A Deep Network-Based Trade and Trend Analysis System to Observe Entry and Exit Points in the Forex Market
Asit Kumar Das,
Debahuti Mishra,
Kaberi Das,
Arup Kumar Mohanty,
Mazin Abed Mohammed,
Alaa S. Al-Waisy,
Seifedine Kadry and
Jungeun Kim ()
Additional contact information
Asit Kumar Das: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Debahuti Mishra: Department of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Kaberi Das: Department of Computer Applications, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Arup Kumar Mohanty: Department of Computer Science and Information Technology, Siksha ‘O’ Anusandhan (Deemed to be) University, Bhubaneswar 751030, Odisha, India
Mazin Abed Mohammed: College of Computer Science and Information Technology, University of Anbar, Anbar 31001, Iraq
Alaa S. Al-Waisy: Computer Technologies Engineering Department, Information Technology Collage, Imam Ja’afar Al-Sadiq University, Baghdad 10064, Iraq
Seifedine Kadry: Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
Jungeun Kim: Department of Software, Kongju National University, Cheonan 31080, Korea
Mathematics, 2022, vol. 10, issue 19, 1-23
Abstract:
In the Forex market, trend trading, where trend traders identify trends and attempt to capture gains through the analysis of an asset’s momentum in a particular direction, is a great way to profit from market movement. When the price of currency is moving in one either of the direction such as; up or down, it is known as trends. This trend analysis helps traders and investors find low risk entry points or exit points until the trend reverses. In this paper, empirical trade and trend analysis results are suggested by two-phase experimentations. First, considering the blended learning paradigm and wide use of deep-learning methodologies, the variants of long-short-term-memory (LSTM) networks such as Vanilla-LSTM, Stacked-LSTM, Bidirectional-LSTM, CNN-LSTM, and Conv-LSTM are used to build effective investing trading systems for both short-term and long-term timeframes. Then, a deep network-based system used to obtain the trends (up trends and down trends) of the predicted closing price of the currency pairs is proposed based on the best fit predictive networks measured using a few performance measures and Friedman’s non-parametric tests. The observed trends are compared and validated with a few readily available technical indicators such as average directional index (ADX), rate of change (ROC), momentum, commodity channel index (CCI), and moving average convergence divergence (MACD). The predictive ability of the proposed strategy for trend analysis can be summarized as follows: (a) with respect to the previous day for short-term predictions, AUD:INR achieves 99.7265% and GBP:INR achieves 99.6582% for long-term predictions; (b) considering the trend analysis strategy with respect to the determinant day, AUD:INR achieves 98.2906% for short-term predictive days and USD:INR achieves an accuracy of trend forecasting with 96.0342%. The significant outcome of this article is the proposed trend forecasting methodology. An attempt has been made to provide an environment to understand the average, maximum, and minimum unit up and/or downs observed during trend forecasting. In turn, this deep learning-based strategy will help investors and traders to comprehend the entry and exit points of this financial market.
Keywords: Forex market trading; Forex market trend analysis; deep learning; Vanilla-LSTM; Stacked-LSTM; Bidirectional-LSTM; CNN-LSTM; Conv-LSTM (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
https://www.mdpi.com/2227-7390/10/19/3632/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/19/3632/ (text/html)
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:19:p:3632-:d:933557
Access Statistics for this article
Mathematics is currently edited by Ms. Emma He
More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().