Comparative Study of FOREX Trading Systems Built with SVR+GHSOM and Genetic Algorithms Optimization of Technical Indicators

Brito, Rodrigo F. B. de; Oliveira, Adriano L. I.

doi:10.1109/ictai.2012.55

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…Automated Forex exchange trading is a popular topic among peer-reviewed studies [3], [4], [12]- [15]. In the last two decades, many studies have been conducted on di erent aspects of foreign exchange technical indicators [3], [4], [13]- [16].…”

Section: R W a Researchmentioning

confidence: 99%

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Ivanov,

Yan

2021

Preprint

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e Foreign Exchange (Forex) is a large decentralized market, on which trading analysis and algorithmic trading are popular. Research e orts have been focusing on proof of e ciency of certain technical indicators. We demonstrate, however, that the values of indicator functions are not reproducible and o en reduce the number of trade opportunities, compared to price-action trading.In this work, we develop two dataset-agnostic Forex trading heuristic templates with high rate of trading signals. In order to determine most optimal parameters for the given heuristic prototypes, we perform a machine learning simulation of 10 years of Forex price data over three low-margin instruments and 6 di erent OHLC granularities. As a result, we develop a speci c and reproducible list of most optimal trade parameters found for each instrument-granularity pair, with 118 pips of average daily pro t for the optimized con guration.

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Section: R W a Researchmentioning

confidence: 99%

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Ivanov,

Yan

2021

Preprint

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“…GAs have been widely employed to solve a large number of classification [19] and combinatorial optimization [20] problems, varying from social network mining [26] to financial analysis [27]. In this work, we propose the use of GA as a new approach for finding an optimized ordering for a chain of classifiers.…”

Section: The Proposed Genetic Algorithmmentioning

confidence: 99%

A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

Gonçalves

Plastino

Freitas

2013

2013 IEEE 25th International Conference on Tools With Artificial Intelligence

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First proposed in 2009, the classifier chains model (CC) has become one of the most influential algorithms for multi-label classification. It is distinguished by its simple and effective approach to exploit label dependencies. The CC method involves the training of q single-label binary classifiers, where each one is solely responsible for classifying a specific label in {l 1 , …, l q }. These q classifiers are linked in a chain, such that each binary classifier is able to consider the labels predicted by the previous ones as additional information at classification time. The label ordering has a strong effect on predictive accuracy, however it is decided at random and/or combining random orders via an ensemble. A disadvantage of the ensemble approach consists of the fact that it is not suitable when the goal is to generate interpretable classifiers. To tackle this problem, in this work we propose a genetic algorithm for optimizing the label ordering in classifier chains. Experiments on diverse benchmark datasets, followed by the Wilcoxon test for assessing statistical significance, indicate that the proposed strategy produces more accurate classifiers.

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“…Pawel et al [20] developed decision trees using evolutionary algorithms and technical indicators to identify buy or sell signals. Rodrigo et al [21] showed that genetic algorithm could also be used to optimize the parameters in technical indicators used for SVR+GHSOM model based trading system and the results outperformed the market. Bernardo et al [22] categorized the market into three different types and used a hybrid system with SVM and genetic algorithm to classify the trend.…”

Section: Introductionmentioning

confidence: 99%

GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading

Zhang

Khushi

2020

2020 International Joint Conference on Neural Networks (IJCNN)

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Foreign exchange is the largest financial market in the world, and it is also one of the most volatile markets. Technical analysis plays an important role in the forex market and trading algorithms are designed utilizing machine learning techniques. Most literature used historical price information and technical indicators for training. However, the noisy nature of the market affects the consistency and profitability of the algorithms. To address this problem, we designed trading rule features that are derived from technical indicators and trading rules. The parameters of technical indicators are optimized to maximize trading performance. We also proposed a novel cost function that computes the risk-adjusted return, Sharpe and Sterling Ratio (SSR), in an effort to reduce the variance and the magnitude of drawdowns. An automatic robotic trading (RoboTrading) strategy is designed with the proposed Genetic Algorithm Maximizing Sharpe and Sterling Ratio model (GA-MSSR) model. The experiment was conducted on intraday data of 6 major currency pairs from 2018 to 2019. The results consistently showed significant positive returns and the performance of the trading system is superior using the optimized rule-based features. The highest return obtained was 320% annually using 5-minute AUDUSD currency pair. Besides, the proposed model achieves the best performance on risk factors, including maximum drawdowns and variance in return, comparing to benchmark models. The code can be accessed at https://github.com/zzzac/rule-based-forextrading-system

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Comparative Study of FOREX Trading Systems Built with SVR+GHSOM and Genetic Algorithms Optimization of Technical Indicators

Cited by 11 publications

References 21 publications

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

Constraint-Based Inference of Heuristics for Foreign Exchange Trade Model Optimization

A Genetic Algorithm for Optimizing the Label Ordering in Multi-label Classifier Chains

GA-MSSR: Genetic Algorithm Maximizing Sharpe and Sterling Ratio Method for RoboTrading

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