CTM201402

TRADING A SHORT-SIDE POUND PATTERN P. 31

Chinas new normal, and the implications for currencies p. 6

Excess volatility and the major FX pairs p. 20

Building an FX system with a

trailing stop p. 16

The beginnings of a panic? p. 10

Strategies, analysis, and news for FX traders

February 2014

Volume 11 No. 2

2 February2014CURRENCY TRADER

CONTENTS

Contributors .................................................4

Global MarketsChinas new normal ....................................6Single-digit GDP growth is the new paradigm, with credit-bubble risks looming in the background. Which currencies are most exposed?By Currency Trader Staff

On the MoneySeeds of a classic panic maybe.........10Was the market disruption toward the end of January a chink in the markets armor, or just another pullback? By Barbara Rockefeller

Trading StrategiesTrailing stops, curtailing losses .............16Starting with a trailing stop rule can make your forex system easier to trade.By Daniel Fernandez

Advanced ConceptsWhen excess becomes predictable: The majors ................................................20Find out whether carry returns from the U.S. dollar, along the money-market yield curve, can predict the excess of implied volatility over historical volatility.By Howard L. Simons

Global Economic Calendar ........................26 Important dates for currency traders.

Events .......................................................26Conferences, seminars, and other events.

Currency Futures Snapshot .................27

BarclayHedge Rankings ........................27Top-ranked managed money programs

International Markets ............................28 Numbers from the global forex, stock, and interest-rate markets.

Forex Journal ...........................................31Taking a swing at the pound/dollar pair

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CONTRIBUTORS


Editor-in-chief: Mark Etzkorn

[email protected]

Managing editor: Molly Goad

[email protected]

Contributing editor:

Howard Simons

Contributing writers:

Barbara Rockefeller,

Marc Chandler, Chris Peters

Editorial assistant and

webmaster: Kesha Green

[email protected]

President: Phil Dorman

[email protected]

Publisher, ad sales:

Bob Dorman

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Classified ad sales: Mark Seger

[email protected]

Volume 11, No. 2. Currency Trader is published monthly by TechInfo, Inc., PO Box 487, Lake Zurich, Illinois 60047. Copyright 2014 TechInfo, Inc. All rights reserved. Information in this publication may not be stored or reproduced in any form without written permission from the publisher.

The information in Currency Trader magazine is intended for educational purposes only. It is not meant to recommend, promote or in any way imply the effectiveness of any trading system, strategy or approach. Traders are advised to do their own research and testing to determine the validity of a trading idea. Trading and investing carry a high level of risk. Past perfor-mance does not guarantee future results.

For all subscriber services: www.currencytradermag.com

A publication of Active Trader

CONTRIBUTORS

qHoward Simons is president of Rosewood Trading Inc. and a strategist for Bianco Research. He writes and speaks fre-quently on a wide range of economic and financial market issues.

qDaniel Fernandez is an active trader with a strong interest in calculus, statistics, and economics who has been focusing on the analysis of forex trading strategies, particularly algorithmic trading and the mathematical evaluation of long-term sys-

tem profitability. For the past two years he has published his research and opinions on his blog Reviewing Every-thing Forex, which also includes reviews of commercial and free trading systems and general interest articles on forex trading (http://mechanicalforex.com). Fernandez is a graduate of the National University of Colombia, where he majored in chemistry, concentrating in computational chemistry. He can be reached at [email protected].

qBarbara Rockefeller (www.rts-forex.com) is an international economist with a focus on foreign exchange. She has worked as a forecaster, trader, and consultant at Citibank and other financial institutions, and currently publishes two daily reports on foreign exchange. Rockefel-ler is the author of Technical Analysis for Dummies, Second Edition (Wiley, 2011), 24/7 Trading Around the Clock, Around the World (John Wiley & Sons, 2000), The Global Trader (John Wiley & Sons, 2001), The Foreign Exchange Matrix (Harriman House, 2013), and How to Invest Internationally, published in Japan in 1999. A book tentatively titled How to Trade FX is in the works. Rockefeller is on the board of directors of a large European hedge fund.

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JointheConversation


GLOBAL MARKETS

China, the worlds second-largest economy, is in transition. Economists increasingly believe a major change is needed for the Chinese economy, including a shift from the export-led model of recent years to a domestic consumption-led model. However, as Beijing tries to orchestrate a major structural transformation, concerns over a potential credit bubble are rising to the surface, which could leave the countrys banking system vulnerable to a shock in 2014.

For the most part, however, the hard-landing scenari-os have eased as overall growth numbers have stabilized, albeit at a much lower rate than in the years prior to the 2008 global financial crisis. China registered GDP growth of 7.7% in 2013, the same level as in 2012. With concerns about social stability in the background, the government has relied on both monetary and fiscal policies to keep the countrys economic engines moving.

But the fiscally driven infrastructure boom of recent years has sparked widespread concerns about the rise of a credit build-up in China and the possibility of a banking crisis lurking in the shadows.

For currency traders, the more immediate issues are whether an evolving Chinese economy portends a more open foreign exchange market for the yuan/renminbi, as well as what other currencies function as good China prox-ies.

Economic outlookFollowing the Communist partys third plenary session in 2013, it became clear the days of 10% GDP were over, according to according to Northern Trust vice president James Pressler. Seven to eight percent would be the

expectation going forward, he says. Pressler estimates 2014 GDP in the 7.5 to 7.6% range.

Most analysts seem to agree a new era of lower, but still quite robust economic growth has emerged in China. The base case is they remain in the 7-8% growth range, says Wells Fargo economist Jay Bryson. I dont think they are ever going back to double digits.

Pressler says Chinas reform strategy includes plans to shift from the export-driven model that has worked well in Asia for 20 years to a point where Chinese consumers exert a greater influence on the economy. As a comparison, he notes the U.S. consumer has a 70% impact on GDP, while the Chinese consumer only has about a 33% impact.

Shifts in the global manufacturing and labor landscape are playing into Chinas desire to decrease its reliance on exports. Now that wages have risen significantly in China, the rest of Asia is becoming more competitive, Presser says. They are losing the low-wage labor force. A mid-level garment worker in China makes $500 a month, while in Bangladesh they earn $50-60 a month. Theyre los-ing the low-end manufacturers and the wage competitive-ness they had 10 to 20 years ago.

Lending spreeAnother factor driving the Chinese government to engi-neer slower overall growth levels is rising credit levels. The Chinese government acted swiftly and aggressively at the end of 2008 to combat the slowdown in the global economy. The government issued 4 trillion yuan of stimu-lus, Pressler notes. A lot of credit was built up to devel-op infrastructure and real estate projects.

Chinas new normal

Single-digit GDP growth is the new paradigm, with credit-bubble risks looming in the background. Which currencies are most exposed?

BY CURRENCY TRADER STAFF

CURRENCY TRADERFebruary2014 7

According to Paul Sheard, chief global economist at Standard & Poors, the Chinese governments fiscal poli-cies were generally successful, as evidenced by the coun-trys spectacular performance of around 9% GDP in the years after the financial crisis. But that success was not without cost. The credit-fueled investment boom focused on infrastructure, he says. The problem with Chinas solution to the global financial crisis is it solved one problem and raised another. Sheard says the country is dealing with the potential aftermath of a credit-fueled splurge.

The Chinese government supported a massive, rapid build-up, including apartment complexes and shopping districts essentially complete cities, many of which currently stand completely empty to service what had been a flood of workers moving east in a search for better-paying jobs. Pressler notes that although the development was meeting a need for new housing, a lot of the projects werent producing the returns that were expected to pay off the projects. Adding to the problem, rising property prices put some of the real estate out of the average work-ers reach.

Shadow banking systemIn recent years a so-called shadow banking system has sprung up in China. It includes non-financial companies, such as export or consumer-goods producers that were looking for an opportunity to invest their excess reserves, according to Pressler. The current low interest rates on sav-ings prompted non-financial companies to expand into the loan business to capture a higher rate of return. Ultimately, however, this exposed these non-financial companies to other areas of the economy especially the real estate sec-tor.

Adding to the worries are these loans lack of visibility. Its very difficult to monitor how much credit is being offered by non-financial organizations, Pressler says. Once you start flying off the radar, its hard to tell what is going on.

JPMorgan estimates the entire shadow banking sec-tor, which includes private non-financial lending, trust accounts, and wealth investment products, totals $6 tril-lion a little less than 70% of GDP, Pressler notes. This figure is in addition to the known total Chinese domestic banking assets that equal about 250% of GDP. In the U.S., as of 2012 (the latest numbers that are available), total assets or loans carried by commercial banks totaled 78.8% of GDP, Pressler notes.

One could argue the pace of development is slowing, and yet you still have credit rising, he says.

Domino effectPressler warns regional and provincial financial institu-tions could be at the biggest risk. If we were to start see-ing defaults at the regional, provincial level or in the worst case, a bank folds then you have a crisis of confi-dence on your hands, he says.

In recent years, wealth management products (WMPs) have risen in popularity in China. These are products sold by banks in which an individual buys into a collateralized obligation with the promise of a specific return say, 8% for the time you hold the vehicle.

Pressler says these risks of these products have are some similarities to the mortgage-backed crisis the shook the U.S. and other Western economies. Its tough to analyze a fund backed by a series of loans into an investment project with the assumption of a return, he says. Our larger con-cern is that if something goes wrong, it will spread inter-nally, and within the financial sector.

In other words, Sheard notes, some of those investments could run into problems. The policy challenge will be if there are some non-performing loans and the losses start to ripple through the system people could panic and lose confidence, he says.

Sean Callow, senior currency strategist at Westpac Institutional Bank, sees a similar risk. Frustrated by capped interest rates at banks and a lackluster local stock market, investors are attracted by high-yielding wealth management products often sold through banks, he says. Recently there has been increased concern over these WMPs as defaults loom. High return of course means high risk; the danger is that WMP defaults will have a spillover effect on the financial system and investor confidence.

While this is an underlying risk for the economy, some argue Chinas tight economic controls could keep a finan-cial crisis in check. Over the last 35 years, the authori-ties have been able to manage their economy very well, Sheard says. There will be localized sporadic events, the question is whether they become systemic. Our base case is no.

This challenge has emerged at a time when the Chinese authorities are attempting to liberalize the financial system somewhat to allow market forces a stronger hand. This will be a longer-term dynamic playing out in China: How do you transition from one economic model to another? Sheard says.


GLOBAL MARKETS

Yuan actionIn the meantime, the Chinese yuan remains under the firm hand of the monetary authorities, with the USD/CNY trading around 6.04 in late January (Figure 1). The yuan appreciated steadily from late 2005 into late 2008, but once the global financial crisis began, the currency leveled out and moved sideways around 6.80 until September 2010, when the appreciation trend restarted. The last several years have seen the USD/CNY rate fall to its recent low around 6.04.

The yuan is now the strongest its been since the rebal-ancing in 1993, Pressler notes. However, he adds it still remains undervalued. I think it should be around 5.4-5.4, he says.

Pressler notes the Chinese government allowed the yuan to appreciate by 2.7% in 2013. We believe given their economy will slow down and they will be sensitive to the needs of exporters, they probably wont let it cross the 6.00 level until the third quarter, he says. Once you let it break into the high 5.90s, youre crossing a boundary.

Callow says the most likely scenario for the CNY is steady gains. He notes Chinas FX reserves grew $157 bil-lion in the fourth quarter 2013, while CNY rose just 1.1% over those three months.

Chinas trade and net foreign direct investment position remains very strong, he says. The clear impression is that USD/CNY is not very close to the equilibrium officials

seek, so intervention will only slow the pace of yuan gains. Investors are likely to continue to view CNY as a one-way bet in 2014, just not a bet with a big payoff perhaps only 3-4%.

Will China have a freer and more open foreign exchange market? Will it be significantly more open this year? No, Bryson says. In five to 10 years? Yes. All policies there are glacial. Everything is measured, everything moves very slowly. They are not up for the big bang experiment.

Spillover impactIf some banking or credit issues explode later in 2014, what countries and other global currencies could see an impact?

The Australian dollar remains very sensitive to any developments that would hurt Chinas economy, so it would be speculators preferred short on any notable China turmoil this year, Callow notes. The Japanese yen is a likely beneficiary, along with the Swiss Franc and U.S. dol-lar, which are the proven safe-havens in the global crisis.

BNP Paribas FX strategist Vassili Serebriakov agrees the Australian dollar is the G-10 currency that is most sensitive to the China story (Figure 2). It is the G-10 economy with the largest share of its exports going to China. The risks are to the downside for the Aussie. The base scenario is that China sees some slowing, but growth remains above 7%. It wont be a source of bad news, but it wont boost the Aussie. But if a more negative scenario plays out it will be

FIGURE 1: YUAN TREND

The yuans steady appreciation vs. the U.S. dollar pushed the USD/CNY to new lows in early 2014.Source: TradeStation


more negative for commodity produc-ers, like the Aussie. The Aussie will be more sensitive to bad news than good news this year.

Looking beyond the G-10 currency landscape, Pressler warns if a credit implosion occurs, the impact would be greatest on Pacific Rim commodity producers, such as Indonesia, Thailand, Taiwan, and Malaysia, which are pro-ducers of copper, palm oil, and rubber goods. Pressler also notes if China slows down and global commodity prices dip, it would benefit the U.S.

He cites the example of commodity prices prior to the 1997 Asian financial crisis. Commodity prices were run-ning briskly until the crisis, and then major consumers fell into deep reces-sion and we saw a significant slow-down, he explains. Their currencies dropped by 40%. Oil fell to $14 barrel. As a developed country, we benefited from the Asian recession.

General playsBarring some type of credit crisis, the Australian dollar remains the top pick for a China play among analysts, although Callow says that within Asia, the Singapore dollar (SGD) and Korean won (KRW) should track the general mood on China (Figure 3). The Aussie dollar is the most popular proxy for the Chinese economy among the free-floating currencies, Callow says. The New Zealand dollar is also somewhat sensitive now that China has overtaken Australia to become New Zealands largest trading partner. y

FIGURE 2: AUSSIE DOLLAR

Some analysts believe the Australian dollar is the major currency that is most susceptible to the China story. Source: TradeStation

FIGURE 3: CHINA PROXIES

The Singapore dollar (top) and Korean won (bottom) should also track China, if not to the same extent as the Aussie dollar.Source: ADVFN.com

By mid-January 2014, FX traders were getting bored by the main themes in the market Fed tapering, European deflation, iffy Chinese growth. Just as FX traders were casting around for some new factors to chew on, equity traders decided to panic about deteriorating conditions in emerging markets.

The tendency toward contagion in stock markets was on display in all its glory. In a single day (Friday, Jan. 24), the ETF on the MSCI emerging markets index fell 2.6% on an opening gap (Figure 1). The Dow dropped 1.96%, the S&P, 2.09%, and the Europe Stoxx 600, 2.39%.

Its uncertain yet whether the one-day sell-off will be a

buy-the-dip opportunity or the start of something bigger, but its a Shock and has the potential to change the global financial environment for the entire year. Not only could a bear market in equities emerge, the Fed may decide to postpone or cut back tapering.

Its interesting that on Jan. 24, the only major stock mar-ket that posted a gain was the Shanghai, which is espe-cially ironic because a sub-par HSBC flash PMI was one of the drivers contributing to the emerging-markets panic. To be fair, nobody really knows whether it was the Chinese PMI, Turkish lira crash, Argentinas devaluation, or some-thing else that was the tipping point. But a critical mass

was reached.

Emerging markets and FXEmerging-market stock markets are joined at the hip with currencies. Political problems in Turkey drove the lira to an all-time low. There were street riots in Bangkok, although the baht and SET stock index fell by only a little. The Argentine peso fell 15% (including an official devaluation), the Russian ruble fell to a five-year low, and the South African rand fell to its lowest level since June 2008.

Meanwhile, advanced-country currencies that sometimes act like emerging-market currencies were also on the defensive the Canadian and Australian dollars were already falling dramatically, and fell some more on the key days. The flight to safety in the Swiss franc and yen was immediate and powerful. The Swiss franc gained 1.88% and the dollar/yen rate lost 2.08% during the last two days of the week (Jan. 23 and 24).

On the Money


ON THE MONEY

Seeds of a classic panic maybe

Was the market disruption toward the end of January a chink in the markets armor, or just another pullback?

BY BARBARA ROCKEFELLER

FIGURE 1: MSCI EMERGING MARKETS INDEX

Led by emerging markets, equity markets took a hard hit on Jan. 24.Source: Chart Metastock; data Reuters and eSignal


You cant hear Thailand-Argentina-Russia without remembering the emerging-market crash of 1997-98. Flight out of emerging-market cur-rencies, equities, and bonds led in fairly short order to default by Argentina and Russia, with high-profile hedge fund Long-Term Capital Management failing in 1998. LTCM underwent a dramatic $3.625 billion rescue by a consortium organized by the New York Fed (resulting one of the best books on finance, When Genius Failed: The Rise and Fall of Long-Term Capital Management by Roger Lowenstein). It was the first big taste of moral hazard, and it set off a flurry of aca-demic papers by everybody and his brother, including the BIS, numerous PhD theses, and the Fed itself.

Moral hazard wasnt the only concept to go mainstream from the Asian crisis of 1997-98.

FIGURE 2: S&P 500

Although the Jan. 24 sell-off dropped the S&P only to its mid-December level, the index held above its 100- and 200-day moving averages.

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ON THE MONEY

There was also contagion and asymmetric information, the one-note pony phenomenon that underlies the herd mentality and leads to financial market disruptions disproportionate to the true underlying financial and economic conditions.

Perspective on U.S. equitiesHow should we put the Jan. 24 global sell-off in perspective? After all, in the U.S. the S&Ps one-day decline of 2.09% is a drop in the bucket of its 600-point gain over the past two years. The move dropped the S&P only to its mid-December level, and the chart shows the index holding above the 100-day and 200-day moving averages, if below the 55-day (Figure 2).

Besides, in 1998, the S&P did not suffer con-tagion: After a minor dip from July to October, it went on to higher levels until its March 2000 peak, and almost everyone agrees the pullback after that was the result of a belated recognition of the ridiculous valuations accorded to high-tech companies. (The pullback was dubbed the Tech Wreck.) Therefore, based on this recent history, the U.S. and presumably other devel-oped equity markets can probably escape direct contagion from emerging-market woes.

The problem is that once a sell-off begins, the sell-off is the Event. Risk-averse market sentiment pays no heed to its origin. Chatter develops about non-EM reasons for U.S. equi-ties to correct, and some of them are fairly convincing. Robert Shiller, for example, offers something called a cyclically adjusted P/E ratio, or CAPE. This divides the current price by the average inflation-adjusted earnings over the previous 10 years. Historically, market peaks tend to occur at around 29 times CAPE. And CAPE is pretty high right now at a 25.4 multiple, implying a pullback from overvalu-

FIGURE 4: US 10-YEAR NOTE YIELD INDEX

The 10-year Treasury yield fell from above 3% at the end of December to 2.735% on Jan. 24.

FIGURE 3: PIMCO EMERGING MARKET CURRENCY FUND (WEEKLY)

The PIMCO Emerging Market Currency Fund gained a 54% between March 2009 and May 2011, but in November 2011 it dipped sharply and rallied just as abruptly to a lower peak in May 2013.


ation would be warranted. Note that in March 2000 as the market was about to experience the Tech Wreck, the CAPE multiple was 43.

Combine a peaky P/E with the prospect of the Fed rais-ing rates, even if more than a year away, and its no won-der equity analysts are worried the EM crisis is just a trig-ger for a bigger and more long-lasting pullback, perhaps of bear proportions. Then add to this fear the idea that some major U.S. companies are dependent on foreign earnings, including earnings from emerging markets, that may now disappoint.

A really interesting tidbit comes from seekingalpha.com Googles non-U.S. revenues are higher than domestic revenues by a giant $4 billion in the first nine months of 2013. The currencies that pose the most risk to Google for-eign earnings are the Japanese yen, the Brazilian real, and the Euro or two out of three from the EM basket.

The underlying conditions that led up to the EM sell-off were in the making for some time before the blow-out. Turkey and Thailand had been having political problems (over corruption and inefficiency) for weeks and even months. In fact, Turkey was eerily like Thailand in 1997 a construction bubble fueled by foreign credit that turned hot faster than anyone had imagined. Its also important to note that some parties getting tarred by the EM brush, such as South Korea, are blameless and dont deserve a currency and equity market sell-off. Thats the toxic nature of contagion. But once a giant sell-off has occurred, the actual causes are no longer the important thing.

All kinds of observations everyone had been ignoring suddenly become relevant. We knew ultra-low returns had been luring yield-hungry investors into various high-risk asset classes and sectors for some time since the Feds quantitative easing began in 2008, to be precise (Figure 3). The PIMCO Emerging Market Currency Fund had gained a whopping 54% between March 2009 and its May 2011 peak, but it then dipped sharply in November 2011 and rallied just as abruptly to a lower peak in May 2013. The current drop echoes the drop in May, when the Fed announced tapering was imminent. In May, the countries

most affected included India and Brazil. Now that India has a highly respected new Reserve Bank chief, the rupee and the Sensex suffered relatively less effect on the crash day. This tells us something valuable that traders are not totally indiscriminate. If the South Korean won was the biggest loser on Jan. 24, as Bloomberg reports, it could be because the country was being used as a proxy for expo-sure to China. Selling South Korea (and Taiwan) was actu-ally selling China, which is notoriously hard to do.

The new round of risk aversion was actually becoming visible on the chart of the 10-year Treasury yield for about two weeks ahead of the EM crisis (Figure 4). The yield fell from above 3% at the end of December to 2.735% on Jan. 24. This was likely more a result of the U.S. stock market putting in a bad first five days, traditionally a harbinger of a bad month, plus the usual market jibber-jabber about too-high P/Es, disappointing sales or earnings, and so on. But the reason the stock indices had gained so much over the previous two years was the search for yield in a world where bonds returned so little the same motivation as in emerging markets.

In fact, falling sovereign bond yields in many places indicate risk aversion was starting to get a grip for several weeks before the EM crisis. In the UK, the 10-year Gilt yield fell from 3.03% at year-end to 2.77% on Jan. 24, while the German Bund yield fell from 1.94% to 1.66%. Irish yields fell to 3.31% after Irelands first post-bailout issu-ance in January was well received. Portugal also made a new five-year offering in January, with the 10-year yield falling from 6.2% at year-end to 3.80% on Jan. 24. Analysts took the drop in peripheral European sovereign yields to mean the debt crisis was behind us, but now that we have the EM crisis, you have to wonder if flight from Turkey helped, say, Portugal. Also, it remains to be seen whether the peripherals will continue to be considered part of the developed basket or slide into the EM basket.

Logically, if equity investors are fleeing emerging mar-kets, the U.S. should be the number one bolt-hole. After all, U.S. equity markets comprise about 34% of total world market capitalization, more than any other single coun-


ON THE MONEY

try. At the end of 2012 U.S. equity markets represented $18.7 trillion in market cap, vs. $54.6 trillion for the world and $11.762 trillion for the top-22 European countries, including Switzerland and the UK (www.quandl.com/economics/stock-market-capitalization-all-countries). The Economist magazine (Jan. 18, 2014 issue) published a world map showing that, apart from government own-ership, many emerging-market stock markets are really tiny. For example, all the equities of India are worth about the same as Nestle. Egypts market cap is the equivalent to Burger King. The Russian stock market is the size of Proctor & Gamble, Mexico is comparable to IBM, and Venezuela is equivalent to Merck.

The first lesson is that emerging economies have small and illiquid markets. The second lesson is that theres really not really much money available to be siphoned from EM markets to developed country markets, including the U.S.

If investors generally are fleeing emerging-market cur-rencies, the U.S. dollar should benefit as a safe haven, alongside the traditional Swiss franc, and home-country advantage for the Japanese, the yen. As the emerging-market crisis exploded on Jan. 23 and Jan. 24, however, the dollar tanked (Figure 5). As with the S&P, the fall in the dollar index is not fatal from a chart perspective yet. The dollar is tracking the 10-year yield and not following the conventional risk-aversion playbook.

Commodities odditiesThe case of the curiously falling dollar brings us to the subject of commodities and the intermarket correlation gremlins. We expect equities and bonds to move inversely thats a classic relationship. We also expect commodities to move inversely to equities, if they are correlated at all, but so far the gains in the CRB index and even oil and gold are not proportionate to the drop in equities.

The time to get worried about contagion from the emerging-market crisis will be when these normal relation-ships do not behave as expected, and we might be seeing the start of that. In other words, equities could be falling but commodities could be falling, too.

The underlying reason for commodities to be losing their appeal is the commodity super-cycle may be ending. The landscape for oil changed when the U.S. got an 18% rise in domestic production in 2013 back to levels not seen in 25 years. The International Energy Agency says the U.S. will be the top producer by 2015. In fact, in the first eight months of 2013, the U.S. was able to produce 86% of its energy needs from all sources (natural gas, petroleum, nuclear ,and renewables such as wind and solar). This is a game-changer.

Another game-changer is the 2013 slowdown in Chinese growth from double digits to 7.7%. No one knows how big Chinese commodity stockpiles are, but they are not negli-gible for things like copper and aluminum. As the Chinese government institutes reforms, including reining in the

FIGURE 5: DOLLAR INDEX

As the emerging-market crisis exploded on Jan. 23 and Jan. 24, the dollar tanked.

shadow banks that fund uneconomic building and manu-facturing projects, demand for commodities from China could well be in decline. In fact, for all anyone knows, the build-up of commodity inventories in China may have been financed by the very shadow banks that are now under the gun. We shouldnt be surprised. Financing illiq-uid assets that are long-term by nature with short-term funding is a classic beginners mistake. At a guess, China intends to manage the contraction of its shadow banking industry (and the restructuring of the official banking sec-tor) very, very carefully. This is not to forecast a bursting bubble, but rather a cut in demand from the country that has demanded more than half the worlds commodities in recent years.

Until the EM crisis, the only real distress was in Canada and Australia, two countries that are sane and well-man-aged. But both countries face recessionary and deflationary conditions coupled with real estate booms that might well be bubbles, and thus endanger the banking system down the road. The solution? Jawboning the currency lower to promote exports and give manufacturing whatever minor boost it can use, even if cutting rates is not on the table because it would exacerbate the housing bubble. However, a housing bubble is only a mask for excessive household debt, which means the central bank might be better off raising rates to cool down housing at the expense of that weak sector, manufacturing (and thus, employment).

Where does this leave the United States? The U.S. is actually in pretty good shape, considering it has emerged from its worst recession since the 1930s. The bond market, after a stumble in May and June when the first announce-ment of tapering sent yields too high, now accepts the Fed is truthful when it says lower for longer. (Overreaction was the problem in May and June, when the initial announcement of tapering took the yield from a low of 1.614% on May 1 to 2.214% on May 31, and thence to an intermediate high of 2.725% on July 5.)

In one of his farewell speeches, Fed chief Ben Bernanke pointed out real GDP is up in 16 of the past 17 quarters. By Q3 2013, GDP was up 5.5% above the peak in 2008, before the recession hit. Unemployment dropped from 10% in 2009 to 7% by the end of 2013 to. And all this in the face of contracting fiscal policy that took as much as 1.5% off GDP, according to the Congressional Budget Office. Bernanke didnt mention it, but households deleveraged, too. As of the end of Q3 2013, household debt was $11.28 trillion (covering mortgages, autos, education and credit cards), from the peak of $12.68 trillion in the third quarter of 2008, according to the New York Fed. Mortgage and credit card delinquencies are also lower.

The possibly developing emerging-market crisis could put the kybosh on the U.S. recovery but not for reasons originating in the U.S. Instead, we might be seeing the classic features of a financial crisis, just not all in one place. One feature is excessive credit creation by incompetent bankers who are insufficiently regulated by central banks and other supervisors, so the backlog of non-performing loans gets out of hand. This is probably the case in China and Turkey, for example, as well as several European coun-tries. Another contributor to a classic crisis is artificially low rates, whether from central bank policy or a savings glut, which drive investors into riskier assets and sectors, including real estate in a phrase, over-leveraging and bubbles.

The other shoeThe catalyst for a wider, deeper crisis is always the fail-ure of a big financial institution, as in the September 2008 Lehman Brothers collapse. Now the task is to identify which financial institution is going to fail. Chances are its not one in the U.S. the banks may not be completely clean, but they are in sounder shape than in 2008. Chances are its not in China, either the government there can be counted on to control (and disguise) a banking crisis.

That pretty much leaves Europe, where the banks just got a reprieve on the timetable and amount of new capital adequacy requirements and other balance-sheet house-cleaning. This observation is not a forecast of a European bank failure, but it is a warning. Other countries can fol-low the example of the U.S. and reduce leverage, improve household, corporate, and bank balance sheets, and prick housing bubbles without the fallout reaching American shores, but dont count on it. Once fear gets a grip, its per-vasive and contagious.

As for the dollar, it doesnt get credit for having a health-ier and faster-growing economy, but it may get credit in the end, so to speak, for having a more stable economic and financial environment. Unfortunately, it will probably take some Euro-negative development to make that point clear, such as some form of ECB easing or a European bank failure.

Until then, the dollar is susceptible to the perception that a global crisis will stay the hand of the Fed and tapering will be postponed or cut back. y

Barbara Rockefeller (www.rts-forex.com) is an international econo-mist with a focus on foreign exchange, and the author of the new book The Foreign Exchange Matrix (Harriman House). For more information on the author, see p. 4.

16 October2010CURRENCY TRADER16 February2014CURRENCY TRADER

Trading systems are often difficult to execute because of their tendency to surrender profits after a position moves into favorable territory a problem especially noticeable in trend-following systems. Many traders watch signifi-cant gains evaporate and turn into losses when a trading system fails to take action when market conditions move against it.

There are different ways to address this problem, but most represent some type of trade-off: Long-term profit-ability is typically sacrificed to accommodate a rule that takes profits in some way as to avoid the psychologically challenging experience of watching winners turn into los-ers.

Traders can turn things in their favor by designing systems that are easier to trade from the start. Here well explore a trading system along these lines that uses a trail-ing stop. Well see how the stop mechanism makes trading easier, and examine its impact on the trading strategys performance.

Entry rulesThe system, which was generated using one-hour data in the Euro/U.S. dollar pair (EUR/USD), uses a few simple rules to execute entries and exits:

Long entry (short exit):1. The hour is 6 (GMT +1 (DST = GMT+2)).2. The high of the previous hourly bar is less than the

open 31 bars ago (High[1] < Open[31]).3. The open nine bars ago is greater than the high 44

bars ago (Open[9] > High[44]).

Short entry (long exit):1. The hour is 6 (GMT +1 (DST = GMT+2)).

2. The low of the previous hourly bar is greater than the open 31 bars ago (Low[1] > Open[31]).

3. The open nine bars ago is less than the low 44 bars ago (Open[9] < Low[44]).

Where,0, 1, 2, etc., reference the most recently closed hourly

bar, the previous bar, the bar two bars ago, etc. A new trade is entered on a new bar whenever one of the sig-nal conditions is met.

The systems initial stop-loss is two times the 20-period average true range (ATR). A trade is closed whenever a stop-loss is hit or a signal in the opposite direction occurs. In the case of a signal in the same direction, the stop-loss is updated as if a new trade had been opened and the cur-rent price bar (which would represent a new entry if there wasnt already an open position) is used to calculate the trailing stop, which well discuss next.

The trailing stopThe systems trailing stop adjusts to price as the market moves in the positions favor that is, moving higher as price rises in a long trade, and moving lower as price declines in a short trade. When price moves a specific dis-tance in this case, two times the 20-period ATR the system moves the trades stop-loss to break-even.

For every favorable move above this ATR profit level, the system places (for a long position) a stop-loss two times the 20-period ATR below the current hourly bars open. This means once the EUR/USD pair reaches the ATR profit threshold, price can never trade below breakeven, but it can oscillate freely above this level and accommo-date potential volatility bursts in the trades favor. (The

TRADING STRATEGIESTRADING STRATEGIES

Trailing stops, curtailing losses

Starting with a trailing stop rule can make your forex system easier to trade.

BY DANIEL FERNANDEZ


stop-loss and trailing-stop values are not optimized.)

Figures 1 and 2 illustrate a sample trade. Figure 1 is a close-up that high-lights the trade-entry setup, which resembles a consolidation pattern. After a successful downside run, Figure 2 shows how the short trade was exited with the trailing stop, which ensured only a limited portion of the open profit was given back as price continued to fall. The next trade (a buy) was exited at breakeven after the EUR/USD pair failed to develop any favorable momentum.

Testing the systemThe system was tested on EUR/USD hourly data from Jan. 1, 1988 to Jan. 1, 2014, with the Deutsche mark/U.S. dollar rate used as a proxy prior to 1999. (Interest earned and paid through swap rates was accounted for in the simulation using historical interest rate values.) The simulation used an initial account balance of $100,000, and risked 1% of account equity on each trades initial stop-loss amount.

The test data was split into two peri-ods: In-sample data from Jan. 1, 1988 to Jan. 1, 2005 was used for strategy generation, while the final eight years were used as an out-of-sample period. The system was generated seeking

FIGURE 1: SHORT-TRADE ENTRY, EUR/USD, 60-MINUTE

This short trade was triggered by the entry setup, which essentially identifies a consolidation pattern.

FIGURE 2: SHORT EXIT, LONG ENTRY, EUR/USD, 60-MINUTE

The short trade was exited with the trailing stop, which prevented losing too much of the open profit. A second, long trade was exited at breakeven.

highly linear in-sample results (i.e., linear regression R2 values greater than 0.98). Note: Other systems cre-ated with this process yielded similar results; this system was picked at ran-dom from the generated pool.

System performanceFigure 3 shows the system, on a non-compounded basis (i.e., always risking $1,000 per trade), produced a highly linear equity curve over the past 26 years a period that encom-passed a wide variety of market conditions. Furthermore, this positive characteristic sustained itself through the out-of-sample period. The R2 value for the entire test in-sample and out-of-sample was 0.96 (see the linear regression line in the chart). Figure 4 shows the systems com-pounded equity curve (i.e., risking 1% per trade).

The system produced a good simulated track record (Table 1). The ratio of overall average yearly profit to maximum drawdown was 0.87 (9.19%/10.56%), with maximum drawdown length of 807 days occur-ring around 1995 (and a maximum drawdown length of less than one year over the past six years). The out-of-sample maximum drawdown length was 565 days. (The in-sample and total maximum drawdown lengths are equal because the longest drawdown occurred in the in-sample period.)

Figure 5 shows the systems annual profits. There were only four los-ing years in the 26-year test period,


TRADING STRATEGIES

FIGURE 4: EQUITY CURVE (COMPOUNDED)

The drawdown near the end of the test period only appears larger because of the effect of compounding.

FIGURE 3: EQUITY CURVE (NON-COMPOUNDED)

The system generated a highly linear equity curve over the wide-ranging simulation period.


and although the system is currently underperforming, it remains within the drawdown boundaries defined by the out-of-sample test. Also, the drawdown near the end of the test in Figure 4, only appears larger because of compound-ing; see the linear chart in Figure 3 to compare this draw-down with previous periods.

The system doesnt trade as much like a higher-frequen-cy, shorter-time frame system as it does like a daily system with fine-tuned entries. This explains why the trailing-stop and stop-loss values are so large (two times the 20-period ATR) and why the system trades infrequently (0.6 trades per week on average, with an average trade duration of six days and 21 hours).

One favorable characteristic of this system is its historically high math-ematical expectancy. The system has an overall reward-to-risk ratio of 1.39 and a winning percentage of 54%. The combination of a reward-to-risk ratio greater than 1.00 and a winning per-centage above 50%, which is a function of the trailing stop, is not very common because it requires a very high per-trade expectancy. This is what makes this system psychologically easier to trade: A system that is expected to win more than 50% of the time is easier to trade than one with a sub-50% win rate.

Easier-to-trade systems using a trailing stopThe trailing stop gives us the opportunity to develop strategies with higher mathematical expectancies, linear-ity, and better out-of-sample testing results. When systems are developed to complement a position-management approach that forces an increase in the signals mathemati-cal expectancy, a better trading strategy is the result. y

Daniel Fernandez is an active trader focusing on forex strategy analysis, particularly algorithmic trading and the mathematical evaluation of long-term system profitability. For more information on the author, see p. 4.

FIGURE 5: ANNUAL RETURNS

There were only four losing years in the 26-year test period.

TABLE 1 All data In-sample Out-of-sample

Average yearly return 9.19% 9.98% 7.65%Total return 809% 377% 90%

No. of trades 840 553 287Profit factor 1.64 1.8 1.55

Max. drawdown 10.56% 8.16% 10.53%Max. drawdown length 807 days 807 days 565 days

Reward-to-risk ratio 1.39 1.48 1.38Win percentage 54% 55% 53%

Ulcer Index 3.17 3.23 3.05Years in test 26 17 6

The systems simulated track record had mostly positive characteristics, out of sample as well as in sample.

20 October2010CURRENCY TRADER

TRADING STRATEGIES

When excess becomes predictable: The majors

Find out whether carry returns from the U.S. dollar, along the money-market yield curve, can predict the excess of implied volatility over historical volatility.

BY HOWARD L. SIMONS


TRADING STRATEGIESADVANCED CONCEPTS

The course of the past four months has been examining whether the skew and smile of currency options could be used in conjunction with those currencies money-market yield curves to predict carry returns from the USD into individual currencies. In general, the skew of the options curve as measured by risk reversals proved useful, while the smile of the options curve as measured by the butterfly did not.

Now lets turn the tables and ask whether carry returns from the USD in conjunction with the money-market yield curve can be used to predict the excess of implied vola-tility over historical volatility. Readers may be familiar with excess volatility, the markets demand for insurance, defined as the ratio of the implied volatility for three-month non-deliverable forwards to high-low-close (HLC) volatility, minus 1.00.

HLC volatility is defined as:

[[.5*(ln(max(H,Ct1)min(L,Ct1)

))2 .39*(ln( CCt1 ))2]*260

N ]1/2

i=1

N

Where Nisthenumberofdaysbetween4and29thatminimizes the function:

1N *

NVol2i=1

N

* | (P MA) | * |MA |

FIGURE 1: THE EURO AND 90-DAY EXCESS VOLATILITY

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1.98

2.00

2.02

2.04

2.06

2.08

2.10

2.12

Jan-

06

Jul-0

6

Feb-

07

Sep

-07

Apr

-08

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug

-11

Mar

-12

Oct

-12

May

-13

Nov

-13

90-Day Excess Volatility Led 3 M

onths

Log 1

0 USD

Car

ry R

etur

n In

to E

UR

, Jan

. 4, 2

006

= 2.

00 Excess Volatility

Carry

FIGURE 2: THE JAPANESE YEN AND 90-DAY EXCESS VOLATILITY

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1.94

1.96

1.98

2.00

2.02

2.04

2.06

2.08

2.10

2.12

2.14

Jan-

06

Jul-0

6

Feb-

07

Sep-

07

Apr-0

8

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug-

11

Mar

-12

Oct

-12

May

-13

Nov

-13

90-Day Excess Volatility Led 3 Months

Log 1

0 USD

Car

ry R

etur

n In

to J

PY, J

an. 4

, 200

6 =

2.00

Excess Volatility

Carry

CURRENCY TRADEROctober2010 21CURRENCY TRADERFebruary2014 21

As our target now is to predict a measure of excess volatil-ity with the ultimate objective of trading it, lets switch from this dynamic measure to a simple ratio of 90-day volatility to 90-day realized volatility, minus 1.00. The first section of the following discussion will be devoted to mapping returns on the major currencies as the common logarithm of the total carry return from the U.S. dollar into those currencies rein-dexed to January 2006. This both approximates the return path of a continuous currency future and allows for the more intuitively appealing rising line depicting a stronger currency.

The second section will look at this measure of excess volatility as a function of the carry return over the past three months and of the lagged value of the money-market yield curve as measured by the forward rate ratio between six and nine months (FRR6,9) for the major currencies. The FRR6,9 is the rate at which we can lock in borrowing for three months starting six months from now, divided by the nine-month rate itself. The steeper the yield curve, the more this ratio exceeds 1.00; an inverted yield curve has an FRR6,9 less than 1.00.

Excess volatility and returnsThe Euros excess volatility appears to be an asymmetric process with positive spikes greatly exceeding negative ones (Figure 1). The general ebb and flow of excess volatility with a three-month lag to carry returns is expected, but the increas-ing demand for insurance when carry returns rose in early 2008, 2009, and again in mid-2011 is somewhat surprising. This pattern of increasing anxiety in the face of a currencys strength is far more common for emerging market and minor currencies, not for what is still the second-most important currency in the global system.

Critically, when carry returns start to rise from low levels, excess volatility tends to rise as well; this suggests option strategies involving long Euro volatility positions would work.

The yens excess volatility was symmetric prior to the adop-tion of quantitative easing by the U.S., UK, and Switzerland in March 2009 (Figure 2). It then shifted to an asymmetric

FIGURE 3: THE CANADIAN DOLLAR AND 90-DAY EXCESS VOLATILITY

-35%

-25%

-15%

-5%

5%

15%

25%

35%

45%

55%

65%

1.92

1.94

1.96

1.98

2.00

2.02

2.04

2.06

2.08

2.10

Jan-

06

Jul-0

6

Feb-

07

Sep-

07

Apr-0

8

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug-

11

Mar

-12

Oct

-12

May

-13

Nov

-13


Log 1

0 USD

Car

ry R

etur

n In

to C

AD, J

an. 4

, 200

6 =

2.00

Excess Volatility

Carry

FIGURE 4: THE AUSTRALIAN DOLLAR AND 90-DAY EXCESS VOLATILITY

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

1.92 1.94 1.96 1.98 2.00 2.02 2.04 2.06 2.08 2.10 2.12 2.14 2.16 2.18 2.20 2.22 2.24 2.26 2.28

Jan-

06

Jul-0

6

Feb-

07

Sep-

07

Apr-0

8

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug-

11

Mar

-12

Oct

-12

May

-13

Nov

-13


Log 1

0 USD

Car

ry R

etur

n In

to A

UD, J

an. 4

, 200

6 =

2.00

Excess Volatility

Carry

FIGURE 5: THE SWISS FRANC AND 90-DAY EXCESS VOLATILITY

-45%

-35%

-25%

-15%

-5%

5%

15%

25%

35%

45%

55%

65%

75%

1.98

2.00

2.02

2.04

2.06

2.08

2.10

2.12

2.14

2.16

2.18

2.20

2.22

Jan-

06

Jul-0

6

Feb-

07

Sep-

07

Apr-0

8

Oct

-08

May

-09

Dec-

09

Jul-1

0

Jan-

11

Aug-

11

Mar

-12

Oct

-12

May

-13

Nov-

13


Log 1

0 USD

Car

ry R

etur

n In

to C

HF, J

an. 4

, 200

6 =

2.00

Excess Volatility Carry


ON THE MONEY ON THE MONEY


ADVANCED CONCEPTS

pattern as Japan began a series of attempts to drive the JPY lower in late 2012 and early 2013. These attempts pushed excess volatility to negative levels as implied volatility declined in the face of predictable policy.

The pattern for the Canadian dollar has been very dif-ferent (Figure 3). Not only has excess volatility been a symmetric affair, its positive spikes have been unrelated to changes in the CADs carry return. However, just as in the case of the EUR, shifts higher in the carry return have led to shifts higher in the excess volatility measure. A stronger CAD invites a long volatility position.

The pattern for the Australian dollar retains both the symmetry of the Canadian dollars excess volatility and its tendency to move higher after carry returns move higher, but adds irregular episodes of positive spikes following gains in the AUDs carry return (Figure 4).

We should expect the pattern for the Swiss franc to have been distorted by the September 2011 imposition of the franc ceiling and simultaneous pledge to print the CHF in unlimited quantities to enforce that pledge (Figure 5). Excess volatility plunged to deeply negative levels on the imposition of the ceiling and then jumped as 90-day real-ized volatility, the fractions denominator, fell.

The Swedish krona had one of the cleanest patterns and direct linkages to carry returns of any currency prior to the May 2010 backstopping of Greece (Figure 6). Then the SEK became a safe-haven currency, albeit not as much as the CHF was, and implied volatility fell as the carry return moved higher. The market simply accepted the SEKs strength and did not fear a Swiss-like response to it.

Finally, the linkage between the carry into the British pound and its excess volatility has been very direct throughout the data sample (Figure 7). Excess volatility has been asymmetric in a manner very similar to Japans since the U.K. began its quantitative easing program in March 2009. While the U.K. has not engaged in direct and public campaigns to weaken the GBP as Japan has with

FIGURE 8 : 90-DAY EXCESS VOLATILITY FOR THE EURO

FIGURE 6: THE SWEDISH KRONA AND 90-DAY EXCESS VOLATILITY

-30%

-20%

-10%

0%

10%

20%

30%

40%

50%

60%

1.90

1.92

1.94

1.96

1.98

2.00

2.02

2.04

2.06

2.08

2.10

2.12

Jan-

06

Jul-0

6

Feb-

07

Sep

-07

Apr

-08

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug

-11

Mar

-12

Oct

-12

May

-13

Nov

-13


onths

Log 1

0 USD

Car

ry R

etur

n In

to S

EK, J

an. 4

, 200

6 =

2.00

Excess Volatility

Carry

FIGURE 7: THE BRITISH POUND AND 90-DAY EXCESS VOLATILITY

-25%

-15%

-5%

5%

15%

25%

35%

45%

55%

65%

75%

1.90

1.92

1.94

1.96

1.98

2.00

2.02

2.04

2.06

2.08

Jan-

06

Jul-0

6

Feb-

07

Sep

-07

Apr

-08

Oct

-08

May

-09

Dec

-09

Jul-1

0

Jan-

11

Aug

-11

Mar

-12

Oct

-12

May

-13

Nov

-13


onths

Log 1

0 USD

Car

ry R

etur

n In

to G

BP,

Jan

. 4, 2

006

= 2.

00 Excess Volatility

Carry

CURRENCY TRADEROctober2010 23CURRENCY TRADERFebruary2014 23

the yen, no one has had to question the bias of British mon-etary policy.

Leading indications of excess volatilityNow lets look at excess volatility for each of the major currencies as a function of the previous three months carry return and three-month-ago values of its FRR6,9. In Figures 8 through 14, positive levels of excess volatility are depicted with green bubbles, negative levels with red bubbles; the diameter of the bubble corresponds to the absolute magnitude of the excess volatility level. The last datum used is highlighted and the current environment is depicted with a crosshair.

The map for the Euro affirms the earlier observation neg-ative carry returns lead positive movements in excess vola-tility (Figure 8). Observations of negative excess volatility cluster in zones of positive carry returns combined with slightly inverted yield curves. This cluster is too isolated and too small to be of much direct trading use.

The yen has a large band of negative excess volatility readings at FRR6,9 levels less than 0.90; these become inter-spersed with positive observations once the previous three months carry moves over 5.0% (Figure 9). The region of the map with FRR6,9 levels between 0.90 and 1.25 is domi-nated heavily by positive excess volatility levels. These defined clusters suggest direct volatility-trading strategies are available.

The CADs map is somewhat discouraging for volatil-ity trading (Figure 10). The clusters of excess volatility are very well-defined but they are too interspersed with each other to invite direct long- or short-volatility positions.

The Australian dollar, however, has an almost direct split along the dimension of the yield curve for positive and negative excess volatility levels (Figure 11). If the AUD FRR6,9 was below 1.05, implied volatility was relatively cheap. Interestingly, the previous three-month carry returns were largely irrelevant for the AUD.

FIGURE 11 : EXCESS VOLATILITY FOR THE AUSTRALIAN DOLLAR

FIGURE 10 : EXCESS VOLATILITY FOR THE CANADIAN DOLLAR

FIGURE 9 : EXCESS VOLATILITY FOR THE JAPANESE YEN


ON THE MONEY ON THE MONEY


ADVANCED CONCEPTS

We can and probably should ignore the Swiss franc unless we have reason to believe future policies will be as dominant as the franc ceiling has been (Figure 12). The large cluster of negative excess volatility levels in the northwest corner of the map were the direct result of the unusual market conditions created in September 2011.

The map for the Swedish krona has two well-defined zones based on previous returns outside of an absolute 15% range (Figure 13). Positive carry returns lead to negative excess volatility and vice versa.

Finally, the map for the British pound has clean divi-sions along both dimensions (Figure 14). With the excep-tion of a small cluster of negative excess volatility, a GBP FRR6,9 greater than 0.90 following negative carry returns is associated with positive excess volatility. Flatter yield curves have the same mean-reverting division at abso-lute previous carry returns greater than 15% as seen for the SEK. The available data sample suggests the GBP is highly amenable to volatility trading.

The response of excess volatility to both the yield curve and carry returns seen for the majors should not be surprising. Both the very steep yield curves seen in recent years and the less common inverted yield curves are responses to monetary policies often seen as tempo-rary and unstable; these opinions lead traders to insure themselves against policy reversals. Similarly, many strong currency moves in either direction are the result of policies as well.

It would be nicer if the responses seen across curren-cies were more uniform and less anecdotal, but this may be asking far too much in a world where central banks and governments dominate short-term interest rate and currency markets. The key for traders is interpreting whether markets find the latest policy moves stable or not. If not, implied volatility will rise and dictate a long volatility position; if so, short volatility positions are in order.

The responses of selected minor currencies will be examined next month. y

Howard Simons is president of Rosewood Trading Inc. and a strategist for Bianco Research. For more information on the author, see p. 4.

FIGURE 14 : EXCESS VOLATILITY FOR THE BRITISH POUND

FIGURE 13 : EXCESS VOLATILITY FOR THE SWEDISH KRONA

FIGURE 12: EXCESS VOLATILITY FOR THE SWISS FRANC

CURRENCY TRADERFebruary2014 2511-IB13-692

Member - NYSE, FINRA, SIPC. Lower investment costs will increase your overall return on investment, but lower costs do not guarantee that your investment will be profitable Supporting documentation for any claims and statistical information will be provided upon request. The settlement date of foreign exchange trades can vary due to time zone differences and bank holidays. When trading across foreign exchange markets, this may neces-sitate borrowing funds to settle foreign exchange trades. The interest rate on borrowed funds must be considered when computing the cost of trades across multiple markets.

For more information visit

interactivebrokers.com/forex

There is a substantial risk of loss in foreign exchange trading. Past performance is not necessarily indicative of future results.

The same philosophy and technology that enables our clients to achieve superior forex trading results also drives our clients trading and investing of stocks, options, futures, and bonds worldwide on over 100 markets.

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Interactive Brokers

Data provided by forexmagnates.com, includes the impact of any commissions

% Profit % LossTotal

AccountsSpread

MarkupsQ3 2013

Percentage of profitable and unprofitable accounts as reported to the NFA

Interactive Brokers OANDA

FXDD

ILQ

Gain Capital

IBFX/TradeStation

FXCM

MB Trading

44.0%

35.1%

29.1%

22.3%

31.0%

32.0%

28.0%

27.2%

56.0%

64.9%

70.9%

77.7%

69.0%

68.0%

72.0%

72.8%

23,759

20,812

5,118

1,138

11,425

8,718

22,055

3,365

NO

YES

YES

YES

YES

YES

YES

YES

Lower your costs to maximize your return


CPI: Consumer price indexECB: European Central BankFDD(firstdeliveryday):Thefirstday on which delivery of a com-modityinfulfillmentofafuturescontract can take place.FND(firstnoticeday):Alsoknownasfirstintentday,thisisthefirstdayonwhichaclear-inghouse can give notice to a buyer of a futures contract that it intends to deliver a commodity in fulfillmentofafuturescontract.The clearinghouse also informs the seller.FOMC: Federal Open Market CommitteeGDP: Gross domestic productISM: Institute for supply management LTD(lasttradingday):Thefinalday trading can take place in a futures or options contract.PMI: Purchasing managers indexPPI: Producer price index

Economic Release release(U.S.) time(ET)GDP 8:30 a.m.CPI 8:30 a.m.ECI 8:30 a.m.PPI 8:30 a.m.ISM 10:00 a.m.Unemployment 8:30 a.m.Personal income 8:30 a.m.Durable goods 8:30 a.m.Retail sales 8:30 a.m.Trade balance 8:30 a.m.Leading indicators 10:00 a.m.

GLOBAL ECONOMIC CALENDAR

February12

3U.S.: January ISM manufacturing reportCanada: December PPI

45

6

U.S.: December trade balanceUK: Bank of England interest-rate announcementECB: Governing council interest-rate announcement

7

U.S.: January employment reportBrazil: January CPI and PPICanada: January employment reportLTD: February forex options; February U.S. dollar index options (ICE)

89

10 Mexico: Jan.31CPIandJanuaryPPI11 South Africa: Q4 employment report12

13

U.S.: January retail salesAustralia: January employment reportGermany: January CPIJapan: January PPI

14Germany: Q4 GDPIndia: January PPIJapan: Bank of Japan interest-rate announcement

151617 Japan: Q4 GDP

18Hong Kong: November-January employment reportUK: January CPI and PPI

19U.S.: January PPI and housing startsSouth Africa: January CPIUK: January employment report

20

U.S.: January CPI and leading indicatorsBrazil: January employment reportFrance: January CPIGermany: January PPI

21Canada: January CPIHong Kong: January CPIMexico: Q4 GDP and January employment report

22232425 Mexico: Feb. 15 CPISouth Africa: Q4 GDP26 Hong Kong: Q4 GDP

27

U.S.: January durable goodsBrazil: Q4 GDPGermany: January employment reportSouth Africa: January PPI

28

U.S.: Q4GDP(second)Canada: Q4 GDPFrance: January PPIIndia: Q4 GDP and January CPIJapan: January employment report and CPI

March12

3U.S.: January personal income and February ISM manufacturing reportCanada: January PPI

4

5U.S.: Fed beige bookAustralia: Q4 GDPCanada: Bank of Canada interest-rate announcement

The information on this page is sub-ject to change. Currency Trader is not responsible for the accuracy of calendar dates beyond press time.

Event: The Traders Expo New YorkDate: Feb. 16-18Location: New YorkFor more information: Go to www.moneyshow.com

Event: 39thAnnualInternational Futures Industry ConferenceDate: March 11-14Location: Boca Raton Resort & Club, Boca Raton, Fla.For more information: Go to www.futuresindustry.org

Event: The MoneyShow Las VegasDate: May 12-15Location: Caesars Palace, Las VegasFor more information: Go to www.moneyshow.com

Event: The MoneyShow San Francisco Date:Aug.21-23Location: San Francisco For more information: Go to www.moneyshow.com

EVENTS


CURRENCY FUTURES SNAPSHOT as of Jan. 31

The information does NOT constitute trade signals. It is intended only to provide a brief synopsis of each markets liquidity, direction, and levels of momentum and volatility. See the legend for explanations of the different fields. Note: Average volume and open interest data includes both pit and side-by-side electronic contracts (where applicable).

LEGEND:Volume: 30-day average daily volume, in thousands.OI: 30-day open interest, in thousands.10-day move: The percentage price move from the close 10 days ago to todays close.20-day move: The percentage price move from the close 20 days ago to todays close.60-day move: The percentage price move from the close 60 days ago to todays close.The % rank fields for each time window (10-day moves, 20-day moves, etc.) show the percentile rank of the most recent move to a certain number of the previous moves of the same size and in the same direction. For example, the % rank for the 10-day move shows how the most recent 10-day move compares to the past twenty 10-day moves; for the 20-day move, it shows how the most recent 20-day move compares to the past sixty 20-day moves; for the 60-day move, it shows how the most recent 60-day move compares to the past one-hundred-twenty 60-day moves. A reading of 100% means the current reading is larger than all the past readings, while a reading of 0% means the current reading is smaller than the previous readings. Volatility ratio/% rank: The ratio is the short-term volatility (10-day standard deviation of prices) divided by the long-term volatility (100-day standard deviation of prices). The % rank is the percentile rank of the volatility ratio over the past 60 days.

BarclayHedge Rankings: Top 10 currency traders managing more than $10 million

(as of Dec. 31 ranked by December 2013 return)

Trading advisor Decemberreturn 2013 YTD

return$ Under mgmt.

(millions)

1 Gedamo(FXAlpha) 4.71% 22.80% 39.92 AlderCap'l(AlderGlobal20) 3.60% 17.40% 504.03 CenturionFxLtd(6X) 3.50% 40.63% 41.3

4 SequoiaCapitalFundMgmt(FX) 2.90% 3.46% 77.85 DynexCorp(Currency) 2.27% 9.39% 30.06 AlderCap'l(AlderGlobal10) 2.20% 9.37% 15.07 Gedamo(FXOne) 2.01% 12.68% 51.68 Trigon(ForeignExchange) 1.76% 0.44% 103.49 FDOPartners(EmergingMarkets) 1.28% 7.82% 2135.0

10 CambridgeStrategy(EmergingMkts) 1.12% -0.29% 561.0Top 10 currency traders managing less than $10M & more than $1M

1 InvestmentCapitalAdv(ManagedActs) 15.30% 104.73% 5.52 Fornex(Foyle) 6.28% 43.55% 4.03 GloranisGmbH(ForexPrivate1) 2.43% 3.26% 2.3

4 BlueFinCapital(ManagedFX) 2.10% 0.92% 2.05 AGBisset(DefensiveAlpha) 1.60% -17.49% 1.06 SmartBoxCapital(LeveragedFX) 0.99% -19.31% 1.17 HartswellCapitalMgmt(Athena) 0.78% 9.87% 1.18 CambridgeStrategy(ExtendedMkts) 0.73% -10.57% 4.09 AltusTrading(ManagedForexA) 0.67% 5.68% 2.2

10 OrwellCapital(CurrencyAlpha) 0.64% 0.19% 8.5

Based on estimates of the composite of all accounts or the fully funded subset method.Does not reflect the performance of any single account.PAST RESULTS ARE NOT NECESSARILY INDICATIVE OF FUTURE PERFORMANCE.

Market Sym Exch Vol OI 10-day move / rank20-day

move / rank60-day

move / rankVolatility

ratio / rank

EUR/USD EC CME 176.0 241.4 -0.95%/88% -1.23%/61% -0.25%/5% .58/93%

JPY/USD JY CME 129.0 224.0 1.93%/77% 2.30%/75% -3.69%/52% .31/48%GBP/USD BP CME 88.4 198.5 0.47%/40% 0.09%/0% 2.90%/41% .32/90%AUD/USD AD CME 78.9 123.3 -0.69%/23% -1.61%/25% -8.02%/93% .12/3%CAD/USD CD CME 60.8 152.6 -1.83%/33% -4.10%/95% -6.33%/98% .25/15%MXN/USD MP CME 32.2 117.5 -0.43%/11% -1.03%/50% -2.54%/49% .38/28%CHF/USD SF CME 29.8 44.9 -0.31%/0% -0.75%/27% 0.24%/6% .63/98%U.S. dollar index DX ICE 20.9 44.8 0.05%/13% 0.55%/35% 1.05%/59% .69/100%NZD/USD NE CME 11.5 20.5 -3.20%/100% -0.96%/43% -2.46%/85% .84/100%E-Mini EUR/USD ZE CME 5.1 4.9 -0.95%/88% -1.23%/61% -0.25%/5% .58/93%

Note:Averagevolumeandopeninterestdataincludesbothpitandside-by-sideelectroniccontracts(whereapplicable).Priceactivityisbased on pit-traded contracts.

INTERNATIONAL MARKETS


CURRENCIES (vs. U.S. DOLLAR)

Rank CurrencyJan. 30

price vs. U.S. dollar

1-month gain/loss

3-monthgain/loss

6-monthgain/loss

52-week high

52-week low Previous

1 Japanese yen 0.00973 2.31% -4.89% -4.70% 0.011 0.0095 172 New Zealand dollar 0.827185 1.50% 0.06% 2.57% 0.8619 0.7704 93 Swedish krona 0.15526 1.08% -1.18% 0.46% 0.159 0.1464 74 Great Britain pound 1.65711 0.53% 3.01% 7.79% 1.6588 1.4877 25 Taiwan dollar 0.033415 0.41% -1.74% 0.00% 0.0341 0.0326 126 Chinese yuan 0.16386 0.18% 0.38% 1.17% 0.1642 0.1588 67 Hong Kong dollar 0.128805 -0.11% -0.13% -0.09% 0.129 0.1288 88 Thai baht 0.030385 -0.25% -5.62% -5.31% 0.0348 0.0302 14

9 Singapore dollar 0.78473 -0.44% -2.75% -0.67% 0.8136 0.7792 11

10 Euro 1.365875 -0.66% -0.82% 2.90% 1.3802 1.2798 311 Swiss franc 1.113775 -0.73% -0.10% 3.50% 1.1277 1.0274 112 Indian rupee 0.016 -1.02% -2.02% -4.93% 0.0188 0.0147 513 Australian Dollar 0.87768 -1.04% -7.72% -5.01% 1.0545 0.8677 16

14 Brazilian real 0.41131 -3.82% -10.31% -7.13% 0.5137 0.4082 15

15 Canadian dollar 0.8969 -3.97% -6.31% -7.89% 1.0035 0.8968 1016 South African rand 0.090295 -4.85% -10.96% -11.57% 0.1134 0.0897 1317 Russian ruble 0.028675 -6.52% -8.27% -5.94% 0.0337 0.028675 4

GLOBAL STOCK INDICES

Country Index Jan. 30 1-month gain/loss3-month gain/loss

6-month gain loss

52-week high


1 Italy FTSE MIB 19,411.60 2.34% 1.28% 17.34% 19,697.20 15,056.60 92 Canada S&P/TSX composite 13,735.30 1.13% 2.08% 9.17% 14,002.40 11,759.00 73 Switzerland Swiss Market 8,205.00 0.02% -0.28% 5.05% 8,544.10 7,247.30 114 Germany Xetra Dax 9,373.48 -1.87% 4.03% 13.33% 9,794.05 7,418.36 45 South Africa FTSE/JSE All Share 45,178.25 -2.07% -0.95% 10.21% 47,045.44 38,630.54 36 France CAC 40 4,180.02 -2.24% -2.20% 4.85% 4,356.28 3,575.17 127 U.S. S&P 500 1,794.19 -2.55% 1.75% 6.42% 1,849.44 1,485.01 68 UK FTSE 100 6,538.50 -2.86% -3.53% -0.49% 6,875.60 6,023.40 89 Australia All ordinaries 5,199.40 -2.96% -4.17% 3.44% 5,453.10 4,610.60 10

10 India BSE30 20,498.25 -3.05% -2.55% 5.94% 21,483.70 17448.70 211 Singapore Straits Times 3,027.22 -4.00% -6.29% -6.72% 3,464.79 2,990.68 1312 Mexico IPC 41,008.30 -4.54% -0.10% 1.92% 45,811.50 37,034.30 513 Hong Kong Hang Seng 22,035.42 -5.20% -5.44% 0.37% 24,111.60 19,426.40 1514 Japan Nikkei 225 15,007.06 -7.88% 3.48% 8.20% 16,320.20 11,007.80 115 Brazil Bovespa 47,244.00 -8.28% -12.79% -2.71% 60,496.00 44,107.00 14


NON-U.S. DOLLAR FOREX CROSS RATES

Rank Currency pair Symbol Jan. 30 1-month gain/loss3-month gain/loss

6-month gain loss

52-week high


1 Yen / Real JPY/BRL 0.02364 6.32% 5.99% 2.52% 0.0248 0.0196 16

2 Pound / Canada $ GBP/CAD 1.847595 4.69% 9.94% 17.02% 1.847595 1.5286 9

3 Euro / Canada $ EUR/CAD 1.522885 3.45% 5.86% 11.71% 1.522885 1.3005 11

4 Franc / Canada $ CHF/CAD 1.2418 3.38% 6.63% 12.36% 1.2418 1.0528 8

5 Euro / Real EUR/BRL 3.320805 3.28% 10.58% 10.80% 3.320805 2.5251 6

6 Aussie $ / Canada $ AUD/CAD 0.97857 3.06% 3.12% 3.12% 1.0685 0.9224 19

7 Aussie $ / Real AUD/BRL 2.133875 2.89% 2.89% 2.28% 2.1995 1.9633 14

8 Pound / Aussie $ GBP/AUD 1.88806 1.59% 11.62% 13.48% 1.9013 1.4439 3

9 Pound / Franc GBP/CHF 1.487835 1.28% 3.11% 4.14% 1.504 1.4062 15

10 Euro / Aussie $ EUR/AUD 1.556245 0.39% 7.48% 8.33% 1.5744 1.2251 5

11 Euro / Franc EUR/CHF 1.226355 0.06% -0.72% -0.58% 1.256 1.2136 18

12 Canada $ / Real CAD/BRL 2.1780605 -0.28% 4.34% -0.93% 2.3271 1.8879 12

13 Aussie $ / Franc AUD/CHF 0.788025 -0.31% -7.63% -8.22% 0.9942 0.7755 21

14 New Zeal $ / Yen NZD/JPY 85.05 -0.76% 5.22% 7.71% 87.15 75.57 7

15 Euro / Pound EUR/GBP 0.824255 -1.16% -3.71% -4.53% 0.8747 0.8201 17

16 Pound / Yen GBP/JPY 170.38 -1.70% 8.32% 13.18% 174.35 138.9 2

17 Aussie $ / New Zeal $ AUD/NZD 1.060965 -2.51% -7.77% -7.40% 1.2643 1.0548 20

18 Euro / Yen EUR/JPY 140.44 -2.85% 4.30% 8.05% 145.12 119.64 4

19 Franc / Yen CHF/JPY 114.515 -2.92% 5.05% 8.67% 118.41 98.05 1

20 Aussie $ / Yen AUD/JPY 90.245 -3.20% -2.95% -0.25% 105.05 87.02 13

21 Canada $ / Yen CAD/JPY 92.215 -6.12% -1.47% -3.28% 100.65 89.5 10

GLOBAL CENTRAL BANK LENDING RATES

Country Interest rate Rate Last change July 2013 Jan. 2013United States Fed funds rate 0-0.25 0.5(Dec08) 0-0.25 0-0.25

Japan Overnight call rate 0-0.1 0-0.1(Oct10) 0-0.1 0-0.1Eurozone Refi rate 0.25 0.25(Nov13) 0.5 0.75England Repo rate 0.5 0.5(Mar09) 0.5 0.5Canada Overnight rate 1 0.25(Sep10) 1 1

Switzerland 3-monthSwissLibor 0-0.25 0.25(Aug11) 0-0.25 0-0.25Australia Cash rate 2.5 0.25(Aug13) 2.75 3

New Zealand Cash rate 2.5 0.5(Mar11) 2.5 2.5Brazil Selic rate 10.5 0.5(Jan14) 8.5 7.25Korea Korea base rate 2.5 0.25(May13) 2.5 2.75Taiwan Discount rate 1.875 0.125(Jun11) 1.875 1.875India Repo rate 8 0.25(Jan14) 7.25 7.75

South Africa Repurchase rate 5.5 0.5(Jan14) 5 5


INTERNATIONAL MARKETS

GDP Period Release date Change 1-year change Next release

AMERICASArgentina Q3 12/20 -6.7% 24.2% 3/20

Brazil Q3 12/3 -0.4% 10.6% 2/27Canada Q3 11/29 0.9% 3.5% 2/28

EUROPEFrance Q3 12/24 -0.1% 0.1% 3/28

Germany Q3 12/14 0.5% 3.3% 2/14UK Q3 12/20 0.8% 1.9% 3/26

AFRICA S. Africa Q3 11/26 1.5% 8.0% 2/25

ASIA and S. PACIFIC

Australia Q3 12/4 0.6% 2.3% 3/5Hong Kong Q3 11/15 6.3% 2.9% 2/26

India Q3 11/29 3.4% 12.0% 2/28Japan Q3 11/14 0.4% 1.6% 2/17

Singapore Q3 11/22 0.3% 6.3% NLT 2/21

Unemployment Period Release date Rate Change 1-year change Next release

AMERICASArgentina Q3 11/18 6.8% -0.4% -0.8% 2/19

Brazil Dec. 1/30 4.3% -0.3% -0.3% 2/20Canada Dec. 1/10 7.2% 0.3% 0.1% 2/7

EUROPEFrance Q3 12/11 10.5% 0.1% 0.6% 3/6

Germany Dec. 1/7 4.9% -0.1% -0.4% 2/27UK Sep.-Nov. 1/22 7.1% -0.6% -0.6% 2/19

ASIA and S. PACIFIC

Australia Dec. 1/16 5.8% 0.0% 0.4% 2/13Hong Kong Oct.-Dec. 1/20 3.2% -0.1% 0.0% 2/18

Japan Dec. 1/31 3.7% -0.3% -0.6% 2/28Singapore Q4 1/29 1.8% 0.1% 0.0% 4/30

CPI Period Release date Change 1-year change Next release

AMERICASArgentina Dec. 1/15 1.4% 11.0% 2/13

Brazil Dec. 1/10 0.9% 5.9% 2/7Canada Dec. 1/24 -0.2% 1.2% 2/21

EUROPEFrance Dec. 1/14 0.2% 0.0% 2/20

Germany Dec. 1/16 0.4% 1.4% 2/13UK Dec. 1/14 0.4% 2.0% 2/18

AFRICA S. Africa Dec. 1/22 0.3% 5.4% 2/19

ASIA and S. PACIFIC

Australia Q4 1/22 0.8% 2.7% 4/23Hong Kong Dec. 1/21 0.5% 4.3% 2/21

India Dec. 1/31 -1.6% 9.1% 2/28Japan Dec. 1/31 0.1% 1.6% 2/28

Singapore Dec. 1/1 0.3% 1.5% 2/24

PPI Period Release date Change 1-year change Next release

AMERICASArgentina Dec. 1/15 1.5% 14.7% 2/14Canada Dec. 1/6 -0.3% 0.8% 2/3

EUROPEFrance Dec. 1/31 0.3% 0.7% 2/28

Germany Dec. 1/20 0.1% -0.5% 2/20UK Dec. 1/14 0.0% 1.0% 2/18

AFRICA S. Africa Dec. 1/30 0.5% 6.5% 2/27

ASIA and S. PACIFIC

Australia Q4 1/31 0.2% 1.9% 5/2Hong Kong Q3 12/12 -2.6% -5.2% 3/13

India Dec. 1/16 -0.7% 5.6% 2/19Japan Dec. 1/16 0.3% 2.5% 2/13

Singapore Dec. 1/27 0.8% 2.0% 2/28 As of Jan. 31 LEGEND: Change: Change from previous report release. NLT: No later than. Rate: Unemployment rate.


Date: Jan. 9, Jan. 14, and Jan. 22, 2014.

Entry: Short the March British pound/U.S. dollar pair (BPH14) futures at 1.6465, 1.6438, and 1.6535.

Reason for trade/setup: These three trades were predicated on the analysis in the January Spot Check article, which described a (mild) historical tendency for weakness in the pound/dollar pair in January, along with two other factors: the pair had reached the upper end of its three-year trading range, and models of the pairs rally over the preceding weeks indicated favorable odds of down movement over an approximately eight-week horizon.

Januarys initial trading seemed to support the analysis. The pair sold off the first three days of the month, and a plan was put in place to sell on subsequent strength (near resistance), with the goal of taking profits on multiple trades on an anticipated decline to around 1.6200, the mid-December low i.e., somewhat discretionary swing trading based on quantitative analysis of the pounds price action.

The first trade opportunity came on Jan. 9, the third day of a bounce off the Jan. 6 low. All positions were executed in British pound futures with limit orders.

Initial stop (first entry): 1.6646 (a little below the high of the big Jan. 2 down bar).

Initial target (first entry): 1.6320 (just above the Jan. 6 swing low).

Exit: 1.6358 (first entry), 1.6326 (second entry), 1.6482 (third entry, position still open).

Profit/loss: +.0053, marked to market at 1.6434 at 4:15 p.m. ET on Jan. 31, 2014.

Outcome: The first two trades were routine. After a minor bump higher after the first entry, the pound swung lower on Jan. 13 close enough to the initial profit target to take profits, although the position was not exited very efficient-ly. The second entry on Jan. 14 was exited the following day on a similar-sized down swing.

The third entry was established on the jump to a new high on Jan. 22. The rally to 1.6662 early on Jan. 24 might have challenged the credulity of the sell strength game plan, but the pair dropped nearly 200 ticks later in the day after Bank of England Governor Mark Carney stated UK interest rates were likely to remain low for quite a while.Overall, the pair moved mostly sideways to higher in January, rather than down. On Jan. 31, the third position was still open, and the pound had declined to around 1.6434 near the end of the trading session again push-ing the pair in the red for the month, but mostly because it had closed so high at the end of December. With this move, the stop was lowered to a little below breakeven.yNote: Initial trade targets are typically based on things such as the historical per-formance of a price pattern or a trading system signal. However, because individ-ual trades are dictated by immediate circumstances, price targets are flexible and are often used as points at which to liquidate a portion of a trade to reduce expo-sure. As a result, initial (pre-trade) reward-risk ratios are conjectural by nature.

Taking a swing at the pound/dollar pair.

FOREX TRADE JOURNAL

Source: TradeStation

TRADE SUMMARY

DateCurrency

pairEntryprice

Initial stop

Initial target

IRRExit/ MTM

DateP/L

LOP LOLTradelengthpoint %

1/9/14 BPH14 1.6465 1.6320 1.6646 1.25 1.6358 1/13/14 0.0107 0.65% 0.0127 -0.0044 4

1/13/14 1.6438 1.6326 1/15/14 0.0112 0.68% 0.0125 -0.0020 2

1/22/14 1.6535 1.6434 1/31/14 0.0101 0.61% 0.0113 -0.0127 9

Legend IRR: initial reward/risk ratio (initial target amount/initial stop amount). LOP: largest open profit (maximum available profit during lifetime of trade). LOL: largest open loss (maximum potential loss during life of trade). Trade length: duration of trade in calendar days. MTM: marked to market (the trades open profit or loss at a given point in time).

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CTM201402

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