calculate thechange (return) from one day to the next, then calculate the standard deviation of those returns.The last step is to multiply the result with the square root of 252 trading days in order toannualise the volatility.To recap, the standard deviation is calculated by firstly calculating the average return andsecondly summing the squared differences between the average return and each individualreturn and thirdly to divide the sum of the squared differences by the number of days minus one.Once you know the volatility of the underlying you are about to chart, we recommend that youuse one of the suggestions found in Table 4.2 below or Table 4.3 below and overleaf.If you can’t adjust the scale then use table 4.2 where different minimum reversal numbers aresuggested in dependence of the volatility. If you can change the scale then use table 4.3.Table 4.2 – Minimum box reversal numbers
Volatility
Recommendation
Below 25%
Use 2 box reversal
Between 25% and 45%
Use standard 3 box reversal
Above 45%
Use 4 to 5 box reversal
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Table 4.3 – Standard scale
Price
Vol 10
Vol 20
Vol 50
Vol 50
from
to
(Box Size)
(Box Size)
(Box Size)
(Box Size)
6
14
0.05
0.10
0.20
0.40
14
29
0.13
0.25
0.50
1.00
29
60
0.25
0.50
1.00
2.00
60
140
0.50
1.00
2.00
4.00
140
290
1.25
2.50
5.00
10.00
290
600
2.50
5.00
10.00
20.00
600
1400
5.00
10.00
20.00
40.00
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New Optimisation Techniques
Optimisation with a standard software packageAs far as we know, no specific optimisation package for point-and-figure exists on the market.We programmed our own for that very reason, but also because we needed additional statisticalfunctionalities for our academic studies.If you do not have the patience to program your own specific p&f optimisation program, it istill possible to perform optimisations with the standard software packages we have alreadymentioned in this book. (They are listed for reference in Appendix 3 on page 249). What youwould have to do is to chart your favoured stocks with different box sizes and reversal numbers.Advice for doing your own testing:1.
First, make sure that your data is correct and up-to-date.
2.
Choose a limited number of stocks and sector indices.
3.
Use a systematic approach and write down your results. A journal of your optimisationswill help you when you do the next optimisations some months later.
4.
In your systematic approach, just test a few combinations. Consider also ourvolatility/parameter table.
A good start is to test reversals of 2, 3, 4 boxes and standard box sizes and half of the standardsize. That’s all. Then as we mentioned above, you would choose the best combination of the twofor your stock, and use the combination on subsequent data.For example, you do your six combinations (3 reversals times 2 box sizes) on data spanningfrom 1996 to 2001, and then you choose the one you like most and apply it on the data from2001 to 2003. You should also use the same parameter combination for a couple of relatedstocks and see whether the parameter combination works fine for the other stocks too.Through that process you get the best combination of box size and reversal, and can then use itwith confidence for your trading.Most important of all, if you run those optimisations thoroughly you will get a good feeling forthe behaviour of your tested stocks. You will see which parameters work through a quiet phaseand which ones should be applied during a volatile phase. This type of information, as itbecomes evident, should be written down in your journal, as it will help you to economize workon subsequent optimisations. It will also help you to adapt parameters quickly when the marketclimate changes.All the software packages that we recommend allow you to do this. Sure, it is more tedious thanusing a specific program like we do, but it is absolutely feasible.In the section which starts overleaf, we show charts used to visually analyse the parameters fortrading BSKYB with Updata. What you have to do is to analyse the different charts in thecontext of your favourite trading strategy.187
The Complete Guide to Point-and-Figure Charting
Example of a visual determination of an optimal parameter setOn the Updata charts you can see some filled dots, which represent the changes from one monthto the next. Based on those dots it is obvious which parameter sets give frequent signals andwhich do not, as a chart with lots of dots is a chart that covers many months and is thereforecondensing information and giving a small number of signals.(As a quick reminder: the frequency of the signals increases – or, putting it the other wayaround, the compression of the price moves decreases – when the box size or the minimumreversal numbers are decreased.)The logarithmic scale used in the Updata example is similar to the standard scale, whereby thepercentage number indicates the box size in percentage points. For example, in a 1%logarithmically-scaled p&f chart the box size is always 1%, which means that when the stock istrading at 10, the box size is 0.1, or when it is trading at 250 the box size is 2.5.The charts shown are scaled logarithmically with a box size ranging from 1% to 3% andminimum reversal numbers from 2 to 5. What you can clearly see is that small numbers in bothbox size and minimum reversal number increase activity substantially.When you do a graphic analysis of different parameter sets you have first to generate all therelevant charts and then take your time to study each of them and decide which parameter setresults in a chart that you consider to represent the behaviour of the stock the optimal way. Youwould especially look at signal frequency and false signals.Please note that the first four charts are bullish, the fifth is bearish – as the buy signal is notformed yet, and the last – which is very slow – is bullish again, as a preceding bearish signalwas never generated.Also, BSKYB moves quite a lot. For a less volatile stock, less compressing parameters wouldhave to be used, like starting with a 2 box reversal with a log scale number of 0.5%.
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New Optimisation Techniques
Chart 4.1 – BSKYB [BSY.L], 2 box reversal, 1% log scaled
The above is too active for many investors. Small price moves generate signals. For instance, asimple buy could be generated by a reversal of only 18p, that is, 12p for the reversal plus 6 forthe breakout.The parameters used might, however, work better for a less volatile security, and might beacceptable to an active trader who pays low commissions.
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The Complete Guide to Point-and-Figure Charting
Chart 4.2 – BSKYB [BSY.L], 3 box reversal, 1% log scaled
The combination 1% x 3 (3 box reversal, 1% logarithmic) is often used as a starting point,especially for stocks with a volatility in the mid-range. The resulting chart explains the pricemoves of BSY well and would be suited for an active investor or a stock analyst.
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New Optimisation Techniques
Chart 4.3 – BSKYB [BSY.L], 2 box reversal, 2% log scaled
The combination 2% x 2 (2 box reversal, 2% logarithmic) is seldom used for stocks. We can seethat in the above case the chart lacks some of the sharp definition of the 1% x 3 chart. This isdue to the smaller minimum reversal number which allows for more frequent interruptions of agrowing column. Such a combination would need a 6% move for a simple buy or sell signal tobe generated. 6% equals 2% times 3 (2 minimum reversal plus 1 for the signal).
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Chart 4.4 – BSKYB [BSY.L], 3 box reversal, 2% log scaled
The combination 2% x 3 is classic for stocks and always a good starting point. Being ‘slower’in the sense of less frequent signals or new columns, the chart compresses price informationmore than the former parameters and is therefore geared towards a longer investment timehorizon.
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New Optimisation Techniques
Chart 4.5 – BSKYB [BSY.L], 3 box reversal, 3% log scaled
The slow combination of 3% x 3 results in a bearish chart. In contrast to the earlier charts thebuy signal is not formed yet as it would need 4 more Xs to be added to the current column.
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Chart 4.6 – BSKYB [BSY.L], 5 box reversal, 3% log scaled
This extremely slow 3% x 5 is shown in order to give an idea of further compression of the pricemovements. In this chart, for example, the up-move from about 400 to about 1,800 is one singlecolumn formed over the period of 8 months. Such a slow parameter set would be used to analysestocks over a very long time horizon.
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New Optimisation Techniques
How to benefit from optimisationsTrading ultimately comes down to making bets like –‘Is the economy going to grow or shrink?’or‘If a war breaks out, given the political situation, should I bet on the increase of the price ofsome commodity?’and so on. Optimisations tell you what sort of parameters are best in a specific marketenvironment. Standard three point reversal works and is the best way to chart pricemovements.If, on the other hand, you want to add bells and whistles, you can use optimised parameter sets.These parameter sets are optimised for certain market conditions. However – and this is crucial– you have to decide when and which optimised parameters to use.This is very similar to what we experienced in many years of options trading. You can use themost sophisticated models to price options, but you have to be aware that they areparameterised, and ultimately you have to choose which model to use. At nearly every majoroptions conference, new models for pricing some sort of options are presented. However, allthese new models need input, be it the model defining the volatility, the probability distributionsused or whatever.This need for input or parameterisation means that there is never one ‘best’ model that exists forall. It is up to the option dealer to decide which model to apply for pricing a certain option.Similarly, in the optimisation of point-and-figure parameters, the investor or trader has to makesome fundamental choices, especially concerning which security to trade and whether thatsecurity follows a similar behaviour in the future as in the past.We believe that the tables giving the different sets of parameters for different volatility scenarios(Tables 4.2, 4.3 on pages 185-186) are extremely useful in this respect. In order to benefit mostfrom optimisations, you need to use common sense. Do your optimisations on reliable data andoptimise over a price history that you expect to be similar to the future price history.The point about reliable data is obvious. To illustrate the point about the price history, considerthe following example: it would be wrong to use data in the period of the Iraq war to optimisepoint-and-figure parameters for charting and trading Crude Oil futures in the period after thewar, because during the war the high amount of political, exogenous impacts to the price wereuntypical. Parameters found during the Iraq war should be employed during another similarhighly volatile period.
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Chapter
5
Profitability analysis
does P&F charting really work?
•
Why P&F is a good trading system
•
Introduction to the historical studies
•
The profitability of P&F stock trading - theoriginal research
•
The profitability of P&F commodity futurestrading - the original research
•
Later commodity profitability research
•
Profitability of P&F commodity trading 1996-1997
•
Profitability of P&F stock trading 1994 -1996
•
Weber-Zieg study on European stocks 1988-2002
The Complete Guide to Point-and-Figure Charting
Why P&F is a good trading systemThe attributes of a good trading system are:•
ease of maintenance
•
simplicity
•
the ability to understand its functioning
and, of course•
profitability
Many believe that profitability is all that matters, and to some it is the only consideration. Butthe average trader would generally sacrifice a few percentage points of profit for the ability tofully understand the functioning of the system itself. There are many systems which performwell, but because of their complexity do not lend themselves to ready understanding. Theiringredients might include exponential weightings, non-linear relationships, probabilityexperience factors, data smoothing, filtering algorithms, and the like. They require substantialcomputer capability, and few feel truly comfortable or are willing to put complete faith in acomputer output, the derivation of which they only vaguely comprehend.A good trading system must also involve the trader in the decision-making process. For,regardless of the lip service we give to satisfaction derived from the compound annual rate ofcapital appreciation, the thesis expressed by Adam Smith in The Money Game prevails – wereally play for the fun as much as for the money. We must win on occasion, of course, to stay inthe game. And we want to win as often as possible, for this is the measure of our skill at playing.But we need to be involved in the decision-making process if we are truly to enjoy theexcitement of the game. We must feel that we are personally analysing the data, making adecision, and then entering the order. If the trade is a success, we take the credit for superiorjudgment and this makes the game worthwhile. If the trade turns sour, the whippy market is ourscapegoat.When a computer, programmed by another person, gives a signal and it is a success, we can takeno part of the credit. This sophisticated trading game may be more profitable than a simpletechnique, but it certainly is not as much fun to play.In this sense, point-and-figure charting provides the best of both worlds. It is simple tounderstand, costs virtually nothing to maintain, demands daily personal involvement, can serveas a scapegoat for bad trades, yields credit to the user for winning trades – and it is profitable.
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Profitability Analysis
Introduction to the historical studiesHow well has point-and-figure charting performed over an extended period of time, and howwell has it performed recently? These seem like questions for which it should be easy to obtainanswers, as point-and-figure is one of the more popular charting techniques in use today. Butjust try to find the answer to these profitability questions! The answers are nowhere to be found.Point-and-figure chartists have generally accepted the system on faith, and – having onceaccepted it – tend to follow it blindly without the need to rely upon constantly updatedperformance records. It seems that few empirical studies have ever been conducted.Other than our own research and academic studies, the only historic profitability studies on p&fcharting we have discovered are the research results of Robert E. Davis and Charles C. Thiel,Jr., published by Dunn and Hargitt in limited printings.
The profitability of P&F stock trading - the originalresearchIn 1965, Davis authored Profit and Probability, which provided the results of an extensive testof traditional p&f techniques as applied to common stocks. The study covered two stocks forthe period of 1914 through 1964, and 1,100 stocks representing all industrial groups for theperiod 1954-64.In his study, Davis defined sixteen entry points – as discussed in detail in Chapter [ ]. Eightof the sixteen entry points were used for closing short positions and establishing long positions;seven of the other eight formations were used for closing long positions and entering the marketon the short side. Of the sixteen formations, all but the S-8, the Downside Breakout Below aBearish Support Line, were analysed for profitability and reliability.In this study Davis assumed that a position was taken on each appearance of a formation andthe position was held until a stop was executed with a stop being defined as the first appearanceof a simple signal, B-1 or S-1, on the other side. Thus regardless of what formations signalled along position, the long would always be liquidated on the very first appearance of an S-1.Conversely, a short position would be covered on the first occurrence of a B-1.The results of this research are interesting. For example, it was found that Ascending Triple Tops(B-4) represented 6.6% of all buy signals generated. Of the B-4s that occurred, 79.5% wereprofitable and 20.5% were losses. The average profit was 36.0% of the full cash investment(assuming no margin leverage) and was realized in 8.0 months. The average loss was 8.1%realized in 3.6 months.Using the Breakout of a Triple Bottom (S-3) as an example of a short-sale formation, it was found199
The Complete Guide to Point-and-Figure Charting
that this formation represented 15.5% of all short-sale formations, was profitable 93.5% of thetimes that it occurred, and realized an average profit of 23.0% in 3.4 months. In the 6.5% of theoccurrences which resulted in losses, the losses averaged 8.7% and were realized in 3.4 months.But most importantly the often quoted frequency of occurrence of the point-and-figure signalswas established in this study. We have cited the results before, but include them again:Table 5.1 – The Davis Study: frequency of occurrence of buy and sell formations
Buys
Freq in %
Sells
Freq in %
B1
12.8
S1
13.5
B2
56.9
S2
56.4
B3
15.3
S3
15.5
B4
6.6
S4
8.4
B5
2.4
S5
1.6
B6
1.2
S6
2.6
B7
3.9
S7
1.4
B8
0.799.8
99.4
Davis has also published profitability numbers associated with the signals. We have looked indepth into his research and have found some questions regarding those numbers and prefertherefore not to quote them.
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Profitability Analysis
The profitability of P&F commodity futures trading —the original researchUpon completing this, the only comprehensive analysis of the profitability and reliability of themore complex point-and-figure formations applied to stocks, Davis’s interest and researchswitched to commodity futures.His next book, co-authored with Thiel, entitled Point and Figure Commodity Trading: AComputer Evaluation, was published in 1970. It was a brilliant research model designed toprovide trading results for a variety of point-and-figure box sizes and reversal distances, in anattempt to determine the optimal box size and reversal distance for each commodity. Althoughthe optimal box and reversal values seldom coincided with the values generally used by chartiststoday, the results are invaluable because for the period of 1960-69 they reveal, using twodelivery months six months apart, the maximum results which would have been attainedregardless of the box size or reversal distance. The returns using conventional box sizes and thestandard three-box reversal value would naturally be lower, simply because this is not alwaysthe best set of rules by which to play.The results show once again the profitability of point-and-figure: 53% of all 799 signals wereprofitable, and the average net profit on all trades (including both profitable and unprofitableones) was $311 realized in an average of 49.8 days.However, when Dr Zieg came to the subject, he was disappointed to find that the Davis-Thielresearch represented the complete universe of published research on the profitability of p&fcharting. Furthermore, as good as this research was, it ended in 1969; and we as traders areinterested in the here and now, in markets almost unrelated to those of the 1960s in terms ofvolume, volatility, and values. It was therefore imperative to continue the Davis and Davis-Thielresearch and examine more modern markets.
Later commodity profitability researchIn 1974, Zieg conducted his first research by analysing the profitability of commodity pointand-figure charting during late 1973 and early 1974.Initially it seemed that all that would be necessary would be to view charts over a given periodof time, assuming that a long position was taken at each buy signal, and at each sell signal thelong was liquidated and a short position taken which remained in force until the next buy signal.When each position is closed and a new position of the opposite type is taken, the profit or losson the closed trade is calculated, a commission is charged, and the results of all completed tradesare added and then divided by the number of transactions.But unfortunately the research study was not this simple.201
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Problems of research
1.
Trading gapsPoint-and-figure charts do not reveal gaps which, if occurring at a signal point, wouldmean that the transaction price would not coincide with the signal price, and that anyanalysis assuming an entry price equivalent to the signal price would bias the results infavour of the system. For example, if a potential buy signal occurred at 27.00, and ifsugar had a daily high and closing price of 26.99 on one day, thus missing the signal, andopened at 27.99 the following day, all stop orders to buy at 27.00 would be filled at27.99. Therefore, analysis based solely upon the chart and not taking into account actualtrading data would show the system to be more profitable than it would actually havebeen. This is certainly one problem.
2.
Duration of tradesThe next problem, and one of major interest to traders, involves the determination ofthe duration of the trades, meaning the length of time that each position is open.. Sincep&f charts are not keyed to time, making an analysis of the duration of positions from thecharts themselves is impossible.
To overcome these two problems, it was necessary to computerise the three-point reversalmethod of point-and-figure charting, drawing on historic daily price data to determine realisticentry and exit points, and maintaining a file of the days of duration of each transaction.First study
In the first study it was assumed that all entries and exits were on simple signals, B-1 and S-1. Thestudy also assumed standard or non-optimised box values and reversal distances existing in 1974.First study (Zieg 1974) summary
••••••••••202
Analysis covered a period of only 135 trading days ending May 17, 1974.During the period there were 375 signals generated in two delivery months of 22commodities.Of the 44 contract months analysed, 28 or 63.6% were profitable on a cumulative basis.Of the 375 signals, 150 or 40% were profitable.The average net profit after commissions per transaction was $306.48.The average duration of a position was 12.4 days.The total return was $114,930.71.The minimum margin investment being $64,400.The profit represented a 178.46% return on minimum margin (with minimum margindefined as 10% of the contract value).The profit was realized in only 135 trading days, a period slightly over six months.
Profitability Analysis
Second study
A second study was then conducted to review the profitability of the Simple Buy and SellSignals using a slightly different calendar period (135 trading days ending June 28, 1974), 23commodities, (cotton was added), and examining only one delivery month of each commodity,with the delivery month selected generally being different from those used in the earlier study.Furthermore, in this second study, like the first, no attempt was made to optimise the box sizesand reversal distances.The results were very similar to the earlier study in terms of reliability, with the second studyyielding 41% profitable trades compared with 40% in the first study. But in terms ofprofitability, the second study showed a 100% return on minimum margin, down from 178%earlier. The lower return on margin resulted primarily from the higher commodity prices andthus higher margins during the later period.Third study
A third study was also undertaken to test the changes in profitability and reliability of the simpleentry points by optimising the box sizes and reversal distances in a manner similar to, but moreextensive than, that done by Davis and Thiel.The contracts tested and the calendar period under review were held constant with the secondstudy, while box sizes and reversal distances were changed to optimal values. This third studyrevealed that the profitability nearly doubled to 199% while the reliability increased by morethan 50% to 66% meaning that two of every three trades were profitable.The optimisation process and the resulting higher returns have been hailed by students andpractitioners of the markets alike as a significant improvement in point-and-figure charting.Table 5.2 – Summary of the 3 Zieg studies, P&F commodity trading profitability, 1974
Study
Profitability (%)
Reliability (%)
Notes
1
178
40
Non-optimised, 2 delivery monthsfor 22 commodities
2
100
41
Non-optimised, 1 delivery monthfor 23 commodities
3
199
66
Optimsed, [other parameters as Study 2]
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The Complete Guide to Point-and-Figure Charting
Profitability of P&F commodity trading 1996-1997To update all of the prior profitability research, several new studies were undertaken by Dr. Ziegfor both commodities and stocks.The first review dealt with commodity profitability for the 12 month period ending January10th, 1997.Study parameters
•
Only one delivery month of each of 38 commodities including agricultural, industrial,precious metals, energy, currency, bond and index future contracts was examined.
•
One contract position was established at all initial long and short signals.
•
Commissions were set at $40 per round trip trade.
•
Actual average initial margin rates in effect at the major discount commodity brokeragefirm of Lind-Waldock was utilised to set investment capital requirements and forthe calculation of return on investment.
Summary of study results
There were a total of 261 total trades during the 12 months.•
120, or 45%, of the trades were profitable.
•
The average commodity had nearly 7 trades for the year, for an average duration of 52calendar days.
•
The net profit per trade was $238.98, representing a 79% annual return on initial margin*.
* This net of $238.98 per trade is obtained after subtracting all commission costs and losses onbad or loss trades. Thus the trader made an average of $238.98 times the number of tradesexecuted. Or stated differently, for this 12 month time period, had $78,008 in initial margin beenemployed, and every original B-1, B-2, S-1, and S-2 been acted upon in the one delivery monthof the 38 commodities, a net profit of $62,375 would have been generated.In all three time periods, point-and-figure trading of commodity futures contracts was extremelyprofitable.The average profit per trade was $311 in 1960-69, $306 in 1974 and $239 in 1997. Thereliability percentage (percentage of profitable trades to total trades) ranged from a high of 53%in 1960-69 to a low of 40% in 1974 and increased to 45% in 1997. Time duration was 49.8 daysin 1960-69 and 52 days in 1997.No matter how you view the results, it is obvious that p&f commodity trading is profitable.
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Profitability Analysis
Table 5.3 – Profitability analysis of commodity futures, for the 12 months ending January10th, 1997
Commodity
Month
TotalTrades
Cattle - liveCattle - feederCocoaCoffeeCopperCornCottonCrude OilGasolineGoldHeating OilHogsLumberNatural GasOatsOrange JuicePlatinumPork BelliesSilverSoybeansSoybean MealSoybeam OilWheatSugar - WorldT BillsT BondsT NotesMuni BondsS&P 500NYSE CompositeAustralian DollarBritish PoundCanadian DollarEuro DollarGerman MarkJapanese YenMexican PesoSwiss Franc
apr-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97feb-97jun-97mar-97mar-97mar-97feb-97mar-97mar-97apr-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97mar-97
781211689541777914911259610111215646954599422
43362264213343431433435814242323333321
6001,000(4,380)3,6851,2651,3301,4402,100(1,756)6,960(406)2,0403,320(930)(1,060)1,4401,516(1,420)2,0002,5402,760(1,000)5602458510,0502,5103,0277,760(8,110)5,7152,000(2,360)1,2655,5904,4206,295
6758107002,4501,6005401,0652,4252,4251,0132,4259452,1005,4008101,2751,2151,6004001,3501,0805401,0805605502,7001,7552,29515,1207,8001,3251,9006754751,3501,7004,1251,755
261
120
62,375
78,008
TOTALAverage Trade ProfitAverage Trade Length
52 days
# ProfitableTrades
Total Profit 1(Loss) ($)
Margin 2($)
TotalReliabilityReturn (%)(%)8