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tive moving average behaves identically to a standard exponential moving average
with period m; however, the effective period of the exponential moving average is
monotonically reduced with an increasing ratio of short-term to long-term volatility,
and increased with a declining ratio of short-term to long-term volatility.

TYPES OF MOVING AVERAGE ENTRY MODELS
A moving average entry model generates entries using simple relationships
between a moving average and price, or between one moving average and another.
Trend-following and countertrend models exist. The most popular models follow
the trend and lag the market. Conversely, countertrend moving average models
anticipate reversals and lead, or coincide with, the market. This is not to imply that
countertrend systems trade better than trend-following ones. Consistently entering
trends, even if late in their course, may be a more reliable way to make money than
anticipating reversals that only sometimes occur when expected. Because of the
need for a standard exit, and because no serious trader would trade without the pro-
tection of money management stops, simple moving average models that are
always in the market are not tested. This kind of moving average reversal model
will, however, be fairly well-approximated when fast moving averages are used,
causing reversal signals to occur before the standard exit closes out the trades.
Trend-following entries may be generated in many ways using moving aver-
ages. One simple model is the ntoving average C˜SSDWE The trader buys when
prices cross above the moving average and sells when they cross below. Instead of
waiting for the raw prices to cross a moving average, the trader can wait for a faster
moving average to cross a slower one: A buy signal occurs when the faster moving
average crosses above the slower moving average, and a sell is signalled when the
crossover is in the other direction. Smoothing the raw price series with a fast mov-
ing average reduces spurious crossovers and, consequently, minimizes whipsaws.
Moving averages can also be used to generate countemend entries. Stock prices
often react to a moving average line as they would to the forces of support or resis-
tance; this forms the basis of one countertrend entry model. The rules are to buy when
prices touch or penetrate the moving average from above, and to sell when penetration
is from below. Prices should bounce off the moving average, reversing direction.
Countertrend entries can also be achieved by responding in a contrary manner to a
standard crossover: A long position is taken in response to prices crossing below a
moving average, and a short position taken when prices cross above. Being contrary-
doing the opposite of what seems “right”+ften works when trading: It can be prof-
itable to sell into demand and buy when prices drop in the face of heavy selling. Since
moving averages lag the market, by the time a traditional buy signal is given, the mar-
ket may be just about to reverse direction, making it time to sell, and vice versa.
Using a moving average in a counter&end model based on support and resis-
tance is not original. Alexander (1993) discussed retracement to moving average sup
port after a crossover as one way to set up an entry. Tilley™s (1998) discussion of a
two parameter moving average model, which uses the idea of support and resistance
to trade mutual funds, is also relevant. Finally, Sweeney (1998) described the use of
an end-of-day moving average to define inuaday levels of support and resistance.

CHARACTERISTICS OF MOVING AVERAGE
ENTRIES
A trend-following moving average entry is like a breakout: Intuitively appealing
and certain to get the trader aboard any major trend: it is also a traditional, readily
available approach that is easy to understand and implement, even in a spreadsheet.
However, as with most trend-following methods, moving averages lag the market,
i.e., the trader is late entering into any move. Faster moving averages can reduce lag
or delay, but at the expense of more numerous whipsaws.
A countertrend moving average entry gets one into the market when others
are getting out, before a new trend begins. This means better fills, better entry
prices, and greater potential profits. Lag is not an issue in countertrend systems.
The danger, however, is entering too early, before the market slows down and turns
around. When trading a countertrend model, a good risk-limiting exit strategy is
essential; one cannot wait for the system to generate an entry in the opposite direc-
tion. Some countertrend models have strong logical appeal, such as when they
employ the concept of support and resistance.

ORDERS USED TO EFFECT ENTRIES
Entries based on moving averages may be effected with stops, limits, or market
orders, While a particular entry order may work especially well with a particular
model, any entry may be used with any model. Sometimes the entry order can
form part of the entry signal or model. A basic crossover system can use a stop
order priced at tomorrow™s expected moving average value. To avoid intraday
whipsaws, only a buy stop or a sell stop (not both) is issued for the next day. If the
close is above, a sell stop is posted; if below, a buy stop.
Entry orders have their own advantages and disadvantages. A market order
will never miss a signalled entry. A stop order will never miss a significant trend
(in a trend-following model), and entry will never occur without contirmation by
movement in favor of the trade; the disadvantages are greater slippage and less
favorable entry prices. A limit order will get the best price and minimize transac-
tion costs, but important trends may be missed while waiting for a retracement to
the limit price. In countertrend models, a limit order may occasionally worsen the
entry price: The entry order may be filled at the limit price, rather than at a price
determined by the negative slippage that sometimes occurs when the market
moves against the trade at the time of entry!

TEST METHODOLOGY
In all tests that follow, the standard portfolio is used. The number of contracts to
buy or sell on entry, in any market, at any time, was chosen to approximate the dol-
lar volatility of two S&P 500 contracts at the end of 1998. Exits are the standard
ones. All tests are performed using the C-Trader toolkit. The portfolios, exit strate-
gies, and test platform are identical to those used previously, making all results
comparable. The tests are divided into trend-following and counter&end ones.
They were run using a script containing instructions to set parameters, run
Moving-Average Models
CHAPTER 6


optimizations, and generate output for each combination of moving average type,
model, and entry order.
The code below is more complex than for breakouts: Instead of a different
routine for each combination of moving average, entry rule, and trading order,
there is one larger routine in which parameters control the selection of system ele-
ments. This technique is required when systems are genetically evolved. Although
no genetics are used here, such algorithms are employed in later chapters. This
code uses parameters to control model elements, making it easier to handle all the
combinations tested in a clean, systematic way.




local variables for clearer reference
// period for faster moving average
// period for slower moving average
I/ type Of entry model
I/ type of moving average
I/ type Of entry order
I/ maximum holding period
I/ profit target in volatility units
// stop loss in volatility units
116



// skip invalid parameter combinations
if(fastmale" >= slovmlale") {
set-"ector˜eqcls, 1, rib, 0.01;
return;
)
I/ perform whole-series computations using vector routines
// ATR for exit
*"gTr"eRa"ses(exitatr, hi, 10, ClS, 50, "b);
switchbvgtype) ( // select type of moving average
case 1: // simple moving averages
A"erageS˜fastma, cls, fastmale", "b);
AverageSislovmla, ClS, slowmale*, nbl;
break;
,, exponential mo"i"g averages
case 2:
xA"erageS(fastma, ClS, fastmale", "b, ;
xA"erageS˜slowma, CL%, s1ovmden. "b) ;
break;
case 3: // front-weighted triangular moving averages
FwTn"erages˜fastma, ClS, fastmale", nb,;
FwTAverageS ˜slowma, ClS, slowmale", "b, ;
break;
case 4: // "IDYA-style adaptive moving averages
"rn"erages˜fastma, ClS. fastmale". 10, "b, ;
"IA"erageS˜slowma, ClS. *lowmale", 10. &I;
break;
default: "rerror˜"I""alid mo"i"g average selected");
1;




// avoid placing orders on possibly limit-locked days
if (hi L&+11 == lo L&+11 1 continue;

,, generate entry signals, stop prices and limit prices
// using the specified moving average entry model
// enter trades using specified order type
ifCts.positionO c= 0 && signal == 1) (
switch(ordertype1 ( // select desired order type
case 1: ts.b"yopen˜˜1'. ncontracts,; break;
case 2: ts.buylimitC˜Z', limprice, ncontracts); break;
The code contains three segments. The first segment calculates moving aver-
ages. A parameter (avgrype) selects the kind of average: 1 = simple; 2 = expo-
nential: 3 = front-weighted triangular; and 4 = the modified VIDYA. Even if the
model requires only one moving average, two of the same type are computed, this
allows the selection of the moving average to be independent of model selection.
The average true range is also computed and is required for setting stop-losses and
profit targets in the standard exit strategy. Two additional parameters, fastmalen
and slowmalen, specify the period of the faster and the slower moving averages,
respectively. The moving averages are saved in the vectors,fismzma and slowma.
The next block uses the selected model to generate entry signals, stop prices,
and limit prices. First, simple relationships (CrossesAbove, CrossesBelow,
TurnsUp, and TurnsDown) are defined. Depending on modeltype, one of four mov-
ing average models then generates the signals: 1 = the classic, trend-following dual
moving average crossover; 2 = a slope-based trend-following model; 3 = a coun-
tertrend crossover model; and 4 = a countertrend support/resistance model. In the
classic, trend-following dual moving average crossover, the trader goes long if a
faster moving average of the closing price crosses above a slower moving average,
and goes short if the faster moving average crosses below the slower one. As a spe-
cial case, this model contains the classic crossover that compares prices to a mov-
ing average. The special case is achieved when the period of the shorter moving
average is set to 1, causing that moving average to decay to the original series, i.e.,
the closing prices. With a slope-based trend-following model, the trader buys when
the moving average was decreasing on previous bars but increases on the current
bar (slope turns up), and sells when the opposite occurs. This model requires only
the fast moving average. The countertrend model is the opposite of the classic
trend-following crossover: The trader buys when the shorter moving average (or the
price itself) crosses below the longer moving average, and sells when it crosses
above. This model is the contnuian™s delight: Doing exactly the opposite of the
trend-followers. Last is a crude countertrend support/resistance model in which
prices are expected to bounce off the moving average, as if off a line of support or
resistance. The rules are almost identical to the countertrend crossover model,
except that the slower moving average must be moving in the direction of the entry:
If the slower moving average is trending upward, and prices (or the faster moving
average) move from above that slower moving average into or below it, the trader
buys; conversely, if prices (or the faster moving average) are currently below a
downward trending slower moving average, and prices bump into or penetrate that
moving average from below, then the trader sells. The additional trend rule prevents
an immediate reversal of position after contact or penetration. Without the trend
rule, a quick penetration followed by a reversal would trigger two entries: The fast
would be the desired countertrend entry, occurring when the prices penetrate the
moving average, and the second entry (in the opposite direction) would occur as the
prices bounce back, again crossing the moving average. The trend check only
allows entries to be taken in one direction at a time. A penetration, followed by a
bounce in an up-trending market, will take the long entry; a penetration and rever-
sal in a down-trending market will result in a short entry.
In the last block of code, the entry order to be posted is determined by the
ordertype parameter: 1 = market at open; 2 = limit; 3 = stop. Whether a buy or
sell is posted, or no order at all, is determined by whether any signal was generat-
ed in the previous block of code; a variable called signal carries this information: 1
= buy; - 1 = sell (go short); 0 = do nothing. The limit price (limpn™ce) is com-
puted as the sum of today™s high and low prices divided by two. Because many of
the models have no natural price for setting a stop, a standard stop was used. The
standard entry stop price (stpprice) is obtained by taking the closing price of the
previous bar and adding (if a long position is signalled) or subtracting (if a short is
signalled) the 50-bar average true range multiplied by 0.50; the market must move
at least one-half of its typical daily range, in the direction of the desired entry, for
the entry to occur. This type of stop order adds a breakout test to the moving aver-
age system: Once a signal is given, the market must move a certain amount in the
direction of the trade to trigger an entry. Because of the large number of tests, sta-
tistical significances were not reported unless something notable occurred.

TESTS OF TREND-FOLLOWING MODELS
The group of tests presented here involve moving averages as trend-followers. The
models vary in the kinds of moving average, the rules that generate signals, and
the orders posted to effect entry. The moving averages tested include the simple,
120




the exponential, the front-weighted triangular, and a modification of VIDYA. Both
single and dual moving average crossovers, and models that use slope (rather than
crossover) to determine entry, are examined. Entry orders are market-at-open,
stop, and limit.
Tests 1 through 12 were of the crossover models, Optimization was per-
formed by stepping the length of the shorter moving average from 1 to 5, in incre-
ments of 1, and the longer moving average from 5 to 50, in increments of 5. Only
cases in which the longer moving average was strictly longer than the shorter mov-
ing average were examined. Brute force optimization was used, controlled by the
testing script. The parameters were chosen to maximize the risk-to-reward ratio or,
equivalently, minimize the probability (based on a t-test of daily returns) that any
profitable performance was due to chance. In Tests 13 through 24 (the slope mod-
els), brute-force optimization was performed by stepping the length of the first
and, in this case, only moving average from 3 to 40, in increments of 1. As in Tests
1 through 12, the risk-to-reward ratio was maximized. Optimization was only car-
ried out on in-sample data.
Tables 6-l and 6-2 show, for each of the 24 tests, the specific commodities that
the model traded profitably and those that lost, for the in-sample (Table 1) and out-
of-sample (Table 6-2) runs. The SYM column represents the market being studied;
the first row identifies the test. The data provides relatively detailed information
about which markets were and were not profitable when traded by each of the mod-
els: One dash (-) indicates a moderate loss per trade, i.e., $2,000 to $4,000, two
dashes (- -) represent a large loss per trade, i.e., $4,000 or more; one plus sign (+)
means a moderate profit per trade, i.e., $1,000 to $2,000; two pluses (+ +) indicates
a large gain per trade, i.e., $2,000 or more; a blank cell means that the loss was
between $0 and $1,999 or the profit was between $0 and $1,000 per trade.
Table 6-3 shows, for the entire portfolio, the return-on-account (ROA%) and
average dollar-per-trade ($TRD) broken down by the moving average, model,
entry order, and sample. The last two columns on the right, and the last four rows
of numbers on the bottom, are averages. The numbers at the bottom have been
averaged over all combinations of moving average type and model. The numbers
at the right are averaged over order type.
None of the trend-following moving average models was profitable on a port-
folio basis. More detailed examination reveals that for the crossover models the
limit order resulted in a dramatic benefit on the in-sample data. When compared
with an entry at open or on a stop, the limit cut the loss of the average trade almost
in half. Out-of-sample, the improvement was not as dramatic, but still significant.
The return-on-account showed a similar pattern: The least loss was with the limit
order. For slope models, the limit order worked best out-of-sample in terms of dol-
lars per trade. The return-on-account was slightly better with a stop (due to distor-
tions in the ROA% numbers when evaluating losing systems), and worse for entry
at open. In-sample, the stop order performed best, but only by a trivial amount.
TABLE 6-l

In-Sample Performance Broken Down by Test and Market




In-sample, the simple moving average provided the best results in average
dollars-per-trade. The worst results were for the adaptive moving average. The
other two moving averages fell in-between, with the exponential better in the
crossover models, and the front-weighted triangular in the slope models. Of the
crossover models, the ROA% was also the best for the simple moving average.
Overall, the crossover models did as well or better than the slope models, possi-
bly because of a faster response to market action in the former. Out-of-sample,
the simple moving average was the clear winner for the crossover models, while
the front-weighted triangular was the best for the slope models. In terms of the
TABLE 6-2

Out-of-Sample Performance Broken Down by Test and Market




ROA%, the exponential moving average appeared the best for the crossover
models, with the front-weighted triangular still the best for the slope models.
When looking at individual tests, the particular combination of a front-
weighted triangular moving average, the slope model, and entry on stop (Test 21)
produced the best out-of-sample performance of all the systems tested. The out-of-
sample results for the front-weighted triangular slope models seemed to be better
across all order types. There apparently were some strong interactions between the
various factors across all tests, e.g., for the crossover model on the in-sample data,
entry at the open was consistently close to the worst, entry on stop was somewhere
in between, and entry on limit was always best, regardless of the moving average
6-3
TABLE

Performance of Trend-Following Moving Average Entry Models
Broken Down by Order, Moving Average Type, Model, and Sample




used. Out-of-sample, the findings were much more mixed: With the simple moving
average, the pattern was similar to that for the in-sample period; however, with the
exponential moving average, the limit performed worst, the stop best, and the open
not far behind. Out-of-sample, with the front-weighted triangular average, the stop
performed by far the worst, with the limit back to the best performer. These results
indicate interaction between the moving average, entry order, and time.
The slope model, in-sample, had the entry at open always performing worst;
however, although the results were often quite close, the limit and stop orders were
hvice seen with the limit being favored (simple moving average and adjusted moving
average), and twice with the stop being favored (exponential moving average and
front-weighted triangular moving average). As before, great variation was seen out-
of-sample.
For the simple moving average, the limit order performed best and the stop
worst. The more typical pattern was seen for the exponential moving average: The
entry at open performed worst, the limit best, and the stop was on the heels of the
limit. As already stated, the front-weighted triangular moving average performed
very unusually when combined with the stop order. The limit was best for the
adaptive moving average, the stop was worst, and the open was slightly better
albeit very close to the stop.
As a whole, these models lost on most markets. Only the JapaneseYen and Pork
Bellies were profitable both in- and out-of-sample; no other markets were profitable
in-sample. Out-of-sample, some profits were observed for Heating Oil, Unleaded
Gasoline, Palladium, Live Hogs, Soybean Meal, Wheat, and Coffee. The strong out-
of-sample profit for Coffee can probably be explained by the major run-up during the
drought around that time. On an individual model-order basis, many highly profitable
combinations could be found for Live Hogs, JapaneseYen, Pork Bellies, Coffee, and
Lumber. No combinations were profitable in either sample for Oats.
In terms of equity averaged over all averages and models, entry at the open
performed, by far, the worst. Entry on limit or stop produced results that were
close, with the limit doing somewhat better, especially early in the test period. It
should be noted that, with the equity curves of losing systems, a distortion takes
place in their reflection of how well a system trades. (In our analyses of these los-
ing systems, we focused, therefore, on the average return-per-trade, rather than on
risk-reward ratios, return-on-account, or overall net profits.) The distortion
involves the number of trades taken: A losing system that takes fewer trades will
appear to be better than a losing system that takes more trades, even if the better
appearing system takes trades that lose more on a per-trade basis. The very heavy
losses with entry at open may not be a reflection of the bad quality of this order;
it may simply be reflecting that more trades were taken with an entry at the open
than when a stop or limit order was used.
Figure 6-l presents the equity curves for all eight model and moving average
combinations. The equity curves were averaged across order type. Figure 6-l pro-
vides a useful understanding of how the systems interact with time. Most of the sys-
tems had their heaviest losses between late 1988 and early 1995. The best
performance occurred before 1988, with the performance in the most recent period
being intermediate. In Curve 3, the simple moving average crossover model was the
most outstanding: This pattern was greatly exaggerated, making the equity curve
appear very distinct; it actually showed a profit in the early period, a heavier rela-
tive loss in the middle period, and levelled off (with a potential return to flat or prof-
itable behavior) toward the end of the third period. Finally, it is dramatically evident
that the crossover systems (Curves 1 through 4) lost much less heavily than the
FIGURE 6-1

Equity Curves for Each Model and Moving Average Combination




slope-based models (Curves 5 through S), although this reflects a larger number of
trades, not greater losses on a per trade basis.

TESTS OF COUNTER-TREND MODELS
As with trend-following models, countertrend models vary in the moving averages
used, the rules that generate signals, and the orders posted to effect entry. The
same moving averages used in the trend-following models are studied here. Both
single and dual moving average models are examined. Entry orders studied are the
market-at-open, stop, and limit.
Tests 25 through 36 evaluate the standard moving average crossover model
turned upside-down. As before, entry signals occur when prices cross the average,
or when a shorter average crosses a longer one. In traditional trend-following
crossover models, the trader buys when the price (or a shorter moving average)
crosses above a longer moving average, and sells when it crosses below. In the
contrarian crossover model, the trader sells when prices cross above, and buys
when prices cross below. In these tests, brute force optimization was performed on
the in-sample data by stepping the length of the shorter moving average from 1 to
7, in increments of 1; the longer moving average was stepped from 5 to 50, in
increments of 5. Only cases where the longer moving average was longer than the
shorter moving average were considered. The parameters minimized the probabil-
ity that any observed profitable performance was due to chance. The model was
run on the out-of-sample data using the best parameter set found in-sample.
In tests of the support/resistance model (37 through 48), the trader buys when
prices bounce off a moving average from above, and sells when they hit a moving
average from below. In this way, the moving average serves as a line of support or
resistance where prices tend to reverse. The rules are almost the same as for Tests 25
through 36, except that not every penetration of a moving average results in an entry.
If prices are above the moving average and penetrate it, a buy is generated; howev-
er, when prices bounce back up through the moving average, the second crossover
is not allowed to trigger a sell. If prices arc below the moving average and rise into
it, a sell is triggered, no buy is triggered when the prices bounce back down. This
behavior is achieved by adding one element to the contrarian crossover model: A sig-
nal is generated only if it is in the direction of the slope of the slower moving aver-
age. Performance was brute force optimized on the in-sample data by stepping the
length of the shorter moving average from 1 to 5, in increments of 1, and the longer
moving average from 5 to 50, in increments of 5. A moving average length of 1

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