r superior to the simple moving average. (However, it should be noted that
 FIGURE  16.2 e-Mini nasdaq 100 continuous futures with 12-Day Moving average
 Note: /uni2191 = buy signal: prices cross moving average from below and close above line; /uni2193 = sell
signal: prices cross moving average from above and close below line.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

242a coMPLeTe guiDe To THe fuTureS MarKeT
 FIGURE  16.3 e-Mini nasdaq 100 continuous futures with 48-Day Moving average
 Note: /uni2191 = buy signal: prices cross moving average from below and close above line; /uni2193 = sell
signal: prices cross moving average from above and close below line.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.
 FIGURE  16.4 e-Mini nasdaq 100 continuous futures with Moving average crossover
 Note: /uni2191 = buy signal: short-term moving average (12-day) crosses long-term moving average
(48-day) from below; /uni2193 = sell signal: short-term moving average crosses long-term moving
average from above.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

243
TecHnicaL TraDing SySTeMS: STrucTure anD DeSign
by including some of the trend-following-system modifi cations discussed in a later section, even the
simple moving average system can provide the core for a viable trading approach.) The weaknesses of
the crossover moving average system and possible improvements are discussed later.
 breakout Systems
 The basic concept underlying breakout systems is very simple: the ability of a market to move to a
new high or low indicates the potential for a continued trend in the direction of the breakout. The
following set of rules provides an example of a simple breakout system:
 1. cover short and go long if today’s close exceeds the prior N -day high.
 2. cover long and go short if today’s close is below the prior N -day low .
 The value chosen for N will defi ne the sensitivity of the system. if a short-duration period is used
for comparison to the current price (e.g., N = 7), the system will indicate trend reversals fairly
quickly, but will also generate many false signals. in contrast, the choice of a longer-duration period
(e.g., N = 40) will reduce false signals, but at the cost of slower entry.
 figure 16.5 compares the trade signals generated by the preceding simple breakout system in silver
continuous futures using N = 7 and N = 40. The following three observations, which are evidenced in
figure 16.5 , are also valid as generalizations describing the trade-off s between fast and slow breakout
systems:
 1. a fast system will provide an earlier signal of a major trend transition (e.g., the october 2012
sell signal).
 FIGURE  16.5 Breakout System Signals, fast versus Slow Systems: Silver continuous futures
 Note: B, S = signals for N = 7; , = signals for N = 40.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

244
A Complete Guide to the Futures mArket
 2. a fast system will generate far more false signals.
 3. The loss per trade in the slower system will be greater than the loss for the corresponding trade
in the faster system. in some cases, a fast system might even realize a small profit on a minor
trend that results in a loss in a slower system. for example, the N = 40 system’s august buy
signal that was liquidated in november resulted in a net loss of approximately $2.54 (exclud-
ing commissions). The corresponding buy signal for the N = 7 version—triggered in July and
exited in September—resulted in a net gain of around $2.46.
as indicated by the preceding illustration, fast and slow systems will each work better under dif-
ferent circumstances. in the case of the chosen illustration, on balance, the slow system was much
more successful. of course, one could just as easily have chosen an example in which the reverse
observation was true. However, empirical evidence suggests that, in most markets, slower systems
tend to work better.
in any case, the choice between a fast and a slow system must be based on up-
to-date empirical testing.
The previous example of a breakout system was based on the current day’s close and prior period’s
high and low . it should be noted that these choices were arbitrary. other alternative combinations
might include current day’s high or low versus prior period’s high or low; current day’s close versus
prior period’s high close or low close; and current day’s high or low versus prior period’s high close
or low close.
although the choice of the condition that defines a breakout will affect the results, the
differences between the variations just given (for the same value of N) will be largely random and not
overwhelming. Thus, while each of these definitions might be tested, it probably makes more sense to
focus research efforts on more meaningful modifications of the basic system.
The pitfalls of breakout-type systems are basically the same as those of moving average systems and
are detailed in the following section.
 ■ Ten Common Problems with Standard
Trend-Following Systems
 1. too many similar systems. Many different trend-following systems will generate similar
signals. Thus, it is not unusual for a number of trend-following systems to signal a trade during
the same one- to five-day period. Because many speculators and futures funds base their deci-
sions on basic trend-following systems, their common action can result in a flood of similar
orders.
under such circumstances, traders using these systems may find their market and stop
orders filled well beyond the intended price, if there is a paucity of offsetting orders.
 2. Whipsaws. Trend-following systems will signal all major trends; the problem is that they
will also generate many false signals.
a major frustration experienced by traders using trend-
following systems is that markets wil


er such circumstances, traders using these systems may find their market and stop
orders filled well beyond the intended price, if there is a paucity of offsetting orders.
 2. Whipsaws. Trend-following systems will signal all major trends; the problem is that they
will also generate many false signals.
a major frustration experienced by traders using trend-
following systems is that markets will frequently move far enough to trigger a signal and then
reverse direction. This unpleasant event can even occur several times in succession; hence, the
term whipsaw.
for example, figure 16.6, which indicates the trade signals generated by a break-
out system (close beyond prior N-day high-low) for N = 10, provides a vivid illustration of the
dark side of trend-following systems.
245
TecHnicaL TraDing SySTeMS: STrucTure anD DeSign 3. Failure to exploit major price moves. Basic trend-following systems always assume an
equal-unit-size position. as a result, given an extended trend, the best such a system can do
is to indicate a one-unit position in the direction of the trend. for example, in figure 16.7 a
breakout system with N = 40 would signal a long position in December 2012 and remain long
throughout the entire uptrend until february 2014. although this outcome is hardly unfavor-
able, profi tability could be enhanced if the trend-following system were able to take advantage
of such extended trends by generating signals indicating increases in the base position size.
 4. Nonsensitive (slow) systems can surrender a large percentage of profi ts. although slow
variations of trend-following systems may often work best, one disturbing feature of such systems
is that they may sometimes surrender a large portion of open profi ts. in figure 16.8 , for example, a
breakout system with N = 40 catches a major portion of the october–December 2014 price advance
in silver, but then surrenders more than the entire gain before an opposite signal occurs. The June buy
signal is initially profi table, but then realizes a much larger loss by the time a sell signal is received.
 5. Cannot make money in trading range markets. The best any trend-following system can
do during a period of sideways price action is to break even—that is, generate no new trade sig-
nals. in most cases, however, trading range markets will be characterized by whipsaw losses. This
is a particularly signifi cant consideration since sideways price action represents the predominant
state of most markets.
 6. temporary large losses. even an excellent trend-following system may witness transitory peri-
ods of sharp equity retracement. Such events can be distressing to the trader who enjoys a profi t
cushion, but they can be disastrous to the trader who has just begun following the system’s signals.
 FIGURE  16.6 Breakout Signals in Trading range Market: october 2015 natural gas futures
 Note: B = buy signal: close above prior 10-day high; S = sell signal: close below 10-day low .
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

246a coMPLeTe guiDe To THe fuTureS MarKeT
 FIGURE  16.7 failure of System to exploit Major Price Move: russell 2000 Mini futures
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.
 FIGURE  16.8 Surrender of Profi ts by nonsensitive System: Silver continuous futures
 Note: B = buy signal: close above prior 40-day high; S = sell signal: close below 40-day low .
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

247
Technical Trading SySTemS: STrucTure and deSign
 7. extreme volatility in best-performing systems. in some cases, the trader may find that
the most profitable trend-following systems are also subject to particularly sharp retracements,
thereby implying an unacceptable level of risk.
 8. System works well in testing but then bombs. This scenario is perhaps the most common
tale of woe among traders who have used mechanical trading systems.
 9. Parameter shift.2 frequently, the trader may perform an exhaustive search to find the best
variation of a system based on past data (e.g., the optimum value of N in a breakout system),
only to find that the same variation performs poorly (relative to other variations) in the ensuing
period.
 10. Slippage. another common experience: the system generates profits on paper, but simultaneously
loses money in actual trading. Slippage is discussed in chapter 19.
 ■ Possible Modifications for Basic
Trend-Following Systems
even simple systems, such as moving average or breakout systems, will probably prove profitable if
traded consistently over a broad range of markets for a sufficient length of time (e.g., three to five
years or longer). However, the simplicity of these systems is a vice as well as a virtue. in essence,
the rules of these systems are perhaps too simple to adequately account for the wide variety of pos-
sible market situations.
even if net profitable over the long run, simple trend-following systems will
typically leave the trader exposed to periodic sharp losses. in fact, the natural proclivity of many, if
not most, users of such systems to abandon the approach during a losing period will lead them to
experience a net loss even if the system proves profitable over the longer run.
in this section, we discuss some of the primary ways to modify basic trend-following systems in
an effort to improve their performance. for simplicity, most of the examples will use the previously
described simple breakout system. However, the same types of modifications could also be applied to
other basic trend-following systems (e.g., crossover moving average).
Confirmation Conditions
an important modification that can be made to a basic trend-following system is the requirement for
additional conditions to be met before a signal is accepted. if these conditions are not realized before
an opposite direction signal is received

eBooks\A Complete Guide to the Futures Market Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads\A Complete Guide to the Futures Market Technical Analysis, Trading Systems, Fundamental Analysis, Options, Spreads.pdf


the same types of modifications could also be applied to
other basic trend-following systems (e.g., crossover moving average).
Confirmation Conditions
an important modification that can be made to a basic trend-following system is the requirement for
additional conditions to be met before a signal is accepted. if these conditions are not realized before
an opposite direction signal is received, no trade occurs. confirmation rules are designed specifi-
cally to deal with the nemesis of trend-following systems: false signals. The idea is that valid signals
will fulfill the confirmation conditions, while false signals generally will not. The range of possible
2 The meaning of the term parameter as it is used in trading systems is detailed in chapter 19.
248a coMPLeTe guiDe To THe fuTureS MarKeT
choices for confi rmation conditions is limited only by the imagination of the system designer. Here
are three examples:
 1. Penetration. a trade signal is accepted only if the market moves a specifi ed minimum amount
beyond a given reference level (e.g., signal price). This confi rming price move can be measured
in either nominal or percentage terms. figure 16.9 compares the trade signals generated by a
standard breakout system with N = 12 and the corresponding system with a confi rmation rule
requiring a close that exceeds the prior N -day high or low by at least 3 percent.
3 note that in
this example, although the confi rmation rule results in moderately worse entry levels for valid
signals, it eliminates fi ve of six losing buy signals. (The sell signals following the nonconfi rmed
buy signals are also eliminated, since the system is already short at these points.)
 2. time delay. in this approach, a specifi ed time delay is required, at the end of which the signal is
reevaluated. for example, a confi rmation rule may specify that a trade signal is taken if the mar-
ket closes beyond the signal price (higher for a buy, lower for a sell) at any time six or more days
beyond the original signal date. figure 16.10 compares the signals generated by a basic breakout
system with N = 12, and the corresponding system with the six-day time delay confi rmation
condition. again, the confi rmation rule eliminates fi ve of the six losing buy signals.
 3 Because figure 16.9 depicts a continuous futures series, percentage price changes would be equal to the
price changes shown on this chart divided by the corresponding nearest futures price, which is not shown.
recall from chapter 5 that continuous futures accurately refl ect price swings but not price levels. conse-
quently, continuous futures cannot be used as the divisor to calculate percentage changes.
 FIGURE  16.9 Penetration as confi rmation condition: coff ee continuous futures
 Note: B, S = signals for breakout system with N = 12; , = signals for breakout system
with N = 12 and 3 percent closing penetration confi rmation.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

249
TecHnicaL TraDing SySTeMS: STrucTure anD DeSign
 3. Pattern. This is a catch-all term for a wide variety of confi rmation rules. in this approach, a
specifi ed pattern is required to validate the basic system signal. for example, the confi rmation
rule might require three subsequent thrust days beyond the signal price. 4 figure 16.11 com-
pares the signals generated by the basic breakout system, with N = 12 and the signals based upon
the corresponding system using the three-thrust-day validation condition. The thrust-day count
at confi rmed signals is indicated by the numbers on the chart. Here, too, the confi rmation rule
eliminates fi ve of six losing buy signals.
 The design of trading systems is a matter of constant trade-off s. The advantage of confi rmation
conditions is that they will greatly reduce whipsaw losses. However, it should be noted that confi rma-
tion rules also have an undesirable side eff ect—they will delay entry on valid signals, thereby reducing
gains on profi table trades. for example, in figures 16.9 through 16.11 , note that the confi rmation
rules result in worse entry prices for all the valid trade signals. The confi rmation condition will be
benefi cial as long as reduced profi ts due to delayed entry are more than off set by avoided losses. a sys-
tem that includes confi rmation conditions will not always outperform its basic system counterpart,
but if properly designed it will perform signifi cantly better over the long run.
 4 a thrust day, which was originally defi ned in chapter 9 , is a day with a close above the previous day’s high or
below the previous day’s low .
 FIGURE  16.10 Time Delay as a confi rmation condition: coff ee continuous futures
 Note: B, S = signals for breakout system with N = 12; , = signals for breakout system with
 N = 12 and six-day time delay confi rmation.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

250a coMPLeTe guiDe To THe fuTureS MarKeT
 Filter
 The purpose of a fi lter is to eliminate those trades that are deemed to have a lower probability of suc-
cess. for example, the technical system might be combined with a fundamental model that classifi es
the market as bullish, bearish, or neutral. T echnical signals would then be accepted only if they were
in agreement with the fundamental model’s market designation. in cases of disagreement, a neutral
position would be indicated. in most cases, however, the fi lter condition(s) will also be technical in
nature. for example, if one could derive a set of rules that had some accuracy in defi ning the pres-
ence of a trading range market, signals that were received when a trading range market was indicated
would not be accepted. in essence, in developing a fi lter, the system designer is trying to fi nd a com-
mon denominator applicable to the majority of losing trades.
 W e will use the frequently unsatisfactory simple moving average system t


of rules that had some accuracy in defi ning the pres-
ence of a trading range market, signals that were received when a trading range market was indicated
would not be accepted. in essence, in developing a fi lter, the system designer is trying to fi nd a com-
mon denominator applicable to the majority of losing trades.
 W e will use the frequently unsatisfactory simple moving average system to provide a specifi c
example of a fi lter condition. The noncircled signals in figure 16.1 illustrate the typical tendency of
the simple moving average system to generate many false signals—even in trending markets. These
whipsaw trades can be substantially reduced by applying a fi lter rule that requires trade signals to be
consistent with the trend of the moving average. for example, price crossing the moving average from
below and closing above the moving average would be accepted as a buy signal only if the moving
average was up relative to the previous day’s level. This fi lter condition makes intuitive sense because
it adheres to the basic technical concept of trading with the major trend.
 FIGURE  16.11 example of a Pattern confi rmation condition: coff ee continuous futures
 Note: B, S = signals for breakout system with N = 12; , = signals for breakout system with
 N = 12 and three-thrust-day confi rmation.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

251
Technical Trading SySTemS: STrucTure and deSign
Two points should be clarified regarding the application of this rule:
 1. a rejected signal could be activated later if the moving average subsequently turned in the direc-
tion of the signal before an opposite-direction crossover of the price and moving average.
 2. Signals that occur after rejected signals are ignored because the net position is already consistent
with the implied trade. This observation is true because the simple moving average system is
always in the market.
The circled signals in
figure 16.1 indicate the trades that would have been accepted if the filter
rule just described were applied. (in both instances these trades occurred after delays, as previously
described, rather than upon immediate penetration of the moving average.) as can be seen, the rule
substantially reduces the number of false signals. although in some cases the application of the filter
condition results in adversely delayed trade entries (for example, the July sell signal), on balance the
benefits clearly outweigh the disadvantages.
of course, a single illustration doesn’t prove anything.
However, the implication of figure 16.1 does have a more general applicability. Most empirical testing
would reveal that, more often than not, the inclusion of the type of filter rule depicted in figure 16.1
tends to improve performance.
in fact, a crossover between price and the moving average that is opposite to the direction of the
moving average trend can often provide a good signal to add to rather than reverse the original position.
for example, in figure 16.1 the March and May 2014 downside penetrations of the moving average
could be viewed as buy rather than sell signals because the moving average trend was still up in those
instances. The rationale behind this interpretation is that in a trending market, reactions often carry to
the vicinity of a moving average before prices resume their longer-term trend (see
chapter 12). Thus,
in effect, such rejected signals could actually provide the basis for a method of pyramiding.
it should be noted that, in a sense, the confirmation conditions detailed in the previous section
represent one type of filter, insofar as signals that fulfill a subsequent set of conditions are accepted,
while those that do not are eliminated. However, the distinction here is that a filter implies a set
of screening rules applied at the time the base system signal is received.
in other words, the sorting
procedure occurs without any dependency on subsequent developments (although, to be perfectly
accurate, subsequent developments could still permit a delayed acceptance of a rejected signal).
con-
sequently, as we have defined the terms, a system can include both a filter and a confirmation rule. in
such a system, only signals that were accepted based on the filter definition and subsequently validated
by the confirmation rule(s) would actually result in trades.
Market Characteristic adjustments
one criticism of simple trend-following systems is that they treat all markets alike. for example, in a
breakout system, with N = 20, both highly volatile and very quiet markets will require the same condi-
tions for a buy signal—a 20-day high. Market characteristic adjustments seek to compensate for the fact
that a system’s optimum parameter value settings will depend on market conditions.
for example, in
the case of a breakout system, instead of using a constant value for N, the relevant value for N might be
contingent on the market’s volatility classification. as a specific illustration, the average two-day price
252
A Complete Guide to the Futures mArket
range during the past 50-day period might be used to place the market into one of five volatility
classifications.5 The value of N used to generate signals on any given day would then depend on the
prevailing volatility classification.
V olatility appears to be the most logical choice for classifying market states, although other cri-
teria could also be tested (e.g., fundamentally based conditions, average volume level). in essence,
this type of modification seeks to transform a basic trend-following system from a static to a dynamic
trading method.
Differentiation between buy and Sell Signals
Basic trend-following systems typically assume analogous conditions for buy and sell signals (e.g., buy
on close above 20-day high, sell on close below 20-day low). However, there is no reason to make this
assumption automatically.
it can be argued that bul


seeks to transform a basic trend-following system from a static to a dynamic
trading method.
Differentiation between buy and Sell Signals
Basic trend-following systems typically assume analogous conditions for buy and sell signals (e.g., buy
on close above 20-day high, sell on close below 20-day low). However, there is no reason to make this
assumption automatically.
it can be argued that bull and bear markets behave differently. for example,
a survey of a broad spectrum of historical price data would reveal that price breaks from major tops
tend to be more rapid than price rallies from major bottoms.
6 This observation suggests a rationale
for using more sensitive conditions to generate sell signals than those used to generate buy signals.
However, the system designer using such an approach should be particularly sensitive to the danger
of overfitting the system—a pitfall discussed in detail in
chapter 19.
Pyramiding
one inherent weakness in basic trend-following systems is that they automatically assume a constant
unit position size for all market conditions. it would seem desirable to allow for the possibility of
larger position sizes in the case of major trends, which are almost entirely responsible for the success
of any trend-following system.
one reasonable approach for adding units to a base position in a major
trend is to wait for a specified reaction and then initiate the additional unit(s) on evidence of a resump-
tion of the trend. Such an approach seeks to optimize the timing of pyramid units, as well as to provide
exit rules that reasonably limit the potential losses that could be incurred by such added positions.
an
5 a two-day price range is used as a volatility measure instead of a one-day range since the latter can easily yield
a distorted image of true market volatility. for example, on a limit day, the one-day range would equal zero, in
extreme contrast to the fact that limit days reflect highly volatile conditions. of course, many other measures
could be used to define volatility.
6 The reverse statement would apply to short-term interest rate markets, which are quoted in terms of the
instrument price, a value that varies inversely with the interest rate level. in the interest rate markets, interest
rates rather than instrument prices are analogous to prices in standard markets. for example, there is no upper
limit to a commodity’s price or interest rates, but the downside for both of these items is theoretically limited. as
another example, commodity markets tend to be more volatile when prices are high, while short-term interest
rate markets tend to be more volatile when interest rates are high (instrument prices are low). The situation for
long-term (i.e., bond) markets is ambiguous since although interest rates can fall no lower than approximately
zero, the pricing mathematics underlying these instruments result in an accelerated price advance (for equal
interest rate changes) as interest rates fall.
253
Technical Trading SySTemS: STrucTure and deSign
example of this type of approach was detailed in chapter 12. another example of a possible pyramid
strategy would be provided by the following set of rules:
Buy Case
 1. a reaction is defined when the net position is long and the market closes below the prior 10-day
l ow.
 2. o nce a reaction is defined, an additional long position is initiated on any subsequent 10-day high
if the following conditions are met:
a. The pyramid signal price is above the price at which the most recent long position was
initiated.
b.
 The net position size is less than three units. (This condition implies that there is a limit of
two pyramid units.)
Sell Case
 1. a reaction is defined when the net position is short and the market closes above the prior 10-day
high.
 2. o nce a reaction is defined, an additional short position is initiated on any subsequent 10-day low
if the following conditions are met:
a.
 The pyramid signal price is below the price at which the most recent short position was
initiated.
b.
 The net position size is less than three units. (This condition implies that there is a limit of
two pyramid units.)
figure 16.12 illustrates the addition of this pyramid plan to a breakout system with N = 40
applied to the 2012–2013 gold market. ( for now , ignore the “stop level” signals; they are explained
shortly.)
risk control becomes especially important if a pyramiding component is added to a system. gen-
erally speaking, it is usually advisable to use a more sensitive condition for liquidating a pyramid posi-
tion than the condition required to generate an opposite signal. The following is one example of a set
of stop rules that might be employed in a system that uses pyramiding. Liquidate all pyramid positions
whenever either condition is fulfilled:
 1. a n opposite trend-following signal is received.
 2. The market closes above (below) the high (low) price since the most recently defined reaction
that was followed by a pyramid sell (buy).
figure 16.12 illustrates the stop levels implied by this
rule in the case of the 2012–2013 gold market.
trade exit
The existence of a trade exit rule in a system (e.g., a stop rule) would permit the liquidation of a
position prior to receiving an opposite trend-following signal. Such a rule would serve to limit losses
254a coMPLeTe guiDe To THe fuTureS MarKeT
on losing trades as well as limit the amount of open profi ts surrendered on winning trades. although
these are highly desirable goals, the trade-off implied by using a trade exit rule is relatively severe. if a
trade exit rule is used, rules must be specifi ed for reentering the position; otherwise, the system will
be vulnerable to missing major trends.
 The danger in using a trade exit rule is that it may result in the premature liquidation of a good
trade. although the reentry rule will serve as a backstop, the combination of an activated trade exit
rule and a subsequent reentry


ively severe. if a
trade exit rule is used, rules must be specifi ed for reentering the position; otherwise, the system will
be vulnerable to missing major trends.
 The danger in using a trade exit rule is that it may result in the premature liquidation of a good
trade. although the reentry rule will serve as a backstop, the combination of an activated trade exit
rule and a subsequent reentry is a whipsaw loss. Thus, it will not be at all uncommon for the addition
of a trade exit rule (and implied reentry rule) to have a negative impact on performance. neverthe-
less, although it is not easy, for some systems it will be possible to structure trade exit rules that
improve performance on balance. (in terms of return, and usually in terms of return/risk measures
as well, if a trade exit rule helps performance, the use of the trade exit rule as a reversal signal—as
opposed to just a liquidation signal—will help performance even more.) Trade exit rules can also be
made dynamic. for example, the trade exit condition can be made increasingly sensitive as a price
move becomes more extended in either magnitude or duration.
 ■ Countertrend Systems
 General Considerations regarding Countertrend Systems
 countertrend systems often appeal to many traders because their ultimate goal is to buy low and sell
high. unfortunately, the diffi culty of achieving this goal is inversely proportional to its desirability.
 FIGURE  16.12 Pyramid Signals: gold continuous futures
 Note: S = base position sell signal; = pyramid sell signal; rD = reaction defi ned.
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

255
Technical Trading SySTemS: STrucTure and deSign
a critical distinction to keep in mind is that whereas a trend-following system is basically self-
correcting, a countertrend system implies unlimited losses. Therefore, it is essential to include some
stop-loss conditions in any countertrend system (unless it is traded simultaneously with trend-following
systems). otherwise, the system could end up being long for the duration of a major downtrend or
short for the duration of a major uptrend. (Stop-loss conditions are optional for most trend-following
systems, since an opposite signal will usually be received before the loss on a position becomes
extreme.
7)
one important advantage of using a countertrend system is that it provides the opportunity for
excellent diversification with simultaneously employed trend-following systems. in this regard, it
should be noted that a countertrend system might be desirable even if it was a modest net loser, the
reason being that if the countertrend system was inversely correlated to a simultaneously traded
trend-following system, trading both systems might imply less risk than trading the trend-following
system alone. Therefore, it is entirely possible that the two systems combined might yield a higher
percent return (at the same risk level), even if the countertrend system alone lost money.
types of Countertrend Systems
The following are some types of approaches that can be used to try to construct a countertrend
system:
Fading minimum move. This is perhaps the most straightforward countertrend approach.
a
sell signal is indicated each time the market rallies by a certain minimum amount above the low
point since the last countertrend buy signal. Similarly, a buy signal is indicated whenever the
market declines by a minimum amount below the high point since the last countertrend sell
signal. The magnitude of the price move required to generate a trade signal can be expressed
in either nominal or percentage terms.
figure 16.13 illustrates the trade signals that would be
generated by this type of countertrend system for a 7.5 percent threshold level in the January–
September 2015 natural gas market.
it is no accident this chart depicts the same market that
was previously used in this chapter to illustrate whipsaw losses for a sensitive trend-following
system (see
figure 16.6). countertrend systems will tend to work best under those types of
market conditions in which trend-following systems fare poorly.
Fading minimum move with confirmation delay. This is similar to the preceding counter-
trend system, with the exception that some minimum indication of a trend reversal is required
before the countertrend trade is initiated.
for example, a one-thrust-day confirmation might
be required to validate countertrend signals based on fading a given percent price move.
Oscillators. a countertrend system could use oscillators to generate trade signals. However, as
discussed in chapters 11 and 12, although using oscillators to signal countertrend trades may
work well in a trading-range market, in a trending market such an approach can be disastrous.
7 Stop-loss rules, however, might be mandatory for an extremely nonsensitive trend-following system—for
example, a breakout system with N = 150.
256a coMPLeTe guiDe To THe fuTureS MarKeT
Contrary opinion. a countertrend system might use contrary opinion as an input in timing
trades. for example, once the contrary opinion rose above a specifi ed level, a short position
would be indicated contingent on confi rmation by a very sensitive technical indicator. (con-
trary opinion was discussed in chapter 14 .)
 ■ Diversifi cation
 The standard interpretation attached to the term diversifi cation is that trading is spread across a broad
range of markets. although this is the single most important type of diversifi cation, assuming the
availability of suffi cient funds, there are two additional levels of possible diversifi cation. first, each
market can be traded with several systems. Second, several variations of each system can be used. for
example, if two contracts of cocoa are being traded using the breakout system, each contract can be
traded using a diff erent value of N (i.e., the number of days whose high or low must be penetrated
to trigger


nt funds, there are two additional levels of possible diversifi cation. first, each
market can be traded with several systems. Second, several variations of each system can be used. for
example, if two contracts of cocoa are being traded using the breakout system, each contract can be
traded using a diff erent value of N (i.e., the number of days whose high or low must be penetrated
to trigger a signal).
 in the following discussion, the term single market system variation (SMSV) will refer to the concept
of a specifi c variation of a given system traded in a single market. Thus, the simple breakout system,
with N = 20, traded in the cocoa market would be an example of an SMSV . in the simplest case in
 FIGURE  16.13 countertrend Signals: october 2015 natural gas futures
 Note: Percentages are calculated as price changes in continuous futures divided by corresponding
nearest futures price levels. B = buy signal: 7.5% decline from prior high; S = sell signal: 7.5%
advance from prior low .
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.

257
Technical Trading SySTemS: STrucTure and deSign
which a single system is used for all markets, and a single system variation is used in each market,
there would be only one SMSV for each market traded. This simplified case represents the typical
application of trading systems and employs only the standard diversification across markets. However,
if sufficient funds are available, additional benefits can be obtained by also diversifying across different
systems and different variations of each system.
There are three important benefits to diversification:
 1. Dampened equity retracements. Different SMSVs will not witness their losses at precisely
the same periods. Thus, by trading a wide variety of SMSVs, the trader can achieve a smoother
equity curve. This observation implies that trading 10 SMSVs with equivalent profit/risk char-
acteristics could provide lower risk at the same return level than trading 10 units of a single
SMSV .
or, alternatively, by trading larger size, 10 SMSVs with equivalent profit/risk character-
istics could provide higher return at the same risk level than trading 10 units of a single SMSV .
up to a point, diversification would be beneficial even if the portfolio included SMSVs with
poorer expected performance. a key consideration would be a given SMSV’s correlation with
the other SMSVs in the portfolio.
 2. ensured participation in major trends. Typically, only a few of the actively traded futures
markets will witness substantial price trends in any given year. Because the majority of trades in
most trend-following systems will lose money,
8 it is essential that the trader participate in the
large-profit trades—that is, major trends. This is a key reason for the importance of diversifica-
tion across markets.
 3. bad luck insurance. futures systems trading, like baseball, is a game of inches. given the
right combination of circumstances, even a minute difference in the price movement on a sin-
gle day could have an extraordinary impact on the profitability of a specific SMSV .
 T o illustrate
this point, we consider a breakout system (N = 20) with a confirmation rule requiring a single
thrust day that penetrates the previous day’s high or low by a minimum amount. in system
a this amount is 0.05 cents; in system B it is 0.10 cents. This is the only difference between
the two systems.
figure 16.14 compares these two systems for the December 1981 coffee market and represents
the most striking instance i have ever encountered of the sensitivity of system performance to minute
changes in system values. The basic system buy signal (i.e., close above the 20-day high) was received
on July 16. This buy was confirmed by system
a on July 17 as the close was 0.09 cents above the
previous day’s high (point a1). System B, however, which required a 0.10-cent penetration, did not
confirm the signal until the following day (point B1).
The buy signal for system a would have been executed at approximately $0.97 (point a2).
However, due to the ensuing string of limit moves, the buy signal for system B could not be
filled until prices surpassed $1.22 (point B2). Thus, during this short interim, system
a gained
8 Such systems can still be profitable because the average gain significantly exceeds the average loss.
258a coMPLeTe guiDe To THe fuTureS MarKeT
25¢/lb ($9,375 per contract), while system B, which was unable to reverse its short position,
lost a similar amount. The failure of the market to close 0.01 cent higher on a given day (a price
move equivalent to less than $4) resulted in an incredible $18,750 per contract diff erence in the
performance of the two nearly identical system variations! it should be emphasized this example
refl ects the randomness in commodity price movements rather than the instability of the tested
system. any system, other than a day trading system, could refl ect the same degree of instability,
since the performance diff erence was due to just a single trade in which the signals were separated
by only one day.
 This example should explain how it is possible for a trader to lose money in a given market
using a system that generally performs well—he may just have chosen a specifi c variation that
does much worse than most other variations (even very similar ones). By trading several varia-
tions of a system, the speculator could mitigate the impact of such isolated, abnormally poor
results.
9 of course, in so doing, the trader would also eliminate the possibility of gains far exceed-
ing the average performance of the system. on balance, however, this prospect represents a desir-
able trade-off , since it is assumed that the basic trading goal is consistent performance rather than
windfall profi ts.
 FIGURE  16.14 System Trading: a game of inches (December 1981 coff ee)
chart created using TradeStation. ©TradeStation T ec


d also eliminate the possibility of gains far exceed-
ing the average performance of the system. on balance, however, this prospect represents a desir-
able trade-off , since it is assumed that the basic trading goal is consistent performance rather than
windfall profi ts.
 FIGURE  16.14 System Trading: a game of inches (December 1981 coff ee)
chart created using TradeStation. ©TradeStation T echnologies, inc. all rights reserved.
 9 in the preceding example, system a and system B were deliberately chosen to be nearly identical in order to
make the point about the potential impact of chance in its strongest possible form. However, in practice, the
trader should choose system variations that are substantially more diff erentiated.
259
Technical Trading SySTemS: STrucTure and deSign
 ■ Ten Common Problems with Trend-Following Systems
Revisited
W e are now ready to consider possible solutions to the previously enumerated problems with stan-
dard trend-following systems. The problems and the possible solutions are summarized in Table 16.2.
table 16.2 Problems with Standard trend-Following Systems and Possible Solutions
Problems with Standard trend-
Following Systems Possible Solutions
1. T oo many similar systems 1a. Try to construct original systems in order to avoid the
problem of “trading with the crowd.”
1b. if trading more than one contract, spread out entry.
2. Whipsaws 2a. employ confirmation conditions.
2b. Develop filter rules.
2c. employ diversification.
3. failure to exploit major price moves 3. add pyramiding component.
4. nonsensitive (slow) systems can
surrender a large percentage of profits.
4. employ trade exit rules.
5. cannot make money in trading-range
markets
5. Trade trend-following systems in conjunction with
countertrend systems.
6. T emporary large losses 6a. if funds permit, trade more than one system in each market.
6b. When beginning to trade a system, trade more lightly if
entering positions at a point after the signal has been received.
7. extreme volatility in best-performing
systems
7. By employing diversification, the trader can allocate some
funds to a high-profit-potential system that is too risky to
trade on its own.
8. System works well in testing but then
bombs.
8. The danger of such a development can be reduced if systems
are properly tested. This subject is discussed in detail in
chapter 19.
9. Parameter shift 9a. if funds permit, diversify by trading several variations of each
system.
9b. experiment with systems that incorporate market
characteristic adjustments.
10. Slippage 10. use realistic assumptions (discussed in chapter 19).

261
Nothing works at all times in all kinds of markets.
—Adam Smith
T
he previous chapter provided two examples of generic trading systems—moving averages and
breakouts. This chapter details several original trading systems that are based on some of the pat-
terns introduced in Chapter 9. Although the systems detailed here represent fully automated trading
strategies, the primary goal of this chapter is not to offer specific trading systems, but rather to give read-
ers a feel for how technical concepts can be utilized to construct a mechanical trading approach. Study-
ing these examples should provide readers with ideas as to how to design their own trading systems.
 ■ Wide-Ranging-Day System
Basic Concept
A wide-ranging day, which was introduced in Chapter 9, is a day with a much wider true range 1
than recent trading sessions. The high volatility inherent in wide-ranging days gives these days special
significance. Typically, the market will tend to extend in the direction of the initial price move beyond
Examples
of Original
Trading Systems
Chapter 17
1 The true range is equal to the true high minus the true low. The true high is the maximum of the current day’s high
and the previous day’s close. The true low is the minimum of the current day’s low and the previous day’s close.
262
A Complete Guide to the Futures mArket
the boundaries of the wide-ranging day. However, situations in which the market originally penetrates
one side of the wide-ranging day and then reverses to penetrate the other side also have significance.
The wide-ranging-day system defines trading ranges based on wide-ranging days. Signals are generated
when prices close above or below these trading ranges. In the simplest case, the trading range is defined
as the wide-ranging day itself. However, we make the system more general by defining the trading range
as the price range encompassing all the true highs and true lows during the period extending from N1
days before the wide-ranging day to N2 days after, where N1 and N2 are parameter values that must be
defined. For example, if both N1 and N2 equal 0, the trading range would be defined by the wide-ranging
day itself (i.e., the range between the true high and true low of the wide-ranging day). If N1 = 2 and
N2 = 4, the trading range would be defined as the range between the highest true high and lowest true
low in the interval beginning two days before the wide-ranging day and ending four days after it.
Definitions
Wide-ranging day. A day on which the volatility ratio (VR) is greater than k (e.g., k = 2). The
VR is equal to today’s true range divided by the average true range of the past N-day period
(e.g., N = 10).
price trigger range (ptr). The range defined by the highest true high and lowest true low in
the interval between N1 days before the most recent wide-ranging day to N2 days after. Note
that the PTR cannot be defined until N2 days after a wide-ranging day. (If N2 = 0, the PTR
would be defined as of the close of the wide-ranging day itself.) The PTR will be redefined each
time there is a new wide-ranging day (i.e., N2 days after such an event).
trading Signals
Buy case. On a close above the high of the PTR, reverse from short to long.
Sell case. On a close below the low of the PTR, reverse from long to short.
Daily Checklist
T o generate trading sign


f N2 = 0, the PTR
would be defined as of the close of the wide-ranging day itself.) The PTR will be redefined each
time there is a new wide-ranging day (i.e., N2 days after such an event).
trading Signals
Buy case. On a close above the high of the PTR, reverse from short to long.
Sell case. On a close below the low of the PTR, reverse from long to short.
Daily Checklist
T o generate trading signals, perform the following steps each day:
 1. If short and today’s close is above the high of the PTR, liquidate short and go long.
 2. If long and today’s close is below the low of the PTR, liquidate long and go short.
 3. Check whether exactly N2 days have elapsed since the most recent wide-ranging day. If this
condition is met, redefine the PTR.
The order of these steps is very important. Note that the check for new trading signals precedes
the check whether the PTR should be redefined. Thus, if the day a new PTR is defined also signals a
trade based on the prevailing PTR going into that day, a signal would be generated. If step 3 preceded
steps 1 and 2, trade signals could get delayed each time a signal occurred on the day a new PTR is
defined (N2 days after the most recent wide-ranging day, which would be the wide-ranging day itself
when N2 = 0). For example, assume the system is long, N2 = 0, and the close on a new wide-ranging
day is below the low of the preceding wide-ranging day. According to the listed step order, the new
wide-ranging day would signal a reversal from long to short. If steps 1 and 2 followed step 3, no signal
263
ExAMPlES OF ORIgINAl TRADINg SySTEMS
would occur, since the PTR would be redefined, and the market would have to close below the new
wide-ranging day to trigger a signal.
System parameters
N1. The number of days prior to the wide-ranging day included in the PTR period.
N2. The number of days after the wide-ranging day included in the PTR period.
k. The value the volatility ratio (VR) must exceed in order to define a wide-ranging day.
Note: N, the number of past days used to calculate the VR, is assumed to be fixed (e.g., N = 10).
parameter Set List
Table 17.1 provides a sample parameter set list. Readers can use this list as is or adjust it as desired.
The subject of testing multiple parameter sets and deciding which one to use in actual trading is
addressed in Chapter 19.
taBLe 17.1 parameter Set List
k N1 N2
1. 1.6 0 0
2. 1.6 2 0
3. 1.6 4 0
4. 1.6 0 2
5. 1.6 2 2
6. 1.6 4 2
7. 1.6 0 4
8. 1.6 2 4
9. 1.6 4 4
10. 2.0 0 0
11. 2.0 2 0
12. 2.0 4 0
13. 2.0 0 2
14. 2.0 2 2
15. 2.0 4 2
16. 2.0 0 4
17. 2.0 2 4
18. 2.0 4 4
19. 2.4 0 0
20. 2.4 2 0
21. 2.4 4 0
22. 2.4 0 2
23. 2.4 2 2
24. 2.4 4 2
25. 2.4 0 4
26. 2.4 2 4
27. 2.4 4 4
264A COMPlETE gUIDE TO THE FUTURES MARKET
 FIGURE  17.1 Wide-Ranging Day System, Chart 1: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
 N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 an Illustrated example
 T o illustrate how the system works, Figures 17.1 through 17.5 superimpose trading signals on copper
charts spanning late October 2013 to November 2015, a period the weekly chart inset in Figure 17.1
shows consisted mostly of a choppy, longer-term price descent, interspersed with short-term uptrends
in mid-2014 and early 2015. Note these charts are continuous futures to coincide with the price series
used to generate signals. As will be fully detailed in the next two chapters, continuous futures are usu-
ally the most suitable price series to use in trading systems. T o help provide continuity between charts,
each chart overlaps one to two months of the preceding chart.
 Two types of signals are indicated on the accompanying charts:
 1. The noncircled signals are generated by the system when both N 1 and N 2 are set to zero. In
other words, the PTR is defi ned by the true high and true low of the wide-ranging day.
 2. The circled signals are generated by the system when N 1 = 2 and N 2 = 4. (In other words, the
PTR is defi ned by the true price range encompassing the interval beginning two days before the
wide-ranging day and ending four days after it.)
 Occasionally, both sets of parameter values will yield identical signals. In most cases, however, the
second system version will trigger signals later or not at all. (The reverse can never occur, since the
PTR based on N 1 = 2 and N 2 = 4 must be at least as wide as the PTR based on N 1 = 0 and N 2 = 0.
Therefore any penetration of the former PTR must also be a penetration of the latter PTR, but not
vice versa.)
265
ExAMPlES OF ORIgINAl TRADINg SySTEMS
 FIGURE  17.3 Wide-Ranging Day System, Chart 3: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.2 Wide-Ranging Day System, Chart 2: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
 N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.

266A COMPlETE gUIDE TO THE FUTURES MARKET
 FIGURE  17.4 Wide-Ranging Day System, Chart 4: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
 N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.5 Wide-Ranging Day System, Chart 5: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
 N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies


for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.5 Wide-Ranging Day System, Chart 5: Copper Continuous Futures
 Note: Thicker bars are wide-ranging days. B, S = buy and sell signals for N 1 = 0 and
 N 2 = 0; , = buy and sell signals for N 1 = 2 and N 2 = 4.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
267
ExAMPlES OF ORIgINAl TRADINg SySTEMS
First, we examine the trading signals generated for the system version when both N1 and N2 equal
zero (the noncircled signals). Therefore, for now , ignore the circled signals, which are based on the
parameter set consisting of N1 = 2 and N2 = 4. W e will subsequently examine the instances in which
the two parameter sets yield different signals.
The first signal occurs in December 2013 when a close above the high of the December 4 wide-
ranging day triggers a buy (Figure 17.1). The system then reverses to short modestly higher in January
2014 when the market closes below the low of the wide-ranging day formed in late December. The
January short position profits from the ensuing downtrend and remains intact until late April when
the market closes above the high of the second wide-ranging day that formed in March, triggering a
buy signal.
The April 2014 long position remains intact for several months, capturing a portion of the ensuing
uptrend, until it is reversed in early August when the market closes below the low of the early July
wide-ranging day (Figure 17.2). The early August short position is short-lived and results in the first
losing trade when a subsequent market bounce forms a wide-ranging day that is exceeded by a closing
price two days later, triggering a buy signal. This August buy signal proves to be a whipsaw trade as the
system reverses back to short in September 2014 (Figure 17.3).
The next buy signal materializes near the same level in late October 2014 when the market closes
above the true high of the October wide-ranging day. This buy signal proves to be another whipsaw
loss as the market immediately turns lower and eventually closes below the same October wide-
ranging day, triggering a sell signal in November. Note, as is the case here, a single wide-ranging day
can trigger multiple trades (in opposite directions) in the absence of intervening wide-ranging days.
The November sell signal yields a small profit before leading to a third successive losing buy signal in
December 2014. The January 2015 sell signal is exited at a profit in February 2015 when the market
closes above the high of the second wide-ranging day formed in January (Figure 17.4). Additional
trades are shown in Figures 17.4 and 17.5.
Next we examine how the signals generated by the second parameter set (N 1 = 2, N 2 = 4;
circled on charts) differ from those that result from the first parameter set (N 1 = 0, N 2 = 0).
One pattern the reader will notice is that whenever both parameter sets had signals in the same
cycle—a signal in the same direction before the first parameter set triggered an opposite signal—
the delay caused by using the second parameter set almost invariably resulted in a less favorable
entry level. In most cases the differences in entry levels were moderate (e.g., the signals shown
in Figure 17.1). In some instances, however, the difference in entry levels was quite substantial.
For example, in Figure 17.2, the second parameter set went long in late June, more than two
months after the first parameter set, because prices needed to close not just above the high of the
March 11 wide-ranging day, but above the high of the two days preceding that day. Occasionally,
both parameter sets may trigger signals on the same day (e.g., the September 2015 buy in Figure
17.5), but there are no instances where the second parameter set has a better entry. The poorer
entry levels generated by the second parameter set are no accident, since the wider PTRs defined
by the nonzero N 1 and N 2 values will always result in equal or higher buy signals and equal or
lower sell signals.
268
A Complete Guide to the Futures mArket
The reader might well wonder why one would ever want to use nonzero values for N1 and N2,
since the resulting delayed entries are invariably equal to or worse than entries based on keeping
N1 and N2 equal to zero. The answer lies in the fact that the broader PTRs that result from nonzero
N1 and N2 values will tend to filter out some losing signals—a characteristic that can have a major
impact on the system’s profitability. For example, following the August 2014 sell signal, the second
parameter set avoids the three successive losing buy signals generated by the first parameter set
(Figure 17.3). As a result, the second parameter set generates a substantial profit during this period
while the series of trades generated by the first parameter set results in a net loss, despite the pre-
vailing major downtrend.
On balance, in the market example illustrated in Figures 17.1 through 17.5, the benefit of filtering
out some losing trades far outweighs the cumulative negative impact of the worse entries that result
from using nonzero values for N1 and N2: For the entire period, the second parameter set generates
a cumulative profit of $0.488 per pound ($12,200 per contract) versus a cumulative loss of –$0.379
per pound (–$9,475 per contract) for the first parameter set.
Although in some cases parameter sets with more sensitive entry conditions will experience the
better performance, the outcome in our example is more typical.
generally speaking, the parameter
sets with more restrictive entry conditions will do better, as the benefit of reducing whipsaw trades
outweighs the disadvantage of worse entries. Ironically, human nature will lead most traders, espe-
cially novices, to choose more sensitive parameter sets because they will be attracted by the better
entries and smaller surrender


our example is more typical.
generally speaking, the parameter
sets with more restrictive entry conditions will do better, as the benefit of reducing whipsaw trades
outweighs the disadvantage of worse entries. Ironically, human nature will lead most traders, espe-
cially novices, to choose more sensitive parameter sets because they will be attracted by the better
entries and smaller surrender of open profits on individual trades offered by these sets, failing to fully
appreciate the cumulative impact of reduced bad trades—a trait characteristic of more restrictive
parameter sets.
It should be emphasized the selected example was intended to illustrate the mechanics of the wide-
ranging day system across varied market conditions, not to put the system in the best light. Therefore,
this example deliberately contained both intervals of strong wins as well as whipsaw losses. Note that
I could easily have made the system look much more impressive by selecting a market and time period
with much smoother trends. Such cherry-picked illustrations are all too common in trading books,
articles, web sites, and—especially—advertisements. W e return to this subject in the discussion of
“the well-chosen example” in Chapter 19.
 ■ Run-Day Breakout System
Basic Concept
Up and down run days were defined in Chapter 9. As was explained, run days tend to occur in strongly
trending markets. In this system, buy reversal signals are generated when the market closes above the
maximum true high of a specified number of prior down run days. Similarly, sell reversal signals are
generated when the market closes below the minimum true low of a specified number of prior up run
days. The idea is that the ability of the market to close opposite the extreme point defined by one or
more such strongly trending days implies a trend reversal has occurred.
269
ExAMPlES OF ORIgINAl TRADINg SySTEMS
trading Signals
Buy case. Reverse to long whenever both of the following two conditions are met:
 1. The close is above the maximum true high of the most recent N2 down run days. (Note: Only
the run day true highs are considered, not the true highs on the interim days.)
 2. The most recent run day is an up run day. (Without this second condition, in some cases, the
first condition in the sell case would result in an automatic reversal back to a short position.)
Sell case. Reverse to short whenever both of the following two conditions are met:
 1. The close is below the minimum true low of the most recent N2 up run days. (Note: Only the
run day true lows are considered, not the true lows on the interim days.)
 2. The most recent run day is a down run day. (Without this second condition, in some cases, the
first condition in the buy case would result in an automatic reversal back to a long position.)
Daily Checklist
T o generate trading signals, perform the following three steps each day:
 1. Check whether the trading day N1 days prior to the current day can be defined as an up or a
down run day.
2 (Recall that a run day cannot be defined until the close N1 days after the run
day.) Keep track of all run days and their true highs and true lows.
 2. If short, check whether today’s close is above the maximum true high of the past N2 down run
days. If it is, check whether the most recent run day was an up run day. If it was, reverse from
short to long.
 3. If long, check whether today’s close is below the minimum true low of the past N2 up run days.
If it is, check whether the most recent run day was a down run day. If it was, reverse from long
to short.
parameters
N1. The parameter used to define run days. For example, if N = 3, a day would be defined as an up
run day if its true high was greater than the maximum true high of the prior three days and its
true low was less than the minimum true low of the following three days.
N2. The number of prior down run days used to compute the maximum true high that must be
exceeded by a close for a buy signal. (Also, the number of prior up run days used to compute
the minimum true low that must be penetrated by a close for a sell signal.)
2 Although uncommon, a day can be both an up run day and down run day. This unusual situation will occur if a
day’s true high is greater than the true highs during the prior and subsequent N1 days, and its true low is lower
than the true lows during the prior and subsequent N1 days. Days that fulfill both the up and down run day defini-
tions are not considered run days.
270
A Complete Guide to the Futures mArket
parameter Set List
Table 17.2 provides a sample parameter set list. Readers can use this list as is or adjust it as desired.
an Illustrated example
T o illustrate the mechanics of the run-day breakout system, Figures 17.6 through 17.9 show the buy
and sell signals generated by the system for the parameter set N1 = 5 and N2 = 4 in the WTI crude
oil market. Down run days are denoted by downward-pointing arrows and up run days by upward-
pointing arrows.
A close below the minimum true low of the four most recent up run days triggers a sell signal
in January 2014 (Figure 17.6). Note the second condition for a sell signal—that is, the most recent
run day is a down run day—was fulfilled on the day of the signal. Had the signal occurred one day
earlier, no trade would have been taken because the December 31, 2013, down run day (the first in
the string of four) would not yet have been confirmed, and the most recent run day would have been
the December 19 up run day. (Remember that each run day marked with an arrow can be confirmed
only after five days have passed.)
A buy signal occurs in February 2014 when the market closes above the true high of the December
31 down run day (the maximum true high of that string of four down run days). The second condition
is also met as the most recent run day is an up run day.
The market drifts higher into June (note the predominance of up run days versus down run days)
and then begins


nly after five days have passed.)
A buy signal occurs in February 2014 when the market closes above the true high of the December
31 down run day (the maximum true high of that string of four down run days). The second condition
is also met as the most recent run day is an up run day.
The market drifts higher into June (note the predominance of up run days versus down run days)
and then begins to sag into July (Figure 17.7). The system next goes short in July when the market
closes below the minimum true low of the prior four up run days. The system stays short through the
entire ensuing downtrend, which is characterized by a tremendous predominance of down run days,
eventually reversing nearly nine months later (in April 2015) on the close above the true high of the
cluster of down run days in March (Figure 17.8). The system holds this position through June as the
market moves sideways to slightly higher. The downturn in July generates a flurry of down run days,
and the system turns short on July 22 with a close below the March 25 low (Figure 17.9). Note that
taBLe 17.2 parameter Set List
N1 N2
1. 3 2
2. 3 3
3. 3 4
4. 3 5
5. 5 2
6. 5 3
7. 5 4
8. 7 2
9. 7 3
10. 7 4
271
ExAMPlES OF ORIgINAl TRADINg SySTEMS
 FIGURE  17.7 Run-Day Breakout System ( N 1 = 5; N 2 = 4), Chart 2: WTI Crude Oil
Continuous Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.6 Run-Day Breakout System ( N 1 = 5; N 2 = 4), Chart 1: WTI Crude Oil
Continuous Futures
 Note: The direction of the arrows indicates the direction of the run day.
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.

272A COMPlETE gUIDE TO THE FUTURES MARKET
 FIGURE  17.8 Run-Day Breakout System ( N 1 = 5; N 2 = 4), Chart 3: WTI Crude Oil
Continuous Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.9 Run-Day Breakout System ( N 1 = 5, N 2 = 4), Chart 4: WTI Crude Oil
Continuous Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
273
ExAMPlES OF ORIgINAl TRADINg SySTEMS
although the sharp rebound in late August is large enough to rally the market above the maximum
true high of the four most recent down run days, there is no buy signal because there is no interven-
ing up run day.
Overall, the system successfully exploits the major downtrend (July 2014 to August 2015) that
occurs during the two-year survey period, capturing about half of the total profit that would be realized
by a hypothetical trader who goes short at the high of the two-year period and covers the position at
the low of the period. Readers, however, are cautioned against generalizing the system’s performance
based on this single market/single parameter set example. In most cases, the system will not attain the
level of performance exhibited in this illustration.
 ■ Run-Day Consecutive Count System
Basic Concept
This system also uses run days as the key input in generating trading signals. In this system, reversal
signals occur whenever there are a specified number of up run days without any intervening down
run days, or vice versa.
Definitions
The system uses the following definitions:
Buy count. The buy count is activated whenever a sell signal is received. The count starts at
zero and increases by one whenever a new up run day is defined. The count is reset to zero
whenever there is a down run day. In effect, the buy count represents the number of up run
days that occur without any intervening down run days. The buy count is closed when a buy
signal is received.
Sell count. The sell count is activated whenever a buy signal is received. The count starts at
zero and increases by one whenever a new down run day is defined. The count is reset to zero
whenever there is an up run day. In effect, the sell count represents the number of down run
days that occur without any intervening up run days. The sell count is closed when a sell signal
is received.
trading Signals
Buy case. Reverse to long whenever the buy count reaches N2. Keep in mind that the fulfill-
ment of this condition will not be known until N1 days after the N2th consecutive up run day.
(Consecutive here means that there are no intervening down run days, not that the up run days
occur on consecutive days.)
274
A Complete Guide to the Futures mArket
Sell case. Reverse to short whenever the sell count reaches N2. Keep in mind that the fulfill-
ment of this condition will not be known until N1 days after the N2th consecutive down run day.
(Consecutive here means that there are no intervening up run days, not that the down run days
occur on consecutive days.)
Daily Checklist
T o generate trading signals, perform the following three steps each day:
 1. Check whether the trading day N1 days prior to the current day can be defined as an up or a
down run day. (Recall that a run day cannot be defined until the close N1 days after the run day.)
If the day is an up run day, increase the buy count by one if the buy count is active (i.e., if the
current position is short); otherwise, reset the sell count to zero. (Either the buy or sell count is
always active, depending on whether the current position is short or long.) If the day is defined
as a down run day, increase the sell count by one if the sell count is active (i.e., if current posi-
tion is long); otherwise, reset the buy count to zero.
 2. If the buy count is active, check whether it is equal to N2 after step 1. If it is, cover short, go
long, close buy count, and activate sell count.
 3. If the sell count is active, check whether it is equal to N2 after step 1. If it is, cover long, go
short, close sell count, and activate buy count.
parameters
N1. The parameter used to define run days.
N2. The number of consecutive run days required for a signal.
parameter Set List
Table 17.3 provides a sample parameter set list. Readers can use th


, close buy count, and activate sell count.
 3. If the sell count is active, check whether it is equal to N2 after step 1. If it is, cover long, go
short, close sell count, and activate buy count.
parameters
N1. The parameter used to define run days.
N2. The number of consecutive run days required for a signal.
parameter Set List
Table 17.3 provides a sample parameter set list. Readers can use this list as is or adjust it as desired.
taBLe 17.3 parameter Set List
N1 N2
1. 3 1
2. 3 2
3. 3 3
4. 3 4
5. 5 1
6. 5 2
7. 5 3
8. 7 1
9. 7 2
10. 7 3
275
ExAMPlES OF ORIgINAl TRADINg SySTEMS
 an Illustrated example
 Figures 17.10 through 17.14 illustrate the signals generated by the run day consecutive count system
for N 1 = 5 and N 2 = 3. In other words, the system reverses from long to short whenever there are
three consecutive down run days and from short to long whenever there are three consecutive up
run days. (Consecutive here means that there are no intervening run days in the opposite direction;
not consecutive days.) Keep in mind that the actual trade signal will not be received until the fi fth close after
the third consecutive run day, since a run day is not defi ned until N1 days after its occurrence (N1 = 5 in this
example).
 The fi rst signal in Figure 17.10 —a buy in December 2013—occurs during a brief trading range
and is reversed by a sell signal that occurs near the January 2014 low—a good example of how even
profi table systems can generate terrible individual trade signals. The three consecutive up run days
that start off February trigger a long position on February 12 (fi ve days after the third up run day).
Figure 17.11 shows this position remains intact until June 12, when the system reverses to short.
Note that although the signal occurs on the fi fth consecutive down run day in the sequence, it is
the fact that this day is fi ve days after the third consecutive down run day that triggers the trade.
The downtrend that begins in June witnesses 18 down run days with no intervening up run days. In
contrast, the preceding February–May uptrend contained 12 up run days and only one down run day.
 The system reverses to the upside at the start of November 2014 (Figure 17.12 ), one week into a
two-and-a-half-month trading range. The three consecutive down run days, which lead to the down-
side breakout of this range, turn the system short in January 2015. The next signal is the worst trade
in the survey period, as the system reverses to long in February, shortly before the March 2 relative
 FIGURE  17.10 Run-Day Consecutive Count System, Chart 1: Soybean Continuous Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
276A COMPlETE gUIDE TO THE FUTURES MARKET
 FIGURE  17.11 Run-Day Consecutive Count System, Chart 2: Soybean Continuous
Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.12 Run-Day Consecutive Count System, Chart 3: Soybean Continuous
Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.

277
ExAMPlES OF ORIgINAl TRADINg SySTEMS
 FIGURE  17.13 Run-Day Consecutive Count System, Chart 4: Soybean Continuous
Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.
 FIGURE  17.14 Run-Day Consecutive Count System, Chart 5: Soybean Continuous
Futures
Chart created using TradeStation. ©TradeStation T echnologies, Inc. All rights reserved.

278
A Complete Guide to the Futures mArket
high (Figure 17.13). The system does not generate a sell signal until late May, just before a relative
low . Fortunately, the system reverses back to long two weeks later in June 2015 just before a sharp,
but short-lived, rally (Figure 17.14). The subsequent downside reversal is equally abrupt, and the
system surrenders most of its profit on the long position by the time the next sell signal is gener-
ated in July. The final two signals occur in October and November 2015 within a relatively narrow
consolidation phase.
It should be noted that our intention was to select a realistic market illustration of the system and
not to cherrypick an example in which the system performed particularly well, as is typical in most
books on trading. The foregoing example provided a market with both favorable (two 4-month trends)
and unfavorable (a more than yearlong wide-swing trading range) price environments. On balance, the
system was net profitable (a cumulative gain of 76.25 cents per bushel, or $3,812.50 per contract) as
the profits during the two trending periods outweighed the losses during the extended trading range
period.
 ■ Conclusion
In this chapter we have introduced some original trading systems. Although they are viable as
described, readers may wish to experiment with modifications that use the concepts of these systems
as the core of more complex approaches. The ultimate goal of this chapter was not to present specific
trading systems, but rather to illustrate how basic chart concepts can be transformed into trading
systems. The number of possible systems that can be constructed from the technical patterns and
concepts already discussed in this volume are limited only by the imagination of the reader.
279
Chapter 18
Garbage in, garbage out.
—Anonymous
S
ystem traders wishing to test their ideas on futures prices have always faced a major obstacle: the
transitory life span of futures contracts. In contrast to the equities market, where a given stock is
represented by a single price series spanning the entire test period, in futures each market is repre
sented by a string of expiring contracts. Proposed solutions to this problem have been the subject of
many articles and a great deal of discussion. In the process, substantial confusion has been generated,
as evidenced by the use of identical terms to describe different types of price series. Even worse, so
much misinforma


series spanning the entire test period, in futures each market is repre
sented by a string of expiring contracts. Proposed solutions to this problem have been the subject of
many articles and a great deal of discussion. In the process, substantial confusion has been generated,
as evidenced by the use of identical terms to describe different types of price series. Even worse, so
much misinformation has been provided on this subject that many market participants now believe
the equivalent of “the earth is flat” theory.
There are four basic types of price series that can be used. The definition, advantages, and dis
advantages of each are discussed in turn.
 ■ Actual Contract Series
At a surface glance, the best route might seem to be simply to use the actual contract series. How
ever, there are two major problems with this approach. First, if you are testing a system over a
meaningful length of time, each market simulation will require a large number of individual price
Selecting the Best
Futures Price Series
for System T esting
280
A Complete Guide to the Futures mArket
series. For example, a 15year test run for a typical market would require using approximately 60 to
90 individual contract price series. Moreover, using the individual contract series requires an algo
rithm for determining what action to take at the rollover points. As an example of the type of problem
that may be encountered, it is entirely possible for a given system to be long in the old contract and
short in the new contract or vice versa. These problems are hardly insurmountable, but they make the
use of individual contract series a somewhat unwieldy approach.
The awkwardness involved in using a multitude of individual contracts is not, however, the main
problem. The primary drawback in using individual contract series is that the period of meaningful
liquidity in most contracts is very short—much shorter than the already limited contract life spans.
T o see the scope of this problem, examine a cross section of futures price charts depicting the price
action in the one
year period prior to expiration. In many markets, contracts don’t achieve meaning
ful liquidity until the final five or six months of trading, and sometimes even less. This problem was
illustrated in Chapter 5. The limited time span of liquid trading in individual contracts means that any
technical system or method that requires looking back at more than about six months of data—as
would be true for a whole spectrum of longer
term approaches—cannot be applied to individual
contract series. Thus, with the exception of shortterm system traders, the use of individual contract
series is not a viable alternative. It’s not merely a matter of the approach being difficult but, rather, its
being impossible because the necessary data simply do not exist.
 ■ Nearest Futures
The problems in using individual contract series as just described has led to the construction of vari
ous linked price series. The most common approach is almost universally known as nearest futures.
This price series is constructed by taking each individual contract series until its expiration and then
continuing with the next contract until its expiration, and so on. This approach may be useful for
constructing long
term price charts for purposes of chart analysis, but it is worthless for providing a
series that can be used in the computer testing of trading systems.
The problem in using a nearest futures series is that there are price gaps between expiring and new
contracts—and quite frequently these gaps can be very substantial. For example, assume the July corn
contract expires at $4 and that the next nearest contract (September) closes at $3.50 on the same day.
Assume that on the next day September corn moves from $3.50 to $3.62. A nearest futures price series
will show the following closing levels on these two successive days: $4, $3.62. In other words, the near
est futures contract would imply a 38
cent loss on a day on which longs would have enjoyed (or shorts
would have suffered) a price gain of 12 cents. This example is by no means artificial. In fact, it would
be easy to find a plethora of similarly extreme situations in actual price histories. Moreover, even if the
typical distortion at rollover is considerably less extreme, the point is that there is virtually always some
distortion, and the cumulative effect of these errors would destroy the validity of any computer test.
Fortunately, few traders are naive enough to use the nearest futures type of price series for computer
testing. The two alternative linked price series described in the next sections have become the approaches
employed by most traders wishing to use a single price series for each market in computer testing.
281
SElECTINg THE BEST FuTurES PrICE SErIES For SySTEM TESTINg
 ■ Constant-Forward (“Perpetual”) Series
The constantforward (also known as “perpetual”) price series consists of quotes for prices a constant
amount of time forward. The interbank currency market offers actual examples of constantforward
price series. For example, the threemonth forward price series for the euro represents the quote for
the euro three months forward from each given day in the series. This is in contrast to the standard
u.S. futures contract, which specifies a fixed expiration date.
A constant forward series can be constructed from futures price data through interpola
tion. For example, if we were calculating a 90 day constant forward (or perpetual) series and
the 90day forward date fell exactly one third of the way between the expirations of the nearest
two contracts, the constant forward price would be calculated as the sum of two thirds of the
nearest contract price and one third of the subsequent contract price. As we moved forward in
time, the nearer contract would be weighted less, and the weighting of the subsequent contract
would increase proportionately. Eventually, the neares


ne third of the way between the expirations of the nearest
two contracts, the constant forward price would be calculated as the sum of two thirds of the
nearest contract price and one third of the subsequent contract price. As we moved forward in
time, the nearer contract would be weighted less, and the weighting of the subsequent contract
would increase proportionately. Eventually, the nearest contract would expire and drop out of
the calculation, and the constant
forward price would be based on an interpolation between the
subsequent two contracts.
As a more detailed example, assume you want to generate a 100day forward price series based on
euro futures, which are traded in March, June, September, and December contracts. T o illustrate the
method for deriving the 100
day constantforward price, assume the current date is January 20. In
this case, the date 100 days forward is April 30. This date falls between the March and June contracts.
Assume the last trading dates for these two contracts are March 14 and June 13, respectively. Thus,
April 30 is 47 days after the last trading day for the March contract and 44 days before the last trad
ing day for the June contract. T o calculate the 100
day forward price for January 20, an average price
would be calculated using the quotes for March and June euro futures on January 20, weighting each
quote in inverse proportion to its distance from the 100
day forward date (April 30). Thus, if on Janu
ary 20 the closing price of March futures is 130.04 and the closing price of June futures is 130.77, the
closing price for the 100
day forward series would be:
44
91 1300 4 130 77 130 42(. )( .) .+=47
91
Note that the general formula for the weighting factor used for each contract price is:
W CF
CC W FC
CC1
2
21
2
1
21
= −
− = −
−
where C1 = number of days until the nearby contract expiration
 C2 = number of days until the forward contract expiration
 F = number of days until forward quote date
 W1 = weighting for nearby contract price quote
 W2 = weighting for forward contract price quote
282
A Complete Guide to the Futures mArket
So, for example, the weightings of the March and June quotes that would be used to derive a
100day forward quote on March 2 would be as follows:
Weighting for March quot e
Weighting for
 = −
− =1031 00
103 12
3
91
JJune quote = −
− =100 12
103 12
88
91
As we move forward in time, the nearer contract is weighted less and less, but the weighting for
the subsequent contract increases proportionately. When the number of days remaining until the
expiration of the forward contract equals the constant forward time (100 days in this example), the
quote for the constant forward series would simply be equal to the quote for the forward contract
(June). Subsequent price quotes would then be based on a weighted average of the June and Septem
ber prices. In this manner, one continuous price series could be derived.
The constant
forward price series eliminates the problem of huge price gaps at rollover points and
is certainly a significant improvement over a nearest futures price series. However, this type of series
still has major drawbacks. T o begin, it must be stressed that one cannot literally trade a constant

forward series, since the series does not correspond to any real contract. An even more serious
deficiency of the constant
forward series is that it fails to reflect the effect of the evaporation of time
that exists in actual futures contracts. This deficiency can lead to major distortions—particularly in
carrying
charge markets.
T o illustrate this point, consider a hypothetical situation in which spot gold prices remain stable at
approximately $1,200/ounce for a oneyear period, while forward futures maintain a constant pre
mium of 1 percent per twomonth spread. given these assumptions, futures would experience a steady
downtrend, declining $73.82/ounce1 ($7,382 per contract) over the oneyear period (the equivalent
of the cumulative carryingcharge premiums). Note, however, the constantforward series would com
pletely fail to reflect this bear trend because it would register an approximate constant price. For
example, a two
month constantforward series would remain stable at approximately $1,212/ounce
(1.01 × $1,200 = $1,212). Thus, the price pattern of a constant forward series can easily deviate
substantially from the pattern exhibited by the actual traded contracts—a highly undesirable feature.
 ■ Continuous (Spread-Adjusted) Price Series
The spreadadjusted futures series, commonly known as continuous futures, is constructed to elimi
nate the distortions caused by the price gaps between consecutive futures contracts at their transi
tion points. In effect, the continuous futures price will precisely reflect the fluctuations of a futures
position that is continuously rolled over to the subsequent contract N days before the last trading
day, where N is a parameter that needs to be defined. If constructing their own continuous futures
data series, traders should select a value of N that corresponds to their actual trading practices.
1 This is true since, given the assumptions, the oneyear forward futures price would be approximately $1,273.82
(1.016 × $1,200 = $1,273.82) and would decline to the spot price ($1,200) by expiration.
283
SElECTINg THE BEST FuTurES PrICE SErIES For SySTEM TESTINg
For example, if a trader normally rolls a position over to a new contract approximately 20 days before
the last trading day, N would be defined as 20. The scale of the continuous futures series is adjusted so
the current price corresponds to a currently traded futures contract.
Table 18.1 illustrates the construction of a continuous futures price for the soybean market. For
simplicity, this example uses only two contract months, July and November; however, a continuous
price could be formed using any number of traded contract months. For example, the continuous futures
price could be constructed using


e current price corresponds to a currently traded futures contract.
Table 18.1 illustrates the construction of a continuous futures price for the soybean market. For
simplicity, this example uses only two contract months, July and November; however, a continuous
price could be formed using any number of traded contract months. For example, the continuous futures
price could be constructed using the January, March, May, July, August, September, and November
soybean contracts.
table 18.1 Construction of a Continuous Futures price Using July and November Soybeans
(cents/bushel)*
Date Contract actual price
Spread at rollover
(Nearby Forward)
Cumulative
adjustment
Factor
Unadjusted
Continuous Futures
(Col. 3 + Col. 5)
Continuous
Futures price
(Col. 6 – 772.5)
6/27/12 Jul 12 1,471 1,471 698.5
6/28/12 Jul 12 1,466 1,466 693.5
6/29/12 Jul 12 1,512.75 1,512.75 740.25
7/2/12 Nov 12 1,438 85 85 1,523 750.5
7/3/12 Nov 12 1,474.75 85 1,559.75 787.25
***
10/30/12 Nov 12 1,533.75 85 1,618.75 846.25
10/31/12 Nov 12 1,547 85 1,632 859.5
11/1/12 Jul 13 1,474 86.25 171.25 1,645.25 872.75
11/2/12 Jul 13 1,454 171.25 1,625.25 852.75
***
6/27/13 Jul 13 1,548.5 171.25 1,719.75 947.25
6/28/13 Jul 13 1,564.5 171.25 1,735.75 963.25
7/1/13 Nov 13 1,243.25 312.5 483.75 1,727 954.5
7/2/13 Nov 13 1,242.5 483.75 1,726.25 953.75
***
10/30/13 Nov 13 1,287.5 483.75 1,771.25 998.75
10/31/13 Nov 13 1,280.25 483.75 1,764 991.5
11/1/13 Jul 14 1,224.5 45.5 529.25 1,753.75 981.25
11/4/13 Jul 14 1,227.75 529.25 1,757 984.5
***
6/27/14 Jul 14 1,432 529.25 1,961.25 1,188.75
6/30/14 Jul 14 1,400.5 529.25 1,929.75 1,157.25
7/1/14 Nov 14 1,147.5 243.25 772.5 1,920 1,147.5
7/2/14 Nov 14 1,141.5 772.5 1,914 1,141.5
*Assumes rollover on last day of the month preceding the contract month.
284
A Complete Guide to the Futures mArket
For the moment, ignore the last column in Table 18.1 and focus instead on the unadjusted con
tinuous futures price (column 6). At the start of the period, the actual price and the unadjusted
continuous futures price are identical. At the first rollover point, the forward contract (November
2012) is trading at an 85
cent discount to the nearby contract (July 2012). All subsequent prices of
the November 2012 contract are then adjusted upward by this amount (the addition of a positive
nearby/forward spread), yielding the unadjusted continuous futures prices shown in column 6. At
the next rollover point, the forward contract (July 2013) is trading at an 86.25
cent discount to the
nearby contract (November 2012). As a result, all subsequent actual prices of the July 2013 contract
must now be adjusted by the cumulative adjustment factor—the total of all rollover gaps up to that
point (171.25 cents)—in order to avoid any artificial price gaps at the rollover point. This cumulative
adjustment factor is indicated in column 5. The unadjusted continuous futures price is obtained by
adding the cumulative adjustment factor to the actual price.
The preceding process is continued until the current date is reached. At this point, the final cumu
lative adjustment factor is subtracted from all the unadjusted continuous futures prices (column 6),
a step that sets the current price of the series equal to the price of the current contract (November
2014 in our example) without changing the shape of the series. This continuous futures price is indi
cated in column 7 of Table 18.1. Note that although actual prices seem to imply a net price decline of
329.50 cents during the surveyed period, the continuous futures price indicates a 443
cent increase—
the actual price change that would have been realized by a constant long futures position.
In effect, the construction of the continuous series can be thought of as the mathematical equiva
lent of taking a nearest futures chart, cutting out each individual contract series contained in the
chart, and pasting the ends together (assuming a continuous series employing all contracts and using
the same rollover dates as the nearest futures chart).
In some markets, the spreads between nearby and forward contracts will range from premiums to
discounts (e.g., cattle). However, in other markets, the spread differences will be unidirectional. For
example, in the gold market, the forward month always trades at a premium to the nearby month.
2 In
these types of markets, the spreadadjusted continuous price series can become increasingly disparate
from actual prices.
It should be noted that when nearby premiums at contract rollovers tend to swamp nearby dis
counts, it is entirely possible for the series to eventually include negative prices for some past periods
as cumulative adjustments mount, as illustrated in the soybean continuous futures chart in Figure 18.1.
The price gain that would have been realized by a continuously held futures position during this period
2 The reason for this behavioral pattern in gold spreads is related to the fact that world gold inventories exceed
annual usage by many multiples, perhaps even by as much as a hundredfold. Consequently, there can never ac
tually be a “shortage” of gold—and a shortage of nearby supplies is the only reason why a storable commodity
would reflect a premium for the nearby contract. (Typically, for storable commodities, the fact that the forward
contracts embed carrying costs will result in these contracts trading at a premium to more nearby months.)
gold prices fluctuate in response to shifting perceptions of gold’s value among buyers and sellers. Even when
gold prices are at extremely lofty levels, it does not imply any actual shortage, but rather an upward shift in the
market’s perception of gold’s value. Supplies of virtually any level are still available—at some price. This is not
true for most commodities, in which there is a definite relevant limit in total supplies.
285
SElECTINg THE BEST FuTurES PrICE SErIES For SySTEM TESTINg
far exceeded the net price gain implied by nearest futures, and the su