GPB/USD : SELL
| Entry | Stop Loss | Take Profit 1 | Take Profit 2 |
| 1.6140->1.3670 | 1.3230 | 1.6080 | 1.6000 |
Thứ Năm, 24 tháng 2, 2011
Thứ Tư, 23 tháng 2, 2011
Volume Weighted Average Price (VWAP)
Volume-Weighted Average Price (VWAP) is exactly what it sounds like: the average price weighted by volume. VWAP equals the dollar value of all trading periods divided by the total trading volume for the current day. Calculation starts when trading opens and ends when trading closes. Because it is good for the current trading day only, intraday periods and data are used in the calculation.
Tick versus Minute
Traditional VWAP is based on tick data. As one can imagine, there are many ticks (trades) during each minute of the day. Active securities during active time periods can have 20-30 ticks in one minute alone. With 390 minutes in a typical stock exchange trading day, many stocks end up with well over 5000 ticks per day. There are over 5000 stocks traded every day and these ticks start adding up exponentially. Needless to say, tick-data is very resource intensive.
Instead of VWAP based on tick data, StockCharts.com offers intraday VWAP based on intraday periods (1, 5, 10, 15, 30 or 60 minute). Note that VWAP is not defined for daily, weekly or monthly periods due to the nature of the calculation (see below).
Instead of VWAP based on tick data, StockCharts.com offers intraday VWAP based on intraday periods (1, 5, 10, 15, 30 or 60 minute). Note that VWAP is not defined for daily, weekly or monthly periods due to the nature of the calculation (see below).
Calculation
There are five steps involved in the VWAP calculation. First, compute the typical price for the intraday period. This is the average of the high, low and close {(H+L+C)/3)}. Second, multiply the typical price by the period's volume. Third, create a running total of these values. This is also known as a cumulative total. Fourth, create a running total of volume (cumulative volume). Fifth, divide the running total of price-volume by the running total of volume.
The example above shows 1-minute VWAP for the first 30 minutes of trading in IBM. Dividing cumulative price-volume by cumulative volume produces a price level that is adjusted (weighted) by volume. The first VWAP value is always the typical price because volume is equal in the numerator and the denominator. They cancel each other out in the first calculation. The chart below shows 1-minute bars with VWAP for IBM. Prices ranged from 127.36 on the high to 126.67 on the low for the first 30 minutes of trading. It was actually a pretty volatile first 30 minutes. VWAP ranged from 127.21 to 127.09 and spent its time in the middle of this range.
Cumulative(Volume x Typical Price)/Cumulative(Volume)
The example above shows 1-minute VWAP for the first 30 minutes of trading in IBM. Dividing cumulative price-volume by cumulative volume produces a price level that is adjusted (weighted) by volume. The first VWAP value is always the typical price because volume is equal in the numerator and the denominator. They cancel each other out in the first calculation. The chart below shows 1-minute bars with VWAP for IBM. Prices ranged from 127.36 on the high to 126.67 on the low for the first 30 minutes of trading. It was actually a pretty volatile first 30 minutes. VWAP ranged from 127.21 to 127.09 and spent its time in the middle of this range.
Characteristics
Like moving averages, VWAP lags price because it is an average based on past data. The more data there is, the greater the lag. A stock has been trading for some 331 minutes by 3PM. As a cumulative "average", this indicator is akin to a 330 period moving average. That is a lot of past data. The 1-minute VWAP value at the end of the day is often quite close to the ending value for a 390 minute moving average. Both moving averages are based on the 1 minute bars for that day. At the close, both are based on 390 minutes of data (one full day). One cannot compare the 390 minute moving average to VWAP during the day though. A 390 minute moving average at 12:00PM will include data from the previous day. VWAP will not. Remember, VWAP calculations start fresh at the open and end at the close. 150 minutes of trading have elapsed by 12:00PM. Therefore, VWAP at 12:00 would need to be compared with a 150 minute moving average.
Despite this lag, chartists can compare VWAP with the current price to determine the general direction of intraday prices. It works similar to a moving average. In general, intraday prices are falling when below VWAP and intraday prices are rising when above VWAP. VWAP will fall somewhere between the day's high-low range when prices are range bound for the day. The next three charts show examples of rising, falling and flat VWAP.
Despite this lag, chartists can compare VWAP with the current price to determine the general direction of intraday prices. It works similar to a moving average. In general, intraday prices are falling when below VWAP and intraday prices are rising when above VWAP. VWAP will fall somewhere between the day's high-low range when prices are range bound for the day. The next three charts show examples of rising, falling and flat VWAP.
Uses for VWAP
VWAP is used to identify liquidity points. As a volume-weighted price measure, VWAP reflects price levels weighted by volume. This can help institutions with large orders. The idea is not to disrupt the market when entering large buy or sell orders. VWAP helps these institutions determine the liquid and illiquid price points for a specific security over a very short time period.
VWAP can also be used to measure trading efficiency. After buying or selling a security, institutions or individuals can compare their price to VWAP values. A buy order executed below the VWAP value would be considered a good fill because the security was bought at a below average price. Conversely, a sell order executed above the VWAP would be deemed a good fill because it was sold at an above average price.
VWAP can also be used to measure trading efficiency. After buying or selling a security, institutions or individuals can compare their price to VWAP values. A buy order executed below the VWAP value would be considered a good fill because the security was bought at a below average price. Conversely, a sell order executed above the VWAP would be deemed a good fill because it was sold at an above average price.
Conclusions
VWAP serves as a reference point for prices for one day. As such, it is best suited for intraday analysis. Chartists can compare current prices with the VWAP values to determine the intraday trend. VWAP can also be used to determine relative value. Prices below VWAP values are relatively low for that day or specific time. Prices above VWAP values are relatively high for that day or specific time. Keep in mind that VWAP is a cumulative indicator, which means the number of data points progressively increases throughout the day. On a 1 minute chart, IBM will have 90 data points (minutes) by 11AM, 210 data points by 1PM and 390 data points by the close. The number dramatically increases as the day extends. This is why VWAP lags price and this lag increases as the day extends.
Thứ Ba, 22 tháng 2, 2011
The Three Skills of Top Trading: Behavioral Systems Building, Pattern Recognition, and Mental State Management
Hank Pruden "The Three Skills of Top Trading: Behavioral Systems Building, Pattern Recognition, and Mental State Management (Wiley Trading)"
Wiley (2007-04-06) | ISBN 0470050632 | 300 Pages | P
| 4.7 Mb
Wiley (2007-04-06) | ISBN 0470050632 | 300 Pages | P
| 4.7 Mb Praise for The Three Skills of Top Trading. "Professor Pruden's new book, The Three Skills of Top Trading, is unquestionably the best book on a specific trading method and the necessary attributes for trading that I have read. His logic, understanding of human foibles, and use of the Wyckoff method of trading are broadly referenced, readable, understandable, and entertaining."
- Charles D. Kirkpatrick, II, CMT, coauthor of Technical Analysis: The Complete Resource for Financial Market Technicians, Editor of the Journal of Technical Analysis, and board member of the Market Technicians Association
"At long last, someone has taken the time and effort to bring the work and insight of Wyckoff to wider public attention-and Hank Pruden has done so masterfully, with great clarity and eloquence. Hank has taken the best of Wyckoff's work, combining it with the essential aspects of trader discipline and psychology, to provide a highly readable and particularly useful guide to trading. MUST READING!"
- Jacob Bernstein, www.trade-futures.com
"Hank Pruden puts all of the elements needed for successful trading into one volume. This book not only belongs on every trader's shelf but should be close enough for continuous reference."
- Martin J. Pring, President, www.Pring.com
"Dr. Pruden has brought together his lifetime of work in developing a modern approach to analyzing and trading the markets built upon classic market analysis from the early part of the twentieth century and topped off with modern-day tenets of behavioral finance and mental state management."
- Thom Hartle, Director of Marketing for CQG, Inc. (www.cqg.com)
"I usually consider a book to be well worth reading if it gives me one paradigm shift. I believe that this book will give the average investor a lot more than just one."
- Van K. Tharp, PhD, President, Van Tharp Institute
Thứ Hai, 21 tháng 2, 2011
Force Index
The Force Index is an indicator that uses price and volume to assess the power behind a move or identify possible turning points. Developed by Alexander Elder, the Force Index was introduced in his classic book, Trading for a Living. According to Elder, there are three essential elements to a stock's price movement: direction, extent and volume. The Force Index combines all three as an oscillator that fluctuates in positive and negative territory as the balance of power shifts. The Force Index can be used to reinforce the overall trend, identify playable corrections or foreshadow reversals with divergences.
Calculation
Force Index(1) = {Close (current period) - Close (prior period)} x Volume
Force Index(13) = 13-period EMA of Force Index(1)
Three factors affect Force Index values. First, the Force Index is positive when the current close is above the prior close. The Force Index is negative when the current close is below the prior close. Second, the extent of the move determines the volume multiplier. Bigger moves warrant larger multipliers that influence the Force Index accordingly. Small moves produce small multipliers that reduce the influence. Third, volume plays a key role. A big move on big volume produces a high Force Index values. Small moves on low volume produce relatively low Force Index values. The table below shows the Force Index calculations for Pfizer (PFE). Line 27 marks the biggest move (+84 cents) and the biggest volume (162,619). This combination produces the biggest Force Index value on the table (136,600).
The chart above shows the Force Index in action. Notice how the 1-period Force Index fluctuates above/below the zero line and looks quite jagged. Elder recommends smoothing the indicator with a 13-period EMA to reduce the positive-negative crossovers. Chartists should experiment with different smoothing periods to determine what best suits their analytical needs.
Interpretation
As noted above, there are three elements to the Force Index. First, there is either a positive or negative price change. A positive price change signals that buyers were stronger than sellers, while a negative price change signals that sellers were stronger than buyers. Second, there is the extent of the price change, which is simply the current close less the prior close. The "extent" shows us just how far prices moved. A big advance shows strong buying pressure, while a big decline shows strong selling pressure. The third and final element is volume, which, according to Elder, measures commitment. Just how committed are the buyers and sellers? A big advance on heavy volume shows a strong commitment from buyers. Likewise, a big decline on heavy volume shows a strong commitment from sellers. The Force Index quantifies these three elements into one indicator that measures buying and selling pressure.
Trend Identification
The Force Index can be used to reinforce or determine the trend. The trend in question, short-term, medium-term or long-term, depends on the Force Index parameters. While the default Force Index parameter is 13, chartists can use a higher number for more smoothing or a lower number for less smoothing. The chart below shows Home Depot with a 100-day Force Index and a 13-day Force Index. Notice how the 13-day Force Index is more volatile and jagged. The 100-day Force Index is smoother and crosses the zero line fewer times. In this regard, the 100-day Force Index can be used to determine the medium or long-term trend. Notice how a resistance breakout on the price chart corresponds to a resistance breakout on the 100-day Force Index. The 100-day Force Index moved into positive territory and broke resistance in mid February. The indicator remained positive during the entire uptrend and turned negative in mid May. The early June support break on the price chart was confirmed with a support break in the Force Index.
Divergences
Bullish and bearish divergence can alert chartists of a potential trend change. Divergences are classic signals associate with oscillators. A bullish divergence forms when the indicator moves higher as the security moves lower. The indicator is not confirming weakness in price and this can foreshadow a bullish trend reversal. A bearish divergence forms when the indicator moves lower as the security moves higher. Even though the security is moving higher, the indicator shows underlying weakness by moving lower. This discrepancy can foreshadow a bearish trend reversal.
Confirmation is an important part of bullish and bearish divergences. Even though the divergences signal something is amiss, confirmation from the indicator or price chart is needed. A bullish divergence can be confirmed with the Force Index moving into positive territory or a resistance breakout on the price chart. A bearish divergence can be confirmed with the Force Index moving into negative territory or a support break on the price chart. Chartists can also use candlesticks, moving average crosses, pattern breaks and other forms of technical analysis for confirmation.
The chart above shows Best Buy (BBY) with the Force Index (39) sporting a series of divergences. The green lines show bullish divergences, while the red lines show bearish divergences. A bullish divergence is confirmed when the Force Index (39) crosses into positive territory (green dotted lines). A bearish divergence is confirmed when the Force Index (39) crosses into negative territory (red dotted lines). Chartists can also use trendline breaks on the price chart for confirmation.
This chart shows two versions of the Force Index. The Force Index (13) captures short-term fluctuations and is more sensitive. The Force Index (39) captures medium-term fluctuations and is smoother. The 39-day Force Index produces fewer zero line crossovers and these crossovers last longer. There is no right or wrong answer for these settings. It depends on trading objectives, time horizon and analytical style.
Confirmation is an important part of bullish and bearish divergences. Even though the divergences signal something is amiss, confirmation from the indicator or price chart is needed. A bullish divergence can be confirmed with the Force Index moving into positive territory or a resistance breakout on the price chart. A bearish divergence can be confirmed with the Force Index moving into negative territory or a support break on the price chart. Chartists can also use candlesticks, moving average crosses, pattern breaks and other forms of technical analysis for confirmation.
The chart above shows Best Buy (BBY) with the Force Index (39) sporting a series of divergences. The green lines show bullish divergences, while the red lines show bearish divergences. A bullish divergence is confirmed when the Force Index (39) crosses into positive territory (green dotted lines). A bearish divergence is confirmed when the Force Index (39) crosses into negative territory (red dotted lines). Chartists can also use trendline breaks on the price chart for confirmation.
This chart shows two versions of the Force Index. The Force Index (13) captures short-term fluctuations and is more sensitive. The Force Index (39) captures medium-term fluctuations and is smoother. The 39-day Force Index produces fewer zero line crossovers and these crossovers last longer. There is no right or wrong answer for these settings. It depends on trading objectives, time horizon and analytical style.
Identifying Corrections
The Force Index can be used in conjunction with a trend following indicator to identify short-term corrections within that trend. A pullback from overbought levels represents a short-term correction within an uptrend. An oversold bounce represents a short-term correction within a downtrend. Yes, corrections can be up or down, it depends on the direction of the bigger trend. Alexander Elder recommends using a 22-day EMA for trend identification and a 2-day Force Index to identify corrections. The trend is up when the 22-day EMA is moving higher, which means the 2-day Force Index would be used to identify short-term pullbacks for buying. The trend is down when the 22-day EMA is moving lower, which means the 2-day Force Index would be used to identify short-term bounces for selling. This is an aggressive strategy best suiting for active traders. The timeframe can be adjusted by using a longer moving average and timeframe for the Force Index. For example, medium-term traders might experiment with a 100-day EMA and 10-day Force Index.
There are two-schools of thought regarding the correction play. Traders can either act as soon as the correction is evident or act when there is evidence the correction has ended. Let's look at an example with the 22-day EMA and 2-day Force Index. Keep in mind that this is designed to identify very short corrections within a bigger trend. The chart below shows Texas Instruments (TXN) with the 22-day EMA turning up in mid September.
With the 22-day EMA rising, traders are looking for very short-term pullbacks when the 2-day Force Index turns negative. Traders can act when the Force Index turns negative or wait for it to move back into positive territory. Acting when negative may improve the reward-to-risk ratio, but the correction could extend a few more days. Waiting for the Force Index to turn positive again shows some strength that could signal the correction has ended. The green dotted lines show when the 2-day Force Index turns negative.
There are two-schools of thought regarding the correction play. Traders can either act as soon as the correction is evident or act when there is evidence the correction has ended. Let's look at an example with the 22-day EMA and 2-day Force Index. Keep in mind that this is designed to identify very short corrections within a bigger trend. The chart below shows Texas Instruments (TXN) with the 22-day EMA turning up in mid September.
With the 22-day EMA rising, traders are looking for very short-term pullbacks when the 2-day Force Index turns negative. Traders can act when the Force Index turns negative or wait for it to move back into positive territory. Acting when negative may improve the reward-to-risk ratio, but the correction could extend a few more days. Waiting for the Force Index to turn positive again shows some strength that could signal the correction has ended. The green dotted lines show when the 2-day Force Index turns negative.
Conclusions
The Force Index is uses both price and volume to measure buying and selling pressure. The price portion covers the trend, while the volume portion determines the intensity. At its most basic, chartists can use a long-term Force Index to confirm the underlying trend. The bulls have the edge when the 100-day Force Index is positive. The bears have the edge when the 100-day Force Index is negative. Armed with this information, traders can then look for short-term setups in harmony with the larger trend, such as bullish setups in a larger uptrend or bearish setups within a larger downtrend. As with all indicators, traders should use the Force Index in conjunction with other indicators and analysis techniques.
Chủ Nhật, 20 tháng 2, 2011
Candlesticks, Fibonacci, and Chart Pattern Trading Tools: A Synergistic Strategy to Enhance Profits and Reduce Risk by Robert Fischer [MF]
Publisher: Wiley (August 14, 2003) | ISBN-10: 0471448613 | ISBN-13: 978-0471448617 | 256 Pages | File type: PDF | 2 mb
An in-depth examination of a powerful new trading strategy
"Fischer provides an intriguing and thorough look at blending the Fibonacci series, candlesticks, and 3-point chart patterns to trade securities. Backed by explicit trading rules and numerous examples and illustrations, this book is an invaluable tool for the serious investor. Read it."
–Thomas N. Bulkowski author of Encyclopedia of Chart Patterns and Trading Classic Chart Patterns
In this groundbreaking new book, Fibonacci expert Robert Fischer and coauthor Dr. Jens Fischer successfully merge Fibonacci applications with candlestick charting to create an innovative trading strategy that will help you enhance profits and reduce risk.
Filled with in-depth insights, helpful charts and graphs, and practical real-world examples, Candlesticks, Fibonacci, and Chart Pattern Trading Tools reveals how correctly combining these different strategies can give you a noticeable edge in challenging market times–regardless of whether you are a short-term or long-term trader–and improve your chances of success under a variety of market conditions.
You’ll be introduced to the critical aspects of this synergistic approach through in-depth analysis and detailed explanations of:
Core combinations of Fibonacci trading tools with candlesticks and regular 3-point chart patterns
The magic figure "three" and its relevance in pattern recognition
Fibonacci-related trading strategies, selected candlestick chart patterns, and regular 3-point chart patterns
Applications of these trading strategies–double tops, Fibonacci price extensions, PHI-channel applications
PHI-ellipses as trading tools
And much more
From the Inside Flap
If you’re unhappy with the performance of your investment advisors and their choices, it is time to take control of your own trading decisions, and Candlesticks, Fibonacci, and Chart Pattern Trading Tools will provide you with the analytical tools and advice to do just that. Candlestick charting, Fibonacci applications, and 3-point Chart Pattern analysis are three of the most popular technical tools used by stock, options, and futures traders. By merging the three techniques, Fibonacci expert Robert Fischer, along with his son Dr. Jens Fischer, have created a new, cutting-edge trading strategy that will help you enhance profits and reduce risk.
No matter how good a trading approach may be, if you don’t have the proper understanding of yourself as well as other traders, you’ll never be successful. That’s why Candlesticks, Fibonacci, and Chart Pattern Trading Tools opens with a discussion of some basic principles of trading psychology and investor behavior–including ego, discipline, and patience. From here, you’ll be introduced to the heart of this synergistic approach through in-depth analysis and detailed explanations of: Download here
Thứ Bảy, 19 tháng 2, 2011
Standard Deviation (Volatility)
Standard deviation is a statistical term that measures the amount of variability or dispersion around an average. Standard deviation is also a measure of volatility. Generally speaking, dispersion is the difference between the actual value and the average value. The larger this dispersion or variability is, the higher the standard deviation. The smaller this dispersion or variability is, the lower the standard deviation. Chartists can use the Standard Deviation to measure expected risk and determine the significance of certain price movements.
Calculation
Stockcharts.com calculates the Standard Deviation for a Population, which assumes that the periods involved represent the whole data set, not a sample from a bigger data set. The calculation steps are as follows:
The spreadsheet above shows an example for a 10-period Standard Deviation using QQQQ data. Notice that the 10-period average is calculated after the 10th period and this average is applied to all 10 periods. Building a running standard deviation with this formula would be quite intensive. Excel has an easier way with the STDEVP formula. The table below shows the 10-period Standard Deviation using this formula. Here's an Excel Spreadsheet that shows the Standard Deviation calculations.
- Calculate the average (mean) price for the number of periods or observations.
- Determine each period's deviation (close less average price).
- Square each period's deviation.
- Sum the squared deviations.
- Divide this sum by the number of observations.
- The standard deviation is then equal to the square root of that number.
The spreadsheet above shows an example for a 10-period Standard Deviation using QQQQ data. Notice that the 10-period average is calculated after the 10th period and this average is applied to all 10 periods. Building a running standard deviation with this formula would be quite intensive. Excel has an easier way with the STDEVP formula. The table below shows the 10-period Standard Deviation using this formula. Here's an Excel Spreadsheet that shows the Standard Deviation calculations.
Standard Deviation Values
Standard Deviation values are dependent on the price of the under security. Securities with high prices, such as Google (±550), will have higher Standard Deviation values than securities with low prices, such as Intel (±22). These higher values are not a reflection of higher volatility, but rather a reflection of the actual price. Standard Deviation values are shown in terms that relate directly to the price of the underlying security. Historical Standard Deviation values will also be affected if a security experiences a large price change over a period of time. A security that moves from 10 to 50 will most likely have a higher Standard Deviation at 50 than at 10.
On the chart above, the left scale relates to the Standard Deviation. Google's Standard Deviation scale extends from 2.5 to 35, while the Intel range runs from .10 to .75. Average price changes (deviations) in Google range from $2.5 to $35, while average price changes (deviations) in Intel range from 10 cents to 75 cents.
Despite the range differences, chartists can visually assess volatility changes for each security. Volatility in Intel picked up from April to June as the Standard Deviation moved above .70 numerous times. Google experienced a surge in volatility in October as the Standard Deviation shot above 30. One would have to divide the Standard Deviation by the closing price to directly compare volatility for the two securities.
On the chart above, the left scale relates to the Standard Deviation. Google's Standard Deviation scale extends from 2.5 to 35, while the Intel range runs from .10 to .75. Average price changes (deviations) in Google range from $2.5 to $35, while average price changes (deviations) in Intel range from 10 cents to 75 cents.
Despite the range differences, chartists can visually assess volatility changes for each security. Volatility in Intel picked up from April to June as the Standard Deviation moved above .70 numerous times. Google experienced a surge in volatility in October as the Standard Deviation shot above 30. One would have to divide the Standard Deviation by the closing price to directly compare volatility for the two securities.
Measuring Expectations
The current value of the Standard Deviation can be used to estimate the importance of a move or set expectations. This assumes that price changes are normally distributed with a classic bell curve. Even though price changes for securities are not always normally distributed, chartists can still use normal distribution guidelines to gauge the significance of a price movement. In a normal distribution, 68% of the observations fall within one standard deviation. 95% of the observations fall within two standard deviations. 99.7% of the observations fall within three standard deviations. Using these guidelines, traders can estimate the significance of a price movement. A move greater than one standard deviation would show above average strength or weakness, depending on the direction of the move.
The chart above shows Microsoft (MSFT) with a 21-day Standard Deviation in the indicator window. There are around 21 trading days in a month and the monthly Standard Deviation was .88 on the last day. In a normal distribution, 68% of the 21 observations should show a price change less than 88 cents. 95% of the 21 observations should show a price change of less than 1.76 cents (2 x .88 or two standard deviations). 99.7% of the observations should show a price change of less than 2.64 (3 x .88 or three standard deviations. Price movements that were 1,2 or 3 standard deviations would be deemed noteworthy.
The 21-day Standard Deviation is still quite variable as it fluctuated between .32 and .88 from mid August until mid December. A 250-day moving average can be applied to smooth the indicator and find an average, which is around 68 cents. Price moves larger than 68 cents were greater than the 250-day SMA of the 23-day Standard Deviation. These above average price movements indicate heightened interest that could foreshadow a trend change or mark a breakout.
The chart above shows Microsoft (MSFT) with a 21-day Standard Deviation in the indicator window. There are around 21 trading days in a month and the monthly Standard Deviation was .88 on the last day. In a normal distribution, 68% of the 21 observations should show a price change less than 88 cents. 95% of the 21 observations should show a price change of less than 1.76 cents (2 x .88 or two standard deviations). 99.7% of the observations should show a price change of less than 2.64 (3 x .88 or three standard deviations. Price movements that were 1,2 or 3 standard deviations would be deemed noteworthy.
The 21-day Standard Deviation is still quite variable as it fluctuated between .32 and .88 from mid August until mid December. A 250-day moving average can be applied to smooth the indicator and find an average, which is around 68 cents. Price moves larger than 68 cents were greater than the 250-day SMA of the 23-day Standard Deviation. These above average price movements indicate heightened interest that could foreshadow a trend change or mark a breakout.
Conclusions
The Standard Deviation is a statistical measure of volatility. These values provide chartists with an estimate for expected price movements. Price moves greater than the Standard Deviation show above average strength or weakness. The Standard Deviation is also used with other indicators, such as Bollinger Bands. These bands are set 2 standard deviations above and below a moving average. Moves that exceed the bands are deemed significant enough to warrant attention. As with all indicators, the Standard Deviation should be used in conjunction with other analysis tools, such as momentum oscillators or chart patterns.
Thứ Năm, 17 tháng 2, 2011
Signals [18/2/2011]
GPB/USD : BUY
| Entry | Stop Loss | Take Profit 1 | Take Profit 2 |
| 1.6140->1.6160 | 1.6120 | 1.6230 | 1.6270 |
The Complete Practitioner's Guide to the Bond Market
The Complete Practitioner's Guide to the Bond Market
Publisher: McGraw-Hill | pages: 464 | 2009 | ISBN: 0071637141 | P | 2,4 mb
Publisher: McGraw-Hill | pages: 464 | 2009 | ISBN: 0071637141 | P
| 2,4 mbSpeaking directly to the practitioner, this thorough guide covers everything there is to know about bonds—from basic concepts to more advanced bond topics. The Complete Practitioner’s Guide to the Bond Market addresses the principles of the bond market and offers the tools to apply them in the real world. By tying the concepts of fixed-income products to big-picture aspects of the economy, this book prepares readers to apply specific tools and methods that will help them glean profits from the bond market.
Download Here
Percentage Price Oscillator
The Percentage Price Oscillator (PPO) is a momentum oscillator that measures the difference between two moving averages as a percentage of the larger moving average. As with its cousin, MACD, the Percentage Price Oscillator is shown with a signal line, a histogram and a centerline. Signals are generated with signal line crossovers, centerline crossovers and divergences. Because these signals are no different than those associated with MACD, this article will focus on a few differences between the two. First, PPO readings are not subject to the price level of the security. Second, PPO readings for different securities can be compared, even when there are large differences in the price. See the ChartSchool article on MACD for information on signals common to both MACD and PPO.
Calculation
Percentage Price Oscillator (PPO): {(12-day EMA - 26-day EMA)/26-day EMA} x 100
Signal Line: 9-day EMA of PPO
PPO Histogram: PPO - Signal Line
Click here for download this spreadsheet example.
Standard PPO is based on the 12-day Exponential Moving Average (EMA) and the 26-day EMA, but these parameters can be changed according to investor or trader preferences. Closing prices are used to calculate the moving averages so PPO signals should be measured against closing prices. A 9-day EMA of PPO is plotted as a signal line to identify upturns and downturns in the indicator.
Interpretation
As with MACD, the PPO reflects the convergence and divergence of two moving averages. PPO is positive when the shorter moving average is above the longer moving average. The indicator moves further into positive territory as the shorter moving average distances itself from the longer moving average. This reflects strong upside momentum. The PPO is negative when the shorter moving average is below the longer moving average. Negative readings grow when the shorter moving average distances itself from the longer moving average (goes further negative). This reflects strong downside momentum. The histogram represents the difference between PPO and its 9-day EMA, the signal line. The histogram is positive when PPO is above its 9-day EMA and negative when PPO is below its 9-day EMA. The PPO-Histogram can be used to anticipate signal line crossovers in the PPO. See the ChartSchool article on the MACD Histogram for signal details.
MACD, PPO and Price
MACD levels are affected by the price of a security. A high priced security will have higher or lower MACD values than a low priced security, even if volatility is basically equal. This is because MACD is based on the absolute difference in the two moving averages. Chart 2 shows Google with MACD and PPO for comparative purposes. The 12-day EMA is around 495, the 26-day EMA is around 512 and the difference is -17 (double digits). Notice that Google's MACD reached double digits on the upside and the downside, but the Percentage Price Oscillator ranged from +2.5 to -3.5. MACD values appear higher because Google is priced at a relatively high level. MACD for the Dow Industrials, which is above 10,000, hits triple digits on a regular basis. However, the PPO ranges from -2 to +2, which is a much more definable range.
Although the indicator lines look the same, there are often subtle differences between MACD and PPO. In the Google example, notice how the PPO broke below the February low, but MACD has yet to break its February low. The lower low in the PPO shows expanding downside momentum.
Although the indicator lines look the same, there are often subtle differences between MACD and PPO. In the Google example, notice how the PPO broke below the February low, but MACD has yet to break its February low. The lower low in the PPO shows expanding downside momentum.
Large Price Changes
Because MACD is based on absolute levels, large price changes can affect MACD levels over an extended period of time. If a stock advances from 20 to 100, its MACD levels will be considerably smaller around 20 than around 100. The PPO solves this problem by showing MACD values in percentage terms. Chart 3 shows Baidu (BIDU) advancing from 25 to 75 over a 12 month period. MACD values around 25-30 are going to be generally smaller than MACD values around 70-80. Notice that MACD broke above its July and March highs, but the PPO did not break these corresponding highs. Also note that Baidu becomes overbought when the PPO exceeds +5.
Comparing Different Securities
Because the Percentage Price Oscillator (PPO) is a percentage version of MACD, its values can be compared against other securities. Dell (DELL) and Hewlett Packard (HPQ) are in the same industry group, but their stock prices are at different levels. As of late May 2010, DELL was trading in the high teens and HPQ was trading in the mid 40s. The absolute price level has nothing to do with fundamentals, but it does affect the level of MACD. HPQ will no doubt have a higher MACD than DELL. However, we can apply the Percentage Price Oscillator (PPO) to compare momentum. First, notice that the PPO for DELL ranges from -4 to +4 for an 8 point range). The PPO for HPQ ranges from -3 to +2 for a range of 5. Right off the bat we can see that DELL is more volatile than HPQ because its PPO range is greater. Second, we can see that upside momentum for DELL was stronger than HPQ in March-April. The PPO for DELL advanced from negative territory and exceeded 4. The PPO for HPQ turned positive before the PPO for DELL, but did not exceed 1.6.
Conclusions
The Percentage Price Oscillator (PPO) generates the same signals at MACD, but provides an added dimension as a percentage version of MACD. The PPO levels of the Dow Industrials (price ~11000) can be compared against the PPO levels of IBM (price ~122) because the PPO levels the playing field so to speak. In addition, PPO levels in one security can be compared over extended periods of time, even if the price has doubled or tripled. This is not the case for MACD. Despite its advantages, PPO is still not the best oscillator to identify overbought or oversold conditions because movements are unlimited (in theory). Levels for RSI and the Stochastic Oscillator are limited and this makes them better suited to identify overbought and oversold levels.
Thứ Tư, 16 tháng 2, 2011
Free forex bonus [no deposit required - 2011]
FBS - $5 account - no deposit required
ForexCent - $5 account - no deposit required
FXOptimax - $10 account - no deposit required
GIGFX - $25 account - no deposit required
Marketiva - $5 account - no deposit required
NordFX - $8 account - no deposit required [You must chose “Welcome!”-account! to get this bonus]
Plus500 - $25 account - no deposit required
RoboForex - $5 account - no deposit required
RVMarkets - Safe - 100 cents bonus; Start - $5 account - no deposit required
Rate of Change (ROC)
The Rate-of-Change (ROC) indicator, which is also referred to as simply Momentum, is a pure 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. The plot forms an oscillator that fluctuates above and below the zero line as the Rate-of-Change moves from positive to negative. As a momentum oscillator, ROC signals include centerline crossovers, divergences and overbought-oversold readings. Divergences fail to foreshadow reversals more often than not so this article will forgo a discussion on divergences. Even though centerline crossovers are prone to whipsaw, especially short-term, these crossovers can be used to identify the overall trend. Identifying overbought or oversold extremes comes natural to the Rate-of-Change oscillator.
Calculation
ROC = [(Close - Close n periods ago) / (Close n periods ago)] * 100
The table above shows the 12-day Rate-of-Change calculations for the Dow Industrials in May 2010. The yellow cells show the Rate-of-Change from April 28th to May 14th. It is actually 13 trading days, but the close on the 28th acts as the starting point on the 29th. The blue cells show the 12-day Rate-of-Change from May 7th until May 25th.
Interpretation
As noted above, the Rate-of-Change indicator is momentum in its purest form. It measures the percentage increase or decrease in price over a given period of time. Think of its as the rise (price change) over the run (time). In general, prices are rising as long as the Rate-of-Change remains positive. Conversely, prices are falling when the Rate-of-Change is negative. ROC expands into positive territory as an advance accelerates. ROC dives deeper into negative territory as a decline accelerates. There is no upward boundary on the Rate-of-Change. The sky is the limit for an advance. There is, however, a downside limit. Securities can only decline 100%, which would be to zero. Even with these lopsided boundaries, Rate-of-Change produces identifiable extremes that signal overbought and oversold conditions.
Trend Identification
Even though momentum oscillators are best suited for trading ranges or zigzag trends, they can also be used to define the overall direction of the underlying trend. There are approximately 250 trading days in a year. This can be broken down into 125 days per half year, 63 days per quarter and 21 days per month. A trend reversal starts with the shortest timeframe and gradually spreads to the other timeframes. In general, the long-term trend is up when both the 250-day and 125-day Rate-of-Change are positive. This means that prices are higher now than they were 12 and 6 months ago. Long positions taken 6 or 12 months ago would be profitable and buyers would be happy.
Chart 2 shows IBM with the 250-day, 125-day, 63-day and 21-day Rate-of-Change. There have been three big trends in the last three years. The first was up as the 250-day Rate-of-Change was largely positive until September 2008 (1). The second was down as the indicator turned negative from October 2008 until September 2009 (2). The third is up as the indicator turned positive in late September 2009 (3). Even though the big uptrend remains in force, IBM flattened out on the price chart and this affected the 125-day and 63-day Rate-of-Change. The 63-day Rate-of-Change (quarterly) has been flirting with negative territory since February (4). The 125-day Rate-of-Change (six month) dipped into negative territory for the first time since April 2009 (5). This shows some deterioration in IBM that serves as an alert to watch the stock carefully. A break below the six month trading range would be a bearish development (6).
Chart 2 shows IBM with the 250-day, 125-day, 63-day and 21-day Rate-of-Change. There have been three big trends in the last three years. The first was up as the 250-day Rate-of-Change was largely positive until September 2008 (1). The second was down as the indicator turned negative from October 2008 until September 2009 (2). The third is up as the indicator turned positive in late September 2009 (3). Even though the big uptrend remains in force, IBM flattened out on the price chart and this affected the 125-day and 63-day Rate-of-Change. The 63-day Rate-of-Change (quarterly) has been flirting with negative territory since February (4). The 125-day Rate-of-Change (six month) dipped into negative territory for the first time since April 2009 (5). This shows some deterioration in IBM that serves as an alert to watch the stock carefully. A break below the six month trading range would be a bearish development (6).
Overbought/Oversold Extremes
There are basically three price movements: up, down and sideways. Momentum oscillators are ideally suited for sideways price action with regular fluctuations. This makes it easier to identify extremes and forecast turning points. Security prices can also fluctuate when trending. For example, an uptrend consists of a series of higher highs and higher lows as prices zigzag higher. Pullbacks often occur at regular intervals based on the percentage move, time elapsed or both. A downtrend consists of lower lows and lower highs as prices zigzag lower. Counter trend advances retrace a portion of the prior decline and usually peak below the prior high. Peaks can occur at regular intervals based on the percentage move, time elapsed or both. The Rate-of-Change can be used to identify periods when the percentage change nears a level that foreshadowed a turning point in the past.
Chart 3 shows Aetna (AET) with an uptrend from April 2009 until April 2010. Notice how the stock zigzagged up with a series of higher highs and higher lows. Because the overall trend was up, the Rate-of-Change indicator was used to identify short-term oversold levels as a chance to partake in the bigger uptrend. Short-term overbought signals were ignored because the bigger trend was up. Based on the May-June bounces, -10% was set as the oversold boundary. Movements below this level indicated that prices were at a short-term extreme. Overbought and oversold settings depend on the volatility of the underlying security. A more volatile stock may use -15% for oversold, while a less volatile stock may use -5%. Oversold readings serve as an alert to be ready for a turning point. Prices are oversold, but have yet to actually turn. Remember, a security can become oversold and remain oversold as the decline continues. A 20-day moving average was overlaid to identify an actual upturn. After ROC became oversold in early October, AET moved above its 20-day SMA in late October to confirm an upturn (1). The second oversold reading occurred in early February and AET moved above its 20-day SMA in late February (2).
Chart 4 shows Microsoft (MSFT) in a downtrend from November 2007 until March 2009. This example uses a 20-day Rate-of-Change to identify oversold levels within a bigger downtrend. The number of time periods depends on the individual security and the desired trading timeframe. The late December high occurred with an overbought reading above +10%. This means Microsoft was up over 10% in a 20-day period, which is about a month. That's a pretty good bounce within a bigger downtrend. The next overbought reading did not occur until April when the Rate-of-Change again exceeded +10%. MSFT broke trendline support in May to signal a continuation of the downtrend. The next overbought reading occurred in early August 2008. It took a while, but the stock eventually broke support at 24 in mid September and again in early October.
Chart 5 shows Abercrombie & Fitch (ANF) within a trading range from October 2006 to February 2008. The 20-day Rate-of-Change indicator sets overbought at +10% and oversold at -10%. The overbought and oversold levels identify extremes quite well, but timing the actual turn is more difficult because of the volatility. The next chart reduces this volatility by using a exponential moving average in place of the price plot.
Chart 6 shows ANF as a 10-day EMA (black) and the actual price plot is invisible. A 30-day EMA has been overlaid as a signal line. Furthermore, the 20-day Rate-of-Change is shown with a 5-day SMA to smooth out the fluctuations. There are fewer overbought and oversold readings using the 5-day SMA. Focusing only on the buy signals, the green dotted line shows when ROC exceeds -10% and the green arrow shows when the 10-day EMA crosses above the 30-day SMA. The oversold readings are usually early, but the moving average crossovers are usually late. Such is life with technical analysis. The point here is to reduce whipsaws by smoothing the data. SharpCharts subscribers can click the chart to see and save the settings. A 10-day EMA was used because it is faster than a 10-day SMA. A 30-day SMA was used because it is slower than a 30-day EMA. Speeding up the shorter moving average and slowing down the longer moving average makes for slightly quicker signals.
Chart 3 shows Aetna (AET) with an uptrend from April 2009 until April 2010. Notice how the stock zigzagged up with a series of higher highs and higher lows. Because the overall trend was up, the Rate-of-Change indicator was used to identify short-term oversold levels as a chance to partake in the bigger uptrend. Short-term overbought signals were ignored because the bigger trend was up. Based on the May-June bounces, -10% was set as the oversold boundary. Movements below this level indicated that prices were at a short-term extreme. Overbought and oversold settings depend on the volatility of the underlying security. A more volatile stock may use -15% for oversold, while a less volatile stock may use -5%. Oversold readings serve as an alert to be ready for a turning point. Prices are oversold, but have yet to actually turn. Remember, a security can become oversold and remain oversold as the decline continues. A 20-day moving average was overlaid to identify an actual upturn. After ROC became oversold in early October, AET moved above its 20-day SMA in late October to confirm an upturn (1). The second oversold reading occurred in early February and AET moved above its 20-day SMA in late February (2).
Chart 4 shows Microsoft (MSFT) in a downtrend from November 2007 until March 2009. This example uses a 20-day Rate-of-Change to identify oversold levels within a bigger downtrend. The number of time periods depends on the individual security and the desired trading timeframe. The late December high occurred with an overbought reading above +10%. This means Microsoft was up over 10% in a 20-day period, which is about a month. That's a pretty good bounce within a bigger downtrend. The next overbought reading did not occur until April when the Rate-of-Change again exceeded +10%. MSFT broke trendline support in May to signal a continuation of the downtrend. The next overbought reading occurred in early August 2008. It took a while, but the stock eventually broke support at 24 in mid September and again in early October.
Chart 5 shows Abercrombie & Fitch (ANF) within a trading range from October 2006 to February 2008. The 20-day Rate-of-Change indicator sets overbought at +10% and oversold at -10%. The overbought and oversold levels identify extremes quite well, but timing the actual turn is more difficult because of the volatility. The next chart reduces this volatility by using a exponential moving average in place of the price plot.
Chart 6 shows ANF as a 10-day EMA (black) and the actual price plot is invisible. A 30-day EMA has been overlaid as a signal line. Furthermore, the 20-day Rate-of-Change is shown with a 5-day SMA to smooth out the fluctuations. There are fewer overbought and oversold readings using the 5-day SMA. Focusing only on the buy signals, the green dotted line shows when ROC exceeds -10% and the green arrow shows when the 10-day EMA crosses above the 30-day SMA. The oversold readings are usually early, but the moving average crossovers are usually late. Such is life with technical analysis. The point here is to reduce whipsaws by smoothing the data. SharpCharts subscribers can click the chart to see and save the settings. A 10-day EMA was used because it is faster than a 10-day SMA. A 30-day SMA was used because it is slower than a 30-day EMA. Speeding up the shorter moving average and slowing down the longer moving average makes for slightly quicker signals.
Conclusions
The Rate-of-Change oscillator measures the speed at which prices are changing. An upward surge in the Rate-of-Change reflects a sharp price advance. A downward plunge indicates a steep price decline. Even though chartists can look for bullish and bearish divergences, these formations can be misleading because of sharp moves. Sustained advances often start with a big surge out of the gate. Subsequent advances are usually less sharp and this causes a bearish divergence to form in the Rate-of-Change oscillator. It is important to remember that prices are constantly increasing as long as the Rate-of-Change remains positive. Positive readings may be less than before, but a positive Rate-of-Change still reflects a price increase, not a price decline. Like all technical indicator, the Rate-of-Change oscillator should be used in conjunction with other aspects of technical analysis.
Chủ Nhật, 13 tháng 2, 2011
On Balance Volume [OBV]
On Balance Volume (OBV) measures buying and selling pressure as a cumulative indicator that adds volume on up days and subtracts volume on down days. OBV was developed by Joe Granville and introduced in his 1963 book, Granville's New Key to Stock Market Profits. It was one of the first indicators to measure positive and negative volume flow. Chartists can look for divergences between OBV and price to predict price movements or use OBV to confirm price trends.
Calculation
The On Balance Volume (OBV) line is simply a running total of positive and negative volume. A period's volume is positive when the close is above the prior close. A period's volume is negative when the close is below the prior close.
Data in the table above comes from Wal-mart (WMT). Volume figure were rounded off and are shown in 1000's. In other words, 8,200 really equals 8,200,000 or 8.2 million shares. First, we must determine if Wal-mart closed up (+1) or down (-1). This number is now used as the volume multiplier to compute positive or negative volume. The last column (OBV) forms the running total for positive/negative volume. Because OBV has to start somewhere, the first value (8200) is simply equal to first period's positive/negative volume. The chart below shows Wal-mart with volume and OBV.
If the closing price is above the prior close price then:
Current OBV = Yesterday's OBV + Current Volume
If the closing price is below the prior close price then:
Current OBV = Yesterday's OBV - Current Volume
If the closing prices equals yesterday's closing price then:
Current OBV = Yesterday's OBV (no change)
Data in the table above comes from Wal-mart (WMT). Volume figure were rounded off and are shown in 1000's. In other words, 8,200 really equals 8,200,000 or 8.2 million shares. First, we must determine if Wal-mart closed up (+1) or down (-1). This number is now used as the volume multiplier to compute positive or negative volume. The last column (OBV) forms the running total for positive/negative volume. Because OBV has to start somewhere, the first value (8200) is simply equal to first period's positive/negative volume. The chart below shows Wal-mart with volume and OBV.
Interpretation
Granville theorized that volume precedes price. OBV rises when volume on up days outpaces volume on down days. OBV falls when volume on down days is stronger. A rising OBV reflects positive volume pressure that can lead to higher prices. Conversely, falling OBV reflects negative volume pressure that can foreshadow lower prices. Granville noted in his research that OBV would often move before price. Expect prices to move higher if OBV is rising while prices are either flat or moving down. Expect prices to move lower if OBV is falling while prices are either flat or moving up.
The absolute value of OBV is not important. Chartists should instead focus on the characteristics of the OBV line. First define the trend for OBV. Second, determine if the current trend matches the trend for the underlying security. Third, look for potential support or resistance levels. Once broken, the trend for OBV will change and these breaks can be used to generate signals. Also notice that OBV is based on closing prices. Therefore, closing prices should be considered when looking for divergences or support/resistance breaks. And finally, volume spikes can sometimes throw off the indicator by causing a sharp move that will require a settling period.
The absolute value of OBV is not important. Chartists should instead focus on the characteristics of the OBV line. First define the trend for OBV. Second, determine if the current trend matches the trend for the underlying security. Third, look for potential support or resistance levels. Once broken, the trend for OBV will change and these breaks can be used to generate signals. Also notice that OBV is based on closing prices. Therefore, closing prices should be considered when looking for divergences or support/resistance breaks. And finally, volume spikes can sometimes throw off the indicator by causing a sharp move that will require a settling period.
Divergences
Bullish and bearish divergence signals can be used to anticipate a trend reversal. These signals are truly based on the theory that volume precedes prices. A bullish divergence forms when OBV moves higher or forms a higher low even as prices move lower or forge a lower low. A bearish divergence forms when OBV moves lower or forms a lower low even as prices move higher or forge a higher high. The divergence between OBV and price should alert chartists that a price reversal could be in the making.
The chart for Starbucks (SBUX) shows a bullish divergence forming in July. On the price chart, SBUX moved below its June low with a lower low in early July. OBV, on the other hand, held above its June low to form a bullish divergence. OBV went on to break resistance before SBUX broke resistance. This was a classic case of volume leading price. SBUX broke resistance a week later and continued above 20 for a 30+ percent gain. The second chart shows OBV moving higher as Texas Instruments (TXN) trades within a range. Rising OBV during a trading range indicates accumulation, which is bullish.
The chart for Medtronic (MDT) shows a bearish divergence with volume leading price lower. The blue dotted lines identify the divergence period. MDT moved higher (43 to 45) as OBV moved lower. Also notice that OBV broke support during this divergence period. The uptrend in OBV reversed with the break below the February low. MDT, on the other hand, was still moving higher. Volume ultimately won the day as MDT followed volume lower with a decline into the low 30s. The second chart shows Valero Energy (VLO) with OBV forming a bearish divergence in April and a confirming support break in May.
The chart for Starbucks (SBUX) shows a bullish divergence forming in July. On the price chart, SBUX moved below its June low with a lower low in early July. OBV, on the other hand, held above its June low to form a bullish divergence. OBV went on to break resistance before SBUX broke resistance. This was a classic case of volume leading price. SBUX broke resistance a week later and continued above 20 for a 30+ percent gain. The second chart shows OBV moving higher as Texas Instruments (TXN) trades within a range. Rising OBV during a trading range indicates accumulation, which is bullish.
The chart for Medtronic (MDT) shows a bearish divergence with volume leading price lower. The blue dotted lines identify the divergence period. MDT moved higher (43 to 45) as OBV moved lower. Also notice that OBV broke support during this divergence period. The uptrend in OBV reversed with the break below the February low. MDT, on the other hand, was still moving higher. Volume ultimately won the day as MDT followed volume lower with a decline into the low 30s. The second chart shows Valero Energy (VLO) with OBV forming a bearish divergence in April and a confirming support break in May.
Trend Confirmation
OBV can be used to confirm a price trend, upside breakout or downside break. The chart for Best Buy (BBY) shows three confirming signals as well as confirmation of the price trend. OBV and BBY moved lower in December-January, higher from March to April, lower from May to August and higher from September to October. The trends in OBV matched the trend in BBY.
OBV also confirmed trend reversals in BBY. Notice how BBY broke its down trendline in late February and OBV confirmed with a resistance breakout in March. BBY broke its up trendline in late April and OBV confirmed with a support break in early May. BBY broke its down trendline in early September and OBV confirmed with a trendline break a week later. These coincident signals indicated that positive and negative volume were in harmony with price.
Sometimes OBV moves step-for-step with the underlying security. In this case, OBV is confirming the strength of the underlying trend, be it down or up. The chart for Autozone (AZO) shows prices as a black line and OBV as a pink line. Both moved steadily higher from November 2009 until October 2010. Positive volume remained strong throughout the advance.
OBV also confirmed trend reversals in BBY. Notice how BBY broke its down trendline in late February and OBV confirmed with a resistance breakout in March. BBY broke its up trendline in late April and OBV confirmed with a support break in early May. BBY broke its down trendline in early September and OBV confirmed with a trendline break a week later. These coincident signals indicated that positive and negative volume were in harmony with price.
Sometimes OBV moves step-for-step with the underlying security. In this case, OBV is confirming the strength of the underlying trend, be it down or up. The chart for Autozone (AZO) shows prices as a black line and OBV as a pink line. Both moved steadily higher from November 2009 until October 2010. Positive volume remained strong throughout the advance.
Conclusions
On Balance Volume (OBV) is a simple indicator that uses volume and price to measure buying pressure and selling pressure. Buying pressure is evident when positive volume exceeds negative volume and the OBV line rises. Selling pressure is present when negative volume exceeds positive volume and the OBV line falls. Chartists can use OBV to confirm the underlying trend or look for divergences that may foreshadow a price change. As with all indicators, it is important to use OBV in conjunction with other aspects of technical analysis. It is not a stand alone indicator. OBV can be combined with basic pattern analysis or to confirm signals from momentum oscillators.
Thứ Bảy, 12 tháng 2, 2011
Dividend Stocks For Dummies
Lawrence Carrel, "Dividend Stocks For Dummies"
For Dummies | 2010 | ISBN: 0470466014 | 360 pages | P | 4,5 MB
For Dummies | 2010 | ISBN: 0470466014 | 360 pages | P
| 4,5 MBExpert advice on a mature, reliable way to invest money
According to Fortune magazine, investing in dividends is one of the top five ways to survive market instability. Dividend Stocks For Dummies gives you the expert information and advice you need to successfully add dividends to your investment portfolio, revealing how to make the most out of dividend stock investing-no matter the type of market.
* Explains the nuts and bolts of dividends, values, and returns
* Shows you how to effectively research companies, gauge growth and return, and the best way to manage a dividend portfolio
* Provides strategies for increasing dividend investments
Weather a down market-reach for Dividend Stocks for Dummies!
Expert advice on a mature, reliable way to invest money
Investing in dividend stocks is one of the top strategies to survive market instability. This hands–on guide gives you expert information and advice to successfully add dividend stocks to your investment portfolio, revealing how to make the most out of dividend stock investing — no matter the type of market.
Get the 411 — find out what dividend stocks are, how they benefit investors, and why they′re a good investment choice in today′s market climate Make a plan — determine your goals, implement a strategy, and discover what it takes to be a successful dividend investor Weigh risk and reward — learn how to minimize risk and use your level of risk tolerance to guide your choices Know which industries are "in" — get the inside scoop on industries that are ripe for dividend investment Divide and conquer — build and manage your portfolio, buy the stock you want, and keep up with dividend taxation
Open the book and find:
The advantages of utilizing a dividend strategy What makes a good dividend stock and how they′re calculated Important dates in the life of a dividend stock Popular styles of investing Ways to analyze a stock′s price–to–earnings ratio and yield Dividend reinvestment plans and direct purchase programs How to avoid dividend investment mistakes Experienced investment advisors to contact for help
Learn to:
Successfully add dividend stocks to your investment portfolio Effectively research companies Gauge risk, growth, and return Increase your dividend investments
Download Here
Thứ Sáu, 11 tháng 2, 2011
The Complete Guide to Online Stock Market Investing: The Definitive 20-Day Guide
The Complete Guide to Online Stock Market Investing: The Definitive 20-Day Guide
Alexander Davidson | Kogan Page | Pages:234 | 2007-03-01 | ISBN:0749447478 | P | 6.11 MB
The Complete Guide to Online Stock Market Investing provides all the information and techniques needed to make money as an online stock market investor. The strategies revealed are tried and tested. In 20 easy modules, readers will discover the secrets of buying bargain stocks and trading. Drawing on the author's most recent experience in the City (London's financial district), this latest edition of the classic guide shows how to: get the most from the broker, select value and growth stocks, read the charts, choose promising investment funds, trade derivatives for fast profit, deal foreign exchange, and manage your money and win.
Alexander Davidson | Kogan Page | Pages:234 | 2007-03-01 | ISBN:0749447478 | P
| 6.11 MBThe Complete Guide to Online Stock Market Investing provides all the information and techniques needed to make money as an online stock market investor. The strategies revealed are tried and tested. In 20 easy modules, readers will discover the secrets of buying bargain stocks and trading. Drawing on the author's most recent experience in the City (London's financial district), this latest edition of the classic guide shows how to: get the most from the broker, select value and growth stocks, read the charts, choose promising investment funds, trade derivatives for fast profit, deal foreign exchange, and manage your money and win.
Money Flow Index (MFI)
The Money Flow Index (MFI) is an oscillator that uses both price and volume to measure buying and selling pressure. Created by Gene Quong and Avrum Soudack, MFI is also known as volume-weighted RSI. MFI starts with the typical price for each period. Money flow is positive when the typical price rises (buying pressure) and negative when the typical price declines (selling pressure). A ratio of positive and negative money flow is then plugged into an RSI formula to create an oscillator that moves between zero and one hundred. As a momentum oscillator tied to volume, the Money Flow Index (MFI) is best suited to identify reversals and price extremes with a variety of signals.
Calculation
There are a several steps involved in the Money Flow Index calculation. The example below is based on a 14-period Money Flow Index, which is the default setting in SharpCharts and the setting recommended by the creators.
First, notice that Raw Money Flow (2) is essentially dollar volume. It is the volume multiplied by the typical price. Raw Money Flow turns into Positive Money Flow when the typical price advances from one period to the next. Raw Money Flow turns into Negative Money Flow when the typical price declines from one period to the next. The Money Flow Ratio in step 5 forms the basis for the Money Flow Index (MFI) as the0 RSI formula is applied to create a volume-weighted RSI. The table below shows a calculation example taken from an excel spreadsheet.
Click here for an MFI calculation in an Excel Spreadsheet.
* 1. Typical Price = (High + Low + Close)/3
* 2. Raw Money Flow = Typical Price x Volume
* 3. Positive Money Flow = Sum of positive Raw Money Flow over 14 periods.
* 4. Negative Money Flow = Sum of negative Raw Money Flow over 14 periods.
* 5. Money Flow Ratio = (Positive Money Flow)/(Negative Money Flow)
* 6. Money Flow Index = 100 - 100/(1 + Money Flow Ratio)
Click here for an MFI calculation in an Excel Spreadsheet.
Interpretation
As a volume-weighted version of RSI, the Money Flow Index (MFI) can be interpreted similar to RSI. The big difference is, of course, volume. Because volume is added to the mix, the Money Flow Index will act a little differently than RSI. Theories suggest that volume leads prices. RSI is a momentum oscillator that already leads prices. Incorporating volume can increase this lead time.
Quong and Soudack identified three basic signals using the Money Flow Index. First, chartists can look for overbought or oversold levels to warn of unsustainable price extremes. Second, bullish and bearish divergence can be used to anticipate trend reversals. Third, failure swings at 80 or 20 can also be used to identify potential price reversals. For this article, the divergences and failure swings are be combined to create one signal group and increase robustness.
Quong and Soudack identified three basic signals using the Money Flow Index. First, chartists can look for overbought or oversold levels to warn of unsustainable price extremes. Second, bullish and bearish divergence can be used to anticipate trend reversals. Third, failure swings at 80 or 20 can also be used to identify potential price reversals. For this article, the divergences and failure swings are be combined to create one signal group and increase robustness.
Overbought/Oversold
Overbought and oversold levels can be used to identify unsustainable price extremes. Typically, MFI above 80 is considered overbought and MFI below 20 is considered oversold. Strong trends can present a problem for these classic overbought and oversold levels. MFI can become overbought (>80) and prices can simply continue higher when the uptrend is strong. Conversely, MFI can become oversold (<20) and prices can simply continue lower when the downtrend is strong. Quong and Soudack recommended expanding these extremes to further qualify signals. A move above 90 is truly overbought and a move below 10 is truly oversold. Moves above 90 and below 10 are rare occurrences that suggest a price move is unsustainable. Admittedly, many stocks will trade for a long time without reaching the 90/10 extremes. However, chartists can use the StockCharts.com scan engine to find those that do. Links to such scans are provided at the end of this article.
JB Hunt (JBHT) became oversold when the Money Flow Index moved below 10 in late October 2009 and early February 2010. The preceding declines were sharp enough to produce these readings, but the oversold extremes suggested that these declines were unsustainable. Oversold levels alone are not reason enough to turn bullish. Some sort of reversal or upturn is needed to confirm that prices have indeed turned a corner. JBHT confirmed the first oversold reading with a gap and trendline break on good volume. The stock confirmed the second oversold reading with a resistance breakout on good volume.
Aeropostale (ARO) became overbought when the Money Flow Index moved above 90 in late September and late December 2009. Extremes in MFI suggested that these advances were unsustainable and a pullback was imminent. The first overbought reading led to a sizable decline, but the second did not. Notice that ARO peaked with the first overbought reading and formed lower highs into October. The late October support break signaled a clear trend reversal. After the December overbought reading, ARO moved above 23 and consolidated. There were two down gaps and a support break, but these did not hold. Price action was stronger than the overbought reading. ARO ultimately broke resistance at 24 and surged back above 28. The second signal did not work.
JB Hunt (JBHT) became oversold when the Money Flow Index moved below 10 in late October 2009 and early February 2010. The preceding declines were sharp enough to produce these readings, but the oversold extremes suggested that these declines were unsustainable. Oversold levels alone are not reason enough to turn bullish. Some sort of reversal or upturn is needed to confirm that prices have indeed turned a corner. JBHT confirmed the first oversold reading with a gap and trendline break on good volume. The stock confirmed the second oversold reading with a resistance breakout on good volume.
Aeropostale (ARO) became overbought when the Money Flow Index moved above 90 in late September and late December 2009. Extremes in MFI suggested that these advances were unsustainable and a pullback was imminent. The first overbought reading led to a sizable decline, but the second did not. Notice that ARO peaked with the first overbought reading and formed lower highs into October. The late October support break signaled a clear trend reversal. After the December overbought reading, ARO moved above 23 and consolidated. There were two down gaps and a support break, but these did not hold. Price action was stronger than the overbought reading. ARO ultimately broke resistance at 24 and surged back above 28. The second signal did not work.
Divergences and Failures
Failure swings and divergences can be combined to create more robust signals. A bullish failure swing occurs when MFI becomes oversold below 20, surges above 20, holds above 20 on a pullback and then breaks above its prior reaction high. A bullish divergence forms when prices move to a lower low, but the indicator forms a higher low to show improving money flow or momentum.
On the Aetna (AET) chart below, a bullish divergence and failure swing formed in January-February 2010. First, notice how the stock formed a lower low in February and MFI held well above its January low for a bullish divergence. Second, notice how MFI dipped below 20 in January, held above 20 in February and broke its prior high in late February. This signal combination foreshadowed a strong advance in March.
A bearish failure swing occurs when MFI becomes overbought above 80, plunges below 80, fails to exceed 80 on a bounce and then breaks below the prior reaction low. A bearish divergence forms when the stock forges a higher high and the indicator forms a lower high, which indicates deteriorating money flow or momentum.
On the Aetna chart above, a bearish divergence and failure swing formed in August-September. The stock moved to a new high in September, but MFI formed a significantly lower high. A bearish failure swing occurred as MFI became overbought above 80 in late August, failed to reach 80 with the September bounce and broke the prior lows with a decline in late September.
On the Aetna (AET) chart below, a bullish divergence and failure swing formed in January-February 2010. First, notice how the stock formed a lower low in February and MFI held well above its January low for a bullish divergence. Second, notice how MFI dipped below 20 in January, held above 20 in February and broke its prior high in late February. This signal combination foreshadowed a strong advance in March.
A bearish failure swing occurs when MFI becomes overbought above 80, plunges below 80, fails to exceed 80 on a bounce and then breaks below the prior reaction low. A bearish divergence forms when the stock forges a higher high and the indicator forms a lower high, which indicates deteriorating money flow or momentum.
On the Aetna chart above, a bearish divergence and failure swing formed in August-September. The stock moved to a new high in September, but MFI formed a significantly lower high. A bearish failure swing occurred as MFI became overbought above 80 in late August, failed to reach 80 with the September bounce and broke the prior lows with a decline in late September.
Conclusions
The Money Flow Index is a rather unique indicator that combines momentum and volume with an RSI formula. RSI momentum generally favors the bulls when the indicator is above 50 and the bears when below 50. Even though MFI is considered a volume-weighted RSI, using the centerline to determine a bullish or bearish bias does not work as well. Instead, MFI is better suited to identify potential reversals with overbought/oversold levels, bullish/bearish divergences and bullish/bearish failure swings. As with all indicators, MFI should not be used by itself. A pure momentum oscillator, such as RSI, or pattern analysis can be combined with MFI to increase signal robustness.
Đăng ký:
Bài đăng (Atom)
