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

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

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

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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 † , Harry Chan Harry Chan Scilit 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: January 30, 2022 / Revised: March 15, 2022 / Accepted: March 23, 2022 / Published: March 27, 2022

Everything You Need To Know About Forex Trading, In Three Minutes

Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms are based on rule-based expert systems, which are a series of complex if/then rules that must be manually adjusted to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can learn market patterns and behavior directly from historical trading data and incorporate this into trading decisions. This article proposes a complete end-to-end system for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system utilizes multiple state of the art (SOTA) machine learning strategies combined in an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize strategies to maximize profits. The system also includes a money management strategy to mitigate risk and a backtesting framework to evaluate system performance. The models were trained from January 2006 to December 2019 using forex data from the EUR-USD pair and then evaluated from January 2020 to December 2020 using previously unknown samples. System performance is promising under ideal conditions. Based on 2020 trading data, the ensemble model achieved a net P&L of approximately 10% with a drawdown of −0.7%. Further work is required to calibrate trading costs and execution bias under real market conditions. It concludes that with increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger.

The ability to make consistent profits in Forex trading continues to be a challenging endeavor, especially given the numerous factors that can influence price movements [1]. To be successful, traders must not only correctly predict the market signals but also carry out risk management to limit their losses in case the market moves against them [2]. Therefore, there is increasing interest in developing automated, system-driven solutions that help merchants make informed decisions about what actions they should take given the circumstances [3]. However, these solutions are typically rule-based or require input from subject matter experts (SMEs) to develop the knowledge base for the system [4]. This approach would, in the long run, negatively impact the performance of the system given the dynamics of the market and make updating cumbersome [5].

Recently, recent innovations have introduced smarter approaches through the use of advanced technologies such as ML algorithms [6]. Unlike the traditional rule-based approach, machine learning is able to analyze the Forex data and extract useful information from it to help traders make decisions [7]. With the explosion of data and the fact that it is becoming more readily available these days, it has fundamentally changed Forex trading with its fast-paced automated trading, requiring little human intervention and providing accurate analysis, forecasting and timely execution of trades [8 ].

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

This study proposes a complete end-to-end system solution, referred to as AlgoML, which includes trading decisions as well as a risk and cash management strategy. The system is able to automatically extract data for an identified forex pair, predict the expected market signal for the next day and execute the most optimal trade decided by the built-in risk and cash management strategy. The system integrates multiple SOTA reinforcement learning, supervised learning, and optimized conventional strategies into a collective ensemble model to obtain the predicted market signal. The ensemble model collects the predicted output of each strategy to produce a final overall forecast. The risk and cash management strategy within the system helps mitigate risk during the trade execution phase. Additionally, the system is designed to make it easier to train and retest strategies to observe performance prior to actual deployment.

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The paper is organized as follows: Section 2 examines related work on prediction-based models for the Forex market. Section 3 presents the high-level architecture of the system and its individual modules. Section 4 explains the ML model designs used in the system. Section 5 provides the results on the performance of the system.

In the last decade, there have been a number of works in the literature proposing various prediction-based models for trading in the Forex market. One of the most popular time-series forecasting models was the autoregressive integrated moving average (ARIMA) by Box and Jenkins [3], which is still being studied by other forex forecasting researchers [9, 10]. However, it is noted that ARIMA is a general univariate model and is based on the assumption that the projected time series are linear and stationary [11].

As machine learning has evolved, most research has focused on using machine learning techniques to develop predictive models. One such area is the use of supervised machine learning models. Kamruzzaman et al. studied artificial neural networks (ANNs)-based predictive modeling of foreign exchange rates and made a comparison with the best-known ARIMA model. The ANN model was found to outperform the ARIMA model [12]. Thu et al. implemented a Support Vector Machine (SVM) model with actual forex transactions and outlined the advantages of using SVM compared to transactions made without using SVM [13]. Decision trees (DT) have also been used in forex prediction models. Juszczuk et al. created 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 is also research that uses an ensemble model instead of relying on single individual models for forex prediction. Nti et al. constructed 25 different ensemble regressors and classifiers using DTs, SVMs and NNs. They evaluated their ensemble models using data from various exchanges and showed that stacking and blending ensemble techniques provide higher prediction accuracy of (90–100%) and (85.7–100%), respectively, compared to bagging ( 53-97.78%). and boosting (52.7–96.32%). The root mean square error (RMSE) recorded for stacking (0.0001-0.001) and mixing (0.002-0.01) was also lower than for filling (0.01-0.11) and boosting (0, 01-0.443) [15].

Besides supervised machine learning models, another area of ​​machine learning technique used for forex prediction is the use of deep learning models. Examples of such models are long-term memory (LSTM) and convolutional neural networks (CNNs). Qi et al. performed a comparative study of several deep learning models involving long short-term memory (LSTM), bidirectional long short-term memory (BiLSTM), and the gated recurrent unit (GRU) versus a base model of a simple recurrent neural network (RNN) [ 16]. ]. They concluded that their LSTM and GRU models outperformed the base RNN model for EUR/GBP, AUD/USD and CAD/CHF currency pairs. They also reported that their models outperformed the models proposed by Zeng and Khushi [17] in terms of RMSE, reaching a value of 0.006 × 10

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Some research has attempted a hybrid approach by combining multiple deep learning models. Islam et al. introduced the use of a hybrid GRU-LSTM model. They tested their proposed model on 10-minute and 30-minute time periods and rated performance based on MSE, RMSE, MAE, and R

Score. They reported that the hybrid model outperforms the standalone LSTM and GRU

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