−1avg.
0.01−0.02−0.04−0.10−0.12−0.05
−0.04−0.08−0.1−0.15−0.2−0.11
0.030.050.080.120.160.09
0.020.130.210.300.390.21
0.020.080.150.210.280.15
−0.030.090.220.370.510.23
0.010.120.220.340.450.23
0−0.07−0.15−0.23−0.33−0.16
0
−0.19
−0.27
0.23
0.53
0.35
0.73
0.61
−0.47
12345avg.
−0.16−0.14−0.10−0.06−0.02−0.10
−0.19−0.27−0.1−0.07−0.03−0.13
0.170.100.070.040.030.08
0.390.280.170.07−0.050.17
0.280.20.120.04−0.030.12
0.580.470.340.270.100.35
0.480.380.240.130.000.25
−0.4−0.3−0.25−0.16−0.05−0.23
Euro leads
Lagdays
Euro lags
Predictive correlation of weekly % changes between the euro and related markets
A popular indicator used to quantify the trend is Wilder’s ADX. This was my firstchoice but it did not perform as expected on testing, as it was too slow to indicatea trend change, giving up a substantial portion of profits during the early part ofthe trend. I then tested the Congestion Index (CI), first introduced in Chapter 9,which performed considerably better than the ADX, as it was quicker to indicate thebeginning of a trend.The system was then enhanced using a simple intermarket rule, filtering out tradeswhen the euro was moving against two of its intermarket peers: 10-year US bondyields (TNX) and commodities (the CRB index).This simple intermarket rule improved the results by a wide margin, nearly doublingthe profit factor of the system.The problem in back-testing intermarket rules, encountered in the yen test, wasalso present here as the correlation with the CRB index was not constant during the
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281
optimization and out-of-sample testing periods and almost doubled, from 0.26 to0.57, during the most recent out-of-sample test.As a result, the older (in-sample) test did not perform as well as the most recent outof-sample test. By the time this book is published correlations with the CRB indexmight once again change and the reader should reevaluate the results and reoptimizethe CRB moving average parameters if necessary, as soon as more data becomeavailable. Correlations with the TNX, except for a brief period in the beginning of2005, were more or less stable through the euro’s nine-year life span.The system can be summarized as follows. Buy whenMA − lowest (MA,3)>0.7∗ filterAnd ABS (CI) < 40 and CI-lowest (CI,3)>3
(17.1)(17.2)
And CRB>MA (CRB,35) and TNX < MA(TNX,5)
(17.3)
where MA is the 10-day moving simple average; the filter is the 20-day standarddeviation of daily moving average changes; MA(CRB,35) is the 35-day movingaverage of the CRB index; and MA(TNX,5) is the five-day moving average of theTNX.The first condition was triggered only when the moving average exceeded its threeday lowest value (rather than the current day’s value) by an amount greater than thefilter in order to take into account small gradual movements which would not exceedthe filter in a single day but would be significant when added over a three-day span.The same conditions in reverse were used to sell short.MA-Highest(MA) < 0.7∗ filterAnd ABS (CI) < 40 and CI-highest (CI, 3) < −3And CRB < MA (CRB,35) and TNX>MA(TNX,5)
(17.4)(17.5)(17.6)
The moving average is not the best indicator to use for exits as it lags price, givingup a considerable amount of profits. Using the reverse rules for liquidating tradesgave up a substantial amount of profits as the signals were sometimes late to close aposition and other times did not materialize at all.I spent a lot of time testing various exit conditions and finally decided to useWilder’s Parabolic Stop and Reverse (SAR) formula. By increasing the accelerationfactor from the default value of 20 % to 40 % Wilder’s stop-loss formula producedtimely exits with minimum profit loss and reasonable drawdown.For the benefit of readers not familiar with the parabolic I thought that a briefdescription would be useful. The Parabolic Stop and Reverse was introduced byJ Welles Wilder in his 1978 book, New Concepts in Technical Trading Systems(see Bibliography) and is a trailing stop designed to address the problem of laggingexit signals in trend-following systems. The parabolic formula solves the price lagproblem by tightening stops at an accelerating rate. The price must continue to move
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Intermarket Trading Strategies
in the direction of the trend, otherwise the trade is stopped out. The trailing stopmoves with the trend, accelerating closer to price action as time passes, giving aparabolic look to the indicator path, and hence its name.The Parabolic is calculated according to the following formula:SAR = AF × (EP − SAR(−1)) + SAR(−1)where SAR(−1) is yesterday’s SAR; AF is the acceleration factor; and EP is theextreme high or low point of the previous day.Wilder used an initial value of 0.02 (20 %) as the accelerating factor (AF). TheAF was then increased by 0.02 each time a new extreme point was made and upto maximum value of 0.2. These are the default values used by most software butcan be adjusted by the user. Changing the AF will tighten the stops, thus makingthe system faster but more sensitive to noise. The choice for the AF should reflectthe price characteristics of the base security being analyzed, and the strategy andpersonal preferences of the trader using the indicator.In the euro’s case a slightly faster AF of 0.04 combined with a maximum value of0.10 was found to produce more timely exits. Long positions were liquidated whenClosing Price < SAR (0.04,0.1)And Closing Price > SAR (0.04,0.1)
(17.7)(17.8)
for short exits.Even though my original intention was to use the parabolic only for exit signals,it had to be added to the entry and short conditions as well, otherwise a long or shortentry was stopped out the same day if the price was below the SAR, thus distortingthe number of trades and probability of win statistics.
Testing ProcedureThe total time span of the available data for the euro was from 1 January 1998 to21 January 2008 divided into one testing and one out-of-sample segment. The firstsegment, the testing and development period, was from 1 January 1998 to 21 January2003 and the period from 22 January 2003 to 21 January 2008 was reserved for outof-sample testing. Keep in mind that for the test to start producing trades, an extra47 days had to be loaded which were required for the calculation of the CI indicatorand its moving average.The default trade size was one round lot of 100 000 units (or €100 000) per trade.Long and short signals were executed the same day at the close. When the systemwas long the euro, the interest difference between euro and the USD, currently at3.75 − 4.30 = −0.55 % was debited to the account and when short the interest was
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283
credited to the account. The average during the test period interest difference of 2 %was subtracted from the buy and hold profits.A commission of 1 pip (or 100 000* 0.0001 = €10), which is the commissioncharged currently by Oanda, was deducted from each trade. Returns were calculatedas a percent of the account required to trade each system and compared to the buy andhold method which involved buying €100 000 and selling the corresponding amountof dollars for the duration of the test. The compound formula was used to annualizepercentage returns.The amount of the account required was the 2 % minimum margin required by theNFA and US based forex brokers, plus the initial drawdown. The percent standarddeviation of change in equity was calculated monthly and the risk-adjusted returnwas calculated by dividing the annualized return by the monthly percent standarddeviation of the change in equity.
17.7
EVALUATION OF RESULTS
The current system is a perfect example of how to use intermarket conditions toimprove on classic technical analysis indicators.The results of both tests are summarized in Table 17.6. I have also marked inthe accompanying chart in Figure 17.2 when each buy/sell signal occurred but only(because of space limitations) from November 2006 to January 2008. The net profit isshown on the sixth row and the total profit (including the interest difference betweenthe two currencies) on the next row. The results of the buy and hold strategy are inthe second column, the classic moving average system results are shown in the thirdcolumn and the intermarket enhanced combined system profit/loss statistics are inthe last column.Buying and holding €100 000 (and selling dollars) produced a considerable profitbut this was offset by the “carry interest” difference of −€10 000 during the five-yearperiod as the average US dollar interest rate was 2 % more than the euro’s during thetest period.In the period tested, the results of the intermarket enhanced system were veryimpressive. During the course of the five-year simulation, the system made morethan €45 000 with only 38 trades, which were roughly 58 % accurate. The profitfactor was 4.1 and the maximum drawdown was only €7950 or 8 % of equity, whichoccurred on 24 February 2004 mainly due to an untimely short signal which was notclosed in time to limit the losses.The risk and return statistics are also shown in Table 17.6. The monthly standarddeviation of equity was only 1.64 % during the test duration which produced a 48 %risk-adjusted annualized return and a more than adequate Sharpe ratio of 45. The buyand hold investor, on the other hand, suffered a peak to valley maximum drawdown of€20 000 and a €2580 initial drawdown which, because of the margin calls, increasedthe account required to buy the EUR/USD from the initial minimum amount of 2 %
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Intermarket Trading Strategies
Table 17.6 Profit/loss report. You can see in Column 3 the test results of the classic movingaverage test and Column 4 the intermarket enhanced results. The intermarket enhanced testoutperformed the classic moving average considerably.Test
Buy & hold
MA
MA & Intermarket
Security on testIntermarket securityNumber of unitsAccount required
EUR/USD
EUR/USD
€100 000€4580
€100 000€3460
EUR/USDCRB, TNX€100 000€2660
Total net profitTotal profit (incl. interest)Profit/loss %Annualized % gain/lossTotal number of tradesWinning tradesLosing tradesPercent profitableGross profitGross lossAvg. winning tradeAvg. losing tradeRatio avg. win/avg. lossProfitability coefficientProfit factorMax. drawdownDate of max drawdownMax. % drawdownMax. initial drawdownDate of max initial drawdownMax. lossMax. consecutive lossesRisk/rewardStandard deviation % of equityRisk-adjusted annualized returnSharpe ratioReference bars neededstart date/loaded dataStart date/testEnd dateTest period (years)Test period (days)Total trade length (days)Out of market (days)Average trade length – daysTrades/yearTime in market
€37 090€27 093592 %56 %110100 %€37 090
€28 910$27 893806.2 %55 %62283445 %€70 250−€41 340€2509−€12162.061.251.70−€11 90010-10-05−11.9 %−€14605-15-03−€3300641.2 %2.03 %27 %25.14711-17-021-22-031-21-085.0182410897351812.459.7 %
€45 750$44 6781679.6 %78 %38221658 %€60 620−€14 870€2755−€9292.963.274.08−€795002-24-04−8.0 %−€6605-15-03−€2980417.4 %1.64 %48 %44.84711-17-021-22-031-21-085.018247791045217.642.7 %
€37 090
−€19 93011-16-05−19.9 %−€25803-21-03
53.7 %2.48 %22 %20.64711-17-021-22-031-21-085.018251825018250.2100.0 %
Forex Trading Using Intermarket Analysis
285
Table 17.6 (continued)Test
Buy & hold
MA
MA & Intermarket
System ParametersMA – barsCI – barsMA CRBMA TNX
1040
1040355
or $2000 to $4580. There is no doubt that the system’s relatively low drawdown canbe attributed to the parabolic trailing stop. The parabolic, however, is no panacea.Although it solves the lag problem of trend-following systems by trailing stops closerto price, it still suffers when the market turns choppy. Although the parabolic was onlyused to close trades initiated by trending signals, it was sometimes late to materialize,especially in sideways markets.The classic moving average test, although profitable, did not manage to outperformthe buy and hold method on a net profit basis but it did produce slightly higher profitsand risk-adjusted return when the interest rate differential was subtracted from bothresults. This test was only included here to demonstrate the dramatic improvementthat can be accomplished by adding a couple of intermarket rules to your tradingsystem.The inferences drawn from the tests presented above is that while intermarketrelationships can be a stand-alone method for trading, you can also incorporate themby any means in a trend-following discipline such as a moving average system toimprove, sometimes dramatically, the system profitability.
17.8
THE AUSTRALIAN DOLLAR
The Australian (or Aussie) dollar is the fifth most actively traded currency in theforeign exchange markets, trailing behind the United States dollar, Japanese yen,euro, and British pound and it accounts for approximately 5.5 % of worldwide foreignexchange transactions.The Aussie dollar along with the Canadian dollar, the New Zealand dollar and theSouth African rand is regarded as a “commodity currency”, owing to the sizable roleof commodity production in the Australian economy. It was first introduced in 1966when it replaced the Australian pound and introduced a decimal system to the nation.In September 1974, Australia replaced the peg to the US dollar in favor of a peg toa basket of currencies, and in December 1983 the Australian dollar was allowed tofloat, controlled mainly by the forces of supply and demand.
286
Intermarket Trading Strategies
Figure 17.2 Chart of the euro (EUR/USD) from October 2006 to January 2008. Test signalsgenerated by the intermarket MA system are superimposed on the chart. The equity line isplotted in the top window, the 40-day CI indicator below and the CRB index in the third fromthe top window.
Forex Trading Using Intermarket Analysis
287
Figure 17.3 Weekly composite chart of the Australian dollar (in bold) and gold bullion (thinline) from 1998 until March 2008. The Australian dollar decoupled briefly from gold in 2005,thus correcting from the 2003–2004 excesses.
The Australian dollar had been in a long-term bear market, trading as low as 47cents to the dollar until a double bottom reversal in 2001. Since then, economic growthin China and the secular bull market in commodities have helped propel it by morethan 100 %. The Aussie dollar is popular amongst currency traders because of its highyield (currently at 7.4 %) and the government’s limited intervention in the FX market.Additionally, the $AUD, because of its strong correlation with gold (see Figure 17.3)and other commodities, offers diversification in a foreign currency portfolio.The Aussie dollar appreciated by more than 70 % against the Japanese yen, to107 yen per A$ from its low of 62 yen in September 2002. Japanese banks andtraders played the “currency carry” game, borrowing yen in Tokyo at zero percent,and buying Aussie dollars which were invested in short-term deposits yielding 7 %or more. Besides the higher yield on the carry trade they also enjoyed a significantcurrency gain until the summer of 2007 when the start of the bear market in equitiestriggered an unwinding of the carry trade and an appreciation of the yen from theall-time high of 107 yen to the A$ to a low of 88 in March of 2008 (see Figure 17.4).
288
Intermarket Trading Strategies
Figure 17.4 Daily composite chart of the Australian All Ordinaries (AORD) (in bold) andthe AUD/JPY exchange rate (thin line) from September 2005 until May 2008. Notice the highcorrelation of the AUD/JPY stemming from the yen carry trade.
Most interesting is the interplay of capital flows versus trade flows in currencymarkets. The Australian dollar, despite a long string of trade deficits, appreciatedsharply against the Japanese yen, which enjoyed a significant trade surplus withAustralia. In this case the interest rate difference was wide enough to propel amassive flow into the Australian dollar. But Japan is Australia’s largest export marketand the overvalued Aussie dollar has hurt businesses outside of the resource sector,thus dichotomizing the Australian economy into two sections. The resources boomextended employment and housing in the resource rich regions such as WesternAustralia, Queensland, and some parts of South Australia, while there was stagnationin the industrial capitals of Sydney and Melbourne where the unemployment rate wasmuch higher.Despite its booming commodity exports, Australia is importing more goods fromabroad than it exports, resulting in a current account deficit of 7 % of GDP in 2007.
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Table 17.7 Pearson’s correlation matrix of weekly percent changes between the Australiandollar (AUD/USD) and other major currencies using 10 years of data from 1 January 1998 to31 December 2007. In the third column is the Canadian dollar (CAD/USD) rate, in the sixthcolumn is the dollar index and in the last column is the yen (YEN/USD) rate.
AUDCADEuroGBPDollarYen
AUD
CAD
Euro
GBP
Dollar
Yen
10.500.490.44−0.530.29
0.5010.320.27−0.400.16
0.490.3210.68−0.970.32
0.440.270.681−0.730.29
−0.53−0.40−0.97−0.731−0.52
0.290.160.320.29−0.521
The high deficit forces the central bank to keep high interest rates in order to attractforeign lenders and carry traders.The direction of commodity and gold prices and their impact on trade flows, interestrate differentials with the US, Japan and other countries, commodity prices and theAUD/JPY carry trade, are all key variables moving the Aussie dollar.I have calculated in Tables 17.7–17.10 the correlation of weekly and monthlychanges between the Australian dollar and related commodities, currencies and equityindices. A large part of both the Australian and Canadian exports consists of basicmaterials and this is reflected in their currencies. The Aussie dollar had the strongestcorrelation with the Canadian dollar (r = 0.61 in Table 17.9). It is also interestingTable 17.8 Pearson’s correlation matrix of weekly percent changes between the Australiandollar (AUD/USD) and other related commodity and equity indices. The correlations werecalculated using 10 years of data from 1 January 1998 to 31 December 2007. In the fourthcolumn is COMEX copper futures, in the sixth column is the Canadian Venture Compositeindex, in the seventh column is the Australian All Ordinaries index and in the last column isCanada’s TSX Composite index. Notice that the Australian dollar correlates better withCanadian rather than with Australian equity indices.
AUDCRBGoldXAUHGVXAORDTSX
CRB
Gold
XAU
HG
VX
AORD
TSX
0.3510.460.440.380.500.150.27
0.460.4610.690.300.570.140.19
0.470.440.6910.310.670.260.33
0.310.380.300.3110.440.240.29
0.480.500.570.670.4410.530.61
0.240.150.140.260.240.5310.58
0.270.270.190.330.290.610.581
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Intermarket Trading Strategies
Table 17.9 Pearson’s correlation matrix of monthly percent changes between the Australiandollar (AUD/USD) and other major currencies using 10 years of data from 1 January 1998 to31 December 2007. In the third column is the Canadian dollar (CAD/USD) rate and in thelast column is the yen (YEN/USD) rate. Notice the high correlation between the AUD and theCAD.
AUDCADEuroGBPDollarYen
AUD
CAD
Euro
GBP
Dollar
Yen
10.610.540.46−0.550.31
0.6110.360.29−0.410.16
0.540.3610.75−0.980.39
0.460.290.751−0.780.34
−0.55−0.41−0.98−0.781−0.55
0.310.160.390.34−0.551
to note that the correlation is stronger with Canadian equity indices. Its correlationwith the Canadian Venture index is 0.53 and with the TSX index it is 0.39 whereasthe correlation with the Australian All Ordinaries index is only 0.34 (Table 17.10).This is not the case, however, with the AUD/JPY rate, whose higher correlation withAustralian equities (see Figure 17.4) reflects the influence of the yen carry trade astraders borrow yen at near zero rates and invest them in the Australian stock market.A strong long-term relationship between the Australian dollar and gold is evidentfrom the chart in Figure 17.3. While this relationship broke down briefly in 2005, thecorrelation was restored in the first quarter of 2006. It is also evident that the priceof gold is leading the Aussie dollar at bottoms but seems to lag slightly at tops. Gold
Table 17.10 Pearson’s correlation matrix of monthly percent changes between theAustralian dollar and the CRB Index, gold bullion (cash), the XAU, COMEX copper futures,Canada’s Venture Index, Australia’s All Ordinaries and Canada’s TSX index. Data from 1January 1998 to 31 December 2007 were used to calculate the correlations. Notice that theAustralian dollar correlates better with the XAU (r = 0.47) than with the price of gold(r = 0.41).
AUDCRBGoldXAUHGVXAORDTSX
CRB
Gold
XAU
HG
VX
AORD
TSX
0.3810.490.500.400.510.160.29
0.410.4910.700.330.580.140.17
0.470.500.7010.340.690.310.30
0.380.400.330.3410.440.260.29
0.530.510.580.690.4410.560.66
0.340.160.140.310.260.5610.69
0.390.290.170.300.290.680.681
Forex Trading Using Intermarket Analysis
291
Table 17.11 The value of Australian exports of mineralcommodities in Australian dollars and as percentage of total exportsduring 2006–07. Coal includes both thermal (A$ 6762) andmetallurgical coal (A$ 15035). Source: Australian Bureau ofAgricultural and Resource Economics.Mineral
A$
Percent
CoalIron & steelOil and gasGoldNickelCopperAluminaAluminumOther mineralsZincLeadTitanium mineralsDiamonds & gemsUraniumManganeseZirconSilver
2179717262156761032083776520622456484972430615791176774660482478221
20.5 %16.2 %14.7 %9.7 %7.9 %6.1 %5.8 %5.3 %4.7 %4.0 %1.5 %1.1 %0.7 %0.6 %0.5 %0.4 %0.2 %
Total
106 472
100 %
bottomed out in February 2001, six months before the Aussie dollar, after it brokeout from a double bottom formation. By the time that the price of gold touched anew high on 17 March 2008, bursting through the psychological barrier of $1000an ounce on the spot market, the Australian dollar had already peaked, twelve daysearlier.Currency traders should therefore be quick on their feet and sell quickly when thegold market looks overextended and due for a correction, as the Aussie can move1–2 % in a single day at the first sign of trouble.Beyond its relationship to gold, the Australian dollar correlates well with the wholecommodity complex, as commodity exports accounted for 63 % of the country’sexports in 2007.As you can see from Table 17.11, coal is Australia’s biggest export accountingfor 20 % of total exports. In addition, Australia is the world’s biggest exporter ofmetallurgical coal, the type used by steelmakers. According to the Australian Bureauof Agricultural and Resource Economics, Australia exported 120 million tons ofmetallurgical coal in 2007, which accounted for a whopping 57 % of total worldexports. Australia is the world’s fourth largest producer of gold bullion, behind
292
Intermarket Trading Strategies
China, South Africa and the United States and despite the strong correlation with theAustralian dollar (r = 0.46), gold accounts for only 9.7 % of total exports.Disclaimer: The analysis presented in this chapter is based upon data gathered fromvarious sources believed to be reliable. However, no guarantee is made by MarkosKatsanos as to the reliability and accuracy of the data.All statements and expressions are the opinion of the author and are not meant tobe investment advice or solicitation or recommendation to establish market positions.Readers are strongly advised to conduct thorough research relevant to trading decisionsand verify facts from various independent sources.
18Conclusion
I returned and saw under the sun, that the race is not to the swift, nor the battleto the strong, nor yet riches to men of understanding, nor yet favor to men ofskill; but time and chance happeneth to them all.–Ecclesiastes (King James Bible)Traditional boundaries of the various marketplaces are coming down everywhere andthe interbreeding is almost out of control. In order to prevent the process of aging, itis important to focus on the changing environment and reassess your trading systemsand methodology.There is clear and incontrovertible evidence that the markets are linked to eachother. Monitoring these relationships and incorporating them in your trading modelscan give you a trading advantage. Intermarket correlations, however, change overtime and you must periodically subject your trading system to a testing regime thatwill permit your system to profitably mimic the unknown economic laws that causethese changes.Although technical intermarket analysis considers only the action of the marketsand is not based on any economic theory, the statistical evidence and test resultspresented in this book and elsewhere, have convinced me that it is a viable technique.Contrary to popular belief by the non-believers, statistics don’t lie. They canbe distorted, bent, misused, and even misinterpreted but unless they are