verages  Again
What we’ve come back to, quantitatively, is an evocation of an oldidea:  moving  averages.  Without  going  into  all  the  ideas  behind  cy-
34  Tradine   the  Trend
FIGURE  2.6 CYCLE  TREND  LINE.  A  cycle  trend  line,  defined  by  anaverage  of  half-cycle  length,  can  define  noncyclic-hopefully,trending-behavior.  Here,  the  machine,  using  MESA  for  Windows,  seestrending  after  the  instantaneous  trend  line  (an  average)  stats  upward.
cles*  (or  signal  processing  theory),  it  turns  out  that  moving  aver-ages were a pretty good idea all along, a simple form of what engi-neers call a filter.9
Why not use the simple “price above or below” the price  n daysago, which amounts to the same thing? Well, it turns out that aver-ages have some other advantages. Besides being simple to calculate,averages  can  be  applied  and  their  length  adjusted  visually.  Simplemoving averages even have a look-back feature because you knowthe  expiring  values  and  thus  have  a  clue  to  the  average’s  futuremovement.
While  you  could  employ  quantitative  techniques  such  as  Fourieranalysis  or  MESA  to  define  average  “length”  properly,  my  experi-
8  John J. Murphy,  Technical Analysis of  the Futures Markets (New  York: NewYork Institute of Finance,  1986),   pp.  414-455.   See  also  John  Ehlers’s  various  arti-cles in Stocks and Commodities, 1986-1988, for an explanation for laymen of theengineering  approach  to  cycles.q  I’d  like  to  report  that  more   exotic  approaches  w o r k   better,  but  after  13  years  ofreviewing  what’s  publicly  available, trading  signalsfrom  those  given  by  averages.  Perhaps  proprietary  techniques  do  better.
I’ve  seen  scant 
improvement 
in 
Trends 35
ence  is that results are sufficiently vague (i.e., smeared spectra) andvariable  (peaks  move  constantly)  that  visual  inspection  of  yourcharts  does  just  as  well.  Although  for  the  purposes  of  campaigningany consistent approach will do, averages not only provide evidenceof  trend  but  also  provide  opportunities  for  additional  trades  (add-on  trades)  during  the  trend.
Finally,  averages  can’t  go  wrong.  Even  though  they  get  whip-sawed  in  trading  ranges,  it’s  impossible  for  them  to  miss  a  trend,and in trading, any element of certainty is welcome.
For  example,  Figure  2.7  shows  a  weekly  chart  of  General  Mo-tors. A variety of averages shows different cutoffs of trend reversalsand  good  “skimming”  along  tops  and  bottoms  of  trends.  You.  thetrader, just pick visually which does the best job
FIGURE  2.7 GM’S  AVERAGES.  The  longer  the  average,  the  slower  thedefinition  of  trend.  Inspecting  visually,  pick  the  average  it  gives  the  bestcombination of skimming tops or bottoms, as well as reversing withtrend reversals.
Trading the Trend
Trends  37
Skimming  refers  to  the  habit  of  defining  the  upper  edge  of  adowntrend and the lower edge of an  uptrend, given the appropriatelength.  This  selection  of  length  isn’t  perfect.‘O   For  one  thing,  somemarkets  advance  and  retreat  at  different  rates,  notably  bonds  andother  longer-term  financial  instruments  where  the  mathematics  ofyield  enhance  this  phenomenon.  For  such  markets,  averages  ofdifferent  lengths  may  be  used,  though  I’ve  rarely  found  this  prac-tice  worthwhile.  For  another,  volatility  changes  with  a  variety  offactors  affecting  the  “best”  length.  Dedicated  technicians  handlethese factors with averages whose length adjusts to the behavior ofthe  market,”  but  to  illustrate  the  tactics  of  campaigning,  simpleaverages  will  do.
For now, a trend will be in the direction in which both (1) a skim-ming average of a weekly chart and (2) a skimming average of thedaily chart are headed up or down. Since New York Light Crude isour test bed,12  Figure 2.8 shows an exemplary daily chart.13
I like to use daily charts for the short averages and weekly chartsto  pick  up  the  longer  average.  Figure  2.9  shows  a  typical  Crudeweekly  chart.‘j
The value for 12 weeks converts to 60 trading days. This is thelonger  average  on  the  daily  chart.  Would  58  or  63  do  better?  Idoubt  we  could  discern  much  difference.  The  roughness  of  thistechnique  would  overwhelm  any  refinement  in  this  number.  Any-way,  success  won’t  depend  on  the  refinement  of  the  entry  signalsbut on the management of losses.
I0   Trading  bands  can  also  define  the  ranges  of  trends,  but  most  of  these  schemesalso  start  w~ith   moving  averages.
  p.  49.
‘I  See  one  well-done  shot  at  this,  the  Variable  Index  Dynamic  Average  in  TusharChande  and  Stanley  Kroll,  T/E   ,Vew   Teclm~cul   Trader  (New   York:  John  Wiley  &Sons,  Inc.,  1994).I2 I’m using a CSI perpetual contract here for convenience’s sake. CSI and othervendors  provide  a  variety  of  continuous  contracts  that  are  manufactured  from  thereal thing to give an uninterrupted data series for analysis. I prefer the  90-day for-ward  approximation because it gives a realistic set of daily ranges,  as  well  as  a  rel-atively  consistent  tracking  basis.I3 For a weekly chart of the entire series used in this book, see the appendix.I4  See the appendix for charts of all  I I yeais used  iri  this  book.
16.5
EXEMPLARY CRUDE.  A 12.week  werage  and a 12-dayFIGURE  2.8average  skim  tops  nicely  (not  perfectly)  and  cut  off  trends  well.  Almostany  rule  will  serve  to  define  trend,  but  averages  are  conceptually  andpractically  good  filters.
Still,  looking  at  the  Crude  charts  will  convince  you  that  this  two-moving-average scheme isn’t perfect. The longer average skims thebottoms  of  the  daily  chart  nicely  three  times  as  prices  rise  and  onetime  as  they  fall,  then  plows  right  through  the  middle  of  a  tradingrange  between  18.0  and  19.0.  The  short  average  has  similar  suc-cesses and failures.
A  secret  number  that  will  generate  the  perfect  average  does  notexist. I’ve eyeballed 12 days and 12 weeks,  lpoking  for values thatcaught  the  overall  price  movements  well,  reversed  as  timely  as  alagging  average  can  in  the  great  movements,  and  nipped  along  therises  and  falls  as  neatly  as  I  thought  possible.  This  is  pure  judge-ment,  and  I’m  sure  others  have  more  refined  techniques.  Whateverthe  technique,  a  campaigning  trader  simply  needs  rules  that  definethe mode he or she is in, and as we’ll see, averages are a serviceabletool that do that for us.
38  Trading  the  Trend
Summary  39
EXEMPLARY  TRADING
Given  this  simplicity,  Figure  2.10  shows  an  equity  summary  forclosed  trades  for  New  York  Light  Crude,  1984-1994,  using  a12day  short average and a 12-week  long average.
The  summary  for  closed  trades  in  Figure  2.10  does  go  uphill,which is good, but it’s only indicative of the potential for success ofthis simple system because the data were perpetual data, not actualtrading  data.  No  stops  are  used.  Experienced  traders  will  note  therunups  on just a few trades and the long, slow drawdowns and flatsin  the  equity  curve.  These  are  realistic  for  this  type  of  trading  sys-tem,  and  dealing  with  them  is  the  point  of  inquiry  for  the  nextchapter, which gets at a key point in campaigning: managing losses.
SUMMARY
FIGURE  2.9 WEEKLY CRUDE. Over six years of widely varied priceaction,  the  12  week  (60.trading   day)  average  does  a  creditable  job  offollowing  prices  and  skimming  the  price  channel.
Chapter  2  has  taken  us  from  needing  a  trading  idea  to  begin  ourcampaign  to  a  rough  cut  at  what  the  results  might  be  like.  Alongthe  way,  the  rationale  for  using  simple  averages  has  been  their  vi-
TRADING  RULES
In Figure 2.8, the longer average (12 weeks) converted to its dailyequivalent  skims  along  the  tops  and  bottoms  of  the  price  move-ments while the daily shows nice  fit: but closer. For this book, “up”is a one-period increase in an average’s value and “down” is a  one-period  decrease 
the  average’s  value.
in 
Our  exemplary  trading  rule  for  trending  will  be  equally  simple:If  both  are  moving  up,  we  go  long  at  the  close;  if   both  down,  short  atthe  close.  If  in  disagreement,  we’re  out  at  the  close.
This  simple  set  of  trading  rules  has  two  essentials:  an  entry  ruleand  an  exit  rule.  This  bounding  of  time  by  the  entry  date  and  exitdate  lets  us  define  extreme  price  movements,  both  favorable  andunfavorable  from  the  point  of  entry.  This  will  be  important  later.Plus, the two rules don’t conflict.  we get in when both averages arein  agreement  and  out  when  one  changes~direction.  Let’s  see  howthis works out.
TRADING  TWO  MOVING  AVERAGES,  1984-1994.  Just
FIGURE  2.10a  couple  big  winners  made  lots  of  the  money  here,  as  for  most  moving-average  systems.  The  two-moving  average-system  also  has  the  usualdrawdowns  after  runups  in  equity.
40  Tradinc  the Trend
sual  convenience  and  the  innate  ability  to  catch  trending  activity(and  the  reverse:  nontrending  activity).
Campaigning  starts  with  some  such  idea  and  usually,  as  I’m  do-ing here, with the idea of catching a trend. The chart in Figure 2.10shows that we can catch trends. Now we will explore how to playthem.
Handling  the  Bad  News
During  any  campaign,  some  things  go  well  and  some  things  gobadly. The enemy makes sure of that. As it happens, a trader’s ene-mies  are  literally  numberless,  and  each  can  have  an  effect  on  themarketplace in which he or she trades. Therefore, it makes sense tospend  a  lot  of  time  thinking  about  what  can  go  wrong.  Indeed,there are some traders who feel you can successfully forget puttinga  lot  of  time  and  effort  into  new  entry  strategies  and  just  worryabout  minimizing  losses.  They  wonder,  as  do  I,  whether  you  can’tsimply  enter  and  exit  randomly  so  long  as  you  have  an  effectivescheme  of  loss  minimization.
This isn’t the book to go into that idea, but at every stage of thecampaign  (or  trading  cycle),  we  must  minimize  our  losses.  I  arguethat the best way to analyze the problem of minimizing losses is toanalyze the actual experience of losses taken in past trades.
Because so much can go wrong at any time; traders have adopteda simple approach to controlling losses: stops. They are the tool I’lldiscuss  here,  but  I  point  out  there  are  more  sophisticated  ways  ofdealing with potential losses the easiest and most fungible of whichis using  options.  Retail traders rarely take advantage of these pliableand effective instruments, and even institutional accounts may neverfully employ them, but they’re worth a long look when you want al-ternatives other than “I’m in” or “I’m out.”
4 1
42 Handling  the  Bad  News
HOW  BAD  DOES  IT  GET?
At  some  point  in  a  trade,  whatever  path  we  take  becomes  thewrong  path  for  the  immediate  future.  In  trading,  we  are  blessedwith  a  loud,  clear  alarm:  losing  money.  Even  though  it’s  easy  tocount  and  losses  are  reported  by  vast  arrays  of  equipment  secondby  second,  losses  remain  tough  to  acknowledge.  Just  as  it  wastough  when  we  were  children  to  look  under  the  bed  or  in  a  darkcloset for night monsters, it’s equally tough to look at a loss and ac-knowledge it. It was easier to hide under the covers back then, andnow it’s easier to adopt some defense mechanism. (The one I hearthe  most  is  “Oh,  that  trading  rule  doesn’t  work!”  as  if  the  entrystrategy  caused  the  loss.)  To  conclude,  it’s  necessary  to  actuallydefine and measure exactly what we fear: losing money.
In  this  book,  we  actually  gather  information  on  how  bad  thingsget during our trades. We’ll determine whether there’s any pattern inthe  bad  news-whether  there’s  a  difference  between  the  pattern  oflosing trades and the pattern of winning trades, If there is a pattern,can we exploit it? Wouldn’t knowing how much adverse price move-ment we were in for tell us how far to hold on? And wouldn’t know-ing  the  size  and  frequency  of  our  losses  tell  us  how  much  moneywe’d  need  to  trade  a  given  scheme?
What  about  the  formal  definitions  of  risk?  Although  I  wastrained  to  use  them,  academic  definitions  of  risk  don’t  define  theelement  we  fear:  losses.  Variance  is  an  excellent  theoretical  con-struct  and  commendably  tractable  mathematically,  but  the  fear  wehave  deals  only  with  the  downside  of  variance,  not  the  upside.  Solet’s look at the downside in depth.
Measuring  Losses
Our  operational  problem  is  that,  at  some  point,  we  must  cut  ourloss,  That  point  could  be  defined  by  time,  by  variables  (other  dataseries,  judgment,  or  politics)  independent  of  price,  by  portfolioconstraints,  or  by  financing,  but  usually  the  limit  is  set  by  the  price
How  Bad  Does  It  Get?
43
itself.  The  one  thing  of  which  we  are  relatively  sure  is  the  price  atany  given  moment  irrespective  of  what  is  driving  it.  Because  priceis  what  drives  our  loss  and  we  can  track  price,  let’s  just  measurehow  bad  the  price  movement  is  during  our  winning  trades,  Thenlet’s ask how bad it gets during a losing trade.
If  we  know  anything  about  winning  trades,  it’s  that  they  end  upin the black. Do they get there randomly? That is, are they at a lossone moment and, erratically, at a profit the next? This isn’t our gen-eral  experience  (though  it  may  seem  so  in  the  heat  of  an  individualsituation). Trades that are winners rarely go from being deep lossesto  overnight  winners.  So,  if  winners  don’t  become  winners  errati-cally, is there some order to their performance?
It turns out there is. If you measure the worst price move againstyour position, there is a difference between that movement for win-ning  trades  and  that  for  losing  trades.  Specifically,  winning  tradesjust don’t have as much adverse price excursion as losing trades,
Excursion
I  choose  the  word  excursion  pointedly  because,  throughout  thisbook,  I’m  most  interested  in  what  the  price  does&m   rhe  point  ofentry.  I  want  to  look  at  the  market’s  behavior  in  the   awe   of  mytrading  action.  Excursion  is  a  neutral  term  that  conveys  neither  di-rection  nor  amplitude,  simply  movement.  Excursion  also  conveysthat, because we don’t know where price is headed, our unbiasedexpectation  is  that  price  will  return  to  where  it  started-our  entryprice  (see  Figure  3.1).
We  don’t  know  whether  there  will  be  movement  after  our  entryor, if there is to be movement- where, when, or how much. Excur-sion  captures  this  ignorance  well  and,  having  acknowledged  it,  al-lows us to study price movement rather than ignore it.
Furthermore, when we find prices moving away from our expec-tation,  we  have  evidence  to  suspect  that  something  other  than“noise” is causing the motion. That is, the more prices depart fromour  previous  expectation  that  they  would  be  the  same  tomorrow
44
Handling  the  Bad  News
How  Bad  Does  It  Get?
45
Price
r
-;‘I-- Excursion
PRICE  EXCURSION.  Excursion 
FIGURE 3.1price  from  the  point  of  entry.  Usually,  highs  and  lows  are  the  mostin  measuring  favorable  or  unfavorable  price  movement.important 
refers 
to 
rhe   movement  of
(or  perhaps  oscillate  lightly  around  today’s  price),  the  more  evi-dence there is that our expectation was wrong and the more oppor-tunity for profit from price  change.
For now, note only that price excursion can be favorable or unfa-vorable,  hence  terminology  you’ll  see  later:  favorable  excursion(shown 
in  Figure  3.1)  or  adverse’  excursion.
What Is Maximum Adverse Excursion?
Maximum  adverse  excursion  is  the  worst  intraday  price  movementagainst a position at any time during the trade. For the purposes oftesting,  this  is  the  high  or  the  low  of  the  day,  keeping  in  mind  thevagueness  of  these  reported  figures.  These  numbers  usually  havebeen  touched  only  momentarily;  sometimes,  the  actual  number  iseven  outside  the  reported  number,  which  should  give  you  somepause  when  relying  on  them.  Figure  3.2  shows  some  examples  ofmaximum  adverse  excursion  (MAE).
’ Adverse [ME, fr. ME adws,  fr. L adversus, pp. of adverrere] I: acting against orin  a  contrary  direction  2:  opposed  to  one’s  inrerests.
t
---> -if” -tt t h  )d”er.. priceShortUo”ement--------_t,r
0”’
.8ct
-ow
/tc‘1tt ----__Max
AdverseExcursion
-Itc
dvwse  Price
vement
- -----_ /
Time
IS  MAE?  MAE  (maximum  adverse  excursion) 
FIGURE  3.2 WHAT worst  intraday  price  movement  against  a  position  measured  from  thepoint  of  entry.  The  short  on  the  top  shows  the  large  MAE  of  a  losingtrade  while  the  long  on  the  bottom  hits  its  MAE  just  two  days  after  entry.
is  the
46 Handling the Bad News
When  inspecting  a  trading  strategy  visually,  it’s  easy  to  pick  outfrom the chart where the worst of the trade was. However, for pur-poses  of  analysis,  it’s  tedious  to  inspect  a  chart,  look  up  the  lowand the high, calculate the MAE, and record it. As long as you un-derstand the idea, this is a job better left to a computer2
Measuring  Maximum  Adverse  Excursion
We’re interested in measuring the adverse price movement from thepoint of entry In other words, how bad does the situation get whenwe have a winning trade? Or a losing trade? This section lays out themechanics  of  actually  capturing  and  displaying  this  information.
1.  Define  an entry and an  exit.  Recall  from  Chapter  2  that  oursample  trading  system  had  the  following  rules:  go  long  whenboth  averages  turn  up,  short  when  both  turn  down,  and  stayout when they aren’t in agreement. Any other rrading rules canbe  used  as  long  as  they  define  the  entry  and  the  exit  withoutstops.3
2.  Tabulate  how  far  things  go  bndfor
  each  trade.  For  each  day  (inthis example) of each trade, calculate the worst intraday pricemovement,  if  any.  If  there  is  none,  enter  a  zero.  For  longtrades, use the low for the day. For shorts, use the high of theday. Table 3.1 shows an example of this method of tabulation.Although  this  method  is  straightforward,  questions  cancome  up.  For  example,  what  if  the  price  first  moves  againstthe  position  and  then  moves  favorably?  The  rule  is  that  MAE
* I generally do this work on a spreadsheet because this ability has  not  been  addedto  any  publicly  available  trading  software.  Spreadsheet  software  also  has  greatflexibility  in  logically  defining  precise  trading  rules,  imports  and  organizes  the  datawell,  and  usually  has  good  graphic  capabilities.3  This  simple  requirement  not  only  lets  you  apply  any  coherent  trading  rules  butalso  lets  you  evaluate  trading  systems  without  knowing  their  trading  rules,  justtheir  entries  and  exits.  Trading  managers  can  also  treat  a  trader  in  the  same  man-ner, studying his entries and exits only.
47
48 Handling  the  Bad  News
never   declines. No matter what the sequence, we shall endurethe  worst  at  some  point,  so  look  for  the  worst  that  happenswhen we trade a specific rule set. Check each day to see if theadverse price movement has grown. (See Table 3.2 for an ex-ample of this process.)
3.  Break 
the 
trading  results 
into  winners  and 
losers.  When  you’vegone through all your data, you’ll have a list of trades and foreach an entry, an exit, and an MAE  value.4  Next, separate thetrades  into  two  categories:  winners  and  losers.  List  all  thewinning  trades  in  one  table  and  all  the  losing  trades  in  an-other  (see  Table  3.3).  Sophisticates  can  use  a  third  category,draws  (setting the range of profits and losses to define drawsis an education in price excursion all by itself), but a third cat-egory is not necessary. Here we just need to see the differencebetween winners and losers, as well as see if there’s a differ-ence  we  can  use.
4.  Tabulate  the  MAE  for  the  winners  and,  separately,  tabulate  itfor the losers. To measure MAE, we’ll use trading points. Dol-lars  are  just  as  feasible-and  preferable  for  comparing  differ-ent  tradables-but  to  emphasize  the  connection  of  adversityto  trading  movement,  Table  3.3  uses  the  actual  quoted  priceof Crude.
5.  Sort 
the 
tabulations 
into  categories  of  loss.  Here,  we  must  ad-
dress the size of the losses we can afford to take.
We  want  to  aggregate  the  actual  adverse  movements  intochunks  that  will  tell  us  something  about  the  relation  of  adver-sity  to  dollar  stops.  For  example,  every  adverse  movement  inTable  3.3  is  lumped  together  into  a  unit  that  could  be  called“any  size.”  There  are  ten  winning  trades  and  four  losingtrades with  MAEs   of  “any  size.”
If we broke up “any size” into two bins, one for  MAEs lessthan 30 points and one for  MAEs greater than 30 points, wecould  say,  “There  are  three  losses  with  MAEs  under  30
4  If  you’re  setting  this  up  as   you  read,  yoqtiight   also   include  the  dates  so  you  canlater  get  data  on  time-in-trade.
50 Handling  the  Bad  News
How  Bad  Does  It  Get?
51
TABLE  3.3
TABULATING  WINNERS  AND  LOSERS.
Tabulating  Winners
Tabulating  Losers
ClosingDate
?/S/83ai1   71832/l  41842/l  61843/l  61846/4/846/12/84V/l 8184V/20/84i  011  1 la4
Total:
Profit  orLoss
0.090.71.890.080.350.340.250.570.050.24
4.56
MAE
0.070.010.0800.0600.040.150.070.27
ClosingDate
¶I1   5183V/26/834112/844/l  6184
Profit orLoss
- 0 . 4- 0 . 0 4-0.12-0.02
MAE
0.460.080.250.02
Total
- 0 . 5 8
points,”  or  “There  is  only  one  loss  with  an  MAE  over  30points,  trading  this  way.”  That  would  give  us  more  detailedsummary  information.  The  trick  is  to  establish  whether  to  use30 points or some other number.
I  recommend  you  start  from  your  trading  capita1.j   Of  thatnumber, say $30,000, 2% should be the maximum loss on anyone trade. Before we even get started, we know we’d like thestop to be no more than 2% of our capital.
Convert  that  2%  figure  into  trading  points.  That’s  thebiggest  bin  size  that  would  work  operationally.  Bigger  thanthat and we won’t be able to see if the  MAEs  fit  our  capitalstop.  Smaller  might  be  helpful,  though,  so  I  recommend  wetake the 2% chunk size and divide it by 2. By the way, statisti-cians  call  these  chunks  bins  when  they’re  doing  their  worksheets. This term refers to the idea of tossing each instance oroccurrence into  a  bin  for counting.
5  Be  straight  with  yourself  on  this  one.  People  tend  to  say,  “I  have  $3,000,  but  ifit’s working,  I’ll make a lot more capital available,” or sometimes, “I have $100,000available”  when  they  actually  won’t commit more than $10,000.
To  summarize:
Total  trading  capitalMaximum  loss  percentageMaximum  loss  in  dollarsMaximum  loss  in  Crude  oiltrading  points  at  $lO/tick.Maximum  bin  size
$30,0002 %$600.6   (60  ticks)
.3  (30 ticks)
This  exercise  will  be  helpful  later.  If  we  examine  the  dataand find that too many trades hit