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

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

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

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By Leonard Kin Yung Loh Leonard Kin Yung Loh Skillet Preprints.org Google Scholar † , Hee Kheng Kueh Hee Kheng Kueh Skillet Preprints.org Google Scholar † , Nirav Janak Parikh Skillet Preprints.org Google Scholar † , Harry Chan Harry Chan Skillet Preprints.org Google Scholar † , Nicholas Joon Hui Ho Nicholas Joon Hui Ho Skillet Preprints.org Google Scholar and Matthew Chin Heng Chua Matthew Chin Heng Chua Skillet 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 norm in the financial markets. Traditionally, most algorithms have relied on rule-based expert systems which are a set of complex if/then rules that need to be manually updated according to 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 incorporate this into trading decisions. In this paper, a complete end-to-end system for automated low-frequency quantitative trading in foreign exchange (forex) markets is proposed. The system uses several state-of-the-art (SOTA) machine learning strategies that are combined under an ensemble model to derive market signals for trading. Genetic algorithms (GAs) are used to optimize strategies to maximize profits. The system also includes a money management strategy to minimize risk and a back-testing framework to evaluate system performance. The models were trained on EUR-USD pair forex data from January 2006 to December 2019, and subsequently evaluated on unseen samples from January 2020 to December 2020. The performance of the system under ideal conditions is promising. The Ensemble model achieved a net P&L of around 10% with a -0.7% drawdown level based on 2020 trading data. Further work is needed to verify the reduction in trading costs and execution in real market conditions. It is concluded that with increasing market volatility due to the global pandemic, the momentum behind machine learning algorithms that can adapt to the changing market environment will become even stronger.

Being able to make consistent profits in forex trading remains a challenging endeavor, especially given the many factors that can affect price movements [1]. To be successful, traders must not only anticipate market signals correctly, but also manage risk to minimize their losses in case the market moves against them [2]. Thus, there is increasing interest in developing automated systems-driven solutions to help traders make informed decisions about the course of action according to circumstances [3]. However, these solutions tend to be rule-based or require the input of subject matter experts (SMEs) to develop a knowledge database for the system [4]. This approach will negatively impact system performance in the long run given the dynamic nature of the market, as well as making it cumbersome to update [5].

Recently, new innovations have introduced more intelligent approaches through the use of advanced technologies, such as ML algorithms [6]. Unlike traditional rule-based approaches, machine learning is capable of analyzing forex data and extracting useful information from it to help traders make decisions [7]. Given the explosion of data and how it is becoming more readily available nowadays, it has been a game-changer in the field of forex trading with its high-speed automated trading as it requires less human intervention. And it provides accurate analysis, forecast and timely. Execution of trades [8].

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

This study proposes a complete end-to-end system solution, modeled as AlgoML, which includes both risk and cash management strategies along with trading decisions. The system is capable of automatically extracting data for the identified forex pair, forecasting the expected market signal for the next day and executing the most optimal trade as decided by the integrated risk and cash management strategy. The system incorporates several SOTA reinforcement learning, supervised learning and traditional strategies optimized in an ensemble ensemble model to obtain predictive market signals. The composite model aggregates the output forecast signals of each strategy to give the overall final forecast. The risk and cash management strategy within the system helps in mitigating risk during the trade execution phase. Furthermore, the system is designed in such a way that it becomes easy to train and backtest strategies to observe performance before actual deployment.

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The paper is structured as follows: Section 2 explores related works on prediction-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 design used in the system. Section 5 provides results on the performance of the system.

Over the past decade, there have been many works 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 forecasted time series is linear and stationary [11].

With the advancement of machine learning, most of the research work has focused on the use of machine learning techniques to develop predictive models. One such area is the use of supervised machine learning models. Kamaruzzaman et al. Artificial Neural Network (ANN) based prediction modeling of foreign exchange rates is examined and compared with the most famous ARIMA models. It was found that the ANN model performed better than the ARIMA model [12]. Thu et al. applied a support vector machine (SVM) model with real forex transactions, and outlined the advantages of using SVM compared to transactions conducted without the use of SVM [13]. Decision trees (DTs) have also seen some use in foreign exchange 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 that uses an ensemble model instead of relying on a single individual model for Forex prediction. Nti et al. Built 25 different ensemble regressors and classifiers using DT, SVM, and NN. They evaluated their ensemble model on data from different stock exchanges and showed that stacking and blending ensemble techniques provided higher prediction accuracy of (90–100%) and (85.7–100%) respectively compared to bagging (53–97.78%). Does and boost (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].

In addition to supervised machine learning models, another area of ​​machine learning techniques that is employed for forex prediction 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. A comparative study of several deep learning models was conducted, including long short-term memory (LSTM), bidirectional long short-term memory (BILSTM), and gated recurrent unit (GRU) against a simple recurrent neural network (RNN) baseline model. , They concluded that their LSTM and GRU models outperformed the baseline RNN models for the EUR/GBP, AUD/USD, and CAD/CHF currency pairs. They also reported that their models outperformed the models proposed by Zeng and Khushi [17] in terms of RMSE, and obtained a value of 0.006 × 10.

Foreign Exchange Options

Some research work has attempted a hybrid approach by combining multiple deep learning models. Islam et al. The use of a hybrid GRU-LSTM model was introduced. They have tested their proposed model on 10-minute and 30-minute timeframes and evaluated the performance based on MSE, RMSE, MAE and R.

Score. He reported that the hybrid model outperforms standalone LSTM and GRU.

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