Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression. (27th August 2019)
- Record Type:
- Journal Article
- Title:
- Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression. (27th August 2019)
- Main Title:
- Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression
- Authors:
- Chihab, Younes
Bousbaa, Zineb
Chihab, Marouane
Bencharef, Omar
Ziti, Soumia - Other Names:
- Yang Miin-Shen Academic Editor.
- Abstract:
- Abstract : 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 timesAbstract : 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. … (more)
- Is Part Of:
- Applied computational intelligence and soft computing. Volume 2019(2019)
- Journal:
- Applied computational intelligence and soft computing
- Issue:
- Volume 2019(2019)
- Issue Display:
- Volume 2019, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 2019
- Issue:
- 2019
- Issue Sort Value:
- 2019-2019-2019-0000
- Page Start:
- Page End:
- Publication Date:
- 2019-08-27
- Subjects:
- Computational intelligence -- Periodicals
Soft computing -- Periodicals
006.305 - Journal URLs:
- https://www.hindawi.com/journals/acisc/ ↗
- DOI:
- 10.1155/2019/8342461 ↗
- Languages:
- English
- ISSNs:
- 1687-9724
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 12012.xml