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8/6/2019 Profitability and the Impact of Complexity on Technical Trading Systems in the Foreign Exchange Market
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PROFITABILITY AND THE IMPACT OF COMPLEXITY ON TECHNICAL TRADING
SYSTEMS IN THE FOREIGN EXCHANGE MARKET
Brian Kenneth Edward Leip*
[May 2011]
* Brian Leip is an undergraduate student in the College of Business Administration HonorsProgram at California State University, Long Beach, CA 90840. This manuscript serves to fulfillhis Honors Thesis requirement. Address correspondence to Brian Leip: [email protected].
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Technical Trading Systems in the Forex Market i
ACKNOWLEDGEMENTS
I must begin by offering my deepest gratitude towards Dr. Pamela Miles Homer, Director of
the CSULB and my thesis sponsor, without whom this thesis would likely not have been
completed. Her deep understanding of the academic thesis process coupled with her persistence
and constant encouragement was vital in seeing this paper through to completion. I would also
like to thank Dr. Peter Ammermann for supporting me in the CSULB Student Research
Competition as well as giving a final review of this paper. Dr. Sam Min also deserves my
genuine thanks for the time he spent guiding the CBA Honors Program while Dr. Homer was on
sabbatical.
I conclude with a heartfelt thanks to my friends, parents, siblings, and girlfriend who
patiently supported me throughout this most challenging endeavor. Completing my thesis
demanded a significant portion of my time. Their kind words, understanding, and encouragement
made the sacrifices easier to bear and the joy of completing the thesis that much greater.
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Technical Trading Systems in the Forex Market 1
PROFITABILITY AND THE IMPACT OF COMPLEXITY ON TECHNICAL TRADING
SYSTEMS IN THE FOREIGN EXCHANGE MARKET
ABSTRACT
This study examines the profitability of 63 publicly available automated technical trading
strategies across six complexity levels over a 35-year period (1/1/1975 to 6/30/2010). The
strategies are tested on the spot foreign exchange market where technical analysis usage is most
prevalent. Prior studies on technical analysis in the foreign exchange market argue that simple
technical trading systems generate excess profits that are eroded over time to near zero, yet more
complex technical trading systems are profitable and are able to retain profitability over time.
However, to my knowledge, the impact of complexity has not been tested empirically.
Complexity is here quantified (operationalized) and its impact on risk-adjusted excess profits is
tested.
In addition, the scope of technical trading systems studied in the past is limited by the
number of trading systems tested and the number of foreign currencies used. This study attempts
to expand on past findings by using genetic optimization techniques on the sampled 63 strategies
on seven major currency pairs for a total of 441 optimization cases. Each optimization case had
an average of 69,030 tests, resulting in 30,442,069 total tests.
Findings show that although the majority of trading systems are profitable, a substantial
portion of those profits can be explained as compensation for the bearing of risk, consistent with
the efficient market hypothesis. However, when examining the effect of complexity, there is a
clear link between complexity and risk-adjusted excess profits. This implies that technical
trading system excess profits are the result of skill, rather than luck, in opposition to the efficient
market perspective.
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Technical Trading Systems in the Forex Market 2
I. INTRODUCTION
The past history of stock prices cannot be used to predict the future in anymeaningful way. Technical strategies are usually amusing, often comforting, but
of no real value.
This quote in a bestselling finance book (over 1.5 million copies sold as of January 10, 2011)
written by Princeton University economics professor and a leading proponent of the efficient
market hypothesis, Burton Malkiel (2011, p. 161), is one notable motivation for this study.
Contrary to Malkiel¶s argument, empirical evidence shows that 59 percent of modern academic
studies on technical analysis yielded positive results versus 21 percent negative and 20 percent
mixed (Park and Irwin 2007). Yet despite this evidence, vocal critics like the above have resulted
in a negative stigma against the practice of technical analysis amongst certain circles.
Technical analysis can be defined as "the study of market action, primarily through the use of
charts, for the purpose of forecasting future price trends" (Murphy 1999). With the majority of
studies favoring the effectiveness of technical analysis, a layperson would likely conclude that
academics would be the ones staunchly praising technical analysis with practitioners being
behind the curve, but evidence suggests otherwise (Menkhoff and Taylor 2007). The bias against
technical analysis is also occasionally found in popular finance textbooks such as Strong (2009).
Academia¶s skepticism of technical analysis is largely due to its conflict with the efficient
market hypothesis (Fama 1970) that is the foundation of modern finance theory. The efficient
market hypothesis²even in its weakest form²states that technical analysis should be
ineffective. Practitioners, on the other hand, used technical analysis prior to the emergence of the
efficient market hypothesis in the 1970¶s, and have continued its use, relatively unmoved by
critics such as Malkiel and Fama (Cheung and Chinn 2001).
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Technical Trading Systems in the Forex Market 3
This divide between ³the classroom´ and ³the street´ has created a knowledge and perception
gap and begs the question, if not in the classroom, then where are future practitioners to learn
proper technical analysis techniques? This study seeks to bridge this gap via an expansive
exploration of technical analysis in the foreign exchange (forex) market.
Moving beyond the broad academic perception of technical analysis, many studies on
technical analysis in the forex market test a limited number of technical trading systems,
currencies, and time frames. For example, Okunev and White (2003) test four trading systems on
eight forex currency pairs over a 20-year period. As can be expected, as more trading systems
and currencies are tested and the time frame is extended, more time, money, and computing
power is required. The current study is ambitious in that it analyzes 63 trading systems on seven
forex currency pairs over a 35-year period. To my knowledge, there are no other empirical
studies on technical analysis with this same broad scope.
In addition, I introduce a yet untested qualifying factor, complexity. Most simply, complexity
is defined as the utilization of sophisticated formulas, multiple technical indicators,
independently defined exits, intermarket analysis, and/or dynamically self-adjusting technical
trading rules within the trading system. While previous studies have stated that complexity has a
positive correlation with market returns and improved resistance to the ever more efficient
markets (Neely, Weller and Ulrich 2009), there is no empirical test of that theoretical argument
in the literature. In summary, this study builds on previous studies of technical analysis in the
foreign exchange market by testing (1) the profitability an extensive number of publicly
available technical trading systems, and (2) the effect of complexity on strategy returns and
robustness.
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Technical Trading Systems in the Forex Market 4
II. THE PRACTICE OF TECHNICAL ANALYSIS
Technical Analysis
As stated previously, technical analysis can be defined as "the study of market action,
primarily through the use of charts, for the purpose of forecasting future price trends" (Murphy
1999). Price charts are the most frequently used tool by the market technician, hence the
outdated nickname "chartist". Today, practitioners of technical analysis are typically referred to
as ³technicians.´ Figure 1 presents a price chart with technical indicators.
[Insert Figure 1 about here.]
Though evidence of technical analysis usage appears in some form since the 17th century
Dutch tulip market and the 18th century Japanese rice markets, modern technical analysis traces
back to W all Street Journal articles written at the end of the 19th century by Charles Dow
(namesake of the Dow Jones Industrial Average), though Dow did not use the term technical
analysis. According to textbooks on the subject, technical analysis is based on three basic tenets:
(1) market action efficiently summarizes all microeconomic, macroeconomic and behavioral
information; (2) prices move in trends; and (3) history repeats itself (Murphy 1999).
As an extension of the belief that all available information is contained within price history,
some technical purists do not perform fundamental or economic analyses because that
information is already ³built in´ to the price data. However, the majority of market technicians
use a combination of technical and fundamental analysis and the more recently adopted flow
analysis (Menkhoff and Taylor 2007). Technicians believe that price data forecasts
fundamentals, and not the reverse (Murphy 1999), since there is a period of learning where some
traders recognize and anticipate changes before others. Engel and West (2005) tested and
validated this theoretical position.
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Technical Trading Systems in the Forex Market 5
Other common beliefs of technical analysts include ³the trend is your friend´, ³cut your
losers short and let the winners ride,´ and ³prices don¶t lie´ (Lefevre 1994). As suggested by the
first, trends need to form and sustain themsemselves long enough to be identified, capitalized
upon, and held until reversed in order to profit from them. This ties in to the second belief that
profitable positions are held as long as possible, while unprofitable positions exit as soon as
possible. This same concept was discussed nearly 200 years ago by economist and trader James
Grant (1838).
The third concept that ³prices don¶t lie´ acknowledges that some companies are not
completely forthright with the public about their company¶s financial position. This was most
blatantly illustrated with the Enron and WorldCom frauds, though it is quite common practice for
public companies to ³manage´ their numbers and work within GAAP rules in order to show
financial statements in the best possible light. In addition to financial statement manipulation,
television pundits fill the airwaves with contradictory information and Wall Street analysts use
tactics such as publicly promoting the positive aspects of a position so that their firm will have a
market to sell into. Technicians feel that the best way to cut through this ³noise´ and decipher the
true direction of the position is through price trends because no matter what is publicly disclosed,
if a financial vehicle (stock, bond, forex pair, etc.) is being bought or sold, that represents
³putting your money where your mouth is´. [Please refer to Murphy (1999) and Pring (1991) for
a more in depth coverage of technical analysis.]
The Efficient Market Hypothesis and Academic Skepticism
The Efficient Market Hypothesis is a theory popularized by Eugene Fama (1970) that
operates on the belief that the markets have become efficient enough where the price one would
pay at any given point in time is fair and accurate. This means that there are no inefficiencies in
the market to exploit. Nothing is under or overpriced, rendering fundamental analysis useless,
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Technical Trading Systems in the Forex Market 6
and there are no trends or historical patterns to exploit, rendering technical analysis useless. The
core belief of the Efficient Market Hypothesis is that the return one will receive for the purchase
of a financial vehicle such as stocks or commodities is equal to the risk one bears while holding
that vehicle. Low risk securities will earn a relatively low return and conversely high risk
securities garner high returns.
After its emergence in the 1970s, the efficient market hypothesis (EMH) quickly gained in
popularity until it became the dominant paradigm and the foundation of Modern Portfolio
Theory in the 1980s. The subsequent two decades have seen the decline of the EMH and the
emergence of behavioral finance, a field of study in finance organized under the belief that the
market is an aggregate of human actions replete with inefficient and imperfect decisions.
Efficient markets should not have bubbles or crashes, so the dot-com crash and 2008 financial
crisis exposed holes in the theory and practitioners sought elsewhere for theoretical arguments
that better explain modern markets.
One alternative perspective, the Adaptive Market Hypothesis (Lo 2004; Lo 2005; Neely,
Weller, and Ulrich 2005) posits that profit opportunities from inefficiencies exist in financial
markets, but are eroded away as the knowledge of the efficiency spreads throughout the public
and the opportunity is capitalized upon. As opposed to its mutually exclusive relationship with
EMH, technical analysis dovetails nicely with AMH. With EMH falling out of favor over the last
twenty years, technical analysis gained.
C ategories of Technical Analysis
Technical analysis can be classified as qualitative and quantitative. Qualitative technical
analysis involves discovering visual patterns in a chart of historic data. Patterns range from the
popular ³head and shoulders´ pattern (Osler and Chang 1995) to the lesser known ³island
reversal´ pattern. See Figure 2 for an illustration of a head and shoulders pattern.
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Technical Trading Systems in the Forex Market 7
[Insert Figure 2 about here.]
Qualitative technical analysis is subjective in nature and thus, can be difficult to test. The
efficacy of chart patterns are not included in this study, but there others have attempted to
quantify and analysis this method (e.g., Chang and Osler 1995, 1999; Lo, Mamaysky, and Wang
2000). Despite the difficulty in recreating subjective human pattern recognition abilities, results
from prior endeavors indicate that some patterns do have predictive power. [Please refer to
Bulkowski (2005) for a thorough chart pattern reference book.]
Quantitative technical analysis performs mathematical and statistical calculations on historic
data, typically price and volume data, in an attempt to forecast future prices. Technicians use
tools called ³technical indicators´ that are visual representations of the quantitative calculations
on a chart. For example, the most commonly used technical indicator is likely the simple moving
average (SMA). The simple moving average is used to smooth the ³noise´ from price
fluctuations in an attempt to distinguish a trend (see Figure 3).
[Insert Figure 3 about here.]
Technical indicators are rarely used in isolation by practitioners, but are merely a set of tools
that can be combined together to form a trading system. This is described in detail later.
III. TECHNICAL INDICATORS, TRADING SYSTEMS, BACKTESTING,
AND OPTIMIZATION
Technical Indicators
Technical indicators are important tools used by technical analysts. A technical indicator can
be defined as a numerical and/or visual representation of current and historical price, volume
and/or market composition data in order to isolate trends, turning points or optimal entry/exit
points. They range from the very simple (moving averages) to the more complex (Commodity
Channel Index). While moving averages have been covered in depth, notably in Brock,
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Technical Trading Systems in the Forex Market 8
Lakonishok, and LeBaron's seminal paper (1992), other popular indicators used widely in the
industry have been explored by the academic community less frequently. For example,
practitioners frequently use the moving average convergence-divergence indicator (MACD),
relative strength index (RSI), Bollinger Bands, and Stochastic indicators, though few have tested
them as part of a trading system. Bollinger Bands are illustrated in Figure 4.
[Insert Figure 4 about here.]
Technical indicators typically have inputs in order to customize the indicator to the
underlying financial vehicle. For example, the Bollinger Bands indicator allows the user to
customize the length of time used (e.g., 30 days) and the standard deviations of the upper and
lower bands. The inputs can be arbitrarily chosen or they can be ³optimized´ by cycling through
a range of inputs to determine what would have been most effective over historic price data.
[Optimization is explained in more detail in a later section.]
Currently, the most extensive amount of information on technical indicators is outside the
academic community (e.g., Achelis 2001; Elder 1993; Katz and McCormick 2000; Kaufman
2005; Murphy 1991, 1999; Pring 1991; Wilder 1978). Indicators are very popular in technical
analysis because they consolidate decisions into a simple Boolean decision-making process, and
eliminate subjectivity as well as natural human emotions (e.g., fear, greed, anger, frustration).
An example of a technical indicator used here is the Relative Strength Index or RSI (Wilder
1978) that is mathematically represented as:
where n is the number of trading days, U is the change on all ³up´ days (days where the close is
higher than the open), D is the change on all ³down´ days (days where the close is lower than the
open), and EMA is an exponential moving average. The RSI indicator is normalized between 0
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Technical Trading Systems in the Forex Market 9
and 100, with zero indicating weakness (closing price is near recent lows) and 100 indicating
strength (closing price is near recent highs). It is typically used as a mean reversion indicator,
which designates a reversal of a trend. Crossing the 70 line is a sell trigger and crossing the 30
line is a buy trigger.
If one only had to follow the buy and sell signals on one indicator to earn a profit in the
markets, there would be no need for this study ² all traders would currently be using that
indicator. Unfortunately, there is no single indicator that will predict profitable trades on a
perfectly consistent basis. Traders instead use combinations of trading indicators, rules, filters,
money management, entries, and exits in order to create a trading system (also referred to as a
trading strategy), described in the next section.
Beginners often look for a magic bullet-a single indicator for making money. If they get lucky for a while, they feel as if they discovered the royal road to profits.
W hen the magic dies, amateurs give back their profits with interest and golooking for another magic tool. The markets are too complex to be analyzed by a
single indicator. (Elder 1993)
Although it is rare for practitioners to use individual indicators in isolation for trading
decisions, there are some academic studies that assess the efficacy of technical analysis based on
this flawed assumption (e.g., Dempster and Jones 2000). To their credit, Dempster and Jones
(2000) acknowledge this flaw at the end of their paper. The majority of the trading systems
tested in this study utilize multiple indicators, rules, and filters. However, for the sake of
understanding the impact of complexity with regard to single indicator trading systems (which
are relatively simple), some single indicator trading systems are tested here as well.
Trading Systems
A trading system is the culmination of all weapons in the technical analyst's arsenal. Trading
systems ideally begin with a theory based on the technician's observations of market activity ² a
pattern or trend that has played out consistently over time or appears to be developing in the near
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Technical Trading Systems in the Forex Market 10
future that the technician believes can be capitalized upon. The technician decides which tools
would most accurately identify and capture the essence of that theory. Robust trading systems
typically incorporate trading rules, filters, money management, entries, and exits.
Trading Rules. Trading rules explicitly state when to enter and when to exit the position. The
rules may utilize one or more technical indicators (e.g., the MACD indicator rises from below to
above the zero line), price action (e.g., the closing price is higher than previous day¶s closing
price for 3 consecutive days), fundamental data (e.g., US GDP increasing quarter over quarter),
or any other quantifiable metric.
Filters. In addition to rules, some trading strategies also employ filters in order to limit the
number of trades the system creates. Filters are considered a subset of trading rules because they
also involve rules based on technical indicators. A definition of a filter is that buy/sell signals
created by the trading system rules are ignored unless the filter criterion is also met. Since no
indicator or system is accurate every time, filters are employed in the attempt to capture only the
best opportunities and avoid "whipsaw", the unfortunate situation induced by a rapid succession
of buy and sell signals in non-trending markets, that leads to a large number of unprofitable
trades. Filters are also created either with technical indicators or non-indicator data. ³Ignore the
buy signal generated by a trading rule if the ADX indicator (shows strength of a trend) is below
10´ is an example of a filter.
Money Management. Another important factor in a trading system, perhaps the most
important factor, is money management. Money management is a general term that covers topics
such as the amount of capital to be used in each trade, the maximum allowable loss per trade,
how and when to close profitable trades, the number of open trades allowed to be open at any
time, etc. A basic concept in trading²letting winners run and cutting losers short²falls under
the category of money management.
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Technical Trading Systems in the Forex Market 11
Some traders believe that money management is the most important ingredient ina trading program, even more crucial than the trading approach itself. I¶m not
sure I¶d go that far, but I don¶t think it¶s possible to survive for long without it.(Murphy 1999)
Entries. All trading systems must have entries and exits. Entries, as the name suggests, are
the point when the trading system enters the market either by going long (buying) or going short
(selling short) the investment vehicle. Entries occur when predetermined trading rules, and
possibly filters, defined in the trading system are triggered.
Exits. Exits can be broken into three categories: reversals, targets, and stop losses. Exits are
an important part of a trading system¶s money management and thus, a very important part of the
trading system. Reversal stops, the simplest of the three, take the opposite side of the current
trade, occurring when an entry signal is triggered in the opposite direction. Targets are exits
performed when the trade has reached an acceptable level of profit or a predetermined price or
percentage change from the entry point.
Stop losses are safeguard exits put in place to prevent exorbitant losses. They can be fixed,
trailing, or dynamic. Fixed stop losses generate an exit signal when the trade has either lost a
predetermined dollar amount, or the price has moved adversely by a predetermined number or
percentage. Trailing stop losses move in tandem with the currency, trailing below (above for
short trades) for each incrementally higher (lower) movement in price. With any movement
against the direction of the trade, the trailing stop stays fixed in place. Should the price move far
enough against the trade, the stop loss will be triggered, the trade will be exited, and the trading
system position will now be ³flat.´ Dynamic stop losses are the most complex form of stop
losses and are a blend of technical indicators and stop losses. The similarities to technical
indicators lie in that they incorporate mathematical or statistical calculations and additional
factors (such as volatility) besides price highs and lows within the stop loss formula. In fact,
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Technical Trading Systems in the Forex Market 12
some technical indicators are designed to be used as stop losses, and include the Parabolic Stop
and Reverse (Wilder 1978).
Trading Systems: An Example. An example of a relatively simple trading system used in
this study is the channel breakout system. The entry rule is: go long (short) when the closing
price crosses above (below) the maximum (minimum) of the closing prices over the last x days.
No additional indicators or filters are used, and exits are dynamic and executed by the Parabolic
Stop and Reverse (SAR) indicator, an intelligent trailing stop. There are three inputs: (1) number
of lookback days for the channel highs/lows, (2) the Parabolic SAR acceleration factor, and (3)
the Parabolic SAR acceleration limit. Mathematically, the entry rule is expressed as
���
���
where C is closing price, t is today, n is the number of lookback days given as an input. The
channel breakout system creates a ³channel´ around the price history over n days, the idea being
that a breakout to the upside (downside) is a strong directional movement and the start of a new
trend.
Backtesting
Backtesting is the process of applying a trading system to actual historical data to evaluate
how well the system would have performed. Before the proliferation of advanced computing
software, backtesting was done manually. This was an immensely time consuming process and
even when it was finally completed, it was prone to errors. There are also subtle nuances that
may be overlooked: e.g., what buy or sell price the strategy should get as opposed to what it
would actually get given live market conditions. Computing software programs have vastly
improved over time, thus reducing human error and unrealistic market expectation problems.
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Technical Trading Systems in the Forex Market 13
O ptimization
Optimization is the process of cycling through a vast number of possible trading system
inputs and backtesting each combination on historic data in order to determine the optimal
inputs. As stated previously, both technical indicators and trading systems²which utilize
technical indicators²have inputs that can be customized to the users preferences and to the
financial vehicle (stock, forex pair, etc.).
To optimize, the user defines the input starting point, ending point, and increment value. For
example, if a trading system has two inputs (indicator 1-number of days, indicator 2-number of
days) and sets the minimum at five, maximum at 400, and increment of five for each of the two
inputs, that would result in 80 for each input [400/5]. Because there are two inputs, the number
of tests are multiplicative and the total backtests for this example would be 6,400 [80 x 80]. This
process is extremely computer intensive and time-demanding, depending on the number of tests
and the formulas built into the trading system.
Brute Strength O ptimization. ³Brute strength´ optimization, also referred to as ³exhaustive´
optimization, is the process of backtesting the full spectrum of tests that results from the input
range. Using the previous example, the full 6,400 backtests would be executed.
Genetic O ptimization. Genetic optimization is a technique developed to reduce the
computing time required for optimizations. It mimics Darwinian evolution by defining
chromosomes (input parameters), establishing a fitness metric (e.g., net profit), and performing
backtests where weak input parameters (low net profit) ³die out´ and strong input parameters
(high net profit) live on to create future generations of similar but slightly different inputs. This
repeats until the strongest set of input parameters remain. The TradeStation platform, used
exclusively for optimization in this study, describes genetic optimization in general terms as
following these steps:
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Technical Trading Systems in the Forex Market 15
Optimization solves the problem of what input parameters to use by cycling through a large
number of possible inputs to determine the best one, however it introduces another problem
called ³curve fitting´. Curve fitting occurs when the inputs chosen are picture perfect for the
sample time period being tested, yet perform poorly in a future time period(s). In order to control
for these problems, a two-step process is taken. First, the available data is divided into two
sections: in-sample data and out-of-sample data. Optimization is performed on the in-sample
data in order to isolate potential input parameters. Those input parameters are then backtested on
the non-optimized out-of-sample data to see if the positive results from optimization are real or
the result of curve fitting.
IV. BACKGROUND AND HYPOTHESES
Technical Analysis in the Stock Market
Early studies on the effectiveness of technical analysis focus on the stock market. They are
notable both in that most find technical analysis to be ineffective, and also that many are
performed by original proponents of the Efficient Market Hypothesis (EMH). For example,
Fama and Blume (1966) report that using filter rules on US stocks is unprofitable when taking
transaction costs into account. Fama (1970) then declared technical analysis to be a futile
undertaking, at which time he introduced the EMH based on his doctoral thesis.
A frequently cited study on the usefulness of technical analysis in the stock market (Brock,
Lakonishok, and LeBaron 1992) gained notoriety because the findings conflict with Fama
(1970). The authors illustrate that the use of simple moving average crossovers, a basic technical
analysis technique, can yield excess returns in the stock market that cannot be accounted for by
null ³random walk´ models such as ARMA or GARCH. Note that the focus of this paper is on
the foreign exchange market and thus, discussion on the stock market is constrained. [Please
refer to Park and Irwin (2007) for a comprehensive review of the technical analysis literature as a
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Technical Trading Systems in the Forex Market 16
whole, and Menkhoff and Taylor (2007) for an equally good review of the literature specifically
within the forex market.]
The Use of Technical Analysis in the Foreign Exchange (forex) Market
Ever since floating exchange rates were created in the early 1970¶s to relieve the untenable
pressure to peg the US dollar to gold prices, researchers have suspected that technical analysis
was used frequently by practitioners in the forex market. However, this belief was not
systematically tested until Allen and Taylor (1990, 1992) surveyed chief forex dealers in
London. The results were striking as nearly 90 percent of sampled dealers reported placing some
importance on technical analysis. In addition, technical analysis was overwhelmingly preferred
on shorter time frames (intraday), whereas fundamental analysis preferred on longer time frames
(over one year).
More recently, similar studies were performed for forex dealers in Austria (Gehrig and
Menkhoff 2004; Oberlechner 2001), Germany (Gehrig and Menkhoff 2004; Menkhoff 1997;
Oberlechner 2001), Hong Kong (Cheung and Wong 2000; Lui and Mole 1995), Singapore
(Cheung and Wong 2000), Switzerland (Oberlechner 2001), Tokyo (Cheung and Wong 2000),
the United Kingdom (Cheung, Chinn, and Marsh 2004; Oberlechner 2001), and the US (Cheung
and Chinn 2001). Though the number of responses and response rates are quite different for each
of these studies, results are notably similar. Findings show that (1) practitioners who use some
type of technical analysis range from 90 percent to 100 percent, (2) technical analysis is more
important for short time frames, and (3) fundamental analysis is more important for longer time
frames. Based on these independent findings, it is safe to say that technical analysis is a widely
used and integral part of the foreign exchange market.
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Technical Trading Systems in the Forex Market 17
P rofitability of Technical Analysis in the Foreign Exchange (forex) Market
While it is well-established that technical analysis is an important part of the foreign
exchange market, the question of profitability shows mixed results. Early studies on simple
moving average and filter rules in the foreign exchange market find sizeable net profits (Cornell
and Dietrich 1978; Dooley and Shafer 1984; Logue and Sweeney 1977; Logue Sweeney and
Willett 1978; Poole 1967; Sweeney 1986). However, with the exception of Dooley and Shafer
(1984) and Sweeney (1986), the studies have many shortcomings by today¶s standards. For
example, commissions, slippage, and interest rate carry costs are not included. The authors also
neglect to perform statistical tests to determine if the profitability occurred by chance, and they
fail to test if the profits are compensation for the bearing of risk²which is an implication of the
EMH. Because Dooley and Shafer (1984) and Sweeney (1986) attempt to address these issues,
they are the most cited of these early studies. Interestingly, modern studies that use improved
methodologies and analytical techniques support many findings of these early flawed studies
(LeBaron 1999; Menkhoff and Schlumberger 1995; Neely 1997; Pilbeam 1995; Saacke 2002;
Surajaras and Sweeney 1992). On balance, the majority of studies find technical analysis to be
profitable, though recent studies suggest that those profits have been eroded over time to close to
zero since the 1990s. More complex forms of technical analysis can still find modest profits
(Neely, Weller and Ulrich 2009).
Accounting for Risk
Perhaps the biggest challenge faced in technical analysis research is determining the ideal
method of accounting for risk. As stated previously, proponents of the EMH do not state that
technical analysis rules must be unprofitable, only that any excess profit is compensation for the
bearing of risk. Cornell and Dietrich (1978) were the first to address this by using an
international capital asset pricing model (ICAPM). Unfortunately, the study is flawed since the
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beta is calculated on the foreign currencies themselves and not on the trading positions taken on
the foreign currencies.
Sweeney (1986) is an oft-cited pioneer in measuring risk compensation who compares
trading rules on currencies versus buy and hold currency strategies. He reports significant profit
opportunities that are not explained by the bearing of risk. This result is questioned due to the
fact that expecting a positive return on a buy and hold currency strategy implies that one
currency has a positive outlook while the other has a negative outlook (cf. Cornell and Dietrich
1978). In addition, the assumption that the forex risk premium is constant and does not change
over time is not realistic. Taylor (1992) uses a first-order autoregressive process to create a time-
varying risk premium calculation and does not find that returns are due to risk, though it is
possible that the model does not calculate risk premium perfectly. In contrast, Kho (1996) finds
that a good portion of the excess returns from technical trading rules is a result of the bearing of
risk when excess returns are related to the world stock portfolio (MSCI) using ICAPM and
GARCH-m models to calculate expected risks.
Another method commonly used by modern researchers to account for risk is the Sharpe
Ratio (Sharpe 1966) that relates the net returns to the standard deviation of those returns.
Mathematically, this is shown as
where R is the asset return, R f is the return on a benchmark asset (frequently the risk free rate of
return), and is the standard deviation of the excess of the asset return. The Sharpe ratio on the
trading rule returns is then compared to the Sharpe ratio on a broad portfolio index like the S&P
500 or MSCI (Neely 1997; Chang and Osler 1999; Saacke 2002). These studies show that
technical trading rule risk-adjusted returns are higher than that of the benchmark. It should be
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noted that the Sharpe ratio, like other risk measurement techniques is imperfect since it requires a
long period of time (22 years for significance at the five percent level) and it does not measure
the perception of risk, which is inherently difficult to quantify. Alternatively, Menkhoff and
Schlumberger (1995) stress the monthly difference in trading rule profitability to buy and hold
profitability to determine practitioner myopic loss aversion posture. Due to the instability of the
monthly returns, their data show that excess profits cease to be significant at the 5% level.
In this study, I expand on the extant literature and utilize the Sharpe ratio of trading rule
returns in relation to the Sharpe ratio of buy and hold returns on the US stock market as
represented by the S&P 500 index to determine if the excess returns are in the form of risk
premia.
H1: Technical trading systems have out-of-sample excess profits that cannot be accounted for by the bearing of risk.
The Impact of C omplexity
In addition to the main profit effects examined in H1, I also test a potentially important
qualifying factor, complexity. While previous studies have stated that more complex forms of
technical analysis outperform less complex forms and that their returns are more stable over time
(Menkhoff and Taylor 2007; Neely, Weller and Ulrich 2009), there is no empirical test of that
theoretical argument in the literature. Furthermore, a clear definition of complexity with regard
to technical trading systems is non-existent.
Most simply, complexity in technical trading systems is here defined as the utilization of
sophisticated formulas, multiple technical indicators, independently defined exits, intermarket
analysis, and/or dynamically self-adjusting technical trading rules within the trading system. I
test the theoretical relationship between complexity and profit stability by operationalizing
complexity and comparing the average Sharpe ratio across complexity levels.
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Technical Trading Systems in the Forex Market 20
H2: More complex technical trading systems yield higher returns than less complex systems.
V. METHODOLOGY
General Information and P rocedural Steps
The method used to determine the efficacy of trading systems is to program and
optimize/backtest them using the Tradestation software platform. The process involves the
following steps:
1. Select the target market [forex], target vehicles [7 major currency pairs], and target time
frame [daily].
2. Write programs in computer code that accurately reflect the technical trading systems.
3. Bifurcate the available test data. One half are used to generate optimal inputs for thetechnical trading system (in-sample data). The other half are used to test the tradingsystem with optimized inputs in a non-optimized environment (out-of-sample data). Thisis done to prevent data snooping. It is recommended that the optimization be performedon the more recent block of data so that it is better suited to handle future marketconditions.
4. Run optimizations for each trading system [63] on each currency pair [7] for a total of 441 optimization cases, with a minimum of 5,000 tests per optimization. This results in atleast 2,205,000 tests that must be filtered down to the top 441. The minimum number of
backtests was 499 and the maximum was 357,604, with a mean of 69,030 backtests.
5. Organize the results and apply a scoring metric to all tests.
6. Select the top performing tests from each of the 441 optimizations.
7. Perform out-of-sample backtest using some of the top optimized inputs from the previousstep.
8. Analyze all out-of sample results.
Forex Market
The focus here is on the forex market. Though technical analysis is used in all financial
markets, it is most prevalent in the foreign exchange market. Studies report that between 90 and
100 percent of forex market traders use technical analysis in some fashion (Gehrig and Menkhoff
2004; Menkhoff 1997; Taylor and Allen 1992). In a later US survey that asked forex respondents
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what best describes their trading style, 29.53% categorized themselves as technical traders, the
largest percent of the four available categories (Cheung and Chinn 2001). The forex market was
also chosen based on the popular idea that it forms trends more often than other markets such as
the stock market (Clements 2010). There are additional benefits to the forex market over other
markets: e.g., it has a manageable number of currency pairs to work with (versus the massive
universe of stocks) and continuous price data (versus futures data that is broken up every few
months by contract expirations and must be blended together).
C urrency P airs
The currency pairs used in this study are the most widely traded, and therefore, the most
liquid currency pairs. Liquidity is crucial in the forex market because it narrows the loss from the
bid/ask spread. If one were to immediately buy and sell a currency pair, it would result in a loss
equal to the size of the spread in addition to any commissions involved. This is commonly
referred to as ³slippage.´ Therefore, more liquid currency pairs with narrow spreads are
preferred. The six most liquid currency pairs are:
y EUR/USD - Euro/US Dollar
y GBP/USD - Great Britain Pound/US Dollar
y USD/JPY - US Dollar/Japanese Yen
y USD/CHF - Us Dollar/Swiss Franc
y USD/CAD - US Dollar/Canadian Dollar
y AUD/USD - Australian Dollar/US Dollar
In addition to the most liquid currency pairs, one additional currency pair was included
because it has gained a wide following amongst high frequency currency traders due to it having
the highest level of volatility. The higher the volatility of a currency pair, the more likely that
trading opportunities will appear over a given time frame. This currency pair is:
y GBP/JPY - Great Britain Pound/Japanese Yen
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Technical Trading Systems in the Forex Market 22
Backtesting and O ptimization Software
The Tradestation platform is recognized as the best of the best when it comes to backtesting
and rule based trading, winning numerous awards from well respected practitioner publications.
Awards include:
y Barron¶s ± Best Online Broker (2011)
y Stocks & Commodities ± Best Institutional Platform (9 years in a row)
y Stocks & Commodities ± Best Professional Platform (9 years in a row)
y Stocks & Commodities ± Best Online Analytical Platform (8 years including 2011)
y Stocks & Commodities ± Best Futures Trading System (7 years in a row)
y Stocks & Commodities ± Best Stock Trading System (7 years in a row)
y Stocks & Commodities ± Best Real Time Data (2009, 2011)
y Brokerage Star Awards ± 1st Place (2010)
For this study, all indicators, trading systems, optimization, and backtests are programmed
and executed using Tradestation 9.0 (update 8585), the most advanced version when the data
were analyzed.
Data
All forex price data are provided by TradeStation and aggregated on a daily basis. Data
ranges used in this study for AUD/USD, GBP/USD, USD/CAD, USD/CHF and USD/JPY
include a 35.5-year time frame (1/1/1975 to 6/30/2010). The data ranges for EUR/USD and
GBP/JPY are from 1/1/1999 to 6/30/2010 (11.5 years).
The data was divided in half for in-sample and out-of-sample testing. In-sample data ranges
for AUD/USD, GBP/USD, USD/CAD, USD/CHF and USD/JPY are from 1/1/1993 to 6/30/2010
(17.5 years). In-sample data ranges for EUR/USD and GBP/JPY are from 7/1/2004 to 6/30/2010
(6 years). Out-of-sample data ranges for AUD/USD, GBP/USD, USD/CAD, USD/CHF and
USD/JPY are from 1/1/1975 to 12/31/1992 (18 years). Out-of-sample data ranges for EUR/USD
and GBP/JPY is 1/1/1999 to 6/30/2004 (5.5 years). All trades assumed one position of 100,000
forex lots in a given direction, long or short, or no position (flat).
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Technical Trading Systems in the Forex Market 23
C ommissions and Slippage
Commission costs and slippage are based on TradeStation commission rates and the bid/ask
spread (slippage) tested on 2/15/2011. To be conservative, actual commission and slippage rates
are slightly increased. For example, actual commission costs for AUD/USD was $2.50 per trade
side, but $3.00 was used. Commission costs are estimated at:
y AUD/USD ----------------------------------------------------------------- $3.00 per trade
y EUR/USD ------------------------------------------------------------------ $3.25 per trade
y GBP/JPY ------------------------------------------------------------------- $4.25 per trade
y GBP/USD ------------------------------------------------------------------ $4.25 per trade
y USD/CAD ------------------------------------------------------------------ $3.00 per trade
y USD/CHF ------------------------------------------------------------------ $3.00 per trade
y USD/JPY ------------------------------------------------------------------- $3.00 per trade
Slippage costs are estimated at:
y AUD/USD ----------------------------------------------------------------- $20.00 per trade
y EUR/USD ------------------------------------------------------------------ $20.00 per trade
y GBP/JPY ------------------------------------------------------------------- $60.00 per trade
y GBP/USD ------------------------------------------------------------------ $20.00 per trade
y USD/CAD ------------------------------------------------------------------ $30.00 per trade
y USD/CHF ------------------------------------------------------------------ $20.00 per trade
y USD/JPY ------------------------------------------------------------------- $20.00 per trade
Trading Systems Tested in this Study
I test a wide variety of 63 trading systems that are available to the open public, located from
a variety of sources (Ammermann 2010; Bollinger 2002; Elder 1993; Katz and McCormick
2000; Murphy 1999; Pruitt and Hill 2003), including some that are self-created. The names and
sources of the trading systems are listed in Table 1. [Note that the strategy numbering system is
not strictly sequential due to changes from the original 64. Strategies 7, 8 and 9 were removed
and strategies 10 and 11 were subdivided into A and B. Original 64 strategies ± 3 + 2 = 63.] For
more details on the trading system rules, technical indicators, and programming code for each
system, please contact the author directly.
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Technical Trading Systems in the Forex Market 24
[Insert Table 1 about here.]
Trading System C omplexity
As a clear definition of trading system complexity is neglected in the scientific literature, I
created an original operationalization that is based on concepts found in various technical
analysis textbooks (e.g., Kaufman 2005; Murphy 1999; Pring 1991; Pruitt and Hill 2003).
Complexity (defined above) is here operationalized via a formative scale created by adding one
point for each of the following complex tactics:
y Entry uses more than 1 indicator type or calculation
y Entry is dynamic (changes based on volatility)
y System has an independently defined exity Exits are dynamic
y System uses more than 1 time frame
y System changes modes based on market condition.
The complexity score begins with a minimum score of one and cumulative points are added
if any of the previous six tactics are used. Therefore, all trading systems have a score ranging
from one to a possible maximum of seven. Applying the scoring metric to each of the 63 trading
systems yields the following distribution:
y Complexity level 1 ------------------------------------------------------------ 10 (15.9%)
y Complexity level 2 ------------------------------------------------------------ 23 (36.5%)
y Complexity level 3 ------------------------------------------------------------ 11 (17.5%)
y Complexity level 4 -------------------------------------------------------------- 7 (11.1%)
y Complexity level 5 ------------------------------------------------------------ 11 (17.5%)
y Complexity level 6 --------------------------------------------------------------- 1 (1.6%)
y Complexity level 7 --------------------------------------------------------------- 0 (0.0%)
Scoring Metric
The optimization process generated over 5,000 sets of parameter inputs for each of the 441
cases (63 trading systems x 7 currency pairs). Each set of parameter inputs has associated
(unique) in-sample profit metrics (e.g., net profit, maximum drawdown, number of trades,
percent of trades that were profitable). In order to select the parameter set that would perform the
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Technical Trading Systems in the Forex Market 25
best in the non-optimized out-of-sample data, the parameter sets must be rank ordered. The
ranking method is determined by the person performing the optimization and can vary
significantly, although net profit or Sharpe ratio are used most frequently. Unfortunately, Sharpe
ratio is not available in the TradeStation output data and thus, an alternative was required. I
chose not to use net profit in isolation due to inherent flaws in the metric. First, net profit does
not take the volatility of the returns into account. In addition, net profit only looks at the result as
of the arbitrarily chosen ending date of the test, and it is possible that all days before and after
were lackluster, but experienced a profit spike on the final day of the optimization. In order to
improve the chances of success for out-of-sample testing, I created a scoring metric that
combines multiple profitability measures.
The scoring metric utilizes four performance measurements:
y Net Profit [Gross profit ± Gross Loss]
y Average trade [net profit / total number of trades]
y Return on account [net profit / maximum drawdown]
y Winning days [average days in winning trade * number of winning trades]
The four metrics are ranked as a percentage of the maximum of the parameter sets (N=5,000)
generated by the optimization process, and then summed together. Since there are four
performance measurements, the maximum possible score for a parameter set is 400%, indicating
that the parameter set matched the maximum in all four categories. Furthermore, any parameter
set that resulted in 2 or fewer trades was excluded, as there was insufficient data to ensure that
the results would hold up in the out-of-sample data set. All parameter sets were then sorted by
the scoring metric, and the highest score was selected. The scoring metric is advantageous
because it rewards high net profit, fewer trades (though a minimum of 3 is required), low
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Technical Trading Systems in the Forex Market 26
volatility via return on account, and a higher percentage of time ³in the market´ versus being
³flat´ via winning days.
VI. RESULTS
Risk Adjustment
This study follows the risk adjustment methodology used by Neely (1997), Chang and Osler
(1999), LeBaron (2000) and Saacke (2002). The basic procedure is: (1) to prevent data snooping
and curve fitting, the available price data is split in half, (2) optimization is performed on in-
sample data, (3) a scoring metric is applied to the optimized parameter sets, (4) the top
performing set is selected, then (5) applied to the out-of-sample portion of the data. The key risk-
adjusted return metric, the Sharpe ratio, is used to determine the success of the out-of-sample
results of the technical trade returns. That figure is then compared to the Sharpe ratio on a buy
and hold strategy of the S&P 500 US stock market index over the same time period.
Excess P rofit: H1
Recall that H1 predicts that technical trading systems have out-of-sample excess profits that
cannot be accounted for by the bearing of risk. As expected, the optimal trading system
parameters for the in-sample data yield significant net profits for all 441 cases (63 trading
systems x 7 currencies). The real test is to determine excess profits on out-of-sample data and to
compare the risk adjusted returns to that of the S&P 500 index over the same time period.
Approximately two-thirds (=67.1%) of the cases (285 of the 425 (441 ± 16 with no trades)) are
profitable. Seventy-six of the 425 cases (=17.9%) had a Sharpe ratio that outperformed the
benchmark, with an average Sharpe ratio below zero at -0.02186. Table 2 presents the out-of-
sample results and comparisons to the S&P 500 Sharpe ratio for the total sample.
[Insert Table 2 about here.]
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Technical Trading Systems in the Forex Market 27
For a cross-section look at the data, out-of-sample results aggregated on the currency level
are displayed in Table 3.
[Insert Table 3 about here.]
The Impact of C omplexity: H2
Data also show support for H2: i.e., more complex technical trading systems yield higher
returns than less complex systems. Applying the scoring metric, each of the 441 cases were then
aggregated and averaged via the Sharpe ratio for each level of complexity. Figure 5 displays the
average Shape ratio by complexity level.
[Insert Figure 5 about here.]
The overall regression model with the Sharp Ratio as the dependent variable and complexity
as the independent variable is significant ( F (1,439)=6.81, p=.009; R2=.015). The individual beta
coefficient for complexity replicates that result (b=.12, t =2.61, p=.009). As presented above, only
one percent of the cases are at the highest complexity levels. When those cases are excluded, the
data are relatively unaffected, only slightly enhanced: ( F (1,432)=7.49, p=.006; R2=.017; b=.13).
Isolating the more robust trading systems (complexity level 4 and above) yields much
stronger results. Eighty-nine out of 124 cases (=71.8%) are profitable, 30 out of 124 (=24.2%)
have a Sharpe ratio that beats the benchmark, and the average Sharpe ratio is positive at .00226.
Examination of simple trading systems (complexity 3 and below) shows that 196 out of 301
(=65.1%) are profitable, 46 out of 301 (=15.3%) have a Sharpe ratio that outperforms the
benchmark, and the average Sharpe rate is below zero at -0.03179. Table 4 displays various
performance metrics by complexity level.
[Insert Table 4 about here.]
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Technical Trading Systems in the Forex Market 28
VII. DISCUSSION AND CONCLUSIONS
This study builds on previous studies of technical analysis in the foreign exchange market by
testing (1) the profitability an extensive number of publicly available technical trading systems
over a 35-year period, and (2) the effect of complexity on strategy returns and robustness. The
profitability of the trading systems is mixed with 67.1% of the trading systems showing positive
net income, and 17.9% with risk-adjusted excess returns (as measured by the Sharpe ratio)
outperforming the risk-adjusted benchmark returns over the same time period. It is not surprising
that some of the trading systems offer sub-par performance as a wide variety of trading strategies
are used in this study, including those that are known to be relatively ³simple´ in order to
determine the impact of complexity. However, results are similar to recent evidence (Neely,
Weller, and Ulrich 2009) that reports Sharpe ratios between -.35 and .65. The Sharpe ratio range
for this study is lower, ranging from -0.78 to .34. While the relatively high profitability
percentage (including simple strategies) points to the effectiveness of technical analysis, the
lower risk-adjusted excess returns percentage implies that a good portion, but not all of the
excess returns can be explained as compensation for the bearing of risk.
Looking at average returns across currency levels provides a unique cross-section view of the
results. In a thorough review of technical analysis literature in the forex market, Menkhoff and
Taylor (2007) argue that technical analysis tends to be more profitable with volatile currencies.
Extending this theoretical perspective to the current study, GBPJPY²the most volatile of the
currencies²was expected to provide the most opportunity for profit. However, GBPJPY shows
the lowest profitability percentage (12.7%) and the lowest average Sharpe ratio (-0.16). This
result is surprising as that currency was particularly selected because its¶ high volatility makes it
popular amongst practitioners. Conversely, USDJPY is the most profitable (95.2%) and has the
highest average Sharpe ratio (.077) of the seven currencies tested. These results suggest that
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Technical Trading Systems in the Forex Market 29
strongly trending currencies such as USDJPY vastly outperform volatile currencies such as
GBPJPY. This also supports the oft-used technical analysis phrase, ³the trend is your friend.´
Trends reverse too frequently for highly volatile currencies for trading opportunities to develop,
to be observed, and to then be capitalized on.
The analysis of profitability becomes most interesting when the risk-adjusted profit metrics
are examined in relation to complexity. Excluding complexity level six (which was limited to
only one trading system of the 63), incrementally moving upwards from level one shows a
consistent increase in Sharpe ratios, though there is a slight decrease from complexity level four
to five. Regression analysis shows that complexity is a statistically significant contributor to risk-
adjusted excess profits ( p<.01). Complexity is obviously not the only factor that can determine
risk-adjusted excess returns, as indicated by the relatively low model R 2 of .015. Additional
factors that affect profitability include the technical indicators used in the trading system, the
type of stop loss, the foreign currency pair, and most importantly, how capable the theory behind
the trading system is able to take advantage of the target market anomaly and the varying degrees
in which the anomaly is available at a given point in time.
The slight decline in profitability moving from complexity level four to five shows support
for practitioners who prefer to avoid ³bloated´ trading systems and lean towards efficiency and
effectiveness (Kaufman 2005; Murphy 1999; Pruitt and Hill 2003). Simply adding random
technical indicators or filters without regard to the underlying theory will lead to a poor trading
system. Overly complicated systems collapse under their own weight and the technical trading
tools must be selected carefully. Similar to the writing process, perfection in trading systems ³is
achieved, not when there is nothing more to add, but when there is nothing more to take away.´
(Antoine de Saint-Exupery)
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To filter out the effects of including relatively simple trading systems, the trading systems
were divided in half (simple systems: complexity levels one through three, and robust systems:
complexity levels four through six). Profitability in both groups is above 50%, though the robust
group outperforms the simple group by 6.7%. Risk-adjusted excess returns for the two groups is
also much better in the robust group, with an average Sharpe ratio of .02 versus -.03 for the
simple group.
In conclusion, these results provide strong evidence supporting statements by earlier
researchers that more complex strategies outperform those that are less complex (e.g., Neely et
al. 2009; Menkhoff and Taylor 2007). Additionally, this implies that technical analysis involves
skill and excess profits are not a product of luck, which flies contrary to the view of efficient
market proponents.
Limitations and Future Research
The most challenging limitation encountered during the course of this study is the vast
amount of time and computing power required for the optimization process, that took six months
to complete using five computers running 24/7. Due to the time constraint, only the top
performing parameter set from each optimization was tested on the out-of-sample data. Further
insight may emerge by examining out-of-sample results on more parameter sets. In addition, the
recency of data from EURUSD and GBPJPY forced a study over a separate time period than the
other five currency pairs, over which the S&P 500 benchmark provided negative returns. This
skewed the percentage of cases that outperformed the benchmark for those two currencies.
Though it is the ³best of the breed´, TradeStation is constantly working to improve their
award winning software platform, including handling multi-core threading, 64-bit technology
and feature set optimizations that will speed up the optimization process and facilitate future
research efforts. For example, TradeStation just released a new update that allows simultaneous
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Technical Trading Systems in the Forex Market 31
optimization and out-of-sample results output (April 2011). Another area of improvement for
TradeStation is the limited data output from the optimization process. The output is in comma
delimited text format rather than modern spreadsheet format and Sharpe ratios are notably
excluded from the output data. Thus, I developed the scoring metric as a suitable replacement for
the Sharpe ratio. Additional challenges with TradeStation include the unique calculation of
Sharpe ratio over the most recent 36 months rather than the whole time period that may have
been a factor in the lower Sharpe ratio results.
Continued improvements to the TradeStation platform²in addition to those mentioned
above²will provide even more avenues for exploration. On June 17, 2010, TradeStation
acquired the Grail Genetic Optimization (GGO) system noted for its superiority in feature set
compared to their own optimization tool. This system includes walk-forward optimization,
additional optimization fitness criteria, and improved analytics. The integration of the GGO is
being beta tested by users (including myself), and is scheduled to be completed in 2011. This is a
big step forward in the area of optimization.
The strongest test of trading systems is to further test them on out-of-sample data in future
periods (cf. Neely et al. 2009). I urge others to pursue such research. Recall that an untested
operationalization of trading system complexity was used here. A comprehensive test of
construct reliability and validity is warranted ± for the current measurement scale and/or
alternative conceptualizations.
Future analysis can be performed through the study of additional trading systems not
included in this study (publicly available or proprietary), analysis of technical trading systems on
intra-day data, and lastly²an area that arguably needs the most attention²a universally
accepted method of measuring risk-adjusted returns that addresses the flaws of current methods.
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Technical Trading Systems in the Forex Market 32
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Technical Trading Systems in the Forex Market 37
TABLE 1
List of Trading Systems
# System Name Source
1 ADX and Moving Average Strategy Building Winning Trading Systemswith Tradestation
2 Momentum, RSI-Based Strategy with Built-in MoneyManagement
Building Winning Trading Systemswith Tradestation
3 King Keltner Building Winning Trading Systemswith Tradestation
4 The Bollinger Bandit Building Winning Trading Systemswith Tradestation
5 The Thermostat-B Building Winning Trading Systemswith Tradestation
6 The Dynamic Break Out II Building Winning Trading Systemswith Tradestation
7 The Super Combo Day Trading Strategy - REMOVED Building Winning Trading Systemswith Tradestation
8 The Ghost Trader Trading Strategy - REMOVED Building Winning Trading Systemswith Tradestation
9 The Money Manager Trading Strategy - REMOVED Building Winning Trading Systemswith Tradestation
10A Triple Screen Trading System-EMA (w/ parabolic exit)- 1 (long term)
Trading For A Living
10B Triple Screen Trading System-EMA (w/ parabolic exit)- 2 (short term)
Trading For A Living
11A Triple Screen Trading System-MACD (w/ parabolicexit) - 1 (long term)
Trading For A Living
11B Triple Screen Trading System-MACD (w/ parabolicexit) - 2 (short term)
Trading For A Living
12 Channel Trading System (Elder) Trading For A Living
13 Welles Wilder's Parabolic and Directional MovementSystems (Parabolic ADX)
Technical Analysis of the FinancialMarkets
14 Bollinger Method I: Volatility Breakout (The Squeeze) ± Parabolic exit
Bollinger on Bollinger Bands
15 Bollinger Method I: Volatility Breakout (The Squeeze) ± Bollinger exit
Bollinger on Bollinger Bands
16 Bollinger Method II: Trend Following ± Parabolic exit Bollinger on Bollinger Bands
17 Bollinger Method II: Trend Following ± Bollinger exit Bollinger on Bollinger Bands
18 Close-Only Channel Breakout (no ADX) The Encyclopedia of TradingStrategies
19 Close-Only Channel Breakout (ADX) The Encyclopedia of TradingStrategies
20 Highest High / Lowest Low Breakouts (no ADX) The Encyclopedia of TradingStrategies
21 Highest High / Lowest Low Breakouts (ADX) The Encyclopedia of TradingStrategies
22 2 moving average crossover - SMA (no ADX) The Encyclopedia of Trading
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# System Name Source
Strategies
23 2 moving average crossover - EMA (no ADX) The Encyclopedia of TradingStrategies
24 2 moving average crossover - Triangular (no ADX) The Encyclopedia of Trading
Strategies 25 2 moving average crossover - VIDYA (no ADX) The Encyclopedia of Trading
Strategies 26 2 moving average crossover - SMA (ADX) The Encyclopedia of Trading
Strategies 27 2 moving average crossover - EMA (ADX) The Encyclopedia of Trading
Strategies 28 2 moving average crossover - Triangular (ADX) The Encyclopedia of Trading
Strategies 29 2 moving average crossover - VIDYA (ADX) The Encyclopedia of Trading
Strategies 30 3 moving average crossover - SMA (no ADX) The Encyclopedia of Trading
Strategies 31 3 moving average crossover - EMA (no ADX) The Encyclopedia of Trading
Strategies 32 3 moving average crossover - Triangular (no ADX) The Encyclopedia of Trading
Strategies 33 3 moving average crossover - VIDYA (no ADX) The Encyclopedia of Trading
Strategies 34 3 moving average crossover - SMA (ADX) The Encyclopedia of Trading
Strategies 35 3 moving average crossover - EMA (ADX) The Encyclopedia of Trading
Strategies
36 3 moving average crossover - Triangular (ADX) The Encyclopedia of TradingStrategies 37 3 moving average crossover - VIDYA (ADX) The Encyclopedia of Trading
Strategies 38 moving average slope - SMA Brian Leip
39 moving average slope - EMA Brian Leip
40 moving average slope - Triangular Brian Leip
41 moving average slope - VIDYA Brian Leip
42 Overbought/Oversold - RSI The Encyclopedia of TradingStrategies
43 Overbought/Oversold - Stochastic The Encyclopedia of TradingStrategies
44 Overbought/Oversold - CCI Avg Brian Leip
45 Signal Line - Stochastic The Encyclopedia of TradingStrategies
46 Signal Line - MACD - SMA - Open The Encyclopedia of TradingStrategies
47 Signal Line - MACD - EMA - Open The Encyclopedia of TradingStrategies
48 Signal Line - MACD - Triangular - Open The Encyclopedia of Trading
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# System Name Source
Strategies
49 Signal Line - MACD - VIDYA - Open The Encyclopedia of TradingStrategies
50 Signal Line - MACD - SMA - With Trend The Encyclopedia of Trading
Strategies 51 Signal Line - MACD - EMA - With Trend The Encyclopedia of Trading
Strategies 52 Signal Line - MACD - Triangular - With Trend The Encyclopedia of Trading
Strategies 53 Signal Line - MACD - VIDYA - With Trend The Encyclopedia of Trading
Strategies 54 Signal Line - CCI Average Brian Leip
55 Zero Line - MACD - SMA The Encyclopedia of TradingStrategies
56 Zero Line - MACD - EMA The Encyclopedia of Trading
Strategies 57 Zero Line - MACD - Triangular The Encyclopedia of TradingStrategies
58 Zero Line - MACD - VIDYA The Encyclopedia of TradingStrategies
59 Zero Line - CCI Average Brian Leip
60 50 Line - RSI The Encyclopedia of TradingStrategies
61 Moving Average Percent Bands - SMA Ammermann
62 Moving Average Percent Bands - EMA Ammermann
63 Moving Average Percent Bands - Triangular Ammermann
64 Moving Average Percent Bands - VIDYA Ammermann
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TABLE 4
Technical Trading System Rules by Complexity Level
# of cases % of total Profitable Avg.Sharpe
Change
in avg.Sharpe Sharpe >Bench Rank (Sharpe)
Complexity level 1 70 16.5% 58.6% (0.06757) 10.0% 6
Complexity level 2 155 36.5% 67.7% (0.02258) 0.04499 16.1% 4
Complexity level 3 76 17.9% 65.8% (0.01763) 0.00495 18.4% 3
Complexity level 4 49 11.5% 73.5% 0.01490 0.03253 30.6% 1
Complexity level 5 68 16.0% 70.6% (0.00412) (0.01902) 20.6% 2
Complexity level 6 7 1.6% 71.4% (0.02429) (0.02017) 14.3% 5
425*
# of cases
% of total Profitable
Avg.Sharpe
Sharpe >Bench
Rank (Sharpe)
Simple (Complexity 1-3) 301 70.8% 65.1% (0.03179) 15.3% 2
Robust (Complexity 4-6) 124 29.2% 71.8% 0.00226 24.2% 1
425*
*425 cases: 63 trading systems x 7 currencies = 441 total cases. 441 ± 16 with 0 trades = 425.
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FIGURE 1
Sample Chart with Technical Indicators
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FIGURE 2
Head & Shoulders Pattern
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FIGURE 3
Simple Moving Average
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FIGURE 4
Bollinger Bands
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FIGURE 5
Average Sharpe Ratio by Complexity