Algorithmic Trading: Consistent Profit Strategies For Canadian Forex

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Algorithmic Trading: Consistent Profit Strategies For Canadian Forex

Algorithmic Trading: Consistent Profit Strategies For Canadian Forex

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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 † lit , Harry Chan Harry Chan lit , Harry Chan 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 : 30 January 2022 / Revised : 15 March 2022 / Accepted : 23 March 2022 / Published : 27 March 2022

Machine Learning In Algorithmic Trading

Algorithm trading has become the standard in the financial market. Traditionally, most algorithms have relied on specialized rule-based systems that are complex sets of if/then rules that must be manually updated to changing market conditions. Machine learning (ML) is the next natural step in algorithmic trading as it can learn market patterns and behaviors directly from historical trading data and factor this into trading decisions. In this paper, a complete end-to-end system is proposed for low-frequency automated quantitative trading in the foreign exchange (Forex) markets. The system uses a number of state-of-the-art machine learning (SOTA) strategies that are combined under an ensemble model to find the market signal for trading. Genetic Algorithm (GA) is used to optimize the strategies to maximize profits. The system also includes a cash management strategy to mitigate risk and a back-testing framework to evaluate system performance. The models were trained on Forex EUR-USD pair data from January 2006 to December 2019, and subsequently evaluated on unseen samples from January 2020 to December 2020. System performance is guaranteed under ideal conditions. The ensemble model achieved around 10% net P&L with a −0.7% drawdown level based on 2020 trade data. More work is needed to calibrate trade costs & execution slippage in real market conditions. It is concluded that due to the increased market volatility due to the global pandemic, the momentum behind the machine learning algorithms that can adapt to the changing market environment will increase.

Being able to profit consistently in Forex trading is always a challenging endeavor, especially given the many factors that can influence price movements [1]. To be successful, traders must not only predict the market signals correctly, but also perform risk management to mitigate their losses in the event that the market moves against them [2]. Therefore, there is a growing interest in developing automated system-driven solutions to help traders make informed decisions about the way they should act given the circumstances [3]. However, these solutions are usually rule-based or require inputs from subject matter experts (SMEs) to develop the knowledge database for the system [4]. This approach would have a negative impact on the performance of the system in the long term given the dynamic nature of the market, and it would also be difficult to update [5].

Recently, newer innovations have introduced smarter approaches using 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 to help traders make a decision [7]. Due to the explosion of data and how it is becoming easier to obtain today, this is a game changer in the field of Forex trading with its fast speed automated trading since it requires little human intervention it and provides accurate, forecast and timely analysis. execution of trades [8].

Algorithmic Trading: Consistent Profit Strategies For Canadian Forex

This study proposes a complete end-to-end system solution, conceived as AlgoML, that incorporates trading decisions as well as risk and cash management strategy. The system is able to automatically extract data for a known Forex pair, predict the expected market signal for the next day and execute the best trade determined by the integrated risk and money management strategy. The system incorporates several SOTA reinforcement learning, supervised learning, and conventional optimization strategies into a collective ensemble model to obtain the predicted market signal. The ensemble model collects the predicted signal output of each strategy to give a final overall prediction. The risk and money management strategy within the system helps mitigate risk during the trade execution phase. In addition, the system is designed in such a way that it will be easier to train and reverse test strategies to observe the performance before actual deployment.

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The structure of the paper is as follows: Section 2 explores related works on prediction-based models for the Forex market. Section 3 presents the high-level architecture of the system and the individual modules. Section 4 details the ML model designs used in the system. Section 5 provides the results on the system performance.

Over the past decade, several works in the literature have proposed various prediction-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 being 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 forecast is linear and stationary [11].

With the advancement of machine learning, most of the research works have focused on the use of machine learning techniques to develop the predictive models. One such area is the use of supervised machine learning models. Kamruzzaman et al. investigated predictive modeling based on artificial neural networks (ANNs) on foreign exchange rates and compared it with the better 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 actual Forex transactions, and outlined 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 containing three classes of decisions (BUY, SELL or WAIT). There are also research works that use an ensemble model rather than relying on individual individual models for Forex forecasting. Nti et al. built 25 different ensemble regressors and classifiers using DTs, SBMs and NNs. They evaluated their ensemble models over data from different stock exchanges and showed that stacking and ensemble blending techniques give higher prediction accuracy of (90-100%) and (85.7-100%) respectively, compared to bagging (53-97.78%). and reinforcement (52.7–96.32%). The root mean square error (RMSE) recorded by stacking (0.0001–0.001) and blending (0.002–0.01) was also lower than that of bagging (0.01–0.11) and boosting (0.01–0.443) [15].

Apart from 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 (CNNs). Qi et al. conducted a comparative study of several deep learning models, including long-short-term memory (LSTM), bi-directional 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 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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Several research works have attempted a hybrid approach by combining different deep learning models. Islam et al. 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.

Score. They reported that the hybrid model outperforms the single LSTM and the GRU

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