................____1X_____________X_....OXO............XO....OXO..X.X.......XO....O.OX.XOXO......XO....|.OXOXOXO......XO______OXOXOXO______XO....|.OXOXOXOX.....XO....|.O.OXOXOXO....XO....|...O.OXOXO....XO....|.....OXO2O....XO__________O_O_O__X_XO....|.........OX.XOXO....|.........OXOXOX.....|.........OXOXOX.....|.........OXOXOX.______________OXOXOX_
880860840820800780760740720700680660640620600590580570560550540530520510500490480470
The sequence of trades shown in the chart above is:••••
Short at 510, taking a profit at 480.A losing, or false, long at 520, covered with a loss at 450Simultaneously reversing the position to go short at 450, taking a profit at 440.Long at 530, taking a profit at 590==
For the use of non-trailing stops, however, one obviously needs a way to calculate or estimatethe profit targets. This can be done either visually, by scrutinizing the long-term chart, tryinghorizontal and vertical counts, or by applying statistical methods.We have also studied variable profit targets. We suggest an efficient though simple variableprofit target, which works fine for most of the European blue-chip stocks:Take a profit when it reaches a level equal to 1.5 times the average height of thepreceding three columns.The concept of profit targets is extended over several columns and is not only applied to a pole.170
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As shown in the following example, where we went short at 270 and simultaneously establishedthe profit target to introduce the stop-order. The target is the average over three columnsmultiplied by 1.5, therefore being the sum of the last three column’s boxes divided by threewhich equals 6, multiplied by 1.5 equalling 9. We keep this profit target until the nature of thechart changes – i.e. an opposite signal occurs – or until it is hit. In our example, it was hit at 180,therefore we liquidate the position with a profit and wait for the next signal, which occurs sometime later, namely a buy at 240.Example 3.9 – Profit target - variable, moving average type; at 1.5 times averagepreceding three columnsChart 3.31 – Profit targets360350340330320310300290280270260250240230220210200190180170160150140130
......................X.X........XOXO.......XOXO.......XOXO.......XOXO.......XOXO........O.O..........O....X.....O....X.....O....X.....OX...X.....OXOX.X.....OXOXOX.....O.OXOX.......O.OX.........OX.........OX.........OX.........O....................................
360350340330320310300290280270260250240230220210200190180170160150140130
Explanation: a sell signal is generated at 270. The three preceding columns before the sell-signalaverage a box size of 6. The profit target is established by taking 1.5 times 6 which equals 9.Hence, the profit target is put 9 boxes below the level of the sell signal.
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Example 3.10 – Profit targets, taking a profit earlyChart 3.32 – Profit target, HBOS [HBOS.L]HBOS.L / 3 box rev,880 ....|................860 ....|................840 _____________________820 ....|................800 .B..|................780 .XOX|................760 OXOXO................740 OXOXO________________720 OXOXO................700 OXOXO................680 OXO.O................660 O...C................640 ____1X_____________X_620 ....OXO............XO600 ....OXO..X.X.......XO590 ....O.OX.XOXO......XO580 ....|.OXOXOXO......XO570 ______OXOXOXO______XO560 ....|.OXOXOXOX.....XO550 ....|.O.OXOXOXO....XO540 ....|...O.OXOXO....XO530 ....|.....OXO2O....XO520 __________O_O_O__X_XO510 ....|.........OX.XOXO500 ....|.........OXOXOX.490 ....|.........OXOXOX.480 ....|.........OXOXOX.470 ______________OXOXOX_460 ....|.........OXO.OX.450 ....|.........O...OX.440 ....|.............OX.430 ....|.............OX.420 __________________OX_410 ----0-------------O-400 ----0----------------
880860840820800780760740720700680660640620600590580570560550540530520510500490480470460450440430420410400
We went short at 660. We calculate the profit target, namely 1.5 times the average over the lastthree columns. The last three columns are highlighted. The average is 6 (the sum of 7+6+5divided by three) multiplied by 1.5 equals 9. This target is hit nicely at 540, avoiding aliquidation through a buy at 600.=====Trading the reversals
If you like to trade fast and have low trading costs, try the following: Get into the positions withthe reversal, get out, either based on a set limit (i.e. 1 to 1.5 times average of last three columnsto five columns) or on the next reversal.This strategy is a very active trading strategy. You change positions every new column. Theadvantage is that you follow the minor swings very closely. As an example we use Shire Pharma.172
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Example 3.11– Getting in and out on reversalsChart 3.33 – Shire Pharmaceuticals [SHP.L], generating profits in excess of 1200 pointsSHP.L / 3 box rev,1450 ....|........X............................ 14501400 _____________XO___________________________ 14001380 ....|........XO........................... 13801360 ....|......X.XO........................... 13601340 ....|......XOXO........................... 13401320 ....|......XOXO........................... 13201300 ___________XOXO__________________________X 13001280 ....|......XOXO....X.....................X 12801260 ....|......XOXO....XO....................X 12601240 ....|......XOXO....XO....................X 12401220 ....|......XOXO....XO....................X 12201200 ___________XO_O____XO____________________X 12001180 ....|......X..O....XO......X...........X.X 11801160 ....|......X..O....XO......XO..........XOX 11601140 ....|......X..O....XO......XO........X.XOX 11401120 ....|......X..O....XO......XO........XOXO7 11201100 ___________X__OX_X_XO______XO________XOXO_ 11001080 ....|......X..OXOXOXO......XO........XOX.. 10801060 ....|......3..OXOXOXO......XO......X.XOX.. 10601040 ....|......X..OXO4OXO......XO..X...XOXOX.. 10401020 ....|......X..OXO.O.O......XOX.XO..XOXO... 10201000 ___________X__OX____O______XOXOXOX_XOX____ 1000980 ....|......X..OX....OX.X...XOXOXOXOXO..... 980960 ....|......X..OX....OXOXO..XOXOXOXOX...... 960940 ....|......X..OX....OXOXO..5OXOXO.OX...... 940920 ....|....X.X..O.....OXOXO..XOXOX..6....... 920900 _______X_XOX________OXOXO__XO_OX__________ 900880 ....|..XOXOX........O.OXOX.X..OX.......... 880860 ....|..XOXOX..........OXOXOX..OX.......... 860840 ....|..XO.OX..........OXOXOX..OX.......... 840820 ....|..X..2X..........OXOXO...O........... 820800 _______X__OX__________OXOX________________ 800780 ....|..X..O...........OXOX................ 780760 ....|..X..............OXOX................ 760740 .X..|..X..............OXOX................ 740720 .XO.|X.X..............OXOX................ 720700 _CO__XOX______________OXO_________________ 700680 .BO.|XOX..............OX.................. 680660 .XOX|XO...............O................... 660640 .XOXOX.................................... 640620 .XOXOX.................................... 620600 _XO_OX____________________________________ 600590 .X..1X.................................... 590580 .A..O..................................... 580570 .X..|..................................... 570560 OX..|..................................... 560550 OX________________________________________ 550540 OX..|..................................... 540530 OX..|..................................... 530520 OX..|..................................... 520510 9X..|..................................... 510500 O_________________________________________ 500490 ....|..................................... 490390 ....|..................................... 390380 ....|..................................... 380205 ----0------------------------------------- 205200 ----0------------------------------------- 200
The chart shows the trades produced by the strategy to simply trade the reversals. This strategyis a non-orthodox trading style which distances itself from classic point-and-figure as it ignoresthe formation of trading signals. For example, the simple buy or sell signal is formed over three173
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columns, whereas in trading the reversals on every column a trade takes place. It’s advantage isthe continuous realization of profits and its simplicity. It can be very profitable, but should beused with caution as it is very sensitive to the chosen box size.===
Forget about the RSIMany scholars of point-and-figure claim that they can improve its profitability by filteringsignals based on RSI readings.The RSI (Relative Strength Index), invented by the well-known technician J. Welles Wilder, isan oscillator defined by one variable (number-of-days), that measures the amount of daily upmoves against the amount of daily down moves over a set period of time, which is usually setto 14, 10 or 5.For example, if the time span is set to 10 days, then if the last five days had increasing closingprices (Day 10 higher than Day 9, Day 9 higher than Day 8 etc.) the RSI reading would be 100.If, on the other hand, the prices were decreasingly lower, then the RSI would be 0. And if overthe 10 days the amount of the positive difference was equal to the amount of negative difference,then the RSI would be 50.If the RSI filter did work, then false buy signals should be much more frequent where RSIindicates an over-bought situation and false-sell signals should be more frequent when the RSIshows low, over-sold readings. It would be possible to set an RSI level above which a buy signalshould be ignored and a level below which a sell signal should be ignored. And using that filter,it would be possible to increase the profitability of point-and-figure.Our rigorous statistical studies show, however, that the correlations between both false pointand-figure signals and RSI, and correct point-and-figure signals and RSI are insignificant. Thismeans that filtering point-and-figure with RSI does not increase profitability. Based on ourresearch we do not see any reason for combining the two. However, we do not dismiss thepossibility that there could be ways of using functions of RSI readings, like differentials, inorder to filter signals efficiently.But in general, forget about the RSI.
But don’t forget the big picture day-tradingIf you day-trade stocks, always keep the end-of-day chart in mind. If the end-of-day chart hasclearly defined channels or trend line break-throughs, keep that in mind and day-tradeaggressively in the direction that the end-of-day chart indicates.Thus, before you start your trading session, look at the end-of-day chart of the stock and defineresistances, supports and direction. Then incorporate that information in your strategy for the174
Trading Applications
day. Say you trade an index basket. Short heavily the stocks with bearish end-of-day charts andbuy the index. Go long the stocks with bullish end-of-day charts and sell the index.If you trade a currency on an intraday basis, then consider the long-term chart with the biggerbox sizes. If the long-term chart is bullish, be more aggressive on the intraday buy signals thanon the intraday sell signals.
SummaryIn this chapter we have shown several modifications used by experienced traders.Most important of all is taking the charting seriously and doing it with surgical precision. Thetechniques discussed involved:1.
Keeping the size of losses to a minimum by entering a position near a stop-loss price.
2.
Evaluating signals by real money risks.
3.
Using a reversal criterion for artificially placing a stop-loss point close to the market aftera move of dramatic proportions.
4.
Using price targets to liquidate profitable positions.
5.
Trading the reversals.
These are just five of the techniques practised by successful chartists. They are not mandatory,but are simply ideas and suggestions which should be considered by each chartist. Those whichmake sense should be adopted, and those which don’t, ignored.Develop your own modifications
The list above is certainly not a comprehensive list of good ideas. After using the point-andfigure technique for a while, each chartist should modify it to fit their specific needs, desires,risk capital structure and personality. The implementation of modifications to personalize thesystem is half the fun of this technique. But before any modification is made, think through thelogic of the change to make sure that it is in accord with the basic philosophy of support andresistance patterns, which is what p&f charts are all about.
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Chapter
4
New optimisation techniques
•
Optimisation techniques — making thegood better
•
Optimisations and simulations
•
Point-and-figure parameter sets fordifferent volatilities
•
Optimisation with a standard softwarepackage
•
How to benefit from optimisations
The Complete Guide to Point-and-Figure Charting
Optimisation techniques — making the good better
Why optimise?Point-and-figure charting with a reversal of 3 boxes works fine, but the question remainswhether it can be made any better.Let’s be clear what we want to achieve: we want to find adjustments to the traditional p&ftechniques which work with a higher profitability level in the future with a high level ofconfidence.Optimisation fallacy
The one thing we want to avoid is the optimisation fallacy. When working with past data thereis always the danger of finding trading systems that would have worked optimally in the past,but are not valid for the future.If you optimise any trading strategy on the past data of FTSE stocks, say in the period 1995 to2000, you will definitely come up with a parameter set that would have generated optimalresults during that period. However, to be of any use, you would need to have known thatparameter set in 1995! Or, put another way, for that parameter set to be of any use in the future,you need to test it on the 2000 to 2002 data to see if the results correlate.Optimisation methodology
What we have to investigate first is whether optimisation is a valuable concept at all in thecontext of the current discussion. In order to assess that, we have carried out various analyses.What we have done first is split our data into segments of random length. Then we have runsome brute-force optimisation on those segments and have used the found parameters insubsequent data.As an example we have taken FTSE stock data for the years 1988 to 1991, optimised theparameters (box-size, minimum reversal, strategy) and used those parameters for the data 1992to 2003. Then we have compared the result with the traditional 3 box reversal method.We have also studied all sorts of statistical correlations between values generated by theoptimisations and other statistical data regarding the stock data, like different types of volatility.The results have been positive, and therefore we think it is worth explaining the concepts in a bitmore detail.But one thing should be clear from the start. We mainly see the benefits of optimising forintraday applications. For end-of-day data analysis we actually prefer the traditional 3 boxand standard-scaled chart.178
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The familiarity of traditional parameters for end-of-day P&F
The reason we prefer the traditional parameter set for end-of-day is that the benefit fromoptimising is too small in comparison with the loss in familiarity with the charts. What we meanby this is that we have worked with the traditional 3 box reversal charts for such a long time thatwe are used to them and are able to compare different stocks and market conditions. And also,as we already know from the Davis Study, standard point-and-figure works well.This point about familiarity will be easily understood by experienced traders or investors. Inp&f charting, you want to compare different types of securities over different time spans, andso the parameters regarding minimum reversal value, box size and scaling should be the same.You cannot compare a Citigroup, logarithmic scale with 1% growth, 4 box reversal, with aHSBC, linear scale, 2 box reversal. And especially for trading arbitrage and spreads you need tocompare several securities, which is best done with the 3 box reversal.
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Optimisations and simulationsOverviewOptimisations and simulations are dealt with in the same chapter because they are both relatedto the testing of parameter sets with computers. In the optimisations we seek a set of parametersthat works optimally for a given data series. In the simulation part we check whether the resultscalculated in the past would have worked for the data following the last data point of the datafed into the simulation.Selection of data sets
You can run simulations on historic data or on generated data:1.
Either you take the optimised parameter set and use it on a data sample of real historicprices. That sample should not be included in the dataset you used to find the parameters.
2.
Or you can generate hypothetical prices – normally via a Monte Carlo simulation – anduse your parameters on those prices.
It is important to understand that optimisation and simulation have to be conducted on differentdata series; most logical is the use of successive data. For example, optimisation run on datafrom January 95 to January 2000, which yields the new parameter set; simulation on the datawith the new parameter set from February 2000 until February 2003.The aim is for the simulation to be as close to the real trading as possible. Therefore, issues suchas transaction costs (including commission, bid-ask spread) have to be modelled correctly.Parameter-determination is done in the most straightforward way possible. All reasonableparameter sets are tested and their results scored. The parameter set with the best score is chosenand used in the simulation. This is only possible because point-and-figure charting is such abeautifully uncomplicated system defined by very few parameters.The ‘over-fitting’ fallacy
We strongly warn against using point-and-figure in combination with other indicators, becausesuch an approach leads to the over-fitting fallacy. This fallacy is best described as using differenttrading systems, say point-and-figure, and some oscillator or some index, and based on suchacombination, creating complex trading rules, such as,BUY IF point­figure buy signal AND RSI > SomeValue ANDoscillatorX < SomeOtherValue.
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Such complex trading rules can – by tinkering with the parameters – be adapted to any past dataseries and generate great hypothetical profits. But again, they will not work in the real worldwith real data. Traders using backtesting software usually fall into this trap.Be on your guard against authors or fund managers who claim to have improved point-andfigure by combining it with some other technical indicator. It probably won’t work. The wholeissue is described with lots of humour in the classic book Fooled by Randomness4.Goal of optimisations and simulationsIt is important to briefly discuss the goal of conducting optimisations and simulations.It is indispensable for a trader to look at the charts of the securities he’s trading. When we runoptimisations, we basically try to find the point-and-figure chart that tells the story of the stockin the most convincing way. If you trade regularly or professionally, you know exactly what wemean. If not, let us explain. Every trader has a certain style of trading, be it risk arbitrage,scalping, day-trading or swing trading. The trader’s mind gets coupled with the stock orwhatever security he’s trading. As a trader you will do two to three optimised charts and decidewhich one is closest to your own interpretation of a stock’s behaviour. By trading in volume,market makers and other specialists develop an intense relationship with the product they aretrading. They can use the point-and-figure efficiently, adjusting only two parameters, in order toget the best chart for their trading style. That is the essence of optimisation.The optimised chart is a fingerprint of the stock.Simulations give us the certainty that point-and-figure works. Not always, but with a high andconstant probability. And that is the confidence we love to have when we trade. Due to thesimplicity of its parameters and the ease of adjusting it to personal trading styles, we considerpoint-and-figure the best technical analysis tool ever used.Parameters involvedThe other beauty of p&f is the definition of the system by only two basic parameters:1.2.
box sizereversal-number
The box size is a function of the scaling algorithm and can be optimised further. We optimisebased on three types of scaling and dozens of strategies. We leave the minimum reversal numberclose to three.We have also worked extensively to correlate those parameters with inherent values of pricedata, especially volatility.4 Nassim Taleb,
Fooled by Randomness, Texere 1999181
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The output of such optimisation runs are immense, often hundreds of printed pages. The resultsof all that research make it possible for us to talk with confidence about the subject.Pole lengths
We also measure the frequency of the occurrence and the length of poles of the charts. Thatinformation helps us hammer out some strategies. The well known strategy up 10, down 3 andout can be geared to the expected value of the majority of poles. You can do this the most basicway by looking at the long-term chart, or the most sophisticated way by doing a Monte Carlosimulation and deriving most probable pole lengths. If you see that the majority of poles have alength of nine, then you would probably adapt the strategy to up 9, down 3 and out.Strategies testedDifferent trading styles exist and we try to model them with different strategies. Some tradersprefer an asymmetric approach to entries and exits. For example, they enter on basic signal andexit on reversals/calculated stops.Types of strategy:•
Some traders do not enter positions on the simple buys or sells (B1, S1). [2 Strategies]
•
Some traders take profits early and get fast out of loosing trades.[4 Strategies]
•
Some traders use reversals to either enter into or exit positions or both.[3 Strategies ]
•
Some traders pyramid aggressively.[2 Strategies]
•
Some traders do not trade on trend line induced signals, for some they areprimordial.[3 Strategies]
Moreover, strategies are clearly dependant on one’s cost of trading. Strategies which generate alot of trades have normally – according to our research – a less volatile P&L. Therefore wemodel strategies with different trading frequencies. The most active strategy would be to usesmall box sizes and to trade every reversal. Such a strategy would generate in average a tradeevery two days. When defining strategies for day-trading, they have to be adapted to marketmicro-structure limitations, such as bid-ask spreads and the estimated depth of the market.All these different strategies are modelled by our software. The number of the resultingstrategies is quite large because it represents a multiplication of the different alternatives, whichare stipulated above in square brackets. For example, 2 x 4 x 3 x 2 x 3 equals 144 differentstrategies, however the set is reduced to 11 which have shown to be the most profitable overdifferent time periods and instruments).The table opposite lists the 12 strategies tested.
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Table 4.1 – Strategies tested
Strategy
Description
1
Pyramiding, Entry (B1..B4,S..S4), Exit (B1..B4,S1..S4)
2
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4)
3
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4) and big VariablePrice Target
4
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4) and smallVariable Price Target
5
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4) and Trailing Stop
6
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4), big VariablePrice Target and on Pole Trailing Stop
7
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4), small VariablePrice Target and on Pole Trailing Stop
8
Pyramiding, Entry (B2..B4,S2..S4), Exit (B2..B4,S2..S4) and Reversal
9
Trade inside Trend-Channels (Parallel trend lines)
10
Trade Reversals, Exit also on Profit Target
11
Trade Reversals and Trend lines
OptimisationsAs mentioned above, we use the brute force approach. We test all reasonable box size, reversaldistance, scaling and strategy combinations and rank them. The ranking can be done in manydifferent ways, but here as well, we prefer simplicity. We like:1.2.3.
a good P&Lstable parametersa minimum number of trades
We do not like parameter combinations that have made all the profits with just very few trades.Based on our experience we know that they usually do not perform in the simulations.183
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How to avoid over-fitting
In order to avoid the over-fitting of the data, we distil from the optimisation results a fewparameter sets and strategies only, namely the parameter sets that work for the greatest amountof securities in a group. Such a group could be FTSE high beta stocks, FTSE low beta stocks,EURO stocks with a high average volatility, AA-bonds and so forth.We do not optimise parameters for a single stock or security, so we avoid over-fitting.Once again, over-fitting is the result of testing many trading rules and choosing the one whichhas worked best, ignoring the fact that it may have worked by pure luck. For any given stockone can use a random generator of trading strategies, test the randomly generated strategies andcome up with a strategy that would have worked brilliantly in the tested data.It is difficult to avoid that over-fitting problem. We try (as mentioned above) to alleviate it bythe grouping of the data and therefore optimising over an entire portfolio and by studying theresults carefully.
SimulationsDuring the simulation we take the parameters found in the past-data optimisation and applythem on data that is subsequent to the data used for the optimisation.In order to study the optimised p&f approach, we shift the optimisation/simulation window fromleft to right, from older data to the most recent data.It is also crucial not to change parameters too often. We recommend a cycle of not less than 1year for end-of-day-trading and 10 days for high-frequency data.
Summary recommendations for optimising point-and-figureWe would like to summarize our recommendations for optimising point-and-figure:1.2.3.4.5.
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Optimise the parameters over an entire group of securities.Keep the reversal number close to three boxes.Use realistic assumptions about transaction costs.Use log scales for long-term simulations.Simulate trading strategies that correspond to your trading style.
New Optimisation Techniques
P&F parameter sets for different volatilitiesWe have studied the impact of volatilities on p&f parameters extensively. The studies showedclearly that the profitability of point-and-figure is enhanced if the parameters – scale andminimum reversal number – are adapted to the volatility of the security charted.There are many ways to calculate volatility, but in order to make our results widely applicable,we have chosen one of the most simple methods. We calculate volatility as the annualisedstandard deviation of one year’s price returns, calculating the returns – the differences from oneday to the next in percentage points – based on the closing prices.If you calculate this simple volatility yourself, take the closing prices of one year,