9-10-10_Fri_11.30pm_doc_Channel Breakout-Pull Back Pattern (CB-PB).doc

9-1= 0-10_Fri_11.30pm_MHT_Channel Breakout-Pull Back Pattern (CB-PB).mht

Channel Breakout-Pull Back Pattern (CB-PB) of Currenc= ies, Commodities, Equities, Fixed Income Securities Markets Automated Trading Systems (ATS) Holding Periods Wins Audit- Testing Biases Towards Promoting Self-Interest

Avi Rushinek

Department of Accounting, University of Miami, Coral Gables, Fl 33124, USA Email: arus= h@miami.edu

Sara Rushinek

Department of Computer Information Systems, University of Miami, Coral Gables, Fl 3312= 4, USA Email: s.rushinek@miami.edu

Abstract:

This study describes methods of testing the hypothesis that certain Automated Trading Systems (ATS) strategies, also called programmed trading, robotic trading, and/or algorithmic trading apply to an excessive number of applications. Namely, that "one size fits all," of the securities, markets, and trading periods. Typically, developers optimized such ATS strategies to specific securities, such as equities, specific markets or index such as the S&P500, and specific holding time period ranges, such as tick by tick, minutes for day traders, hours, days, weeks, months, etc, for swing traders and so on. Such systems have self-surveillance records that show the percentage of winning trades, the elapsed time, 5 day= s or 10n days of a holding period, of an exit time, producing a record for back-testing paper trading experiments.&nb= sp; This study uses such records to develop a methodology of testing hypothesis concerning the assertion of some ATS sellers, in order to valida= te or invalidate them. This study demonstrates such a method, rejecting the null hypothesis stating that a specific strategy applies equally to all the securities, markets and trading durations tested. It conclude= s that the sample strategy produces significantly higher percentage winning trades= for certain combinations of securities, markets, and trade durations.

Overview

Currencies, Commodities, Equities, Fixed Income Securities Markets Automated Trading Systems (ATS) Holding Periods Wins Aud= it- Testing Biases Towards Promoting Self-Interest

Economic Decisions with Related Biases towards Promoting Self-Interest in Behavio= ral Economics & Finance

The problems of automated trading systems (ATS) strategies are the Biases Towards Promoti= ng Self-Interest as Behavioral Econo= mics & Finance explain it. “Behavioral economics[1] and its related area of study, be= havioral finance, use social, cognitive and emotional factors in understanding the economic decisions of individuals and institutions performing” This study looks at a differe= nce perspective of Automated Trading Systems developed by individuals and institutions, and used by them and others for “economic functions, including consumers, borrowers and investors, and their effects on m= arket prices, returns and the resource allocation. The fields are primarily concerned with the bounds of ratio= nality (selfishness, self-control) of economic agents. Behavioral models typically integrate insigh= ts from psychology with theory. Behavioral analysts are not only concerned with the effects of= market deci= sions but also with public choice, which describes another source of economic decisions with related biases towards promoting self-interest: Heurist= ics: People often make decisions based on approximate rules of thumb, not strict logic. See also cognitive biases and bounded rationality, Framing; The collection of anecdotes and stereot= ypes that make up the mental emotional filters individuals rely on to understand= and respond to events; Market inefficiencies: These include miss-pricing, non-r= ational decision making, and return anomalies. Daniel, Hirshleifer, and Subrahmanyam (1998)[2] built models based on extrapolation (seeing patterns in random sequences) a= nd overconfidence to explain security market under- and overreactions, though their source continues to be debated. These models assume that errors or bi= ases are positively correlated across agents so that they do not cancel out in aggregate. This would be the case if a large fraction of agents look at the same signal (such as the advice of an analyst) or have a common bias.” Likewise, agents use advice of sel= lers of automated trading systems and strategies, misleading them to trade in an area that may not be best suited for such systems.

Back-testing ATS audit Apps, iPhone, Goo= gle Droid, free OS HTML & Java

The biases towards promoting self-intere= st at the expense of the facts, is the focus of the present study. This study aims at developing an automated backtesting system, that will test ATS in difference securities, markets, and holding periods to test the claims that sellers of automated trading strategies frequently make concerning the “one size fits all” claims in an attempt to maximize the sales of their trading strategy.

This study demonstrates “off-the-shelf” quantitative method that can easily be programm= ed into Microsoft Office Excel or similar users that is not a programmer. Using a program code generator or = an embedded Macro Programming Language like Visual BASIC for Applications, included in Microsoft Office Excel 10, the user simply records the applications, while the code is generated automatically by the Excel progra= m, or a similar program, producing a back-testing ATS audit validation applica= tion (App). Such Apps can eventual= ly be recompiled into Java script and HTML (Hyper Text Mark Up Language) running = on any web server or mobile device such as iPhone, Google Droid, and other PDAs (Personal Digital Assistants) and computers, without Excel or any other proprietary program or Operating System (OS) such as Microsoft Windows. Instead it will use free open sour= ce OS, such as UNIX or LINUX.

Introduction & Liter= ature Review

Counterparty Surveillance Promotes Transparency and Clarity of the Facts across Financial Transaction= s

Counterparty surveillance promotes transparency and clarity of the facts across financial transaction= s so users of systems can discover the bias or certain optimization, or over-optimization, built into the systems and deal with it effectively, and= efficiently. In the case that Automated Trading Systems (ATS) that are optimized for certain securities such as equities, stocks or stock funds, may be much less successful in trading currencies. Thus they may generate a much high= er rate of winning trades in equities, compared to currencies. Likewise, they may be optimized or= over optimized for a maximum trading period of 5 days, and may not be as effecti= ve for longer holding periods, such as periods longer than 5 days. However, sellers of such systems m= ay falsely promote them as equally effective for all securities and all trading time frameworks to maximize their sales volume and revenues. Such sellers may falsely promote t= hese systems, as “one size fits all,” due to the seller’s self-interest bias, which again has to be assessed and discounted properly.=

Uncooking the Books and the Decision Making Process From Biased and Self-Interest of Material Misstatements

Recently the pre= ss has been flooded with cases of cooking and uncooking the books from toxic paper sub-prime mortgages CDS and CSOs material misstatements of the financial services industry: crisis challenges and counterparty surveillance of collateralized debt obligations', as reported by Rushinek and Rushinek (2010 a-l) in the International Journal of Economics and Accounting, Volume 1, No. 1, 2010. Such articles stress the importance = of automated audit applications (Apps) that can effectively and efficiently counteract the flood of false, misleading and inaccurate information that is flooding the air-ways and the internet.&nb= sp; This study is trying to develop a methodology for such apps that will automatically assess the bias of certain claims, and reject the false claim= s in a simple numerical way that a computer program, can easily issue an alert f= or the risk of following a suggestion that is bias and may lead to less than optimal decisions. An example= of such process can be a series of statistical hypothesis tests that reject the hypothesis that a certain automated trading system (ATS) strategy performs equally well for a variety of securities, such as equities, commodities, currencies, for a variety of trading periods such as 5, 10, 15, and 20n day= s, etc. In the absence of such a= udit apps ATS sellers are more likely to mislead users to apply less then optimal ATS strategies, lose their capital, and go out of business, before they had= a chance to remediate the deficiency. This is especially true in economically slow periods, suc= h as the years 2009-2010, where it is not clear if the recession has expired or = the economy is about to get into another recession.

The following literature= deals with surveillance, testing and forecasting method to correctly assess and discount variety of biases. Back-testing of securities trades is such area where validity, foren= sic accounting and audit testing is very important. This study focuses on employing research methodologies that highlight the importance of forensic accounting, auditing, fraud mitigation, and litigation service issues. These instructional methodologies include multi-media capabilities, data manageme= nt and data engineering (Rushinek and Rushinek, 2000b; Rushinek and Rushinek, 2001). Software was developed that analyses and improves forensic accounting research skills, research tools and research techniques for all that use the software.

Rushinek and Rushinek (1997, 1998, 19= 99, 2000a) demonstrate a self-vetting system for ‘Rating and Ranking Best Practices of the British Petroleum Oil Company and the Oil & Gas Industry’. As the system runs repeatedly, eventually the ‘Sales= to Cash Forecasting Univariate Regression Trend Analysis’ emerges as the best practice among a large variety of ‘Computer Modeling’ (Rushinek and Rushinek, 2005c; Rushinek and Rushinek, 2007). Even though, t= his was done for the Oil and Energy industry, the lessons apply to any search f= or best practices. This study promotes audio visual (AV) surveillance publications, and their download and viewing rates, ranking and rating publication, as a self-vetting system of best practices of e-learning and educational materials.

Standards for the eval= uation of skills and e-competences

Standards for the evaluation of skills and e-competences are different in the USA and EU. Such differences have to be closed for the pur= pose of consolidated financial statements. To close such difference in different time zones, different continents, different language, require minimal IT sk= ills and e-competences. Rushinek et al. (1983) reported about empirical models of ‘Development and Testing of a Discriminate Model for Measuring Change= s in Instructor Evaluations’. They demonstrated software programs based on= a discriminate linear function for automating the gain of instructors’ ratings for applications of computers in mathematics and science teaching (Rushinek et al., 1983). Rushinek and Rushinek (1983) show that such CAI and testing that are fast and responsive satisfying users in ‘An Evaluati= on of Mini/Micro Computer Systems’, using ‘An Empirical Multivaria= te Analysis’. The results of the evaluations are stored in a database, f= or comparative time series analysis (Rushinek and Rushinek, 1983; Friedman et = al., 2006).

IFRS compliant AV HD recording audi= t of standards of effective learning strategies

Rushinek and Rushinek (2009a) have been conducting workshops f= or the American Accounting Association (AAA) about International Financial Reporting Standards (IFRS). These workshops raised the issue of compliance = with multiple sets of unrelated standards in one integrated compliance efforts. Course Programmers for E-competences (CPE) for accountants are training programmers and workshops for e-competences, making sure that they can keep their Certified Public Accountant (CPA) or similar certificates current. The following is a description of training course programmers and workshops for e-competences. Automating the process of recording, distributing, making the instructor an instant author of the materials, and rewarding them further w= ith royalties of sales of the videos, the transcripts, the tests and assessments automatically generated from these transcripts will improve their overall competencies and performance (Rushinek and Rushinek, 2004).

The present= ers, Avi Rushinek, University of Miami, and Sara Rushinek, University of Miami, describe it as follows: “Effective Learning Strategies (ELS) that = repurposes stand-up traditional classroom instruction AV (Audio Video) surveillance recorded to a DVD, for real-time auditing, distance learning, CPE coursewar= e, as well as tutor CDs and DVDs. As soon as the class is over, the instructor’s PC uploads the AV session to Google Video and to a DVD-burner” (Rushinek and Rushinek, 2009b).

Cell phone real-time forensic audit on the World Wide Web (WWW)

To complement the traditional after-the-fact auditing, this ca= se study deals with real-time auditing. This means that the auditor watches the activities in while they occur; hence the term ‘real-time’. The system is using the World Wide Web instead of limiting the access to the intranet; adding a potentially profitable online distances learning service (Rushinek and Rushinek, 2005a; Rushinek and Rushinek, 2009b).

Rushinek and Rushinek (2003c, 2008) demonstrate how surveillance video playback, peer evaluation and discussion of the ‘Role Play Forensic Accounting, Audi= ting and Tax Expert Witness’, can be very effective in ‘Providing Testimony and Computer L= itigation Support Teaching, Services, Research and Development’. It shows that = such visual aids, as AV surveillance materials can dramatically enhance existing instructional technologies, while improving the security for the participan= ts (Rushinek and Rushinek, 2008).

1 Using wireless c= ell phone and surveillance technology in the forensic accounting

Let us review how forensic accounting instructors can use some= of the above technologies in the classroom. The instructors can demonstrate ‘Wireless Access Points’ by sharing the recorded classroom vide= os wirelessly with students in the class, letting them download the media in real-time into their devices, and recording the in-coming video signal as i= t is displayed on their media player. This also demonstrates ‘AV (Audio Video), Surveillance Recording’, the students see how the camera that= is situated at the back of the classroom can pan, tilt, and zoom (PTZ) an imag= e into their laptop showing the display on their screen.” The instructor can= let students, with a cell phone and internet connectivity; control the PTZ IP (Internet Protocol) surveillance camera by using the phone as a remote cont= rol. An instructor/lecturer could have anyone in the world be the cameraperson s= ince class is being broadcasted real time over the Internet Protocol.=

As the surveillance video recording e= nds, the videos are automatically stored and uploaded to social video websites f= or viewing. Such websites include but are not limited to iTunes U^{®,
Google Video^® and You Tube^®. The Forensic
Accounting instructor uses the videos to analyze the performance of the exp=
ert
witnesses together with the session participants. The discussion highlights=
the
pros and cons of the court behavior of the experts, letting the experts exp=
ress
their views. This activity al=
so
instructs the participants about Search Engine Optimization (SEO) of videos=
on
the web (Rushinek and Rushinek, 2003a). They learn how to garnish the top 1=
00
positions, on Google Video^®, using meta-tags, lecture
transcriptions, video transcriptions, court-reporter techniques to index and
internet searches of court document databases. They learn how an expert wit=
ness
can promote his brand on Google Video^®, by monopolizing the top
100 videos on certain search keyword meta-tags, such as Nets-Expert
(http://video.google.com/videosearch?hl=3Den&q=3Dnets-expert&num=3D=
100#). The
instructor could invite practicing attorneys as guest speakers. The students
can role-play as judges, prosecutors, defense attorneys and experience the
‘examination of the experts’ all while being recorded for later
discussion and review (Rushinek and Rushinek, 2005b).}

Counterparty Surveillance audit AV im= proves Records what is going on inside and Outside the Computer

Counterparty surveillance applies to the surveillance of a seller of ATS strategies. The buyer can evaluate the veracit= y of the seller by test the claims that it trades equally well for all securitie= s, markets, and time periods. Product/services in the manner of course/lecture blending, distance instruction and self-study education all add to additional funds generated = for all parties involved. If security surveillance equipment is already in place and working, the cost of equipment is already been realized by the institut= ion and only minimum capitalization may be needed to begin revenue generation (Rushinek and Rushinek, 2002).

Currencies, Commodities, Equities, Fixed Income Securities Markets Automated Trading Systems (ATS) Holding Periods Wins Aud= it

Hypothesis Testing

This study tests the hypothesis that there is no significant difference among Automated Trading Systems (ATS) percentage wins (% Winning Trades) as some ATS developers and resellers claim on the World Wide Web (WWW). To promote their product and maxim= ize their sales some ATS developers and resellers claim that “one size fi= ts all.” In other words, t= hat their ATS will be equally successful in any market and in any securities portfolios. This study test s= uch a null hypothesis claiming that there is no difference in the % percentage of winning trades of this ATS for the Securities Traded of Currencies, Commodities, Equities, Fixed Income Securities, & Holding Periods in the number of Days of Exits for, with the duration of 5, 10, 15, Exit Days. These systems have been back-teste= d in the following markets: S&P-500, 10-year T-note, Wheat, Eurodollar, Corn, Cotton, Crude oil, Sugar, Japanese yen, Live hogs, Soybeans, U.S. bond, Bri= tish pound, Deutsche mark, Gold, Canadian dollar, Orange juice, Silver, Coffee, Heating oil, Copper, Swiss franc.

= Data Collection

The data collected by the Automated Trading System (ATS) includes Securities Traded Currencies % Wins, & Holding Periods in the number of Days of Exits for Commodities, Equit= ies, Fixed Income, with the duration of 5, 10, 15, Exit Days. These systems have been back teste= d in the following markets: S&P-500, 10-year T-note, Wheat, Eurodollar, Corn, Cotton, Crude oil, Sugar, Japanese yen, Live hogs, Soybeans, U.S. bond, Bri= tish pound, Deutsche mark, Gold, Canadian dollar, Orange juice, Silver, Coffee, Heating oil, Copper, Swiss franc.

Channel Breakout-Pull Back Pattern (CB-PB)

= The data has been collected by the Automated Trading Systems CB-PB, as follows (Chan= de, 1999)[3] describes: “This section discusses a trading system based on a pattern observed in mature markets, that is, markets with a large volume of institutional activity. In these markets, the big players have a tende= ncy to fade market moves. Thus, they will resist advances and support declines.= For example, when a market makes a new 20-day high, many big players will short it heavily, and push the market back into the previous consolida= tion. If the fundamental forces underlying the market are strong, the uptrend will resume after a brief consolidation. A trading system that trades the long s= ide only, by going long during the pull back after new 20-day highs, is called = the channel breakout-pull back (CB-PB) system.

<= o:p>

We begin with a few examples of how the CB-PB system works, and show the actual code used for the tests. Next, we test the basic CB-PB entry strategy acros= s 22 markets to illustrate the general validity of the idea. Then, we discuss th= ree different exit strategies to show how you can convert the same entry strate= gy into vastly different trading systems. These systems vary from a short-term system, which is in the market for 7 to 9 days, to a long-t= erm trend-following system. We will also explore the effect of using a $1,500 "close" initial stop versus a $5,000 "wide" stop. The analysis focuses on the following mature markets: coffee, Eurodollar, Japanese yen, Swiss franc, S&P-500, 10-year T-Note, and the U.S. bond.<= o:p>

T= he channel breakout-pull back pattern is for long trades only. The assumptions= underlying this system are:

1. The market will begin an uptrend after the consolidation ends, because it has recently made a new 20-day high.

2. The entry during the consolidation is a low-risk entry point.

3. Exits could be placed at the nearby 20-day high, by using trail&= shy;ing stops, or by exiting after .r-days in the trade.

The reality is that markets ma= y have an extended consolidation after making a new 20-day high, or could even make new 20-day lows.”

Hence, a bias to the long side may be correct only 50 to 70 percent= of the time. It is also difficult to find consistent exits, since the markets do not follow the same script every rime. Hence, another difficulty with the CB-PB system is finding a consistent exit strategy. A third area of difficulty is where to place= the initial stop. If the market rolls over and starts a new downtrend, then an initial stop is critical for risk management and loss control, whether it i= s a simple-dollar stop or a volatility-based stop.

The first exa= mple of the CB-PB pattern uses the March 1995 deutsche mark contract. ….&#= 8221; The data “shows the daily ba= rs and, superimposed on the bars, the 20-bar trading range. The 20-day range l= ines have a 13-rick barrier added to both the lines to filter out some false breakouts. The chart shows that the deutsche mark broke above its 20-day ra= nge in December 1995 and then consolidated for 7 days before moving higher. Upon moving higher, it made a higher high, and consolidated again.

Data Analysis=

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9-10-10_Fri_12.15pm_Channel Breakout-Pull Back Pattern (CB-PB).docx

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Table 1 descr= ibes the Descriptive statistics of Currencies, Commodities, Equities, Fixed Income Securities Markets Automated Trading Systems (ATS) Holding Periods Wins. It includes the: %Winners of Curre= ncies, Commodities, Equities, Fixed Income and Exit-Days. The table describes the Mean of 53= .89772727 % overall wins compared to % of losses. The mean overall differs greatly ac= ross currencies, which may indicate that the claim that many of systems develope= rs in this case is not supported by the statistics. With commodities at about 30% wins= , it shows that the commodities loss rate is much higher than the overall loss r= ate.

Likewise, it describes the following statistics: Standard Error, Median, Mode, Standard Deviation, Sample Variance, Kurtosis, Skewness, Range, Minimum, Maximum, an= d Sum, Count, Largest(1), Smallest(1), Confidence Level(95.0%). Reviewing the descriptive statisti= cs demonstrates the large variance of wins across securities, hinting that the= reseller’s claims may not apply to this sample. Furthermore, the Confidence Level of (95.0%), suggest that it may not apply to a broader population, since they are outside the 95% confidence interval. Thus, the reader may expect that an ANOVA (Analysis of Variance) will reject the null hypothesis claiming that there is no significant difference among the securities (equities, currencies, commodities, & fixed income) market (Dollars, Go= ld, etc) win %, and Holding Days.

In other word= s, buyers and users of ATS should be aware of the potentially misleading claims that ATS sellers are making as they apply an ATS to every category of securities, markets and holding periods.&n= bsp; They should be aware that most likely a single ATS may be optimized = to have higher wins in a certain security such as currencies, in a certain mar= ket such as Euros, and for a certain holding period, or Exit-days such as 5 days. Thus if they are not pl= anning to trade in that specific parameters, they may be better off with another A= TS trading strategy.

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Table 2 describes the Correlation Among Securities Winning% Trades & Holding Periods Exit-Days. It confirm= s the notion that the descriptive statistics raised concerning the difference amo= ng the wins across securities markets and exit-days. It shows that some correlations are positive and some are negative. For example, it shows that Winners correlation with = Currencies of -0.06520143, and Commodities of -0.148368368, are both negative, the correlation with Equities of 0.254099558, and Fixed Income (10 year T-Notes, & US-Bonds) are positive. Likewise, the Days correlates negatively with all with a correlation value of -0.279277638, yet correlates positively with Commodities with a correlation of 7.25195E-18, which means that as the holding period rises th= e % wins also increases, as opposed to the rest of the securities, which reduces wins as the holding period increases.

Again the sa= me pattern emerges that “one size” does not fit all securities tra= des equally. It seems that for a = single ATS a longer holding period improves the winners, while for the others it drives the winners down.

9-10-10_Fri_= 1.00pm_Channel Breakout-Pull Back Pattern (CB-PB).docx

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Table 3 the Covariance Analysis of Day-Exit Holding Period & Securities Win % of the CB-PB ATS, shows similar results to the previous results. Namely, that the variables do not = ovary in sync. In other words, some= co-vary negatively, like 5-Day Exit co-varies negatively with Currencies, at a valu= e of -0.045454545, and with Commodities at a value of -0.636363636. In contrast 5-Day Exit co-varies positively with Equities at a value of 0.363636364, and with = Fixed at a value of 0.318181818. In summary, the evidence in this s= ample does not support the “one size” of CB-BC ATS “fits all.” In contrast, it s= eems that Equities and Fixed income securities co-vary positively, while, Curren= cies and Commodities co-vary negatively.

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Table 4 the ANOVA of Securities and Exit-days Winning Percentages of the CB-PB ATS, rejects the = null hypothesis that states that there are no significant differences across the different securities, win %, and holding time periods of Exit-days. It demonstrates that there may be differences within and across groups, with the F exceeding the critical F, F-Critical in all categories. Accordingly it shows that the ANOVA with Two-Factor With Replication, distinguished within group such as currencies, where it compares, in the summary, SUMMARY, the British pound, Canadian dollar, in the commodities: Coffee, Copper, Corn, Cotton, C= rude oil, etc, and in the Fixed Income: the 10-year T-note, U.S. bond, etc.=

The ANOVA shows the Sour= ce of Variation, SS, df, MS, F, P-value, F crit, and the Difference=3DF-F_Crit= , F less F-Critical have a positive difference for all categories, meaning the differences are statistically significant at the P-value of .05 or lower of= statistical significance level. For example for this sample the F, P-value, F crit, and the Difference-F_Crit a= re respectively as follows: 6.40= 93E-147, 3.912874918, and 20664.87321. The same applies to the Columns, the securities, and the interactions (the hold= ing periods) all vary significantly with respect to the percentage of winners of this automated trading strategy system.

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Summary, Co= nclusions and Implications

Summary

For the development of Automated Trading System (ATS) strategi= es, users remember to test the bias that may often be built into the selling process of such systems. This= study tested such methods as descriptive statistics, correlations, covariance and analysis of variance (ANOVA) to conclude that the null hypothesis cannot be rejected at the .05 level of statistical confidence.

Conclusions

This study has concluded that one can reject the null hypothesis that states that there are no statistically significant differences among different securities, markets, and trade duration, with respect to one ATS strategy. Therefore, concluding that such st= rategy will most likely perform better in certain currencies, markets, and holding time periods.

Implications

The future implications are that more= test like this have to be done on a regular basis. Such back-testing should be built = into trading platforms that use ATS strategies, and should not require programmi= ng, as to make them more user friendly. As of now, most of these methods are not built-in and do require some programming, or at least understanding of some programming. Therefore, many users do not back-= test systems sufficiently, which is leading to excessive loss of capital and premature exit from the securities trading industries, as well as the expan= sion of fraudsters, making false and exaggerated claims about the performance of= the systems that they promote. Su= ch systems should be integrated into the educational curriculum and training, which is not currently the case. Due primarily to the reluctance of higher education instructors to c= over such topics as technical analysis, day trading, etc., claiming that it is p= ure gambling and therefore does not belong in higher education, they also avoid teaching about auditing such applications and calculating their bias, which will remediate some of the deficiencies and the tendency of traders to treat the activity as pure gambling.

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References

Chande, Tushar S. “Beyond Technical Analy= sis: How to Develop and Implement a Winning Trading System,” John Wiley & Sons, Inc. New York, 1995<= /p>

Retrieved: Beyond Technical Analysis.doc - Beyond Technical AnalysisEffect of Exits and Portfolio Strategies on Equity Curves 186 .... T= he systems could use different types of data, such as 5-minute bars or weekly data. ...... sell at a point five ticks below twice the previous day's trading range subtracted ... The August cru= de oil contract with curve-fitted system www.fxf1.com/books/english/Beyond%20Technical%20Analysis.doc<= /i>

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Technology in the managerial accounti= ng course’, The Accounting Educators’ Journal, <= /span>

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for measuring changes in instructor evaluations due to using computer assisted instruction’, <= /span>

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petroleum oil company and the oil &am= p; gas industry: sales to cash forecasting univariate

regression trend analysis and computer modeling’, Oil and Energy Quarterly.

Rushinek, A. and Rushinek, S. (1998, September) ‘Litigat= ion in Pennzoil and the petroleum

refining industry: a financial ratio automated peer review analysis of variance internet

software development’, Oil, = Gas & Energy Quarterly, Vol. 47, No. 1, pp.17–32.

Rushi= nek, A. and Rushinek, S. (1999, September) ‘Correlating investors’ preferences to stock characteristics: an algorithm for internet software decision support system development in the crude oil & refining industry’, Oil, Gas & Energy Quarterly, Vol. 48, No. 1, pp.87–101.

Rushinek, A. and Rushinek, S. (2000a) ‘Oil & gas market, sector & company relative strength congrui= ty trading theory (MSCRSSCTT): an internet automation approach applied to Chev= ron Corporation’s Stocks’, Oil, Gas & Energy Quarterly, = Vol. 48, No. 4, pp.671–682.

Rushinek, A. and Rushinek, S. (2000b) ‘Internet fraud auditing: a simulated health care industry case study’, Journal of Forensic Accounting, Vol. 1, No. 1, pp.125–234.

Rushinek, A. and Rushinek, S. (2001) ‘The role of the forensic accountant in calculating the damages using= the “but-if” analysis in a case of internet day trader & online broker misconduct litigation’, Journal of Forensic Accounting,= Vol. 1, No. 2, pp.241–250.

Rushinek, A. and Rushinek, S. (2002) ‘E-commerce security measures: are they worth it?’, ACM: Ubiquity, Vol. 3, No. 39, pp.1–12.

Rushinek, A. and Rushinek, S. (2003a) ‘What makes a publisher important? Maximization of internet citations methodology’, ACM: Ubiquity, Vol. 4, No. 12.

Rushinek, A. and Rushinek, S. (2003b) ‘The University of DVD’, ACM: Ubiquity, Vol. 4, No. 41. =

Rushinek, A. and Rushinek, S. (2003c) ‘Forensic audits of stock market forecasts for Texaco & The Inter= national Oil and Gas Industry & Financial Models for Online Broker Litigation’, Oil, Gas, & Energy Quarterly, Vol. 51, No. 4, pp.659–678.

Rushinek, A. and Rushinek, S. (2004) ‘Patents & Provisional Business Method Patent Application (PPA) Disclosure Document Program (DDP) For Building & Testing Quality Resear= ch Productivity Of Expert Witness Testimony & Computer Litigation Support = Of Trade Secrets & IP’, Productivity and Quality Management Front= iers.

Rushinek, A. and Rushinek, S. (2005a) ‘What makes users unhappy: share-point team services web server security’, ACM: Ubiquity, Vol. 6, No. 2.

Rushinek, A. and Rushinek, S. (2005b) ‘Sarbanes-Oxley Microsoft^® Accelerator Forensic Accounting, Ex= pert Witness Testimony, and Computer Litigation Support’, 2005 American Accounting Association (AAA) Annual Meeting, San Francisco, California.=

Rushinek, A. and Rushinek, S. (2005c) ‘Internet web server comparative forecasting sales models: reversed accounting of Texaco Corp and The Oil & Gas Industry linear, exponentia= l, power, polynomial & logarithmic univariate regressions’, Oil, Gas, &a= mp; Energy Quarterly, Vol. 54, No. 2, pp.87–95.

Rushinek, A. and Rushinek, S. (2007, November) ‘An automated business intelligent stock market matching en= gine for the oil and gas industry: materiality of violating the normality assump= tion of linear regression control analysis’, Oil, Gas, & Energy Quarterly, Vol. 56, No. 2, pp.435–448.

Rushinek, A. and Rushinek, S. (2008, August) ‘Role Play Forensic Accounting, Auditing & Tax Expert Witness: Providing Testimony and Computer Litigation Support Teaching, Services, Research and Development’, 2008 American Accounting Association (AAA) Annual Meeting Anaheim, CA. Available online at: http://aaahq.org/AM2008/cpe/cpe11.htm (accessed on 27 October 2009). <= /o:p>

Rushinek, A. and Rushinek, S. (2009a) ‘IFRS compliant real-time 2-way AV (audio/video) HD (high definition) recording audit of effective learning strategies for forensic IT networks of expert witnesses research that focuses on investigative accounting testimon= y, & computer litigation support, effective learning strategies’, 2009 American Accounting Association (AAA) Annual Meeting, 3 August 2009, New York. Available online at: http://aaahq.org/ AM2009/ELS01.cfm (accessed on = 27 October 2009).

Rushinek, A. and Rushinek, S. (2009b) ‘Cell Phone Real Time Forensic Audit of Tele-Medicine e-Bill Records = over the WWW AV IP Network Security Surveillance Reconnaissance PTZ Cam Systems, CCTV & DVD-Recording: Repurposed Stand-Up Instructions for Distance or Blended Education and CPE, Emerging and Innovative Research Session’,= 2009 American Accounting Association (AAA) Annual Meeting, Tuesday 4 August 2009, NY. Available online at: http://aaahq.org/AM2009/Emerging.cfm (access= ed on 27 October 2009).

Rushinek, A. and Rushinek S. (2010a) 'Uncooking the books from toxic paper sub-prime mortgages CDS and CSOs material misstatements of the &= nbsp; financial services industry: crisis challenges and counterparty surveillance of collateralized debt obligations', The International Journal of Economics and Accounting, Volume 1, No. 1, 2010. (Accepted for publication)

&= nbsp; Retrieved on: 9/1/2010: http://www.inderscience.com/search/index.php?action=3Drecord&= amp;rec_id=3D33906"Uncooking the books from toxic paper sub-prime mortgages CDS and CSOs material misstatements of the financial services industry: crisis challenges and counterparty surveillance of collateralized debt obligations"

http://inderscience.metapress.com/app/home/contribution.asp?refer= rer=3Dparent&backto=3Dissue,9,10;journal,1,1;linkingpublicationresults,= 1:121805,1

Rushinek, A. and Rushinek S. (2010b) 'Oil ETN Trading Rule Linear & Polynomial Da= ily Open & Close Regression Forecasts- An Automated Software &= nbsp; Development- Exit Strategy And Automated Stop Rules', Oil, Gas, Energy Quarterly, June 2010, Volume 58, No. 4. (Accepted for publication)=

Rushinek, A. and Rushinek S. (2010c) 'Assessment automation software for accounting of domain name auctions, forensic audit = appraisal, ANOVA = of price and cost, R= OI univariate and multivariate linear regression forecasting models', <= /span>International Journal of Managerial and Financial Accounting, = &= nbsp; Issue 4, 2010. (Accepted for publication)

<= /span>

Rushine= k, A. and Rushinek S. (2010d) '= Forensic-free AV surveillance audit podcasts for e-competence of academics and staff – repurposed traditional instructions for blended, distance and self-study e-learning to maximize lecture- capture ROI', = ; International Journal of Cont. Engineering Ed. And Life-Long Learning Volume 20 2010 (Accepted for publication)

Rushinek, A., Rushinek, S., Stutz, J. (2010e) 'Audit= ing Employer Discrimination Compliance Analysis/ Alert System and the &= nbsp; Convergence of Forensic Experts in Accounting and Computing', 2010 Annual Americ= an Accounting Association (AAA) <= /span>Forensic and Investigative Accounting (FIA) Section Research Conference, Baton Rouge= , LA. May 7 - 8, 2010. Retrieved &nbs= p; 9/10/2010: http://aaahq.org/= meetings/2010FIA_program.htm

Rushinek, A. and Rushinek, S. (2010f) 'Assessing pun= itive damage awards upheld by the courts when academics retaliate &= nbsp; defame-Anti sex-discrimination EEOC Gender compliance Forensic WWW Audit & Salary Forecast Class Action Lawsuit &= nbsp; &nbs= p; RSS Controls- Multivariate Regression of eMBA Rankings,' American Accoun= ting Association AAA Annual Meeting = 2010, San Francisco, CA., August 4, 2010.

&= nbsp; Accessed 9/10/2010: http://aaahq.org/AM2010/abstract.cfm?submissionID=3D969<= /span>

Rushinek, A., Rushinek, S., Stutz, J. (2010g) 'Autom= ated Diversity Compliance real-time AV audit, network security, disaster planning, backup & recovery co= sts reduction sponsor ad funding, Ecommerce podcast revenues from digital media streaming,' American Accounting Association Annual Meeting 2010, San= Francisco, CA., August 3, 2010.

Accessed 9/10/2010: http://aaahq.org/AM2010/abstract.cfm?submissionID=3D1368

Rushinek, A. and Rushinek, S. (2010h) 'Assessment Automation Software for Accounting, Teaching, and Recruiting Programs: Ecommerce of Lecture Capture DVDs & VOD Casting WWW Methodology<= /em>', American Accounting Association Annual Meeting 2010, San Francisco, CA. Aug= ust 4, 2010.

Accessed 9/10/2010: http://aaahq.org/A= M2010/ELS01.cfm   http://aaahq.org/AM2010/Emerg= ing.cfm

Rushinek, A. and Rushinek, S. (2010i) 'e-Collaborati= on Publishing Lecture Capture Contents on the Internet Using Surveillance Technologies among University Administrators, Professors, Students, Auditor= s, A/V Security, Audio to Text Transcribers', American Accounting Association Annual Meeting 2010, San Francisco, CA., August 4, 2010.

Accessed 9/10/2010: http://aaahq.or= g/AM2010/Emerging.cfm

Rushinek, A. and Rushinek, S. (2010j) 'Lecture Captu= re Cell Phone Forensic Audit of Business Continuity Contingency Planning, Emergency Preparedness Response, Disaster & Backup Recovery Using AV Recordings Online via the World Wide Web', American Accounting Association AAA Annual Meeting 2010, San Francisco, CA., August 4, 2010.

Accessed 9/10/2010: http://aaahq.or= g/AM2010/Emerging.cfm

Rushinek, A. and Rushinek, S. (2010k) 'Assessment Automation Software for Accounting, Teaching, and Recruiting Programs: e-Commerce of Lecture Capture DVDs & VOD Casting WWW Methodology', American Accounting Association AAA Annual Meeting 2010, San Francisco, CA.= August 4, 2010.

Accessed 9/10/2010: http://aaahq.org/A= M2010/ELS01.cfm

Rushinek, A. and Rushinek, S. (2010l) 'Forensic Acco= unting Repurposed HD Surveillance Video Podcast Lecture Capture for Distance Ed &a= mp; Blended Learning—Brand Name Audit SEO Social Networking for Expert Witness Testimony & CD Computer Litigation Support', American Accounting Association AAA Annual Meeting 2010, San Francisco, CA., August = 4, 2010.

Accessed 9/10/2010: http://aaahq.org/A= M2010/ELS01.cfm

9-10-10_Fri_8.15pm_Channel Breakout-Pull Back Pattern (CB-PB) (Recovered).docx

=

Appen= dix:

Appendix 1: Percent winning trades for CB-PB entry signal calculated over all available data from January 1, 1975, through July 10, 1995

Market

5-Day Exit=

10-Day Exit=

15-Day Exit=

20-Day Exit=

Currencies

Commodities

Equities

Fixed

S&P-500

0

0

1

0

10-year T-note=

0

0

0

1

Wheat

1

0

0

0

Eurodollar

1

0

0

0

Corn

0

1

0

0

Cotton

0

1

0

0

Crude oil=

0

1

0

0

Sugar

0

1

0

0

Japanese yen=

1

0

0

0

Live hogs=

0

1

0

0

Soybeans

0

1

0

0

U.S. bond=

0

0

0

1

British pound=

1

0

0

0

Deutsche mark=

1

0

0

0

Gold

0

1

0

0

Canadian dollar=

1

0

0

0

Orange juice=

0

1

0

0

Silver

0

1

0

0

Coffee

0

1

0

0

Heating oil=

0

1

0

0

Copper

0

1

0

0

Swiss franc=

1

0

0

0

Sum

1232

1175

1183

1153

7

12

1

2

Average

Appendix 1: Percent winning trades for CB-PB entry signal calculated over all available data from January 1, 1975, through July 10, 1995

Market

5-Day Exit=

10-Day Exit=

15-Day Exit=

20-Day Exit=

Currencies

Commodities

Equities

Fixed Income<= /o:p>

S&P-500

0

0

1

0

10-year T-note=

0

0

0

1

Wheat

1

0

0

0

Eurodollar

1

0

0

0

Corn

0

1

0

0

Cotton

0

1

0

0

Crude oil=

0

1

0

0

Sugar

0

1

0

0

Japanese yen=

1

0

0

0

Live hogs=

0

1

0

0

Soybeans

0

1

0

0

U.S. bond=

0

0

0

1

British pound=

1

0

0

0

Deutsche mark=

1

0

0

0

Gold

0

1

0

0

Canadian dollar=

1

0

0

0

Orange juice=

0

1

0

0

Silver

0

1

0

0

Coffee

0

1

0

0

Heating oil=

0

1

0

0

Copper

0

1

0

0

Swiss franc=

1

0

0

0

Sum

1232

1175

1183

1153

7

12

1

2

Average

Legend<= /span>

5-Day Exit=

10-Day Exit=

15-Day Exit=

20-Day Exit=

Currencies

Commodities

Equities

Fixed Income<= /o:p>

Market

S&P-500

10-year T-note

Wheat

Eurodollar

Corn

Cotton

Crude oil=

Sugar

10-year T-note=

Sugar

/////////////////////////

Appendix 1: Percent winning trades for CB-PB entry signal calculated over all available data from January 1, 1975, through July 10, 1= 995        &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;

Market   &= nbsp;        5-Day Exit       10-Day Exit     15-Day = Exit     20-Day Exit     Currencies       Commo= dities   Equities           Fixed Income

S&P-500  &n= bsp;        64        &= nbsp;           54=         &= nbsp;           56=         &= nbsp;           61=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0

10-year T-note  = ;            &n= bsp;         63        &= nbsp;           57=         &= nbsp;           60=         &= nbsp;           57=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 1

Wheat   &n= bsp;         63        &= nbsp;           52=         &= nbsp;           51=         &= nbsp;           47=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Eurodollar  &nb= sp;    60        &= nbsp;           58=         &= nbsp;           60=         &= nbsp;           60=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Corn   &nb= sp;            = 57        &= nbsp;           52=         &= nbsp;           50=         &= nbsp;           46=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Cotton   &= nbsp;         57        &= nbsp;           62=         &= nbsp;           55=         &= nbsp;           58=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Crude oil  &nbs= p;       57        &= nbsp;           55=         &= nbsp;           62=         &= nbsp;           58=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Sugar   &n= bsp;           57<= span style=3D'mso-tab-count:1'>        &= nbsp;           53=         &= nbsp;           57=         &= nbsp;           48=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Japanese yen 56        &= nbsp;           49=         &= nbsp;           50=         &= nbsp;           55=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Live hogs  &nbs= p;      56        &= nbsp;           51=         &= nbsp;           53=         &= nbsp;           51=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Soybeans   = ;      56        &= nbsp;           58=         &= nbsp;           53=         &= nbsp;           46=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

U.S. bond  &nbs= p;      56        &= nbsp;           53=         &= nbsp;           52=         &= nbsp;           52=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 1

British pound  = 55        &= nbsp;           52=         &= nbsp;           55=         &= nbsp;           54=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Deutsche mark  =             &nb= sp;         55        &= nbsp;           55=         &= nbsp;           54=         &= nbsp;           55=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Gold   &nb= sp;            = 55        &= nbsp;           52=         &= nbsp;           53=         &= nbsp;           53=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Canadian dollar &nbs= p;            &= nbsp;        54        &= nbsp;           48=         &= nbsp;           52=         &= nbsp;           45=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Orange juice  &= nbsp; 54        &= nbsp;           54=         &= nbsp;           50=         &= nbsp;           50=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Silver   &= nbsp;           54=         &= nbsp;           53=         &= nbsp;           56=         &= nbsp;           48=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Coffee   &= nbsp;          52        &= nbsp;           56=         &= nbsp;           45=         &= nbsp;           46=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Heating oil  &n= bsp;   52        &= nbsp;           53=         &= nbsp;           55=         &= nbsp;           54=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Copper   &= nbsp;         51        &= nbsp;           48=         &= nbsp;           52=         &= nbsp;           56=         &= nbsp;           0<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 1        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Swiss franc  &n= bsp;   48        &= nbsp;           50=         &= nbsp;           52=         &= nbsp;           53=         &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0        &= nbsp;           &nbs= p; 0

Sum   &nbs= p;             = 1232        &= nbsp;       1175        &= nbsp;       1183        &= nbsp;       1153        &= nbsp;       7        &= nbsp;           &nbs= p; 12        &= nbsp;           1<= span style=3D'mso-tab-count:1'>        &= nbsp;           &nbs= p; 2

Average   =        56        &= nbsp;           54=         &= nbsp;           54=         &= nbsp;           53=         &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;           &nbs= p;            &= nbsp;
/////////////////////////////////////////////////

Securities Traded: Currencies Wins & Holding Periods = in the number of Days of Exits: Commodities, Equities, Fixed Income, 5, 10, 15, & Day Exit      =

Market: S&P-500, 10-year T-note, Wheat, Eurodollar, C= orn, Cotton, Crude oil, Sugar, Japanese yen, Live hogs, Soybeans, U.S. bond, Bri= tish pound, Deutsche mark, Gold, Canadian dollar, Orange juice, Silver, Coffee, = Heating oil, Copper, Swiss franc

Microsoft Office Excel Binary Worksheet Object=

Appendix 1: Percent winning trades for CB-PB entry signal calculated over all available data from January 1, 1975, through July 10, 1995
Market	5-Day Exit=	10-Day Exit=	15-Day Exit=	20-Day Exit=	Currencies	Commodities	Equities	Fixed
S&P-500	0	0	1	0
10-year T-note=	0	0	0	1
Wheat	1	0	0	0
Eurodollar	1	0	0	0
Corn	0	1	0	0
Cotton	0	1	0	0
Crude oil=	0	1	0	0
Sugar	0	1	0	0
Japanese yen=	1	0	0	0
Live hogs=	0	1	0	0
Soybeans	0	1	0	0
U.S. bond=	0	0	0	1
British pound=	1	0	0	0
Deutsche mark=	1	0	0	0
Gold	0	1	0	0
Canadian dollar=	1	0	0	0
Orange juice=	0	1	0	0
Silver	0	1	0	0
Coffee	0	1	0	0
Heating oil=	0	1	0	0
Copper	0	1	0	0
Swiss franc=	1	0	0	0
Sum	1232	1175	1183	1153	7	12	1	2
Average	Appendix 1: Percent winning trades for CB-PB entry signal calculated over all available data from January 1, 1975, through July 10, 1995
Market	5-Day Exit=	10-Day Exit=	15-Day Exit=	20-Day Exit=	Currencies	Commodities	Equities	Fixed Income<= /o:p>
S&P-500	0	0	1	0
10-year T-note=	0	0	0	1
Wheat	1	0	0	0
Eurodollar	1	0	0	0
Corn	0	1	0	0
Cotton	0	1	0	0
Crude oil=	0	1	0	0
Sugar	0	1	0	0
Japanese yen=	1	0	0	0
Live hogs=	0	1	0	0
Soybeans	0	1	0	0
U.S. bond=	0	0	0	1
British pound=	1	0	0	0
Deutsche mark=	1	0	0	0
Gold	0	1	0	0
Canadian dollar=	1	0	0	0
Orange juice=	0	1	0	0
Silver	0	1	0	0
Coffee	0	1	0	0
Heating oil=	0	1	0	0
Copper	0	1	0	0
Swiss franc=	1	0	0	0
Sum	1232	1175	1183	1153	7	12	1	2
Average	Legend<= /span>
5-Day Exit=
10-Day Exit=
15-Day Exit=
20-Day Exit=
Currencies
Commodities
Equities
Fixed Income<= /o:p>

Market	S&P-500	10-year T-note	Wheat	Eurodollar	Corn	Cotton	Crude oil=	Sugar
		10-year T-note=						Sugar