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The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex tr...
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... results can be seen in Table 5. The bold numbers in the table show the comparison between the transactions use or not use SVM. ...Context 2
... are big gap results of test set compared to the results of training set (see in table 3, about 57% and 83.8 % respectively). The advantage use of SVM in the transaction Robotics can be seen in Table 5. The indicators' results show that the net profit of using SVM in add-on robot is higher than the one without using SVM. ...Similar publications
The main purpose of this article is to investigate a speculative trading system with a constant magnitude of return rate. We consider speculative operations related to the exchange rate given as the quotient of the base exchange medium by the quoted currency. An exchange medium is understood as any currency or any precious metal. The unit return is...
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... Considering the difference between the stock market and the currency market and the experience of professional traders shown in [11], [12] and [13] with technical methods, the following parameters are taken to formulate the problem. ...
... However, despite the free-form likelihood being a powerful tool, difficulties are encountered when the probability model is unknown. A typical example of this is the modeling of cash flows for investing in financial instruments [Thu andXuan, 2018, Sidehabi et al., 2016]. The demand for machine learning frameworks for algorithmic trading has been rapidly increasing in recent years. ...
Gaussian process regression can flexibly represent the posterior distribution of an interest parameter providing that information on the likelihood is sufficient. However, in some cases, we have little knowledge regarding the probability model. For example, when investing in a financial instrument, the probability model of cash flow is generally unknown. In this paper, we propose a novel framework called the likelihood-free Gaussian process (LFGP), which allows representation of the posterior distributions of interest parameters for scalable problems without directly setting their likelihood functions. The LFGP establishes clusters in which the probability distributions of the targets can be considered identical, and it approximates the likelihood of the interest parameter in each cluster to a Gaussian using the asymptotic normality of the maximum likelihood estimator. We expect that the proposed framework will contribute significantly to likelihood-free modeling, especially from the perspective of fewer assumptions for the probability model and low computational costs for scalable problems.
Background: When you make a forex transaction, you sell one currency and buy
another. If the currency you buy increases against the currency you sell, you profit, and
you do this through a broker as a retail trader on the internet using a platform known
as meta trader. Only 2% of retail traders can successfully predict currency movement in
the forex market, making it one of the most challenging tasks. Machine learning and its
derivatives or hybrid models are becoming increasingly popular in market forecasting,
which is a rapidly developing field.
Objective: While the research community has looked into the methodologies used by
researchers to forecast the forex market, there is still a need to look into how machine
learning and artificial intelligence approaches have been used to predict the forex
market and whether there are any areas that can be improved to allow for better
predictions. Our objective is to give an overview of machine learning models and their
application in the FX market.
Method: This study provides a Systematic Literature Review (SLR) of machine learning
algorithms for FX market forecasting. Our research looks at publications that were
published between 2010 and 2021. A total of 60 papers are taken into consideration.
We looked at them from two angles: I the design of the evaluation techniques, and (ii)
a meta-analysis of the performance of machine learning models utilizing evaluation
metrics thus far.
Results: The results of the analysis suggest that the most commonly utilized assessment
metrics are MAE, RMSE, MAPE, and MSE, with EURUSD being the most traded
pair on the planet. LSTM and Artificial Neural Network are the most commonly used
machine learning algorithms for FX market prediction. The findings also point to many
unresolved concerns and difficulties that the scientific community should address in
the future.
Conclusion: Based on our findings, we believe that machine learning approaches in
the area of currency prediction still have room for development. Researchers interested
in creating more advanced strategies might use the open concerns raised in this work
as input.
