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Common Forex Trading Challenges In Las Vegas: Legal Solutions
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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 are based on rule-based expert systems, which are a set of complex if/then rules that must be manually updated to changing market conditions. Machine learning (ML) is the natural next step in algorithmic trading because it can instantly learn patterns and buying behaviors from historical trading data and factor that into trading decisions. In this paper, a complete end-to-end system is proposed for automated quantitative low-frequency trading in the foreign exchange (Forex) markets. The system uses several state-of-the-art machine learning (SOTA) strategies combined into 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 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. The performance of the system is promising under ideal conditions. The ensemble model achieved approximately 10% net P&L with a draw level of −0.7% based on 2020 trading data. Further work is required to calibrate trading costs and execution slippage in real market conditions. It is concluded that with increased market volatility due to the global pandemic, the dynamic behind machine learning algorithms that can adapt to a changing market environment will become even stronger.
Being able to profit consistently in Forex trading still remains a challenge, especially given the numerous 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 should the market move against them [2]. Therefore, there has been a growing interest in developing automated system-based solutions to help traders make informed decisions about the course of action to take under the circumstances [3]. However, these solutions tend to be rule-based or require the input of subject matter experts (SMEs) to develop the knowledge base for the system [4]. This approach would negatively affect system performance in the long run given the dynamic nature of the market, as well as make updating cumbersome [5].
More 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 readily available these days, this has been a game changer in Forex trading with high-speed automated trading as it requires little human intervention and provides accurate analysis, forecasting and timely execution of transactions [8].
This study proposes an end-to-end integrated system solution, coined as AlgoML, that integrates both trading decisions and a risk and cash management strategy. The system is able to automatically extract data for a recognized Forex pair, predict the expected market signal for the next day and execute the most optimal trade decided by the integrated risk and cash management strategy. The system integrates several SOTA reinforcement learning, supervised learning and optimized conventional strategies into a collective ensemble model to obtain the predicted market signal. The ensemble model aggregates the predicted output signal of each strategy to give an overall final prediction. The risk and cash management strategy within the system helps mitigate risk during the execution phase of trades. Additionally, the system is designed to facilitate training and back-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 forecast-based models for the foreign exchange market. Section 3 presents the high-level architecture of the system and its individual modules. Section 4 elaborates on the ML model designs used in the system. Section 5 provides the results on the system performance.
Over the last decade, there have been a number of works in the literature proposing various forecast-based models for trading in the foreign exchange market. One of the most popular time series forecasting models was Box and Jenkins autoregressive integrated moving average (ARIMA) [3], which is still being investigated 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 of the research works have focused on using machine learning techniques to develop predictive models. One such area is the use of supervised machine learning models. Kamruzzaman et al. investigated forecasting modeling of foreign currency rates based on artificial neural networks (ANN) and compared it with the more well-known ARIMA model. It was found that the ANN model outperformed the ARIMA model [12]. Thu et al. applied a support vector machine (SVM) model to real Forex trades and described the advantages of using SVM compared to trades made without using SVM [13]. Decision trees (DT) have also been used in Forex forecasting 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 categories (BUY, SELL or WAIT). There are also research works that use an ensemble model instead of relying on individual models for Forex forecasting. Nti et al. built 25 different global regressors and classifiers using DT, SVM and NN. They evaluated their ensemble models on data from various stock markets and showed that ensemble stacking and ensemble mixing techniques offer higher prediction accuracy (90–100%) and (85.7–100%) respectively, compared to that of bagging (53–97 .78%) and enhancement (52.7–96.32%). The root mean square 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-short-term memory (LSTM) and convolutional neural networks (CNN). Qi et al. conducted a comparative study of several deep learning models, which included long-short-term memory (LSTM), bi-directional long-short-term memory (BiLSTM), and tiered recurrent unit (GRU) against a basic recurrent neural network (RNN) model [16. ]. They concluded that the LSTM and GRU models performed better than the basic RNN model for 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, achieving 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 et al. introduced the use of a hybrid GRU-LSTM model. They have tested their proposed model in 10 min and 30 min time frames and evaluated the performance based on MSE, RMSE, MAE and R
Score. They reported that the hybrid model outperforms standalone LSTM and GRU
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