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The Quality Trading Coefficient. General Formula to Qualify a Trade and a Trading Methodology

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Trading the financial markets is a wide activity nowadays. There are several indicators to measure this activity. The drawdown, the profit factor and the trading efficiency are some of them. This paper will present a different one: the quality trading coefficient. The new formula will indicate several particular aspects originating from the human psychological emotions and fears during transactions on the way to make profit. If the trade was fast and the price moved continuous to the profit target level, the quality trading coefficient will have a positive value close to one. When most of the time the trading position was on loss and the profit was made on the last part of the trade time, the coefficient will have a positive value more close to zero. If the trade is closed on losses, the coefficient will have a negative value tends to minus one if the market goes against the direction of the trade on the entire interval. Measuring this coefficient for all trades made with a trading strategy we can compare and categorize the strategies for any market and for any time interval. The quality trading coefficient became a new optimization criterion for the trading strategies used in automated trading systems.
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Informatică Economică vol. 22, no.3/2018 97
DOI: 10.12948/issn14531305/22.3.2018.09
The Quality Trading Coefficient. General Formula
to Qualify a Trade and a Trading Methodology
Cristian PĂUNA
Economic Informatics Doctoral School, Bucharest University of Economic Studies
cristian.pauna@ie.ase.ro
Trading the financial markets is a wide activity nowadays. There are several indicators to
measure this activity. The drawdown, the profit factor and the trading efficiency are some of
them. This paper will present a different one: the quality trading coefficient. The new formula
will indicate several particular aspects originating from the human psychological emotions and
fears during transactions on the way to make profit. If the trade was fast and the price moved
continuous to the profit target level, the quality trading coefficient will have a positive value
close to one. When most of the time the trading position was on loss and the profit was made
on the last part of the trade time, the coefficient will have a positive value more close to zero.
If the trade is closed on losses, the coefficient will have a negative value tends to minus one if
the market goes against the direction of the trade on the entire interval. Measuring this
coefficient for all trades made with a trading strategy we can compare and categorize the
strategies for any market and for any time interval. The quality trading coefficient became a
new optimization criterion for the trading strategies used in automated trading systems.
Keywords: Algorithmic trading, Financial markets, Quality trading coefficient, Trading
Introduction
Trading and investing on financial mar-
kets is a common activity today. In the context
when enterprises grow or change to meet
market demands and competitive situations,
new business requirements drive the expan-
sion of IT resources in the computing environ-
ment” [1]. After the electronic trading was
globally implemented, the number of the mar-
ket participants is increasing day by day.
Traders, investors, private or public funds are
buying and selling every day on different mar-
kets using different trading strategies to make
profit. The fast evolution in the price volatility
involves computers to be the main part of all
trading systems today. “The existence of im-
portant price differences makes possible the
profit when an automated system buy cheaper
and sell more expensive[2]. The price evo-
lution is automatically analyzed in real-time
and the trading decisions are made almost in-
stantly by servers using different trading strat-
egies and algorithms. The interest for this re-
search field is growing.
Data mining has become an increasingly
powerful technology, being applied in a vari-
ety of areas, from investment management to
astronomy [3]. The trading strategies in-
volved in the financial trading systems use
data mining procedures adapted to find partic-
ular patterns in the time price series in order
to initiate good trades. A trade is the process
to buy, to keep and to sell different equities in
order to make profit. But what means a good
trade is a relative question. What some con-
sider is good enough, others are not. An exact
criterion is needed.
In the trading activity there are several indica-
tors to measure different aspects involved.
The drawdown describes the maximum capi-
tal exposure of a trade, the profit factor indi-
cates how much is the realized profit com-
pared with the capital exposure and the trad-
ing efficiency will indicate the profit rate in
the time unit. We use all of these in order to
compare different trades. These classical co-
efficients are well known and they are not the
subject of this paper The continuous growth
of the challenges and the complexity of pro-
jects, lead to the development of new ap-
proaches to model and support uncertainties
and risks [4]. This article will present a dif-
ferent factor in order to qualify the trade: the
quality trading coefficient (QTC).
Starting from the psychological part of the
1
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trading activity and counting the emotions and
fears of the trader and investor during a trade,
this paper will open a new direction to com-
pare and categorize a trade and a trading meth-
odology. It will be developed a coefficient that
will indicate if a trade is on the right direction
or not, if the price has reversed and goes
against the trade direction on the main period
of time. The indicator will have a positive
value more close to one when the trade took
place more close to the ideal transaction.
The notion of the perfect trade will be intro-
duces as base for this model. The perfect trade
is that trade in which the price goes higher and
higher every time unit until the profit target is
touched. If in a significant time period of the
real trade the position was on loss and the
profit was made in the last small part of the
trade, the QTC will have a positive value but
more close to zero. For negative trades the
QTC will have a negative value. A very close
value to minus one is possible when the trade
was never on profit and the price moved
against the trade.
The QTC can be used in order to qualify a
trade and a trading strategy. With QTC the
price behavior during the trade can be easily
quantified by a single number. Measuring the
QTC for all trades made by a trading method-
ology we can categorize that model for a spec-
ified market in a given time unit. With this
method more types of trading strategies can be
found. In addition this model can be a good
filter in order to optimize the trading model
and to initiate only that trades that satisfy the
investor expectancy. If a trade is making the
profit in the last part of the trading time, as
most of the investment strategies are doing,
the beneficiary of that trading system can be
informed priori about the methodology speci-
ficity and the expectancy of the investor can
be adapted and prepared for the reality in the
trading activity.
2 The necessity for a quality trading coeffi-
cient
There are several aspects in the trading
activity that ask for a quality trading
coefficient. A part of these are related with the
psychology in the trading activity. The main
fear of a trader is to open a trade that will go
against the direction of the trade and will
record a loss. Even stop loss procedures are
used in the trading strategies in order to limit
the losses, the psychology of fear is always the
same. A manual investor or an algorithmic
trader wants to know any time how the trade
goes. The main financial indicator is of course
the profit or loss value. During the trade this
will indicate if the trade is close to be finished
or if the price is far away to the profit target
and it is approaching to the stop loss level. But
the information about the price history is not
covered looking only on the profit level. We
don’t know what the price behavior is. Is the
price in a continuous growth or is far away the
predicted trajectory?
In order to underline the necessity for a quality
indicator in the trading activity, this paper will
include some dialogue from a trading floor in
a private financial investment company. The
capital manager is asking the trading assistant
“how that trade is going on?” In this situation
the answer is usual a long explanation even
the manager wants a short one. To describe the
situation the answer is usual a story about the
movement of the price during the time after
the trade was opened. Something like “we was
on loss with about 0.33% for thirty hours but
the price reversed after the yesterday news and
now we are on profit with about 0.12% and the
price is going up”. The manager must have
patience to hear the entire explanation in order
to know the situation of any trade, especially
when it is about longer trades.
After the QTC methodology was
implemented in the trading system the dialog
became very efficient. After the question
“what we got? the answer of the trading
assistant about the current trade is only a
number “0.65”. “Great!” The value of the
QTC indicates the state of the trade and the
main information about the price history is
condensed in the QTC value. For someone
who knows the specificity of the trade, the
target level and the time period predicted for
that trade, the QTC value will indicate how the
price evolved during the transaction. The QTC
is not giving the profit level or the distance
until the target. As we will see, the QTC is
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giving an indication about how close is the
current trade to the perfect imaginary trade.
The QTC is a good measure to qualify the
price evolution during the trade. The QTC can
be computed anytime during the trade to tell
us the state of the price evolution. After the
trade is closed, the QTC has a final value
which will remain unchanged. Having the
QTC final values for all trades made with a
specified trading strategy we can classify that
method and we can compare more models to
find the better one. Each trading strategy has
its own data mining methodology. The better
strategy will open trades with QTC higher and
close to one. If the main trades of a trading al-
gorithm obtain positive values closed to zero
that means the most of the profit is made in the
last part of the trade. This will be an important
aspect for the trader in order to make a realis-
tic expectation about that trade methodology.
More, if a strategy will generate more trades
with QTC closed to the one value that strategy
can be categorized to be more accurate when
it is about the entry point and it will be pre-
ferred instead of others. The necessity for
QTC derives also from the necessity to cate-
gorize and compare the trading strategies used
and to find an optimization factor in order to
improve the trading system.
3 The perfect trade
In order to build the QTC the current trade will
be compared with a perfect imaginary trade.
This kind of trade is hypothetical and rarely
exists in the current practice. However, this
trade exists in the mind of traders and inves-
tors as to be the wish of anyone. In the perfect
trade, once the transaction was opened, the
price goes continuous up and evolves with the
same speed in each time interval until the
profit target is reached.
Fig. 1. The perfect trade
The perfect trade is sketched in the fig. 1. We
will note with qe the entry price and with qt the
target price of the perfect trade. In the perfect
trade the price makes the same Δ movement
in each time unit. We have qe=pe and qt=pt,
where pe is the entry price or the real trade and
pt is the target price. The trade step of the per-
fect trade is given by:
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npp
nqq etet
(1)
where n is the number of the time intervals
spent on that trade. The main property of the perfect trade is that we can generalize the term
formula as an arithmetic progression with:
ipiqq eei
for any
ni ,1
(2)
In the fig. 1. was drawn the Frankfurt Stock
Exchange Deutscher Aktienindex DAX30 [5]
time price series on a hourly timeframe
between 2nd and 3rd July 2018. As we can see
in this sample, the real trade is far away from
the perfect trade even in the case when the
price range is close to the perfect evolution.
Because of the market volatility, the price
goes up and down and rarely makes
movements only in the up direction. The
perfect trade is only a model. It represents the
ideal price movement between the open and
target price.
4 The correlation coefficient
To build QTC we want to find a math formula
to measure how far away is the real time price
series from the price levels of the perfect
trade. We will note here the price variable
from the real trade with P=(p1, p2, … , pn) and
the hypothetical price levels from the perfect
trade with Q=(q1, q2, , qn), where n is the
number of the time intervals spend on the
current trade.
In order to measure how correlated are these
two variables, both measured on the same
moments of time, we will start from the
correlation coefficient of Galton-Paerson [6]
which is given by:
   
QP
QP
QP
,cov
,
(3)
where
 
QP,cov
is the covariance between P
and Q series,
and
are the standard
mean deviations of the P respectively Q
variables. The covariance is the coefficient “to
measure the intensity of linear dependency
between two variables” [7] and is given by
formula:
 
 
QqPp
n
QP i
n
ii
1
1
,cov
(4)
where
P
and
Q
are the simple means of the P and Q variables given by:
n
ii
p
n
P1
1
and
n
ii
q
n
Q1
1
(5)
The standard deviation for the P and Q can be computed using the known relations:
 
1
1
2
n
Pp
n
ii
P
and
 
1
1
2
n
Qq
n
ii
Q
(6)
With all of these, the correlation coefficient will be given by:
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 
 
 
 
n
ii
n
ii
n
iii
QqPp
QqPp
n
n
QP
1
2
1
2
1
1
,
(7)
An important observation regarding the (7)
formula is about the term (n-1)/n. Usual, for
series with large number of observations,
these term can be considered tends to one, but
for small numbers of observations, this
hypothesis is not true. With this
presupposition the correlation coefficient is
given without the term (n-1)/n in many
statistical books or papers. In our case it was
found important to keep the term (n-1)/n in the
correlation coefficient formula because we
want to measure the correlation factor even for
small number of time intervals.
5 The quality trading coefficient
The QTC is in fact the correlation coefficient
of the actual trade price series with the perfect
hypothetical trade price levels described in
chapter 3. The QTC will measure how far
away is the price evolution on the current
trade from the perfect price wanted. Using the
properties of the perfect trade price series, the
formula (7) will suffer some changes as we
will see.
First of all, the simple mean of the perfect
trade price series can be expressed by:
2222
n
p
n
q
ppqq
Qee
etet
(8)
where
ee pq
is the open price level and
tt pq
s the profit target level of the current
trade. In order to simplify the formula (7) we
will consider (2) and (8):
2
n
iQqi
and
 
4
2
22
2n
iniQqi
(9)
and
 
 
12 2
1
4
2
2
1
2
11
22
1
2
nnn
iniQq n
i
n
i
n
i
n
ii
(10)
With all of these, the quality trading coefficient will be given by formula:
   
n
ii
n
iiPp
n
iPpQTC 1
2
1/
2
where
2
12122
nn
n
n
for any
2n
(11)
As we can see, the QTC depends only on the
i
p
price levels and it is not depending at all
on the target price
t
p
. The QTC is referring
about the past price movement, not about the
future. The formula (11) can be applied for
any number n of intervals higher or equal with
2. For those trades which are closed in the
same time interval in they were opened, the
formula cannot be applied. For n=1 the
QTC=0. For this case it must to be used a
shorter time interval in order to have a higher
n and more price observations to apply
formula (11). If the trade is closed
immediately after it was opened, as many
cases in high-frequency trading exists, that
trade will be considered a perfect trade and the
QTC will be assimilated with the value of one.
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6 Different trading cases
As we proposed, the QTC scope is to qualify
the price evolution during a trade. The
evolution is due to the price behavior, but
using a data mining applied to the time price
series, the QTC values will qualify also the
trading model. A methodology with higher
values obtained for QTC will be more adapted
to find those price patterns which assure
opening trades for better price evolution
cases. In order to see differences between
some trades, three examples will be
considered in this chapter.
The first case is the case of a trade made on
DAX30 market between 2nd and 3rd July 2018
by TheDaxInvestor [8], an automated
investment software. The trade was opened on
2nd July at 17:06 at 12,220 price level with an
initial target established at 12.346 and moved
during the trade at 12,398. On the entire
period of the trade the QTC has positive
values. The minimum value for the QTC was
0.57 and the maximum values was 0.92. As it
can be seen in the fig. 2, the price levels were
close to the perfect trajectory, there were no
substantial retracements and the price trend
was continuously up approaching the target in
any time interval. This is a good example for
a trade very close to the perfect trade.
Fig. 2. Profitable trade close to the perfect trade
In the next example we will see a profitable
trade with a lower QTC value. This is the case
for a trade opened with TheDaxDealer [9], an
automated trading software for DAX30
market which makes short term trades with
variable targets. The entry trade was initiated
at 12th June 2018 at 13:00 at 12,850 price level
with an initial target at 12,900. The price
evolution during this trade can be seen in the
fig. 3. It can be observed that after the opening
of the trade, the price has fallen continuously
and it was under the perfect trade price levels
almost all period of the trade time. This trade
was closed on profit due to a price recovery on
the last period of the trade. The final QTC of
this trade was 0,16 after its minimal value was
-0.68 in the middle of the trade. Looking on
the QTC value during the trade, anyone can
qualify that trade.
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Fig. 3. Profitable trade far away the perfect trade
The third example is for a trade closed on loss.
We present the example of a trade initiated by
TheDaxTrader [10], an automated trading
system which makes short time trades on the
DAX30 market. The trade was opened on 28
June at 9:38 at 12,330 price level with an
initial target at 12,368. After opening the trade
the price goes well for about two hours. Due
to some unexpected economical news
released, the price turned down and described
a short strong trend. An automated stop loss
procedure closed the trade at 12.098 in order
to limit the losses. The QTC of this trade has
negative values immediately after the first two
hours. The trade was closed with the final
value for the QTC at -0.96. This value
describes a trade far away the wish trade in an
inverse correlation with the perfect trade,
close enough to the worst trade.
An interesting observation is that the final
value of the QTC for a closed trade depends
on the decisions during the trade. As we can
see in the fig. 4, if a larger stop loss would
have been used, the automated stop loss
procedure would not have closed that trade.
After a short time the price recovered all loses
and the trade could be closed on profit. In this
case the QTC would have a small but positive
value as in the second example. This
consideration tell us that QTC is defining a
trading strategy including the data-mining
methodology in order to find those price
patterns proper to open a trade and also the
decisions regarding exits for the current trade.
In this example the stop loss procedure was
also automated but in some trading systems
this is the case when the human can intervene.
The capital protection and the risk
management for all trades presented were
made automatically using the methodology of
the “Global stop loss” [11]. The third example
is the typical case when a stop loss rule must
to be respected. Using this kind of functional
limits, the capital is protected and the
exposure of the trading capital is kept in the
limits established by the investor. “Loss are
part of trading” [12] and the risk management
is the key for capital protection. In trading
large losses must be avoided, any small loss
can be recovered in the next period of time
with a proper capital exposure management.
From this reason trading models with values
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for the QTC higher are preferred.
Fig. 4. Loss trade far away the perfect trade
7 A new design criterion
As we have seen in the chapter above, the
QTC can indicate the state of the current price
evolution compared with the perfect desired
trade. The final value of the QTC can also
indicate the quality for the entire trade.
In the algorithmic trading environment, when
an automated trading system is designed and
tested, several aspects about the trades made
by that system are taken into considerations.
First is the obtained profit level. The second
as importance is the maximal drawdown,
which is the maximum capital exposure
obtained in that trade. This factor describes
the real capital risk involved in that trade. The
trading efficiency is also important and will
indicate the profit obtained per each time unit,
as a measure for the trading system. Another
indicator used in the design process of the
automated trading strategies is the longest
time trade. This is an important measure and
an optimization parameter as it was presented
in [11].
Analyzing the values for all these indicators,
the functional parameters can be optimize to
obtain a better trading strategy. Even so, it was
found that using only these indicators, a
trading strategy can make a lot of profitable
trades with QTC close to zero. This means that
trades become profitable in the last part of the
trade and on the major time during the trade
the positions are on loss. This is not a desired
case. In these cases the entry price level is too
high. For this reason the QTC become an
additional optimization criteria. Using QTC
methodology we can filter those data mining
procedures that make better trades. In this way
the efficiency of the entire trading system is
improved. In addition, using QTC as
optimization criterion, the trading system can
be adapted for some particular requests. For
example if an investor will ask for a trading
system with a specified efficiency level, the
QTC will be a very powerful factor in order to
optimize all the functional parameters. More,
after the design process of a trading system,
the measure of QTC minimal and maximal
values obtained for all trades permits an
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enlightening presentation of the trading
system capabilities.
8 Conclusions
The QTC is a measure of the price behavior
during the trade. For a closed trade, the QTC
is a measure for the quality of the trading strat-
egy used, including the quality for the data
mining process used to open the trades to-
gether with the capital and risk management
decisions during the trade and with the exit
strategy precision used by the trading system
in order to close the trades. All of these to-
gether can categorize an entire trading meth-
odology. In this way the QTC becomes an im-
portant optimization factor in the design, test-
ing and optimization process of any trading
system. The general conclusion is that the
QTC is a good measure in order to qualify a
trade, a trading strategy and a trading system.
References
[1] L. Hurbean and D. Fotache, “Enterprise
Resource Planning and E-Business”,
Informatica Economica Journal, 3/2006.
ISSN 1453-1305
[2] C. Păuna, “Arbitrage Trading Systems for
Cryptocurrencies. Design Principles and
Server Architecture”, Informatica
Economica Journal, vol. 22, 2/2018. ISSN
1453-1305
[3] A. Tudor, A. Bara and I. Botha, “Data
Mining Algorithms and Techniques
Research in CRM Systems”, Recent
Researches in Computational Techniques,
Non-Linear Systems and Control, ISBN:
978-1-61804-011-4
[4] A. Purnusa and C. Bodea, “Considerations
on Project Quantitative Risk Analysis”,
26th IPMA World Congress, Crete,
Greece, 2012, Procedia Social and
Behavioral Sciences 74, 2013. pp. 144-
153. Available: http://sciencedirect.com
[5] Börse. (2018, July 2). Frankfurt Stock
Exchange Deutsche Aktienindex DAX30
Components. Available: http://www.
boerse-frankfurt.de/index/dax
[6] I. Purcaru, Informație și corelație, Editura
Științifică și Enciclopedică, 1988, pp. 91.
[7] T. Andrei, Statistică și econometrie,
Editura Economică, București, 2003
ISBN: 973-590-764-X. pp.258.
[8] C. Păuna. (2018, July 2). TheDaxInvestor
automated investment software online
presentation, 2015. Available:
https://pauna.biz/thedaxinvestor
[9] C. Păuna. (2018, July 2). TheDaxDealer
automated investment software online
presentation, 2016. Available:
https://pauna.biz/thedaxdealer
[10] C. Păuna. (2018, July 2). TheDaxTrader
automated investment software online
presentation, 2010. Available:
https://pauna.biz/thedaxtrader
[11] C. Păuna, “Capital and Risk management
for automated trading Systems”,
Proceeding of the 17th International
Conference on Informatics in Economy,
May 2018. Available:
https://pauna.biz/Capital_and_Risk_Mana
gement
[12] S. Ward, High performance trading,
Harriman House, 2009. ISBN: 978-1-
905641-61-1. pp. 137.
Cristian PĂUNA graduated the Faculty of Cybernetics, Statistics and
Economic Informatics of the Economic Studies Academy in 1999 and he is also
a graduate of the Aircraft Faculty from the Bucharest Polytechnic University in
1995. He got the title of Master of Science in Special Aerospace Engineering
in 1996. In the last decades he had a sustained activity in the software
development industry, especially applied in the financial trading domain. Based
on several original mathematical algorithms, he is the author of more automated trading
software for financial markets. At present he is the Principal Software Developer of Algo
Trading Service Ltd. and he is involved as PhD student in the Economic Informatics Doctoral
School from the Bucharest University of Economic Studies.
... When the TPB is increasing, even STR is decreasing, good trading opportunities can be found. All of these are given by the trading signals assembled with the formula: (15) It was found that the trading signals given by (15) give us good trading results for the daily and four hours timeframes. To automate the cases for the oversold price intervals detected with PPB, the trading signals can be given by: ...
... When the TPB is increasing, even STR is decreasing, good trading opportunities can be found. All of these are given by the trading signals assembled with the formula: (15) It was found that the trading signals given by (15) give us good trading results for the daily and four hours timeframes. To automate the cases for the oversold price intervals detected with PPB, the trading signals can be given by: ...
... For these cases when the STR is increasing, good trading opportunities can be found using (13). For the intervals when the price is contracting, sustained trading signals are also built with (15). All of these signals traded together obtained a risk to reward ratio value equal with 1:8.22 for the study case took as an example. ...
Article
Full-text available
After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
... The longest trade period for these signals obtained n the study above was 218 hours. The Quality Trading Coefficient (QTC) [16] obtained was between 0.52 and 0.87. All these values indicate that PPB trading methodology is a reliable one. ...
... As it was found in this paper, using the price level related to the PPB values, good opportunities for oversold buy trades can be found using formula (16). These additional trading signals offer us a significant number of trades even the risk and reward ratio is higher. ...
Conference Paper
Full-text available
After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
Thesis
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After several attempts to publish my Ph.D. thesis with different prestigious publishers, I have decided to make this work public and free of charge for anyone. Enjoy! Cristian Păuna
Article
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When dot.com has become a quaint idea, when electronic shops have lost the mass attention, while classical and margin trading has become obsolete, something new is coming: cryptocur-rencies. Hundreds of virtual coins have been invented for a single reason: the profit. The high price volatility of these new markets and the fact that the virtual coins price is not regulated by a central bank or a single exchange, gives us opportunities for arbitrage trading. The existence of important price differences makes possible the profit when an automated system buy cheaper and sell more expensive in the same time. This paper will present the general principles underpinning the implementation of arbitrage trading software for virtual coins market. The very large number of cryptocurrencies and exchanges fundamentally change the server architecture of the trading software. The distributed price data in hundreds of sources and the technical differences of each of these data providers make all the things difficult to be implemented in a single application. The low-latency order calculation needed for the fast delivery before a significant price change, in the presence of thousands of price quotes coming from hundreds of distributed servers makes everything special. Keywords: Cryptocurrency (CRYC), Arbitrage trading (ART), Algorithmic trading (AT), High Frequency Trading (HFT), Automated trading software (ATS) (Published at: http://revistaie.ase.ro/content/86/04%20-%20pauna.pdf)
Conference Paper
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The most important part in the design and implementation process of automated trading systems in any financial investment company is the capital and risk management solution. Starting from the principle that the trading system must run fully automated, the design process gets several particular aspects. The global stop loss is a special approach for the risk management strategy that will ensures a positive expectancy in algorithmic trading. A case study based on an already optimized trading algorithm will be used to reveal how important the risk level optimization is, in order to improve the efficiency of the trading software. The main optimal criteria are as usual the profit maximization together with the minimization of the allocated risk, but these two requirements are not enough in this case. This paper will reveal an additional optimization criterion and the main directions to build a reliable solution for an automated capital and risk management procedure. Keywords: automated trading software (ATS), business intelligence systems (BIS), capital and risk management (CRM), algorithmic trading (AT), high frequency trading (HFT). (Available at: https://pauna.biz/Capital_and_Risk_Management)
Article
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It is inevitable that as enterprises grow or change to meet market demands and competitiv situations, new business requirements drive the expansion of IT resources in the computing environment. Successful business growth depends heavily on the ability to update, integrate, customize and deploy applications rapidly and provide fast, reliable, interactive data access to end users, from employees to suppliers, customers and partners. In the Internet age, businesses in all industries need to move from intra-company integration to inter-company integration in order to increase competitiveness. New enterprise applications rely on Internet infrastructure for e-business solutions and Web-based collaboration, so customers, employees, suppliers and business partners work together as if they were all one company.
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  • A Tudor
  • A Bara
  • I Botha
A. Tudor, A. Bara and I. Botha, "Data Mining Algorithms and Techniques Research in CRM Systems", Recent Researches in Computational Techniques, Non-Linear Systems and Control, ISBN: 978-1-61804-011-4
  • A Purnusa
  • C Bodea
A. Purnusa and C. Bodea, "Considerations on Project Quantitative Risk Analysis", 26th IPMA World Congress, Crete, Greece, 2012, Procedia Social and Behavioral Sciences 74, 2013. pp. 144-153. Available: http://sciencedirect.com
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Börse. (2018, July 2). Frankfurt Stock Exchange Deutsche Aktienindex DAX30 Components. Available: http://www. boerse-frankfurt.de/index/dax
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  • I Purcaru
I. Purcaru, Informație și corelație, Editura Științifică și Enciclopedică, 1988, pp. 91.
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  • C Păuna
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  • S Ward
S. Ward, High performance trading, Harriman House, 2009. ISBN: 978-1-905641-61-1. pp. 137.