Foreign exchange currency rate prediction using a GRU-LSTM hybrid network

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Foreign exchange currency rate prediction using a GRU-LSTM hybrid network

The rationale behind this approach is based on the idea that a strong economic environment and potentially high growth are more likely to attract investments from foreign investors. And, in order to purchase investments in the desired country, an investor would have to purchase the country's currency—creating increased demand that should cause the currency to appreciate. The core belief behind fundamental analysis is that it can identify a currency that is mispriced and will eventually correct itself. This is part of the reason why fundamental analysis is generally better at predicting longer-term price movements, although it does have its uses for short-term strategies. Evans, “A hybrid artificial neural network-gjr modeling approach to forecasting currency exchange rate volatility,” Neurocomputing, vol.

Again, the case of 200 iterations shows huge differences from the other cases, generating less than half the number of the lowest number of transactions generated by the others. The data set was created with values from the period January 2013–January 2018. This 5-year period contains 1234 data points in which the markets were open.

predicting forex

A transaction is successful and the traders profit if the prediction of the direction is correct. Bid price is the price at which the trader can sell the base currency. Ask price is the price at which the trader can buy the base currency. A lower spread means the trader can profit from small price changes.

Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

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  • Methodologically, we employ the Foreign Exchange Volatility Index as a measure of FX volatility.
  • After the preprocessing phase, the ME_LSTM model was trained using all of these macroeconomic factors together with the closing values of the EUR/USD pair.
  • Simply put, when you rely on sentiment analysis, you check who is selling and who is buying in the market, with the emphasis on who.

Testing is also performed using subperiod analysis to investigate whether data deviations and outliers affect model training. Such subperiod analysis has been commonly implemented in previous studies (Sharma et al. , García and Kristjanpoller , Ramos-Pérez et al. , and Choi and Hong ). Specifically, we split the entire sample period into three subperiods called Period 1 , Period 2 , and Period 3 .


This indicator can be used to highlight a new trend or warn against extreme conditions. Moreover, CCI identifies overbought and oversold conditions (Özorhan 2017). The memory cell of the initial LSTM structure consists of an input gate and an output gate. While the input gate decides which information should be kept or updated in the memory cell, the output gate controls which information should be output. This standard LSTM was extended with the introduction of a new feature called the forget gate (Gers et al. 2000).

predicting forex

This is a type of conservative approach to trading; it reduces the number of trades and favors only high-accuracy predictions. After the preprocessing phase, the ME_LSTM model was trained using all of these macroeconomic factors together with the closing values of the EUR/USD pair. ganna chart Interest and inflation rates are two fundamental indicators of the strength of an economy. In the case of low interest rates, individuals tend to buy investment tools that strengthen the economy. If supply does not meet demand, inflation occurs, and interest rates also increase .

High-Frequency News Sentiment and Its Application to Forex Market Prediction

MA can not only identify the trend direction but also determine potential support and resistance levels . 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. Both macroeconomic and technical indicators are used as features to make predictions.

The initial LSTM structure solves this problem by introducing the constant error carousel . In this way, the architecture ensures constant error flow between the self-connected units . Chart analysis and price analysis using technical indicators are the two main approaches in technical analysis. While the former is used to detect patterns in price charts, the latter is used to predict future price actions (Ozorhan et al. 2017). Fundamental analysis and technical analysis are the two techniques commonly used for predicting future prices in Forex. While the first is based on economic factors, the latter is related to price actions .

The main motivation for our hybrid model solution was to avoid the drawbacks of the two different LSTMs (i.e., macroeconomic and technical LSTMs). When the ME_LSTM and TI_LSTM were executed separately using the features of their corresponding data sets (i.e., macroeconomic features and technical indicator features), they generated too many transactions. Some of these transactions were generated with not very good signals and thus had lower accuracy results.

“Discussion” and “Conclusion” sections discuss the experimental results and provide insight for future research directions. Forex forecasting software is an analytical toolkit used to help currency traders with foreign exchange trading analysis through technical charts and indicators. Based on the empirical findings in Section 4, some implications can be observed.

predicting forex

Therefore, we expect that our approach will be suitable for FX volatility prediction because it combines the merits of these two models. Methodologically, we employ the Foreign Exchange Volatility Index as a measure of FX volatility. In particular, the three major FXVIX indices from 2010 to 2019 are considered, and we predict future prices using the proposed hybrid model.

With that simulator, he managed to make profit in all six stock domains with an average of 6.89%. This is also a particularly good model considering that the main variables that weigh on one currency differ from those that weigh on another, and that the relationship between currency pairs also varies. For example, a trader trying the physician philosopher's guide to personal finance to calculate where the USD/CAD exchange rate will head over time might consider the likes of the interest rate differential between the two countries, or their GDP or income growth rates. Before deciding what approach to take forex investors need to define the basics of their strategy, including what currency pairs to trade.

Proposed model: hybrid LSTM model

In fact, most of them aren't available in a higher resolution than monthly. Interpretation of market sentiment information is done based on specific Forex forecasting methodology. In general, it is believed that large institutional speculators from the CoT report are more often correct in their anticipations compared to the positions of retail traders.

Based on fluctuations caused by the Brexit movement, the data were divided into subsets from 2010 to 2015, 2016, and 2017 to 2019 based on instabilities in 2016. The first period represents the period of recovery following the subprime mortgage crisis and contains the most data . As shown in Figure 1, the variability of the entire section appears to be large. The standard deviations of BPVIX, JYVIX, and EUVIX in this section are the largest among all periods, excluding BPVIX in 2016.

They found that ANN, with an accuracy of 75.74%, performed significantly better than SVM, which had an accuracy of 71.52%. Predicting current and future market trends using existing data and facts is called forecasting. In Forex , which is the largest financial market in the world, with a daily volume of more than $7.5 trillion, forecasting is the main tool for traders to open and close positions in currency pairs. Another common method used to forecast exchange rates involves gathering factors that might affect currency movements and creating a model that relates these variables to the exchange rate.

Significant sentiment data, based on a representative sample of 25 to 50 leading trading advisors for 5 years. Do not follow a single guru but rather a balanced group of well chosen experts. The advantages and disadvantages of floating exchange rate system Forex Forecast Poll offers a condensed version of several expert's opinions. Only outlooks are considered that have been committed to publication and therefore have an influence on the market.

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