It is recommended that traders use the indicator when trading the currency pair in the range. Thus, when the price approaches very close to the upper line, the pair of currency becomes overbought. On the other hand, the outer band widens automatically after the volatility increases and narrows down when there is a drop in volatility. Also, it is only ideal that you use the Bollinger Bands only after receiving confirmation from any other technical tool or indicator. Bollinger bands usually work well with trendlines, candlestick patterns, as well as other signals.

WHAT IS THE FOREX FORECAST POLL?

It indicates when the forex market is unable to move in a particular direction and thus needs rest or correction. Thus, MACD revolves around moving averages but also requires certain other formulas. In fact, it is a sort of technical indicator referred to as the oscillator. The oscillators are displayed in boxes under the pricing chart. Once an oscillator goes up, it must return back. Typically, this is what happens in the pricing chart too. However, while the MACD must return to 0 or even lower, the price drop will most likely be less or smaller.

Thus, this is exactly how the MACD predicts price turns. Traders who use the MACD indicator must look for dramatic fall and rise. As a result, they must sell once the histogram begins to decline after making a big advancement. On the contrary, they must buy after the histogram starts going up after a massive decline. Also, the crossovers that take place between the signal line and histogram are helpful in predicting precise market movements.

Thus, traders must buy after MACD histogram goes beyond the signal line. On the other hand, they must sell after the MACD and histogram drops under the forex signal line. Apart from this, zero line also serves as another source for confirmation.

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After the MACD surpasses the zero line, it displays the bears or bulls strength. The foreign exchange market, known as Forex or FX, is a financial market where currencies are bought and sold simultaneously. It is a decentralized market that operates 24 h a day, except for weekends, which makes it quite different from other financial markets. The characteristics of Forex show differences compared to other markets. These differences can bring advantages to Forex traders for more profitable trading opportunities. Two types of techniques are used to predict future values for typical financial time series—fundamental analysis and technical analysis—and both can be used for Forex.

The former uses macroeconomic factors while the latter uses historical data to forecast the future price or the direction of the price. The main decision in Forex involves forecasting the directional movement between two currencies. Traders can profit from transactions with correct directional prediction and lose with incorrect prediction. Therefore, identifying directional movement is the problem addressed in this study. In recent years, deep learning tools, such as long short-term memory LSTM , have become popular and have been found to be effective for many time-series forecasting problems.

In general, such problems focus on determining the future values of time-series data with high accuracy. However, in direction prediction problems, accuracy cannot be defined as simply the difference between actual and predicted values. Therefore, a novel rule-based decision layer needs to be added after obtaining predictions from LSTMs.

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We first separately investigated the effects of these data on directional movement. After that, we combined the results to significantly improve prediction accuracy. This can be interpreted as a fundamental analysis of price data. The other model is the technical LSTM model, which takes advantage of technical analysis. Technical analysis is based on technical indicators that are mathematical functions used to predict future price action. A popular deep learning tool called LSTM, which is frequently used to forecast values in time-series data, is adopted to predict direction in Forex data.

A novel hybrid model is proposed that combines two different models with smart decision rules to increase decision accuracy by eliminating transactions with weaker confidence. The proposed model and baseline models are tested using recent real data to demonstrate that the proposed hybrid model outperforms the others. The rest of this paper is organized as follows. Moreover, the preprocessing and postprocessing phases are also explained in detail.

Various forecasting methods have been considered in the finance domain, including machine learning approaches e. Unfortunately, there are not many survey papers on these methods. Cavalcante et al. The most recent of these, by Cavalcante et al. Although that study mainly introduced methods proposed for the stock market, it also discussed applications for foreign exchange markets. There has been a great deal of work on predicting future values in stock markets using various machine learning methods.

We discuss some of them below. Selvamuthu et al. Patel et al. In the first stage, support vector machine regression SVR was applied to these inputs, and the results were fed into an artificial neural network ANN. SVR and random forest RF models were used in the second stage. They reported that the fusion model significantly improved upon the standalone models. Guresen et al. Weng et al.

Market prices, technical indicators, financial news, Google Trends, and the number unique visitors to Wikipedia pages were used as inputs. They also investigated the effect of PCA on performance. Huang et al. They compared SVM with linear discriminant analysis, quadratic discriminant analysis, and Elman back-propagation neural networks. They also proposed a model that combined SVM with other classifiers. Their direction calculation was based on the first-order difference natural logarithmic transformation, and the directions were either increasing or decreasing.

430# Forex Prediction

Kara et al. Ten technical indicators were used as inputs for the model. They found that ANN, with an accuracy of In the first approach, they used 10 technical indicator values as inputs with different parameter settings for classifiers. Prediction accuracy fell within the range of 0. In the other approach, they represented same 10 technical indicator results as directions up and down , which were used as inputs for the classifiers. Although their experiments concerned short-term prediction, the direction period was not explicitly explained.

Ballings et al.

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They used different stock market domains in their experiments. According to the median area under curve AUC scores, random forest showed the best performance, followed by SVM, random forest, and kernel factory. Hu et al. Using Google Trends data in addition to the opening, high, low, and closing price, as well as trading volume, in their experiments, they obtained an Gui et al.

That study also compared the result for SVM with BPNN and case-based reasoning models; multiple technical indicators were used as inputs for the models. That study found that SVM outperformed the other models with an accuracy of GA was used to optimize the initial weights and bias of the model.

Two types of input sets were generated using several technical indicators of the daily price of the Nikkei index and fed into the model. They obtained accuracies Zhong and Enke used deep neural networks and ANNs to forecast the daily return direction of the stock market. They performed experiments on both untransformed and PCA-transformed data sets to validate the model.

In addition to classical machine learning methods, researchers have recently started to use deep learning methods to predict future stock market values. LSTM has emerged as a deep learning tool for application to time-series data, such as financial data. Zhang et al. By decomposing the hidden states of memory cells into multiple frequency components, they could learn the trading patterns of those frequencies.

They used state-frequency components to predict future price values through nonlinear regression. They used stock prices from several sectors and performed experiments to make forecasts for 1, 3, and 5 days. They obtained errors of 5. Fulfillment et al. He aimed to predict the next 3 h using hourly historical stock data.

The accuracy results ranged from That study also built a stock trading simulator to test the model on real-world stock trading activity. With that simulator, he managed to make profit in all six stock domains with an average of 6. Nelson et al. They used technical indicators i. They compared their model with a baseline consisting of multilayer perceptron, random forest, and pseudo-random models.

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The accuracy of LSTM for different stocks ranged from 53 to They concluded that LSTM performed significantly better than the baseline models, according to the Kruskal—Wallis test. They investigated many different aspects of the stock market and found that LSTM was very successful for predicting future prices for that type of time-series data. They also compared LSTM with more traditional machine learning tools to show its superior performance. Similarly, Di Persio and Honchar applied LSTM and two other traditional neural network based machine learning tools to future price prediction.

They also analyzed ensemble-based solutions by combining results obtained using different tools.