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Forex Trading And Systemic Risk: Las Vegas Attorney Guidance
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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 Harncil † 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 have relied on rule-based expert systems, which are a set of complex if/then rules that must be manually updated to reflect changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading, as it can directly learn market patterns and behaviors from historical trading data and translate this into trading decisions. In this paper, a complete end-to-end system is proposed for automated low-frequency quantitative trading in the foreign exchange (Forex) markets. The system uses state-of-the-art (SOTA) machine learning strategies that are combined under an ensemble model to generate market signals for trading. Genetic Algorithm (GA) is used to optimize profit maximizing strategies. The system also includes a money management strategy to reduce risk and a back-testing framework to evaluate system performance. The models were trained on EUR-USD forex data from January 2006 to December 2019 and then evaluated on unseen samples from January 2020 to December 2020. System performance is promising under ideal conditions. The ensemble model achieved approximately 10% net P&L with a decline level of −0.7% based on 2020 trading data. Further work is needed to calibrate trading costs and performance slippage under real market conditions. It is concluded that with the increase in market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to the changing market environment will further increase.
Consistently making profits in forex trading continues to be a challenge, especially given the many factors that affect price movements [1]. To be successful, traders must not only correctly predict market signals, but also perform risk management to mitigate losses in case the market moves against them [2]. As such, there has been increased interest in developing automated system-driven solutions to help traders make informed decisions about the course of action they should take given the circumstances [3]. However, these solutions are typically rule-based or require input from subject matter experts (SMEs) to build a knowledge database for the system [4]. This approach will negatively affect the performance of the system in the long run, given the dynamic nature of the market, as well as making it difficult to update [5].
Recently, new innovations have introduced more intelligent approaches using advanced technologies such as ML algorithms [6]. Unlike the traditional rule-based approach, machine learning can analyze Forex data and extract useful information from it to help traders make decisions [7]. Given the explosion of data and how it is becoming more accessible today, 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, predictive and timely analysis. Trade performance [8].
This study offers a complete system solution designed as AlgoML that includes both trading solutions and risk and cash flow management strategy. The system can 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 an integrated risk and cash management strategy. The system combines multiple SOTAs with reinforcement learning, supervised learning, and optimized conventional strategies in a collective ensemble model to obtain a predictive market signal. The ensemble model aggregates the predictive signal output of each strategy to give an overall final forecast. A risk and cash management strategy in the system helps to reduce risk during the trade execution phase. Additionally, the system is designed to facilitate training and testing strategies to monitor performance prior to actual deployment.
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The paper is structured as follows: Section 2 surveys related work on forecasting-based models for the forex market. Section 3 presents the high-level architecture of the system and its individual modules. Section 4 discusses the ML model designs used in the system. Section 5 provides results on system performance.
Over the past decade, there have been a number of papers in the literature that have proposed various forecast-based models for trading in the forex market. One of the most popular time series forecasting models was Box and Jenkins’ Automatic Regressive Integrated Moving Average (ARIMA) [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 it is developed based on the assumption that the forecasted time series is linear and stationary [11].
As machine learning advances, most research efforts focus on using machine learning techniques to build predictive models. One such area is the use of supervised machine learning models. Kamruzaman et al. Investigated artificial neural network (ANN) forecasting modeling of foreign exchange rates and compared it with the most well-known ARIMA model. It was found that the ANN model outperformed the ARIMA model [12]. if etc. implemented a support vector machine (SVM) model with actual forex transactions and highlighted the advantages of using SVM compared to transactions performed without using SVM [13]. Decision trees (DT) have also been used in Forex forecasting models. Yushchuk et al. created a model capable of generating datasets from real FOREX market data [14]. The data is transformed into a decision table with three decision classes (buy, sell or wait). There are also research works using an ensemble model rather than relying on individual models for forex forecasting. Nti et al. Built 25 different ensemble regressors and classifiers using DTs, SVMs and NNs. They evaluated their ensemble models on different stock market data and showed that ensemble stacking and shuffling techniques offer higher prediction accuracy (90–100%) and (85.7–100%) respectively, compared to binning (53–97.78%) . and strengthening (52.7–96.32%). The root mean square error (RMSE) observed with stacking (0.0001–0.001) and mixing (0.002–0.01) was also lower than bagging (0.01–0.11) and boosting (0.01–0.443) [ 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 are long-short-term memory (LSTM) and convolutional neural networks (CNN). Qi et al. conducted 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) against a simple recurrent neural network (RNN) baseline model [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 studies have tried a hybrid approach by combining multiple deep learning models together. Islam and others. introduced the use of a hybrid GRU-LSTM model. They tested their proposed model on 10-min and 30-min timescales and evaluated the performance based on MSE, RMSE, MAE, and R .
Score. They reported that the hybrid model outperforms independent LSTM and GRU
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