Chipre Forex Brokers - Bienvenidos a nuestra extensa lista de corredores de Forex regulados por Chipre. Hay ciertos riesgos asociados con el comercio de divisas, y si tiene alguna duda, debe tomar el asesoramiento de un asesor financiero independiente. Los errores y las omisiones pueden ocurrir en declaraciones hechas por, o opiniones expresadas por, autores individuales, y usted debe observar que FXHQ no y no ha verificado la exactitud o de otra manera de tales opiniones o declaraciones. Estoy realmente impresionado de sus habilidades educativas, ya que tienen sound mind investing promotion code manera eficaz *pelaburan forex 2012 ford* impartir conocimientos. Lee mas. Sin embargo, siempre quise ser parte de un equipo de la divisa con una buena estrategia para aumentar equidad. Lee mas ''.

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. 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. In addition to traditional exchanges, many studies have also investigated Forex. Some studies of Forex based on traditional machine learning tools are discussed below. Galeshchuk and Mukherjee investigated the performance of a convolutional neural network CNN for predicting the direction of change in Forex.

That work used basic technical indicators as inputs. Ghazali et al. To predict exchange rates, Majhi et al. They demonstrated that those new networks were more robust and had lower computational costs compared to an MLP trained with back-propagation.

In what is commonly called a mark-to-market approach, market prices are increasingly being used to calibrate models to quantify risk in several sectors. The net present value of a financial institution, for example, is an important input for estimating both bankruptcy risk e.

In such a context, stock price crashes not only dramatically damage the capital market but also have medium-term adverse effects on the financial sector as a whole Wen et al. Credit risk is a major factor in financial shocks. Therefore, a realistic appraisal of solvency needs to be an objective for banks. At the level of the individual borrower, credit scoring is a field in which machine learning methods have been used for a long time e. In one recent work, Shen et al. They were able to show that deep learning approaches outperformed traditional methods.

Even though LSTM is starting to be used in financial markets, using it in Forex for direction forecasting between two currencies, as proposed in the present work, is a novel approach. Forex has characteristics that are quite different from those of other financial markets Archer ; Ozorhan et al.

To explain Forex, we start by describing how a trade is made. If the ratio of the currency pair increases and the trader goes long, or the currency pair ratio decreases and the trader goes short, the trader will profit from that transaction when it is closed.

Otherwise, the trader not profit. When the position closes i. When the position closes with a ratio of 1. Furthermore, these calculations are based on no leverage. If the trader uses a leverage value such as 10, both the loss and the gain are multiplied by Here, we explain only the most important ones. Base currency, which is also called the transaction currency, is the first currency in the currency pair while quote currency is the second one in the pair.

Being long or going long means buying the base currency or selling the quote currency in the currency pair. Being short or going short means selling the base currency or buying the quote currency in the currency pair.

In general, pip corresponds to the fourth decimal point i. Pipette is the fractional pip, which corresponds to the fifth decimal point i. In other words, 1 pip equals 10 pipettes. Leverage corresponds to the use of borrowed money when making transactions. A leverage of indicates that if one opens a position with a volume of 1, the actual transaction volume will be After using leverage, one can either gain or lose times the amount of that volume. Margin refers to money borrowed by a trader that is supplied by a broker to make investments using leverage.

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. Spread is the difference between the ask and bid prices. A lower spread means the trader can profit from small price changes. Spread value is dependent on market volatility and liquidity. Stop loss is an order to sell a currency when it reaches a specified price.

This order is used to prevent larger losses for the trader. Take profit is an order by the trader to close the open position transaction for a gain when the price reaches a predefined value. This order guarantees profit for the trader without having to worry about changes in the market price.

Market order is an order that is performed instantly at the current price. Swap is a simultaneous buy and sell action for the currency at the same amount at a forward exchange rate. This protects traders from fluctuations in the interest rates of the base and quote currencies. If the base currency has a higher interest rate and the quote currency has a lower interest rate, then a positive swap will occur; in the reverse case, a negative swap will occur.

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 Archer Fundamental analysis focuses on the economic, social, and political factors that can cause prices to move higher, move lower, or stay the same Archer ; Murphy These factors are also called macroeconomic factors.

Technical analysis uses only the price to predict future price movements Kritzer and Service This approach studies the effect of price movement. Technical analysis mainly uses open, high, low, close, and volume data to predict market direction or generate sell and buy signals Archer It is based on the following three assumptions Murphy :. 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. LSTM is a recurrent neural network architecture that was designed to overcome the vanishing gradient problem found in conventional recurrent neural networks RNNs Biehl Errors between layers tend to vanish or blow up, which causes oscillating weights or unacceptably long convergence times. In this way, the architecture ensures constant error flow between the self-connected units Hochreiter and Schmidhuber 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.

The forget gate is responsible for resetting a memory state that contains outdated information. LSTM offers an effective and scalable model for learning problems that includes sequential data Greff et al. It has been used in many different fields, including handwriting recognition Graves et al. In the forward pass, the calculation moves forward by updating the weights Greff et al. The weights of LSTM can be categorized as follows:. The other main operation is back-propagation.

Calculation of the deltas is performed as follows:. Then, the calculation of the gradient of the weights is performed. The calculations are as follows:. Using Eqs. A technical indicator is a time series that is obtained from mathematical formula s applied to another time series, which is typically a price TIO These formulas generally use the close, open, high, low, and volume data.

Technical indicators can be applied to anything that can be traded in an open market e. They are empirical assistants that are widely used in practice to identify future price trends and measure volatility Ozorhan et al. By analyzing historical data, they can help forecast the future prices. According to their functionalities, technical indicators can be grouped into three categories: lagging, leading, and volatility. Lagging indicators, also referred to as trend indicators, follow the past price action.

Leading indicators, also known as momentum-based indicators, aim to predict future price trend directions and show rates of change in the price. Volatility-based indicators measure volatility levels in the price. BB is the most widely used volatility-based indicator. Moving average MA is a trend-following or lagging indicator that smooths prices by averaging them in a specified period. In this way, MA can help filter out noise. MA can not only identify the trend direction but also determine potential support and resistance levels TIO It is a trend-following indicator that uses the short and long term exponential moving averages of prices Appel MACD uses the short-term moving average to identify price changes quickly and the long-term moving average to emphasize trends Ozorhan et al.

Rate of change ROC is a momentum oscillator that defines the velocity of the price. This indicator measures the percentage of the direction by calculating the ratio between the current closing price and the closing price of the specified previous time Ozorhan et al. Momentum measures the amount of change in the price during a specified period Colby It is a leading indicator that either shows rises and falls in the price or remains stable when the current trend continues.

Momentum is calculated based on the differences in prices for a set time interval Murphy The relative strength index RSI is a momentum indicator developed by J. Welles Wilder in RSI is based on the ratio between the average gain and average loss, which is called the relative strength RS Ozorhan et al.

RSI is an oscillator, which means its values change between 0 and It determines overbought and oversold levels in the prices. Bollinger bands BB refers to a volatility-based indicator developed by John Bollinger in the s. It has three bands that provide relative definitions of high and low according to the base Bollinger While the middle band is the moving average in a specific period, the upper and lower bands are calculated by the standard deviations in the price, which are placed above and below the middle band.

The distance between the bands depends on the volatility of the price Bollinger ; Ozturk et al. CCI is based on the principle that current prices should be examined based on recent past prices, not those in the distant past, to avoid confusing present patterns Lambert This indicator can be used to highlight a new trend or warn against extreme conditions. 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.

In the opposite case, the economy becomes fragile. If supply does not meet demand, inflation occurs, and interest rates also increase IRD In such economies, the stock markets have strong relationships with their currencies. The data set was created with values from the period January —January This 5-year period contains data points in which the markets were open.

Table 1 presents explanations for each field in the data set. Monthly inflation rates were collected from the websites of central banks, and they were repeated for all days of the corresponding month to fill the fields in our daily records. The main structure of the hybrid model, as shown in Fig. These technical indicators are listed below:. Our proposed model does not combine the features of the two baseline LSTMs into a single model. The training phase was carried out with different numbers of iterations 50, , and Our data points were labeled based on a histogram analysis and the entropy approach.

At the end of these operations, we divided the data points into three classes by using a threshold value:. Otherwise, we treated the next data point as unaltered. This new class enabled us to eliminate some data points for generating risky trade orders. This helped us improve our results compared to the binary classification results. In addition to the decrease and increase classes, we needed to determine the threshold we could use to generate a third class—namely, a no-action class—corresponding to insignificant changes in the data.

Algorithm 1 was used to determine the upper bound of this threshold value. The aim was to prevent exploring all of the possible difference values and narrow the search space. We determined the count of each bin and sorted them in descending order. Then, the maximum difference value of the last bin added was used as the upper bound of the threshold value. As can be seen in Algorithm 1, it has two phases.

In the first phase, which simply corresponds to line 2, the whole data set is processed linearly to determine the distributions of the differences, using a simple histogram construction function. The second phase is depicted in detail, corresponding to the rest of the algorithm. The threshold value should be determined based on entropy. Entropy is related to the distribution of the data.

To get balanced distribution, we calculated the entropy of class distribution in an iterative way for each threshold value up until the maximum difference value. However, we precalculated the threshold of the upper bound value and used it instead of the maximum difference value.

Algorithm 2 shows the details of our approach. In Algorithm 2, to find the best threshold, potential threshold values are attempted with increments of 0. Dropping the maximum threshold value is thus very important in order to reduce the search space. Then, the entropy value for this distribution is calculated. At the end of the while loop, the distribution that gives the best entropy is determined, and that distribution is used to determine the increase, decrease, and no-change classes.

In our experiments, we observed that in most cases, the threshold upper bound approach significantly reduced the search space i. For example, in one case, the maximum difference value was 0. In this case, the optimum threshold value was found to be 0. The purpose of this processing is to determine the final class decision. If the predictions of the two models are different, we choose for the final decision the one whose prediction has higher probability.

This is a type of conservative approach to trading; it reduces the number of trades and favors only high-accuracy predictions. Measuring the accuracy of the decisions made by these models also requires a new approach. If that is the case, then the prediction is correct, and we treat this test case as the correct classification. We introduced a new performance metric to measure the success of our proposed method. We can interpret this metric such that it gives the ratio of the number of profitable transactions over the total number of transactions, defined using Table 2.

In the below formula, the following values are used:. After applying the labeling algorithm, we obtained a balanced distribution of the three classes over the data set. This algorithm calculates different threshold values for each period and forms different sets of class distributions. For predictions of different periods, the thresholds and corresponding number of data points explicitly via training and test sets in each class are calculated, as shown in Table 3.

This table shows that the class distributions of the training and test data have slightly different characteristics. While the class decrease has a higher ratio in the training set and a lower ratio in the test set, the class increase shows opposite behavior. This is because a split is made between the training and test sets without shuffling the data sets to preserve the order of the data points.

We used the first days of this data to train our models and the last days to test them. If one of these is predicted, a transaction is considered to be started on the test day ending on the day of the prediction 1, 3, or 5 days ahead. Otherwise, no transaction is started.

A transaction is successful and the traders profit if the prediction of the direction is correct. For time-series data, LSTM is typically used to forecast the value for the next time point. It can also forecast the values for further time points by replacing the output value with not the next time point value but the value for the chosen number of data points ahead. This way, during the test phase, the model predicts the value for that many time points ahead. However, as expected, the accuracy of the forecast usually diminishes as the distance becomes longer.

They defined it as an n-step prediction as follows:. They performed experiments for 1, 3, and 5 days ahead. In their experiments, the accuracy of the prediction decreased as n became larger. We also present the number of total transactions made on test data for each experiment. Accuracy results are obtained for transactions that are made.

For each experiment, we performed 50, , , and iterations in the training phases to properly compare different models. The execution times of the experiments were almost linear with the number of iterations. For our data set, using a typical high-end laptop MacBook Pro, 2. As seen in Table 4 , this model shows huge variance in the number of transactions. Additionally, the average predicted transaction number is For this LSTM model, the average predicted transaction number is The results for this model are shown in Table 6.

The average predicted transaction number is One major difference of this model is that it is for iterations. For this test case, the accuracy significantly increased, but the number of transactions dropped even more significantly.

In some experiments, the number of transactions is quite low. Basically, the total number of decrease and increase predictions are in the range of [8, ], with an overall average of When we analyze the results for one-day-ahead predictions, we observe that although the baseline models made more transactions Table 8 presents the results of these experiments. One significant observation concerns the huge drop in the number of transactions for iterations without any increase in accuracy.

Furthermore, the variance in the number of transactions is also smaller; the average predicted transaction number is There is a drop in the number of transactions for iterations but not as much as with the macroeconomic LSTM. The results for this model are presented in Table However, the case with iterations is quite different from the others, with only 10 transactions out of a possible generating a very high profit accuracy.

On average, this value is However, all of these cases produced a very small number of transactions. When we compare the results, similar to the one-day-ahead cases, we observe that the baseline models produced more transactions more than The results of these experiments are shown in Table Table 13 shows the results of these experiments.

Again, the case of iterations shows huge differences from the other cases, generating less than half the number of the lowest number of transactions generated by the others. Table 14 shows the results of these experiments. Meanwhile, the average predicted transaction number is However, the case of iterations is not an exception, and there is huge variance among the cases. From the five-days-ahead prediction experiments, we observe that, similar to the one-day- and three-days-ahead experiments, the baseline models produced more transactions more than This extended data set has data points, which contain increases and decreases overall.

Applying our labeling algorithm, we formed a data set with a balanced distribution of three classes. Table 16 presents the statistics of the extended data set. Below, we report one-day-, three-days-, and five-days-ahead prediction results for our hybrid model based on the extended data. The average the number of predictions is The total number of generated transactions is in the range of [2, 83]. Some cases with iterations produced a very small number of transactions. The average number of transactions is Table 19 shows the results for the five-days-ahead prediction experiments.

Interestingly, the total numbers predictions are much closer to each other in all of the cases compared to the one-day- and three-days-ahead predictions. These numbers are in the range of [59, 84]. On average, the number of transactions is Table 20 summarizes the overall results of the experiments.

However, they produced 3. In these experiments, there were huge differences in terms of the number of transactions generated by the two different LSTMs. As in the above case, this higher accuracy was obtained by reducing the number of transactions to Moreover, the hybrid model showed an exceptional accuracy performance of Also, both were higher than the five-days-ahead predictions, by 5.

The number of transactions became higher with further forecasting, for It is difficult to form a simple interpretation of these results, but, in general, we can say that with macroeconomic indicators, more transactions are generated. The number of transactions was less in the five-days-ahead predictions than in the one-day and three-day predictions.

The transaction number ratio over the test data varied and was around These results also show that a simple combination of two sets of indicators did not produce better results than those obtained individually from the two sets. Hybrid model : Our proposed model, as expected, generated much higher accuracy results than the other three models.

Moreover, in all cases, it generated the smallest number of transactions compared to the other models The main motivation for our hybrid model solution was to avoid the drawbacks of the two different LSTMs i. Some of these transactions were generated with not very good signals and thus had lower accuracy results.

Although the two individual baseline LSTMs used completely different data sets, their results seemed to be very similar. Even though LSTMs are, in general, quite successful in time-series predictions, even for applications such as stock price prediction, when it comes to predicting price direction, they fail if used directly.

Moreover, combining two data sets into one seemed to improve accuracy only slightly. For that reason, we developed a hybrid model that takes the results of two individual LSTMs separately and merges them using smart decision logic. That is why incorrect directional predictions made by LSTMs correspond to a very small amount of errors. This causes LSTMs to produce models making many such predictions with incorrect directions. Many traders misapply neural nets because they place too much trust in the software they use all without having been provided good instructions on how to use it properly.

To use a neural network in the right way and thus, gainfully, a trader ought to pay attention to all the stages of the network preparation cycle. It is the trader and not their net that is responsible for inventing an idea, formalizing this idea, testing and improving it and, finally, choosing the right moment to dispose of it when it's no longer useful.

Let us consider the stages of this crucial process in more detail:. A trader should fully understand that their neural network is not intended for inventing winning trading ideas and concepts. It is intended for providing the most trustworthy and precise information possible on how effective your trading idea or concept is. Therefore, you should come up with an original trading idea and clearly define the purpose of this idea and what you expect to achieve by employing it.

This is the most important stage in the network preparation cycle. Next, you should try to improve the overall model quality by modifying the data set used and adjusting the different the parameters. Every neural-network based model has a lifespan and cannot be used indefinitely. The longevity of a model's life span depends on the market situation and on how long the market interdependencies reflected in it remain topical.

However, sooner or later any model becomes obsolete. When this happens, you can either retrain the model using completely new data i. Many traders make the mistake of following the simplest path. They rely heavily on and use the approach for which their software provides the most user-friendly and automated functionality.

This simplest approach is forecasting a price a few bars ahead and basing your trading system on this forecast. Other traders forecast price change or percentage of the price change. This approach seldom yields better results than forecasting the price directly.

Both the simplistic approaches fail to uncover and gainfully exploit most of the important longer-term interdependencies and, as a result, the model quickly becomes obsolete as the global driving forces change. A successful trader will focus and spend quite a bit of time selecting the governing input items for their neural network and adjusting their parameters.

They will spend from at least several weeks—and sometimes up to several months—deploying the network. A successful trader will also adjust their net to the changing conditions throughout its lifespan. Because each neural network can only cover a relatively small aspect of the market, neural networks should also be used in a committee. Use as many neural networks as appropriate—the ability to employ several at once is another benefit of this strategy.

In this way, each of these multiple nets can be responsible for some specific aspect of the market, giving you a major advantage across the board. However, it is recommended that you keep the number of nets used within the range of five to ten.

Finally, neural networks should be combined with one of the classical approaches. This will allow you to better leverage the results achieved in accordance with your trading preferences. You will experience real success with neural nets only when you stop looking for the best net. After all, the key to your success with neural networks lies not in the network itself, but in your trading strategy.

Therefore, to find a profitable strategy that works for you, you must develop a strong idea about how to create a committee of neural networks and use them in combination with classical filters and money management rules. Automated Investing.

Trading Skills. Portfolio Management. Your Money. Personal Finance. Your Practice. Popular Courses. Table of Contents Expand. Table of Contents. Common Misconceptions. Uncover Opportunities. The Best Nets. Is Faster Convergence Better? Correct Application of Neural Nets. The Optimal Approach to Neural Nets. The Bottom Line.

Neural network forex trading | It has been used in many different fields, including handwriting recognition Graves et al. The average the number of predictions is Introduction The foreign exchange market, known as Forex or FX, is a financial market where currencies are bought and sold simultaneously. Article Google Scholar. This order is used to prevent larger losses for the trader. 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. |

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More info about ratio sharpe. I have chosen a ratio shape because in its base the ratio shape includes a risk renatability analysis based on a reference environment and we therefore want our fitness function to include a risk analysis of each investment. In summary, the more benefit based on ratio shape the better. This is the neural network that we are going to train with a small change in the software it could be of any dimension and have more hidden layers.

In summary our genetic algorithm need perform optimization of This library implement a genetic algorhitm to evolve each population. And we deploy a full conected network with inputs 3 hidden layers with 35 neurons each with 40 outputs. Now we can analize 2 most important metohds Function crossover recibe two individual species and perform a random crossover and return new specie completly evolved. This library deploy class CNET to perform a internal neural network calculation and perform trades bases on optimized weigth.

Let's go to analize how we call a full conected network of 3 hidden layers with 35 neurons each. After running some evolution we can see optimization grah where each blue dot represent the result for each specie. In our case i take as a reference a network of inputs 3 hidden layers and 40 outputs with small change we can use any dimension.

Skip to content. Star Hands On Neural Network inside Metatrader 14 stars 7 forks. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Branches Tags. Could not load branches. Could not load tags. Latest commit. Git stats 83 commits. Failed to load latest commit information. View code. Mandatory Literature It is assumed that readers are familiar with the basic concepts of deep networks and financial markets.

What are we trying to do with quantitative trading? This sounds interesting python and r have hundreds of liberties for machine learning some of them interesting as recently google library TensorFlow In other words we have price for time T Motivations about MarketsGA and Quantitative Traders problems The main problems about quantitative trader encounters when operating in real market is that they are exposed to market volatility, rapid market movements, price slippage and the constant noise produced by the randomness of the market structure itself.

Basic concepts about GA Genetic algorithms are based on the genetic process of biologically living organisms. Genetics algorithms basis Neural Network weigth optimization As indicated in the introduction of the article we will try to optimize a neural network of any input dimension any internal dimension and any output dimension. Loss function vs fitness function The loss function in a machine learning represents the error between the predicted value vs. The main difference between the gradient descent method and the genetic algorithms to optimize the weights of a neural network, is that, while in the first we must process over and over again the loss function epoch to epoch to see how the error decreases, with the genetic algorithm we do not need to process the network any epoch of training simply we should run several networks piloted by the genes of a species check how good is each specie and finally evolve to new population only based on the best species To know how good is a species we will process all the actions performed to the fitness function.

Our Fitness function The main objective of the document is the creation of a neural network optimized by genetic algorithm in order to discover a complex trading system capable of making any decision available on the platform. For this reason our loss function will be based on: More info about ratio sharpe. Releases No releases published. Packages 0 No packages published.

Neural Network Forex Trading Example:. Target Variable: Tomorrow Close price. Forex trading classification problem: Input: Yesterday open price, Yesterday high price, Yesterday low price, Last 7 days high price, LAst 7 days low price, Relative Strength Index for Daily chart time frame.

Target Variable: 1 or 0, where 1 is profit trade, 0 is loss trade. Neural network systems utilize data and analyze it. The process in which neural networks analyze information is similar to the cause-effect relationship in human thinking. The probabilities of a situation are analyzed before making a final decision. This is also true for neural network systems.

The neural network analyzes past information to make a more informed decision in the future. This is similar to when a child makes an error when doing a puzzle and corrects it with their next move. This is how the system processes information and makes educated decisions. The biological neural network operates very similarly to the nerves in the human body. For example, all system elements communicate with each other to determine a final answer.

The neural network is multi-layered and detail-oriented. There are two main databases involved with neural networks. There is a training base as well as a test base. Database improvements are completed through trial and error. The network maintains permanent progress. The system is always using new information to improve the result. The Forex market has been increasingly expanding its technology to improve trading outcomes.

Tech developers have the ability to improve the effectiveness of all forms of artificial intelligence greatly. The most important feature of neural networks is their ability to gather data and analyze it. This information is then stored and used when it comes time to make predictions.

The system takes time to recognize and learn patterns before it can be used consistently with guaranteed success. The process of system learning does not take long, which is another benefit of this quick network. The different features of the network include immersion, extraction, neural training, and decision-making.

These are the steps involved in creating an accurate prediction. Neural networks have the ability to benefit the forex market significantly. The main reason for this is due to their accuracy and intuitive instincts. They have the ability to analyze fundamental data as well as technical data. Mechanical systems are not well-equipped to analyze this type of data. Human errors are even more common when faced with analyzing this data.

This is why neural networks have the ability to benefit traders greatly. Another major benefit of neural networks is their quick adaptability. Neural networks do not take a long time to train. This is beneficial as it saves time and resources.

Neural networks can help bridge the gap between human intelligence and computers. Neural networks are already in use today. Popular search engines such as Google already use neural networks to improve their system. Google uses neural networks to analyze and classify images, text, and other data. The neural network has the ability to sort images and distinguish certain features from others. Google translate also utilizes neural networks in part. For example, the translations have become more accurate with the use of neural networks.

The benefits of these systems include self-learning, highly improved reaction speed, and problem-solving capabilities. Many people want to know if the system is fully compatible with Forex and how to generate a successful outcome. Neural networks have the ability to make a forecast. They can also generalize and highlight the data as well.

The network is trained and can make educated predictions based upon the historical information it has saved. Classical indicators are different from neural networks. Neural networks have the ability to view dependencies between data and therefore make adjustments based upon this information.

In forex trading, trader has to predict the risk in forex transaction and how to gain or increase the profits based on analysis. The purpose of this study is to. In this paper we investigate and design the neural networks model for FOREX prediction based on the historical data movement of USD/EUR exchange rates. Developed algorithm was applied for trading of historical forex ex-change rates. APPLICATION OF NEURAL NETWORK FOR FORECASTING OF EXCHANGE RATES.