Combining Forex Trading And Mining For Easy Money In Coventry, Uk

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Combining Forex Trading And Mining For Easy Money In Coventry, Uk

Combining Forex Trading And Mining For Easy Money In Coventry, Uk

The background papers represent state-of-the-art research with significant potential for high impact in the field. A Feature Paper should be an original and substantial article involving several techniques or approaches, providing perspective for future research directions, and describing possible 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 †, 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 / Reviewed: March 15, 2022 / Accepted: March 23, 2022 / Published: March 27, 2022

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Algorithmic trading has become the standard in the financial market. Traditionally, most algorithms have relied on rule-based expert systems, which are a set of complex if/then rules that need to be manually updated based on changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can directly learn market patterns and behaviors from historical trading data and factor them into trading decisions. A complete end-to-end system for automated low-frequency quantitative trading in the foreign exchange (Forex) markets is proposed in this document. The system uses several state-of-the-art machine learning (SOTA) 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 on EUR-USD Forex data from January 2006 to December 2019 and subsequently evaluated on unseen samples from January 2020 to December 2020. System performance is promising under ideal conditions. The ensemble model achieved approximately 10% net profit and loss with a drawdown level of −0.7% based on 2020 trading data. More work is needed to calibrate trading costs and execution slippage under real market conditions. It concludes that as market volatility due to the global pandemic increases, the momentum behind machine learning algorithms that can adapt to a changing market environment will become even stronger.

Making a consistent profit in Forex trading continues to be challenging, especially given the many factors that can influence price movements [1]. To be successful, traders must not only predict market signals correctly, but also perform risk management to mitigate losses in case the market moves against them [2]. Therefore, there has been growing interest in developing systems-driven automated solutions to help traders make informed decisions about the course of action to take given the circumstances [3]. However, these solutions tend to be rule-based or require the input of subject matter experts (SMEs) to develop the knowledge database for the system [4]. This approach would have a negative impact on system performance in the long run, given the dynamic nature of the market, as well as making it difficult to upgrade [5].

More recently, the latest 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 Forex data and extract useful information from it to help traders make a decision [7]. Given the explosion of data and how it is becoming more readily available nowadays, this represented a game-changer in the field of Forex trading with its fast-paced automated trading as it requires little human intervention and provides analytics accurate, forecast and timely execution of operations [8].

Combining Forex Trading And Mining For Easy Money In Coventry, Uk

This study proposes a complete end-to-end system solution, called AlgoML, which incorporates both trading decisions and a risk and liquidity 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 optimal trade decided by the integrated risk and liquidity management strategy. The system incorporates different methods of SOTA reinforcement learning, supervised learning, and conventional strategies optimized into a collective ensemble model to obtain the expected market signal. The ensemble model collects the expected output signal of each strategy to provide a final overall prediction. The risk and liquidity management strategy within the system helps mitigate risk during the execution phase of the trade. In addition, the system is designed to make it easier to train and backtest strategies to observe performance before actual implementation.

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The paper is structured as follows: Section 2 explores related work on forecast-based models for the Forex market. Section 3 presents the high-level architecture of the system and its individual modules. Section 4 delves into the ML model projects used in the system. Section 5 provides system performance results.

Over the past decade, a number of papers have been published in the literature proposing various forecast-based models for trading in the Forex market. One of the most popular time series forecasting models has been the Autoregressive Integrated Moving Average (ARIMA) by Box and Jenkins [3], which is still being explored by other researchers for Forex forecasting [9, 10]. However, it is noted that ARIMA is a general univariate model and is developed on the basis of the assumption that the predicted time series are linear and stationary [11].

With the advancement of machine learning, most research work has focused on the use of machine learning techniques to develop prediction models. One such area is the use of supervised machine learning models. Kamruzzaman et al. studied foreign exchange rate prediction models based on artificial neural networks (ANNs) and compared with the more well-known ARIMA model. The ANN model was found to outperform the ARIMA model [12]. Gio et al. implemented a Support Vector Machine (SVM) model with actual Forex transactions and outlined the benefits of using SVM over transactions made without the use of SVM [13]. Decision trees (DTs) have also seen some use in Forex forecasting models. Juszczuk et al. created a model that can generate datasets from real-world FOREX market data [14]. The data is transformed into a decision table with three decision classes (BUY, SELL or HOLD). There are also research works that use an ensemble model rather than relying on single individual models for forex forecasting. Nti et al. built 25 different regressors and classifiers together using DT, SVM and NN. They evaluated their models together on data from various stock exchanges and showed that the stacking and blending ensemble techniques offer higher prediction accuracy of (90–100%) and (85.7–100%), respectively, than to that of bagging (53–97.78%) and enhancement (52.7–96.32%). The root mean squared error (RMSE) recorded by stacking (0.0001–0.001) and mixing (0.002–0.01) was also lower than that of bagging (0.01–0.11) and boosting (0, 01–0.443) [15].

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

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

Point. They reported that the hybrid model outperforms LSTM and autonomous GRUs

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