trend. As long as the series of lower highs and lower lows continued, the downtrend remained intact. A higher low in late June was followed by a higher high in July to call into question the strength and validity of the downtrend. Once the higher high arrived, the signals for StochRSI may have required adjustments to protect against whipsaws.
Trying to buy the stock on advances from oversold levels back above .20 would have proved difficult. There were whipsaws in March and May that would have resulted in some bad trades. This choppy action around .20 could have also led to some premature exits from profitable short positions. When a stock is trending lower, it is sometimes prudent to raise the level in order to close short positions (or to generate buy signals). In this case, a trader could have required StochRSI to move from oversold to above .50 before closing short positions. This would have eliminated the March and May whipsaws.

[url=]StochRSI and SharpCharts[/url]
The StochRSI indicator can be charted as an indicator using the SharpCharts tool. The Parameter box value specifies the number of periods used in calculation (default is 14).
[url=http://stockcharts.com/h-sc/ui?c=INTC,UU[L,A]DACLNIMY[P][VC60][IUO14]]Click here[/url] to see a live example of StochRSI.

[url=]Conclusion[/url]It is important to remember that StochRSI is an indicator of an indicator. It is designed to predict extreme readings in RSI before the actual RSI reaches these extremities. As an indicator of an indicator, it is further removed from the actual price of the underlying security. Because it is actually predicting RSI, but being used to predict price changes in the underlying security, it will have greater sensitivity and be prone to false signals, especially if used incorrectly. As with other indicators, StochRSI should be used in conjunction with other indicators and aspects of technical analysis.
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 楼主| 发表于 2009-3-15 12:36 | 显示全部楼层
[url=]TRIX[/url]
[url=]Introduction[/url]TRIX is a momentum indicator that displays the percent rate-of-change of a triple exponentially smoothed moving average of a security's closing price. It was developed in the early 1980's by Jack Hutson, an editor for Technical Analysis of Stocks and Commodities magazine. Oscillating around a zero line, TRIX is designed to filter out stock movements that are insignificant to the larger trend of the stock. The user selects a number of periods (such as 15) with which to create the moving average, and those cycles that are shorter than that period are filtered out.
The TRIX is a leading indicator and can be used to anticipate turning points in a trend through its divergence with the security price. Likewise, it is common to plot a moving average with a smaller period (such as 9) and use it as a "signal line" to anticipate where the TRIX is heading. TRIX line crossovers with its "signal line" can be used as buy/sell signals as well.

[url=]Calculation[/url]To calculate TRIX, you must first pick a period with which to create an exponential moving average of the closing prices. For a 15-day period:
    Calculate the 15-day exponential moving average of the closing price.
    Calculate the 15-day exponential moving average of the moving average calculated in step #1.
    Calculate the 15-day exponential moving average of the moving average calculated in step #2. You now have triple exponentially smoothed the moving average of closing prices, greatly reducing volatility.
  • Finally, calculate the 1-day percent change of the moving average calculated in step #3.

[url=]Use[/url]Since TRIX measures the rate-of-change of closing prices, a positive TRIX value is interpreted as a steady rise in the closing price of a security. A positive TRIX is thus akin to a positive trending price, allowing the indicator to act as a buy signal whenever it crosses up above the zero line. Similarly, crossing below the zero line suggests the price is tending to close down at the end of each period, which can be a sell signal.
The "signal line" mentioned earlier is also a useful buy/sell indicator. Since the signal line period is shorter, a cross above it suggests that recent stock prices are closing much higher. A buy signal is triggered when TRIX crosses above its signal line, and a sell signal is triggered when TRIX crosses below its signal line. This method can generate false signals during sideways price movements, so it works best when prices are trending. It is therefore wise to use TRIX in tandem with other indicators for confirmation.

[url=]Example[/url]
In the Microsoft example, three bullish crossovers between the TRIX and its "signal line" were all followed by uptrends. These crossovers represented ideal buy points, for they quickly anticipated an increasing demand for the security.

[url=]TRIX and SharpCharts[/url]
The TRIX indicator can be plotted using our SharpCharts charting tool. The number of periods is specified in the first parameter value, and a signal line can be added with the second parameter value. The default parameters are a 15-day period with a 9-day signal line. A 30-day period can be used for more conservative trend confirmations, though a 15-day will respond faster to potential trend formations.




[url=]Ultimate Oscillator[/url]
[url=]Introduction[/url]Developed by Larry Williams and first described in a 1985 article for Technical Analysis of Stocks and Commodities magazine, the "Ultimate" Oscillator combines a stock's price action during three different time frames into one bounded oscillator. Values range from 0 to 100 with 50 as the center line. Oversold territory exists below 30 and overbought territory extends from 70 to 100.
Three time frames are used by the Ultimate Oscillator and can be specified by the user. Typically values of 7-periods, 14-periods and 28-periods are used. Note that these time periods all overlap, i.e. the 28-period time frame includes both the 14-period time frame and the 7-period time frame. This means that the action of the shortest time frame is included in the calculation three times and has a magnified impact on the results.

[url=]Calculation[/url]
    Calculate Today's "True Low (TL)". TL = the lower of today's low or yesterday's close.
    Calculate Today's "Buying Pressure (BP)". BP = Today's close - Today's TL.
    Calculate Today's "True Range (TR)". TR = the higher of 1.) Today's High - Today's Low; 2.) Today's High - Yesterday's Close; 3.) Yesterday's Close - Today's Low.
    Calculate BPSum1, BPSum2, and BPSum3 by adding up all of the BPs for each of the three specified time frames.
    Calculate TRSum1, TRSum2, and TRSum3 by adding up all of the TR's for each of the three specified time frames.
  • The Raw Ultimate Oscillator (RawUO) is equal to:
4 * (BPSum1 / TRSum1) + 2 * (BPSum2 / TRSum2) + (BPSum3 / TRSum3)

  • The Final Ultimate Oscillator is equal to:
( RawUO / (4 + 2 + 1) ) * 100

Note: The '*' sign in the above formula denotes multiplication.

[url=]Use[/url]The Ultimate Oscillator can be used on intraday, daily, weekly or monthly data. The time frame and number of periods used can vary according to desired sensitivity and the characteristics of the individual security.
It is important to remember that overbought does not necessarily imply time to sell and oversold does not necessarily imply time to buy. A security can be in a downtrend, become oversold and remain oversold as the price continues to trend lower. Once a security becomes overbought or oversold, traders should wait for a signal that a price reversal has occurred. One method might be to wait for the oscillator to cross above or below -50 for confirmation. Price reversal confirmation can also be accomplished by using other indicators or aspects of technical analysis in conjunction with the Ultimate oscillator.

[url=]Example[/url]
This chart of Alcoa (AA) with the Ultimate Oscillator illustrates some key points:
    A bearish divergence appeared well before the top and persisted until the stock reversed.
    Extreme values are rare for the Ultimate Oscillator.
    Crosses above and below the center line (50) are relatively common.
  • As always, confirmation from other signals usually increases the accuracy of any analysis.

[url=]Ultimate Oscillator and SharpCharts[/url]
Using SharpCharts, the Ultimate Oscillator can be charted using any number of periods for the three separate oscillators. The Ultimate Oscillator is the weighted sum of these three oscillators. The typical values are 7, 14 and 28 which are the default entries for this indicator.
[url=http://stockcharts.com/h-sc/ui?c=,uu[h,a]dhclnymy[pi!d20,2!f][vc60][ilya7,14,28]&style=]Click here[/url] to see a live example of the Ultimate Oscillator.
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 楼主| 发表于 2009-3-15 12:37 | 显示全部楼层
[url=]Williams %R[/url]
[url=]Introduction[/url]Developed by Larry Williams, Williams %R is a momentum indicator that works much like the Stochastic Oscillator. It is especially popular for measuring overbought and oversold levels. The scale ranges from 0 to -100 with readings from 0 to -20 considered overbought, and readings from -80 to -100 considered oversold.
William %R, sometimes referred to as %R, shows the relationship of the close relative to the high-low range over a set period of time. The nearer the close is to the top of the range, the nearer to zero (higher) the indicator will be. The nearer the close is to the bottom of the range, the nearer to -100 (lower) the indicator will be. If the close equals the high of the high-low range, then the indicator will show 0 (the highest reading). If the close equals the low of the high-low range, then the result will be -100 (the lowest reading).

[url=]Calculation[/url]%R = [(highest high over ? periods - close)/(highest high over ? periods - lowest low over ? periods)] * -100

Typically, Williams %R is calculated using 14 periods and can be used on intraday, daily, weekly or monthly data. The time frame and number of periods will likely vary according to desired sensitivity and the characteristics of the individual security.

[url=]Use[/url]It is important to remember that overbought does not necessarily imply time to sell and oversold does not necessarily imply time to buy. A security can be in a downtrend, become oversold and remain oversold as the price continues to trend lower. Once a security becomes overbought or oversold, traders should wait for a signal that a price reversal has occurred. One method might be to wait for Williams %R to cross above or below -50 for confirmation. Price reversal confirmation can also be accomplished by using other indicators or aspects of technical analysis in conjunction with Williams %R.
One method of using Williams %R might be to identify the underlying trend and then look for trading opportunities in the direction of the trend. In an uptrend, traders may look to oversold readings to establish long positions. In a downtrend, traders may look to overbought readings to establish short positions.

[url=]Example[/url]
The chart of Weyerhaeuser with a 14-day and 28-day Williams %R illustrates some key points:
    14-day %R appears quite choppy and prone to false signals.
    28-day %R smoothed the data series and the signals became less frequent and more reliable.
    When the 28-day %R moved to overbought or oversold levels, it typically remained there for an extended period and the stock continued its trend.
  • Some good entry signals were given with the 28-day %R by waiting for a move above or below -50 for confirmation.

[url=]Williams %R and SharpCharts[/url]
With the SharpCharts charting tool, the Williams %R can be added in the Indicators section. The Parameters box value specifies the number of periods used. The default number of periods is 14.
[url=http://stockcharts.com/h-sc/ui?c=INTC,UU[L,A]DACLNIMY[P][VC60][IUK14]]Click here[/url] to see a live example of Williams %R.




[url=]Introduction to Market Indicators[/url]
[url=]Introduction[/url]This article is designed to introduce the concept of market indicators and explain how to use them in your analysis. We will see how market indicators differ from technical indicators, and why they are just as important for making investment decisions. Most of this article covers specific market indicators so you can begin using them to your advantage right away.
Here is a list of the market indicators covered in this article:

[url=]What Is a Market Indicator?[/url]Like a technical indicator, a market indicator is a series of data points derived from a formula. In this case, however, the formula for market indicators is applied to the price data for multiple securities within the market, instead of just one security. Price data can come from open, high, low or close points for the securities, their volume, or both. This data is entered into the indicator formula and the data point is produced.
Unlike technical indicators, market indicators are not charted above or below the chart. Market indicators are what is being charted, and as such have their own ticker symbols. There are often many symbols that apply the market indicator formula simply to different markets. For example, the $BPSPX and $BPNDX track the Bullish Percent Index for the S&P 500 and the NASDAQ 100 respectively.

[url=]Bullish Percent Index (BPI)[/url]The Bullish Percent Index (BPI) is a popular market breadth indicator that is calculated by dividing the number of stocks in a given group (an exchange, an industry, etc.) that are currently trading with Point and Figure buy signals, by the total number of stocks in that group. Bullish Percent levels that are above 70% are considered overbought, whereas levels below 30% are considered oversold. Strong buy signals occur when the Bullish Percent Index falls below 30% and then reverses up by at least 6%. Conversely, promising sell signals occur when it goes above 70%, and then reverses down by at least 6%.
It is important to note that the Bullish Percent Index is not something that can be applied to a single stock but rather an index that is calculated for a group of stocks.
The most popular version of this chart is the NYSE Bullish Percent () which is mentioned prominently in Thomas Dorsey's book, Point & Figure Charting, however it is important to remember that the Bullish Percent index can be calculated for any grouping of stocks.
Because the NYSE's Bullish Percent Index is so closely followed, each day we also publish the list of NYSE stocks with P&F buy signals as well as the list of all stocks in the current NYSE "universe". Those lists can be found on our NYSE BPI Components page.
Traditionally, the Bullish Percent indicator is charted on a Point and Figure chart (example) using a 2 point box size. However, the indicator can also be charted and studied using standard charts (example) as well.

[url=]Arms Index (TRIN)[/url]Richard Arms developed the TRIN, or Arms index, as a contrarian indicator to detect overbought and oversold levels in the market. Because of its calculation method, the TRIN has an inverse relationship with the market. Generally, a rising TRIN is bearish and a falling TRIN is bullish. Sometimes you will see the scale of the TRIN inverted to reflect this inverse relationship.

[url=]Calculation[/url]The TRIN is the advance/decline ratio divided by the advance volume/decline volume ratio:
(Advancing issues/declining issues)/(advancing volume/declining volume)

Examples of TRIN calculations:

In the first example, the ratios were equal and the TRIN was 1, which indicates a standoff. Volume flowing into advancing stocks was virtually equal to volume flowing into declining stocks. In the second example, the up volume/down volume ratio did not keep up with the advance/decline ratio and the TRIN rose above 1.
A TRIN above 1 indicates that the volume in declining stocks outpaced the volume in advancing stocks. In the final example the TRIN was below 1, indicating the volume in advancing stocks was healthy and outpaced the volume in declining stocks.

[url=]Use[/url]
A number of TRIN interpretations have evolved over the years. Richard Arms, the originator, uses the TRIN to detect extreme conditions in the market. He considers the market to be overbought when the 10-day moving average of the TRIN declines below .8 and oversold when it moves above 1.2. Other interpretations seek to use the direction and absolute level of the TRIN to determine bullish and bearish scenarios. In the momentum-driven markets, the TRIN can remain oversold or overbought for extended periods of time.

[url=]Volatility Index (VIX)[/url]Introduced by the CBOE in 1993, the Volatility Index (or VIX) is a weighted measure of the implied volatility for 8 OEX put and call options. The 8 puts and calls are weighted according to time remaining and the degree to which they are in or out of the money. The result forms a composite hypothetical option that is at-the-money and has 30 days to expiration. (An at-the-money option means that the strike price and the security price are the same.) VIX represents the implied volatility for this hypothetical at-the-money OEX option.

OEX options are by far the most traded and most liquid index options on the CBOE. Because of their dominant activity, OEX options represent a good proxy for implied volatility of the market as a whole. As OEX trades, VIX is updated throughout the day, and can be tracked as an intraday, daily, weekly or monthly indicator of implied volatility and market expectations.
Typically, VIX (and by extension implied volatility) has an inverse relationship to the market. A chart of the VIX will usually be shown with the scale inverted to show the low readings at the top and high readings at the bottom. The value of VIX increases when the market declines and decreases when the market rises. It seems that volatility would be a two-way street. The stock market, on the other hand, has a bullish bias. A rising stock market is viewed as less risky, and a declining stock market more risky. The higher the perceived risk is in stocks, the higher the implied volatility and the more expensive the associated options, especially puts. Hence, implied volatility is not about the size of the price swings, but rather the implied risk associated with the stock market. When the market declines, the demand for puts usually increases. Increased demand means higher put prices and higher implied volatilities.

[url=]Use[/url]For contrarians, comparing VIX action with that of the market can yield good clues on future direction or duration of a move. The further VIX increases in value, the more panic there is in the market. The further VIX decreases in value, the more complacency there is in the market. As a measure of complacency and panic, VIX is often used as a contrarian indicator. Prolonged and/or extremely low VIX readings indicate a high degree of complacency, and are generally regarded at bearish. Some contrarians view readings below 20 as excessively bearish. Conversely, prolonged and/or extremely high VIX readings indicate a high degree or anxiety – or even panic – among options traders, and are regarded at bullish. High VIX readings usually occur after an extended or sharp decline and sentiment is still quite bearish. Some contrarians view readings above 30 as bullish.
Conflicting signals between VIX and the market can also yield sentiment clues for the short term. Overly bullish sentiment or complacency is regarded as bearish by contrarians. On the other hand, overly bearish sentiment or panic is regarded as bullish. If the market declines sharply and VIX remains unchanged or decreases in value (towards complacency), it could indicate that the decline has further to go. Contrarians might take the view that there is still not enough bearishness or panic in the market to warrant a bottom. If the market advances sharply and VIX increases in value (towards panic), it could indicate that the advance has further to go. Contrarians might take the view that there is not enough bullishness or complacency to warrant a top.

[url=]$VIX and $OEX[/url]
The chart above shows the relationship between VIX and OEX. Generally, VIX decreases in value as OEX rises, and visa versa. A 10-day SMA was applied to both the VIX and OEX for smoothing. Over the last three years (Oct-97 to Sept-00), VIX produced roughly 7 extreme readings greater than 30 or less than 20. The four readings above 30 indicated excessive bearishness, panic or an extremely high implied volatility: Nov-97, Sept-98, Feb-99 and Apr-00 (green arrows). The three readings below 20 indicated excessive bullishness, complacency or low implied volatility (red arrows).
Once the extreme readings were recorded, a confirmation signal was given when VIX returned above 20 or below 30 (vertical dotted line). Except for the first bearish signal in Mar-98 (black circle), most of the signals were pretty timely. Two of the bullish signals produced small double bottoms in the VIX that could have led to small whipsaws, but the subsequent "second" signals proved quite profitable. As of this writing (13 September 2000), the VIX 10-day SMA has just risen above 20 and this could be considered the fourth signal of excessive bullishness or complacency among option traders.
Note on Rex Takasugi's VIX chart: Rex inverts VIX by taking the reciprocal of the open, high, low and close. If VIX is 30, then 1/30 = .033
(Click here to see a live example of the VIX)

[url=]CBOE NASDAQ Volatility Index ($VXN)[/url]The CBOE NASDAQ Volatility Index ($VXN) employs the same formula used to calculate $VIX, which is based on the implied volatility of S&P 500 index options. This formula is derived from a basket of put and call options. Some are out of the money, some in the money, and some at the money. The resulting $VXN represents the implied volatility of a hypothetical 30-day option that is at the money.

(Click here to see a live example of the VXN)

[url=]The "Original" VIX ($VXO)[/url]The $VXO is the ticker created to track the "original VIX" that was calculated using the prices of S&P 100 options. The new VIX uses the ticker $VIX and is calculated using the prices of S&P 500 options. The fundamental nature of the VXO is the same as the VIX, but it is less robust and not as simple as the VIX.

(Click here to see a live example of the VXO)
For more information on options, option pricing and volatility, see the following:
    CBOE Web site
    Trading Index Options by James B. Bittman
  • Buying and Selling Volatility by Kevin Connolly
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[url=]Advance and Decline Line[/url]The Advanced-Decline Line (AD Line) is a popular indicator of market breadth that is used to help determine the direction of the stock market. Since this indicator includes price data from the entire market, analysts often prefer using it to gauge the strength of the market, rather than using a smaller sample such as the Dow Jones Industrial Average.
It is calculated each day by taking the difference between the number of advancing stocks and the number of declining stocks. If that difference is positive, the AD Line goes up by that amount. It is a running cumulative total of the number of advancing stocks minus the number of declining stocks. Therefore, a rising AD Line implies that most stocks are advancing (bullish), while a falling AD Line implies that most stocks are declining (bearish).
As mentioned earlier, plotting the AD Line allows insight into market strength. When compared to a market average such as the S&P 500, divergence from that average could be an early indication of a possible trend reversal.

The above chart shows the NYSE AD Line compared to the S&P 500. The divergence that occurred in mid-May suggested that the market was gaining strength even as the S&P 500 was still declining. This Bullish AD Line reversal was confirmed by the following trend reversal of the S&P 500.

[url=]McClellan Oscillator[/url]Developed by Sherman and Marian McClellan, the McClellan Oscillator is a breadth indicator derived from each day's net advances, the number of advancing issues less the number of declining issues. Subtracting the 39-day exponential moving average from the 19-day exponential moving average of net advances forms the oscillator.
Similar to MACD, the McClellan Oscillator is a momentum indicator that is applied to the advance/decline statistics. When the 19-day EMA (shorter moving average) moves above the 39-day (longer moving average) EMA, it signals that advances are gaining the upper hand. Conversely, when the 19-day EMA declines below the 39-day EMA, it signals that declining issues are dominant. As a momentum indicator, the McClellan Oscillator attempts to anticipate positive and negative changes in the AD statistics for market timing.
Buy and sell signals are generated as well as overbought and oversold readings. Usually, readings above +100 are considered overbought and below -100 oversold. Overbought and oversold readings may vary among indices and historical precedent. Buy signals are generated when the oscillator advances from oversold levels to positive territory. Sell signals are generated on declines from overbought to negative territory. Traders may also look for positive or negative divergences to time their trades. A series of rising troughs would denote strength, while a series of declining peaks weakness.

[url=]Calculation[/url]When calculating the McClellan Oscillator, the ratio adjusted index is often used for easier comparisons over long periods of time. The basic input for the ratio-adjusted version is no longer the daily advances minus declines. Rather, you
    Subtract declines from advances
    Divide the result by the total of advances plus declines, and
  • Multiply that result by 1000. (Multiplying by 1000 is simply cosmetic and lets us work with whole numbers instead of decimals.)
The rest of the calculations for the Oscillator are the same.

[url=]Example[/url]
The above chart shows the breakdown of the McClellan Oscillator. The top window shows the 19-day EMA and the 39-day EMA of the NYSE advance-decline issues, and the lower window shows the ratio adjusted McClellan Oscillator line. Notice that the 19-day and 39-day EMA crossovers correspond with zero-line crossovers on the McClellan Oscillator.
StockCharts.com provides one-year charts of the McClellan Oscillator for the NYSE and NASDAQ markets.
Or click for a live example of the McClellan Oscillator for [url=http://stockcharts.com/h-sc/ui?c=$NYSI,uu[m,a]daclynay[dd][p]&pref=G]NYSE[/url] or for [url=http://stockcharts.com/h-sc/ui?c=$NASI,uu[m,a]daclynay[dd][p]&pref=G]NASDAQ[/url].

[url=]McClellan Summation Index[/url]The McClellan Summation Index is a popular market breadth indicator that is ultimately derived from the number of advancing and declining stocks in a given market. It is derived from the McClellan Oscillator by tracking its daily accumulation or "summation". This provides a longer-term view of the McClellan concept. Many people regard it as an excellent indicator of the overall "health" of the market and the market's current trend. It was developed by Sherman and Marian McClellan and first presented in their book, Patterns for Profit (available from McClellan Financial Publications).

[url=]Calculation[/url]There are two methods for calculating the Summation Index. The first method (the one originally used by the McClellans) simply maintains a running total of the values of the McClellan Oscillator (which is defined here). The second method uses the following formula:
Summation Index = 1000 + (10%Trend - 5%Trend) - [(10 x 10%Trend) + (20 x 5%Trend)]

where:
5%Trend = 39-day EMA of (Advancers-Decliners)

10%Trend = 19-day EMA of (Advancers-Decliners)

The McClellan Summation index generally oscillates between 0 and 2000 although it can move outside of this range during extreme or unusual market conditions. Historically, major market bottoms occur after the index falls below -1000. Readings above +1600 often indicate a major top is near. Top and bottom signals carry more significance if the index is also diverging from the associated market average. According to the McClellans, the beginning of a new bull market is signaled if the NYSE-based Summation index first moves below the -1200 level and then quickly rises above +2500.
The Summation Index is simply a longer range version of the McClellan Oscillator. Whereas the McClellan Oscillator is used for short to intermediate trading purposes, the Summation Index provides a longer range view of market breadth and is used to spot major market turning points.

The chart above shows the ratio adjusted NYSE McClellan Summation Index. The sharp move from -650 in late September to 675 in less than four months can be interpreted as a major bullish turning point in the market.
The ratio adjusted index is calculated differently and is used for easier comparisons over long periods of time. First, the basic input for the ratio-adjusted version is no longer the daily advances minus declines. Rather you (1) subtract declines from advances, (2) divide the result by the total of advances plus declines, and (3) multiply that result by 1000. (Multiplying by 1000 is simply cosmetic and lets us work with whole numbers instead of decimals.) The rest of the calculations for the Oscillator are the same. The second difference is that zero (0) is now considered neutral for the Summation Index, so you no longer begin with 1000 in your Summation Index calculation.
StockCharts.com provides one-year charts of the McClellan Summation Index for the NYSE and NASDAQ markets.
Or click for a live example of the McClellan Summation Index for NYSE or for NASDAQ.

[url=]Stocks above Their 200-Day, 150-Day, or 50-Day Simple Moving Averages[/url]These market breadth indicators represent the number of stocks in a given group that have closing prices that are currently above their 200, 150, or 50-day simple moving average. Because the number of stocks in the underlying group can vary from day to day, the actual value of these indicators is less important than the shape of their charts.
Common techniques for using these indicators include locating overbought/oversold levels and finding positive or negative divergences between them and the underlying group's composite index. Standard peak and trough trend line analysis is used in most cases.
At StockCharts.com, you can track all stocks that have exceeded these moving average levels within the most popular indices such as the NASDAQ 100 or the S&P 500. The symbols ending in "R" track the ratio of stocks exceeding the moving average to the stocks that are not.
For example, the symbol $OEXA200R tracks the percentage of stocks that are above their 200-day moving average, while $OEXA200 tracks the number of stocks above their 200-day moving average.
Click on a symbol below to see how many (or what percent of) stocks are exceeding their moving averages:
Ticker Symbol Ticker Ratio Name $DOWA150$DOWA150RDJ Industrials Stocks Above 150-Day Moving Average$DOWA200$DOWA200RDJ Industrials Stocks Above 200-Day Moving Average$DOWA50$DOWA50RDJ Industrials Stocks Above 50-Day Moving Average$NAA150$NAA150RNASDAQ Stocks Above 150-Day Moving Average$NAA200$NAA200RNASDAQ Stocks Above 200-Day Moving Average$NAA50$NAA50RNASDAQ Stocks Above 50-Day Moving Average$NDXA150$NDXA150RNASDAQ 100 Stocks Above 150-Day Moving Average$NDXA200$NDXA200RNASDAQ 100 Stocks Above 200-Day Moving Average$NDXA50$NDXA50RNASDAQ 100 Stocks Above 50-Day Moving Average$NYA150$NYA150RNYSE Stocks Above 150-Day Moving Average$NYA200$NYA200RNYSE Stocks Above 200-Day Moving Average$NYA50$NYA50RNYSE Stocks Above 50-Day Moving Average$OEXA150$OEXA150RS&P 100 Stocks Above 150-Day Moving Average$OEXA200$OEXA200RS&P 100 Stocks Above 200-Day Moving Average$OEXA50$OEXA50RS&P 100 Stocks Above 50-Day Moving Average$SPXA150$SPXA150RS&P 500 Stocks Above 150-Day Moving Average$SPXA200$SPXA200RS&P 500 Stocks Above 200-Day Moving Average$SPXA50$SPXA50RS&P 500 Stocks Above 50-Day Moving Average$TSXA150$TSXA150RS&P/TSX Composite Stocks Above 150-Day Moving Average$TSXA200$TSXA200RS&P/TSX Composite Stocks Above 200-Day Moving Average$TSXA50$TSXA50RS&P/TSX Composite Stocks Above 50-Day Moving AverageIf you are very interested in using market indicators to improve your investment decisions, we recommend you visit DecisionPoint.com
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 楼主| 发表于 2009-3-15 12:39 | 显示全部楼层
[url=]Dow Theory[/url]
[url=]Introduction[/url]The Dow theory has been around for almost 100 years, yet even in today's volatile and technology-driven markets, the basic components of Dow theory still remain valid. Developed by Charles Dow, refined by William Hamilton and articulated by Robert Rhea, the Dow theory addresses not only technical analysis and price action, but also market philosophy. Many of the ideas and comments put forth by Dow and Hamilton became axioms of Wall Street. While there are those who may think that it is different this time, a read through The Dow Theory will attest that the stock market behaves the same today as it did almost 100 years ago.
The Dow theory presented below has been taken from Robert Rhea's book, The Dow Theory. Although Dow theory is attributed to Charles Dow, it is William Hamilton's writings that serve as the corner stone for this book and the development of the theory. Also, it should be noted that most of the theory was developed with the Dow Jones Rail and Industrial averages in mind. Even though many concepts can be applied to individual stocks, please keep in mind that these are broad concepts and best applied to stocks as a group or index. When possible, we have also attempted to link some of the realities of today's market with the Dow theory as explained by Dow, Hamilton and Rhea.

[url=]Background[/url]Charles Dow developed the Dow theory from his analysis of market price action in the late 19th century. Until his death in 1902, Dow was part owner as well as editor of The Wall Street Journal. Although he never wrote a book on the subject, he did write some editorials that reflected his views on speculation and the role of the rail and industrial averages.
Even though Charles Dow is credited with developing the Dow theory, it was S.A. Nelson and William Hamilton who later refined the theory into what it is today. Nelson wrote The ABC of Stock Speculation and was the first to actually use the term "Dow theory." Hamilton further refined the theory through a series of articles in The Wall Street Journal from 1902 to 1929. Hamilton also wrote The Stock Market Barometer in 1922, which sought to explain the theory in detail.
In 1932, Robert Rhea further refined the analysis of Dow and Hamilton in The Dow Theory. Rhea read, studied and deciphered some 252 editorials through which Dow (1900-1902) and Hamilton (1902-1929) conveyed their thoughts on the market. Rhea also referred to Hamilton's The Stock Market Barometer. The Dow Theory presents the Dow theory as a set of assumptions and theorems.

[url=]Assumptions[/url]Before one can begin to accept the Dow theory, there are a number of assumptions that must be accepted. Rhea stated that for the successful application of the Dow theory, these assumptions must be accepted without reservation.

[url=]Manipulation[/url]The first assumption is: The manipulation of the primary trend is not possible. When large amounts of money are at stake, the temptation to manipulate is bound to be present. Hamilton did not argue against the possibility that speculators, specialists or anyone else involved in the markets could manipulate the prices. He qualified his assumption by asserting that it was not possible to manipulate the primary trend. Intraday, day-to-day and possibly even secondary movements could be prone to manipulation. These short movements, from a few hours to a few weeks, could be subject to manipulation by large institutions, speculators, breaking news or rumors. Today, Hamilton would likely add message boards and day-traders to this list.
Hamilton went on to say that individual shares could be manipulated. Examples of manipulation usually end the same way: the security runs up and then falls back and continues the primary trend. Examples include:
    PairGain Technology rose sharply due to a hoax posted on a fake Bloomberg site. However, once the hoax was revealed, the stock immediately fell back and returned to its primary trend.
    Books-A-Million rose from 3 to 47 after announcing an improved web site. Three weeks later, the stock settled around 10 and drifted lower from there.
  • In 1979/80, there was an attempt to manipulate the price of silver by the Hunt brothers. Silver skyrocketed to over 50$ per ounce, only to come back down to earth and resume its long bear market after the plot to corner the market was unveiled.
While these shares were manipulated over the short term, the long-term trends prevailed after about a month. Hamilton also pointed out that even if individual shares were being manipulated, it would be virtually impossible to manipulate the market as a whole. The market was simply too big for this to occur.

[url=]Averages Discount Everything[/url]The market reflects all available information. Everything there is to know is already reflected in the markets through the price. Prices represent the sum total of all the hopes, fears and expectations of all participants. Interest rate movements, earnings expectations, revenue projections, presidential elections, product initiatives and all else are already priced into the market. The unexpected will occur, but usually this will affect the short-term trend. The primary trend will remain unaffected.
The chart below of Coca-Cola (KO) is a recent example of the primary trend remaining intact. The downtrend for Coca-Cola began with the sharp fall from above 90. The stock rallied with the market in October and November 1998, but by December started to decline again. According to Dow theory, the October/November rally would be called a secondary move (against the primary trend). It is likely that the stock was caught up in the general market advance at the time. However, when the major indices were hitting new highs in December, Coca-Cola was starting to flounder and resume its primary trend.

Hamilton noted that sometimes the market would react negatively to good news. For Hamilton, the reasoning was simple: the market looks ahead. By the time the news hits the street, it is already reflected in the price. This explains the old Wall Street axiom, "buy the rumor, sell the news". As the rumor begins to filter down, buyers step in and bid the price up. By the time the news hits, the price has been bid up to fully reflect the news. Yahoo! (YHOO) and the run up to earnings is a classic example. For the past three quarters, Yahoo! has been bid up leading right up to the earnings report. Even though earnings have exceeded expectations each time, the stock has fallen by about 20%.


[url=]Theory Not Perfect[/url]Hamilton and Dow readily admit that the Dow theory is not a sure-fire means of beating the market. It is looked upon as a set of guidelines and principles to assist investors and traders with their own study of the market. The Dow theory provides a mechanism for investors to use that will help remove some of the emotion. Hamilton warns that investors should not be influenced by their own wishes. When analyzing the market, make sure you are objective and see what is there, not what you want to see. If an investor is long, he or she may want to see only the bullish signs and ignore any bearish signals. Conversely, if an investor is out of the market or short, he or she may be apt to focus on the negative aspects of the price action and ignore any bullish developments. Dow theory provides a mechanism to help make decisions less ambiguous. The methods for identifying the primary trend are clear-cut and not open to interpretation.
Even though the theory is not meant for short-term trading, it can still add value for traders. No matter what your time frame, it always helps to be able to identify the primary trend. According to Hamilton (writing in the early part of the 20th century), those who successfully applied the Dow theory rarely traded more than four or five times a year. Remember that intraday, day-to-day and possibly even secondary movements can be prone to manipulation, but the primary trend is immune from manipulation. Hamilton and Dow sought a means to filter out the noise associated with daily fluctuations. They were not worried about a couple of points, or getting the exact top or bottom. Their main concern was catching the large moves. Both Hamilton and Dow recommended close study of the markets on a daily basis, but they also sought to minimize the effects of random movements and concentrate on the primary trend. It is easy to get caught up in the madness of the moment and forget the primary trend. After the October low, the primary trend for Coca-Cola remained bearish. Even though there were some sharp advances, the stock never forged a higher high.

[url=]Market Movements[/url]Dow and Hamilton identified three types of price movements for the Dow Jones Industrial and Rail averages: primary movements, secondary movements and daily fluctuations. Primary moves last from a few months to many years and represent the broad underlying trend of the market. Secondary (or reaction) movements last from a few weeks to a few months and move counter to the primary trend. Daily fluctuations can move with or against the primary trend and last from a few hours to a few days, but usually not more than a week.

[url=]Primary Movement[/url]Primary movements represent the broad underlying trend of the market and can last from a few months to many years. These movements are typically referred to as bull and bear markets. Once the primary trend has been identified, it will remain in effect until proved otherwise. (We will address the methods for identifying the primary trend later in this article.) Hamilton believed that the length and the duration of the trend were largely indeterminable. Hamilton did study the averages and came up with some general guidelines for length and duration, but warned against attempting to apply these as rules for forecasting.
Many traders and investors get hung up on price and time targets. The reality of the situation is that nobody knows where and when the primary trend will end. The objective of Dow theory is to utilize what we do know, not to haphazardly guess about what we don't know. Through a set of guidelines, Dow theory enables investors to identify the primary trend and invest accordingly. Trying to predict the length and the duration of the trend is an exercise in futility. Hamilton and Dow were mainly interested in catching the big moves of the primary trend. Success, according to Hamilton and Dow, is measured by the ability to identify the primary trend and stay with it.

[url=]Secondary Movements[/url]Secondary movements run counter to the primary trend and are reactionary in nature. In a bull market a secondary move is considered a correction. In a bear market, secondary moves are sometimes called reaction rallies. Earlier in this article, a chart of Coca-Cola was used to illustrate reaction rallies (or secondary movements) within the confines of a primary bear trend. Below is a chart illustrating a correction within the confines of a primary bull trend.

In Sept-96, the DJIA ($INDU) recorded a new high, thereby establishing the primary trend as bullish. From trough to peak, the primary advance rose 1988 points. During the advance from Sept-96 to Mar-97, the DJIA never declined for more than two consecutive weeks. By the end of March, after three consecutive weeks of decline, it became apparent that this move was not in the category of daily fluctuations and could be considered a secondary move. Hamilton noted some characteristics that were common to many secondary moves in both bull and bear markets. These characteristics should not be construed as rules, but rather as loose guidelines to be used in conjunction with other analysis techniques. The first three characteristics have been applied to the example above.
    Based on historical observation, Hamilton estimated that secondary movements retrace 1/3 to 2/3 of the primary move, with 50% being the typical amount. In actuality, the secondary move in early 1997 retraced about 42% of the primary move. (7158 - 5170 = 1988; 7158 - 6316 = 842, 842/1988 = 42.35%).
    Hamilton also noted that secondary moves tend to be faster and sharper than the preceding primary move. Just with a visual comparison, we can see that the secondary move was sharper that the preceding primary advance. The primary move advanced 38% (1988/5170 = 38%) and lasted from Jul-96 to Mar-97, about 8 months. The secondary move witnessed a correction of 11.7% (842/7158 = 11.7%) and lasted a mere five weeks.
    At the end of the secondary move, there is usually a dull period just before the turnaround. Little price movement, a decline in volume, or a combination of the two can mark this dullness. Below is a daily chart focusing on the Apr-97 low for the secondary move outlined above.



    April 7 through 10 marked the dull point (red line on volume). There was little price movement and volume was the lowest since the decline began. The DJIA ($INDU) then gapped down on an increase in volume. After the down gap, there was a reversal day and then the DJIA proceeded with a gap up and breakout to a reaction high on increasing volume (green line on volume). The new reaction high combined with the increase in volume indicated that the secondary move was over and the primary trend had resumed.
  • Lows are sometimes accompanied by a high-volume washout day. The September/October lows in 1998 were accompanied by record volume levels. At the time, the low on Sept-1 witnessed the highest volume ever recorded and the Oct-8 low recorded the second highest volume ever. Although these high-volume lows were not a signal in and of themselves, they helped form a pattern that preceded a historical advance. This advance took the DJIA ($INDU) from below 8000 to over 11000 in less than one year. Further confirmation of a change in trend came in the form of a new reaction high with high volume on Oct-15.



[url=]Dow Theory Note[/url]There is still debate as to whether the crash of 1998 was a bear market or merely a secondary move within the confines of a larger bull market. In hindsight, it would appear to be a secondary move. Even though the DJIA recorded a lower low on August 4 and had lost just over 20% by September 4, the two-month time frame makes it difficult to justify as a bear market.
Hamilton characterized secondary moves as a necessary phenomenon to combat excessive speculation. Corrections and counter moves kept speculators in check and added a healthy dose of guesswork to market movements. Because of their complexity and deceptive nature, secondary movements require extra careful study and analysis. Investors often mistake a secondary move for the beginning of a new primary trend. How far does a secondary move have to go before the primary trend is affected? This issue will be addressed in Part 3 of this article, when we analyze the various signals based on Dow theory.

[url=]Daily Fluctuations[/url]Daily fluctuations, while important when viewed as a group, can be dangerous and unreliable individually. Due to the randomness of the movements from day to day, the forecasting value of daily fluctuations is limited at best. At worst, too much emphasis on daily fluctuation will lead to forecasting errors and possibly losses. Getting too caught up in the movement of one or two days can lead to hasty decisions that are based on emotion. It is vitally important to keep the whole picture in mind when analyzing daily price movements. Think of the pieces of a puzzle. Individually, a few pieces are meaningless, yet at the same time they are essential to complete the picture. Daily price movements are important, but only when grouped with other days to form a pattern for analysis. Hamilton did not disregard daily fluctuations, quite to the contrary. The study of daily price action can add valuable insight, but only when taken in context of the larger picture. There is little structure in one, two or even three days' worth of price action. However, when a series of days is combined, a structure will start to emerge and analysis becomes better grounded.

[url=]The Three Stages of Primary Bull Markets and Primary Bear Markets[/url]Hamilton identified three stages to both primary bull markets and primary bear markets. These stages relate as much to the psychological state of the market as to the movement of prices. A primary bull market is defined as a long sustained advance marked by improving business conditions that elicit increased speculation and demand for stocks. A primary bear market is defined as a long sustained decline marked by deteriorating business conditions and subsequent decrease in demand for stocks. In both primary bull markets and primary bear markets, there will be secondary movements that run counter to the major trend.

[url=]Primary Bull Market - Stage 1 - Accumulation[/url]Hamilton noted that the first stage of a bull market was largely indistinguishable from the last reaction rally of a bear market. Pessimism, which was excessive at the end of the bear market, still reigns at the beginning of a bull market. It is a period when the public is out of stocks, the news from corporate America is bad and valuations are usually at historical lows. However, it is at this stage that the so-called "smart money" begins to accumulate stocks. This is the stage of the market when those with patience see value in owning stocks for the long haul. Stocks are cheap, but nobody seems to want them. This is the stage where Warren Buffet stated in the summer of 1974 that now was the time to buy stocks and become rich. Everyone else thought he was crazy.
In the first stage of a bull market, stocks begin to find a bottom and quietly firm up. When the market starts to rise, there is widespread disbelief that a bull market has begun. After the first leg peaks and starts to head back down, the bears come out proclaiming that the bear market is not over. It is at this stage that careful analysis is warranted to determine if the decline is a secondary movement (a correction of the first leg up). If it is a secondary move, then the low forms above the previous low, a quiet period will ensue as the market firms and then an advance will begin. When the previous peak is surpassed, the beginning of the second leg and a primary bull will be confirmed.

[url=]Primary Bull Market - Stage 2 - Big Move[/url]The second stage of a primary bull market is usually the longest, and sees the largest advance in prices. It is a period marked by improving business conditions and increased valuations in stocks. Earnings begin to rise again and confidence starts to mend. This is considered the easiest stage to make money as participation is broad and the trend followers begin to participate.

[url=]Primary Bull Market - Stage 3 - Excess[/url]The third stage of a primary bull market is marked by excessive speculation and the appearance of inflationary pressures. (Dow formed these theorems about 100 years ago, but this scenario is certainly familiar.) During the third and final stage, the public is fully involved in the market, valuations are excessive and confidence is extraordinarily high. This is the mirror image to the first stage of the bull market. A Wall Street axiom: When the taxi cab drivers begin to offer tips, the top cannot be far off.

[url=]Primary Bear Market - Stage 1 - Distribution[/url]Just as accumulation is the hallmark of the first stage of a primary bull market, distribution marks the beginning of a bear market. As the "smart money" begins to realize that business conditions are not quite as good as once thought, they start to sell stocks. The public is still involved in the market at this stage and become willing buyers. There is little in the headlines to indicate a bear market is at hand and general business conditions remain good. However, stocks begin to lose a bit of their luster and the decline begins to take hold.
While the market declines, there is little belief that a bear market has started and most forecasters remain bullish. After a moderate decline, there is a reaction rally (secondary move) that retraces a portion of the decline. Hamilton noted that reaction rallies during bear markets were quite swift and sharp. As with his analysis of secondary moves in general, Hamilton noted that a large percentage of the losses would be recouped in a matter of days or perhaps weeks. This quick and sudden movement would invigorate the bulls to proclaim the bull market alive and well. However, the reaction high of the secondary move would form and be lower than the previous high. After making a lower high, a break below the previous low would confirm that this was the second stage of a bear market.

[url=]Primary Bear Market - Stage 2 - Big Move[/url]As with the primary bull market, stage two of a primary bear market provides the largest move. This is when the trend has been identified as down and business conditions begin to deteriorate. Earnings estimates are reduced, shortfalls occur, profit margins shrink and revenues fall. As business conditions worsen, the sell-off continues.

[url=]Primary Bear Market - Stage 3 - Despair[/url]At the top of a primary bull market, hope springs eternal and excess is the order of the day. By the final stage of a bear market, all hope is lost and stocks are frowned upon. Valuations are low, but the selling continues as participants seek to sell no matter what. The news from corporate America is bad, the economic outlook bleak and not a buyer is to be found. The market will continue to decline until all the bad news is fully priced into stocks. Once stocks fully reflect the worst possible outcome, the cycle begins again.

[url=]Signals[/url]Through the writings of Dow and Hamilton, Rhea identified 4 separate theorems that addressed trend identification, buy and sell signals, volume, and trading ranges. The first two were deemed the most important and serve to identify the primary trend as bullish or bearish. The second two theorems, dealing with volume and trading ranges, were not considered instrumental in primary trend identification by Hamilton. Volume was looked upon as a confirming statistic and trading ranges were thought to identify periods of accumulation and distribution.

[url=]Identification of the Trend[/url]The first step in identifying the primary trend is to identify the individual trend of the Dow Jones Industrial Average (DJIA), and Dow Jones Transportation Average (DJTA), individually. Hamilton used peak and trough analysis in order to ascertain the identity of the trend. An uptrend is defined by prices that form a series of rising peaks and rising troughs (higher highs and higher lows). In contrast, a downtrend is defined by prices that form a series of declining peaks and declining troughs (lower highs and lower lows).
Once the trend has been identified, it is assumed valid until proved otherwise. A downtrend is considered valid until a higher low forms and the ensuing advance off of the higher low surpasses the previous reaction high. Below is a chart of the Dow Jones Transportation Average in 1992. Even though Hamilton and Dow did not make specific references to trend lines, a line has been drawn to emphasize the downward trajectory of the trend. Since the peak in February, a series of lower lows and lower highs formed to make a downtrend. There was a secondary rally in April and May (green circle), but the March high was not surpassed.

The DJTA ($TRAN) continued down until the high volume washout day (red arrow). As discussed in this article, high volume days signal that a possible change is looming. Alone, a high volume washout day is not a buy signal, but rather an indication to monitor price action a little closer. After this high volume day, the DJTA dipped again and then moved above 1250, creating a higher low (green arrow). Even after the higher low is in place, it is still too early to call for a change in trend. The change of trend is not confirmed until the previous reaction high is surpassed (blue arrow).
Conversely, an uptrend is considered in place until a lower low forms and the ensuing decline exceeds the previous low. Below is a line chart of the closing prices for the DJIA. An uptrend began with the Oct-98 lows and the DJIA formed a series of higher highs and higher lows over the next 11 months. Twice, in Dec-98 (red circle) and Jun-99 (blue arrows), the validity of the uptrend came into question, but the uptrend prevailed until late September. (The Dec-98 price action is addressed below.) There were lower highs in Jun-99, but there were never any lower lows to confirm these lower highs and support held. Any bears that jumped the gun in June were made to sit through two more all-time highs in July and August. The change in trend occurred on September 23 when the June lows were violated. Some traders may have concluded that the trend changed when the late August lows were violated. This may indeed be the case, but it is worth noting that the June lows represented a more convincing support area. Keep in mind that the Dow theory is not a science and Hamilton points this out numerous times. The Dow theory is meant to offer insights and guidelines from which to begin careful study of the market movements and price action.

Looking at the line chart above (DJIA ($INDU) 1998/1999 daily close semi-log scale), it may be difficult to distinguish between a valid change in trend and a simple correction. For instance: Was a change in trend warranted when the December low penetrated the November low? (red circle) After the November peak, a lower high formed in December and then the November reaction low was broken. In order to eliminate false signals, Hamilton suggested excluding moves of less than 3%. This was not meant to be a hard and fast rule, but the idea is worth noting. With the increased volatility of today's markets comes the need to smooth the daily fluctuations and avoid false readings.
Hamilton and Dow were interested in catching the big moves and would have been apt to use weekly charts to establish reaction highs and lows. However, in today's fast moving markets, weekly charts may not portray the detail that investors need. One possible solution is to apply a short moving average to the price plot. Although not mentioned by Hamilton and Dow, a 5-day moving average could be applied to smooth the price series and still allow for detail. The chart below (DJIA ($INDU) 1998/1999 daily close 5-EMA) uses a 5-day exponential moving average to smooth the price plot. Notice that the November reaction low now appears quite immaterial. Also, the September reaction high (red arrow) still shows up.


[url=]Averages Must Confirm[/url]When the Dow theory was being developed at the turn of the century, the railroads were a vital link in the economy. Hamilton argued that many times activity would begin in the Rail Average before the Industrial Average. He attributed this to the fact that before economic activity began, raw materials would have to be moved from the suppliers to manufacturers. Before General Motors could increase production, more steel would need to be transported. Therefore, an increase in activity among the rail stocks would foreshadow an increase in business activity for the industrial stocks.

[url=]Why the Rails?[/url]There is no doubt that today's economy is much different and the makeup of the DJTA has changed to favor the airlines. However, there is still some credibility in using the DJTA to confirm movements in the DJIA. Transport stocks are much more dependent on the economic environment than the average stock and will likely foreshadow economic growth.
    The airline business is cyclical and revenues are highly susceptible to economic changes.
    Airline companies typically carry above average levels of debt and will be more vulnerable to changes in interest rates.
  • Energy and Labor costs form a large portion of expenses.
To reflect the added risks above, airline stocks have traditionally sold significantly below market multiples. If the PE for the S&P 500 is 28, the average airline might sell for only 8-10 times earnings.
Even though we are possibly entering into a "new economy," the majority of businesses will somehow be affected by changes in economic activity, interest rates, energy costs and labor costs. Airline companies, bearing the burden of all of the above, are still likely to act as a leading indicator of the general economic environment.
However, one caveat must be added as well. Possibly the greatest fear of the airlines is that people will stop flying in airplanes. Business travel accounts for a large portion of airline revenues, especially the high margin revenues. With the development of the Internet and networking, the need for business travel could be greatly reduced in the future. Federal Express has already experienced a slowdown in the quantity of business documents being shipped. This could ultimately spill over into the business of the airlines.

[url=]How Averages Confirm[/url]Hamilton and Dow stressed that for a primary trend buy or sell signal to be valid, both the Industrial Average and the Rail Average must confirm each other. If one average records a new high or new low, then the other must soon follow for a Dow theory signal to be considered valid.

Combining the guidelines set forth for trend identification with the theorem on confirmation, it is now possible to classify the primary trend of the market. The chart above shows an array of signals that occurred during a 7-month period in 1998.
    In April, both the DJIA ($INDU) and DJTA ($TRAN) recorded new all-time highs (blue line). The primary trend was already bullish, but this confirmation validated the primary trend as bullish.
    In July, trouble began to surface when the DJTA failed to confirm the new high set by the DJIA. This served as a warning sign, but did not change the trend. Remember, the trend is assumed to be in force until proved otherwise.
    On July 31, the DJTA recorded a new reaction low. Two days later, the DJIA recorded a new reaction low and confirmed a change in the primary trend from bullish to bearish (red line). After this signal, both averages went on to record new reaction lows.
    In October, the DJIA formed a higher low while the DJTA recorded a new low. This was another non-confirmation and served notice to be on guard for a possible change in trend.
    After the higher low, the DJIA followed through with a higher high later that month. This effectively changes the trend for the average from down to up.
  • It was not until early November that the DJTA went on to better its previous reaction high. However, at the same time the DJIA was also advancing higher and the primary trend had changed from bearish to bullish.

[url=]Volume[/url]The importance of volume was alluded to above with the chart of the Apr-97 bottom in the DJIA. Rhea notes that while Hamilton did analyze volume statistics, price action was the ultimate determinant. Volume is more important when confirming the strength of advances and can also help to identify potential reversals.

[url=]Volume Confirmation[/url]Hamilton thought that volume should increase in the direction of the primary trend. In a primary bull market, volume should be heavier on advances than during corrections. Not only should volume decline on corrections, but participation should also decrease. As Hamilton put it, the market should become "dull and narrow" on corrections, "narrow" meaning that the number of declining issues should not be expanding dramatically. The opposite is true in a primary bear market. Volume should increase on the declines and decrease during the reaction rallies. The reaction rallies should also be narrow and reflect poor participation of the broader market. By analyzing the reaction rallies and corrections, it is possible to judge the underlying strength of the primary trend.

[url=]Volume and Reversals[/url]Hamilton noted that high volume levels could be indicative of an impending reversal. A high volume day after a long advance may signal that the trend is about to change or that a reaction high may form soon. In his StockCharts.com commentary on 25-Jun-99, Rex Takasugi discusses the correlation between volume and peaks in the market. Even though his analysis reveals a lag time between volume peaks and market reversals, the relationship still exists. Takasugi's analysis reveals that since 1900 there have been 14 cycles and volume peaked on average 5.6 months ahead of the market. He also notes that the most recent volume peak occurred in Apr-99.

[url=]Trading Ranges a.k.a. Lines[/url]In his commentaries over the years, Hamilton referred many times to "lines." Lines are horizontal lines that form trading ranges. Trading ranges develop when the averages move sideways over a period of time and make it possible to draw horizontal lines connecting the tops and bottoms. These trading ranges indicate either accumulation or distribution, but it was virtually impossible to tell which until there was a break to the upside or the downside. If there were a break to the upside, then the trading range would be considered an area of accumulation. If there were a break to the downside, then the trading range would be considered an area of distribution. Hamilton considered the trading range neutral until a breakout occurred. He also warned against attempting to anticipate the breakout.


[url=]Performance of the Dow Theory[/url]Mark Hulbert, writing in the New York Times - 6-Sept-98, notes a study that was published in the Journal of Finance by Stephen Brown of New York University and William Goetzmann and Alok Kumar of Yale. They developed a neural network that incorporated the rules for identifying the primary trend. The Dow theory system was tested against buy-and-hold for the period from 1929 to Sept-98. When the system identified the primary trend as bullish, a long position was initiated in a hypothetical index fund. When the system signaled a bearish primary trend, stocks were sold and the money was placed in fixed income instruments. By taking money out of stocks after bear signals, the risk (volatility) of the portfolio is significantly reduced. This is a very important aspect of the Dow theory system and portfolio management. In the past few years, the concept of risk in stocks has diminished, but it is still a fact that stocks carry more risk than bonds.
Over the 70-year period, the Dow theory system outperformed a buy-and-hold strategy by about 2% per year. In addition, the portfolio carried significantly less risk. If compared as risk-adjusted returns, the margin of out-performance would increase. Over the past 18 years, the Dow theory system has under-performed the market by about 2.6% per year. However, when adjusted for risk, the Dow theory system outperformed buy-and-hold over the past 18 years. Keep in mind that 18 years is not a long time in the history of the market. The Dow theory system was found to under-perform during bull markets and outperform during bear markets.

[url=]Criticisms of Dow Theory[/url]The first criticism of the Dow theory is that it is really not a theory. Neither Dow nor Hamilton wrote proper academic papers outlining the theory and testing the theorems. The ideas of Dow and Hamilton were put forth through their editorials in the Wall Street Journal. Robert Rhea stitched the theory together by poring over these writings.
Secondly, the Dow theory is criticized for being too late. The trend does not change from bearish to bullish until the previous reaction high has been surpassed. Many traders feel that this is simply too late and misses much of the move. Dow and Hamilton sought to catch the meat of the move and enter during the second leg. Even though this is where the bulk of the move will take place, it is also after the first leg and part way into the second leg. And, if one has to wait for confirmation from the other average, it could even be later in the move.
Thirdly, because it uses the DJIA and DJTA, the Dow theory is criticized as being outdated and no longer an accurate reflection of the economy. This may be a valid point, but as outlined earlier, the DJTA is one of the most economically sensitive indices. The stock market has always been seen as a great predictor of economic growth. To at least keep the industrials up to speed, Home Depot, Intel, Microsoft and SBC Corp have been added to the average to replace Chevron, Goodyear, Sears and Union Carbide, as of 1-Nov-99.

[url=]Conclusions[/url]The goal of Dow and Hamilton was to identify the primary trend and catch the big moves. They understood that the market was influenced by emotion and prone to over-reaction both up and down. With this in mind, they concentrated on identification and following: identify the trend and then follow the trend. The trend is in place until proved otherwise. That is when the trend will end, when it is proved otherwise.
Dow theory helps investors identify facts, not make assumptions or forecast. It can be dangerous when investors and traders begin to assume. Predicting the market is a difficult, if not impossible, game. Hamilton readily admitted that the Dow theory was not infallible. While Dow theory may be able to form the foundation for analysis, it is meant as a starting point for investors and traders to develop analysis guidelines that they are comfortable with and understand.
Reading the markets is an empirical science. As such there will be exceptions to the theorems put forth by Hamilton and Dow. They believed that success in the markets required serious study and analysis that would be fraught with successes and failures. Success is a great thing, but don't get too smug about it. Failures, while painful, should be looked upon as learning experiences. Technical analysis is an art form and the eye grows keener with practice. Study both successes and failures with an eye to the future.
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 楼主| 发表于 2009-3-15 12:40 | 显示全部楼层
[url=]Elliott Wave Theory[/url]R. N. Elliott believed markets had well-defined waves that could be used to predict market direction. In 1939, Elliott detailed the Elliott Wave Theory, which states that stock prices are governed by cycles founded upon the Fibonacci series (1-2-3-5-8-13-21…).
According to the Elliott Wave Theory, stock prices tend to move in a predetermined number of waves consistent with the Fibonacci series. Specifically, Elliott believed the market moved in five distinct waves on the upside and three distinct on the downside. The basic shape of the wave is shown below.

Waves one, three and five represent the 'impulse', or minor up-waves in a major bull move. Waves two and four represent the 'corrective,' or minor down-waves in the major bull move. The waves lettered A and C represents the minor down-waves in a major bear move, while B represents the one up-wave in a minor bear wave.
Elliott proposed that the waves existed at many levels, meaning there could be waves within waves. To clarify, this means that the chart above not only represents the primary wave pattern, but it could also represent what occurs just between points 2 and 4. The diagram below shows how primary waves could be broken down into smaller waves.

Elliott Wave theory ascribes names to the waves in order of descending size:
    Grand Supercycle
    Supercycle
    Cycle
    Primary
    Intermediate
    Minor
    Minute
    Minuette
  • Sub-Minuette
The major waves determine the major trend of the market, and minor waves determine minor trends. This is similar to the way Dow Theory postulates primary and secondary trends. Elliott provided numerous variations on the main wave, and placed particular importance on the golden mean, 0.618, as a significant percentage for retracement.
Trading using Elliott Wave patterns is quite simple. The trader identifies the main wave or Supercycle, enters long, and then sells or shorts, as the reversal is determined. This continues in progressively shorter cycles until the cycle completes and the main wave resurfaces. The caution to this is that much of the wave identification is taken in hindsight and disagreements arise between Elliott Wave technicians as to which cycle the market is in.
Here is an example of an Elliott Wave cycle. Ideally, Wave Two would not retrace more than 66%, but you can get a real sense of the wave patterns in action from the chart, just as well.

For more information, check out Elliott Wave Principle: Key to Market Behavior by Robert Prechter.
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 楼主| 发表于 2009-3-15 12:41 | 显示全部楼层
[url=]The 'Last' Stochastic Technique[/url]The Stochastic Oscillator is a momentum or price velocity indicator developed by George Lane. The calculation is very simple:
K=[(C-L)/(H-L)]*100

Where:
    K = Lane's Stochastic
    C = latest closing price
    L = then-period low price
  • H = the n-period high price
Additionally, Lane's methods specifically required that the K be smoothed twice with three-period simple moving averages. Two other calculations are then made:
    SK = three period simple moving average of K
  • SD = three period simple moving average of SK
The classic interpretation of a stochastic can be complicated. The basic method is to buy when the SK is above the SD, and sell when the SK moves below the SD. However, the stochastic employs a fixed period-to-period calculation that can move about erratically as the earliest data point is dropped for the next day's calculation. Due to this instability and false signals generated, using a stochastic for entry and exit signals can incur a lot of unprofitable trades. To compensate for this inherent weakness, buy signals are generally reinforced when the crossover occurs in the 10-15% ranges, and sells in the 85-90% range.

Unfortunately, many techniques for using the stochastic oscillator can produce consistent losses over time. Some analysts have recommended smoothing the data further, or looking for a confirming overbought/oversold ratio prior to selling or buying. Most secondary filters such as overbought/oversold indicators degrade the performance of the stochastic in that one does not take advantage of major trends, getting whipsawed in and out.

[url=]K39 - The Last Stochastic Technique[/url]A study published in "The Encyclopedia of Technical Market Indicators" found that some very good signals were given by an un-smoothed 39 period stochastic oscillator (K = 39, no signal line). A buy signal is generated when K crosses above 50% and the closing price is above the previous week's high close. Sell and/or sell short signals are created when the K line crosses below 50% and the closing price is below the previous week's low close. Taking a longer period, and not smoothing the data over a 3-period moving average allows the analyst to view Lane's Stochastic.
Note: You can add the Last Stochastic to our SharpChart charting tool by adding a "Slow Stochastics" indicator with parameters of 39 and 1. Here is an example. Alternately, you can choose "Chip's Mutual Funds" from the "ChartStyles" dropdown.
In the chart below for MSFT, we see that the 39 period K crossed above 50% on June 14, at around $36.00.

Weekly, Daily and Hourly through Minute data can all be used effectively for the 39 period stochastic. Using weekly data for three years, we see that the 39-Week Stochastic for MSFT didn't cross below 50% until late February, 2000.

The whipsaw that occurred for MSFT the following month shows the need for signal confirmation. If we look at CSCO for the last year on daily data, we see that by the 39 day stochastic, it was a hold from November 1999 at $17 through early April 2000 at $32 a share. Here again, we see a false rally at the end of April. What can be used for confirmation?

Since the Stochastic is a price momentum indicator, one should pair it with a volume assessment for trade confirmation. In the chart below, the On Balance Volume (OBV) indicator has been added along with a 30 day MA as a signal line.

Notice that there was a bullish OBV crossover in early November 1999 and again in early June 2000 soon after the K line moved back above 50%. Although the Last Stochastic reversed in April, the OBV crossover did not occur. When the K line moved above 50% again in early June, confirmation soon followed.
One last point to remember is that all stocks are unique, and while the 39 period Stochastic is a useful technical indicator, one should always map the performance against your specific stock. Recently, most Tech stocks have evidenced a tendency to signal entry at a K crossover above 40% and a sell with K crossing below 60%. However, in volatile equities a second price or sentiment indicator along with a volume indicator provides the best confirmation.
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 楼主| 发表于 2009-3-15 12:44 | 显示全部楼层
[url=]Arthur Hill On Moving Average Crossovers[/url]A popular use for moving averages is to develop simple trading systems based on moving average crossovers. A trading system using two moving averages would give a buy signal when the shorter (faster) moving average advances above the longer (slower) moving average. A sell signal would be given when the shorter moving average crosses below the longer moving average. The speed of the systems and the number of signals generated will depend on the length of the moving averages. Shorter moving average systems will be faster, generate more signals and be nimble for early entry. However, they will also generate more false signals than systems with longer moving averages.

For Inter-Tel (INTL), a 30/100 exponential moving average crossover was used to generate signals. When the 30-day EMA moves above the 100-day EMA, a buy signal is in force. When the 30-day EMA declines below the 100-day EMA, a sell signal is in force. A plot of the 30/100 differential is shown below the price chart by using the Percentage Price Oscillator (PPO) set to (30,100,1). When the differential is positive, the 30-day EMA is greater than the 100-day EMA. When it is negative, the 30-day EMA is less than the 100-day EMA.
As with all trend-following systems, the signals work well when the stock develops a strong trend, but are ineffective when the stock is in a trading range. Some good entry points for long positions were caught in Sept-97, Mar-98 and Jul-99. However, an exit strategy based on the moving average crossover would have given back some of those profits. All in all, though, the system would have been profitable for the time period shown.

In the example for 3Com (COMS), a 20/60 EMA crossover system was used to generate buy and sell signals. The plot below the price is the 20/60 EMA differential, which is shown as a percent and displayed using the Percentage Price Oscillator (PPO) set at (20,60,1). The thin blue lines just above and below zero (the centerline) represent the buy and sell trigger points. Using zero as the crossover point for the buy and sell signals generated too many false signals. Therefore, the buy signal was set just above the zero line (at +2%) and the sell signal was set just below the zero line (at -2%). When the 20-day EMA is more than 2% above the 60-day EMA, a buy signal is in force. When the 20-day EMA is more than 2% below the 60-day EMA, a sell signal is in force.
There were a few good signals, but also a number of whipsaws. Although much would depend on the exact entry and exit points, I believe that a profit could have been made using this system, but not a large profit and probably not enough to justify the risk. The stock failed to hold a trend and tight stop-losses would have been required to lock in profits. A trailing stop or use of the parabolic SAR might have helped lock in profits.
Moving average crossover systems can be effective, but should be used in conjunction with other aspects of technical analysis (patterns, candlesticks, momentum, volume, and so on). While it is easy to find a system that worked well in the past, there is no guarantee that it will work in the future.
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 楼主| 发表于 2009-3-15 12:45 | 显示全部楼层
[url=]Multicollinearity[/url]Multicollinearity is a statistical term for a problem that is common in technical analysis. That is, when one unknowingly uses the same type of information more than once. Analysts need to be careful and not utilize technical indicators that reveal the same type of information.
Here is how John Bollinger states it: "A cardinal rule for the successful use of technical analysis requires avoiding multicollinearity amid indicators. Multicollinearity is simply the multiple counting of the same information. The use of four different indicators all derived from the same series of closing prices to confirm each other is a perfect example."
The issue of multicollinearity is a serious issue in technical analysis when your money is at stake. It is a problem because collinear variables contribute redundant information and can cause other variables to appear to be less important than they really are. One of the real problems is that sometimes multicollinearity is difficult to spot.
Technical indicators should be arranged in categories to keep from using too many from the same category. Here is a table that categorizes the indicators available at StockCharts.com:
CategoryIndicators
MomentumRate of Change (ROC)
Stochastics (%K, %D)
Relative Strength Index (RSI)
Commodity Channel Index (CCI)
Williams %R (Wm%R)
StochRSI
TRIX
Ultimate Oscillator (ULT)
Aroon
TrendMoving Averages
Moving Average Convergence Divergence (MACD)
Average True Range (ATR)
Wilder's DMI (ADX)
Price Oscillator (PPO)
VolumeAccumulation Distribution
Chaikin Money Flow (CMF)
Volume Rate of Change
Volume Oscillator (PVO)
Demand Index
On Balance Volume (OBV)
Money Flow Index
The best way to quickly determine if an indicator is collinear with another one is to chart it. Make sure you have enough data on the chart to get a good indication. If they basically rise and fall in about the same areas, the odds are that they are collinear and you should just use one of them.
The first chart below shows some examples of indicators that are collinear. Notice that all three indicators are basically saying the same thing. If your analysis was that this was supportive information, you would be falling into the multicollinearity trap. Pick one of the indicators for your analysis and do not use the others.

Below are some examples of indicators that are not collinear. These three are not similar at all and, when interpreted correctly, each will give different information. It may be supportive or it may not.

Bottom Line: If you are randomly selecting indicators to support your analysis, you will more than likely fall into the multicollinearity trap of using multiple indicators that are all saying the same thing. They are not giving you any additional information; in fact, they are restricting your overall view of the market. Don't search for supporting information among collinear indicators, it is just misleading.
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