9-13-10_Mon_2.03pm_mht_Channel Breakout-Pull Back
Pattern (CB-PB) Securities Markets Time Frames Win Rate
9-13-10_Mon_2.03pm_doc_Channel Breakout-Pull Back
Pattern (CB-PB).doc
9-11-10_Sati_2.03pm_doc_Channel Breakout-Pull Back
Pattern (CB-PB).doc
9-11-10_Sati_2.03pm_MHT_Channel Breakout-Pull Back
Pattern (CB-PB).mht
9-10-10_Fri_11.30pm_doc_Channel Breakout-Pull Back
Pattern (CB-PB).doc
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Breakout-Pull Back Pattern (CB-PB).mht
Winning Trade Rate Psychological Framing Mental Accounting The House-Money & Framing Effects On Securities Markets & Time Frame Choices
Avi
Rushinek
Department
of Accounting, University of Miami, Coral Gables, Fl 33124, USA Email:
arush@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 paper looks at Behavioral finance to see how psychologi= cal factors influence securities (Equities, Currencies, Fixed Income, or Commod= ities) trading choice decision makin= g in financial markets (S&P500, US Dollar/Yen, 10-year Notes, or Gold)[1]. This includes the study of how information processing by investors may be influenced by psychological factors, such as Automated Trading System (ATS) seller self-promoting bias salesmanship overconfidence, or traders and GAAP conservatism. It deals with framing a trader as day-trader (with negative connotation) and long-term investor (positive connotation), mental accounti= ng- the data frequency (Tick, Second, Minute, Hour, Daily, Weekly Bars, etc) and the house-money effect (will higher win rate in commodities will drive more commodities trades?) as explanations of investor decisions (will higher ATS= win rate in currencies lead to focus on the exchange markets?), and considers market and modeling factors (correlations and covariance among securities, markets, time frames) that can limit arbitrage. (Chelley-Steeley and Steeley, 2010)
Overview
This paper psychological surveillance methods to measure fac= tors that influence securities (Equities, Currencies, Fixed Income, or Commoditi= es) trading choice decision makin= g in financial markets (S&P500, US Dollar/Yen, 10-year Notes, or Gold). This includes how to decide whether Automated Trading System (ATS) seller is self-promoting and has a bias of overconfidence in selling a “one size fits all” strategy. How= would ATS users deal with framing a trader as day-trader (with negative connotati= on) and long-term investor (positive connotation) is there a difference among t= ime frames or does “1 size fits all?.” How can I measure mental accounting- the data frequency (Tick, Second, Minute, Hour, Daily, Weekly B= ars, etc) using win trades rate? – by applying ANOVA and surveillance audit. Would that help evalua= te the house-money effect (will higher win rate in commodities will drive more commodities trades?) as explanations of investor decisions (will higher ATS= win rate in currencies lead to focus on the exchange markets?), and considers market and modelling factors (correlations and covariance among securities, markets, time frames) that can limit arbitrage since traders will be more a= ware of the correlations among securities, markets, and time-frames and will con= sider them in their trades. Firstly, they have to measure such parameters. This study demonstrates such measu= re and proposes to incorporate them into ATS. (Chelley-Steeley and Steeley,= 2010)
Currencies, Commodities, Equities, Fixed Income
Securities Markets Automated Trading Systems (ATS) Holding Periods Wins Aud=
it-
Testing Biases Towards Promoting Self-Interest
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 peri=
ods. Typically, developers optimized su=
ch ATS
strategies to specific securities, such as equities, specific markets or in=
dex
such as the S&P500, and specific holding time period ranges, such as ti=
ck
by tick, minutes for day traders, hours, days, weeks, months, etc, for swing
traders and so on. Such syste=
ms
have self-surveillance records that show the percentage of winning trades, =
the
elapsed time, 5 days or 10 days of a holding period, of an exit time, produ=
cing
a record for back-testing paper trading experiments. This study uses such records to de=
velop
a methodology of testing hypothesis concerning the assertion of some ATS
sellers, in order to validate or invalidate them. This study demonstrates such a met=
hod,
rejecting the null hypothesis stating that a specific strategy applies equa=
lly
to all the securities, markets and trading durations tested. It concludes that the sample strat=
egy
produces significantly higher percentage winning trades for certain
combinations of securities, markets, and trade durations.
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[2]<=
/a>
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 differ=
ence
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-rational decision making, and return anomalies. Daniel, Hirshleifer, and
Subrahmanyam (1998)[3] built
models based on extrapolation (seeing patterns in random sequences) and
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.”
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-s=
helf”
quantitative method that can easily be programmed into Microsoft Office Exc=
el
or similar users that is not a programmer.=
Using a program code generator or an embedded Macro Programming Lang=
uage
like Visual BASIC for Applications, included in Microsoft Office Excel 10, =
the
user simply records the applications, while the code is generated automatic=
ally
by the Excel program, or a similar program, producing a back-testing ATS au=
dit
validation application (App). Such
Apps can eventually be recompiled into Java script and HTML (Hyper Text Mar=
k 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 source 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 s=
uch
as equities, stocks or stock funds, may be much less successful in trading
currencies. Thus they may gen=
erate
a much higher rate of winning trades in equities, compared to currencies.
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 e=
ducational
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” (Rushi=
nek
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 image
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
control. An instructor/lecturer could have anyone in the world be the
cameraperson since 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)[4=
]
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.
….” The data
“shows the daily bars and, superimposed on the bars, the 20-bar tradi=
ng
range. The 20-day range lines have a 13-rick barrier added to both the line=
s to
filter out some false breakouts. The chart shows that the deutsche mark bro=
ke
above its 20-day range in December 1995 and then consolidated for 7 days be=
fore
moving higher. Upon moving higher, it made a higher high, and consolidated
again.
Data Analysis=
---- Insert Table 1 ----
Microsoft Office Excel Binary Worksheet Object
[[[[[=
[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[
]]]]]=
]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
9-10-10_Fri_12.15pm_Channel
Breakout-Pull Back Pattern (CB-PB).docx
/////=
///////////////////////////
Table 1 descr=
ibes
the Descriptive statistics of Currencies, Commodities, Equities, and Fixed
Income Securities Markets Automated Trading Systems (ATS) Holding Periods
Wins. It includes the: %Winne=
rs of
Currencies, 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 across currencies, which may indicate that the claim that many of
systems developers 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, Gold, 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.
\\\\\=
\\\\\\\\\\\\\\\\\\\\\\\\\\\
---- Insert Table 2 ----
Microsoft Office Excel Binary Worksheet Object
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 cor=
relation
with Equities of 0.254099558, and Fixed Income (10 year T-Notes, &
US-Bonds) are positive. Likew=
ise,
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 the % 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
---- Insert Table 3 ----
Microsoft Office Excel Binary Worksheet Object
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, a=
t a
value 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.
---- Insert Table 4 ----<=
span
style=3D'font-size:12.0pt;mso-bidi-font-size:10.0pt;line-height:115%'> Microsoft Office Excel Binary
Worksheet Object
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 wit=
hin
group such as currencies, where it compares, in the summary, SUMMARY, the British pound, Canadi=
an
dollar, in the commodities: Coffee, Copper, Corn, Cotton, Crude oil, etc, a=
nd
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:
6.4093E-147, 3.912874918, and 20664.87321. The same applies to the Columns, t=
he
securities, and the interactions (the holding periods) all vary significant=
ly
with respect to the percentage of winners of this automated trading strategy
system. &=
nbsp; &nbs=
p; &=
nbsp; &nbs=
p; &=
nbsp; &nbs=
p; &=
nbsp; &nbs=
p; &=
nbsp; &nbs=
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nbsp; &nbs=
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nbsp; &nbs=
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nbsp; &nbs=
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nbsp; &nbs=
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nbsp; &nbs=
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nbsp; <=
span
style=3D'font-size:8.0pt'>
Summary,
Conclusions 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 users 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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&nb=
sp; =
&=
nbsp; &nbs=
p;
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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 |
|
|
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 |
||||||||
|
5-Day
Exit |
|||||||||
|
10-Day
Exit |
|||||||||
|
15-Day
Exit |
|||||||||
|
20-Day
Exit |
|||||||||
|
Currencies |
|||||||||
|
Commodities |
|||||||||
|
Equities |
|||||||||
|
Fixed Income |
|||||||||
|
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 &=
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p; &=
nbsp; &nbs=
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&=
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style=3D'mso-tab-count:1'> &=
nbsp; &nbs=
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nbsp; &nbs=
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Eurodollar &nb=
sp; 60 &=
nbsp; 58=
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nbsp; 60=
&=
nbsp; 60=
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nbsp; 1<=
span
style=3D'mso-tab-count:1'> &=
nbsp; &nbs=
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nbsp; &nbs=
p; 0 &=
nbsp; &nbs=
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Corn &nb=
sp; =
57 &=
nbsp; 52=
&=
nbsp; 50=
&=
nbsp; 46=
&=
nbsp; 0<=
span
style=3D'mso-tab-count:1'> &=
nbsp; &nbs=
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nbsp; &nbs=
p; 0 &=
nbsp; &nbs=
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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