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mình đang tìm về mối quan hệ của giá chứng khoán và khối lượng dao dịch. bạn nào biết cách sử dụng của "THE VOLUME PRICE CONFIRMATION INDICATOR " chỉ mình với nha.
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05092012, 09:19 AM #2Guest
Dưới đây là hướng dẫn của Hiệp hội phân tích kỹ thuật thế giới (MTA) về VPCI. Bạn tham khảo nhé
Introducing the Volume Price Confirmation Indicator (VPCI): Price & Volume Reconciled
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Tags: Buff Dormeier, Introducing the Volume Price Confirmation Indicator VPCI Price Volume Reconciled
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This paper introduces a new volumeprice measurement tool that could provide the clearest picture of the volumeprice relationship of any indicator devised: the VolumePrice Confirmation Indicator, or PCI. The VPCI reveals the intrinsic relationships between price and volume as a validation or contradiction of price trends. In other words, VPCI identifies the inherent relationship between price and volume as harmonious or inharmonious. This study shows that investors who use the VPCI properly may increase their profits and the reliability of their trades, while simultaneously reducing risk.
In the exchange markets, price results from an agreement between buyers and sellers to exchange, despite their different appraisal of the exchanged item’s value. One opinion may be heavily loaded in meaning and purpose; the other may be pure nonsense. However, both are equal as far as the market is concerned. Price represents the convictions, emotions and volition of investors. I t is not a constant, but rather is changed and influenced by information, opinions and emotions over time. Market volume represents the number of shares traded over a given period. It is a measurement of participation, enthusiasm or interest in a given security. Price and volume are closely linked, yet are independent variables. Together, these individually derived variables give better indications of supply and demand than either can provide independently.
Volume can be thought of as the force that drives the market. Force or volume is defined as power made operative against support or resistance.i In physics, force is a vector quantity that tends to produce an acceleration.ii The same is true of market volume. Volume substantiates and mediates price. When volume increases, it confirms price; when volume decreases, it contradicts price movements. In theory, increases in volume should precede significant price movements, giving quicker downside and upside signals. This basic tenet of technical has been repeated as a mantra since the days of Charles Dow.iii When stocks change hands, there is always an equal amount of buy volume to sell volume on executed orders. When the price moves up, it reflects reasoned demand or the fact that buyers are in control. Likewise, when the price moves down it infers supply or that sellers are in control. Over time, these trends of supply and demand form accumulation and distribution patterns. VPCI was designed to expose price and volume relationships as validation or contradiction of price trends. The following pages discuss the derivation and components of VPCI, explain how to use VPCI, review comprehensive testing of VPCI and present further applications.
Deriving the Components
The market is likened to an orchestra without a conductor. By mediating the intrinsic relationship between price and volume, the VPCI attunes price and volume into an observable accord. Simply put, this could be considered the harmony between price and volume. The basic concept is that measuring the difference between volumeweighted moving averages (VWMAs) and the corresponding simple moving average (SMA), reveals a precise level of pricevolume confirmation or pricevolume contradiction. This occurs because volume weighted averages weight closing prices in exact proportion to the volume traded during each time period.
Since VWMAs are essential to understanding the VPCI, it is important to differentiate them from SMAs. The VWMA was developed to give a more accurate account of trends by modifying the SMA. The VWMA measures the commitment expressed through a closing price, weighted by that day’s corresponding volume (participation), compared to the total volume (participation) of the trading range. Although SMAs exhibit a stock’s changing price levels, they do not reflect the amount of participation by investors. However, with VWMAs, price emphasis is directly proportional to each day’s volume, compared to the average volume in the range of study.
The VWMA is calculated by weighting each timeframe’s closing price with the timeframe’s volume compared to the total volume during the range: volumeweighted average = sum where I = given day’s action.
SMA  simple moving average
VWMA  volumeweighted
moving average
VPC (+/)  volumeprice
confirmation/contradiction
VPR  volumeprice ratio
VM  volume muliplier
Here is an example of how to calculate a twoday moving average, using both SMA and VWMA on a security trading at $10.00 with 100,000 shares on the first day and at $12.00 with 300,000 shares on the second day. The SMA calculation is Day One’s price plus Day Two’s price divided by the number of days, or (10+12)/2, which equals 11. The VWMA calculation would be Day One’s price (10) multiplied by Day One’s volume of the total range expressed as a fraction (100,000/400,000 = 1/4) plus Day Two’s price (12) multiplied by Day Two’s volume of the total range expressed as a fraction (300,000/400,000 = 3/4), which equals 11.5. Keeping in mind how VWMAs work, an investigation of VPCI may begin.
The VPCI involves three simple calculations:
1.) volumeprice confirmation/ contradiction (VPC+/),
2.) volumeprice ratio (VPR), and
3.) volume multiplier (VM).
The first step in calculating VPCI is to choose a longterm and shortterm timeframe. The longterm timeframe number will be used in computing the VPC as the simple and volumeweighted pricemoving average, and again in calculating the VM as a simple, volumemoving average. The shortterm timeframe number will be used in computing the VPR as a simple and volumeweighted pricemoving average and again in calculating the VM as a simple, volumemoving average.
The VPC is calculated by subtracting a longterm SMA from the same timeframe’s VWMA. In essence, this calculation is the otherwise unseen nexus between volume proportionally weighted to price and price proportionally weighted to volume. This difference, when positive, is the VPC+ (volumeprice confirmation) and, when negative, the VPC (volumeprice contradiction).
In effect, this computation reveals price and volume symmetrically distributed over time. The result is quite revealing. For example, a 50day SMA is 48.5, whereas the 50day VWMA is 50. The difference of 1.5 represents pricevolume confirmation. If the calculation were negative, then it would represent pricevolume contradiction. This alone provides purely unadorned information about the intrinsic relationship between price and volume.
The next step is to calculate the volume price ratio. VPR accentuates the VPC+/ relative to the shortterm pricevolume relationship. The VPR is calculated by dividing the shortterm VWMA by the shortterm SMA. For example, assume the shortterm timeframe is 10 days, and the 10day VWMA is 25, while the 10day SMA is 20. The VPR would equal 25/20, or 1.25. This factor will be multiplied by the VPC (+/) calculated in the first step. Volumeprice ratios greater than 1 increase the weight of the VPC+/. Volumeprice ratios below 1 decrease the weight of the VPC+/.
The third and final step is to calculate the volume multiplier. The VM objective is to overweight the VPCI when volume is increasing and underweight the VPCI when volume is decreasing. This is done by dividing the shortterm volume average by the longterm volume average. As an illustration, assume the shortterm average volume for 10 days is 1.5 million shares a day, and the longterm volume average for 50 days is 750,000 shares per day. The VM is 2 (1,500,000/750,000). This calculation is then multiplied by the VPC+/ after it has been multiplied by the VPR.
Now we have all the information necessary to calculate the VPCI. The VPC+ confirmation of +1.5 is multiplied by the VPR of 1.25, giving 1.875. Then 1.875 is multiplied by the VM of 2, giving a VPCI of 3.75. Although this number is indicative of an issue under very strong volumeprice confirmation, this information serves best relative to the current and prior price trend and relative to recent VPCI levels. Discussed next is how best to use the VPCI.
Using VPCI
Confirming Signals
Unlike other volumeprice indicators, the VPCI is not a standalone tool. Most volumeprice indicators may give signals without regard to price trend (although this is not advised). For example, a trader may buy an issue based on a breakout of On Balance Volume, or sell an issue on a Money Flow Index divergence in an overbought zone. However, the VPCI gives virtually no indications outside of its relationship to price; it only confirms or contradicts the price trend. There are several ways to use VPCI in conjunction with price trends and price indicators. These include a VPCI greater than zero, a rising or falling VPCI, a smoothed (moving average) rising or falling VPCI, or a VPCI as a multiplier. Table 1 gives the basic VPCI utilizations:
Table 1. VPCI and Price Trends
Price VPCI Price Trend Relationship Implications
Rising Rising Confirmation Bullish
Rising Declining Contradiction Bearish
Declining Rising Confirmation Bearish
Declining Declining Contradiction Bearish
VPCI in Action
In our first example (Figure 1), the price trend of SIRI is rising and the VPCI is also rising. Here the VPCI is giving three bullish signals, the most important being that the VPCI is rising. Increasing volume and price confirmation demonstrate strengthening commitment to the existing price trend of demand. Secondly, VPCI smoothed is rising and the VPCI has crossed above it, indicating momentum within the confirmation. This is a good indication that the existing bullish price trend will continue. Last and least important, both the VPCI and VPCI smoothed are above the zero line, indicating a healthy longerterm accumulation. All of these VPCI indications are interpreted as bullish only because SIRI’s prevailing trend is rising.
Figure 1. Bullish Confirmation: SIRI’s Rising Price Trend and Rising VPCI. Bottom Red, Wiggly Line is VPCI. Smoother Blue Line is VPCI Smoothed
Next, we look at an example of the VPCI giving a bearish contradiction signal (Figure 2). TASR’s stock price is rising, but the VPCI is falling. This situation suggests caution  a significant price correction could be looming because the intrinsic relationship between price and volume is not harmonious. Although price is rising and volume appears supportive, the VPCI is indicating that demand is no longer in control. Here two bearish signs are given in the presence of a rising stock price. Most significantly, both the VPCI and VPCI smoothed are in downtrends, indicating weakening commitment to the uptrend. Also, both the VPCI and VPCI smoothed are below zero, suggesting an unhealthy uptrend.
Figure 2. Bearish Contradiction: TASR’s Rising Price Trend and Falling VPCI
A falling stock price and a rising VPCI (Figure 3) is an example of volumeprice confirmation. In our illustration, GSK’s stock price is falling and the VPCI is rising, indicating control is clearly in the hands of sellers. The VPCI moves gradually upward, supporting the downward price movement. Gaining momentum, the VPCI crosses above zero and eventually through the VPCI smoothed. GSK’s stock price breaks down shortly afterwards on the selling pressure.
Figure 3. Bearish Confirmation: GSK’s Falling Price Trend and Rising VPCI
RIMM provides an example of a bullish contradiction. In Figure 4, RIMM’s price is declining, as is the VPCI. A decreasing VPCI while the price is falling is usually a sign of increasing demand, especially if the stock has previously been in an uptrend as was the case with RIMM. When RIMM begins to break down, the VPCI takes a sharp nosedive, indicating a weak selloff. Once the VPCI bottoms, the bulls regain control of RIMM and the breakdown is reversed. The VPCI turns upward, confirming the prior uptrend. This is a classic example of the VPCI indicating a countertrend.
Figure 4. Bullish Contradiction: RIMM’s Falling Price and a Falling VPCI
Putting it all together, let us take a look at one final example of the VPCI in action (Figure 5). It’s extremely important to note when using VPCI that volume leads or precedes price action. Unlike most indicators, the VPCI will often give indications before price trends are clear. Thus, when a VPCI signal is given in an unclear price trend, it is best to wait until one is evident. This final example is given in a weekly timeframe to illustrate VPCI signals in a longerterm cycle.
At Point 1 in Figure 5, CMX is breaking out and the VPCI confirms this breakout as it rapidly rises, crossing over the VPCI smoothed and zero. This is an example of a VPCI bullish confirmation. Later, the VPCI begins to fall during the uptrend, suggesting a pause within the new uptrend. This small movement is a bearish contradiction. At Point 2, CMX’s price falls as the VPCI continues to fall below zero and eventually through the VPCI smoothed, gaining momentum. This is a classic example of a countertrend VPCI bullish contradiction. At Point 3, the VPCI has bottomed out and with CMX begins to rise, confirming the last VPCI signal. Later, in Point 3, VPCI moves upward, supporting the higher price movement. By Point 4, CMX breaks through resistance, while the VPCI upward momentum accelerates rapidly, crossing the VPCI smoothed and zero. From this bullish confirmation, one could deduce a high probability of a price breakout, illustrating bullish confirmation once again.
Figure 5. VPCI in action CMX
Testing the VPCI
Applying the VPCI information to a trading system should improve profitability.
To evaluate this VPCI hypothesis, it was tested via a trading system, contrasting two moving average systems. The goal of this study was not to achieve optimum profitability but to compare a system using VPCI signals to that of a system not using them. The crossing of the fiveday and 20day moving averages was used to generate buy and sell signals. The fiveday moving average represents the cost basis of traders in a oneweek timeframe. The 20 day moving average represents the cost basis of traders in a onemonth timeframe. The shorter moving average is more responsive to current price action and trend changes, because it emphasizes more recent price changes. The longerterm moving average comprises more information and is more indicative of the longerterm trend. Because its scope is broader, the longerterm moving average normally lags behind the action of the shorter moving average. When a moving average curls upward, the investors within this timeframe are experiencing positive momentum. The opposite is true when the moving average curls downward. When the shortterm moving average’s momentum is significant enough to cross over the longerterm moving average, this is an indication of a rising trend, otherwise known as a “buy signal.” Likewise, when the shorterterm moving average’s momentum crosses under the longerterm moving average, a “sell signal” is generated.
Backtested first was a five and 20day crossover system. A long position is taken when the shortterm moving average crosses above the longterm moving average. A short position is taken when the shortterm moving average crosses under the longterm moving average. These actions tend to represent shortterm changes in momentum and trend. In the comparative study, I used the same five and 20day crossover, but kept only the trades when the VPCI had crossed over a smoothed VPCI. This indicates a rising VPCI or price confirmation. The VPCI settings will be the same as the moving averages, 20 days for the longterm component and five days for the shortterm component. The VPCI smoothed is the 10day average of the VPCI.
There are a number of limitations to a study framed this way but these settings were chosen deliberately to keep the study simple and uncompromised. First, the five and 20day moving average settings are too short to indicate a strong trend. This detracts from the effectiveness of the VPCI as an indicator of price trend confirmation or contradiction. However, although these settings are short, they provide more trades than a longerterm trend system, creating a more significant sample size. Also, the VPCI settings at five and 20 days, when the price data is only 20 days old (length of the longterm moving average) are too short. By using these time settings, the VPCI may give indications ahead of the price trend or momentum signals given by the moving average. However, changing the settings could be interpreted as being optimized. Accordingly, a 10day lookback delay on the VPCI and a fiveday lookback delay on the VPCI smooth was installed. This delay gives the VPCI confirmation signal more synchronicity with the lagging moving average crossover. Ideally, VPCI delays should be “tuned in” to the individual issue. My testing has shown more responsive high volume and highvolatility issues generally do not require delays as long as slowermoving low volume and lowvolatility issues. One could also use trend lines corresponding to the timeframe being applied to tune the VPCI.
To ensure a broad scope within the sample being studied, the test was broken into several elements. Securities were selected across three areas of capitalization: small, as measured by the S&P Small Cap Index; medium, as measured by the S&P 400 Mid Cap Index; and large, as measured by the S&P 100 Large Cap Index. Equally important are the trading characteristics of each security. Thus, securities were further characterized by volume and volatility. Combining these seven traits forms a total of 12 groups: small cap high volume, small cap low volume, small cap high volatility, small cap lowvolatility, mid cap high volume, mid cap low volume, mid cap high volatility, mid cap low volatility, large cap high volume, large cap low volume, large cap high volatility and large cap low volatility (Table 2).
Table 2. Sixty Securities Organized by Size, Volume and Volatility
To ensure unbiased results, five securities were backtested in each of these 12 subgroups for a total of 60 securities, a significant sample size. For credibility, the five securities representing each group were not selected at random, but by identifying the leaders in the various characteristics being measured. Thus, the five highest and lowestvolume securities, as well as the five highest and lowestvolatility securities of each of the three capitalization groups as identified by Bloomberg (June 22, 2004) were used in the study. Any duplicated securities (highvolume and highbeta stocks were occasional duplicates) were used only once. Securities that lacked sufficient history were removed and replaced by the nextbest suitable issue.
To keep the system objective, both long and shortsystem generated trades were taken into account. A $10,000 position was taken with each crossover. Commissions were not included. The testing period used was August 15, 1996, to June 22, 2004, for a total of 2,000 trading days. The results were measured in terms of profitability, reliability and riskadjusted return.
Profitability
Profitability was tested using a five and 20day moving average crossover and then retested using only those trades also displaying VPCI confirmation signals. The results were impressive (Figure 6). Broadly, the VPCI improved profitability in the three size classes  small, mid, and large caps  and all four style classifications  high and low volume, and high and low volatility. Nine of the 12 subgroups showed improvement. The exceptions were mid cap high volatility issues, and small and large lowvolume issues. Of the 60 issues tested, 39 or 65%, showed improved results using VPCI. The VPCI group made $381,089. This compares to the competing nonVPCI group making only $169,092. Thus, overall profitability was boosted by $211,997 with VPCI.
Figure 6. Profitability Improvement with VPCI
Reliability
Reliability was measured by looking at the percentage of profitable trades. By employing VPCI in the five/20day crossover system, overall profitability improved an average of 3.21% per issue. Improvement was realized by adding VPCI in all three size groups and all four style groups (Figure 7). Of the 12 subgroups, 10 showed improved profitability with the VPCI. Large and small cap lowvolatility issues were the two exceptions. Overall, over 71% (43 of 60 issues) showed improvement with the VPCI.
Figure 7. Average Trading System Reliability
RiskAdjusted Returns
Two tests of riskadjusted performance were conducted to further evaluate VPCI. One was the Sharpe Ratio, which takes the total return subtracted from the riskfree rate of return (US Treasury Note) and divides the result by the portfolio’s monthly standard deviation, giving a riskadjusted rate of return. VPCI improved the results once again across all three size categories and all four style groups. VPCI realized improvement in nine of the 12 subgroups. Mid cap high volatility, large cap low volatility, and large cap low volume were the exceptions. Overall, the Sharpe Ratio showed significant improvement with the addition of the VPCI.
A second way to look at riskadjusted returns is through profit factor (Figure 8). Profit factor takes into account how much money could be gained for every dollar lost within the same strategy, measuring risk by comparing the upside to the downside. It is calculated by dividing gross profits by gross losses.
For instance, one issue may generate $40,000 in losses and $50,000 in gains whereas a second issue may generate $10,000 in losses and $20,000 in gains. Both issues generate a $10,000 net profit. However, an investor could expect to make $1.25 for every dollar lost in the first system, while expecting to make $2 for every dollar lost in the second system. The figures of $1.25 and $2 represent the profit factor. Even more significant improvements across all size, volume and volatility groups again were achieved using the VPCI. Of the 12 subgroups, only large cap lowvolatility issues did not show an improvement with the VPCI. Overall, the profit factor was improved by 19%, meaning one could expect to earn 19% more profit for every dollar lost when applying VPCI to the trading system.
Figure 8. Profit Factor Improvement Using VPCI Among the 12 Subgroups
Other Applications
The raw VPCI calculation may be used as a multiplier or divider in conjunction with other indicators such as moving averages, momentum indicators, or price and volume data. For example, if an investor has a trailing stop loss order set at the fiveweek moving average of the lows, one could divide the stop price by the VPCI calculation. This would lower the price stop when price and volume are in confirmation, which would increase the probability of keeping an issue demonstrating price volume confirmation. However, when price and volume are in contradiction, dividing the stop loss by the VPCI would raise the stop price, preserving capital. Similarly, using VPCI as an addon to various other price, volume, and momentum indicators may not only improve reliability but increase responsiveness as well.
Conclusion
The VPCI reconciles volume and price as determined by each of their proportional weights. This information can be used to confirm or deny the likelihood of a current price trend continuing. This study clearly demonstrates that adding the VPCI indicator to a trendfollowing system results in consistently improved performance across all major areas measured by the study. As a maestro’s baton in the hands of a proficient investor, the Volume Price Confirmation Indicator is a tool capable of substantially accelerating profits, reducing risk and empowering the investor to more reliable investment decisions.
Footnotes
i Ammer, C. (1997). The American Heritage Dictionary of Idioms. Boston: Houghton Mifflin Company.
ii The American Heritage Stedman’s Medical Dictionary. (2002). Boston:Houghton Mifflin Company.
iii Edwards, R.D., & Magee, J. (1992). Technical of Stock Trends. Boston: John Magee Inc.
Biography
Buff Dormeier, CMT began in the securities industry in 1993 with PaineWebber. From PaineWebber Buff joined Charles Schwab where he handled some of the firm’s largest and most active accounts. Do to his growing popularity with the firm’s clientele; Buff was called to train other brokers in the art of communicating technical market analytics to customers. His training program gave birth to Schwab’s Technical Team. Later, Buff became the lead portfolio manager and chief technical analyst at T.P. Donovan Investments. Armed with proprietary indicators and investment programs, Buff now coaches and manages portfolios for individual and institutional clients as a financial advisor at a major international brokerage firm.
Buff’s work with market indicators and trading system design has been both published and referenced in Stock’s & Commodities and Active Trader magazines & has been discussed in seminars across the nation. Further, Buff’s original contributions to the field have been included in John Bollinger’s book, “Bollinger on Bollinger Bands.” Buff is Series 8, 7, 65, 63 and insurance licensed and has previously served on the Market Technicians Association Admissions Committee. Personal hobbies include running and bible study
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