brian leip thesis

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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].



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.



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.




I. INTRODUCTION

The past history of stock prices cannot be used to predict the future in any

meaningful way. Technical strategies are usually amusing, often comforting, butof 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 hypothesis — even in its weakest form — states 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).




This divide between ―the classroom‖ and ―the 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.




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 ―technicians.‖ 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 Wall 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 ―built 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.




Other common beliefs of technical analysts include ―the trend is your friend‖, ―cut your

losers short and let the winners ride,‖ and ―prices 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 ―prices 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 ―manage‖ 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 ―noise‖ 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

―putting your money where your mouth is‖. [Please refer to Murphy (1999) and Pring (1991) for

a more in depth coverage of technical analysis.]

The Eff icient M arket H ypothesis 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,




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.

Categories 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 ―head and shoulders‖ pattern (Osler and Chang 1995) to the lesser known ―island

reversal‖ pattern. See Figure 2 for an illustration of a head and shoulders pattern.





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 ―technical 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 ―noise‖ from price

fluctuations in an attempt to distinguish a trend (see Figure 3).


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 I ndicators

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,




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.


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 ―optimized‖ 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 ―up‖ days (days where the close is

higher than the open), D is the change on all ―down‖ days (days where the close is lower than the

open), and EMA is an exponential moving average. The RSI indicator is normalized between 0




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 — 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. Ifthey get lucky for a while, they feel as if they discovered the royal road to profits.

When the magic dies, amateurs give back their profits with interest and go

looking 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.

Tr ading 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 — a

pattern or trend that has played out consistently over time or appears to be developing in the near




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. ―Ignore 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 trading — letting winners run and cutting losers short — falls under

the category of money management.




Some traders believe that money management is the most important ingredient in

a 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 ―flat.‖ 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,




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 ―channel‖ 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.




Optimization

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 systems — which utilize

technical indicators — have 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 Optimization. ―Brute strength‖ optimization, also referred to as ―exhaustive‖

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 Optimization. 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) ―die 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:




1. An initial population of chromosomes (combination of strategy parameter values) israndomly chosen.

2. The fitness criterion is used to identify the best specimens.

3. Weakest chromosomes are discarded.

4.

Mutation and crossover are used to find new chromosomes to replace the ―weak‖ones.

5. The replacement chromosomes are put in the population, defining the nextgeneration.

6. Return to step 2 for fitness evaluation until the last generation is concluded.

7. Find the chromosome with the best fitness in the population.

8. The final chromosome contains the optimized parameter values.

Genetic optimization significantly reduces the number of backtests required in an

optimization. Using the example above with TradeStation’s suggested genetic optimization

settings, backtests are reduced by 80 percent from 6,400 to 1,280 with results the same or very

close to that of brute force optimization. As a rule, genetic optimization is used in this study

unless the total number of tests is too few (under 1,000) that brute force optimization is rendered

acceptable. Genetic programming is an increasingly popular technique in technical analysis

studies not only for optimization purposes, but also to determine trading rules (Allen and

Karjalainen 1995; Dempster and Jones 2000; Neely, Weller, and Dittmar 1997).

Controll ing for Data Snooping. A frequent problem of early studies of technical analysis

was that input parameters (e.g., number of days for a moving average) were selected either

arbitrarily or based on common usage (Poole 1967). There is no reason to believe that arbitrary

inputs would ever be successful except through luck and therefore, they are poor predictors of

the efficacy of technical analysis. As for testing commonly used input parameters, they are

popular because they were successful in the past (the time period being tested), and may not have

good predictive power.




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 ―curve 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 ―random 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




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 (for ex) 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.




Profi tabil ity of Techni cal 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 risk — which 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).

Accounti ng 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




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




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 Complexi ty

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.




H2: More complex technical trading systems yield higher returns than less complex systems.

V. METHODOLOGY

General I nformation and Procedural 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 of441 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 M arket

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




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).

Cur rency Pairs

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 ―slippage.‖ Therefore, more liquid currency pairs with narrow spreads are

preferred. The six most liquid currency pairs are:

EUR/USD - Euro/US Dollar

GBP/USD - Great Britain Pound/US Dollar

USD/JPY - US Dollar/Japanese Yen

USD/CHF - Us Dollar/Swiss Franc

USD/CAD - US Dollar/Canadian Dollar

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:

GBP/JPY - Great Britain Pound/Japanese Yen




Backtesting and Optimi zation 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:

Barron’s – Best Online Broker (2011)

Stocks & Commodities – Best Institutional Platform (9 years in a row)

Stocks & Commodities – Best Professional Platform (9 years in a row)

Stocks & Commodities – Best Online Analytical Platform (8 years including 2011)

Stocks & Commodities – Best Futures Trading System (7 years in a row)

Stocks & Commodities – Best Stock Trading System (7 years in a row)

Stocks & Commodities – Best Real Time Data (2009, 2011)

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).




Commissions 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:

AUD/USD -------------------------------------------------------------------- $3.00 per trade

EUR/USD -------------------------------------------------------------------- $3.25 per trade

GBP/JPY --------------------------------------------------------------------- $4.25 per trade

GBP/USD -------------------------------------------------------------------- $4.25 per trade

USD/CAD -------------------------------------------------------------------- $3.00 per trade

USD/CHF -------------------------------------------------------------------- $3.00 per trade

USD/JPY --------------------------------------------------------------------- $3.00 per trade

Slippage costs are estimated at:

AUD/USD -------------------------------------------------------------------- $20.00 per trade

EUR/USD -------------------------------------------------------------------- $20.00 per trade

GBP/JPY --------------------------------------------------------------------- $60.00 per trade

GBP/USD -------------------------------------------------------------------- $20.00 per trade

USD/CAD -------------------------------------------------------------------- $30.00 per trade

USD/CHF -------------------------------------------------------------------- $20.00 per trade

USD/JPY --------------------------------------------------------------------- $20.00 per trade

Tr ading 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.




[Insert Table 1 about here.]

Tr ading System Complexi ty

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:

Entry uses more than 1 indicator type or calculation

Entry is dynamic (changes based on volatility)

System has an independently defined exit Exits are dynamic

System uses more than 1 time frame

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:

Complexity level 1 -------------------------------------------------------------- 10 (15.9%)



Complexity level 4 ---------------------------------------------------------------- 7 (11.1%)


Complexity level 6 ----------------------------------------------------------------- 1 (1.6%)

Complexity level 7 ----------------------------------------------------------------- 0 (0.0%)

Scori ng Metri c

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




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:

Net Profit [Gross profit – Gross Loss]

Average trade [net profit / total number of trades]

Return on account [net profit / maximum drawdown]

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




volatility via return on account, and a higher percentage of time ―in the market‖ versus being

―flat‖ 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 Prof it : 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.





For a cross-section look at the data, out-of-sample results aggregated on the currency level

are displayed in Table 3.


The Impact of Complexity: 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.


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.





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 ―simple‖ 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, GBPJPY — the most volatile of the

currencies — was 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




strongly trending currencies such as USDJPY vastly outperform volatile currencies such as

GBPJPY. This also supports the oft-used technical analysis phrase, ―the 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 ―bloated‖ 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 ―is

achieved, not when there is nothing more to add, but when there is nothing more to take away.‖

(Antoine de Saint-Exupery)




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.

L imi tations and Futur e 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 ―best 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




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 platform — in addition to those mentioned

above — will 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 lastly — an area that arguably needs the most attention — a universally

accepted method of measuring risk-adjusted returns that addresses the flaws of current methods.




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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)


11A Triple Screen Trading System-MACD (w/ parabolicexit) - 1 (long term)


11B Triple Screen Trading System-MACD (w/ parabolicexit) - 2 (short term)


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





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 Trading

Strategies 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





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




TABLE 2

Out-of-Sample Profitability Metrics and Benchmark Comparisons

Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST01 AUDUSD 3 (33,680.00) 10 20.00% 33,738.00 400.00 60.00 -1.81% (235.98) 2,014.36 -0.23 0.15 NO NO

ST01 EURUSD 3 (17,739.50 ) 3 66.67% 21,656.50 120.00 19.50 -4.56% (684.18) 3,050.62 -0.26 -0.08 NO NO

ST01 GBPJPY 3 (61,316.08) 38 28.95% 66,967.66 4,560.00 323.00 -13.35% (1,521.11) 4,358.80 -0.29 -0.08 NO NO

ST01 GBPUSD 3 (7,397.50) 15 40.00% 59,394.00 600.00 127.50 -0.45% (1,160.59) 6,316.96 -0.14 0.15 NO NO

ST01 USDCAD 3 (1,179.24) 2 0.00% 1,179.24 1 20.00 12.00 -0.07% (235.13) 514.93 -0.78 0.15 NO NO

ST01 USDCHF 3 46,294.25 10 70.00% 3,086.39 400.00 60.00 2.38% 187.25 2,817.06 0.14 0.15 NO YES

ST01 USDJPY 3 (924.24) 7 42.86% 20,405.40 280.00 42.00 -0.06% 407.78 3,648.83 0.01 0.15 NO NO

ST02 AUDUSD 3 2,232.00 8 37.50% 33,814.00 320.00 48.00 0.14% (52.90) 1,453.17 -0.05 0.15 NO YES

ST02 EURUSD 3 8,585.00 8 8 7.50% 9,046.50 320. 00 52.00 2.30% 613.21 2,233.59 0.22 -0.08 YES YES

ST02 GBPJPY 3 (3,521.53) 6 16.67% 19,430.05 720.00 51.00 -0.97% (199.00) 4,678.90 -0.03 -0.08 YES NO

ST02 GBPUSD 3 63,790.00 20 55.00% 42,065.50 800.00 170.00 3.08% (337.09) 6,948.92 0.07 0.15 NO YES

ST02 USDCAD 3 18,428.35 20 3 5.00% 10,430.89 1,200.00 120.00 1.06% 128.57 1,308.06 -0.03 0.15 NO YES

ST02 USDCHF 3 13,942.49 17 35.29% 51,217.02 680.00 102.00 0.82% (1,298.22) 3,579.32 -0.04 0.15 NO YES

ST02 USDJPY 3 14,914.36 19 26.32% 51,630.79 760. 00 114.00 0.87% (27.90) 3,210.89 0 0.15 NO YES

ST03 AUDUSD 3 30,572.00 48 31.25% 12,294.00 1,920.00 288.00 1.67% (145.20) 1,556.63 0.01 0.15 NO YES

ST03 EURUSD 3 3,362.50 25 8.00% 20,834.50 1,000.00 162.50 0.92% 244.83 2,914.91 -0.01 -0.08 YES YES

ST03 GBPJPY 3 (12,196.66) 16 18.75% 14,145.02 1,920.00 136.00 -3.21% (378.96) 4,714.05 -0.08 -0.08 YES NO

ST03 GBPUSD 3 82,706.00 24 29.17% 62,457.50 960.00 204.00 3.77% (879.09) 8,347.46 0.08 0.15 NO YES

ST03 USDCAD 3 20,157.11 38 18.42% 7,607.42 2,280.00 228.00 1.15% 88.76 1,378.23 -0.02 0.15 NO YES

ST03 USDCHF 3 58,803.82 36 25.00% 41,270.44 1,440.00 216.00 2.89% (995.16) 3,320.57 0.03 0.15 NO YES

ST03 USDJPY 3 57,835.61 24 12.50% 39,104.86 960.00 144.00 2.85% (61.58) 2,640.84 0.03 0.15 NO YES

ST04 AUDUSD 5 23,598.00 32 34.38% 13,682.00 1,280.00 192.00 1.32% (216.95) 2,046.68 0.01 0.15 NO YES

ST04 EURUSD 5 (3,244.00) 8 25.00% 11,0 27.00 320.00 52.00 -0.89% (115.86) 2,516.99 -0.1 -0.08 NO NO

ST04 GBPJPY 5 (17,900.50) 9 33.33% 18,463.66 1,080.00 76.50 -4.60% (335.16) 4,667.60 -0.11 -0.08 NO NO

ST04 GBPUSD 5 169,665.50 37 45.95% 14,601.00 1,480.00 314.50 6.20% 726.13 7,781.67 0.13 0.15 NO YES

ST04 USDCAD 5 13,156.01 16 43.75% 7,847.92 960.00 96.00 0.77% 205.41 1,360.56 -0.01 0.15 NO YES

ST04 USDCHF 5 94,169.25 27 48.1 5% 19,115.03 1,080.00 162.00 4.15% (110.30) 3,069.32 0.1 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST04 USDJPY 5 71,278.75 38 39.47% 37,677.54 1,520.00 228.00 3.36% (481.94) 2,925.50 0.05 0.15 NO YES

ST05 AUDUSD 6 19,616.00 19 21.05% 12,020.00 760.00 114.00 1.12% (259.90) 2,061.14 0 0.15 NO YES

ST05 EURUSD 6 7,703.00 10 30.00% 11,803.00 400.00 65.00 2.07% 102.00 2,681.05 0 -0.08 YES YES

ST05 GBPJPY 6 (51,853.41) 12 8.33% 51,853.41 1,440.00 102.00 -11.67% (1,280.77) 4,308.92 -0.32 -0.08 NO NO

ST05 GBPUSD 6 (48,389.00) 34 38.24% 90,701.50 1,360.00 289.00 -2.47% (344.49) 7,054.27 -0.04 0.15 NO NO

ST05 USDCAD 6 27,155.66 6 50.00% 2,063.90 360.00 36.00 1.50% 109.56 1,297.59 0.03 0.15 NO YES

ST05 USDCHF 6 101,831.91 21 33.33% 16,373.38 840.00 126.00 4.39% 13.48 3,376.07 0.07 0.15 NO YES

ST05 USDJPY 6 84,745.29 25 56.00% 17,738.51 1,000.00 150.00 3.83% (125.26) 2,810.00 0.09 0.15 NO YES

ST06 AUDUSD 5 12,526.00 14 28.57% 8,436.00 560.00 84.00 0.74% (123.10) 2,489.70 -0.01 0.15 NO YES

ST06 EURUSD 5 15,803.50 11 45.45% 5,726.50 440.00 71.50 4.10% 464.81 2,492.32 0.13 -0.08 YES YES

ST06 GBPJPY 5 (19,335.37) 10 30.00% 26,817.63 1,200.00 85.00 -4.94% (752.78) 4,574.48 -0.18 -0.08 NO NO

ST06 GBPUSD 5 71,487.50 5 60.00% 28,717.00 200.00 42.50 3.37% (869.74) 6,641.41 0.09 0.15 NO YES

ST06 USDCAD 5 11,729.65 6 33.33% 5,780.96 360.00 36.00 0.69% 104.25 1,320.51 -0.02 0.15 NO YES

ST06 USDCHF 5 14,018.40 6 33.33% 29,060.37 240.00 36.00 0.82% (775.41) 4,583.60 0.01 0.15 NO YES

ST06 USDJPY 5 41,346.26 16 75.00% 5,855.30 640.00 96.00 2.16% 1,013.24 2,373.95 0.34 0.15 YES YES

ST10A AUDUSD 5 (2,828.00) 8 12.50% 3,282.00 320.00 48.00 -0.17% (108.77) 1,682.42 -0.16 0.15 NO NO

ST10A EURUSD 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST10A GBPJPY 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST10A GBPUSD 5 23,018.00 12 33.33% 12,465.50 480.00 102.00 1.29% 822.07 5,850.76 0.14 0.15 NO YES

ST10A USDCAD 5 3,897.76 9 44.4 4% 3,932.07 540.00 54.00 0.24% 31.91 1,015.66 -0.09 0.15 NO YES

ST10A USDCHF 5 (2,289.55) 3 0.00% 2,289.55 120.00 18.00 -0.14% (338.33) 4,127.65 -0.08 0.15 NO NO

ST10A USDJPY 5 21,868.54 15 40.00% 9,459.37 600.00 90.00 1.24% 194.24 2,939.48 0.1 0.15 NO YES

ST10B AUDUSD 5 (11,840.00) 10 10.00% 12,744.00 400.00 60.00 -0.70% (101.80) 1,738.25 -0.14 0.15 NO NO

ST10B EURUSD 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST10B GBPJPY 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST10B GBPUSD 5 17,568.00 12 25.00% 12,112.50 480.00 102.00 1.01% 627.43 5,849.05 0.1 0.15 NO YES

ST10B USDCAD 5 (3,413.40) 6 0.00% 3,413.40 3 60.00 36.00 -0.21% 294.41 1,420.62 0.09 0.15 NO NO

ST10B USDCHF 5 6,331.83 10 20.00% 3,164.30 400.00 60.00 0.38% 269.89 2,185.53 0.06 0.15 NO YES

ST10B USDJPY 5 33,842.93 14 57.14% 4,352.16 560.00 84.00 1.82% 801.28 3,179.51 0.2 0.15 YES YES

ST11A AUDUSD 5 1,356.00 9 33.3 3% 4,802.00 360.00 54.00 0.08% 58.96 2,160.10 -0.04 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST11A EURUSD 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST11A GBPJPY 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST11A GBPUSD 5 41,615.00 10 40.00% 6,735.50 400.00 85.00 2.17% 1,342.42 5,107.33 0.27 0.15 YES YES

ST11A USDCAD 5 11,013.62 11 45.45% 2,768.76 660.00 66.00 0.65% 196.73 1,037.17 0 0.15 NO YES

ST11A USDCHF 5 16,390.50 5 40.00% 1,660.72 200.00 30.00 0.95% 830.18 2,972.03 0.23 0.15 YES YES

ST11A USDJPY 5 838.54 3 33.33% 2,998.45 120.00 18.00 0.05% 68.01 2,650.57 -0.03 0.15 NO YES

ST11B AUDUSD 5 (11,034.00) 29 24.14% 12,256.00 1,160.00 174.00 -0.65% (50.78) 1,791.62 -0.15 0.15 NO NO

ST11B EURUSD 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST11B GBPJPY 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST11B GBPUSD 5 21,089.00 6 33.33% 12,5 14.00 240.00 51.00 1.20% 1,318.06 5,727.58 0.25 0.15 YES YES

ST11B USDCAD 5 (1,861.59) 7 14.29% 1,861.59 420.00 42.00 -0.12% (166.32) 878.39 -0.38 0.15 NO NO

ST11B USDCHF 5 (79, 438.09) 6 0.00% 79,438.09 240.00 36.00 -3.65% (1,904.90) 2,667.10 -0.76 0.15 NO NO

ST11B USDJPY 5 838.54 3 33.33% 2,998.45 120.00 18.00 0.05% 68.01 2,650.57 -0.03 0.15 NO YES

ST12 AUDUSD 4 (5,642.00) 2 0.00% 5,642.00 80.00 12.00 -0.34% (806.00) 2,085.57 -0.45 0.15 NO NO

ST12 EURUSD 4 17,372.00 8 100.00% - 320.00 52.00 4.47% 1, 930.22 1,261.23 1.32 -0.08 YES YES

ST12 GBPJPY 4 8,509.05 5 8 0.00% 3,553.77 600. 00 42.50 2.28% 686.88 3,658.84 0.16 -0.08 YES YES

ST12 GBPUSD 4 (24,780.50) 13 61.54% 47,819.50 520.00 110.50 -1.38% (750.92) 4,737.72 -0.18 0.15 NO NO

ST12 USDCAD 4 5,161.99 4 75.00% 6,705.55 2 40.00 24.00 0.31% (16.55) 1,278.33 -0.18 0.15 NO YES

ST12 USDCHF 4 7,942.26 25 68.00% 15,225.51 1,000.00 150.00 0.48% (207.09) 2,684.77 0 0.15 NO YES

ST12 USDJPY 4 3,707.59 13 69.23% 7,704.99 520.00 78.00 0.23% 119.72 2,083.50 -0.01 0.15 NO YES

ST13 AUDUSD 5 (30,060.00 ) 15 33.3 3% 31,336.00 600.00 90.00 -1.64% (34.05) 2,250.13 -0.19 0.15 NO NO

ST13 EURUSD 5 25,341.00 6 16.67% 6,896.00 240.00 39.00 6.31% 626.87 3,154.49 0.15 -0.08 YES YES

ST13 GBPJPY 5 3,135.40 8 62.50% 6,618.00 960.00 68.00 0.86% 80.24 5,254.19 0.01 -0.08 YES YES

ST13 GBPUSD 5 (24, 365.50) 3 0.00% 24,365.50 120.00 25.50 -1.36% (1,123.32) 4,776.64 -0.24 0.15 NO NO

ST13 USDCAD 5 14,958.03 17 29.41% 9,524.85 1,020.00 102.00 0.87% (46.39) 1,320.81 -0.05 0.15 NO YES

ST13 USDCHF 5 52,410.37 86 40.70% 27,855.83 3,440.00 516.00 2.63% (154.14) 4,453.15 0.03 0.15 NO YES

ST13 USDJPY 5 143,606.00 33 51.52% 13,633.55 1,320.00 198.00 5.56% 136.91 3,180.23 0.16 0.15 YES YES

ST14 AUDUSD 5 61,848.00 7 71.43% 2,416.00 280.00 42.00 3.01% (272.98) 1,416.52 0.07 0.15 NO YES

ST14 EURUSD 5 3,504.00 4 5 0.00% 3,986.50 160. 00 26.00 0.96% 269.54 2,376.34 0.05 -0.08 YES YES

ST14 GBPJPY 5 (7,317.92) 12 41.67% 17,144.27 1,440.00 102.00 -1.97% (275.84) 4,234.19 -0.08 -0.08 YES NO




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST14 GBPUSD 5 (3,080.50) 33 42.42% 80,652.00 1,320.00 280.50 -0.19% 1,708.45 6,891.45 0.04 0.15 NO NO

ST14 USDCAD 5 4,845.33 15 40.00% 14,948.21 900.00 90.00 0.30% 229.24 1,283.94 -0.06 0.15 NO YES

ST14 USDCHF 5 27,242.71 20 65.00% 9,181.86 800.00 120.00 1.51% 973.15 2,811.33 0.05 0.15 NO YES

ST14 USDJPY 5 74,269.96 33 57.5 8% 17,160.14 1,320.00 198.00 3.47% 270.26 3,027.88 0.08 0.15 NO YES

ST15 AUDUSD 4 3,112.00 3 33.33% 4,396.00 1 20.00 18.00 0.19% 121.85 1,457.31 -0.03 0.15 NO YES

ST15 EURUSD 4 8,874.00 4 5 0.00% 4,853.00 160. 00 26.00 2.37% 328.67 2,650.67 0.07 -0.08 YES YES

ST15 GBPJPY 4 (16,731.48 ) 8 12.50% 17,440.58 960.00 68.00 -4.32% (758.07) 4,673.23 -0.17 -0.08 NO NO

ST15 GBPUSD 4 66,504.50 3 66.67% 16,798.50 120.00 25.50 3.19% (725.53) 5,338.65 0.15 0.15 YES YES

ST15 USDCAD 4 24,846.36 2 100.00% - 120.00 12.00 1.39% 94.21 1,042.71 0 0.15 NO YES

ST15 USDCHF 4 (5,521.56) 7 57.14% 20,410.95 280.00 42.00 -0.34% (639.90) 3,952.65 -0.05 0.15 NO NO

ST15 USDJPY 4 12,688.62 10 60.00% 21,903.65 400.00 60.00 0.75% (589.01) 2,605.88 -0.03 0.15 NO YES

ST16 AUDUSD 5 26,936.00 19 42.11% 7,588.00 760.00 114.00 1.49% (107.50) 1,940.20 0.01 0.15 NO YES

ST16 EURUSD 5 (19,242.50) 25 12.00% 30,464.00 1,000.00 162.50 -4.91% (367.82) 3,501.65 -0.16 -0.08 NO NO

ST16 GBPJPY 5 (45,887.67) 39 25.64% 66,952.87 4,680.00 331.50 -10.55% (1,352.42) 4,795.24 -0.15 -0.08 NO NO

ST16 GBPUSD 5 30,493.50 29 37.93% 64,089.50 1,160.00 246.50 1.66% (1,000.30) 8,619.66 0.04 0.15 NO YES

ST16 USDCAD 5 1,352.46 23 43.48% 9,473.11 1,380.00 138.00 0.08% (73.59) 1,010.26 -0.1 0.15 NO YES

ST16 USDCHF 5 121,107.33 17 47.06% 17,617.29 680.00 102.00 4.96% 476.57 3,573.04 0.1 0.15 NO YES

ST16 USDJPY 5 88,284.19 18 50.00% 9,067.30 720.00 108.00 3.95% (127.60) 2,876.70 0.11 0.15 NO YES

ST17 AUDUSD 4 29,532.00 33 48.48% 20,648.00 1,320.00 198.00 1.62% (102.95) 1,310.18 0.01 0.15 NO YES

ST17 EURUSD 4 (3,218.00) 52 21.15% 16,265.50 2,080.00 338.00 -0.88% (143.71) 3,024.87 -0.07 -0.08 YES NO

ST17 GBPJPY 4 (20,656.82) 75 28.00% 45,903.24 9,000.00 637.50 -5.24% (816.70) 4,826.08 -0.08 -0.08 YES NO

ST17 GBPUSD 4 80,252.00 8 37.50% 44,414.00 320.00 68.00 3.68% (787.35) 8,649.77 0.07 0.15 NO YES

ST17 USDCAD 4 15,620.50 10 30.00% 7,095.01 600.00 60.00 0.91% 56.47 1,339.47 -0.03 0.15 NO YES

ST17 USDCHF 4 10 3,275.24 19 36.84% 14,047.29 760.00 114.00 4.43% (82.36) 3,507.97 0.08 0.15 NO YES

ST17 USDJPY 4 139,150.14 53 62.26% 10,504.03 2,120.00 318.00 5.45% 220.01 2,827.67 0.16 0.15 YES YES

ST18 AUDUSD 3 13,338.00 42 38.10% 16,524.00 1,680.00 252.00 0.78% (169.20) 1,206.53 -0.02 0.15 NO YES

ST18 EURUSD 3 19,762.00 10 50.00% 5,741.00 400.00 65.00 5.04% 498.43 2,932.94 0.12 -0.08 YES YES

ST18 GBPJPY 3 (35,512.38) 17 29.41% 47,256.77 2,040.00 144.50 -8.49% (1,179.09) 3,431.66 -0.29 -0.08 NO NO

ST18 GBPUSD 3 78,160.50 67 4 1.79% 31,185.00 2,680.00 569.50 3.61% 1,084.92 5,595.00 0.1 0.15 NO YES

ST18 USDCAD 3 31,501.13 30 63.33% 1,682.86 1,800.00 180.00 1.71% 99.93 999.09 0.13 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST18 USDCHF 3 116,674.22 105 47.62% 18,762.49 4,200.00 630.00 4.83% 1,055.83 3,531.81 0.11 0.15 NO YES

ST18 USDJPY 3 146,591.64 203 40.39% 18,965.30 8,120.00 1,218.00 5.64% 9.69 3,264.54 0.18 0.15 YES YES

ST19 AUDUSD 5 13,876.00 24 33.33% 10,6 92.00 960.00 144.00 0.81% (6.35) 1,413.04 -0.02 0.15 NO YES

ST19 EURUSD 5 12,618.00 8 75.00% 3,619.50 320.00 52.00 3.32% 467.33 2,373.61 0.13 -0.08 YES YES

ST19 GBPJPY 5 - 0 0.00% - - - 0.00% - - 0 -0.08

ST19 GBPUSD 5 129,551.50 21 57.14% 30,869.50 840.00 178.50 5.19% 1,205.23 8,578.48 0.12 0.15 NO YES

ST19 USDCAD 5 (563.82) 86 39.53% 26,639.55 5,160.00 516.00 -0.04% (302.75) 1,148.66 -0.15 0.15 NO NO

ST19 USDCHF 5 11,384.20 48 33.3 3% 41,323.25 1,920.00 288.00 0.67% 682.52 4,447.52 0.03 0.15 NO YES

ST19 USDJPY 5 56,563.75 24 54.17% 11,196.04 960.00 144.00 2.80% 549.33 3,384.96 0.11 0.15 NO YES

ST20 AUDUSD 3 10,582.00 43 41.86% 19,886.00 1,720.00 258.00 0.63% (169.20) 1,206.53 -0.03 0.15 NO YES

ST20 EURUSD 3 18,309.50 9 55.56% 6,476.50 360.00 58.50 4.70% 469.06 2,845.06 0.11 -0.08 YES YES

ST20 GBPJPY 3 (42,262.86) 16 18.75% 52,061.34 1,920.00 136.00 -9.84% (1,233.22) 4,799.91 -0.18 -0.08 NO NO

ST20 GBPUSD 3 82,559.00 66 45.45% 30,785.00 2,640.00 561.00 3.76% 1,126.17 5,591.23 0.11 0.15 NO YES

ST20 USDCAD 3 28,964.61 26 61.54% 1,682.86 1,560.00 156.00 1.59% 54.13 1,001.62 0.12 0.15 NO YES

ST20 USDCHF 3 118,975.21 112 44.64% 15,065.36 4,480.00 672.00 4.90% 730.35 3,530.58 0.11 0.15 NO YES

ST20 USDJPY 3 141,962.59 214 41.59% 17,725.31 8,560.00 1,284.00 5.52% (55.59) 3,172.19 0.18 0.15 YES YES

ST21 AUDUSD 5 11,848.00 22 31.82% 13,680.00 880.00 132.00 0.70% (25.85) 1,416.82 -0.03 0.15 NO YES

ST21 EURUSD 5 4,424.00 4 7 5.00% 3,196.50 160. 00 26.00 1.21% 368.67 3,010.58 0.08 -0.08 YES YES

ST21 GBPJPY 5 (21, 294.92) 3 0.00% 21,294.92 360.00 25.50 -5.39% (2,354.70) 4,794.59 -0.52 -0.08 NO NO

ST21 GBPUSD 5 (14,242.50 ) 5 40.00% 41,995.50 200.00 42.50 -0.83% (238.51) 4,658.83 -0.06 0.15 NO NO

ST21 USDCAD 5 (12,154.30) 24 29.17% 19,141.14 1,440.00 144.00 -0.72% (4.96) 1,278.77 -0.13 0.15 NO NO

ST21 USDCHF 5 41,400.46 15 60.00% 11,471.53 600.00 90.00 2.16% 478.61 3,362.60 0.15 0.15 YES YES

ST21 USDJPY 5 122,330.47 27 51.8 5% 8,802.58 1,080.00 162.00 4.99% 140.92 2,679.56 0.13 0.15 NO YES

ST22 AUDUSD 1 5,026.00 14 28.57% 18,4 42.00 560.00 84.00 0.31% (104.85) 1,560.98 -0.05 0.15 NO YES

ST22 EURUSD 1 (5,797.00) 18 11.1 1% 22,134.50 720.00 117.00 -1.57% (23.85) 3,025.90 -0.09 -0.08 NO NO

ST22 GBPJPY 1 (51,676.38) 14 14.29% 52,766.21 1,680.00 119.00 -11.63% (1,415.20) 4,628.82 -0.24 -0.08 NO NO

ST22 GBPUSD 1 91,893.50 9 66.67% 31,291.00 360.00 76.50 4.07% (749.85) 7,335.40 0.07 0.15 NO YES

ST22 USDCAD 1 29,924.09 10 40.00% 8,742.81 600.00 60.00 1.64% 111.01 1,268.18 0 0.15 NO YES

ST22 USDCHF 1 40,288.31 14 3 5.71% 31,149.79 560.00 84.00 2.12% (1,255.65) 3,478.08 -0.02 0.15 NO YES

ST22 USDJPY 1 78,797.59 10 40.00% 17,453.03 400.00 60.00 3.63% (105.63) 2,818.72 0.05 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST23 AUDUSD 1 14,982.00 13 30.77% 11,7 24.00 520.00 78.00 0.87% (92.85) 1,436.05 -0.02 0.15 NO YES

ST23 EURUSD 1 9,435.00 10 30.00% 9,182.50 400.00 65.00 2.52% 199.29 2,927.88 0.02 -0.08 YES YES

ST23 GBPJPY 1 1,691.87 4 25.00% 11,985.24 480.00 34.00 0.47% (233.21) 4,843.19 -0.01 -0.08 YES YES

ST23 GBPUSD 1 71,748.00 12 33.33% 77,888.00 480.00 102.00 3.38% (1,714.77) 7,296.39 0.05 0.15 NO YES

ST23 USDCAD 1 17,130.74 29 2 0.69% 14,263.65 1,740.00 174.00 0.99% 101.44 1,318.34 -0.03 0.15 NO YES

ST23 USDCHF 1 72,314.15 16 43.75% 22,907.23 640.00 96.00 3.40% (568.33) 3,671.07 0.03 0.15 NO YES

ST23 USDJPY 1 107,997.92 9 44.44% 9,957.04 360.00 54.00 4.58% 123.93 2,877.41 0.09 0.15 NO YES

ST24 AUDUSD 1 (12,764.00 ) 14 21.4 3% 30,082.00 560.00 84.00 -0.75% (92.35) 1,488.60 -0.08 0.15 NO NO

ST24 EURUSD 1 6,454.50 7 28.57% 11,346.00 280.00 45.50 1.75% 233.61 2,805.15 -0.01 -0.08 YES YES

ST24 GBPJPY 1 (79,341.50) 45 22.22% 93,052.27 5,400.00 382.50 -16.31% (2,072.38) 4,495.68 -0.31 -0.08 NO NO

ST24 GBPUSD 1 105,403.50 9 66.67% 27,212.50 360.00 76.50 4.50% (1,386.14) 7,310.08 0.05 0.15 NO YES

ST24 USDCAD 1 16,162.87 22 31.82% 10,129.88 1,320.00 132.00 0.94% 55.29 1,382.26 -0.03 0.15 NO YES

ST24 USDCHF 1 38,934.35 8 50.00% 27,213.06 320.00 48.00 2.05% (828.42) 3,976.70 0.01 0.15 NO YES

ST24 USDJPY 1 94,051.01 12 41.67% 20,718.13 480.00 72.00 4.14% (231.65) 2,723.98 0.07 0.15 NO YES

ST25 AUDUSD 2 (15,126.00) 26 30.77% 24,292.00 1,040.00 156.00 -0.88% (226.15) 1,406.53 -0.08 0.15 NO NO

ST25 EURUSD 2 3,033.50 21 33.33% 15,632.50 840.00 136.50 0.83% 76.48 2,859.12 -0.02 -0.08 YES YES

ST25 GBPJPY 2 (33,681.41 ) 8 12.50% 33,681.41 960.00 68.00 -8.11% (911.62) 5,041.86 -0.19 -0.08 NO NO

ST25 GBPUSD 2 119,488.00 12 5 8.33% 38,547.00 480.00 102.00 4.91% (953.35) 6,855.48 0.09 0.15 NO YES

ST25 USDCAD 2 9,942.98 8 37.50% 16,112.32 480.00 48.00 0.59% 172.25 1,260.06 -0.05 0.15 NO YES

ST25 USDCHF 2 (294.16) 6 33.33% 39,762.76 240.00 36.00 -0.02% (1,455.68) 3,384.09 -0.05 0.15 NO NO

ST25 USDJPY 2 - 0 0.00% - - - 0.00% - - 0 0.15

ST26 AUDUSD 2 18,342.00 23 39.1 3% 18,214.00 920.00 138.00 1.05% (188.65) 1,472.67 -0.02 0.15 NO YES

ST26 EURUSD 2 5,878.00 8 37.50% 11,626.00 320.00 52.00 1.59% 228.45 2,847.58 -0.03 -0.08 YES YES

ST26 GBPJPY 2 - 0 0.00% - - - 0.00% - - 0 -0.08

ST26 GBPUSD 2 115,605.50 17 4 1.18% 42,659.50 680.00 144.50 4.80% (328.56) 6,541.97 0.09 0.15 NO YES

ST26 USDCAD 2 14,893.98 25 36.00% 12,307.88 1,500.00 150.00 0.87% 18.92 1,332.37 -0.05 0.15 NO YES

ST26 USDCHF 2 36,791.76 10 4 0.00% 28,820.77 400.00 60.00 1.96% (1,131.67) 3,577.27 -0.01 0.15 NO YES

ST26 USDJPY 2 110,062.02 8 50.00% 9,684.03 320.00 48.00 4.64% 118.36 2,888.33 0.09 0.15 NO YES

ST27 AUDUSD 2 35,406.00 24 50.00% 5,144.00 960.00 144.00 1.89% 132.25 1,450.67 0.03 0.15 NO YES

ST27 EURUSD 2 10,051.50 9 33.33% 8,566.00 360.00 58.50 2.67% 214.70 2,810.34 0.03 -0.08 YES YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST27 GBPJPY 2 - 0 0.00% - - - 0.00% - - 0 -0.08

ST27 GBPUSD 2 82,891.00 14 42.86% 80,498.00 560.00 119.00 3.77% (1,687.78) 7,091.43 0.07 0.15 NO YES

ST27 USDCAD 2 15,673.57 18 33.33% 12,314.93 1,080.00 108.00 0.91% 99.48 1,366.01 -0.03 0.15 NO YES

ST27 USDCHF 2 74,142.46 16 43.75% 21,239.59 640.00 96.00 3.47% (508.21) 3,691.92 0.03 0.15 NO YES

ST27 USDJPY 2 91,222.25 11 36.36% 14,008.34 440.00 66.00 4.05% 105.34 2,915.36 0.07 0.15 NO YES

ST28 AUDUSD 2 22,152.00 28 3 9.29% 16,842.00 1,120.00 168.00 1.25% (96.15) 1,496.74 -0.01 0.15 NO YES

ST28 EURUSD 2 (11,897.00) 18 11.11% 27,994.50 720.00 117.00 -3.14% (75.26) 3,191.19 -0.11 -0.08 NO NO

ST28 GBPJPY 2 (55,440.52) 43 30.23% 75,315.04 5,160.00 365.50 -12.32% (1,584.95) 4,731.41 -0.22 -0.08 NO NO

ST28 GBPUSD 2 86,196.50 11 54.5 5% 51,029.50 440.00 93.50 3.88% (1,156.56) 7,210.00 0.07 0.15 NO YES

ST28 USDCAD 2 10,167.49 30 40.00% 9,834.14 1,800.00 180.00 0.60% (87.03) 1,206.56 -0.08 0.15 NO YES

ST28 USDCHF 2 40,062.52 11 5 4.55% 35,739.47 440.00 66.00 2.11% (1,384.60) 3,507.69 -0.01 0.15 NO YES

ST28 USDJPY 2 83,557.62 8 62.50% 13,849.94 320.00 48.00 3.79% (70.21) 2,925.13 0.07 0.15 NO YES

ST29 AUDUSD 3 (1,182.00) 27 22.22% 27,562.00 1,080.00 162.00 -0.07% (153.85) 1,600.73 -0.06 0.15 NO NO

ST29 EURUSD 3 (18,431.00) 54 20.37% 22,799.00 2,160.00 351.00 -4.72% (428.03) 3,547.66 -0.15 -0.08 NO NO

ST29 GBPJPY 3 (33,681.41 ) 8 12.50% 33,681.41 960.00 68.00 -8.11% (911.62) 5,041.86 -0.19 -0.08 NO NO

ST29 GBPUSD 3 119,488.00 12 5 8.33% 38,547.00 480.00 102.00 4.91% (953.35) 6,855.48 0.09 0.15 NO YES

ST29 USDCAD 3 (597.76) 24 33.33% 10,941.72 1,440.00 144.00 -0.04% 179.85 1,257.19 -0.1 0.15 NO NO

ST29 USDCHF 3 81,356.43 41 36.59% 21,242.65 1,640.00 246.00 3.72% 84.30 3,881.12 0.07 0.15 NO YES

ST29 USDJPY 3 174,2 09.52 55 56.36% 6,640.89 2,200.00 330.00 6.30% 336.48 2,660.94 0.2 0.15 YES YES

ST30 AUDUSD 1 (11,322.00) 17 23.53% 27,774.00 680.00 102.00 -0.67% (369.40) 2,379.10 -0.08 0.15 NO NO

ST30 EURUSD 1 1,554.50 7 28.57% 14,916.00 280. 00 45.50 0.43% 25.28 2,492.36 -0.04 -0.08 YES YES

ST30 GBPJPY 1 (39,203.27) 12 16.67% 39,203.27 1,440.00 102.00 -9.24% (1,335.40) 4,059.20 -0.36 -0.08 NO NO

ST30 GBPUSD 1 28,783.50 9 77.78% 60,175.50 360.00 76.50 1.58% (1,814.17) 7,110.27 0.02 0.15 NO YES

ST30 USDCAD 1 16,179.58 30 40.00% 5,878.52 1,800.00 180.00 0.94% (40.44) 1,134.65 -0.04 0.15 NO YES

ST30 USDCHF 1 38,161.67 7 42.86% 46,099.94 280.00 42.00 2.02% (1,386.22) 3,161.72 0.01 0.15 NO YES

ST30 USDJPY 1 62,755.91 11 45.45% 15,293.32 440.00 66.00 3.04% (326.18) 2,826.20 0.06 0.15 NO YES

ST31 AUDUSD 1 26,212.00 23 34.78% 10,186.00 920.00 138.00 1.45% (58.75) 1,322.56 0.01 0.15 NO YES

ST31 EURUSD 1 5,554.50 7 28.5 7% 8,576.50 280.00 45.50 1.51% 62.94 3,165.41 -0.02 -0.08 YES YES

ST31 GBPJPY 1 (14,345.92) 9 22.22% 14,345.92 1,080.00 76.50 -3.74% (260.74) 4,397.98 -0.07 -0.08 YES NO

ST31 GBPUSD 1 67,688.00 12 41.67% 80,169.50 480.00 102.00 3.23% (1,980.03) 6,736.41 0.05 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST31 USDCAD 1 26,057.69 17 29.41% 7,503.38 1,020.00 102.00 1.45% 184.03 1,234.58 0 0.15 NO YES

ST31 USDCHF 1 9,966.70 9 44.44% 3 8,853.66 360.00 54.00 0.59% (1,039.75) 3,599.06 -0.03 0.15 NO YES

ST31 USDJPY 1 97,683.34 10 40.00% 7,047.21 400.00 60.00 4.26% (56.73) 2,686.19 0.07 0.15 NO YES

ST32 AUDUSD 1 (33,914.00) 34 32.35% 39,078.00 1,360.00 204.00 -1.82% (509.10) 1,345.75 -0.15 0.15 NO NO

ST32 EURUSD 1 10,198.00 8 25.00% 12,999.00 320.00 52.00 2.71% 267.91 2,564.80 0.05 -0.08 YES YES

ST32 GBPJPY 1 (19,200.03 ) 6 16.67% 19,200.03 720.00 51.00 -4.90% (642.39) 4,187.42 -0.18 -0.08 NO NO

ST32 GBPUSD 1 8,465.00 10 50.00% 67,375.50 400.00 85.00 0.51% (1,713.21) 7,486.71 -0.01 0.15 NO YES

ST32 USDCAD 1 24,657.21 17 47.06% 8,453.16 1,020.00 102.00 1.38% 112.35 1,270.84 0 0.15 NO YES

ST32 USDCHF 1 51,272.36 10 50.00% 15,184.09 400.00 60.00 2.59% (733.86) 3,173.88 0.05 0.15 NO YES

ST32 USDJPY 1 53,156.10 8 50.00% 27,176.22 320.00 48.00 2.66% (18.52) 2,764.15 0.06 0.15 NO YES

ST33 AUDUSD 2 1,324.00 16 31.25% 14,9 64.00 640.00 96.00 0.08% (263.00) 1,302.65 -0.05 0.15 NO YES

ST33 EURUSD 2 7,560.50 3 6 6.67% 5,886.50 120. 00 19.50 2.04% 174.29 3,371.19 0.02 -0.08 YES YES

ST33 GBPJPY 2 (30,831.01) 9 22.22% 30,831.01 1,080.00 76.50 -7.50% (823.40) 4,584.47 -0.18 -0.08 NO NO

ST33 GBPUSD 2 15,656.50 11 27.27% 94,811.00 440.00 93.50 0.91% (2,770.24) 6,131.38 0 0.15 NO YES

ST33 USDCAD 2 10,905.59 7 57.14% 11,291.34 420.00 42.00 0.65% 121.86 1,276.98 -0.05 0.15 NO YES

ST33 USDCHF 2 (4,564.20) 8 25.00% 60,061.57 320.00 48.00 -0.28% (1,558.03) 3,394.88 -0.07 0.15 NO NO

ST33 USDJPY 2 111,451.21 23 60.87% 8,977.83 920.00 138.00 4.68% 469.41 2,850.63 0.14 0.15 NO YES

ST34 AUDUSD 2 4,168.00 17 29.41% 22,134.00 680.00 102.00 0.26% (498.15) 2,104.05 -0.05 0.15 NO YES

ST34 EURUSD 2 7,931.00 6 50.00% 13,369.50 240.00 39.00 2.13% 273.48 2,734.49 0.05 -0.08 YES YES

ST34 GBPJPY 2 (34,840.26) 13 23.08% 36,422.45 1,560.00 110.50 -8.35% (1,198.46) 4,265.15 -0.33 -0.08 NO NO

ST34 GBPUSD 2 14,309.00 6 50.00% 53,497.00 240.00 51.00 0.84% (1,548.28) 6,097.55 0 0.15 NO YES

ST34 USDCAD 2 16,596.05 24 50.00% 5,270.66 1,440.00 144.00 0.96% 121.38 1,323.51 -0.01 0.15 NO YES

ST34 USDCHF 2 32,879.14 7 28.57% 16,486.80 280.00 42.00 1.78% (97.79) 3,484.62 0.04 0.15 NO YES

ST34 USDJPY 2 48,210.29 10 20.00% 32,099.41 400.00 60.00 2.46% (164.97) 2,808.73 0.04 0.15 NO YES

ST35 AUDUSD 2 7,984.00 16 37.50% 10,550.00 640.00 96.00 0.48% (71.35) 2,419.80 -0.03 0.15 NO YES

ST35 EURUSD 2 6,354.50 7 28.5 7% 8,876.00 280.00 45.50 1.72% 98.36 3,077.79 -0.02 -0.08 YES YES

ST35 GBPJPY 2 (1,353.57) 2 50.00% 11,071.43 240.00 17.00 -0.38% (123.81) 4,328.38 -0.05 -0.08 YES NO

ST35 GBPUSD 2 83,347.50 5 60.00% 30,447.00 200.00 42.50 3.79% (865.60) 7,077.44 0.1 0.15 NO YES

ST35 USDCAD 2 23,836.00 12 50.00% 8,167.96 720.00 72.00 1.34% 184.57 1,249.73 0 0.15 NO YES

ST35 USDCHF 2 46,557.20 10 30.00% 26,176.55 400.00 60.00 2.39% (513.41) 3,927.27 0.05 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST35 USDJPY 2 86,606.31 9 44.44% 10,534.70 360.00 54.00 3.90% (194.53) 2,893.47 0.06 0.15 NO YES

ST36 AUDUSD 2 31,838.00 12 33.33% 6,128.00 480.00 72.00 1.73% 86.35 2,390.68 0.08 0.15 NO YES

ST36 EURUSD 2 13,170.50 3 100.00% - 120.00 19.50 3.45% 627.17 2,987.94 0.17 -0.08 YES YES

ST36 GBPJPY 2 (20,862.52) 12 16.67% 22,181.82 1,440.00 102.00 -5.29% (832.36) 4,174.36 -0.23 -0.08 NO NO

ST36 GBPUSD 2 11,866.50 11 45.45% 16,352.50 440.00 93.50 0.70% 234.44 5,847.70 0.04 0.15 NO YES

ST36 USDCAD 2 23,689.78 13 53.85% 5,812.94 780.00 78.00 1.33% 62.04 1,242.27 0.01 0.15 NO YES

ST36 USDCHF 2 60,834.60 6 50.00% 8,800.26 240.00 36.00 2.97% (421.53) 3,898.15 0.07 0.15 NO YES

ST36 USDJPY 2 58,295.95 8 50.00% 28,504.24 320.00 48.00 2.87% 15.80 2,714.24 0.06 0.15 NO YES

ST37 AUDUSD 3 (3,490.00) 10 20.0 0% 24,694.00 400.00 60.00 -0.21% (720.25) 2,152.86 -0.06 0.15 NO NO

ST37 EURUSD 3 12,152.50 15 33.33% 5,742.50 600.00 97.50 3.20% 714.85 2,937.01 0.2 -0.08 YES YES

ST37 GBPJPY 3 (57,409.52) 10 10.00% 57,409.52 1,200.00 85.00 -12.67% (1,316.82) 3,936.23 -0.27 -0.08 NO NO

ST37 GBPUSD 3 44,932.00 8 37.50% 71,692.50 320.00 68.00 2.32% (1,780.42) 6,501.90 0.04 0.15 NO YES

ST37 USDCAD 3 9,254.65 4 50.00% 4,120.87 240.00 24.00 0.55% (23.84) 991.79 -0.09 0.15 NO YES

ST37 USDCHF 3 16,877.08 7 42.86% 25,173.49 280.00 42.00 0.97% (1,057.90) 3,650.13 -0.02 0.15 NO YES

ST37 USDJPY 3 95,305.79 19 63.16% 7,119.82 760.00 114.00 4.18% 443.45 2,953.51 0.12 0.15 NO YES

ST38 AUDUSD 4 6,844.00 26 3 4.62% 22,374.00 1,040.00 156.00 0.41% (319.40) 1,505.31 -0.05 0.15 NO YES

ST38 EURUSD 4 (2,923.00) 22 40.91% 15,055.00 880.00 143.00 -0.80% 12.66 2,944.86 -0.07 -0.08 YES NO

ST38 GBPJPY 4 (31,077.32) 19 26.32% 32,200.28 2,280.00 161.50 -7.56% (1,138.28) 4,571.07 -0.25 -0.08 NO NO

ST38 GBPUSD 4 148,199.00 26 46.15% 37,092.50 1,040.00 221.00 5.68% 312.80 7,538.70 0.13 0.15 NO YES

ST38 USDCAD 4 905.88 29 37.93% 13,9 32.63 1,740.00 174.00 0.06% (52.77) 1,018.60 -0.11 0.15 NO YES

ST38 USDCHF 4 20,978.51 19 52.63% 17,739.93 760.00 114.00 1.19% (411.70) 4,004.81 0.04 0.15 NO YES

ST38 USDJPY 4 93,163.02 24 50.00% 14,610.64 960.00 144.00 4.11% 70.24 2,874.28 0.09 0.15 NO YES

ST39 AUDUSD 4 854.00 31 25.81% 31,168.00 1,240.00 186.00 0.05% (387.80) 1,975.08 -0.05 0.15 NO YES

ST39 EURUSD 4 20,542.50 15 46.67% 8,792.00 600.00 97.50 5.22% 455.14 2,712.92 0.09 -0.08 YES YES

ST39 GBPJPY 4 3,183.39 11 45.45% 11,026.94 1,320.00 93.50 0.88% (54.32) 4,060.20 0 -0.08 YES YES

ST39 GBPUSD 4 82,627.50 25 44.0 0% 13,855.50 1,000.00 212.50 3.76% 569.98 6,042.99 0.21 0.15 YES YES

ST39 USDCAD 4 (18,628.50) 28 28.57% 25,966.36 1,680.00 168.00 -1.07% 121.22 1,254.90 -0.15 0.15 NO NO

ST39 USDCHF 4 89,769.17 40 62.5 0% 15,533.39 1,600.00 240.00 4.00% 437.66 3,085.33 0.12 0.15 NO YES

ST39 USDJPY 4 10 9,226.20 23 60.87% 12,950.40 920.00 138.00 4.61% 187.83 2,782.03 0.12 0.15 NO YES

ST40 AUDUSD 4 (43,152.00) 47 27.66% 51,504.00 1,880.00 282.00 -2.24% (449.05) 2,178.32 -0.17 0.15 NO NO




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST40 EURUSD 4 (1,325.00) 10 40.00% 15,8 73.00 400.00 65.00 -0.37% 25.20 2,828.64 -0.07 -0.08 YES NO

ST40 GBPJPY 4 (24,725.35) 11 36.36% 27,977.67 1,320.00 93.50 -6.17% (1,046.30) 4,476.09 -0.23 -0.08 NO NO

ST40 GBPUSD 4 48,737.00 18 55.56% 10,847.00 720.00 153.00 2.48% 599.95 5,618.64 0.11 0.15 NO YES

ST40 USDCAD 4 11,434.09 24 58.33% 7,625.28 1,440.00 144.00 0.68% (99.29) 983.47 -0.07 0.15 NO YES

ST40 USDCHF 4 72,986.56 20 60.00% 9,615.55 800.00 120.00 3.42% 642.16 3,137.12 0.18 0.15 YES YES

ST40 USDJPY 4 96,620.30 29 48.2 8% 10,783.84 1,160.00 174.00 4.22% 216.03 2,694.03 0.09 0.15 NO YES

ST41 AUDUSD 4 16,470.00 50 3 6.00% 13,348.00 2,000.00 300.00 0.95% (81.45) 1,694.63 -0.02 0.15 NO YES

ST41 EURUSD 4 10,735.00 30 43.3 3% 13,920.50 1,200.00 195.00 2.85% 589.06 2,726.17 0.03 -0.08 YES YES

ST41 GBPJPY 4 (39,589.33) 27 22.22% 46,243.93 3,240.00 229.50 -9.31% (1,054.50) 3,783.36 -0.26 -0.08 NO NO

ST41 GBPUSD 4 84,263.00 42 45.24% 52,585.50 1,680.00 357.00 3.82% (696.99) 6,849.40 0.08 0.15 NO YES

ST41 USDCAD 4 5,531.13 58 44.83% 9,332.16 3,480.00 348.00 0.34% 194.58 1,323.94 -0.04 0.15 NO YES

ST41 USDCHF 4 32,391.73 196 49.49% 14,693.67 7,840.00 1,176.00 1.75% 901.69 3,141.07 0.16 0.15 YES YES

ST41 USDJPY 4 94,434.51 52 44.2 3% 17,587.33 2,080.00 312.00 4.15% 263.76 2,946.18 0.08 0.15 NO YES

ST42 AUDUSD 1 (28,142.00) 102 27.45% 33,940.00 4,080.00 612.00 -1.55% (266.05) 1,545.52 -0.14 0.15 NO NO

ST42 EURUSD 1 (8,848.00) 12 16.67% 8,981.50 480.00 78.00 -2.37% (589.87) 1,746.19 -0.43 -0.08 NO NO

ST42 GBPJPY 1 (27,486.89) 92 18.48% 34,468.08 11,040.00 782.00 -6.78% (616.64) 4,342.23 -0.13 -0.08 NO NO

ST42 GBPUSD 1 (171,602.00) 332 39.76% 175,261.00 13,280.00 2,822.00 -6.24% (874.72) 7,187.58 0.02 0.15 NO NO

ST42 USDCAD 1 (22,481.05) 11 27.27% 22,849.83 660.00 66.00 -1.27% (186.50) 1,259.62 -0.16 0.15 NO NO

ST42 USDCHF 1 84,537.08 44 25.0 0% 18,061.91 1,760.00 264.00 3.83% (57.12) 3,642.15 0.09 0.15 NO YES

ST42 USDJPY 1 82,858.01 77 33.7 7% 10,253.50 3,080.00 462.00 3.77% (45.98) 3,117.26 0.07 0.15 NO YES

ST43 AUDUSD 1 (47,462.00) 237 36.29% 47,596.00 9,480.00 1,422.00 -2.43% (97.50) 1,259.48 -0.3 0.15 NO NO

ST43 EURUSD 1 (660.50) 17 35.29% 3,981.00 680.00 110.50 -0.18% (55.04) 1,105.85 -0.19 -0.08 NO NO

ST43 GBPJPY 1 (4,949.82) 21 23.81% 13,428.00 2,520.00 178.50 -1.35% (599.96) 4,623.89 -0.15 -0.08 NO NO

ST43 GBPUSD 1 (85,555.00) 130 37.69% 87,624.50 5,200.00 1,105.00 -3.86% (2,112.25) 4,542.71 -0.4 0.15 NO NO

ST43 USDCAD 1 (8,064.45) 118 38.14% 10,356.63 7,080.00 708.00 -0.48% (188.56) 760.73 -0.38 0.15 NO NO

ST43 USDCHF 1 (34,012.55) 389 45.76% 48,971.39 15,560.00 2,334.00 -1.83% (522.72) 2,017.60 -0.16 0.15 NO NO

ST43 USDJPY 1 22,343.78 437 45.08% 14,909.37 17,480.00 2,622.00 1.26% 21.74 2,412.84 0 0.15 NO YES

ST44 AUDUSD 2 (4,770.00) 5 40.00% 4,924.00 200.00 30.00 -0.29% (162.42) 1,879.63 -0.14 0.15 NO NO

ST44 EURUSD 2 (6,846.00) 4 0.00% 6,846.00 160.00 26.00 -1.85% (978.00) 2,474.38 -0.46 -0.08 NO NO

ST44 GBPJPY 2 - 0 0.00% - - - 0.00% - - 0 -0.08




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST44 GBPUSD 2 (2,241.00) 6 16.67% 19,7 94.00 240.00 51.00 -0.14% (83.00) 5,272.47 -0.02 0.15 NO NO

ST44 USDCAD 2 (6,633.44) 6 16.67% 6,633.44 360.00 36.00 -0.40% 135.56 1,328.09 -0.09 0.15 NO NO

ST44 USDCHF 2 (59,102.61) 267 3 8.58% 77,988.02 10,680.00 1,602.00 -2.90% (330.94) 3,093.19 -0.1 0.15 NO NO

ST44 USDJPY 2 3,609.37 2 50.00% 6,932.04 80.00 12.00 0.22% 184.50 3,209.36 0.02 0.15 NO YES

ST45 AUDUSD 1 (15,920.00) 880 39.89% 47,442.00 35,200.00 5,280.00 -0.92% (499.30) 1,555.76 -0.13 0.15 NO NO

ST45 EURUSD 1 (39,928.50) 229 33.19% 44,536.50 9,160.00 1,488.50 -9.38% (650.40) 3,486.92 -0.24 -0.08 NO NO

ST45 GBPJPY 1 (38,136.29) 235 36.60% 44,412.69 28,200.00 1,997.50 -9.02% (464.80) 4,974.80 -0.18 -0.08 NO NO

ST45 GBPUSD 1 (129,040.00) 1380 43.12% 157,015.50 55,200.00 11,730.00 -5.18% (1,133.83) 8,502.83 -0.08 0.15 NO NO

ST45 USDCAD 1 (82,947.51) 1034 37.04% 84,564.18 62,040.00 6,204.00 -3.77% (310.36) 1,112.86 -0.35 0.15 NO NO

ST45 USDCHF 1 (46,598.52) 1027 44.79% 88,801.52 41,080.00 6,162.00 -2.39% (1,544.03) 3,252.97 -0.13 0.15 NO NO

ST45 USDJPY 1 42,307.78 987 43.67% 32,044.58 39,480.00 5,922.00 2.20% 202.74 3,255.13 0.02 0.15 NO YES

ST46 AUDUSD 2 13,334.00 21 47.62% 15,678.00 840.00 126.00 0.78% (71.15) 1,521.22 -0.03 0.15 NO YES

ST46 EURUSD 2 (28,512.00 ) 8 25.00% 28,512.00 320.00 52.00 -7.00% (813.21) 2,741.22 -0.35 -0.08 NO NO

ST46 GBPJPY 2 (7,938.20) 9 55.56% 21,773.57 1,080.00 76.50 -2.13% (161.28) 5,095.26 -0.04 -0.08 YES NO

ST46 GBPUSD 2 (27,051.50) 19 36.84% 127,699.00 760.00 161.50 -1.50% (2,671.14) 6,920.28 -0.11 0.15 NO NO

ST46 USDCAD 2 (32,886.92) 31 22.58% 33,209.15 1,860.00 186.00 -1.78% (40.35) 1,161.19 -0.22 0.15 NO NO

ST46 USDCHF 2 19,738.77 11 4 5.45% 27,606.73 440.00 66.00 1.13% (1,162.80) 3,587.08 -0.02 0.15 NO YES

ST46 USDJPY 2 (25,388.54) 65 40.00% 65,670.19 2,600.00 390.00 -1.41% 379.26 2,932.38 -0.03 0.15 NO NO

ST47 AUDUSD 2 (1,882.00) 22 31.8 2% 21,280.00 880.00 132.00 -0.12% (11.85) 1,480.20 -0.05 0.15 NO NO

ST47 EURUSD 2 13,191.00 6 33.33% 7,086.50 240.00 39.00 3.46% 373.19 2,966.23 0.08 -0.08 YES YES

ST47 GBPJPY 2 (35,625.08) 18 27.78% 43,435.98 2,160.00 153.00 -8.51% (1,247.05) 4,804.99 -0.19 -0.08 NO NO

ST47 GBPUSD 2 123,4 83.50 9 44.44% 52,104.00 360.00 76.50 5.02% (1,433.85) 6,955.01 0.1 0.15 NO YES

ST47 USDCAD 2 (17,533.74) 42 26.19% 26,205.48 2,520.00 252.00 -1.01% (214.79) 1,011.71 -0.19 0.15 NO NO

ST47 USDCHF 2 70,567.31 116 43.97% 2 9,334.97 4,640.00 696.00 3.34% 227.09 4,202.08 0.05 0.15 NO YES

ST47 USDJPY 2 71,362.95 8 37.50% 15,205.68 320.00 48.00 3.37% (167.66) 2,827.91 0.04 0.15 NO YES

ST48 AUDUSD 2 (5,448.00) 23 39.13% 18,460.00 920.00 138.00 -0.33% (197.15) 1,445.80 -0.06 0.15 NO NO

ST48 EURUSD 2 (22,608.50 ) 9 33.33% 22,608.50 360.00 58.50 -5.69% (412.71) 2,584.78 -0.27 -0.08 NO NO

ST48 GBPJPY 2 (40,549.44) 22 18.18% 44,518.57 2,640.00 187.00 -9.51% (936.48) 5,115.08 -0.17 -0.08 NO NO

ST48 GBPUSD 2 43,942.50 35 54.2 9% 59,897.00 1,400.00 297.50 2.28% 142.72 7,468.65 0.03 0.15 NO YES

ST48 USDCAD 2 (36,089.27) 29 24.14% 36,089.27 1,740.00 174.00 -1.93% (180.01) 1,329.05 -0.2 0.15 NO NO




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST48 USDCHF 2 40,449.65 11 54.55% 28,239.90 440.00 66.00 2.12% (858.63) 3,696.63 0.01 0.15 NO YES

ST48 USDJPY 2 (16,485.30) 57 42.11% 61,676.92 2,280.00 342.00 -0.95% 523.97 2,713.34 -0.02 0.15 NO NO

ST49 AUDUSD 3 29,542.00 133 35.34% 1 1,616.00 5,320.00 798.00 1.62% (43.75) 1,449.93 0.01 0.15 NO YES

ST49 EURUSD 3 23,245.50 13 84.62% 4,919.50 520.00 84.50 5.84% 708.35 3,118.80 0.19 -0.08 YES YES

ST49 GBPJPY 3 (42,601.56) 11 54.55% 46,979.71 1,320.00 93.50 -9.91% (1,044.61) 4,522.56 -0.22 -0.08 NO NO

ST49 GBPUSD 3 (17,524.50) 17 41.18% 61,424.00 680.00 144.50 -1.01% (918.85) 7,144.90 0.01 0.15 NO NO

ST49 USDCAD 3 (17,564.55) 40 37.50% 20,977.68 2,400.00 240.00 -1.01% (18.46) 1,335.13 -0.15 0.15 NO NO

ST49 USDCHF 3 16,602.12 16 37.50% 45,538.77 640.00 96.00 0.96% (640.31) 3,696.14 -0.03 0.15 NO YES

ST49 USDJPY 3 78,000.43 45 44.44% 17,861.64 1,800.00 270.00 3.60% (340.60) 2,543.40 0.06 0.15 NO YES

ST50 AUDUSD 2 1,692.00 18 38.89% 20,940.00 720.00 108.00 0.10% (188.75) 1,439.36 -0.05 0.15 NO YES

ST50 EURUSD 2 (14,285.50 ) 7 28.57% 18,516.00 280.00 45.50 -3.73% (439.11) 2,251.65 -0.24 -0.08 NO NO

ST50 GBPJPY 2 (5,800.36) 8 37.50% 19,413.07 960.00 68.00 -1.57% (187.67) 4,734.92 -0.05 -0.08 YES NO

ST50 GBPUSD 2 58,496.50 51 62.75% 47,350.50 2,040.00 433.50 2.88% (333.63) 6,248.63 0.05 0.15 NO YES

ST50 USDCAD 2 (7,579.03) 23 39.1 3% 16,892.79 1,380.00 138.00 -0.46% 87.43 1,110.82 -0.14 0.15 NO NO

ST50 USDCHF 2 48,466.55 16 37.50% 30,167.90 640.00 96.00 2.47% (237.91) 3,648.15 0.03 0.15 NO YES

ST50 USDJPY 2 58,605.81 40 52.5 0% 10,768.98 1,600.00 240.00 2.88% 548.84 2,747.72 0.07 0.15 NO YES

ST51 AUDUSD 2 (10,262.00) 17 23.53% 21,602.00 680.00 102.00 -0.61% (52.35) 1,416.18 -0.07 0.15 NO NO

ST51 EURUSD 2 9,471.00 6 33.33% 10,846.50 240.00 39.00 2.53% 155.84 3,044.67 0.01 -0.08 YES YES

ST51 GBPJPY 2 (28,211.69) 15 33.33% 36,870.30 1,800.00 127.50 -6.94% (872.23) 4,115.20 -0.18 -0.08 NO NO

ST51 GBPUSD 2 66,860.50 7 42.86% 56,395.50 280.00 59.50 3.20% (1,215.60) 7,373.61 0.07 0.15 NO YES

ST51 USDCAD 2 (4,096.46) 33 36.36% 15,975.44 1,980.00 198.00 -0.25% (177.35) 1,063.48 -0.14 0.15 NO NO

ST51 USDCHF 2 35,791.37 12 33.33% 29,123.54 480. 00 72.00 1.91% (827.12) 3,600.52 0 0.15 NO YES

ST51 USDJPY 2 72,258.21 41 56.10% 15,6 83.26 1,640.00 246.00 3.40% 78.94 2,722.55 0.1 0.15 NO YES

ST52 AUDUSD 2 (8,878.00) 18 33.33% 25,444.00 720.00 108.00 -0.53% (197.00) 1,392.18 -0.08 0.15 NO NO

ST52 EURUSD 2 (24,268.50 ) 9 22.22% 24,268.50 360.00 58.50 -6.07% (605.43) 2,279.50 -0.31 -0.08 NO NO

ST52 GBPJPY 2 (36, 503.51) 9 22.22% 45,200.42 1,080.00 76.50 -8.69% (874.68) 4,672.39 -0.2 -0.08 NO NO

ST52 GBPUSD 2 28,393.50 29 51.72% 76,060.50 1,160.00 246.50 1.56% (148.49) 7,274.80 0.02 0.15 NO YES

ST52 USDCAD 2 (23,662.17) 44 27.27% 23,824.57 2,640.00 264.00 -1.33% (183.75) 1,304.22 -0.22 0.15 NO NO

ST52 USDCHF 2 102,209.43 27 40.74% 21,714.44 1,080.00 162.00 4.40% 325.77 3,370.16 0.09 0.15 NO YES

ST52 USDJPY 2 57,982.31 43 51.1 6% 26,565.99 1,720.00 258.00 2.86% 424.98 2,712.22 0.06 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST53 AUDUSD 3 31,420.00 55 41.82% 17,916.00 2,200.00 330.00 1.71% 14.77 1,374.98 0 0.15 NO YES

ST53 EURUSD 3 9,015.00 10 80.00% 8,916.50 400.00 65.00 2.41% 345.80 3,152.88 0.07 -0.08 YES YES

ST53 GBPJPY 3 (44,064.60) 7 0.00% 44,064.60 840.00 59.50 -10.19% (1,331.33) 4,207.59 -0.29 -0.08 NO NO

ST53 GBPUSD 3 (638.00) 8 50.00% 95,214.00 320.00 68.00 -0.04% (2,221.10) 6,775.45 -0.02 0.15 NO NO

ST53 USDCAD 3 (12,378.11) 21 38.10% 19,164.12 1,260.00 126.00 -0.73% 146.86 1,285.97 -0.14 0.15 NO NO

ST53 USDCHF 3 5,038.95 6 50.00% 31,569.70 240.00 36.00 0.31% (753.85) 3,370.04 -0.03 0.15 NO YES

ST53 USDJPY 3 105,354.96 60 46.67% 13,061.75 2,400.00 360.00 4.50% 114.12 2,968.67 0.13 0.15 NO YES

ST54 AUDUSD 2 40,906.00 94 32.98% 12,698.00 3,760.00 564.00 2.14% 43.25 1,386.10 0.04 0.15 NO YES

ST54 EURUSD 2 4,541.00 46 28.26% 14,348.50 1,840.00 299.00 1.24% 86.43 3,078.28 -0.01 -0.08 YES YES

ST54 GBPJPY 2 (36,366.90) 52 17.31% 47,223.02 6,240.00 442.00 -8.66% (1,100.09) 4,091.31 -0.19 -0.08 NO NO

ST54 GBPUSD 2 82,180.50 367 36.78% 58,464.00 14,680.00 3,119.50 3.75% 1,271.05 7,508.15 0.06 0.15 NO YES

ST54 USDCAD 2 (11,376.47) 142 22.54% 22,990.04 8,520.00 852.00 -0.67% (102.77) 1,383.45 -0.13 0.15 NO NO

ST54 USDCHF 2 134,080.59 108 39.81% 18,546.28 4,320.00 648.00 5.31% 741.55 3,849.77 0.14 0.15 NO YES

ST54 USDJPY 2 138,190.30 145 42.07% 9,712.98 5,800.00 870.00 5.42% 121.82 2,885.26 0.16 0.15 YES YES

ST55 AUDUSD 2 11,130.00 25 36.00% 15,968.00 1,000.00 150.00 0.66% (272.15) 1,540.88 -0.04 0.15 NO YES

ST55 EURUSD 2 (5,785.50) 7 28.57% 14,846.00 280.00 45.50 -1.57% (41.89) 3,258.82 -0.1 -0.08 NO NO

ST55 GBPJPY 2 25,973.86 29 51.72% 6,014.45 3,480.00 246.50 6.45% 748.51 4,281.70 0.15 -0.08 YES YES

ST55 GBPUSD 2 23,876.50 11 36.36% 114,241.00 440.00 93.50 1.34% (2,333.85) 6,898.56 0.01 0.15 NO YES

ST55 USDCAD 2 1,651.29 25 24.00% 14,353.97 1,500.00 150.00 0.10% 58.67 1,370.86 -0.07 0.15 NO YES

ST55 USDCHF 2 34,394.26 15 46.67% 20,691.47 600.00 90.00 1.85% (588.05) 3,828.49 0.01 0.15 NO YES

ST55 USDJPY 2 75,192.62 9 44.44% 9,958.32 360.00 54.00 3.50% 102.61 2,911.33 0.06 0.15 NO YES

ST56 AUDUSD 2 12,978.00 12 25.00% 11,9 24.00 480.00 72.00 0.76% (97.85) 1,435.38 -0.02 0.15 NO YES

ST56 EURUSD 2 8,481.00 6 3 3.33% 8,206.50 240. 00 39.00 2.27% 167.27 2,999.01 0.02 -0.08 YES YES

ST56 GBPJPY 2 4,115.99 3 33.3 3% 11,985.24 360. 00 25.50 1.13% (233.21) 4,843.19 0 -0.08 YES YES

ST56 GBPUSD 2 76,689.50 13 46.15% 81,288.00 520.00 110.50 3.56% (1,644.78) 7,175.85 0.06 0.15 NO YES

ST56 USDCAD 2 14,005.31 24 2 5.00% 15,119.87 1,440.00 144.00 0.82% 175.23 1,333.82 -0.04 0.15 NO YES

ST56 USDCHF 2 63,739.57 18 38.89% 28,403.22 720.00 108.00 3.08% (433.22) 3,741.74 0.03 0.15 NO YES

ST56 USDJPY 2 95,165.90 10 40.00% 13,574.67 400.00 60.00 4.18% 105.34 2,915.36 0.07 0.15 NO YES

ST57 AUDUSD 2 7,958.00 27 3 7.04% 18,388.00 1,080.00 162.00 0.48% (180.65) 1,573.18 -0.04 0.15 NO YES

ST57 EURUSD 2 6,821.50 9 44.44% 9,096.00 3 60.00 58.50 1.84% 264.29 2,859.48 -0.01 -0.08 YES YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST57 GBPJPY 2 (2,963.73) 2 50.00% 10,433.41 240.00 17.00 -0.82% (161.30) 4,827.08 -0.03 -0.08 YES NO

ST57 GBPUSD 2 89,826.50 11 54.55% 42,138.00 440.00 93.50 4.00% (679.85) 7,490.65 0.07 0.15 NO YES

ST57 USDCAD 2 8,017.70 27 40.74% 8,329.64 1,620.00 162.00 0.48% 152.52 1,420.05 -0.04 0.15 NO YES

ST57 USDCHF 2 37,242.69 15 33.33% 27,759.08 600.00 90.00 1.98% (89.65) 3,844.59 0.02 0.15 NO YES

ST57 USDJPY 2 73,849.38 9 33.33% 19,767.81 360.00 54.00 3.46% (337.30) 3,366.59 0.04 0.15 NO YES

ST58 AUDUSD 3 (15,202.00) 27 33.33% 22,036.00 1,080.00 162.00 -0.88% (226.15) 1,427.18 -0.08 0.15 NO NO

ST58 EURUSD 3 13,093.00 18 44.44% 7,702.50 720.00 117.00 3.44% 437.99 3,043.33 0.08 -0.08 YES YES

ST58 GBPJPY 3 (34,894.85) 9 55.56% 37,874.79 1,080.00 76.50 -8.36% (837.43) 4,021.05 -0.24 -0.08 NO NO

ST58 GBPUSD 3 82,691.00 14 50.00% 43,801.00 560.00 119.00 3.77% (874.85) 7,298.06 0.06 0.15 NO YES

ST58 USDCAD 3 16,676.35 9 66.67% 6,141.58 540.00 54.00 0.96% 167.19 1,262.98 -0.04 0.15 NO YES

ST58 USDCHF 3 6,064.12 10 40.00% 41,922.55 400.00 60.00 0.37% (1,231.18) 3,434.56 -0.03 0.15 NO YES

ST58 USDJPY 3 84,311.61 14 42.86% 26,928.93 560.00 84.00 3.82% (246.21) 2,940.34 0.05 0.15 NO YES

ST59 AUDUSD 2 (7,494.00) 14 35.7 1% 11,380.00 560.00 84.00 -0.45% (222.80) 1,626.94 -0.21 0.15 NO NO

ST59 EURUSD 2 1,881.00 6 50.00% 4,256.50 2 40.00 39.00 0.52% 125.40 2,474.08 -0.01 -0.08 YES YES

ST59 GBPJPY 2 (22,601.25) 239 37.24% 33,227.67 28,680.00 2,031.50 -5.69% (376.58) 2,684.58 -0.21 -0.08 NO NO

ST59 GBPUSD 2 49,880.50 7 57.14% 23,3 97.00 280.00 59.50 2.53% 1,245.16 5,824.05 0.22 0.15 YES YES

ST59 USDCAD 2 3,495.63 6 50.00% 2,120.85 3 60.00 36.00 0.21% 145.48 1,019.11 -0.02 0.15 NO YES

ST59 USDCHF 2 52,915.27 6 66.67% 12,939.42 240.00 36.00 2.65% 785.51 3,725.99 0.22 0.15 YES YES

ST59 USDJPY 2 28,692.75 10 50.00% 13,703.51 400.00 60.00 1.58% 717.44 3,190.81 0.07 0.15 NO YES

ST60 AUDUSD 1 12,234.00 71 19.72% 22,230.00 2,840.00 426.00 0.72% (346.65) 1,894.73 -0.01 0.15 NO YES

ST60 EURUSD 1 (18,821.50) 71 11.27% 27,587.50 2,840.00 461.50 -4.82% (425.99) 4,007.66 -0.15 -0.08 NO NO

ST60 GBPJPY 1 (47,129.04) 54 11.11% 52,803.39 6,480.00 459.00 -10.78% (1,560.69) 4,914.72 -0.24 -0.08 NO NO

ST60 GBPUSD 1 (17,804.50) 77 25.97% 92,155.50 3,080.00 654.50 -1.02% (1,098.65) 8,359.69 -0.01 0.15 NO NO

ST60 USDCAD 1 32,230.10 18 27.78% 4,522.84 1,080.00 108.00 1.75% 164.32 1,266.27 0.02 0.15 NO YES

ST60 USDCHF 1 13,791.42 65 35.38% 24,725.41 2,600.00 390.00 0.81% (411.66) 3,768.23 -0.01 0.15 NO YES

ST60 USDJPY 1 83,609.82 59 37.2 9% 11,256.87 2,360.00 354.00 3.80% 138.01 2,849.19 0.07 0.15 NO YES

ST61 AUDUSD 2 38,372.00 3 66.67% 7,206.00 120.00 18.00 2.03% (522.40) 1,563.34 -0.02 0.15 NO YES

ST61 EURUSD 2 (14,119.00) 6 16.67% 22,7 96.00 240.00 39.00 -3.69% (83.23) 2,951.52 -0.1 -0.08 NO NO

ST61 GBPJPY 2 5,239.12 2 50.0 0% 12,262.78 240. 00 17.00 1.43% (252.63) 4,928.64 0 -0.08 YES YES

ST61 GBPUSD 2 117,5 60.50 7 42.86% 46,294.00 280.00 59.50 4.86% (1,107.35) 6,097.99 0.1 0.15 NO YES




Strategy

Cur-

rency

Com-

plex-

ity

Total Net

Profit

#

trades % Win

Max

Drawdown

Total

Slippage

Total

Commssn

Annual

ROR

Avg. Mth

Rtn

Std. Dev

of Mth

Rtn

Sharpe

Ratio

Sharpe

Benchmark

Strat >

Bench Profita

ST61 USDCAD 2 - 0 0.00% - - - 0.00% 0 0 0 0.15

ST61 USDCHF 2 87,814.11 17 35.29% 19,905.97 680.00 102.00 3.94% (383.74) 3,593.33 0.05 0.15 NO YES

ST61 USDJPY 2 81,123.97 18 27.78% 35,230.86 720.00 108.00 3.71% 253.97 2,741.28 0.05 0.15 NO YES

ST62 AUDUSD 2 29,688.00 2 100.00% - 80.00 12.00 1.62% 189.25 1,448.14 0.05 0.15 NO YES

ST62 EURUSD 2 (17,726.00) 4 0.00% 17,726.00 160.00 26.00 -4.56% 387.60 2,869.07 0.05 -0.08 YES NO

ST62 GBPJPY 2 (16,330.16) 4 25.00% 31,692.69 480.00 34.00 -4.22% (661.04) 4,812.60 -0.1 -0.08 NO NO

ST62 GBPUSD 2 (59,878.50) 21 33.33% 85,585.00 840.00 178.50 -2.93% (754.70) 6,759.74 -0.02 0.15 NO NO

ST62 USDCAD 2 24,574.98 2 100.00% - 120.00 12.00 1.37% (73.38) 1,196.91 -0.03 0.15 NO YES

ST62 USDCHF 2 14 2,682.57 21 61.90% 10,795.08 840.00 126.00 5.54% 803.25 3,385.96 0.13 0.15 NO YES

ST62 USDJPY 2 10 3,872.22 18 44.44% 12,581.14 720.00 108.00 4.45% (50.59) 2,890.82 0.09 0.15 NO YES

ST63 AUDUSD 2 43,758.00 2 100.00% - 80.00 12.00 2.27% 189.25 1,448.14 0.03 0.15 NO YES

ST63 EURUSD 2 (10,587.00) 18 50.00% 16,358.50 720.00 117.00 -2.81% (283.69) 3,169.95 -0.11 -0.08 NO NO

ST63 GBPJPY 2 (10,246.81 ) 5 20.00% 16,877.07 600.00 42.50 -2.72% (255.03) 4,595.10 -0.06 -0.08 YES NO

ST63 GBPUSD 2 25,742.50 15 53.33% 42,195.50 600.00 127.50 1.43% 62.44 7,011.57 0.04 0.15 NO YES

ST63 USDCAD 2 - 0 0.00% - - - 0.00% 0 0 0 0.15

ST63 USDCHF 2 88,938.34 23 26.09% 31,713.37 920.00 138.00 3.98% (269.42) 3,664.54 0.05 0.15 NO YES

ST63 USDJPY 2 109,283.16 10 40.00% 12,432.77 400.00 60.00 4.61% 253.97 2,741.28 0.09 0.15 NO YES

ST64 AUDUSD 3 26,068.00 2 100.00% - 80.00 12.00 1.45% 189.25 1,448.14 0.06 0.15 NO YES

ST64 EURUSD 3 21,271.00 6 33.33% 7,799.50 240.00 39.00 5.39% 562.51 2,610.50 0.15 -0.08 YES YES

ST64 GBPJPY 3 (39,163.02) 7 14.29% 39,163.02 840.00 59.50 -9.23% (1,155.53) 5,366.63 -0.18 -0.08 NO NO

ST64 GBPUSD 3 (18,867.00 ) 22 36.3 6% 54,205.00 880.00 187.00 -1.08% 340.51 7,373.77 0.01 0.15 NO NO

ST64 USDCAD 3 - 0 0.00% - - - 0.00% - - 0 0.15

ST64 USDCHF 3 18,068.77 4 50.00% 2,176.95 160.00 24.00 1.04% 485.36 3,509.64 0.01 0.15 NO YES

ST64 USDJPY 3 76,793.58 16 37.50% 23,169.69 640.00 96.00 3.56% 186.63 2,782.41 0.06 0.15 NO YES




TABLE 3

Technical Trading System Rules by Currency

Currency # of cases % of

total Profitable Avg.

Sharpe Sharpe >

Bench Rank

(Sharpe)

AUDUSD 63 14.8% 63.5% (0.05762) 0.0% 5

EURUSD** 59 13.9% 62.7% (0.00881) 69.5% 4

GBPJPY** 55 12.9% 12.7% (0.15964) 30.9% 7

GBPUSD 63 14.8% 74.6% 0.04317 7.9% 2

USDCAD 60 14.1% 66.7% (0.08850) 0.0% 6

USDCHF 63 14.8% 87.3% 0.02286 7.9% 3

USDJPY 62 14.6% 95.2% 0.07726 12.9% 1

425*

*425 cases: 63 trading systems x 7 currencies = 441 total cases. 441 – 16 with 0 trades = 425. ** These currencies were tested over a different time frame




TABLE 4

Technical Trading System Rules by Complexity Level

# ofcases % 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*

# ofcases

% oftotal 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.




FIGURE 1

Sample Chart with Technical Indicators




FIGURE 2

Head & Shoulders Pattern




FIGURE 3

Simple Moving Average




FIGURE 4

Bollinger Bands




FIGURE 5

Average Sharpe Ratio by Complexity

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