Agent based genetic algorithm employing financial technical analysis for making trading decisions using historical equity market data

Schoreels, Cyril; Logan, Brian; Garibaldi, Jonathan M.

doi:10.1109/iat.2004.1342986

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…The hybrid genetic algorithm-technical indicators models are applied on stock data in [13], [14], [15] and on equity data in [16]. The rest of the related work includes the hybrid approaches which uses technical indicators and the applied domain is forex currencies.…”

Section: Related Workmentioning

confidence: 99%

Heuristic based trading system on Forex data using technical indicator rules

Öztürk

Toroslu

Fidan³

2016

Applied Soft Computing

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, 115 pages The foreign exchange market, which is widely known as Forex or FX, is the largest financial market with a daily transactional volume of $5 trillion. Due to the huge structure of the market, price analysis on FX market draws attention of many scientists and practitioners. There are 2 main analysis approaches: Fundamental and technical analysis. Fundamental analysis focuses on the macroeconomic factors such as interest rate to explain the market movements. Technical analysis deals with past market price data to forecast the future prices. Technical analysis involves two main approaches: Chart analysis and technical indicator based price analysis. Chart analysis deals with detection of patterns in price charts. Technical indicators transform the price time series data into another time series data to explore patterns. Technical indicators are widely used in FX and other financial markets which are the building blocks of many trading systems. A trading system is based on technical indicators or pattern-based approaches which produces buy/sell signals to trade in the market. In this thesis, a heuristic based trading system on Forex data is developed using popular technical indicators. The system grounds on selecting and combining the trading rules based on indicators using heuristic methods. The selection of the trading rules is realized by using Genetic Algorithm and a local search method. A weighted majority voting method is proposed to combine the technical indicator based trading rules to form a single trading rule. The experiments are conducted on 2 major currency pairs v in 3 different time frames where promising results are achieved.

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Section: Related Workmentioning

confidence: 99%

Heuristic based trading system on Forex data using technical indicator rules

Öztürk

Toroslu

Fidan³

2016

Applied Soft Computing

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“…And also, Schoreels et.al [12] have presented a work which attempts to generate the buying and selling signals against 30 companys' stocks in Germany (DAX30). They use combination of Technical Indexes applied to GA as well as [11], and then rank the stocks according with the strength of signals to restructure the portfolio.…”

Section: Work On Technical Analysis and Gamentioning

confidence: 99%

“…Besides, in order to keep high-fitness individuals, the elite 1% (the top 1% individuals in terms of fitness value) are reserved automatically every generation. In addition, the worst peformers (the bottom 30% in our method) are replaced through randomly generated new genes using concept of immigration, an approach previously suggested by Branke, and also applied by Schoreels [12,13] (see Fig.4). …”

Section: Fig 3 Flow Of the Investment Managementmentioning

confidence: 99%

Optimization of the trading rule in foreign exchange using genetic algorithm

Hirabayashi¹,

Aranha

Iba

2009

Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation

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The generation of profitable trading rules for Foreign Exchange (FX) investments is a difficult but popular problem. The use of Machine Learning in this problem allows us to obtain objective results by using information of the past market behavior. In this paper, we propose a Genetic Algorithm (GA) system to automatically generate trading rules based on Technical Indexes. Unlike related researches in the area, our work focuses on calculating the most appropriate trade timing, instead of predicting the trading prices.

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“…However, the description in that work was too preliminary to allow for a comparison with our system to be made. Reference [43] presented a work which attempts to generate the buying and selling signals against 30 companies' stocks in Germany (DAX30). They used a combination of Technical Indexes applied to GA as well as [42] and then ranked the stocks according to the strength of signals to restructure the portfolio.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

Chihab

Bousbaa

Chihab

et al. 2019

Applied Computational Intelligence and Soft Computing

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In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

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Agent based genetic algorithm employing financial technical analysis for making trading decisions using historical equity market data

Cited by 14 publications

References 6 publications

Heuristic based trading system on Forex data using technical indicator rules

Heuristic based trading system on Forex data using technical indicator rules

Optimization of the trading rule in foreign exchange using genetic algorithm

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

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