don’t fully agree with everything Malkiel has said inhis classic book, I do believe that he has made some good points. I havelong felt that traders who can consistently follow a rational, unopti-mized method of trading and who can, at the same time, employ strictprinciples of risk management can achieve profitable results, possiblyequal to or greater than the results which may be achieved by rigid trad-ing system followers. The essence of any trading system is not the sys-tem itself but rather its approach to risk management and the skill of thetrader implementing that system.
What’s Left?
Am I telling you, therefore, that you ought not to trade according to asystem? Am I saying that there are no trading systems which are worthtrading? Am I saying that trading systems should not be tested or devel-oped? Or do I wish to lead you in an entirely different direction?Frankly, I’m telling you that your time is more efficiently spent ondeveloping your own methods of risk management and trading style asopposed to optimizing trading systems. You will find within this book26 = Chapter Three
few examples of trading system results, no outrageous claims, and fur-thermore, repeated warnings about the limitations of trading systems.This book will not present you with a stable of optimized, perfected, andcurve-fitting trading systems which look great on paper but which willnot perform well for you in the future. System development and systemtesting are illusory. Do not depend upon them too much—they will leadyou astray.
Accordingly, I feel that serious day traders and in fact serious tradersregardless of their time-frame orientation should devote their energiesto the following:
1. Research and develop timing tools which have shown relatively highaccuracy as opposed to trading systems per se.
2. Focus on methods of risk management including limitation of losses,maximization of profits, and such important issues as the use of trail-ing stop losses.
3. Focus on improving your relationship with the markets and develop yourskills as a trader, skills which will serve you well regardless of whetheryou are using a trading system which has been thoroughly back-tested or not.
Markets Change—Market Participants
Change
Another very important consideration in the use of trading systems andin trading system development is the fact that the characteristics of mar-kets change. The markets of today are distinctly different from the mar-kets of the 1950s, the 1960s, the 1970s, and the 1980s. Each era of marketactivity has had its own characteristic issues, and each market has expe-rienced significant fundamental developments which have changed itstone and underlying response styles.
Furthermore, the nature of the players has changed. Whereas institu-tions were involved only marginally in futures trading until the early1980s, they now constitute a major force in the markets; their collectivebuying and selling can produce significant market volatility. A tradingsystem, therefore, which would have worked well in the 1970s may notwork well in the 1990s. This is why trading systems show regression tothe mean the farther back they are tested. You may wish to try a littleexperiment on your own. Develop a trading system based on the last 5years’ worth of price data. Once it has been developed, test that sametrading system on the last 10 years’ worth of data. In all probability,The Illusion of System Testing 27
you'll find that the system has diminished in its performance. Now, testthe last 15 years of data. Result? You'll find in a vast majority of casesthat the system was even less effective. This is a classic example ofregression to the mean. It shows that markets change their characteris-tics and that the players in a market change as well. This in turn affectsthe ability of trading systems to show consistent performance over time.
Striving for Solutions
What are the answers to the significant questions and limitations I’veposed regarding trading system development and back testing? Thereare several potentially valid answers. First and foremost, trading systemresearch should be given less attention rather than more attention. I donot believe that the ultimate trading system using conventional meth-ods of research will ever be uncovered or revealed. I feel very stronglythat the emphasis must be the development of simple but repetitive andreliable market relationships as opposed to systems per se. Second, theday trader must develop specific methods for implementing consistentintraday market relationships within the context of effective risk man-agement. And third, the day trader (in fact, all traders) must be sensitiveto changing market conditions. Monitor your performance closely inorder to determine when conditions are changing and when you oughtto respond by changing your trading style and methods. This book isabout these solutions.
The Role of Artificial Intelligence
There is one potentially significant exception to everything I’ve saidabove. Computer software and hardware technology are now advancingat such a rapid pace that the development of artificial-intelligence-basedtrading systems will, I feel, in the very near future, become a significantforce in the marketplace. I refer here specifically to systems based onneural networks. Until recently, the computer hardware and softwarerequired to mimic the functioning of the human brain were extremelycostly and, in many cases unavailable other than to government andlarge industry. However, with the advent of affordable computer memo-ry as well as faster computer chip speeds, the ability to analyze vastamounts of data on many levels simultaneously has opened the door tomany new and powerful possibilities in the field of artificial intelli-gence. Artificial intelligence is the terminology used to describemachine-based or computer-simulated intelligence which attempts toChapter Three
simulate the human problem-solving process. Applications of this newtechnology are already being used in science, business, and industry,particularly in the area of industrial robotics.
A sophisticated branch of artificial intelligence, termed neural net-works, actually models its software on theories of human thinking,attempting thereby to replicate the human learning process and more-over to create a computer program which learns from its errors andwhich performs better with more experience. Although both these goalsare certainly lofty, they are not unrealistic in terms of current and antici-pated technology. Because neural networks seek to mimic the intellectu-al processes of the human brain, this new technology promises to spawna vast new area of trading methodology, one which is based on comput-er-learning models. If the promise of the neural networks in the area ofstock and futures trading turns out to be as significant as I think it will,then this new technology could very well change system developmentdramatically.
Because each neural analysis program is distinct in terms of the mar-ket forces it examines and the learning model it employs, very few neur-al programs will reach the same conclusions at the same time. Hence,neural analysis also promises a multiplicity of approaches which, in thefinal analysis, would be considered an extremely positive factor for themarkets, since the players would not all be playing from the same sideat the same time or from similar timing frames. Because of how neuralanalysis reaches its conclusions, the focus is primarily on pattern recog-nition and evaluation of current relationships which appear to be driv-ing the markets. Since forces underlying market trends change from dayto day and week to week, neural analysis programs which are sensitiveto such changes will also change. Neural systems are dynamic; they arenot subject to the static limitations of traditional trading systems. This,of course, promises to eliminate one of the major objections to tradition-al trading systems, which is that they are not responsive to underlyingmarket conditions which can change dramatically over time.
Finally, neural analysis programs evaluate market inputs as do tradi-tional trading systems. However, neural analysis programs can evaluatea vast number of inputs (by inputs I mean indicators), analyzing thecombination of indicators which are driving market prices at any giventime. Neural programs can combine the evaluation of technical as wellas fundamental inputs concomitantly. In this way, neural analysis pro-grams represent a quantum leap, possibly a several-quanta leap, fromtraditional systems. You'll be hearing much more about these new sys-tems in the next few years.The Illusion of System Testing 29
Implications for the Future
Inasmuch as neural analysis programs could very well replace tradition-al trading methodologies during the next several years, the questionnaturally arises “why learn what's contained in this book or for thatmatter in any other system related or indicator related book?” Theanswer is simple indeed. As you have realized by now, my emphasis isnot on system and not on technique per se, but rather on the develop-ment of trader skills. Skill is what ultimately separates winners from losers.The most effective trading system in the hands of an unskilled, undisci-plined impulsive trader is a tool of destruction.
Conversely, a mediocre trading system with a knowledgeable, experi-enced, disciplined, and well-heeled trader at the helm can be a vehiclewhich affords vast wealth. Therefore, the skills which you will learn inthis book (provided you are attentive and self-directed) will serve youwell in the future regardless of the trading methodologies you choose toemploy. Furthermore, the skills you learn in this book can be used asintellectual inputs for a neural computer brain which, when combinedwith other inputs can yield outstanding results. Hence, your time andmoney will lead to positive results regardless of what hot new systemsmake their debut. But enough for theory—now on to practice!Day Trading withMoving Averages
Certain assumptions are made throughout this book regarding the exper-tise of my readers. Possibly these assumptions are too optimistic.Nevertheless, I will assume that, since you have purchased a book onday trading, you have already acquired some degree of sophisticationand knowledge about the futures markets. I will, therefore, dispensewith basic definitions in many cases, assuming that you are alreadyfamiliar with the standard trading terminology. From time to time, how-ever, I may choose to insult your intelligence by explaining simple con-cepts, knowing full well that you already have developed your ownunderstanding of them. My purpose in doing so is to rectify what I feelis, at times, an erroneous understanding of these concepts. In addition, Imay wish to redefine some terms within the day-trading context.
Moving Average Basics
Of all systems available to traders in the futures and stock markets, thevast majority are based on moving averages, or on a variation of themoving average theme. Moving averages are not difficult to under-stand, relatively simple to apply, and frequently quite easy to calculate.There are many different types of moving averages. Here is a brief list ofthe moving averages I’ve worked with, followed by a brief definition:
1. Simple Moving Average (MA). This is a moving average inwhich each price of the data series is assigned the same weight or value.Day Trading with Moving Averages 31
A 10-period moving average is calculated simply by summing all 10 val-ues and dividing by 10. The second moving average value is calculatedby dropping the first raw value from the sum and adding the eleventhvalue to the sum. This leaves a window of 10 data points which are thenadded and divided by 10. The result is a second value in the movingaverage series. The process continues with the next raw data point. Amoving average of x time units in length always has x data points.When I refer to a 10-period moving average, I mean specifically 10 days,10 hours, 10 years, 10 months, or 10 segments of 5 minutes each. Thetime length of the unit is referred to as the period.
Typically in our work with intraday moving averages, we will bedealing with moving averages of from one minute to one hour in lengthas the period. Consequently, if I refer to a 10-period simple movingaverage on the one minute data, I am referring to a moving average cal-culated by adding together the most recent ten one minute prices anddividing by ten. If I refer to a ten hour moving average I am referring tothe last ten hours worth of prices (one price per hour), added together,and divided by ten.
2. Exponential Moving Average (EMA). An exponential movingaverage is calculated slightly differently than is a simple moving aver-age inasmuch as it exponentially weights each value. The purpose ofusing an exponential moving average is, theoretically, to provide anaverage which will be more responsive, theoretically, to the underlyingdata. There are numerous sources which you may consult for thespecifics of exponential MA calculation.
3. Weighted Moving Average (WMA). This is one of my favoritemoving averages, because it does not assign equal weight to all values in thedata series. There are two types of weighted moving averages, front-weighted (also called front-loaded), and back-weighted (also called back-loaded). If we refer to the front portion of the data as the most recentdata, then a front-weighted moving average multiplies each valuetoward the most recent data by a constant weight or value in order toprovide for greater impact of current data. A back-weighted movingaverage weights the earliest data in the price series in order to give itmore significance in the final analysis.
4. Smoothed Moving Average (SMA). A smoothed moving aver-age is a weighted moving average of a different type. In this case, thedata is “smoothed” mathematically to keep the averages from movingaround too much. This, it is hoped, will yield more stable signals.32
Chapter Four
5. Triangular Moving Average (TMA). Yet another type of mov-ing average, the TMA is weighted to emphasize the normal statisticaldistribution. In other words, the extremes of the TMA are less heavilyweighted than is the central portion of the TMA. This results in theTMA being more centered and, therefore, more responsive to the normaldistribution qualities of the data series. In order to calculate a 7-dayTMA, for example, the following procedure would be used:
Raw data: A, B,C, D, E, F, G (7 days)
Step 1:x=(1XA)+(2XB)+(XC)+4XD)+(BXE)+(2XF)+0 XG)Step 2:
TMA = x/16
(16 = sum of all multipliers)
 
Traditional Moving Average Systems:Assets and Liabilities
Moving averages have been used with varying degrees of success bystock and futures traders for many years. Although there are literallyhundreds of possible variations on the theme of moving averages, thefact is that moving-average-based trading systems, regardless of theirspecific type, have some distinct limitations and only a few advantages.
Moving-average systems are trend-following systems. They get traderson board once a move has started based on the averages, and they eitherexit positions (i.e., “go flat”) when a move has ended or reversed. MAsperform well when markets are in trends. However, in trendless or side-ways markets, moving averages experience their greatest limitations anddownfall. Since markets are in strong trends perhaps only 30 percent ofthe time, you'll find that most moving-average-based systems are accu-rate (i.e., correct) between 20 and 50 percent of the time. This relativelylow accuracy rate, however, does not mean that MA-based systems can-not make money. They can, provided the essential rules of risk manage-ment and good trading are carefully followed. Before I discuss these,however, I'll give you some of the pros and cons of MAs.
As previously stated, the problem with moving-average-based sys-tems is that they are not very accurate. Moving averages are laggingDay Trading with Moving Averages 33
indicators. A lagging indicator, as its name suggests, is an indicatorwhich follows the market and which, by its very nature, does notchange direction until after the market has changed direction. The positiveaspect of such indicators is that they frequently do not change directionuntil a new trend is under way.
The downside, is, of course, that what the moving-average perceivesto be the start of a new trend may actually prove to be a very brief trendor it may be nothing more than a random variation or “hiccough” with-in the existing trend. But most moving-average systems aren’t smartenough to distinguish between a real trend or a brief variation withinthe existing trend. Hence, they generate many false signals. Movingaverages are basically deaf, blind, and dumb indicators. They say to themarket, “lead me and I will follow.” Provided the market establishes ameaningful trend, moving averages will do well. Yet, in the absence ofameaningful trend, or in a whipsaw type market, moving averages willsuffer terribly, taking loss after loss after loss.
Another severe limitation of moving averages is the fact that in orderto immunize them from false turns, they must necessarily sacrifice sen-sitivity. It is not, therefore, uncommon for a considerable amount ofprofit to’be given back in awaiting an MA signal to exit an existing posi-tion. Similarly, new positions will often be entered well after a trend haschanged, therefore, also sacrificing a good deal of the potential profit.Although there are some ways to minimize this problem period, it is,nevertheless, a serious one.
Yet another significant problem with moving-average-based systemswhich arises from the two foregoing limitations is that the systems arefrequently incorrect. Most moving-average systems are correct between20 and 40 percent of the time, with the upper end of this range being theexception rather than the rule. Yet, in spite of these limitations, moving-average-based systems continue to be very popular among technicaltraders and in particular, among fund managers. Why? There are sever-al reasons: First, the use of moving averages requires very little in theway of sophisticated mathematics. Basic moving averages can be readilycomputed without the assistance of a computer and can be determinedquickly, both on a daily as well as on an intraday basis. Second, moving-average-based systems provide specific trading signals which are afunction of moving averages and/or prices crossing above or below oneanother. This is ideal for the strictly mechanical trader. Third, movingaverage systems in most cases are always-in-the-market systems. Thismeans that with few exceptions, these systems go from long to shortand short to long and rarely maintain a neutral position. The idea ofalways having a position in the market appeals to many traders who34
Chapter Four
know that some of the most spectacular and potentially profitable mar-ket movements occur when traders do not have positions. Fourth, manytraders fear that by not having a position, they take the risk of not beingable to establish one at a reasonable entry price once a major price movehas started. (Of course, commodity fund managers who share in thecommission income of their funds love MA systems which trade fre-quently. After all, they can’t help but have a vested interest if they par-ticipate in the commissions.) Yet a fifth reason for the popularity of mov-ing averages is found in their simplicity. Moving averages do notrequire a great deal of understanding about the markets, yet they offerthe attraction of a mathematical model which rings the bell of moneymanagers who have a public image to maintain.
Because moving averages are so amenable to quick calculation, andbecause the signals derived from moving averages are so precise (do notconfuse precise with accurate), they lend themselves readily to applica-tion for short-term and day trading.
This chapter will focus on a number of specific moving average appli-cations which day traders may use, frequently with considerable suc-cess, provided they follow certain very basic rules of application.Although some of the methods are traditional, you will find some newand interesting ideas in what follows.
Moving Average Crossovers—Priceversus MA
One of the most traditional and time-tested methods of moving-averageapplication is the price-cross-above-MA method. The relationship is avery simple one indeed.
As long as price is above the MA, trend is defined as up
As long as price is below the MA, trend is defined as down
 
When price has been below the MA and then crosses above the MA,the trend changes from down to up. When price has been above the MAand then crosses below the MA, the trend changes from up to down.Figure 4-1 illustrates the basic relationships using a 9-period simple MAon a 30-minute T-bond futures chart. Note my comments.
Day traders may use price/MA relationships and crossovers to gener-ate buy-and-sell signals as indicated in my illustration. As an aside,please note that my comments here are offered within the constraints ofDay Trading with Moving Averages 35
 
Figure 4-1. The basic relationships between the MA and price using a 9-periodsimple MA on a 30-minute T-bond futures chart.
the limitations I have previously given you about the use of movingaverages. The simple rules are:
Buy when price closes above the MA after having been below it.Sell when price has been below the MA after having been above it.
Using these rules yields many false signals, generates considerable com-missions, and in the long run (or short run in this case) results in moreheat than light.
There are two ways to overcome the above problem. First, adjust or fitthe length of the MA to the market, and, second, determine which timeframe is best. Here are some of my suggestions.
1. Length of the MA
Typically, the length of the moving average should be adjusted to accom-modate the characteristics of the market which is being traded. In somecases, a slower MA serves the purpose better than a faster MA. Figure 4-2shows the same market as Figure 4-1 but with an 18-period MA.
As you can see, the results are much, much better. And Figure 4-3illustrates the same MA, 18 periods of 30-minute data on Swiss francfutures. Note my comments on each chart. You can see that buy signalsoccur when the closing price for each price bar has crossed above the36 = Chapter Four
ie
 
 
 
Figure 4-2. The basic relationships between the MA and price using an 18-peri-od simple MA on a 30-minute T-bond futures chart.
 
Figure 4-3. The basic relationships between the MA and price using an 18-periodsimple MA on a 30-minute Swiss franc futures chart.Day Trading with Moving Averages 37
moving-average price, which is the solid line. Conversely, sell signalsoccur when the closing price for a given price bar falls below the MAvalue. The relationships are extremely simple and mechanically applied.
2. Time Frame
The best time frames for a day trader are from 1 minute to 30 minutes. Ican tell you from personal experience that if you want to go crazy you'lluse a 1-minute time frame for trading. If, however, you want to retainsome degree of sanity and order in your life you'll use a 5-minute timeframe at the shortest. Many traders swear by the 3-minute charts inStandard & Poor’s (S&P). I think this has merit as well. You will, how-ever, have to decide what you want to do with your time. Trading by3-minute charts, even by 5-minute charts, will require you to be “glued”to your price quotations. Most of the methods and techniques Ill dis-cuss in this book will be applicable to time frames ranging from 5 to 30minutes.
Evaluation of the MA Crossovers—
Price versus MA as a SystemAlthough this approach is certainly simple enough and easily applied tovirtually any market, it is not a good system to use in markets which arenot trending. It is essentially a method which will lose you moneybetween slippage and commission. Although the rules of entry and exitare specific and objective, using this method profitably is more of an artthan a science. The overall accuracy of this technique is about 25 to 35percent at best. If you are using this method on 30-minute to 60-minutedata, strictly according to the rules outlined above, then an MA length ofabout 10 to 14 units is best. The results, after slippage and commission,however, in most markets will be less than $100 per trade. I suggest youstay away from this method. It will only cost you time and money.
Dual MA Crossover
One way to limit the number of false signals from MA systems is to usetwo MAs as opposed to one. In this case the rules of application are alsovery simple. Typically, the two MAs used are related on about an 8 to 1ratio. In other words, if we were using a 3-period MA of the 5-minuteclosing prices for our first MA, then we would use about a 24-periodMA of the closing price for our second MA. This would give us thedesired ratio. Do note that in some applications of MAs (to be discussed38s
Chapter Four
later) these ratios do not apply. Figure 4-4 shows the basic buy-and-sellsignals on a 30-minute T-bond futures chart.
The purpose of using two MAs is to slow the response time requiredfor a crossover. This slowing, although beneficial because it generatesfewer false signals, is also detrimental in that it requires a slowerresponse time by the moving averages and thereby signals marketentries and exits less responsively. It is, therefore, very important toselect the correct combination of MA lengths, a combination whichstrikes the proper balance of response time and minimization of falsecrossovers. :
Although the application of two MAs seems to offer more profitpotential and fewer false signals than does the price-versus-MAcrossover, this approach is not one of the better ones, since it is subjectto the limitations of all lagging market indicators. As a point of informa-tion, note the performance history test results in Figure 4-5 for the dualMA crossover system in S&P futures. .
As you can see from the accompanying historical performance sum-mary (Figure 4-5), the dual MA crossover system is not impressive. Notethat the results reflect carrying positions overnight. By closing out posi-tions at the end of the day the performance of this method deterioratesinto a losing proposition. Hence, I urge you to stay away from it for thepurpose of day trading. In S&P futures this method is an overall loser.
 
Figure 4-4. Basic dual MA (3 and 24 periods) and buy-and-sell signals on a 30-minute T-bond futures chart.Day Trading with Moving Averages 39
                                                             
  
MovAvg Crossover SP E92-30 min 01/02/92-12/31/92Performance Summary: All Trades
Total net profit $20325.00 Open position P/L $0.00Gross profit $49025.00 Gross loss $—28700.00
Total number of trades 137 Percent profitable 55Number winning trades 76 Number losing trades 61
Largest winning trade $2550.00 Largest losing trade $—800.00Average winning trade $645.07 Average losing trade $—470:49Ratio average win/average loss 1.37 Average trade (win & loss) $148.36
Maximum consec. winners 9 Maximum consec. losers 4Average bars in winners 6 Average bars in losers 5
Maximum intraday drawdown $—3400.00Profit factor 1.71 Maximum contracts held 1Account size required $6400.00 Percent return on account 318
The performance summary above is based on thefollowing assumptions:
3- versus 54-period moving average
30-minute S&P futures
1/2/92-12/31/92
$800 stop loss
No deduction for slippage and commissionPositions exited at the end of the trading session
Figure 4-5. Results of the dual MA crossover system in S&P futures. (Reprinted with permission of OmegaResearch Inc.) .
Moving Averages as Support andResistance
Another approach to the use of MAs is to use them as measures of sup-port and resistance. Generally defined, support is a price level fromwhich the market is expected to recover should it decline following aprice rally. Resistance is a price level from which the market will declinefollowing rallies in a declining trend. Support is used for the purpose ofestablishing long positions on price reactions or corrections during ris-ing markets. Resistance is the price level used to establish positions onrallies during bear trends.
Throughout the history of the stock and futures markets, there havebeen many efforts to specifically define support and resistance levels or40
Chapter Four
areas. Each school of market analysis has developed its own methodolo-gy. Chartists point to certain formations, trend lines, and price-versus-volume patterns as their preferred methods of establishing technicalsupport and resistance levels.
Point and figure chartists have their unique methodology as well.And, of course, those who adhere to the teachings of Gann and Elliottuse percentage retracements and Gann angles as their preferred methodsof establishing support and resistance. While I do not discount the valid-ity or potential of such methods, I find them too intricate. I have devel-oped my own approach which has served me well during the years. Thesections which follow illustrate some of these ideas and techniques.
Dual MA Crossover for Support andResistance
This approach is a very viable one for day traders, and I recommend itstrongly. Its assets are clearly that it provides a specific entry pointeither long or short and that it is relatively simple to use. Its downside isthat it is not an entirely objective or strictly mechanical methodology.
Applications of Intraday MA forSupport and Resistance Trading
The application of this approach is very simple indeed. There are basi-cally three steps involved:
= Use the dual MA crossover method. The 8:1 ratio suggested earliershould be used.
= Determine market trend. This is accomplished by examining the pricechart for the last crossover signal. If the last crossover signal was abullish signal, then the trend is assumed to be up. If the last crossoverwas a bearish signal, then the trend is assumed to be down. Pleaseconsult Figures 4-6 and 4-7 for specifics.
= Buy on price declines to the longer of the two MAs which serves as supportand sell short if the trend is down on price rallies to the longer of the twoMA lines. Again, please consult Figures 4-6 and 4-7 for specific illus-trations of this approach. In any case, you will close your position,win, lose, or draw, by the end of the trading session.
Since there is no way to determine whether a decline to the MA line ina bullish market is the start of a new trend down or just a reaction onwhich to buy, you will need to protect your position with a stop loss.Day Trading with Moving Averages 41
 
Figure 4-7. Dual MA crossover signals using support and resistance.42
Chapter Four
Stop losses may be determined in any of several ways as previouslydiscussed.
You may use a strict money management stop, in other words, risk afixed dollar amount on each trade or you may use a specific techniquefor stop loss placement. The money management or dollar risk stop isself-explanatory; however, I remind you that larger stop losses are oftenpreferable to smaller stop losses, since markets do need room to negoti-ate their moves.
In terms of time frame, I recommend using between 5-minute, 10-minute, or 30-minute data. Anything shorter than 5 minutes would betoo short, and anything longer than 30 minutes would not allow for suf-ficient time before the trading session ends.
In Summary
In the final analysis, I recommend against the use of MA systems for thepurpose of day trading. Although there are variations on the theme ofmoving average (to be discussed), the vast majority of methods do notyield much more than losses and commissions. Avoid these systems
_ since they are not likely to serve you very well. The use of MA systems,’ however, with support and resistance as trend indicators, does have
good potential, but it is difficult to subject to a rigorous test.Intraday Applicationof Stochastics
The application of the stochastic indicator (SI) as originally developedby George Lane offers considerable potential for day traders as well asposition traders. Those who are familiar with the immediate action sig-nals which were presented in the Stochastic Fantastic book co-authoredby Mark Silber and myself are aware of how the SI may be valuableboth in position trading as well as in short-term trading.” The immediateaction signals are also likely to be applicable in the day-trading timeframe; however, they have not yet been fully tested.
This chapter will illustrate some basic applications of stochastics forthe purpose of day trading and will provide some guidelines regardingSI application and applications, possible limitations, advantages,