(O), sc(O),avghi(G), signhl(Q); _{Exit type =1, On-Balance Volume=2, Chiaken Volume Accumulator=3, Negative Volume Index=4, Positive Volume Index=5, Price and Volume Trend=6, Volume oscillator (npsd used for exit level)=7, Volume rate of change=8, Low volume exitFor entry filter use negative type ~
{test exit)type = ttype;if type < 0 then begin -
tentry = 1;type = @absvalue(ttype); “end;
{price index)
map = @average(close,period);
if ddays < 1 then nd = 1 else nd = ddays;if mapind} = 0 then px = 0 else px = (map
{On-Balance Volume)if type = 1 then begin
- map{nd1)*100/map(nd];
VOLUME INDICATOR: 247
4 close > closef1) then KMAvol = KMAvoT + volume
else if close < close(1] then vol = Kyo} - volume;end;Chaikin Volume Accumulator 1 wowit type = 2 then kvol = Kvol +(({close = ow) / (high - Tow)) - .5)*2*volume
(Negative Volume Index!if type = 3 then begintf volume < volume1} then Kvoi = Kvol(1] + (closeelse if Kvol = 0 then Kvol = volumeelse Kvol = Kvolll];end;
- closef1})*kvol/closefl}
[Positive Volume Index}if type = 4 then begin{fF volume > volume(1] then Kvol = Kvoltll + (close ~else if Kvol = 0 then Kyo! = volumeelse Kvol = Kvol(l];end;
close(1i)*kvol(L}/closell)
[Price and Volume Trend}if type = 5 then Kvol = Kvol tvolume* (close - closet1]) / closel1){Volume oscillator}if type = 6 thenKvol = (@average(volume,ddays) = .Guverage( volume period) )*100/@average( volume, ddays);
(Volume rate-of-changel
if type = 7 then beginTf Kvol = 0 then Kyol = volume;4 volumeCperiod] = 0 then Kvol = kvol(l] else _kKyol = (volume - volumefperiod])*100/volumelperiod]s
end;
(Moving average of pricelmadays = 2*period;
ma = @average(close,madays);madiff = ma - ma(nd];
if type < 6 or type = 9 then beginmav = @average(Kvol period); . ,Te mavEnd] = 0 then vx = 0 else vx = (may - mavind])*100/mavind);sdiff = px - Xs{record divergence only} .if yerpx <0 then dvg = sdiff else dvg = 05jf madiff > 0 and madiff(1] <= 0 then beginexitshort at market;ff tentry = 0 and (vx >= maxdn and @absvbuy at marketsend;sf madiff <0 and madiff[1] >= 0 then beginexitlong at market;SS tentry = 0 and (vx >= maxdn and @absvalue(dvasell at market;
end; |if vx < maxdn and @absvalue(dvg) > maxdva then begin
exitlong at market;
exitshort at market; -
end; -end;
alue(dvg) <= maxdvg) then
) <= maxdvg) then 
 
 
 
 
 
ces
SorReY
     
248 VOLUME, OPEN INTEREST, AND BREADTH
if type = 6 or type = 7 hen beginavgv = @average(Kvol,period);sdy = @stddev(kvol period) ;if tentry = 0 then beginif (madiff > 0 and madiff[1} <= 0) or Kvol <= avgv-npsd*sdv thenexitshort at market;if (madiff ¢ 0 and madiff{[1] >= 0) or Kvol <= avgv-npsd*sdv thenexitlong at market;if madiff > 0 and madiff[2] <= 0 then buy at market;if madiff < 0 and madiff{1] >= 0 then sell at market;endelse beginif madiff > 0 and madiff{1] <= 0 then exitshort at market;if madiff < 0 and madiff{1] >= 0 then exitlong at market;if madiff > 0 and madiff{1] <= 0 and Kvol <= avgv-npsd*sdv thenbuy at marketsif madiff < 0 and madiff[1] >= 0 and Kvol <= avgv-npsd*sdv thensel) at market;end;end;{Low volume exit, comparing slow and fast average]if type = 8 then beginavgv = @average( volume, period);avg2 ~ @average(volume,ddays);if tentry = 0 then begin iif (madiff > 0 and madiff{1] <= 0) or avg2 <= avgv*npsdthen exitshort at market;if (madiff ¢ 0 and madiff(1] >= 0) or avg2 <= avgvenpsdthen exitlong at market;if madiff.> 0 and madiff[1} <= 0 then buy at market;if madiff < 0 and madiff[1] >= 0 then sell at market;endelse beginif madiff > 0 and madiff[1] <= 0 then exitshort at market;if madiff <0 and madiff(1} >= 0 then exitlong at market;if madiff > 0 and madiff(1] <= 0 and avg2 <= avgv*npsd then buy at marketjf madiff < 0 and madiffC1] >= 0 and avg2 <= avgv*npsd then sell at market;end;end;
1s One Volume/Breadth Indicator Better Than Another?
‘The final test of a successful indicator is whether it adds value to your trading decisions.‘This can be determined by programming the indicator into the total strategy and testingthe rules over a reasonable historic time period. Before doing that, we should decide whatthe increase or decrease in volume or breadth is telling us. If performance testing does notconfirm our expectations, than we must rethink the approach because the chance of anerror has increased. What appears to be a profitable discovery may only be an unlikelycoincidence. “
Looking at the selection of indicators, we see that small net changes in price can resultin all volume being designated to one market direction. This is the case with Granville’sOn-Balance Volume and Bolton-Tremblay. Is it reasonable to add all volume to the accu-mulated index if the S&P gained only a fraction of one point? On a single day this may seemto be arbitrary, but just as so many of the simple systems, over a long period of time the neteffect of this approach is sound. In addition, we should not forget that these indicators failto account for a range of noise that is similar to the erratic movement of prices; that is,
 
 
INTERPRETINGVOLL 2 SYSTEMATICALLY 249
 
there is a normal variation in volume that does not mean anything special. On the otherside, these indicators fail to isolate outliers, the significant events or price shocks that areassociated with high volatility.
‘The changes made by Mark Chaiken, which take a percentage of volume based on therelative close of prices within the daily range, have a very sensible basis and avoid the all-or-nothing technique used in On-Balance Volume. Sibbett's Demand Index, by using thesum of 10 days’ volume, avoids this problem completely and smooths out results, a situa-tion often needed when using the highly variable values of volume and breadth
On the more confusing side, it has been suggested that the Demand Index, which sim-ply accumulates the net of advances minus declines, be used with the absolute value of thedaily calculation, Advances ~ Declines. That would give the same value when advances out-numbered declines by the same amount as declines outnumbering advances, whichappears to lose information and fails to distinguish becween market direction. Unless youclearly understand why taking the absolute value of any advance-decline formula makessense, they should be avoided, even if historical testing shows a better result than othermethods.
By seeing these indicators close together, they appear to be a collection of minormanipulations of data. The resulting series still requires interpretation using trendlines,divergence, or new highs and lows. Instead of having to interpret only the difficult move-ments of prices, we have added an equally difficult series of volume. While it is not clearthat volume is always useful in analyzing price movement, it can impart valuable informa-tion in certain situations.
 
INTERPRETING VOLUME SYSTEMATICALLY
Most systematic approaches to volume apply a long-term smoothing method to the data,then identify trend changes to. confirm price direction. This can be implemented with anyof the accumulation indices, but not with single-day momentum or oscillator values. Forthe oscillators, most analysts have taken the approach that high volume confirms a newprice direction; therefore, they look for a reversal signal at the same time as peak or near”peak volume. If they do not occur at the same time, a volume peak should precede a trendchange. A decline in volume has also been used to confirm direction, but it is more likelyto indicate that prices have reached equilibrium, and that a further advance or declinerequires additional confirmation.
Moving Average Approaches
A straightforward way of using volume is to calculate a 10-day moving average of the vol-ume to be used as a confirmation of a 20-day wend position.‘ By simply requiring the cur-rent volume to be greater than the average volume over that past 10 days, you introducethe idea of greater participation associated with the new trend. An additional importantbenefit is that this volume condition acts as a filter, eliminating a substantial number oftrades. If the net returns are the same, the volume-filtered approach is far better becauseyou are gut of the market mofe, and are not reversing your position every time there is anew signal.
‘A similar method was proposed by Waxenberg.’ A 10-day moving average of the vol-ume is calculated as the normal level, and a change in trend must be confirmed by a 20%
* Alex Saitta, “A Price and Volume-Based System,” Technical Analysis of Stocks & ‘Commodities (March 1996).5 Howard K. Waxenberg, “Technical Analysis of Volume,” Technical Analysis of Stocks & Commodities (March1986). 
250 VOLUME, OPEN INTEREST, AND BREADTH
increase in volume above this norm. (The 20% band acts as an additional smoothing filter,but may be replaced by a longer trend and smaller band.) Extremes in a trending move canbe found at points that exceed approximately a 40% volume increase. Applied to the stockmarket, Waxenberg used the extreme volumes to indicate the end ofa sell-off. To add moreflexibility for longer test periods, and to adjust for volatility, Bollinger bands (based onabout 2 standard deviations, or 95% probability) can be substituted for the fixed percent-age bands.
Alternately, using 13 days of volume, subtract the total down volume from the total upvolume. A plot of the results will serve as a momentum indicator from which overboughtand oversold levels can be identified. If these values are unstable due to lack of liquidity,they may be smoothed using a short-term moving average. ,
Advance-Decline System
Advance and decline values, as with most volume figures, can be more useful if they aresmoothed. By combining peak values of the net of smoothed advancing and declin-ing shares with a directional move in price, Conners and Hayward have created a basic sys-tem structure that they named CHADTP® (Conners-Hayward Advance-Decline TradingPatterns™). This system tries to identify reversal patterns by applying the following steps:
1. Add the past 5 days of advancing issues (New York Stock Exchange)
2. Add the past 5 days of declining issues (New York Stock Exchange)
3. Subtract the 5-day sum of declining issues (1) from the advancing issues (2)4, Divide by 5 to get the average daily value
CHADTP = (@sum(advNYSE,5) - @sum(decINYSE,5)) /5
To trade using this oscillator, Conners and Hayward have determined that +400 are theextreme levels where the values have been overbought and oversold, Based on this, we canapply the following rules to the S&P futures:
1. Sell when CHADTP > +400 and the SP trades .10 below the low of the previous day;buy when CHADTP <—400 and the SP trades .10 above the high of the previous day.
2. Note that the oscillator does not have to exceed its recent extremes on the day ofthe buy or sell signal. .
3. Timing is best if the signal corresponds to a newspaper commentary indicating “de-pressed volume” (which is seen as an excess of cash waiting to enter).
This system targets returns over a 5- to 7-day period. A drop in the oscillator, which resultsin values in the midrange, is an opportunity to exit, or a standard oscillator of price can beconstructed to generate overbought and oversold signals within this time frame. An oppo-site entry signal would reverse the position. .- ;
AN INTEGRATED PROBABILITY MODEL
If there is a noticeable relationship between price, volume, and open interest (or marketbreadth), then a probability model can be constructed to test itsimportance.’ To do this itis necessary to construct a one-day-ahead forecast using a simple linear regression model,then back-test weighting factors for each element. Because of the powerful software prod.ucts, this has become a very manageable process. Using Omega's Easy Language, each one-day-ahead forecast is determined using ” past days: :
® Laurence A. Conners and Blake E. Hayward, Investment Secrets of a Hedge Fund Manager (Probus, 1995).7 Based on the May 1995 “CSI Technical Journal” (Commodity Systems, Inc., Boca Raton, FL).
 
INTRADAY VOLUME Px. ‘ERNS 251
1. Price forecast: Pf = Price + LinearRegSlope(close,n)
2. Volume forecast: Vf = Volume + LinearRegS] ope(volume,n)
3. Open interest forecast: Of = Opint + LinearRegSlopetopint,n)
4. On-Balance Volume forecast: 08Vf = OBV + LinearRegSlope(O8V,n)
‘The function LinearRegSlope gives the one-period increase or decrease in the value basedon a straight-line fit of the past days, and can be added to the current value to get theone-day-ahead forecast. The period 1 can be selected by subtracting the actual next-dayvalue from the forecast and creating an error series that can be measured using a standarddeviation. The number of days, 7, that generates the smallest standard deviation is the bestforecast period. It is likely that the optimal forecast period will differ for each of the fouritems above, and this becomes a matter of concern, Is there a reason why the forecastperiod be the same for all of the predictions? If we are independently trying to forecast vol-ume, then fixing the periods at the same length does not seem necessary. If we are tryingto discover whether a change in volume is related to a change in price, as we are here, thenthe same regression interval seems important. (These and other testing procedures arethoroughly discussed in Chapter 21 (“Testing”).
Having found the four one-day-ahead forecasts, an index can be created that gives oneweight w, to the price forecast and the balance, 1 — v1, toa combination of the other threefactors. This assumes that price is the most important predictor of price. .
Forecast Index = w, x Pf + (1 — wy) x (10.Vf + w;Of + wsOBVE)
This formula can be backtested for values of w;, w2, ws, and w, between 0 and 1. The finalindex can be used instead of price for determining the trend. It will still require a movingaverage, or some trendline, to signal new uptrends and downtrends; however, the results,if successful, should be more reliable than only using price.
One advantage of testing the weighting factors is that, if one of the four elements is nothelpful in predicting a trend, the weighting factor should be zero. Another approach, thatdoes not cluster the nonprice data together, would be to treat each item separately:
Forecast Index = w,Pf + w2Vf + w;Of+ wsOBVE
andwhere = > w=1
The sum of the weighting factors should always be 1, equal to 100%.
INTRADAY VOLUME PATTERNSIdentifying increases and decreases in volume during the trading day must consider over-
_ riding patterns caused by the way traders enter orders. The pattern of intraday volume can
be seen using tick volume as a practical substitute for actual volume, which is usually notavailable until the next day. To create a meanful chart of 30-minute volume patterns, eachinterval was compared with the first 30 minutes of the day and recorded as a percentage ofthat initial tick volume. Figure 10-2 shows the results of three futures markets, Eurodollars,S&P 500, and Deutschemarks taken over a 1-year period using the nearest delivery.
‘The patterns of the three markets shown in Figure 10-2 are similar in that the greatestvolume is at the beginning and end of the trading day, yet very different in their internalpatterns, Eurodollars, which have the highest volume of all futures markets, show muchlarger volume on the opening and closing 30-minute periods.
It should not be surprising to see the extreme clustering at both ends of the tradingday, Orders enter the market early in reaction to news and events that occurred since the
close of the previous day’s trading, There are also orders resulting from trading decisions -
based on the previous day's data but calculated after the close. The end of day is active
  
252VOLUME,OP INTEREST, AND BREADTH
—
FIGURE 10-2 Patterns of 30-minute tick volume, 1993. (a) Eurodollars. (b) S&P 500.
(c) Deutschemarks.i
30-min. tick volume as % 1st IntervalEurodollars, Jan~Dec 1993
 
 
 
 
 
§8=328s€s2S2
°
850 950 7050 1150=«*1250.-=«1350=«1450Interval ending time (NY)(a)30-min. tick volume as % 1st intervalDeutsche Marks, Jan—Dec 1993
100
S 208=z
5 60PS8=
E4032
= 2
°
 
850 950 1050 1150 1250 1350 1450,~ Interval grsing time (NY)(o)
 
thes
FILTERING LOWVOL. = 283
A
FIGURE 10-2 (Continued)
30-min. tick volume as % 1st Interval‘S&P 500, Jan—Dec 1993
100
80
60
20
Ticks/30 min as % of 1st interval
4280 1330 1430 1530 1615Interval ending time (NY)(o
the
1030 1130
because of juggling for position based on today’s price movements. For most traders,
closing price is the most dependable value of the day.There is a typical rounded bottom formation on most charts, with lowest volume in the
late morning when traders take their breaks. Deutsche marks (Figure 10-2c) differ from theothers by showing more sustained high volume through late morning. This is because theEuropean markets are open during that period. London interbank markets are likely to beopen at 5 pM. (12 noon in New York), to overlap U.S. trading a5 much as possible. Volumeon the IMM Deutschemark contract tapers off after European business hours end.‘When trading based on an increase in volume, these patterns must serve AS the norm.‘To decide whether there is a volume confirmation for a trade to be entered at LL A.M., youmast compare today’s 30-minute volume for the 11 A. interval with the previous averagewitume for this same period only. This is equivalent to deseasonalizing or detrending theVata, Even with this precaution, an increase in volume for a thinly traded market, such ascocoa, may appear significant because the 30-minute volume jumped from 100 contractsto 300 contracts during midday. This can easily be misleading, You ‘will need to considerthe sample error in low-volume markets or demand much larger volume changes to be
more certain.
FILTERING LOW VOLUME
Itseems clear that minutes, hours, or even days that have little market activity are likely tobe associated with uncertain price direction. If the British pound moves the equivalent of§.50 in the early afternoon of the U.S. market, we know that volume is normally light andthe London and European markets are closed. If the intention of a volume indicator is toidentify positive moves in volume that can be used as a confirmation of price direction,then eliminating those days with low volume, or with marginal price moves, may make thevolume indicator dependable. 
254 VOLUME, OPL. «NTEREST, AND BREADTH
 
Removing Low-Volume Periods
The inclusion of low-volume periods into a volume index may make the index movementsunreliable. There is a similar situation in statistics, where a number of statistical values areslightly skewed to one side. Each value on its own is not noticeably important, but collec-tively they cause a bias. Some analysts believe that the collective weight creates a significantsituation, while others favor ignoring any statistic that is too close to the norm to be dis-tinguished. When you ignore the individual values, there is no collective value. In the sameway, by ignoring days with low volume, you do not have to risk the chance of posting aseries of days, all of which may be uncertain, that result in a confirmation of a new pricedirection. Days may be filtered if the volume falls one standard deviation below the aver-age (removing 16% of the total days), or two standard deviations (removing only 2.5% ofthe days), or any other combination. The use of absolute volume numbers, such as a dayon the New York Stock Exchange with volume under 200 million, will not allow consistentfiltering over any prolonged test. Then, on any day, the Volume-Filtered On-Balance Vol-ume would be found by the following program or spreadsheet steps:volume_threshold = @AVG(volume, n) + f*@STOCvolume - volumef{1], nm)if volume < volume_threshold then VFOBV = VFOBV(1]
else VFOBV = VFOBV[1] + ((close - close{1]}/(@ABS(close - close{1])) * volume.
where n is the number of periods in the average and standard deviation
f is the number of standard deviations used to filter minimum volume
 
Removing Volume Associated with Small Price Moves
Indicators such as On-Balance Volume post all volume as either a positive or negative con-tribution to the index, based on the direction of prices on that day. It is fair to question thevalidity of posting all volume to the upside when the S&P 500 closed up a minimum moveof one-twentieth of one point (+.05). It could just as easily have closed down that amount.In a manner similar to filtering low-volume periods, in the section above, periods in whichprices moved very little may be eliminated by using a standard deviation of the pricechanges as a filter. Days which are within +.5 or +1.0 standard deviations of the averagewould be ignored. The Price-Filtered On-Balance Volume would then be found as follows:price_threshold ~ @AVG(price - price[1], n) + f*@STD(price - pricef1],n) \
if @ABS(price - price{1}) < price_threshold then PFOBV = PFOBV[1]
else PFOBV = PFOBVE1] + ({close - close[1])/(@ABS(close - close(1])) * volume
where n is the number of periods in the average and standard deviationf is the number of standard deviations used to filter minimum volume
Note that, in the case of a minimum price threshold, the rules look at price change, whichcan be positive or negative. For a volume threshold there is only a one-sided test. ~
MARKET FACILITATION INDEX
In weighing the likelihood that prices are indicating a direction, rather than a false start,the tick volume compared with the price range for the same period, called the MarketFacilitation Index,* can measure the willingness of the market to move the price. This con-cept is interesting because it is not clear that high volume results in a large price move,although it appears to set up the conditions for high volatility. If the Market FacilitationIndex increases, then the market is willing to move the price; therefore, trading profits aremore likely. -
* Bill Williams, Trading Chaos (John Wiley & Sons, 1995).
 
SOURCES OF INFORM... 1ON 255
 
ao range __ high - lowMarket Facilitation Index = 27 == Volume
‘The results of combining the change in tick volume and the Market Facilitation Index areinterpreted as:
iMarket
 
Tick Volume Facilitation Index InterpretationU Up Confirmation of directionDown Down False direction, do not take tradeDown Up Poor entry timing, approach with cautionUp Down Potential new trend, end of old trend
SOURCES OF INFORMATIONIn addition to the various references in this chapter,taining useful information on volume:
Steven B. Achelis, Technical Analysis from A to Z (Probus, 1995)
Colby and Meyers, The Encyclopedia of Technical Market Indicators (Dow Jones-
Irwin, 1988).;
CompuTrac Manual (Telerate, 1990)
Edwards and Magee, Technical Analysis of Stock Trends (Edwards and Magee, 1948)
Dr. Alexander Elder, Trading for a Living John Wiley & Sons, 1994)
” Omega TradeStation, Easy Language Manual (Omega Research, 1995)Martin J. Pring, Technical Analysis Explained (McGraw-Hill, 1991)Kenneth H. Shaleen, Volume and Open Interest (Probus, 1991)
the following is a list of sources con- 
 
 
 
 
 
 
Point-and-Figure Charting
oint-and-figure charting is credited to Charles Dow, who is said to have used it justprior to the turn of the twentieth century. This method differs from ordinary chartingin three important ways:
1. It has simple, well-defined trading rules.
2, It eliminates price reversals that are below a minimum (box) value.
3. [thas no time factor. As long as prices fail to change direction by the reversal value,the trend is intact. ,
When point-and-figure charting first appeared, it did not contain the familiar boxes ofXs and Os. The earliest book containing the subject is reported to be The Game in WallStreet and How to Play it Successfully, published by Hoyle (not Edmond Hoyle, theEnglish writer) in 1898. The first definitive work on the subject was by Victor de Villiers,
who in 1933 published The Point and Figure Method of Anticipating Stock Price Move.ment. De Villiers worked with Owen Taylor to publish and promote a weekly point-and-figure service, maintaining their own charts; he was impressed by the simple scientificmethodology. As with many of the original technical systems, the application was intendedfor the stock market, and the rules required the use of every price change appearing on theticker. The rationale for a purely technical system has been told many times by now, but anoriginal source is often refreshing. De Villiers said:
“The Method takes for granted:
 
- 1. That the price of a stock at any given time is its correct valuation up to theinstant of purchase and sales (a) by the consensus of opinion of all buyersand sellers in the world and (b) by the verdict of all the forces governingthe laws of supply and demand.
2. That the last price of a stock reflects or crystalizes everything known aboutor bearing on it from its first sale on the Exchange (or prior), up to thattime.
3. That those who know more about it than the observer cannot conceal theirfuture intentions regarding it. Their plans will be revealed in time by thestock’s subsequent action.”!
The unique aspect of the point-and-figure method is that it ignores the passage of time.Unlike bar charts, you do not make a single vertical mark and chen move to the right a uni-form distance. Each column of a point-and-figure chaft can represent any length of time.The measurement of a significant change in price direction alone determines the patternof the chart.
The original figure charts were plotted with only dots or with the exact price in eachbox or with a combination of Xs and occasional digits (usually Os and 5s every 5 boxes) to
‘Victor De Villiers, The Point and Figure Method of Anticipating Stock Price Movements (Trader Pi; ress, New1966 (Reprint of 1933 edition), p. 8). on “es
 
 
PLOTTING PRICES ~ (NG THE POINT-AND-FIGURE METHOD as7
help keep the chart aligned. A geometric representation was also created by connecting thepoints in each column with a vertical line and closing the gap between columns with acrossbar on top for a reversal down and a bar at the bottom if the next column goes up.This would later be called a swing chart. Charts using 1, 3, and 5 points were popular, inwhich each point represented a minimum price move. In the 5-point method, no entry wasrecorded unless the price change spanned 5 points.
Point-and-figure charts, which are still commonly used on the floor of the ChicagoMercantile Exchange and the Chicago Board of Trade, are recorded to show the greatestdetail. Each box represents the minimum allowable price move, and reversals of directionuse the traditional 3-box criteria. Floor traders use the charts to show only the short-termsustained price moves and leave a lot to the interpretation of patterns.
PLOTTING PRICES USING THE POINT-AND-FIGURE METHOD
7o plot prices on a point-and-figure chart, start with a piece of graph paper and mark theleft scale using a conveniently smail price increment. For example, each box may be set at$1 for gold and platinum, one-eighth of a point (4 ticks) for 30-year bonds, 1¢ for soybeansand silver, and so forth (see Figure 11-1). The choice of a box size will make the chart moreor less sensitive to changes in price direction, as will be seen in later examples. The smallerthe price increment, the more changes in direction will be seen. This also corresponds toIonger and shorter trends or major and minor trends. Therefore, a point-and-figurechartist looking for a long-term price movement will use a larger box size. Box sizes areoften related to the current volatility of the markers.
Once the graph paper has been scaled and the prices entered along the left side, thechartist can begin. The first box to be entered is that of the current closing price of the mar-ket. If the price of silver is 852.50 and a 1¢ box is being used, a mark is placed in the boxbeside the value 852. An X or.an O is used to indicate that the current price trend is up ordown, respectively. Either an X or O may be used to begin—after that, it