Combining Forex Trading And Mining For Easy Money In Gatineau, Canada

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Combining Forex Trading And Mining For Easy Money In Gatineau, Canada

Combining Forex Trading And Mining For Easy Money In Gatineau, Canada

Featured papers represent cutting-edge research with significant potential for high impact in the field. A Feature Paper should be a substantive original article that incorporates several techniques or approaches, provides an outlook on future research directions, and describes potential research applications.

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Editor’s Choice articles are based on the recommendations of scientific journal editors from around the world. The editors select a small number of articles recently published in the journal that they believe will be of particular interest to readers, or relevant to the relevant field of research. The aim is to provide a snapshot of some of the most exciting work published in the journal’s various research areas.

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 , Chancilryn † 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

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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 must be manually updated for 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 this into trading decisions. In this paper, a complete end-to-end system is proposed for quantitative automated low-frequency trading in foreign exchange (Forex) markets. The system uses several state-of-the-art machine learning (SOTA) strategies that are combined under an ensemble model to derive the market signal for trading. Genetic Algorithm (GA) is used to optimize strategies for maximizing 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 the data of the EUR-USD Forex pair from January 2006 to December 2019, and then evaluated on unseen samples from January 2020 to December 2020. The performance of the system is promising under ideal conditions. The ensemble model achieved around 10% net P&L with -0.7% drawdown level based on 2020 trading data. Further work is required to calibrate trading costs and execution slippage to real market conditions. It is concluded 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.

Being able to consistently win in Forex trading continues to be a challenging endeavor, especially given the many factors that can affect price movements [1]. To be successful, traders must not only correctly predict market signals, but also perform risk management to mitigate their losses in case the market moves against them [2]. As such, there has been a growing interest in developing automated system-driven solutions to assist marketers in making informed decisions about the course of action to take given the circumstances [3]. However, these solutions tend to be rule-based or require subject matter expert (SME) input to develop the knowledge database for the system [4]. This approach will negatively affect the performance of the system in the long term, given the dynamic nature of the market, as well as making it cumbersome to update [5].

Recently, newer innovations have introduced more intelligent 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 available nowadays, it has been a game changer in the field of Forex trading with its fast-paced automated trading as it requires little human intervention and provides accurate and timely analysis. execution of trades [8].

Combining Forex Trading And Mining For Easy Money In Gatineau, Canada

This study proposes a complete end-to-end system solution, designed as AlgoML, that includes both trading decisions and a risk and money 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 set by the integrated risk and money management strategy. The system incorporates several SOTA reinforcement learning, supervised learning and conventional optimized strategies in a collective ensemble model to obtain the predicted market signal. The ensemble model aggregates the forecast signal at the output of each strategy to give 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 facilitate training and trial strategies to observe performance prior to actual deployment.

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The paper is structured as follows: Section 2 explores related works on forecasting-based models for the Forex market. Section 3 presents the high-level architecture of the system and its individual modules. Section 4 elaborates on the ML model models used in the system. Section 5 gives the results on the system performance.

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

With the advancement of machine learning, most research works have focused on using machine learning techniques to develop prediction models. One such area is the use of supervised machine learning models. Kamruzzaman etc. investigated artificial neural network (ANN)-based forecasting modeling of foreign exchange rates and made a comparison with the more popular ARIMA model. It was found that the ANN model outperformed the ARIMA model [12]. Thu et al. implemented a support vector machine (SVM) model with actual Forex transactions, and described the advantages of using SVM compared to transactions made without using SVM [13]. Decision Trees (DT) 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 WAIT). There is also research work using an ensemble model instead of relying on individual models for Forex forecasting. Nti et al. built 25 different pooled regressors and classifiers using DT, SVM and NN. They evaluated their pooled models on data from different stock markets and showed that ensemble clustering and mixing techniques provide higher forecast accuracy (90-100%) and (85.7-100%), respectively, compared to that of collection (53-97.78%). and encouragement (52.7–96.32%). The root mean square error (RMSE) recorded by clustering (0.0001–0.001) and mixing (0.002–0.01) was also lower than that of clustering (0.01–0.11) and growth (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 short-term memory (LSTM) and convolutional neural networks (CNN). Qi etc. performed a comparative study of several deep learning models, which included long short-term memory (LSTM), bidirectional short-term memory (BiLSTM), and closed recurrent unit (GRU) against a basic simple neural network model recurrent (RNN) [16. ]. They concluded that their LSTM and GRU models outperformed the baseline 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 works have attempted a hybrid approach by combining multiple deep learning models together. Islam etc. introduced the use of a hybrid GRU-LSTM model. They have tested their proposed model on 10-minute and 30-minute time frames and evaluated the performance based on MSE, RMSE, MAE and R.

Point. They reported that the hybrid model outperforms independent LSTM and GRU

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