Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers using advanced mathematical algorithms. This paper will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments.
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Pr i ce Pr obabili ty P redictor.
C apital i nvestments as sisted by a pr obability f ie ld.
C ristian P ău na
E co nomi c In formatics D octoral Sc hool
Ac a demy of Eco nomic S tudies, B ucharest, Ro mania
Th e 14 th Int ernational Co nference o n B u sin ess Excel lence.
B us iness R ev olution in th e Di gital Era.
11- 12 J un e 2020, Buc harest, Rom an ia .
Th is paper wa s fina nced by Al gorith m In vest (a lgoinvest.biz )
T his p aper p re sents:
Pr i ce P robabilit y Pr edictor, a re liable p r obability f ield
b ase d on t i me p rice se ries a p plicable t o a n y c a pital m arke t
Ma the matical p roof of a n i nverse bu t st rong c orrelatio n
b etwe e n th e Pr ice Pr obability P re d ictor a nd t he p rice a ction
A m e thod b ased on t he Pr ice Pr obability Pr edictor t o bu il d
a ut omated si gnals to e ntry a nd e xit on a n y f i na ncial m arke t
A m e thod b ased on t he Pr ice Pr obability Pre dictor t o bu ild
l i mit condi tions i n or der to s tay away fr om t he m arket r i sk
R e al i nv e stment re sults ob tained w i th t he p resented
m e thods t o p r ove t he e f ficiency a nd s implicity i nvolved
Pr ice Prob ability Pre dic tor
i s a fu nction tha t ca n b e a p plied
On r ea l-time
pr i ce s er ies t o
m ak e a pr ofit
By any on e u si ng
a pe r sonal
c om puter
To b u ild a sta b le
i nv estmen t
m et hod
In any f in ancial
m ar ket u si ng
any t i m eframe
C an i m prove an y
ot h er c ap ital
i nv estmen t
st r ategy
C an i m prove any
au t omated
i nves tment
so f t ware
C an i n crea s e t he
c api tal ef fic iency
of an y
i nv estm ent pl a n
A strong and inv erse correlation i s prov ed between PPP and t he price action.
-0.999 ≤ Pearson CC ≤ -0.537
for all equities included in the next capit al markets:
DAX – Frankfurt Stock E xchange Deutscher Aktienindex
DJIA – US Wall Street Dow Jones Industrial Index
S&P – US St andard & P oor' s Market Index
NASDAQ – US Nasdaq Stock Market Index
FTSE – U K Financial Ti mes St ock Ex change
SMI– S wi ss Stock Ex change Market Index
ASX – Australi an Stock Exchang e Market Index
NIKKEI –Japanese Stock E xchange Nikkei Index
CAC– Fra nce Cot at ion Assisté e en Cont inue Index
CURRENCIES – EURUSD , EURJPY, GBPUS D
GOLD – Spot price XAUU AD, XAUEUR, XAUAUD
OIL – BRENT CRUDE O IL
Pr ice Pr obability Pre dicto r
i s d ef ined a s:
P robability t hr ough
P ri ce Cy clicality Fun ction
P ri ce C y clicality Fun ction
Th e prob ab ility fi eld is d efined a s:
P ro bability t hr ough
Sm oothed H eikin-Ashi p ri ce t r ansform
Sm oothed He ikin-Ashi t ransf orm
Th e pr obabilit y fi eld is defined a s:
P robability t hr ough
P ri ce P r ediction L in e an d Trigonometric P rice L i ne
Th e pr obabilit y fi eld is defined a s:
Pr ice Pr ediction Li ne
Tr i gon ometric Pr ice L ine
A strong and inv erse correlation i s prov ed between PPP and t he price action.
-0.999 ≤ Pearson CC ≤ -0.537
for all equities included in the next capit al markets:
DAX – Frankfurt Stock E xchange Deutscher Aktienindex
DJIA – US Wall Street Dow Jones Industrial Index
S&P – US St andard & P oor' s Market Index
NASDAQ – US Nasdaq Stock Market Index
FTSE – U K Financial Ti mes St ock Ex change
SMI– S wi ss Stock Ex change Market Index
ASX – Australi an Stock Exchang e Market Index
NIKKEI –Japanese Stock E xchange Nikkei Index
CAC– Fra nce Cot at ion Assisté e en Cont inue Index
CURRENCIES – EURUSD , EURJPY, GBPUS D
GOLD – Spot price XAUU AD, XAUEUR, XAUAUD
OIL – BRENT CRUDE O IL
Pr ice Pr obability Pre dicto r
i s d ef ined a s:
Aut omated e nt r y si gnals
Au to mate d ex it s ig nal s a nd li mi t co n di tions
A
B
I ns t ead of co nclusions
1:4.96 (4.96€ pro fit fo r each1€ risk)
T hi s re sult me a ns 10 : 49.6, o r 2 0:99 .2 , or 25 :124
Ca p i tal ev olution due to the A type signal s built with t he Price Probabi lity Predict ion.
Ca p ital evolution due to the B type signals built with t he Price Proba bility Predi ction.
R RR : 1 :2.32
R RR : 1 :4.96
Pe rio d: 01 .0 1.2019 - 31.12.2019
Pe rio d: 0 1.01 .2019 - 31 .1 2.2019
In stead o f co nc lusions
I mportant n ote: i f the probability f or a pr ice i ncrease i s lo w, th at
d oes n ot m ean t hat the prob ab ility for a p ri ce d ecre ase is h igher !
DAX30 D1
31.01.2020
64%
P r ice P robability P r ed ictor.
Ca pital i nv estm ents a ssisted b y a p r obabilit y f i eld .
Cr i st ian P ăuna
Emai l: c ri stian.pauna@ie.ase.ro
Phone: +4 07 .4003.0000
Ec on omic I nf orma tics D octoral S chool
A ca demy o f Ec onomic St udies, B ucharest, Ro mania
T hi s p ap e r wa s fi nanced b y A lgorithm In vest (algoinvest.biz )
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SuperCont is an automated capital management system using artificial intelligence to invest in a wide range of capital markets. theServer can be used by any private or institutional investor in ord
er to manage the capital in his own accounts. More details can be found at https://pauna.pro/supercont or at https://algoinvest.pro ... [more] View project The goal of this project is to identify emotions, fears and psychological trust related to the financial investment activity using algorithmic trading and automated investment software systems. A p
articular goal is to identify those conditions that can make possible stress less investment. ... [more] View project Conference Paper
Full-text available
June 2020
Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers
... [Show full abstract] using advanced mathematical algorithms. This paper will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments. View full-text January 2021
Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers
... [Show full abstract] using advanced mathematical algorithms. This chapter will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments. Read more Method
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Measuring investors risk aversion is an important step in the RiskM methodology to include capital management into automated software systems. This model was published here for the first time at 14.10.2018.
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After the introduction of the electronic execution systems in all main stock exchanges in the world, the role of the automated trading software in the business intelligence systems of any financial or investment company became significant. Designing of reliable trading software to build and send automated orders based on quantitative mathematical models applied in the historical and real-time
... [Show full abstract] price data is a challenge for nowadays. Algorithmic trading and high-frequency trading engines become today a relevant part of any trading system and their specific characteristics related with the fast execution trading process and capital management involves specific measures to be used. Smart integration of the trading software in the business intelligence systems is also a sensitive theme for any financial and investment activity, a plenty of functional, control and execution issues being subjects of researches for future improvements. This paper wants to gather together more particular aspects on this subject, based on the experience of last years, opening the way for future topics. View full-text Last Updated: 05 Jul 2022
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