Forex Mining: Strategies For Extracting Profit In Dallas

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Forex Mining: Strategies For Extracting Profit In Dallas

Forex Mining: Strategies For Extracting Profit In Dallas

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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 Harry † 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 a standard in the financial market. Traditionally, most algorithms rely on rule-based expert systems, which are complex sets of rules that must be manually updated based on 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 influence trading decisions. This article offers a complete end-to-end system for automated low-frequency quantitative trading in the currency (Forex) markets. The system uses several state-of-the-art (SOTA) machine learning strategies combined under an ensemble model to derive a market signal for trading. Genetic Algorithm (GA) is used to optimize profit maximization strategies. The system also includes a money management strategy to reduce risk and a retest system to evaluate system performance. The models were trained on Forex data from January 2006 to December 2019 for the EUR–USD pair and then evaluated on unseen samples from January 2020 to December 2020. System performance is promising under ideal conditions. The ensemble model achieved net P&L of approximately 10% with a drawdown rate of −0.7% based on 2020 sales data. Additional work is required to calibrate trading costs and execution drift under actual market conditions. It concluded that with increased market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to changing market environments will only intensify.

The ability to consistently earn a profit in Forex trading remains a difficult task, 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 minimize their losses when the market moves against them [2]. Thus, there is a growing interest in developing automated system-based solutions to help traders make informed decisions about the actions they should take given the situation [3]. However, these solutions are usually rule-based or require input from subject matter experts (SMEs) to develop the database for the system [4]. This approach, taking into account the dynamic nature of the market, has a negative impact on the performance of the system in the long term, as well as makes it difficult to update it [5].

Recently, new innovations have introduced more intelligent approaches by using advanced technologies such as ML algorithms [6]. Unlike the traditional rule-based approach, machine learning has the ability to analyze Forex data and extract useful information from it to help traders make decisions [7]. Given the explosion of data and how readily available it is today, it has changed the game in the Forex trading industry with its fast automated trading as it requires no human intervention and provides accurate analysis, forecasting and forecasting. provides data at time z. making trades [8].

Forex Mining: Strategies For Extracting Profit In Dallas

This study offers a complete end-to-end system solution, designed as AlgoML, which includes trading decisions as well as risk and money management strategies. The system is capable of automatically extracting data for an identified Forex pair, predicting the expected market signal for the next day, and executing the most optimal trade decided by an integrated risk and cash management strategy. The system incorporates multiple SOTA reinforcement learning, supervised learning, and optimized traditional strategies into a collective ensemble model to obtain a forecasted market signal. The ensemble model aggregates the output predicted signal of each strategy to give an overall final prediction. A risk and money management strategy in the system helps reduce risk during the trade execution phase. In addition, the system is designed in such a way that it makes it easy to teach and back test strategies to monitor performance before actually launching.

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The paper is organized as follows: Section 2 reviews the 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 details the ML model designs used in the system. Section 5 gives results on the performance of the system.

Over the past decade, there have been a number of works in the literature proposing different forecast-based models for trading in the Forex market. One of the most popular time series forecasting 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 general univariate model, which is developed based on the assumption that the forecasted time series is linear and stationary [11].

With the development of machine learning, most of the research efforts focus on using machine learning techniques to develop prediction models. One such area is the use of supervised machine learning models. Kamruzzaman and others. studied predictive modeling of foreign exchange rates based on artificial neural networks (ANN) and made a comparison with the most popular ARIMA model. The ANN model was found to be superior to the ARIMA model [12]. Thu et al. implemented a support vector machine (SVM) model with real Forex transactions and identified the advantages of using SVM over transactions without using SVM [13]. Decision trees (DT) have also seen some use in Forex prediction models. Juszczuk et al. created a model that can generate datasets from real-world FOREX market data [14]. The data is converted into a decision table with three decision classes (SELL, SELL, or WAIT). There are also research studies using an ensemble model rather than relying on separate individual models to predict forex. Nti and others. Built 25 different ensemble regressors and classifiers using DT, SVM and NN. They evaluated their ensemble model on data from various stock exchanges and found that the stacking and mixing ensemble techniques achieved (90–100%) and (85.7–100%) coverage compared to (53–97.78%) coverage, respectively. has shown to offer high prediction accuracy. and increasing (52.7–96.32%). The root mean square error (RMSE) recorded by stacking (0.0001–0.001) and unmixing (0.002–0.01) for both coverage (0.01–0.11) and amplification (0.01–0.443 ) was lower than [15].

In addition to supervised machine learning models, another area of ​​machine learning techniques 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 and others. A comparative study of several deep learning models, including long short-term memory (LSTM), bilateral long short-term memory (BiLSTM), and gated recurrent unit (GRU) versus a simple recurrent neural network (RNN) baseline model conducted [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 model achieved a value of 0.006 × 10 from the model proposed by Zeng and Khushi [17] in terms of RMSE.

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Some research works have tried a hybrid approach, combining several deep learning models. Islam and others. introduced the use of a hybrid GRU-LSTM model. They tested their proposed model at 10 min and 30 min time intervals and evaluated the performance based on MSE, RMSE, MAE and R.

Score. They report that the hybrid model outperforms independent LSTM and GRU

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