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Support Vector Machines (svm) In Forex Forecasting
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Deep Learning Based Exchange Rate Prediction During The Covid 19 Pandemic
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By Leonard Kin Yung Loh Leonard Kin Yung Loh Scilit Preprints.org Google Scholar † , Hee Kheng Kueh Hee Kheng Kueh Scilit Preprints.org Google Scholar † , Nirav Janak Parikh Nirav Janak Parikh Scilit Preprints.org Google Scholar , Harry Chanscilit Harry Chan Preprints.org Google Scholar † , Nicholas Jun Hui Ho Nicholas Jun Hui Ho Scilit Preprints.org Google Scholar and Matthew Chin Heng Chua Matthew Chin Heng Chua Scilit Preprints.org Google Scholar *
Received: 30 January 2022 / Revised: 15 March 2022 / Accepted: 23 March 2022 / Published: 27 March 2022
Forecasting Stock Price Movement: New Evidence From A Novel Hybrid Deep Learning Model
Algorithmic trading has become a standard in financial markets. Traditionally, most algorithms have relied on rule-based expert systems which are complex if/then rules that need to be updated manually to change market conditions. Machine learning (ML) is a natural next step in algorithmic trading because it can learn market patterns and behavior directly from historical trading data and apply this to trading decisions. In this book, a complete end-to-end system is proposed for automated trading of low-cost numbers in the foreign exchange (Forex) market. This method uses multiple state of the art (SOTA) machine learning techniques combined under a collective model to derive market signals for trading. A genetic algorithm (GA) is used to optimize the strategy for profit maximization. The process also includes financial management techniques to reduce risk and back-testing techniques to monitor system performance. These models were purchased on the EUR-USD pair Forex data from Jan 2006 to Dec 2019, and analyzed with a blind check from Jan 2020 to Dec 2020. The system performance is guaranteed under the existing conditions. better. The cumulative model achieved approximately 10% nett P&L with an extension level of -0.7% based on 2020 business data. Further work is required to adjust business costs & capital losses to actual market conditions. It is concluded that due to the increase in the market due to the global epidemic, the motivation behind machine learning algorithms that can adapt to the changing market environment will be stronger.
Being able to consistently make a profit in Forex trading remains a difficult endeavor, especially given the many factors that can affect price movements [1]. In order to be successful, traders must not only predict market signals correctly, but also perform risk management to reduce their losses if the market goes against them [2]. As such, there has been an increasing interest in creating automated system solutions to help consumers make the right decision and course of action they should take due to the situation [3]. However, these solutions are usually rule-based or require the input of subject matter experts (SMEs) to develop a knowledge database for the system [4]. This approach will affect the efficiency of the system in the end due to the dynamic nature of the market, as well as making it easier to update [5].
Recently, new innovations have introduced more intelligent methods using advanced technologies, such as ML algorithms [6]. Unlike traditional algorithms, machine learning is able to analyze Forex data and extract useful information from it to help traders make decisions [7]. Given the explosion of data and how fast it is nowadays, this is a game changer in the field of Forex trading and the speed of its trading since it requires less human hands and provides accurate analysis, forecasting, and timing. execution of trades [8].
This study proposes a complete and finite solution system, modeled as AlgoML, which includes both business decision making and risk estimation and financial management. The system can automatically extract data for known Forex pairs, predict the expected market price for the next day and execute the best trades determined by the combined risk and money management strategy. The system integrates multiple SOTA learnings, supervised learning, and conventional strategies into a collective model to achieve predictive marketing signals. The aggregate model aggregates the predictive signals of each strategy to provide a final overall forecast. The risk and investment management plan in the system helps to reduce the risk during the trading process. In addition, the system is designed in such a way that it makes it easy to train and test the system to check the performance before it is actually deployed.
How To Use Ai In Forex Trading?
The paper is organized as follows: Section 2 examines related functions and forecasting models for the Forex market. Section 3 presents the high-level architecture of the system and its modules. Section 4 provides details on the ML models used in the system. Section 5 presents the results and performance of the system.
Over the past decade, there have been many works and books that suggest different types of strategies for trading in the Forex market. One of the most popular forecasting models is Box and Jenkins’ auto-regressive integrated move (ARIMA) [3], which is still being analyzed by other researchers for Forex forecasting [9, 10]. However, ARIMA was found to be a general model and was developed based on the assumption that the forecasted time series is linear and stationary [11].
With the advancement of machine learning, many of the research projects are focused on using machine learning techniques to create predictive models. One such area is the use of supervised machine learning models. Kamruzzaman et al. analyzed artificial neural networks (ANNs) – forecasting foreign exchange rates and measured them with the most popular ARIMA model. It was found that the ANN model outperforms the ARIMA model [12]. Thu et al. implemented a support vector machine (SVM) strategy in real Forex trading, and reported the benefits of using SVM compared to trades made without using SVM [13]. Decision trees (DT) have also seen some use in Forex forecasting models. Juszczuk et al. developed a model that can generate datasets from real FOREX market data [14]. The data is converted into a decision table with three decision classes (Buy, Sell or Wait). There are also research services using aggregate models instead of relying on a single model for Forex predictions. Nti et al. built 25 different clustered regressors and classifiers using DT, SVM and NN. They tested their clustered model on data from different product variables and showed that pooling and mixing clustering methods yielded higher predictions of (90-100%) and (85.7-100%) respectively. one, compared to the bag (53-97.78%). and increase (52.7-96.32%). The root mean square error (RMSE) recorded by the collection (0.0001-0.001) and combination (0.002-0.01) is also lower than that of bag (0.01-0.11) and promotion (0.01-0.443) [15].
Apart from the controllable machine learning model, another area of machine learning technique that is employed for Forex prediciton is the use of deep learning model. Examples of these models include long-term memory (LSTM) and neural networks (CNNs). Qi et al. conducted case studies of several deep learning models, including long-term memory (LSTM), short-term long-term memory (BiLSTM) and gated recurrent unit (GRU) versus simple recurrent neural networks (RNN) ) [16] ]. They concluded that their LSTM and GRU models outperformed the original RNN model for EUR/GBP, AUD/USD and CAD/CHF currency pairs. They also reported that their model outperformed that proposed by Zeng and Khushi [17] in terms of RMSE, achieving a value of 0.006 × 10.
Pdf] Statistical And Machine Learning Approach In Forex Prediction Based On Empirical Data
Some research projects have tried hybrid approaches by combining several types of deep learning together. Islam et al. introduced the use of a hybrid GRU-LSTM model. They tested their proposed model at 10-mins and 30-mins intervals and evaluated the performance based on MSE, RMSE, MAE and R.
Mark. They reported that the hybrid model outperforms the linear LSTM and GRU
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