, theindecision in the market becomes greater as time goes by. Once this rangeis broken, market direction is quickly resumed. If the break out is causedby a gap in the same direction as the prices were trending before theconsolidation, a further move in that direction is certain. Because of thesubjective nature of these formations, the textbook cases will rarely beseen. Basically, they are the same as the Rising and Falling Three Methodsand the Mat Hold, except that no clear arrangement of candlesticks can beused to define them.
Chapter 5Figure 5-17
Data Requirements, caps, and RulesOnly daily price data, which consists of open, high, low, and close priceson a stock or commodity, is being used when explaining these concepts.Many times, the open price is not available on stocks. In such cases theprevious day's closing price has been substituted. The exception to this iswhen the previous day's close is higher than today's high, today's high isused for the open. Similarly, when the previous day's close is lower thantoday's low, today's low is used as the open price. This allows the visibility of gaps from one day's close to the next day's range.Gaps are an important part of candlestick analysis. To demonstrate thatthere is not much difference, the S&P 100 stocks with and without openprice were tested and analyzed. Comprehensive testing was also accomplished on vast amounts of data that contained the open price to see if therewas any statistical information about gaps that could be used when theopen price was not available. Whenever a day's high and low prices weregreater than the high price of the preceding day, a gap-up analysis wasperformed. Likewise, whenever a day's high and low price were less thanthe previous day's low price, a gap-down analysis was done. Once a "gapday" was identified, the following formula was used to determine thelocation of the open price relative to the day's range:
• Inverted Hammer
• Dark Cloud Cover• Piercing Line• Meeting Lines• Upside Gap Two Crows
• Two Crows• Unique Three River Bottom• Kicking• Matching Low• Side-by-Side White Lines
• Three Line Strike• in Neck LineThere are techniques that can be used in computerized candle patternidentification that will still allow these patterns to be used. For example,one could set some parameters that relate the data components from"greater than" to "greater than or equal to." As a result a requirement thatthe open of one day to be less than the close of the previous day could bemodified so that the open could also be equal. Although this may stretchthe philosophy of candle pattern recognition too far, it at least permits theuse of data that do not contain the open price.Today's electronic capabilities let traders watch intraday price movements from single trade ticks, one minute bars, and almost any otherconceivable increment in between. It is not the purpose here to decidewhich type is better, but sometimes the trees do get in the way of theforest. One must also keep in mind that candle patterns reflect the shortterm psychology of trading, including the decision process that occurs aftera market is closed. This is why open and close prices are so important.
Chapter 6Using intraday day data without the benefit of a break is questionable atthe very least.
The IdeaPattern recognition has been around for many years. A computer can checkand scan vast amounts of data and compile unlimited statistics on patternsand their ability to forecast prices. This approach never remains popularfor very long because it is based solely on statistics and overlooks animportant explanation of why some patterns are more successful than others—human psychology.
Enter Human PsychologyIn the first few minutes of the trading day, a great deal of overnightemotion is captured. Sometimes special events will even cause chaos. Forexample, on the New York Exchange, it may take several minutes for thespecialists to open a stock for trading because of a large order imbalance.However, once a stock or commodity does open, a point of reference hasbeen established. From this reference point, trading decisions are madethroughout the day.As the trading day progresses, extremes are reached as speculator emotion is tossed around. These extremes of emotion are recorded as the highand low of the trading day. Finally, the trading day ends and the last tradeis recorded as the closing price. This is the price that many will use to helpmake decisions about their positions and the tactics they will use at theopen of the next trading day.Aside from intraday data, four prices are normally available for thetrader to analyze. One certainly knows the exact open and close prices forany trading day, but at what times during the day the high and low werereached, and in what order, are not known.How does one determine the existence of a candlestick pattern?
The Philosophy Behind candle Pattern IdentificationMost candle patterns require the identification of, not only the datarelationship making the pattern, but also the trend immediately precedingthe pattern. The trend is what sets up the psychology of traders for thecandle pattern to develop. Most of the current literature somehow evadesthis essential ingredient to candle pattern recognition.It must also be stated here that Japanese candlestick analysis is shortterm (one to ten days) analysis. Any patterns that give longer term resultsare surely just coincidental.
Trend DeterminationWhat is a trend? This question, if it could be answered in depth, couldreveal the secrets of the marketplace and maybe even the universe. For thisdiscussion, only a simple and highly reliable short term answer is sought.Trend analysis is a primary part of technical analysis. To some, trendidentification is as important as the timing of reversal points in the market.Technical analysis books deal with the subject of trend quite thoroughlyand define it in numerous ways. One of the most common approaches isthe moving average.
Moving Averages and SmoothingOne of the simplest market systems created, the moving average, worksalmost as well as the best of the complicated smoothing techniques. Amoving average is exactly the same as a regular average except that it"moves" because it is continuously updated as new data become available.Each data point in a moving average is given equal weight in the computation, hence the term arithmetic or simple is sometimes used when referring to a moving average.A moving average smooths a sequence of numbers so that the effectsof short term fluctuations are reduced, while those of longer term fluctuations remain relatively unchanged. Obviously, the time span of the movingaverage will alter its characteristics.
The Philosophy Behind Candle Pattern identification
Chapter 6
J. M. Hurst in The Profit Magic of Stock Transaction Timing (1970)explained these alterations with three general rules:1. A moving average of any given time span exactly reduces themagnitude of the fluctuations of duration equal to that time span tozero.2. The same moving average also greatly reduces (but does not eliminate) the magnitude of all fluctuations of duration less than thetime span of the moving average.3. All fluctuations are greater than the time span of the average "comethrough," or are also present in the resulting moving average line.Those with durations just a little greater than the span of the average are greatly reduced in magnitude, but the effect lessens asperiodicity duration increases. Very long duration periodicitiescome through nearly unscathed.
A somewhat more advanced smoothing technique is the exponentialmoving average. In principle, it accomplishes the same thing as the simple(arithmetic) moving average. Exponential smoothing was developed toassist in radar tracking and flight path projection. A quicker projection oftrend was needed with more influence from the most recent data. Theformula for exponential smoothing appears complex, but it is only anotherway of weighting the data components so that the most recent data receivethe greatest weight. Even though only two data points are required to getan exponentially smoothed value, the more data used the better. All of thedata are used and are a part of the new result.A simple explanation of exponential smoothing is therefore given here.An exponential average utilizes a smoothing constant that approximatesthe number of days for a simple moving average. This constant is multiplied by the difference between today's closing price and the previousday's moving average value. This new value is then added to the previousday's moving average value. The smoothing constant is equivalent to2/(n+l) where n is the number of days used for a simple moving average.
The Trend Method usedAfter conducting numerous tests, a short term exponential smoothing ofthe data was determined to best identify the short term trend. It gives thebest, easiest, and quickest determination of the short term trend and iscertainly a concept which one can understand. Simple concepts are usuallymore reliable and certainly more creditable.Numerous tests were performed on vast amounts of data with the finding that a exponential period of ten days seemed to work as well as any,especially when you recall that candlesticks have a short term orientation.
Identifying the Candle PatternsPrevious chapters presented detailed descriptions of the exact relationshipsamong the open, high, low, and close. Those chapters also dealt with theconcept of trend use, while this chapter focused on trend determination. Inaddition, a method of determining long days, short days, doji days, etc. isneeded, including the relationship between the body and the shadows. Thelatter is essential for proper identification of patterns such as the HangingMan and Hammer. The following sections will show the multitude ofmethods used to accomplish these and similar tasks.
Long DaysAny of three different methods are available, where each, or any combination, of the three can be used to determine long days. The term minimumin these formulas refers to the minimum acceptable percentage for a longday. Any day whose body is greater than this minimum value will beconsidered a long day.
1. Long Body / Price - Minimum (0 to 100%)This method will relate the day in question with the actual value ofthe prices for a stock or commodity. If the value is set at 5% andthe price is at 100, then a long day will be any day whose range
The Philosophy Behind candle Pattern identification
Chapter 6patterns. Patterns such as the Shooting Star and Inverted Hammer use justthe inverse of these settings.
Doji DaysDoji occurs when the open and close prices are equal. This is an exceptionally restrictive rule for most types of data and should have some leewaywhen identifying candle patterns. The formula lets you set a percentagedifference between the two prices that will be acceptable.Doji Body / High to Low Range - Maximum (0 to 100%)This value is a percentage maximum of the prices relative to the rangeof prices on the Doji day. A value in the neighborhood of 1 to 3% seemsto work quite well.
Equal ValuesEqual values occur when prices are required to be equal. This is used forpatterns like Meeting Lines and Separating Lines. Meeting Lines requirethat the close price of each day be equal; while Separating Lines requirethe open prices to be equal. The same concept used in determining a Dojiday can be used here as well. There are a few instances when setting theparameters to the literal definition will restrict, rather than enhance, the
pattern concept.
Computerized Analysis and AnomaliesThe candle pattern statistics in Table 6-1 shows the amount of data used inthis analysis, the type of data used, and various other pertinent statistics.Data were obtained from stocks with and without open price, CSI's perpetual futures contracts, and leading market indexes.A total pattern frequency of slightly less than 11% equates to onecandle pattern about every nine trading days. This represents a good frequency for daily analysis of stocks and futures. Reversal patterns occur
Table 6-1
The Philosophy Behind Candle Pattern Identification
about 30 times more often than continuation patterns. This too is important, as it indicates the reversal of a trend caused by changed positions intrading. In this analysis, there were 48 reversal patterns and 14 continuation patterns, which makes reversal patterns account for about 77% of allpatterns.It is also interesting to note that 6 patterns account for almost 9% of allpatterns. Of this, the Harami pattern accounts for 32% of those 6 patternsand almost 3% of all patterns. Finally, please note that some patternsoccurred quite infrequently. To assess whether or not they have any value,you should refer to the scoring statistics in Chapter 7. When a patternoccurs, you must understand that, statistically, the success or failure doesnot mean much. Success and/or failure of candle patterns is dealt withextensively in Chapter 7.When a particular pattern appears only a few times in a large amountof data, you should realize that its success and/or failure is subject to thetime period under study. Do not let statistics interfere with common sense,and certainly be alert for inaccuracies in the data.
Remember, candle patterns were used as a visual charting techniquefor hundreds of years. With computers there is no way to handle thesubjectivity that classic chart reading offers. Another factor to considerwhen using computers is the quality of the graphics screen: its resolution.The screen consists of small dots of light known as pixel elements. If toomuch data is used, or if the range of the data is too great, then what mightappear as equal on the screen would not be so numerically. The smallestsize (width) horizontal line could have a price range within itself, notvisible to the eye. Not only computer screens, but also computer generatedchart books could have this problem. This is why some flexibility must bebuilt into the identification of, and definition of, the classic patterns.Another computer anomaly arises in handling candle patterns that arewithin, or part of, another candle pattern. A computer will look at the datain chronological order, that is, old data first. As each day is added, a candlepattern may or may not be noted. When a pattern is identified, the resultsare stored and the process continues. If a bullish Engulfing Day is identified and the next day has a white body with a close greater than the firstday of the Engulfing Day, a Three Outside Up pattern is noted and recorded. The data for statistics and testing has been acquired for both patterns. However, only the Three Outside Up pattern will be identified as acandle pattern if it is given higher priority.
Using the identification philosophy developed in the previous chapter, onecan now adapt a method of determining just how successful candle patterns are.
Measures of SuccessThe following three assumptions were used in measuring the successand/or failure of the many different candle patterns:1. The pattern must, of course, be identified based upon its open,high, low, and close relationships..2. For the pattern to be identified, the trend must be determined. Thisis interchangeable with the previous assumption; each must exist inthe methodology.3. Some basis of measurement must be established to determine thesuccess or failure of the candle pattern.To make a creditable prediction, you either know the current trend oryou do not. Both assumptions and possibilities have been used here.
Reliability of Pattern Recognition
Chapter 7
The Trend is KnownCandle patterns fall into two general categories: those that indicate areversal of the current trend and those that indicate trend continuation.Each day (for each tradeable), a prediction is made about whether theknown trend will continue or reverse for each prediction interval. In otherwords, if today's close is above the exponential average (trend), then weassume that we are in an uptrend. The success or failure is measured by thechange in this trend over the prediction interval. The prediction interval isthe number of days into the future the success or failure was based upon.Prediction intervals refer to the time periods between the actual candlepattern and some point in the future.When a candle pattern occurs, it is offering a short-term forecast on thedirection of the underlying market. The prediction interval is the numberof days after the candle pattern that a determination is made as to whetheror not the candle pattern was successful. A prediction interval is a time inthe future that measures the candle pattern's forecasting ability.Once a trend starts, the odds are that it will continue. Every student ofscience or engineering will recognize that this is nothing more thanNewton's First Law of Motion, which says, Every body continues in astate of rest or of uniform motion in a straight line unless it is compelledto change that state by forces applied to it. Simply said, it is easier for amarket to continue its direction than to reverse its direction.Therefore, the continuation of a trend is more common than the reversal of a trend. Remember, we are talking about the short-term future here.If, at the prediction interval, the price is still above the trend, then thecandle pattern was successful. Simply said: If, during the prediction interval, we are still in an uptrend, then it was deemed successful (Figure 7-1).If not, it was a failure. Figure 7-1 graphically shows the relationship ofreversal and continuation patterns with the prediction interval. The relationship of pattern type with prediction interval is based upon the fact that
the trend is known.
Figure 7-1
The Trend is Not KnownSometimes you do not know what the trend is before making the prediction. In such cases, a coin-toss type of prediction is made about whetherthe price will go up or down. If you do not know the trend, the odds of itscontinuing or reversing would fall into the area of 50%. The differenceabove or below 50% would reflect the directional bias of the data used inthe analysis. Again, the success or failure is based upon the price at theprediction interval relative to the change in trend. This fact is also shownin Figure 7-1. Remember, most candle patterns require that the trend beidentified.
Chapter 7
Reverse Current Trendand Continue Current TrendFrom the computer calculations, two primary parameters are determined:Reverse Current Trend and Continue Current Trend. These are furtherbroken down into Up and Down trends (i.e., Reverse Current Trend Upand Reverse Current Trend Down).The sum of Reverse Current Trend success and Continue CurrentTrend success will be equal to the number of days of data used in thetesting process. Since a prediction is made each day, Reverse CurrentTrend success and Continue Current Trend failure would be equal. In otherwords, the success of Reverse Current Trend, is also the failure of Continue Current Trend.Reversal candle patterns (which most are) are compared to ReverseCurrent Trend and further broken down into upturns and downturns. Sincereversal candle patterns must go against the very trend that defines them,their measure of success would not be as rigid as that of a continuationcandle pattern. In fact, their measure of success could actually be less thanthat of a coin toss, since they are predicting a change in the current trend,a trend which is supposedly known.Likewise, continuation candle patterns are compared to Continue Current Trend. Continuation candle patterns say that the trend that helpeddefine them is going to continue. Therefore, for a continuation candlepattern to be considered successful, it must do better than the success ofknowing the trend in the first place. Because we know the current trendand we know that the odds are that the current trend will continue; to beuseful, continuation patterns must be exceptionally good, or are no betterthan the trend-identification process.
Candle Pattern Statistical RankingCandle patterns are predictable psychological trading pictures (windows)that produce reasonable forecasting results when used in the proper manner. This section will explain the technique used to determine the variousstatistics developed to show the success of candle patterns. Note that no
Reliability of Pattern Recognition
magnitude of success is used, only relative success and failure. Keep inmind, though, that success still means that the pattern correctly predictedthe market move and failure means that it did not.Using all of the information about pattern recognition (including trenddetermination) developed in the previous chapters, we will now set out tosee just how good candle patterns are. Because a simple approach is usually best, no elaborate assumptions were used, only the price change overvarious time intervals into the future.Once the relative success or failure of a particular candle pattern wasdetermined, its relationship to the appropriate pattern standard of measurewas calculated. This standard of measure is the Reverse Current Trend andContinue Current Trend, discussed earlier. Recall that continuation patterns must outperform reversal patterns because of their trend relationship.That is why you will see many continuation patterns with a negative ranking, even though their success percentage was high.
Candle Patterns and StocksData for this analysis were the stocks contained in the Standard and Poor's100 Index and 41 futures contracts. The S&P 100 Index is a capitalizationweighted index of 100 stocks from a broad range of industries. These 100stocks present an excellent representation of the U.S. stock market. Thefutures contracts used were perpetual contracts from CSI (CommoditySystems, Inc). These contracts were used so that long-term continuous datacould be analyzed.The results, presented in Tables 7-1 through 7-3, use prediction intervals of three, five, and seven days. This should adequately cover the timeinterval used when doing candle pattern analysis. When prediction intervals from one to ten days are analyzed the results from all ten fall withinthe expected range represented with these tables.Table 7-1 presents the results of the candle pattern ranking system fora prediction interval of three days, using over 82,000 days of data. Noticethat 55 of the 62 possible patterns occurred in the 100 stocks used in thistest, but that a few were somewhat sparse. About 65% (36 out of 55) of the
Ranumty of Pattern Recogn.tlon
Reliability of Pattern Recognitioncandle patterns were deemed successful using the established ranking criteria.The data offer a good example of the difference in importance betweenreversal patterns and continuation patterns. The reversal pattern IdenticalThree Crows had a 100% success and a ranking score of 230.03%. Thecontinuation pattern Falling Three Methods also had a 100% success, butits ranking score was only 49.48%. The difference in ranking scores occursbecause continuation patterns only suggest that the known trend will continue, which, of course, is favored by the odds. In contrast, reversal patterns indicate that the trend will reverse, which is less likely to occur.As Table 7-2 shows, using a prediction interval of five days decreasedthe number of successful patterns somewhat. Only 28 out of 55 patternswere ranked as successful, or just a little over 50%. Notice also that theIdentical Three Crows pattern dropped to the number 5 position. FallingThree Methods, reflecting its name, dropped to the number 46 positionwith a ranking of -33%.Using a prediction interval of seven days reversed the decline in successful patterns with 35 out of the 55, or 63%, ranked successful, asillustrated in Table 7-3. Notice also that the top two patterns were previously near the bottom in the previous tables.
Summary of the Three Stock TablesWhat can be gleaned from the data in Tables 7-1, 7-2, and 7-3? Rememberthat the exact same data were used in each table and that only the prediction intervals were changed. As a result, we can make the following observations:If a pattern rises and falls in the rankings when the prediction intervalis changed, its usefulness is suspect for the data being used. For example, Downside Gap Three Methods moved from 39 when the prediction interval was at three days, to number 20 with the predictioninterval at five, and then to 37 as the prediction interval increased toseven. Even though the jump up to 20 was not exceptional, it did show
Chapter?
that this pattern's predictive ability wasn't steady, which is what weare looking for in these tables.2. Steady movement in a single direction in the rankings can be telling.Matching Low is a good example. In Table 7-1 it ranked at 44 with anegative 35.35% ranking score. As the prediction interval increasedfrom three to five days, Matching Low moved up the rankings to 31.And at seven days, Matching Low was up to 24th place and a 8.71%ranking score. This says that the Matching Low reversal pattern tendsto get better, relatively, with an increase in prediction interval. Saiddifferently, Matching Low has staying power and tends to be longerterm in its trend-reversal prediction capability.Meeting Lines+ is another good example of a pattern that moves upthe list as the prediction interval is increased. Meeting Lines* movedfrom 50 to 14, and then to the number 2 position. This indicates thatMeeting Lines-i- tends to be better at slightly longer term predictions oftrend change. The only problem is that Meeting Lines+ occurred onlytwice, which makes the conclusion somewhat suspect.Identical Three Crows, while number 1 with the prediction interval atthree, moved to number 5, and then down to number 50 when theprediction interval increased to seven days. This shows that it tends tobe much better as a short term reversal indicator than as a longer term3.
one.Patterns that continue to remain in the same relative position are themost stable predictors of trend changes. Out of the first 15 patterns fora prediction interval of three days, 6 patterns remained in the top 15for all three rankings. They were Three Black Crows, Three WhiteSoldiers, Three Inside Up, Three Outside Down, Dark Cloud Cover,and Three Outside Up. These 6 reversal patterns consistently showedgood performance over all prediction intervals tested.
At the other end of the spectrum, 6 patterns remained in the bottom 15ranking for all three prediction intervals. They were Three Line Strike-,Doji Star+, Doji Star-, Three Stars in the South, Side-by-Side White Lines-,
Reliability of Pattern Recognitionand Breakaway*. Three of these patterns, Three Stars in the South, Sideby-Side White Lines-, and Breakaway+, occurred only once in all of thedata, so not much significance should be put on them.It is also interesting to note that when the prediction interval wasincreased to nine days, only Three Black Crows, Three Inside Up, andDark Cloud Cover remained in the top 15 ranking. Three Line Strike- andSide-by-Side White Lines- were the only patterns to remain in the bottom15 ranking. The surprise came when the consistently poor performers,Three Stars in the South and Breakaway*, were in the number 1 and 3positions, respectively.Obviously, one could get overly analytical with the results. One shouldalways strive to make observations that have at least a chance at beingsuccessful when additional data and/or intervals are used.
The candle pattern ranking for 41 different futures contracts was performed on over 49,000 days of data. Table 7-4 shows the results for aprediction interval of three days. Out of 62 possible candle patterns, 57patterns were identified in this data. It is important to note that 7 patternsoccurred only one or two times. Slightly more than half (32 out of 57)were deemed successful using the ranking system previously discussed.Here, just as with the stocks, two patterns had a 100% success rate. Kicking-, a reversal pattern, had only a single occurrence and should not begiven much significance. Side-by-Side White. Lines*, a continuation pattern, had a 100% success rate and a ranking score of 40.65%. Rememberthat continuation patterns have the trend working in their favor and therefore must perform exceptionally well to receive a high ranking score.With the prediction interval at five days, 30 out of 57 patterns hadpositive ranking scores, as shown in Table 7-5. Note, however, that 4patterns had 100% success. Because the number of occurrences of each ofthese patterns was small, their significance should be based upon how theyperformed over varying prediction intervals.
Reliability of Pattern RecognitionSetting the prediction interval at seven days gave 38 successful patterns, or over 66%. Again, 4 patterns had successes of 100%, but alsonotice that it wasn't the same 4 patterns as in Table 7-5.
Summary of the Three Futures Tables1. As when analyzing stocks, patterns that jump around in the rankingsshould be noted. Shooting Star started out with a rank of 45 when theprediction interval was three days. When the prediction interval wasmoved up to five days, Shooting Star improved to a ranking of 23.Finally, with the prediction interval at seven days, Shooting Stardropped to a low of 53. This type of volatility shows that the ShootingStar should not be relied upon when used with this data.2.Steady movement, whether up the list or down, will help identifypatterns that may be used for shorter or longer predictions. The firstpattern to demonstrate this trait is Morning Doji Star. It starts out witha ranking of 3, then moves down slightly to a ranking of 7, and finallydrops to a ranking of 34. This says that Morning Doji Star is bestwhen used for short prediction intervals.In contrast, Side-by-Side White Lines- starts out with a ranking of 44,moves up to a ranking of 21, and then continues up to a ranking of 7.These significant moves strongly suggest that Side-by-Side WhiteLines- is best at making longer term predictions. As you may remember from Chapter 4, Side-by-Side White Lines- would normally showa somewhat bullish pattern in that there are two normally bullish whitelines in a row. This probably accounts for its longer term perspectiveon the trend. Even though it appears as a bullish set of days, it iscorrectly calling the downtrend to continue.3.
Patterns that were stable in their rankings are the best overall performers. Only 7 of the top 15 patterns when the prediction interval wasthree days remained that high for all three tests. They were Kicking-,Three Black Crows, Breakaway*, Three Outside Up, Three Inside Up,Engulfing Pattern-, and Three Outside Down. Of these 7 patterns,
Reliability of Pattern Recognition
Candlestick filtering offers a method of trading with candlesticks that issupported by other popular technical tools for analysis. Filtering is a concept that has been used in many other forms of technical analysis and isnow a proven method with candlesticks.If there is any fault with using a single method for market timing andanalysis, it most certainly will also be a fault with candlesticks. Just likeany price-based technical indicator based upon a singular concept, candlesticks will not work all of the time. When indicators are combined or usedin conjunction with one another, the results can only improve. Again,candlesticks are no different: when used with another indicator, the resultsare superb.
The Filtering ConceptThe filtering concept was developed to assist the analyst in removingpremature candle patterns, or for that matter, eliminate most of the earlypatterns. Because candle patterns are intensely dependent upon the underlying trend of the market, lengthy trends in price will usually cause early
Chapter 8pattern signals, just like most technical indicators. Something else had tobe used to assist in the qualification of the candle pattern signals. Mosttechnical analysts use more that one indicator to confirm their signals, sowhy not do the same with candle patterns? The answer is the use oftechnical indicators. While appearing obvious, technical indicators did notprovide the "how" answer to the problem, only the "what."The following discussion will try to explain the answer to the "how"question. Most indicators have a buy and sell definition to help in theirinterpretation and use. There is a point prior to a buy or sell signal that isnormally a better place for a signal to fire, but it is difficult to define.Most, if not all, indicators lag the market somewhat. This is because thecomponents of indicator construction are the underlying data itself. If anindicator's parameters are set too tight, the result will be too many badsignals, or whipsaws. Therefore, a pre-signal area was calculated basedupon thresholds and/or indicator values, whether positive or negative.Once an indicator reaches its defined pre-signal area, it has beenprimed to await its firing signal. The amount of time an indicator will bein the pre-signal area cannot be determined. The only certainty is that oncean indicator reaches its pre-signal area, it will eventually produce a tradingsignal (buy or sell). Statistically, it has been found that the longer anindicator is in its pre-signal area, the better the actual buy or sell signal willbe.The pre-signal area is the filtering area for each individual indicator; itsfingerprint. Each indicator has a different fingerprint. If the indicator is inthe buying pre-signal area, only bullish candle patterns will be filtered.Likewise, if an indicator is in the selling pre-signal area, only bearishcandle patterns are filtered.
Candlestick Filtering
Figure 8-1
Pre-Signal AreasFor threshold-based indicators, the pre-signal area is the area between theindicator and the thresholds, both above and below (Figure 8-1).For oscillators, the pre-signal area is defined as the area after the indicator crosses the zero line until it crosses the moving average or smoothingused to define the trading signals (Figure 8-2).
Wilder's RSIJ. Welles Wilder developed the Relative Strength Index (RSI) in the late1970s. It has been a popular indicator, with many different interpretations.It is a simple measurement that expresses the relative strength of the current price movement as increasing from 0 to 100. Basically, it averages theup days and the down days. Up and down days are determined by theday'