. Most of these indicators (for example movingaverages) exploit price trends, while others exploit price volatility.I’ll have to admit that trying to trade classic moving average crossover entries inthe traditional sense has never worked for me. Such a simple approach works greatin trending markets but will slaughter your account in sideways markets. Admittedly,the methods I use to trade with today have a few more components than two movingaverages on a chart, and almost always include intermarket indicators.The yen, like the dollar and the euro, correlates better with interest rates andrecently (because of the “carry trade”) with equity indices rather than commodityprices (as is the case with the Australian or Canadian dollar).In Table 17.1 you can see the correlation between 1–20 day percent changes of theyen and the S&P 500, the Nikkei 225, the euro, the dollar index, the 10-year Treasuryyield (TNX) and Japanese Government Bond futures (JGB) using up to 11 years ofvariable time spans.It is clear from Table 17.1 that, mainly because of the “carry trade” the absolute correlation with all related markets (except perhaps the dollar index), has beenincreasing at a steady pace when using more recent data for the correlation calculations and this is more pronounced in the case of equity indices. For example,the correlation of daily changes between the yen and the S&P 500 has increasedfrom nearly zero to a negative correlation of −0.373 during the last two years. Similarly, the correlation of daily changes between the yen and the 10-year Treasuryyield (TNX) also increased significantly from nearly zero to a moderately strongvalue of −0.49. The strengthening in the trend of the negative correlation betweenthe yen and the Nikkei 225 is more evident when using 10 or more days of pricedifferences.The highest and diachronically persistent correlation was with the dollar index andthe TNX and they were both used to develop a trading system using the regressiondivergence indicator and the disparity indicator.
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Table 17.1 Pearson’s correlation of daily and 1–20 day (4 week) percentage changesbetween the yen (YEN/USD) and related forex, interest rates and equity indices using2–11 year of data from 1 January 1997 to 31 December 2007. In the first column is thenumber of years of data used. The TNX is the 10-year Treasury yield, and the JP Bond is the10-year Japanese Government bond futures (JGB). Unfortunately, data for the Japanese Bondhas been available only since 14 March 2005. Notice the increasing trend of negativecorrelations between the yen and equity indices as the older data are discarded. The yen isexpressed in dollars per yen (for example 0.0096154) and not in the usual yen per USD (forexample 104).Years
S&P 500
Nikkei 225
Euro
Dollar Index
TNX
JP Bond
−0.083−0.29−0.377−0.493
n/an/a0.0270.029
−0.05−0.275−0.383−0.488
n/an/a0.1470.183
−0.11−0.285−0.43−0.55
n/an/a0.2380.335
−0.182−0.28−0.447−0.56
n/an/a0.290.377
−0.186−0.27−0.47−0.56
n/an/a0.370.44
Correlation of daily % changes11632
−0.09−0.145−0.243−0.373
0.048−0.02−0.113−0.125
0.3420.4780.4170.36
−0.507−0.59−0.53−0.487
Correlation of weekly % changes11632
−0.028−0.085−0.167−0.259
0.068−0.091−0.263−0.3
0.3450.50.50.456
−0.534−0.62−0.598−0.559
Correlation of 10 day % changes11632
0.014−0.076−0.24−0.312
−0.022−0.09−0.343−0.393
0.3520.540.5470.535
−0.541−0.65−0.637−0.615
Correlation of 15 day % changes11632
0.011−0.07−0.298−0.397
−0.036−0.09−0.39−0.457
0.370.560.60.581
−0.556−0.67−0.685−0.653
Correlation of 20 day % changes11632
0.046−0.065−0.33−0.45
0.06−0.09−0.4−0.48
0.3750.5740.610.56
−0.554−0.675−0.684−0.63
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The fact that the yen is already a minor (13.6 %) component of the dollar indexdoesn’t seem to have serious repercussions on the correlation values as the strongcorrelation between the yen and the euro (which is the major component of the dollarindex) indicates. Nevertheless, just to make sure, I created a pseudo dollar indexusing the formula 4.2 (explained in Chapter 4), taking into account all other currencycomponents except the yen, and calculated the correlation of 15-day percent changesbetween the yen and the pseudo dollar index over a six-year span. The result wasan 11 basis point reduction in correlation from −0.67 to −0.56 which still beats allothers.The S&P 500 was also used, but only as a confirmation of the dollar index divergence signal and not to generate autonomous signals. This last condition wasn’tverified by back-testing as the correlation between the yen and the S&P 500, stemming from the “carry trade”, is something that emerged recently. To deal with thisproblem I used the TNX as an alternative confirmation when back-testing the systemusing older data. Thus the system could switch to TNX if the condition was notverified by the S&P 500.The same problem inherent in all divergence models (discussed in the previouschapter) was also encountered here, as the system generated only 28 trades during theseven-year out-of-sample testing period. To increase the turnover I added a volatilitybreakout component which increased the number of trades to 39, sufficient for astatistical evaluation of the system.To develop the volatility component I used the SD* ADX indicator, introducedin my article “Las Vegas or Los Nasdaq” first published in the January 2005 issueof STOCKS & COMMODITIES (see Bibliography). This indicator is calculated bymultiplying Wilder’s ADX by the standard deviation and dividing by the movingaverage. The SD* ADX indicator was used to detect low volatility periods which, justlike the calm before a storm, usually precede violent breakouts.Low volatility periods can also be detected by a contraction of the Bollinger Bandsor low values of the ADX indicator, but my research has shown that the SD* ADXindicator, which combines both methods, is superior to both when used alone.
Testing ProcedureIn order to develop the system I divided the available data into two segments. I thenused the first segment to test different rules and optimize the indicator parameters andthe second segment of data to test and evaluate the results. No optimization, exceptfor the S&P 500 condition, was carried out in the second phase of testing.Several assumptions underlie the trading model.The default trade size was one round lot of $100 000 per trade. Long and shortsignals were executed the same day at the close. This doesn’t present problems inreal-time trading since forex markets are open 24 hours and a trade can be executedvirtually seconds after the close indicated by the data.
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When the system was long the USD, the interest difference between the dollar andthe yen, currently at 4.15 − 0.80 = 3.35 % was credited to the account and whenshort the 3.35 % interest was debited to the account.The commission of 1.5 pip (or 100 000* 0.015/115 = $13), was deducted from exittrades. Returns were calculated as a percent of account required to trade each systemand compared to the buy and hold method which involved buying $100 000 andselling yen for the duration of the test. The compound formula was used to annualizepercentage returns.The amount of the account required was the 2 % margin required by major forexbrokers plus the initial drawdown. The percent standard deviation of change in equitywas calculated monthly and the risk-adjusted return was calculated by dividing theannualized return by the percent standard deviation of the change in equity.Three different indicators were used to define the intermarket rules: According toTable 17.1 the yen correlated best with the dollar index for 15–20 day percentagechanges. On testing, the 15-day segment produced slightly more profitable resultsand this value was substituted in the regression divergence indicator. The intermarketmomentum oscillator (IMO) was again used to identify extreme divergence levels.The 10-day disparity indicator was used to quantify divergence between the yen andthe TNX, as this indicator performed better than the regression method on testing.The one-day rate of change of the S&P 500 was also used to confirm signals. I couldnot back-test this last condition effectively, as there was no correlation between theyen and the S&P 500 before 2006, but since their relationship (because of the “carrytrade”), has recently changed dramatically, the system should be reevaluated andperhaps the S&P 500 may play a more dominant role in the future.The combined intermarket regression-disparity-volatility system was defined asfollows:Buy condition 1 (intermarket): A buy signal was generated if the 200 bar IMOof the 15-day YEN-DXY regression divergence rose over 70 and declined by morethan 4 points during the next four days. To eliminate premature signals, I used the10-day moving average to confirm the price reversal as follows. The system executedthe IMO buy signals only if the 2-day moving average of the yen crossed over its10-day moving average and the yen rose intraday (closed above the open). To furthereliminate false signals during periods that the yen decoupled from the dollar index, Iused a second intermarket condition which required a positive YEN-TNX disparityor the S&P 500 rising more than 2 %.Buy condition 2 (intermarket): A buy signal was generated if the 10-day disparitydivergence rose above 4 and reversed direction but only during short-term oversoldconditions defined by the 5-day stochastic crossing over 20 from below.Buy condition 3 (volatility): The SD* ADX indicator was used to detect extremelylow volatility and the Bollinger Bands to detect breakouts. This condition tended to
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produce some unprofitable trades so the signals were filtered using an intermarketcondition. Thus a buy signal was generated if the SD* ADX dropped below 0.11 andthe yen subsequently crossed over the Bollinger Band top during the next three days,but only if the disparity with the TNX was positive or the Dollar Index was above its20-day moving average.The opposite of the first two conditions, with the addition of a trailing stop loss,was used to liquidate the trades as follows:Sell condition 1 (trailing stop loss): Any open trades were stopped out if the yencrossed below the lower 10-day Bollinger Band.Sell condition 2 (intermarket): The position was liquidated if the IMO droppedbelow 30 and then reversed while the MACD was below its signal line.Sell condition 3 (intermarket): The position was liquidated when the disparitydropped below −4 and reversed direction but only when the yen was in a short-termoverbought state specified by the five-day stochastic crossing below 80.Sell short: The exact opposite of the first three buy conditions was used to generateshort signals.Buy to cover: Once again, the exact opposite of the sell conditions described abovewere used except that an extra requirement was added for the dollar index to be aboveits 20-day moving average or the yen above its 10-day moving average as the systemtended to close short positions rather prematurely.Please keep in mind that the conditions described above can only be used to tradethe USD/YEN pair which is positively correlated with the dollar index, the TNXand the S&P 500. To trade the YEN/USD pair or Japanese yen futures, which arenegatively correlated with the dollar index, the TNX and the S&P 500, the systemshould be modified as follows.A minus sign should be added to the disparity formula to take into account the negative correlation. Also the disparity extreme values used to signal trades by the firstsystem will have to be adjusted to take into account the reverse sign of the correlationbetween the YEN/USD and the TNX. This is because the disparity between negatively correlated markets is less (in absolute values) than the corresponding disparitybetween positively correlated markets. Another thing to change is the inequality signin all references to the S&P and the DXY moving average. Therefore the inequalitysign in the buy conditions should be reversed from > to < and the opposite in thesell or short conditions. Luckily there is no need to adjust the regression divergenceindicator as the formula detects the correlation sign and adjusts automatically.I did limited testing of the reverse system on JY futures and the system performedas expected, reversing the signals produced by the USD/YEN system. I have notpresented the results as Reuters did not provide enough data to cover the entire
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testing period chosen for the USD/YEN test. The code is, however, included inAppendix A.
Evaluation of ResultsThe results for both tests are summarized in Table 17.2. I have also marked in theaccompanying chart in Figure 17.1 when each buy/sell signal occurred but only (because of space limitations) from January to December 2007. The net profit is shownon the sixth row and the total profit (including the interest income resulting from theinterest rate spread between the two currencies) is shown on the next row. Buyingand holding $100 000 (and selling yen) produced a small loss but this was offsetby the considerable “carry interest” difference of $20 700 credited to the account during the seven-year period. The results of the initial pure intermarket system are shownin the third column and the combined system profit/loss statistics in the last column.In the period tested, the results of the combined system were very impressive.During the course of the seven-year simulation, the system made more than $70 000with only 39 trades, which were roughly 72 % accurate. The profit factor, which isdefined as gross profits divided by gross losses, was 7.8. A profit factor above 4 isconsidered excellent.The system sustained a maximum drawdown of $5900 or 5.9 % of equity, whichoccurred on 30 July 2001, mainly due to a trade initiated by an unpropitious regressiondivergence buy signal during the yen’s temporary decoupling from the dollar index,which was not closed in time to limit the loss.If you achieve a higher return with a lower standard deviation than buy and hold,that is your first piece of evidence that you are achieving a greater return with lessrisk and that your returns are more likely to be stable over any given period. In thiscase, the standard deviation of 1.4 % was 1.2 percentage points lower than that ofbuy and hold and the risk-adjusted return was 42 % compared with a 1 % loss of thebuy and hold.In addition, the buy and hold investor suffered a precipitous $29 000 or 29 %of equity drawdown and a rather steep initial drawdown of $11 184, increased theaccount required to buy the USD/YEN, because of the margin calls, from the initialminimum requirement of 2 % or $2000 to $13 184.Certain drawbacks of a strategy, which are not readily apparent from the profit-lossstatistics, can only be diagnosed by heeding the signals of a trading system once theyare in place. After inspecting carefully the trade signals on the chart, I identified thefollowing weaknesses.The timing of the exits was not always the best as they were sometimes lateand other times too early to close out trades. This occurred more often on signalsproduced by the first system which relied solely on intermarket conditions to enterand exit trades. This is because intermarket generated signals can fail, where there isa temporary decoupling of the base security from its related intermarket.
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Table 17.2 Profit/loss report. In Column 3 you can see the test results of the pureintermarket regression test and Column 4 the volatility enhanced results. All tests wereprofitable and outperformed the buy and hold or short and hold strategies.
Test
Buy & hold
Intermarket
Intermarket +Volatility
Security on testIntermarket securityTrading amountAccount requiredTotal 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. drawdownMax. initial drawdownMax. lossMax. consecutive lossesStandard 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 – daysTime in market
USD/YEN
USD/YENDXY, TNX, SPX$100 000$5216$37 456$35 735718 %35 %2820871.4 %$42 189−$4733$2109−$5923.574.958.91−$4199−$3216−$139731.55 %23 %19.741803-05-9902-01-0131-12-077.0255497115833538.0 %
USD/YENDXY, TNX, SPX$100 000$3058$72 585$71 1522374 %58 %39281171.8 %$83 214−$10 629$2972−$9663.084.737.83−$5914−$1058−$251821.37 %42 %39.241803-05-9902-01-0131-12-077.0255418756794873.4 %
$100 000$13 184−$2726$20 715157.0 %14 %1010%0−$2726
−$29 270−$11 184−€29 27012.54 %−1 %−3.103-05-9902-01-0131-12-077.02554255402554100.0 %
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Table 17.2 (continued)System ParametersDisparity barsRate of change – barsMomentum oscillator – barsMomentum oscillator – buyMomentum oscillator – (long exit)Disparity buyDisparity sell
101520070304−4
101520070304−4
Figure 17.1 Chart of the Japanese yen (USD/YEN) from January to December 2007. Testsignals generated by the regression method are superimposed on the chart. The 10-day disparitywith the TNX (10-year Treasury yield) is plotted in the top window, and below is the intermarketmomentum oscillator of the regression between the yen and the dollar index. The 15-daystandard deviation* ADX product is also plotted above the yen chart.
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Adding the volatility trailing stop loss condition in the second (combined) test, didimprove on the results by eliminating large drawdowns but failed to close trades incases of small and gradual adverse movements. More stringent exit rules, however,failed to improve on profitability as they tended to close out trades far short of theirmaximum profit potential.Premature entry signals, however, did not affect the bottom line, as trades triggeredby early signals usually ended up profitable.Although I was not entirely satisfied with all entry and exit conditions the model wasrobust and maintained a more than acceptable balance between risk and reward. Thetiming of the entry and exit signals, however, can be improved further by combiningconventional technical analysis with intermarket rules and indicators, thus filteringunprofitable or untimely signals.
17.5
THE EURO
The euro is the single currency currently shared by 15 of the European Union’s member states (Belgium, Germany, Greece, Spain, France, Ireland, Italy, Luxembourg,Netherlands, Austria, Portugal, Finland, Slovenia, Cyprus and Malta).When the euro was first launched in 1999, it became the new official currency of11 member states, replacing the old national currencies – such as the Deutschmarkand the French franc – in two stages. It was first used as a virtual currency for cashlesspayments and accounting purposes, while the old currencies continued to be usedfor cash payments. It appeared in physical form, as banknotes and coins, for the firsttime on 1 January 2002.The euro is not the currency of all EU Member States. Two countries (Denmark andthe United Kingdom) agreed an ‘opt-out’ clause in the Treaty exempting them fromparticipation, while the remainder (many of the newest EU members plus Sweden),have yet to meet the conditions for adopting the single currency. Nevertheless theeuro is the single currency for more than 320 million Europeans. Including areasusing currencies pegged to the euro, it directly affects close to 500 million peopleworldwide. The name – the euro – was chosen at the European Council meeting inMadrid in 1995 and the euro symbol – € – was inspired by the Greek letter epsilon(). It also stands for the first letter of the word “Europe” in the Greek alphabet, whilethe two parallel horizontal lines crossing through the symbol signify stability.When the euro came into being, monetary policy became the responsibility ofthe independent European Central Bank (ECB), which was created for that purpose.Fiscal policy (tax and spending) remains in the hands of individual national governments – though they undertake to adhere to commonly agreed rules on public financesknown as the Stability and Growth Pact.The euro’s miserable debut on 1 January 1999 had developed into a stunning bullmarket by 2004. Since breaking out in the spring of 2002, the euro has enjoyed astrong, vigorous bull market over the past six years, which can be mainly attributed to
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Table 17.3 Pearson’s correlation of 15-day (three week) percent changes between the euro(EUR/USD) and related commodity and forex markets using 2–9 years of data from theeuro’s launch on 1 January 1999 to 31 December 2007. In the first column is the number ofyears of data used. The TNX is the 10-year Treasury yield, and the Bund is the 10-yearGerman Bund futures (GBL).Correlation of 15 day % changes between the euro and related marketsDataYears
EuroStoxx
TNX
10-yearbund
Gold
CRB
GBP
AUSD
Yen/USD
9532
−0.23−0.16−0.010.01
−0.26−0.28−0.10−0.27
0.220.27−0.030.17
0.430.510.520.61
0.260.370.480.57
0.730.760.770.77
0.520.620.570.59
0.370.520.580.58
the weakness of the dollar and the US twin deficit rather than strength in the Eurolandeconomy.When currency guru George Soros was asked about the weakness in the US dollarin June 2002, he predicted that the greenback could drop as much as 30 % before themove was over. The dollar has dropped by more than 50 % since, from 0.94 dollars tothe euro to 1.47 by the end of 2007 and there are no indications that the move is over.But how can the euro be predicted using intermarket relationships?To quantify its correlation with related markets I compiled three tables. In Table17.3 you can see how the correlation of three week percent changes between theeuro and European stock indices, US and European interest rates, commodities andother currencies evolved during the nine years of its existence. In Table 17.4 you cansee how different percent changes can affect the correlation, and in Table 17.5 youcan see how well a related market’s weekly percentage price change can predict nextweek’s change of the euro.The most important conclusions drawn from these tables are summarized below:• The relation with the Euro Stoxx 50 index has recently deteriorated from a negativecorrelation of −0.23 to nearly zero during the last two years from 2005 to 2007(Table 17.3). Unlike correlation with other indices, the correlation with Euro Stoxxis stronger when taking 10-day price differences (Table 17.4), suggesting a shortterm relationship. Also a slight lead of the euro is evident from Table 17.5.• The euro correlates better with US rather than European interest rates, especiallyin the longer term (more than three months).• The correlation with gold and the CRB has been trending up when using morerecent data (Tables 17.3 and 17.4), and it is stronger for short to medium percentchanges (two to three weeks). The euro lags both the CRB and gold slightly(Table 17.5).
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Table 17.4 Pearson’s correlation of different percentage changes between the euro andrelated commodity and forex markets using five years of data from 1 January 2003 to 31December 2007. In the first column is the number of trading days used to calculate thepercentage differences, which varied from 1 to 126 days (six months). Weekends andholidays were not included.5 year correlation of % changes between the euro and related markets% change –days
EuroStoxx
TNX
10-yearbund
Gold
CRB
GBP
AUSD
Yen/USD
1510152163126
−0.20−0.19−0.2−0.16−0.13−0.06−0.05
−0.32−0.27−0.28−0.28−0.27−0.32−0.32
0.310.230.240.270.270.210.16
0.520.530.520.510.500.470.3
0.260.350.360.370.380.270.16
0.690.730.750.760.780.790.75
0.570.610.610.620.630.800.79
0.460.470.500.520.540.690.74
• The correlation between the euro and the British pound, stemming from Britain’smembership of the European Union, is the strongest of the group. The relationshipis strong for both short and long term time segments and constant through theyears. A lead of the euro is clearly evident from Table 17.5. This indicates that itis better to use the euro to predict the British pound and not the other way around.• There is also strong correlation with the Australian dollar. This is more pronouncedfor longer term percent change intervals (more than three months). Also it is evidentfrom Table 17.5 that the euro leads the Australian dollar.• The relation with the yen has also been increasing recently (Table 17.3). It isevident from Table 17.4 that the correlation is stronger when taking three or moremonths of percentage changes.• The euro seems to lead all other currencies.17.6
TRADING THE EURO
A trend-following strategy seemed the most appropriate to use in order to takeadvantage of the euro’s strong trends.The classic method involved a plain moving average but trades were not initiatedaccording to the usual rules of price crossing over the moving average but only whenthe moving average turned up by a certain minimum amount or filter. This filteradjusted itself according to volatility. To accomplish this, the filter was defined as 0.7standard deviations of the moving average daily changes. A second filter was alsoemployed to filter out signals in trendless periods and eliminate false signals whenprices were moving sideways.
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Table 17.5 Pearson’s correlation coefficient of weekly percentage changes between theeuro and Euro Stoxx 50, 10-year Treasury yield (TNX), 10-year Bund futures (GBL), gold(cash), the Commodity Research Bureau Index (CRB), the British pound (GBP/USD), theAustralian dollar (AUS/USD) and the yen (JPY/USD). The correlations were calculatedusing data for the five-year period from 2003–2007. The first column depicts the number ofdays that the euro data were shifted in time. A 1-day lag indicates that yesterday’s euroweekly change was correlated with today’s Euro Stoxx, TNX, or CRB data and a lag of −1indicates that tomorrow’s weekly change was correlated with today’s weekly change of thecorresponding commodity. Therefore higher correlations with positive lags indicate a eurolead and higher correlations for negative lag indicate that the euro lagged. The avg row is theaverage of lagging or leading correlations.
EuroStoxx
TNX
10-yearbund
Gold
CRB
GBP
AUSD
Yen/USD
−5−4−3−2