Sky Eye Trading System Edge Abyss

S K Y E Y E T R A D I N G S Y S T E M | 2
Table of Contents
Chapter 1: Introduction ................................................................................................................3
A. History: The Original Project...............................................................................................4
B. Why Microsoft Excel?.......................................................................................................10
Chapter 2: Philosophy.................................................................................................................11
Chapter 3: Methodology..............................................................................................................12
A. Trading Strategy.................................................................................................................13
B. Technical Analysis.............................................................................................................18
C. Trading Section..................................................................................................................20
D. Smart system......................................................................................................................26
E. Extra Features ....................................................................................................................27
Chapter 4: Macrocodes ...............................................................................................................32
Chapter 5: How to Use the SETS’ Excel File ............................................................................41
Chapter 6: Qualitative Study......................................................................................................45
A. Study Design and Procedures ............................................................................................46
B. Pilot Test............................................................................................................................47
C. Questionnaire.....................................................................................................................50
D. Data Analysis Techniques..................................................................................................52
E. Establishing Validity and Reliability.................................................................................54
F. Findings and Discussion ...................................................................................................55
G. Conclusion and Recommendations....................................................................................63
H. Survey ................................................................................................................................63
References ....................................................................................................................................68

Forex Trading System: Sky Eye Trading System – Edge Abyss
International trading (or “Global marketing”) is considered one of the best ways to gain
great profits in a short time for numerous reasons. These reasons include economic globalization,
market size, daily trading size, trading time availability, trading tools’ flexibility, and advanced
technology. In fact, trading in the global markets, such as stock, metals, oil, and foreign
exchange exposure—or “forex,” which will be the focus of this paper—needs a solid foundation
in both fundamental and technical analysis and requires years of trading experience in order to
trade safely in such dangerous markets (Shubita, Harris, Malindretos, & Bobb, 2011). The focus
will be on the forex market simply because it is the largest and the strongest in the world with a
daily trade of 4.0 to 4.7 trillion dollars, with yearly increasing trading rates (see Figure 1) (Bech,
2012, pp. 33 and 40).
Unfortunately, only 5% of traders are gaining all the profits because they have a clear
trading strategy, which answers the “why and the triple how questions.” These questions
according to Drakoln (2008) are “why you are selecting a trade, how to enter a trade, how to
Figure 1. Benchmarked average daily FX trading activity. Adapted
from “FX Volume During the Financial Crisis and Now” by M. Bech,
2012, BIS Quarterly Review, 33, p. 40.

properly monitor the trade you are in, and, finally, how to exit the trade to the best of your
ability” (p. 75). Consequently, many companies and organization have published many books
and articles about how to trade effectively and about how to manage trading accounts safely. In
the other hand, many companies have developed many systems (software) to help traders (users)
to trade directly and easily in any of these global markets without having to read or analyze any
market reports and/or news. Besides, these systems, which been developed by these companies,
use the technical techniques (quantitative methods) more than the fundamental analysis, e.g.,
reading the daily and weekly articles, watching live news, and analyzing the market daily,
weekly, and monthly (Cofnas, 2001; Krutsinger, 2005; Dunis, Laws, & Naïm, 2003, p. 1).
From this point on, this project: Sky Eye Trading System−Edge Abyss (SETS−EA) was
developed, which is about implementing advance quantitative methods (trading indicators) by
using Microsoft Excel 2010 to allow traders to find the best trading decisions automatically.
SETS−EA uses a specific philosophy and methodology to implement a safe and effective trade,
regardless of the situations. This article explains the system’s history, philosophy, methodology
(trading strategy, technical analysis [quantitative methods], trading section [technical analysis],
smart systems, and extra F), and micro codes of the project. This article also explains how to use
SETS−EA’s Excel file correctly.
A. History: The Original Project
It is essential to clarify that the original Sky Eye Trading System (SETS) was developed
as software in the beginning of 2006 using visual studio 2003/2008 as a framework and VB.Net
as a programming language. Moreover, SETS won the first place in the Saudi Arabian national
competition for computer skills programing field in 2007 (see Figure 2). Indeed, this system
went through many different stages of development in the past, transforming from small and

simple software that uses simple formulas, to advanced, multifaceted software using convoluted
formulas, and finally to today’s complete system, which can serve both the beginner and the
professional trader.
Although SETS uses only three major steps to analyze the market: loading and uploading
the data, clicking the calculation button, and then copying the results (decisions) in order to do
the live trading, it needs human effort to complete the job (half automatically system).
Figure 2. National completion for computer skills. Creative certificate: The
Supreme Committee for the National Completion for Computer Skills hereby
certifies that participant Alghenaim, Mohammed Fahad Abdullah is a creative in
computer-programing field for the kingdom of Saudi Arabia for the year 1428H
(2007 AD), hoping him all success. Head of supreme committee for the national
competition for computer skills. Signed by Dr. Ahmad Abdullah Alhamed.

Nevertheless, there is always a need for human intervention to do a complete analysis, such as
loading and uploading the data (the user needs to download the data from Metatrader4 [MT4] or
Metatrader5 [MT5] and then upload it into SETS), watching different news stations, reading the
SETS’ library, analyzing the latest up-to-date reports, comparing the calculation results with the
previous analyses, etc. The aforementioned steps are a normal result of SETS’ services in the
VB.NET version.
SETS includes all the necessary services for any trader, such as the fundamental and the
technical analysis tools; therefore, it is not possible to use all these tools automatically. Further,
while SETS has a professional interface with high-quality design, this version is still not easy to
use because it does not have a friendly interface for the user and requires between one and three
months for traders to become familiar with it, which is considered somewhat time consuming
(see Figures 3 and 4).
Figure 3. SETS (complex interface).

Even though SETS decisions’ model (tree) was very effective and can do 80% of the
work, it requires human intervention (20% of the work) to complete the steps. These eight major
Figure 4. These are some examples of SETS’
features.

steps, as they are shown under “Notes” in Figure 5, require a solid foundation in classic
analyzing, including knowledge of classic indicators, such as relative strength index (RSI),
commodity channel index (CCI), money flow index (MFI), etc. Moreover, traders would need a
lot of practicing to complete the task in a specific period of time.
Figure 5. The complex decisions’ structure (model/tree), which has been used by SETS.

Owing to the complexity of SETS, there was a need to remove many of the manual
features (extra analyzing tools/services) from the system in order to make it simple for the
traders to use and to make it directly connected (automatically connected) to the MT4 system.
Consequently, a new system called SETS−EA was derived from the old system (see Figure 6),
which adopted a new trading philosophy (see the Philosophy section below).
Accordingly, SETS−EA has yielded great results in just six months of daily trading, with
different demo accounts, after which major modifications have been done to the original SETS
decisions model. Still, SETS−EA contains a serious problem (a programming issue or “bug”)
while connecting to the MT4’s dynamic data exchange (DDE) service. This issue was the timer
lag that limited the connection’s time between SETS−EA and MT4 to only five to seven minutes
before the system froze (see Figure 7). In order to solve this issue, SETS−EA needs to be
Figure 6. SETS−EA (a simple version of SETS) can be
connected directly to MT4.

converted to an Excel file, to avoid any future bug fixing (service backs), and to make it easy for
traders to use.
B. Why Microsoft Excel?
One of the best solutions for SETS−EA’s timer lag issue is to use Microsoft Excel,
which is considered the best spreadsheet application to be used in the business decision
management system (BDMS) (Feldman, 2013, p. 9). Moreover, Excel 2010 has the ability to
implement DDE by using Visual Basic for Applications (VBA) (Interactive Brokers, 2001, p.
92). In fact, MT4 provides the DDE service to be used directly in the Microsoft Excel cells
without the need to use VBA codes in Excel. As it is shown in Figure 8, in order to load the live
data from MT4, a code such as MT4|BID!USDCHF must be entered in any cell in the Excel file
(IronForex, n.d., p. 150), which only works in Microsoft Excel 2003/2007. Thus, when using
Microsoft Excel 2010, the code must be changed to be ('MT4'|BID!USDCHF OR
MT4DDE|BID!GBPUSD) in order to load the data.
Figure 7. SETS−EA’s coding problem (timer lag bug).
Figure 8. DDE request formats and their possible results by the example of
"DDE-sample.xls" (IronForex, n.d., p. 150).

Therefore, SETS−EA was converted to an Excel file instead of being a VB.NET
application. Microsoft Excel 2010 was selected because of its extra visual features, in addition to
its ability to eliminate the need to create service pack packages in the future to fix such problems
(bugs). In addition, using Microsoft Excel 2010 helps to keep the focus on upgrading the trading
formulas, which are categorized as a first priority, without the need to upgrade the entire system
(upgrading the system interface and programming codes), which would be too time consuming.
Philosophy
Why Was SETS-EA Successful?
Over the last 10 years of recorded data, which can be loaded from MT4/MT5, each of 14
currency pairs has done both the maximum and minimum daily points’ movements. The idea of
using the daily points’ movements is to find the differences (subtractions) between the average of
all daily maximum points and the average of all daily minimum points that each currency has
reached in the past 10 years. In fact, many quantitative methods have been used, such as close-
points averages, currencies’ standard divisions (SDs), currencies’ correlation, etc., in order to
understand and analyze these daily moving points.
As shown in Figure 9, there are three steps (levels) of daily movements and an average
level for each currency pair. Step [1] represents the normal maximum movement of each
currency for average calculation, while step [2] represents the maximum daily movement for
Max and Min calculations. Step [3], which considers a great opportunity to gain a great profit,
represents the collapse of each currency in case of standard deviation (SD) calculation. On the
other hand, the average (AVG) column represents the average of step [1], step [2], and step [3]
for each currency, which has been used by the system in order to weigh the movements of each
of the three levels.

Consequently, the philosophy is built on
analyzing the data (for the daily chart) of the last
10 years of points’ movements for each currency
pair, such as Euro/US dollar (or “EURUSD”),
Great Britain Pound/US dollar (or “GBPUSD”),
etc. This data is then compared to the current
daily trading points’ movements (Pips) in order to
find out the correct buying/selling (or “shorting”)
points. New data is update in the system, which
will give the system the automatic ability to learn
from its mistakes.
Methodology
MT4, which is a free software (open-source software) and one of the most popular
systems that provides advance-trading solutions in the global markets, especially the forex
markets, and offers DDE service in its tool menu (see Figure 10) (GFT, 2012; Horner, 2010, p.
166; Rosenthal, 2012). The SETS’ methodology is built on using the MT4’s live data
(dynamically) in order to analyze the market by loading the high, low, close, and open points for
each currency’s pair. It then uses specific quantitative methods (math calculations) in different
tables, in the same or different sheets, with different objectives to break down this data into
valuable information that can be used to create other necessary trading indicators to specify what
trading signals need to be given.
Figure 9. SETS-EA’s currencies recorded
daily movements.

A. Trading Strategy
SETS−EA calculates (analyzes) the data’s results and then uses them in trading by taking
the best decisions. In fact, the SETS’ strategy is very aggressive in term of risk management
(RM). For example, if the daily moving point for Australian dollar/US dollar (AUDUSD) has
reach its maximum level, which is in this case 90 point (breakdown/collapse), the system opens
three buying positions because there is a high possibility for market corrections. These
corrections (rebounds) could be any of Fibonacci (c. 1170 – c. 1250) percentages of the total
daily points when a breakdown occurs (see Figure 11) (Hartle, 2001; Marshall & Moubray,
2005). These numbers are “23.6%, 38.2%, 50%, 61.8%, and 100%” (Knaust, 2010, p. 10).
Fibonacci numbers, which are derived from the Fibonacci theory (or “Fibonacci sequence”), an
amazing mathematical procedure that is known as the super sequence ( )
Figure 10. Enable DDE server in MT4 (GFT, 2012, p. 156).

(Canaan, Garai, & Daya, 2011; Kalman & Mena, 2003, p. 168) (see Figures 12 and 13), are used
in SETS−EA, specifically in the LOADING DATA’s sheet (see Figures 14, 15, and 16).
Figure 11. An example of the Fibonacci on the Euro/Japanese yen
(EURJPY) 15 minutes’ chart (Marshall & Moubray, 2005, p. 26).
Figure 12. The Fibonacci theory.
Figure 13. Leonardo Pisano Bigollo
known as Fibonacci (Canaan at el.,
2011).

Figure 14. LOADING DATA’s sheet (hidden section) is where all the data is collected in order
to be converted to valuable information.
Figure 15. LOADING DATA’s shows the turning points for all 14
currency pairs by using the Fibonacci method to determine the
profits targets.

Returning to the previous example, the three AUDUSD buying positions are: direct
contract (first position), support one (second position below the first position), support two (third
position below the second position), and a general and united “stop lose” for all three positions
below the third position (support two). These buying signals will be implemented automatically
at the SYSTEM PLATFORM table in the MAIN PAGE’s sheet (see section C. Trading Section).
In addition, one of the most important trading strategies is to trade at a specific time
period every day. One of the best advantages of trading forex is that there is an ability to trade
24/7 from Monday until Friday every week (O’keefe, 2010, p. 10), excluding some international
events, such the United States’ Independence Day. The best time to trade, in the New York time
zone, or Eastern Standard Time (EST), is from 8 a.m. to 11:59 a.m. EST due to many reasons.
One of these reasons, as shown in Figure 12, is to trade after all the major effects/impacts have
occurred in most of the markets on the same day, so that the maximum daily Pips reached for
each currency pair are known.
Figure 16. LOADING DATA’s presents the Fibonacci numbers on all 14
currency pairs in a dynamic chart.

In other words, the best time to trade is after the close of two of the four biggest markets
(Australian, Japanese/Asian markets) and after five hours of the (Landon/European markets),
which means that the trade should start at the New York/North American markets. According to
Hunt (2011), “The best time to trade is when the market is most active” (para. 4), which in this
case will be from 8 a.m. to 11:59 p.m. EST according to SETS−EA’s trading strategy. It is
essential to recognize that most of the world trades are in both the Landon/European and New
York/North American markets with a 54.7% of world forex trading (BIS, 2010, p. 1).
According to BIS (2010), “The relative ranking of foreign exchange trading centres has
changed slightly from the previous survey. Banks located in the United Kingdom accounted for
36.7%, against 34.6% in 2007, of all foreign exchange market turnover, followed by the United
States (18%), Japan (6%), Singapore (5%), Switzerland (5%), Hong Kong SAR (5%) and
Australia (4%)” (p. 1).
As shown in Figure 17, the trade must start (according to SETS−EA’s trading strategy) at
1 p.m. Greenwich Mean Time (GMT) and end at 4:59 p.m. GMT, which is equal to 8 p.m. to 11:
59 a.m. EST in the New York/North American markets.
Figure 17. This is the global market’s trading times in EST (Bryan, 2007).

B. Technical Analysis
Figure 18 shows the main trading platform, which includes all the necessary functions
and codes to analyze the live data. This platform (or “table”) contains 24 columns. Starting as a
pyramid model, each column represents a different formula which will be the base for other
formulas (columns) (see Figure 19).
Figure 18. The trading platform in SETS−EA.
Figure 19. SETS−EA uses a pyramid model when
implementing the trading formulas where n is an integer
from 1, 2, 3, …, ∞.

Below, the formulas are used for each currency pair in order to determine the trading’s signals.
I. System platform formulas:
1- Bid price: ('MT4'|BID!Currancy’s pair), which is a dynamic parameter from MT4.
2- Ask price: ('MT4'|ASK!Currancy’s pair), which is a dynamic parameter from MT4.
3- High price: ('MT4'|HIGH!Currancy’s pair), which is a dynamic parameter from MT4.
4- Low price: ('MT4'|LOW!Currancy’s pair), which is a dynamic parameter from MT4.
5- Total daily points (PIPS): (High price – Low price) * (10000 or 100)
6- Direction detector : IF (BA12=X1, CHAR(200), IF (BA12=X1, CHAR(199), CHAR(199)))
X1 =IF (Ask price < PIVOT POINT, "D", IF (Ask price > PIVOT POINT, "U", "+"))
PIVOT POINT = (High price + Low price + Ask price + Bid price) / 4
7- H | A: ABS ((High price – Ask price) * (10000 or 100)
8- L | B: ABS ((Bid price – Low price) * (10000 or 100)
9- Rollover of the currency (ROC): (L | B – H | A) / 100
10- Top of steps (TOS): (PIPS – Step1, which is level 1 maximum daily points historical movements)
11- Before/after breaking point (BAB): TOS – ABS (L | B - H | A)
12- Covering the continuing percentage (CCP): ABS (IF (X2 > Y1, (Y1 / ( X2 + Y1)), IF ((Y1 >
X2), ( X2 / (Y1 + X2)), 100)))
Where X2 = (High price – Bid price) and Y1 = (Bid price – Low price)
13- LEVEL: IF (AND (I10 >= SETTINGS!D4, I10< SETTINGS!E4), 0, IF (AND (I10 >= SETTINGS!E4,
I10 < SETTINGS!F4), 1, IF (I10 >= SETTINGS!F4, 2, "")))
II. Risk Management:
1- X3 = ((Total Founds * Risk Level%) / 1000)
2- Position size: IF (X3 >= 1000, ((X3 / 1000)), X3 & IF (Position size <= 9.99999999999999, "", "K")
3- Take profit: IF (X3 * 0.25 >= 1000, (X3 * 0.25 / 1000), X3 * 0.25) & IF(X3 * 0.25 >= 1000, "K", "")

4- Position [1]: IF (X3 * 0.25 >= 1000, (X3 * 0.25 / 1000), X3 * 0.25) & IF (X3 * 0.25 >= 1000, "K", "")
5- Position [2]: IF (X3 * 0.25 >= 1000, (X3 * 0.5 / 1000), X3 * 0.5) & IF (X3 * 0.25 >= 1000, "K", "")
III. Market speed: MODE (M1 : M2)
1- M1 = IF (AND (CCP >0 , CCP < 0.2), 5, IF (AND (CCP >= 0.2, CCP < 0.4), 4, IF (AND (CCP >= 0.4,
CCP < 0.6), 3, IF (AND (CCP >= 0.6, CCP < 0.8), 2, IF (AND (CCP >= 0.8, CCP < 1), 1, 0)))))
2- M2 = IF (AND (CCP > 0, CCP < 0.2), 5, IF (AND (CCP >= 0.2, CCP < 0.4), 4, IF (AND (CCP >= 0.4,
CCP < 0.6), 3, IF (AND (CCP >= 0.6, CCP < 0.8), 2, IF (AND (CCP >= 0.8, CCP < 1), 1, 0)))))
IV. For more formulas’ details, open the excel file (SKY EYE TRADING SYSTEM.
.XLSM).
C. Trading Section
First, regardless of how much the trader’s capital (account) was, the trader must trade 5%
of the account (Whistler, 2004, p. 49), but not more and not less than 5%. This follows the
condition known as pyramiding the profit, which “is a method of increasing a position size by
using unrealized profits from successful trades to increase margin” (Glossary, 2009, p. 38). For
example, if the trader account was $25,000, 5% will equal to $1,250 for each trade. Second, this
5% ($1,250) must also be divided into three different sizes (and must not trade as one) to be in
support or resistance levels (see Figure 20).
In fact, SETS−EA helps traders in calculating and dividing the 5% to be in lot (or “Mini-
Lot”) which, according to Glossary (2009), “is a currency trading lot size that is 1/10 the size of
the standard lot of 100,000 units. One pip of a currency pair based in USD is equal to $1 when
trading a mini‐lot, compared to $10 for a standard-lot trade. Mini‐lots are available to trade if
you open a mini‐account with a forex dealer” (p. 27). In other words, lots are the amounts that
are participating in each trade. Moreover, there is a special calculation to find lot sizes according
to the SETS−EA (see section B. Technical Analysis).

The best way to explain the trading technique is
by providing the following example. When a buying
signal is given for the AUDUSD pair, which can be
seen under the SIGNAL column at the SYSTEM
PLATFORM table, a beep sound will repeated to inform
the trader of a trading action, and four numbers will
appear. These four numbers are TAKE PROFIT, POINT
[1], POINT [2], and STOP LOSE (see Figure 21).
The trader needs to copy all four numbers into Notepad or any similar application, and
then post these numbers in the MT4 for the AUDUSD currency pair in a specific way. As
explained in Figure 21, the first thing that must be done when the system gives a buy signal is to
open a buying position directly (manually). This direct position is not given by the system and it
could be any number at that moment. For example, this direct position will be (0.91400) with the
size of (0.313 lot) and with the target of (0.91953).
After that, the trader needs to place two future orders. The first future order will be the
first supporting point, which will be (0.90874) in this example, and the target will be the original
position (0.91400) in order to support the original position with the size of (0.313 lot). The
second order will be the second supporting point, which will be (0.90474) in this example, and
the target will be the first supporting point (0.90874) with the size of (0.625 lot).
Figure 21. Example of a buying signal.
Figure 20. Risk management section
is responsible for calculating the
three levels’ lots to be used in three
different trading positions.

Finally, all the three positions will have the same stop lose point, which will be (0.90024)
in this example (see Figure 22). The previous steps (trading strategy) were justified because of
the risk management reason to manage the account margin, which “means that there is no
requirement for putting up the full value of the position. In forex trading, the amount of leverage
available could be up to 200 times the value of somebody’s account” (David & Leustean, 2009,
p. 127).
After entering all the values in the MT4, it is important to know what to do next. If the
position (1) SIGNAL-BUY has reached the target (2) TAKE PROFIT, both positions (3) POINT
[1] and (4) POINT [2] must be cancelled. However, if position (3) POINT [1] has opened, then
position (1) SIGNAL-BUY’s target will be changed to the same original position (in order to
close at zero profit/loss). Likewise, if position (4) POINT [2] has opened, then both positions (1)
SIGNAL-BUY and position (3) POINT [1]’s target will be changed to (3) POINT [1]. The idea is
to lose position (1), to close position (3) at zero profit/loss, and to win position (4), which is the
largest in size (lots).
Figure 22. Trading section.

In short, the trading technique is to end up always with profits in total trading. For
example, if the first position has succeeded in reaching the take profit target, then the trade was
successful and there is no need for the other two pending positions, which need in this case to be
cancelled in order to wait for new signals with new details (trading values). If the first position
fails to achieve its TAKE PROFIT, then POINT [1] could be opened as an active second contract.
In this case, there is a need to change the original take profit point to the same value when the
original position has been opened.
For example, if the original position was (0.91400), then the target will be the same
(0.91400) to close at zero profit/loss. In fact, POINT [1]’s target will be the first original position
new target. In other words, the target for POINT [1] will be (0.91400) to close at a profit and
zero profit/loss, which will equal to an overall profit. Consider the below example.
• Original position = Zero (no profit and no loss) and the trading size was (0.313 lot).
• POINT [1] = Profit and the trading size was (0.313 lot).
• Then total profit = Original position + POINT [1] = Profit.
• Then total profit = 0 + 0.625 = 0.313
However, if POINT [1] fails to achieve the original target, then POINT [2] could be
opened as an active third contract. In this case, there is a need to change both of the TAKE
PROFIT targets for both of the original and POINT [1] positions. The new target POINT [1] will
be united for all the three positions (original position, POINT [1], and POINT [3]). In fact, the
size of the third position will be doubled (0.625 lot) in order to close with an overall profit.
Consider the below example.
• Original position = loss and the trading size was (0.313 lot).
• POINT [1] = Zero (no profit and no loss) and the trading size was (0.313 lot).

• POINT [2] = Profit and the trading size was (0.625 lot).
• Then total profit = Original position + POINT [1] + POINT [2] = Profit.
• Then total profit = - 0.313 + 0 + 0.625 = 0.313
Here, it would be beneficial to explain another trading methodology. In fact, the numbers
in Figure 22 could be representing within a specific different range (Fibonacci supporting or
resistaning points). This extra trading strategy is a reasonable methodology. As explained in the
Fibonacci theory, Fibonacci discovered through a mathematical sequence what is called the
Golden Triangle (See Figure 23), which has been used in the system to identify the take profits
targets in three stages. In detail, the first target will be 23.6% of the maximum historical daily
movement (MHDM) for any specific currency pair while the first support will be at 38.2% of the
same MHDM. Also, the second support will be 61.8% of the same MHDM.
The second trading strategy is Fibonacci Targets, which uses Fibonacci numbers in order
to generate the supporting or resentencing points. This strategy uses the Fibonacci Golden
Figure 23. Fibonacci’s golden triangle (Canaan at
el., 2011).

Triangle method, at shown in Figure 28, to implement it as a trading strategy, as shown in Figure
24. The difference between the default strategy and the Fibonacci Targets strategy is that
Fibonacci Targets is used only for the following currency pairs: EURUSD, GBPUSD, EURGBP,
and GBPCHF. This is because Fibonacci Targets works effectively for long-term trading with
these currency pairs and was provided for those who want to trade with these pairs in particular.
However, the default setting will not enable the Fibonacci Targets strategy. It must be
enabled by the trader by checking the Fibonacci Targets box, as shown in Figure 25.
In addition, it is important to notify that both the Correlation and News Trading strategies
are not valid at this moment because they are under testing in order to ensure that they will work
efficiently with the order indicators in the system. In fact, it is beneficial to note how helpful it is
to open demo accounts with those website brokers that offer the FIFO trading rule (policy).
According to Bland, Meisler, and Archer (2009), First-In/First-Out (FIFO) is an “accounting rule
Figure 24. Fibonacci Targets (the second trading strategy).
Figure 25. Fibonacci.

according to which open positions are closed. All positions opened within a particular currency
pair are liquidated in the order in which they were originally opened” (p. 281). This action helps
remind the trader not to change the targets manually; this policy will do all the previous steps
automatically.
FIFO has a specific role that has been assigned by the National Futures Association (the
industry wide, self-regulatory organization for the U.S. futures industry), and it has “forced some
dealers to eliminate the availability of stop and limit orders on individual positions” (O’Keefe,
2010, p. xii). In other words, “the trader must always close the oldest trade of a given pair. This
rule is enforced to maintain your account's synchronization between MT4 and [the] fxTrade
platform, in which the concept of partially closing a trade works differently” (OANDA, 2011,
para. 35). This policy makes using SETS—EA easier when dealing with MT4.
D. Smart system
As shown in Figure 26, SETS−EA includes
some advance indicators, which can update the system
according to time of day, in order to make this system
to be a smart system that learns for its mistakes. In fact,
the new updated data is stored in two Microsoft Access
2010 databases (Control and Database updated) in order to manage these new data and to do
some dynamic queries on it in the future, if necessary, in order to send feedback to the Excel file
(SETS−EA) in the SETTINGS sheet.
Figure 26. Examples of the smart
indicators that are used in the system.

E. Extra Features
The Market Speed indicator (see Figure 27)
explains the market trading speed around the world,
which could be analyzed in five levels. Level zero (or
“DEAD”) means that there is no connection or the
market is not moving for unknown reason, while part
one (or “NEWS!”) means that the market is slow and is
waiting for updated news. In the SD section, as shown
in Figure 28, there are five different SDs, shown to
inform the trader about the market moving mechanism.
However, this information is not used as auto-indicators
because they are under testing. In fact, these parameters
could provide a clear view to the trader regarding the overall direction of the markets by
comparing the left-to-right currency pairs.
This table in Figure 29 explains the top compatible/contradictory correlation relationships
of the 14 currency pairs, which are analyzed individually, as shown in Figure 30. The table in
Figure 30 uses the following function:
CORREL(Data of currency pair1, Data of currency pair2)
Where both of Data of currency pair1and Data of currency pair2 are in a separated sheet,
which is named DATA. In fact, this indicator is not valid at the moment; however, it is presented
Figure 29. Correlation explanation.
Figure 27. Market speed.
Figure 28. SD section.

in the system as a visual table that can be understood easily and can be used to analyze the
market direction for each currency pair as compared to others.
Also there is another table (see Figure 31) which shows the trend’s power and direction
for each currency pair.
This indicator (“Risk-M: Enabled”) (see Figure
32) means risk management has been enabled because
one or more currency pairs have moved above 200
points (in the daily chart), which should be considered
as a collapse of these currency pairs that could reach
levels one, two, or three (the stars), indicating the
aggressive daily movements for each currency pair. It isFigure 32. Risk Management alert.
Figure 30. Correlation table.
Figure 31. Current news impacts indicator.

essential to notify that when the level is higher, the likelihood to gain a profit becomes higher
and faster.
Figure 33 shows the SUMMARY sheet, which is a friendly interface that include only the
necessary things that trader should use in order to trade.
This indicator (“News impact strength”), which is shown in Figure 34, is very critical in
that it shows today currency pairs’ strength movements, which are the results of the market’s
Figure 33. Summary of the main system platform.
Figure 34. Current news impacts indicator.

successive news. This table measures the movement for each currency on its own against all
other currencies. This indicator is not valid at the moment, but will be implemented in the future.
A powerful feature in SETS−EA is to enable the news
power indicator (used as a dynamic code) in the taking
decision system as a critical indicator (see Figure 35), which
plays a major role in risk management in order to ensure that
the given signals (buying/selling signals) are correct. Figure
36 shows a useful calculator that helps the traders to track
their profits and evaluate both of the SETS−EA’s
performance and accuracy. Simply, the user needs to click
any of the two buttons (Win or Lose) to find out the trading
results percentage of how many successful and failed
contracts in a certain time, in order to send feedback for the
developer to improve the system’s quality. In addition the
trader can reset the results, if necessary, and start the
evaluation over again.
Figure 35. News’ code.
Figure 36. System evaluation.

Figure 37 shows the control section, which has
some very critical trading tools (buttons) which give the
trader some necessary flexibility to manage the trading
situations correctly. As shown in the figure, there are
five control parts. The first part includes the following
buttons, [1], [2], [3], [4], [5], [6], and [7]. These buttons
are responsible for changing the YouTube forex
channel to other forex channels that provides different
kinds of news that should help the trader to understand
and learn more about news that could affect the global
market positively or negatively, especially the central
banks and the politic news. The second part is about
sorting the SYSTEM PLATFORM table, which can
happen in six ways. The second way is to sort the Pips
in order to know how the top currency pairs Pips have
done in the market. The third way is to sort the TOP in
order to find out the currency pairs that have broken out
the highest historical daily points’ movement. The
fourth way is to sort the LEVEL in order to know
which currencies pairs have given a trading signals and
what level of opportunity have been given: empty star, half star, or complete star. An empty star
is a symbol of level 1, while half star represents level 2. The best opportunity is level 3, when the
Figure 37. SETS−EA’s control
section provides advance services.

complete star appears. The fifth way is to sort the CCP from least to highest in order to find out
which of the 14 currency pairs are nearest to the top or bottom.
CCP is an important indicator because it is the main
indicator that gives the system the green light to show the trading
signals. The default value for the CCP for the three levels are at
shown in Figure 38.
Macrocodes
SETS−EA uses some VBA codes in order to organize/support the system, such as sorting
the data when it comes to filtering the data, and to make the system more effective for mid- and
long-term trading by giving the traders the choice to use different CCP percentages, which will
give traders more flexibility without affecting the system trading strategy negatively. SETS−EA
includes both direct codes and models, which will be upgraded/updated in the future (see Figure
39). It is important to notify that some VBA functions are under testing and these functions could
be used in the system when they are ready.
Below are some of the codes that are being used at the moment.
Figure 39. SETS−EA’s models.
Figure 38. Relationship
between CCP and Level.

It is necessary to note that this system includes a disabled expiration function in its VBA
code, which will be enabled when this system becomes a commercial product in the future (see
Figure 40) as copyright privacy.
How to Use the SETS’ Excel File
A. Connection Section:
1- Download MT4 from, http://www.metatrader4.com/traders;
2- Run MT4 and enable the DDE service;
Figure 40. The disabled expiration code.

3- Ensure that MT4 is connecting to the live markets (see Figure 41) by checking the
connection symbol at the bottom left corner;
4- Create demo account, see Figures A, B, C, D, E, and F;
Figure A. From menu, select File,
and then Open an Account.
Figure 41. Connection symbol.

Figure B. Fill in all the textboxes, and then check the box next to “I agree to
subscribe to your newsletters.” Then, click the Next > button.
Figure C. Click the Scan button, and then click the Next > button.

The trader can access the MT4 system
any time by using the same username and
password in any place around the world. It is
better to start with a small balance (capital),
such as $1,000 or $10,000 in the demo account
(Schlossberg, 2012, pp. 18 and 22) in order to
learn how to manage small accounts, which are more difficult because small accounts need both
trading patience and a higher level of risk management than those accounts that are equal or
above $50,000. In fact, real accounts should not be less than $25,000 to start from a reasonable
balance. According to Traded FX (n.d.), “You need to trade with the trend in the case of small
accounts ($ 25,000 and under)” (p. 16).
Figure D. Copy and save both the Login number (500068010) and the Password
(a7jxzal) and keep them in a safe place.
Figure E. The demo account under the
navigator section.

5- Enable the DDE in MT4, see Figure 42;
Figure F. The demo account is ready to be used.
Figure 42. Enable DDE server in MT4 (GFT, 2012, p. 156).

6- Open the SETS−EA’s Excel file;
7- Enable the Macro;
8- Choose “Update” and update the data (see Figure 43);
9- As it is shown in Figure 44, SETS−EA is now connected to MT4!
Qualitative Study
A qualitative study was done on SETS−EA, which was protected by two complex
passwords (review tab: protect sheet and protect workbook) to hide the VBA code, cells
formulas, and other sheets, such as SETTING, LOADING DATA, DATA, and DATABASE. These
passwords were applied to protect the intellectual property rights of the researcher. Therefore,
only three sheets were visible to the traders: ABOUT, MAIN PAGE, and SUMMARY. The
objectives of this study were to find the advantages and disadvantages of using this trading
system, and to improve the system’s quality in the future. Also, this study was established in
order to support the trading results of the SETS−EA trading methodology.
Figure 44. SETS−EA has a dynamic platform.
Figure 43. This message shows up each time when opening the excel file.

A. Study Design and Procedures
To conduct a qualitative study on SETS-EA, the researchers developed a questionnaire.
This questionnaire was then distributed to and completed by 10 volunteer students from Middle
Tennessee State University (MTSU), in Murfreesboro, Tennessee, U.S.A. For this study, the
researcher used a random number generator to randomly select participants who were not related
to each other. This sampling method was used because it produces unprejudiced results. Out of a
population of 20 MTSU students, 10 were selected to participate. During the selection process,
each of the 20 students was assigned a number between 1 and 20. A random list of the 20
numbers was generated, and the first 10 numbers were highlighted. The students whose numbers
correlated with the 10 numbers that were randomly chosen were picked for the study. This
procedure ensured that the selection was unbiased because each student had an equal chance of
being selected.
This method of random selection is much quicker and cheaper than other sampling
methods, and the results are generated usually valid. Nevertheless, it must be noted that there is
no guarantee that the information gathered from the unsystematic sampling is entirely accurate.
The data collected in this study is based on the assumption that the sample of participants might
not be a good representation of the entire population.
One of the advantages of a qualitative research procedure is the ability to gain deeper
insight into the occurrence being studied. In this case, the attention was on the unique outlook of
the participants in connection to the topic (Patton, 2001). An appropriate structure was provided
to explore the impact of SET-EA on profit levels for four weeks by using MT4’s demo account
with $25,000 for every participant. The application of a qualitative questionnaire allowed the
participants to be investigated in their natural environment, which meant the materialization of

multidimensional elements was anticipated. At the onset of the study, the researcher clearly
detailed its purpose and objectives. Certain factors were considered during the preparation of the
questionnaire, which involved the following elements:
• Fluent and simplified language;
• Fluidity between phrases and sentences;
• Applicability to the topic;
• Absence of grammatical errors and mistakes.
B. Pilot Test
Conducting a pilot study provided the chance to identify and prepare for the extensive
range of possible challenges that could be experienced while conducting the research. The pilot
study replicated the method of testing, the study procedure, and the instrument before conducting
the research. The aim of pilot testing was not to gather information, but to fine-tune the
procedures and approaches to be used during the later stages of research (Patton, 2001). In this
pilot study, students were selected using a sampling procedure similar to that mentioned in
Section A: a random list of 20 students was used, and the first 10 on the list were chosen. A
considerable benefit of the pilot study was that it gave the opportunity to potentially investigate
new questions and make any necessary changes to the questionnaire before conducting the actual
study. Nonetheless, the pilot study had its challenges. The specific challenges experienced are
listed below:
• Unnecessary questions;
• Confusing questions;
• Ambiguous and unfocused questions (lack of focus on a singular point);
• Leading and objectionable questions.

This pilot study increased the quality of the questionnaire and allowed for improvements
based on the recommendations of professionals in the field. The best way to carry out the pilot
study was to use a retrospective interview technique (Patton, 2001). It was important that all
participants had the same understanding of the questionnaire, and the most important part of the
pilot study was to ensure that the questionnaire was issued in the same way and under the same
circumstances as those intended for the actual study. It was essential to note both the start and
end times so the time required to fill out the questionnaire could be calculated. In addition, the
researcher took into consideration the significance of certain specific instances where the
volunteers showed indecision or hesitation when answering the questions. This helped the
researcher to correct questions that were confusing, ambiguous, or difficult to understand.
Once the pilot study participants were chosen by the random number generator (see
Figure 45), the researcher clearly explained how important it was for them to focus on the way
they comprehended every question and possible answer. It was noted when participants required
clarification on questions. Cultural sensitivity and relevance of questions was considered, and
any objections raised to the questions were recorded (see Figure 46). Instructions explaining how
to handle discrepancies were outlined in written form (Patton, 2001) to ensure that the.
Objectives and procedures were adhered to in order to achieve valid results.

As shown in Figure 45, specific randomization
formulas have been used in an excel file to select the first
10 of the 20 questionnaires that have been collected. As a
result, the random number generator table selected the
following surveys: 1, 2, 3, 6, 8, 9, 14, 16, 18, and 20, all
of which were used in analyzing the trading results after
using the SETS-EA trading methodology and clicking the
F9 key 20 times (because there were 20 surveys in the
list). For more details, all the formulas and “IF
statements” are in the excel file called “Random
selection.”
Figure 46. Reflexive questions: Triangulated inquiry (Patton, 2001, p. 66).
Figure 45. Random number generator
(Excel file).

C. Questionnaire
When developing the questionnaire for this research project, open questions were
intentionally used in order to give the participants flexibility when providing their answers. The
questionnaire was structured in such a way to pull essential information from each participant.
The participants were also required to sign a consent form that stated that their participation was
entirely voluntary. It also stated that the information they provided would remain confidential
and would not be released to any third party. The questions used in the questionnaire are listed
below.
Before using SETS−EA:
1. Please indicate your gender and age.
2. What is your yearly income?
3. What does FOREX mean?
4. Do you have experience trading in the global market, such as stock, currency exchange,
oil markets, etc.?
5. Have you used Metatrader4 (MT4) or Metatrader5 (MT5) before?
6. Have you ever invested your money in global markets?
7. Do you have experience using Microsoft Excel software?
8. Do you think you will succeed in using SETS−EA? Why or why not?
9. Please, write the following in the space provided below this sentence: “I guarantee that I
will search for and read about the following: Forex, MT4, and excel software for at least
three days, and I will follow all the trading guidelines, which are on pages 20–26 in the
file called Sky Eye Trading System Edge Abyss.”

After four weeks of using SETS−EA:
10. Did you find it easy to use the SETS−EA excel sheet? Why or why not?
11. Did you find it easy to use the SETS−EA trading strategies? Why or why not?
12. How many hours did you trade each week?
13. Please indicate the top five advantages and disadvantages of using SETS−EA.
14. How do you evaluate the reliability and credibility of SETS−EA?
15. What was the effect of using SETS−EA on your trading performance?
16. Please describe your overall experience when using SETS−EA.
17. Did you gain profits during this four-week test? If so, please discuss/describe your
experience below.
18. How do you describe the impact of using SETS−EA on your self-efficacy as a trader?
19. Are you planning to continue using SETS−EA in the future? Why or why not?
20. Did you find a similar system to SETS−EA that you believe is much better? If yes, please
explain.
21. Could you provide certain recommendations for improving SETS−EA quality?
The responses to these questions were critically evaluated and are presented in the
proceeding chapters. A major concern with this procedure was the amount of information
collected. It was important to collect as much information as possible in order to show specific
patterns and trends related to the execution of SETS−EA and its effect on trading performance
and self-efficacy of the participants. The questions were developed in a manner that would,
hopefully, illustrate important practical implications pertaining to the issue under investigation.

D. Data Analysis Techniques
The technique of evaluating the qualitative information collected from the questionnaires
relied upon feedback from the participants answering the particular questions. In addition, the
main focus was on assessing the exact purpose of the evaluation. It was, therefore, critical to the
research and collected data that the right questionnaire questions were developed and distributed
(Patton, 2001). Information was also categorized and data coded in order to grasp a better
understanding of the topic under examination. Trends, themes, and patterns were pinpointed
based on the traders’ or students’ thoughts and ideas for using SETS−EA in the foreign exchange
market. Identifying these trends and patterns within and between classes was a very important
part of the procedure because, ultimately, it was the interpretation of this data that gave the study
its meaning.
Software called Statistical Package for the Social Sciences (SPSS) was then used to
analyze the qualitative questionnaires in order to ensure the data analysis quality; therefore, there
was a need to code the item’s variables and deal with the qualitative questions as indirect
quantitative questions (Knafl, n.d.). To achieve this goal, the qualitative questions were coded by
categorizing the written answers subjectively, such as “very optimistic,” “optimistic,” “not sure,”
“pessimistic,” and “very pessimistic.” Below is the coded questionnaire (see Figure 47).

Figure 47. The coded questionnaire.

E. Establishing Validity and Reliability
Establishing validity and reliability is critical to the credibility of a research project. In
this study, it was important to address issues of reliability, lack of objectivity, survey limitations,
and survey distribution consistency. Issues regarding prejudice were avoided by carefully
creating the questions and not including those that could possibly illicit biased responses. The
sample size was small (10 participants); therefore, the limited number of surveys presented a
valid risk to data accuracy. To mitigate this risk, significant efforts were made to ensure that the
participants’ answers were reliable and credible. To help with consistency, the surveys were
presented orally to the participants, and the instructions and purpose of the study were also
carefully explained.
To establish validity in the relevant qualitative research project, it was necessary to
execute the entire study professionally and accurately without withholding any information that
may have been considered relevant to the study. The results and the conclusion were carefully
presented (see Figure 48) (Silverman, 2006). In the current research, data was linked to the
research field involving the impact of technology on the development of learning outcomes
among university students. The responses from the participants were assessed by considering
their reality as university students in context with the answers given. There was relatively high
validity and transparency in the data collected. This data involved the participants’ thoughts and
ideas, and their opinions proved to be central to the study.
Figure 48. Qualitative vs. quantitative research
(Silverman, 2006, p. 4).

The reliability of the study was thought to be high due to the fact that results could be
replicated externally, outside the study’s geographical area of Murfreesboro, Tennessee. The data
collected offered significant insight into the effect of applying SETS-EA to volunteer students’
results in obtaining profits in the foreign exchange market. Furthermore, most of the students had
multicultural backgrounds, which indicates that the results can be applied to trading settings in
different countries.
The participants’ understanding of using the system implied that the method fulfilled the
original objectives of the study, while the degree of openness left room to discover other
appropriate methods that could potentially be applied. The extent to which the researcher was
free to discuss and interpret the collected data was precise (Silverman, 2006). Reliability was the
biggest concern with the procedure because it was defined within the context of qualitative
research. The research procedures had to be properly documented in detail so the consistency of
the procedures could be clearly illustrated.
F. Findings and Discussion
The aim of this research project was to examine, from the MTSU setting, the applicability
of SETS−EA in the foreign exchange trading market and determine whether the use of
SETS−EA technology could improve the performance of foreign exchange traders. Additionally
this methodology made it possible for concerns and issues with the SETS−EA technology to be
illustrated. Therefore, this chapter will present and discuss the findings of the study, including
important implications of the SETS−EA technology implementations on foreign exchange
market trading. Below are the statistical analysis results.

Data composition
The data for this analysis comprises ten responses to a 22-question survey on the use of
SETS-EA, an Excel-based forex market trading plugin.
Gender and Age Composition of Respondents
The respondents’ age and gender is summarized in Table 1. There were a total of 3
female respondents, all of whom were in the 25-30 age bracket. Male respondents were spread
out between the 21-25, 25-30, and 30-35 age brackets.
Income and Trading Experience
Respondents’ annual incomes were between 0–$50,000 with a greater percentage (60%)
in the under-$25,000 category. Cross tabulated with the level of experience, as shown in Table 2
Table 1.
Table 2.

above, respondents with extensive experience or some experience with forex trading had
incomes exceeding $25,000.
Usage of MT4 or MT5 and Investment in Global Markets
The chart below shows the participants’ experience with MT4 or MT5
As the graph shows, only one respondent had previous experience with MT4, a common
global markets trading software.
Table 3.

As Table 3 shows, a majority of respondents had neither invested in global markets nor
experience in trading with MT4 or MT5. Table 4 shows the correlation between the two
variables: experience with MT4/MT5 and investment in global markets. As can be seen, there is
significant positive correlation (p=0.035), suggesting that the respondents did not have previous
backgrounds in global markets trading.
Experience with MS Excel and Projection of Success
The graph below summarizes the respondents’ levels of experience.
Half of the respondents had extensive experience with MS Excel. Because SETS-EA is
based on MS Excel, the researcher felt it necessary to calculate whether there is a correlation
Table 4.

between experience with Excel and the possibility of succeeding with SETS-EA. The results are
shown in Table 5 below.
From Table 6, it can be seen that there is a significant positive correlation at 90%
confidence level. This could mean the participants’ projections were done after using SETS-EA.
Table 5 shows the cross tabulation of experience with MS Excel and the projected likelihood of
success.
Ease of Use, Ease of Strategy, and Time Spent Trading
Ease of use and ease of strategy were almost perfectly correlated (p=0.009). This
suggests that those who thought SETS-EA was easy to use also thought SETS-EA trading
strategies were easy to apply. Analyzing whether the perceived ease of use had some relationship
with the hours spent trading produced Table 7, shown below.
As can be noted from the table, respondents who thought it was easy to use SETS-EA
spent more than 25 hours each week working with the tool. Respondents who used the tool more
Table 5.
Table 6.

than 30 hours each week (accounting for 60% of respondents) thought the tool was easy to use
(Yes [excellent], Yes [good]).
Reliability and Credibility of SETS-EA and its Effect on Trading Performance
A cross tabulation of respondents’ perceptions of reliability and credibility of SETS-EA
and the effect SETS-EA had on the overall trading performance is summarized below.
Half of all the respondents answered that the reliability and credibility of SETS-EA was
excellent and also thought using SETS-EA was effective in improving trading performance.
Overall Experience and the Profit Generated
The pie chart below represents responses about the overall experience of using SETS-EA.
Table 7.
Table 8.

As can be seen, all respondents described SETS-EA as either “good” or “excellent.” An
analysis was also done to see whether this difference in experience could have been reflected in
the profit earned after the four-week test. The results of the ANOVA test confirmed the
hypothesis that there were significant differences (p=0.030) in the amount of profit earned by
respondents with different experience using SETS-EA. The table below shows the summaries of
the tests.
Table 10 shows that testing if there was a significant difference in profit earned based on
the effect the tool had on self-efficacy showed no statistically significant difference (p=0.403).
Table 9.

Future Use of SETS-EA
Based on the respondents’ answer to all the questions, a test was conducted to uncover
the characteristics of respondents who would use the tool in the future. A discriminant test was
conducted to reveal this relationship. The results are summarized in the table below.
As seen in the above table, the only variables used in the analysis (those that discriminate
most for the predictor variable) are age, yearly income, gender, investment in forex, experience
Table 10.
Table 11.

with MT4 or MT5, trading experience, and experience with MS Excel. As seen in the table
below, the model created predicted all the cases correctly (performance 100%).
G. Conclusion and Recommendations
From a statistical analysis point of view, most respondents evaluated SETS-EA
positively. However, discriminant testing shows the continued use of the tool depends on the
following variables: age of user, yearly income of the user, gender, previous investment in forex,
experience with MT4 or MT5, prior trading experience, and experience with Microsoft Excel.
Qualitative responses from the participants indicated some difficulty with initial usage,
especially amongst those with some experience using Excel. The users also suggested that the
tool should be automated to make it more efficient.
H. Survey
A specific survey was completed and associated with the topic investigated in this study.
The survey consisted of numerous multiple choice, multiple answer, and explanation questions
pertaining to the effectiveness of SETS−EA. The respondents were students (volunteers) at
MTSU working at different educational levels. Below is the list of survey questions:
Table 12.

References
Bech, M. (2012). FX volume during the financial crisis and now. BIS Quarterly Review, 33−43.
BIS. (2010). Triennial central bank survey. Basel, Switzerland: Bank for international
settlements: Monetary and economic department. Retrieved from
http://www.forexfactory.com/attachment.php?attachmentid=305072&d=1252336216
Bland, J., Meisler, J., & Archer, M. (2009). Forex essentials in 15 trades: The global-view.com
guide to successful currency trading. In Bland, J., Meisler, J., & Archer, M. (Eds.), Title
of book (pages of chapter). New Jersey: John Wiley & Sons,Inc.
Bryan, P. (2007). The best time to trade the Forex market. Retrieved from articleslash.net:
http://www.articleslash.net/Finance/188482__The-Best-Time-To-Trade-The-Forex-
Market.html
Canaan, C., Garai, M., & Daya, M. (2011). All about Fibonacci: A python approach. World
Applied Programming, 1(1), 72−76. Retrieved from
http://www.waprogramming.com/download.php?download=50ae473e168419.23807424.
pdf
Cofnas, A. (2001). E-forex: Currency trading for the rest of us. Day trading techniques, 30(15),
38−42.
David, O., & Leustean, B. (2009). Forex and the liberalized financial market. U.P.B. Sci. Bull.,
Series D, 71(3), 125−130.
Drakoln, N. (2008). Winning the trading game: Why 95% of traders lose and what you must do
to win. New Jersey, United states of America: John Wiley & Sons,Inc.
Dunis, C., Laws, J., & Naïm, P. (2003). Applied quantitative methods for trading and investment.
In Dunis, C., Laws, J., & Naïm, P. (Ed.), Chichester, England: John Wiley & Sons Ltd.

Feldman, J. (2013). Open Source Business Decision Management System. OpenRules, 1−127.
Retrieved from http://openrules.com/pdf/OpenRulesUserManual.pdf
GFT (2012). MetaTrader 4 and MetaEditor (pp. 155−156). Bedminster, New Jersey: Global
Futures & Forex, Ltd. Retrieved from
http://documents.gftforex.com/software/US_MetaTrader_4_User_Manual.pdf
Glossary (2009). Continental Capital Investment. In Explanation of Key Terms (pp. 1−−−−59).
WSD. Retrieved from http://co-fx.com/key_terms_CCI.pdf
Hartle, T. (2001).Using Fibonacci ratios and momentum. Stocks & Commodities, 15(11), p.
477−482.
Horner, R. (2010). Forex on five hours a week: How to make money trading on your own time. In
Brandzel, J. (Ed.), Hoboken, New Jersey: Wiley Trading & Sons, Inc.
Hunt, D. (2011). How to set a Forex trading schedule. Retrieved from investopedia.com:
http://www.investopedia.com/articles/forex/08/forex-trading-schedule-trading-times.asp
Interactive Brokers (2001). Trader workstation Excel API for beginners. (I. B. Markets, Ed.)
United States of America: Interactive Brokers LLC. Retrieved from
http://www.interactivebrokers.com/download/ExcelApiBeginners.pdf
IronForex. (n.d.). Export of quotes. In IronForex, Solid foundation for successful trading (pp.
1−−−−152). IronForex Limited. Retrieved from
https://www.ironfx.com/content/1/1/3/6/1/74f15c69/MT4.pdf
Kalman, D., & Mena, R. (2003). The Fibonacci numbers—exposed. Mathematical association of
America, 76(3), 167−181. Retrieved from
http://www1.math.american.edu/People/kalman/pdffiles/fibpaper.pdf

Knafl, G. (n.d.). Current Topics in Statistics for Applied Researchers: Factor analysis
[PowerPoint slides]. Retrieved from
http://www.unc.edu/~gknafl/research/FA/factor.analysis.presentation.pdf
Krutsinger, J. (2005). New twist on an old indicator. Futures: Stock, commodity, options, and
forex strategies for the serious trader, 34(11), 48−50.
Marshall, K., & Moubray, R. (2005). Practical Fibonacci methods for forex trading. Forex
systems research company, pp.1−31. Retrieved from http://ebookbrowse.com/practical-
fibonacci-methods-for-forex-trading-pdf-d86450282
O’keefe, R. (2010). Making money in forex: Trade like a pro without giving up your day job.
Hoboken, New Jersey: John Wiley & Sons, Inc. Retrieved from
http://www.borsat.net/downloadfile.ashx?filename=9b7d886a-aa79-49bb-b761-
d4a4223d812a.pdf
OANDA. (2011, May 30). Frequently asked questions. Retrieved on July 18, 2013 from
OANDA: http://fxtrade.oanda.com/trade-forex/metatrader/faq#fifo
Patton, M. Q. (2001). Qualitative research & evaluation methods. Thousand Oaks, CA: Sage
Publications.
Rosenthal, N. (2012). Guide to trading system development. Futures: Stock, commodity, options,
and forex strategies for the serious trader, 41(9), 24−26.
Schlossberg, B. (2012). Don’t trade like Tony Montana: Adventures in FX trading. BKFOREX
ADVISORS LLC, 1−33. Retrieved from http://www.bkforex.com/wp-
content/uploads/2012/01/donttradeliketonymontana.pdf
Shubita, M., Harris, P., Malindretos, J., & Bobb, L. (2011). Foreign exchange exposure: An
overview. International Research Journal of Finance and Economics, 78, 171−177.

Silverman, D. (2006). Interpreting qualitative data. London: Sage Publications.
Traded FX. (n.d.). Powerful tips on forex trading. In Traded FX, A guide to successful forex
trading (pp. 1–23). Retrieved from
http://tradedfx.com/downloads/A%20Guide%20To%20Successful%20Trading.pdf
Whistler, M. (2004). Working money: The politics of money management. Stocks &
Commodities, 22(7), 48−49.

Sky Eye Trading System Edge Abyss

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (16)

Similar to Sky Eye Trading System Edge Abyss

Similar to Sky Eye Trading System Edge Abyss (20)

Sky Eye Trading System Edge Abyss