Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System

Vezeris, Dimitrios; Kyrgos, Themistoklis; Schinas, C. J.

doi:10.3390/jrfm11030056

Cited by 24 publications

(14 citation statements)

References 31 publications

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“…Something that had not been examined prior to our research was the behavior of the sliding and variable ATR-based stop loss technique combined with exclusion barriers when used on an automated trading system, based on a Turtle strategy, similar with the one described by (Vezeris et al 2018b).…”

Section: Related Work and Backgroundmentioning

confidence: 99%

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AdTurtle: An Advanced Turtle Trading System

Vezeris

Karkanis

Kyrgos

2019

JRFM

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For this research, we implemented a trading system based on the Turtle rules and examined its efficiency when trading selected assets from the Forex, Metals, Commodities, Energy and Cryptocurrency Markets using historical data. Afterwards, we enhanced our Turtle-based trading system with additional conditions for opening a new position. Specifically, we added an exclusion zone based on the ATR indicator, in order to have controlled conditions for opening a new position after a stop loss signal was triggered. Thus, AdTurtle was developed, a Turtle trading system with advanced algorithms of opening and closing positions. To the best of our knowledge, for the first time this variation of the Turtle trading system has been developed and examined.

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Section: Related Work and Backgroundmentioning

confidence: 99%

“…We also decided that it is best not to hold open positions over the weekend based on the results by (Vezeris et al 2018b).…”

Section: Data and Implementationmentioning

confidence: 99%

AdTurtle: An Advanced Turtle Trading System

Vezeris

Karkanis

Kyrgos

2019

JRFM

Self Cite

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“…For the sake of simplicity, we will assume that whenever we issue an order to trade, we have determined a profit target and a stop loss level for our trade. Although we may have simplified a little bit our setting, this assumption is quite realistic and done by many practitioners as stated in various papers (Labadie and Lehalle (2010), Giuseppe Di Graziano (2014), Fung (2017), or Vezeris et al (2018)). The profit target ensures that the strategy locks in real money the profit realized and is materialized by a limit order.…”

Section: Settingsmentioning

confidence: 99%

A New Approach to Learning in Dynamic Bayesian Networks (DBNs)

Benhamou,

Atif,

Laraki

et al. 2018

SSRN Journal

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In this paper, we revisit the parameter learning problem, namely the estimation of model parameters for Dynamic Bayesian Networks (DBNs). DBNs are directed graphical models of stochastic processes that encompasses and generalize Hidden Markov models (HMMs) and Linear Dynamical Systems (LDSs). Whenever we apply these models to economics and finance, we are forced to make some modeling assumptions about the state dynamics and the graph topology (the DBN structure). These assumptions may be incorrectly specified and contain some additional noise compared to reality. Trying to use a best fit approach through maximum likelihood estimation may miss this point and try to fit at any price these models on data. We present here a new methodology that takes a radical point of view and instead focus on the final efficiency of our model. Parameters are hence estimated in terms of their efficiency rather than their distributional fit to the data. The resulting optimization problem that consists in finding the optimal parameters is a hard problem. We rely on Covariance Matrix Adaptation Evolution Strategy (CMA-ES) method to tackle this issue. We apply this method to the seminal problem of trend detection in financial markets. We see on numerical results that the resulting parameters seem less error prone to over fitting than traditional moving average cross over trend detection and perform better. The method developed here for algorithmic trading is general. It can be applied to other real case applications whenever there is no physical law underlying our DBNs.E. Benhamou, J. Atif, R. Laraki/Learning in Dynamic Bayesian Graphical Modelwhere the parents of a node x are denoted by π x . A dynamic Bayesian Network (DBN) is defined as a pair (B 0 , B 2d ) where B 0 is a traditional Bayesian network representing the initial or a priori distribution of random variables, that can be related to time 0 and where B 2d is a dynamic two-step Bayesian network describing the transition from time t−1 to time t with the probability P(x t x t−1 ) for any node x belonging to V , in a directed acyclic graph G = (V, E). The joined probability for two sets of nodes V t and V t−1 is given byThe factorized joint probability law is computed by tracing the sequence in the graph over the time sequence. If we denote by T the total length of the path and by P(V 0 ) the joined probability of the initial network B 0 , the probability to go from V 0 to T is given by:A dynamic Bayesian network thus respects the Markov property, which expresses that conditional distributions at time t depend only on the state at time t−1. Dynamic Bayesian networks generalize probabilistic models such as Hidden Markov Model (HMM), and Kalman filter (KF). Apart from the mainstream Kalman filter and HMM models whose DAG is given by 1, more complex DBN can include multi input network with connection between observable and previous latent variables as provided by 2. Another example is the combination of Kalman Filtering (KF) model and echo neural network (ESN) as provided by figure...

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“…first group includesWang et al (2018);Liu et al (2018);Vezeris et al (2018);Xu et al (2018); Ptak-Chmielewska (2019) andHamori et al (2018) Wang et al (2018). proposed two estimation algorithms for extracting cross-lingual news pairs based on machine learning from multilingual financial news articles Luo et al (2019).…”

mentioning

confidence: 99%

“…proposed two estimation algorithms for extracting cross-lingual news pairs based on machine learning from multilingual financial news articles Luo et al (2019). introduced convolutional neural network (CNN) techniques to forecast the short-term crude oil futures prices Vezeris et al (2018). compared the addition of various strategies to a simple Moving Average Convergence Divergence (MACD) automated trading system Xu et al (2018).…”

mentioning

confidence: 99%

Empirical Finance

Hamori

2020

JRFM

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The research field related to finance has made great progress in recent years due to the development of information processing technology and the availability of large-scale data. This special issue is a collection of 16 articles on empirical finance and one book review. The content is six articles on machine learning, five articles based on traditional econometric analysis, and five articles on emerging markets. The large share of articles on the application of machine learning is in line with recent trends in finance research. This special issue provides a state-of-the-art overview of empirical finance from economic, financial, and technical points of view.

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Take Profit and Stop Loss Trading Strategies Comparison in Combination with an MACD Trading System

Cited by 24 publications

References 31 publications

AdTurtle: An Advanced Turtle Trading System

AdTurtle: An Advanced Turtle Trading System

A New Approach to Learning in Dynamic Bayesian Networks (DBNs)

Empirical Finance

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