Strategies For Success: Forex Trading And Mining In Oxford’s Financial Landscape


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Strategies For Success: Forex Trading And Mining In Oxford’s Financial Landscape

Strategies For Success: Forex Trading And Mining In Oxford's Financial Landscape

Papers represent the most advanced research with the greatest potential for maximum impact in the field. A Concept Paper should be an original text that covers a wide range of methods or approaches, providing a vision for future research directions and describe research applications.

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By Leonard Kin Yung Loh Leonard Kin Yung Loh Scilit Google Scholar † , Hee Kheng Kueh Hee Kheng Kueh Scilit Google Scholar † , Nirav Janak Parikh Nirav Janak Parikh Scilit Google Scholar † Scilit Chan , Harry Chan Google Scholar † , Nicholas Jun Hui Ho Nicholas Jun Hui Ho Scilit Google Scholar and Matthew Chin Heng Chua Matthew Chin Heng Chua Scilit Google Scholar *

Received: 30 January 2022 / Revised: 15 March 2022 / Accepted: 23 March 2022 / Published: 27 March 2022

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Algorithmic trading has become an example in the financial market. In general, most algorithms rely on special rule-based systems that are a set of complex if/then conditions that must be updated manually to change market conditions. Machine learning (ML) is the next step in algorithmic trading because it can learn market patterns and behavior directly from data. historical trading prices and related trading options. In this document, a complete decision-making process for automatic low-risk trading in the foreign exchange (Forex) market is presented. The system uses several State of the Art machine learning techniques (SOTA) combined under a standard model. combined to produce the market index for trading. Genetic Algorithm (GA) is used to optimize strategies for increasing profits. The system also includes a risk management strategy and a back-testing framework to evaluate system performance. The models were trained on EUR–USD pair Forex data from Jan 2006 to Dec 2019, and later evaluated on previously unseen samples from Jan 2020 to Dec 2020. The performance of the system is successful in under good conditions. The prototype achieved approximately 10% nett P&L with a −0.7% discount rate based on trading data. trade 2020. More work is needed to differentiate trading costs and operational failures in real market conditions. It is concluded that, with the increase in market changes due to the global pandemic, the power behind the Machine learning algorithms can adapt to a changing market environment.

Being able to always make a profit in Forex trading continues to be a challenging endeavor, especially with the many factors that can affect the price [1]. To be successful, traders must not only see market signals correctly, but also implement risk management to minimize their losses when the market moves against them [2]. As such, there is increasing interest in the development of automated management solutions to assist traders in making informed decisions on the action they should take due to the circumstances [3]. However, these solutions tend to be rule-based or require the use of subject matter experts (SMEs) to develop the information system for the system [4]. This process will affect the performance of the system in the long term due to the dynamic nature of the market, as well as the difficulty to renew [5].

More recently, innovations have introduced more intelligent methods by using advanced technologies, such as ML algorithms [6]. Unlike the traditional algorithm, machine learning can analyze Forex data and extract useful information from it to help traders make a decision [7]. Due to the lack of information and how easy it is to find these days, this has become a game-changer in the field of Forex trading and its fast-paced trading since it requires human intervention and provides analysis. accurate, predictable, and timely. trade performance [8].

Strategies For Success: Forex Trading And Mining In Oxford's Financial Landscape

This study presents a final decision model, named as AlgoML, which includes all trading results as well as a risk and financial strategy. The system can automatically extract information for a certain Forex bar, see the expected market signal for the next day and execute the best trade that is chosen by the combination of risk and investment. The system incorporates a variety of SOTA reinforcement training, supervised training, and simulated internal strategies. a combination model provides the market signal. The composite model aggregates the signal values ​​of each parameter to provide an overall result. The risk and money management in the system helps to reduce the risks during the execution of transactions. In addition, the system is designed to facilitate learning and back-testing strategies to monitor performance before delivery.

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The paper is organized as follows: Part 2 examines related work on the basic principles for the Forex market. Section 3 shows the high-level structure of the system and its individual modules. Section 4 elaborates on the ML modeling patterns used in the system. Section 5 presents the results on the implementation of the system.

In the last ten years, there has been a lot of work in the literature that presents basic strategies for trading in the Forex market. One of the most popular time series models is Box and Jenkins’ auto-regressive integrated moving average (ARIMA) [3], which is still being studied by other researchers for Forex forecasting [9, 10]. However, it is noted that ARIMA is a common model and is based on the idea that the time series is represented by lines and bars [11].

With the advancement of machine learning, many research projects have focused on using machine learning to develop predictive models. One such area is the use of machine learning models. Kamruzzaman et al. Explore the forecasting model of foreign exchange rates and make a comparison with the most popular ARIMA model . It was found that the ANN model outperformed the ARIMA model [12]. Tu et al. implemented a machine support model (SVM) with real Forex trades, and described the efficiency of using SVM compared to trades made without using SVM [13]. Decision trees (DT) have also seen some use in Forex trading models. Juszczuk et al. create an example that can generate data from real FOREX market data [14]. The data is transformed into a decision table with three decision classes (BUY, SELL or PEND). There are also research projects that use a model model instead of relying on individual models for Forex forecasting. Nti et al. build 25 different ensembled regressors and classifiers using DTs, SVMs and NNs. They evaluated their test model on data from different trades and showed that the clustering and mixing of clustering methods offer the highest level of accuracy of (90-100%) and (85.7-100% ), compared to the bag (53-97.78%). and increased (52.7–96.32%). The root mean square error (RMSE) determined by clustering (0.0001-0.001) and mixing (0.002-0.01) was also lower than bagging (0.01-0.11) and forcing (0.01-0.443) [ 15].

Apart from supervised machine learning models, another part of machine learning methods used for Forex prediciton is the use of Deep Learning models. Examples of such models include long-short-term memory (LSTM) and convolutional neural networks (CNNs). Qi et al. conducted a comparative analysis of several deep learning models, which include long-short memory (LSTM), bidirectional long-short memory (BiLSTM) and gated recurrent unit (GRU) against a neural network-based model ( RNN) simple [16]. ]. They concluded that their LSTM and GRU models outperformed the RNN-based model for the EUR/GBP, AUD/USD and CAD/CHF pairs. money flow. They also reported that their model was better than that proposed by Zeng and Khushi [17] in terms of RMSE, reaching a value of 0.006 × 10.

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Some research projects have tried a hybrid approach by combining deep learning models together. Islam et al. introduce the use of a hybrid GRU-LSTM model. They have tested their proposed model on 10-mins and 30-mins timeframes and evaluated the performance based on MSE, RMSE, MAE and R

Points They reported that the hybrid model outperforms LSTM and GRU independently.

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