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Xe Currency Converter. These are the highest points the exchange rate has been at in the last 30 and day periods. These are the lowest points the exchange rate has been at in the last 30 and day periods. These are the average exchange rates of these two currencies for the last 30 and 90 days.

Forex trading neural network elder indicators for forex

Forex trading neural network

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Neural network is the system of self-learning based on the technologies of artificial intelligence. This network collects and analyzes data similar to human brain, by way of trials and errors, generalization and segregation. What are the prospects of using this system in Forex financial markets?

Neural network is a unique system of technical data analysis. Note that its operation process is similar to what people do when they evaluate cause-effect relationships and probabilities. What we see as important issues for making decisions, is also embodied in neural network system, which is evaluation of past experience.

It can be compared with a child who keeps doing a puzzle, eventually making fewer mistakes. And this is operation principle of the multilayered network. The network contains two main databases: a training base and a test base, improvements in their work are made through trial-and-error process, similar to what people do. One of the strong features of the neural network is the ability of permanent progress, as the system uses new data for making more accurate predictions, altering and improving conclusions.

At Forex market a neural network can efficiently analyze both technical and fundamental data, performing the task, which can be difficult for the mechanical systems, and even more difficult for traders. At the same time, training of the neural network does not take a lot of time, resources or forces. It is believed that this is the way, which leads to shortening the distance and the difference between the unique abilities of human mind and capabilities of the computer systems.

Search engines such as Google or Yandex have long been using neural networks to analyze and classify images, sounds, text characters and other data. Google neural network can sort out images and distinguish common and particular features, specific to the same pictures. Such neural systems easily sort out black and white and color images, and can quite easily distinguish images of say, kittens from puppies.

Google translator has also partially switched to the neural network interface, which has improved quality of translation. Neurocomputers are actively used by the American financial conglomerate Citigroup Inc. Chemical Bank has also developed a large software system maintained by the company Neural Data. The system expands opportunities of working with any data. For example, neural network can compress data, highlight links between common parts and provide the data in the short form and in a lesser dimension.

The system can also restore the original data due to associative memory of the neural network if the data was somehow damaged. Today, researchers and developers of the neural networks have some other serious tasks to implement. They shall improve the system of self-learning and analysis, increase the speed of reaction and resolve many other problems, enabling the use of the system for some specific tasks.

Neural network can make a forecast, generalize and highlight the data. Trained network, just like any other technical indicator, can make predictions of the future based on historical data. Before they can be of any use in making Forex predictions, neural networks have to be 'trained' to recognize and adjust for patterns that arise between input and output.

The training and testing can be time consuming, but is what gives neural networks their ability to predict future outcomes based on past data. The basic idea is that when presented with examples of pairs of input and output data, the network can 'learn' the dependencies, and apply those dependencies when presented with new data.

From there, the network can compare its own output to see how close to correct the prediction was, and go back and adjust the weight of the various dependencies until it reaches the correct answer. This requires that the network be trained with two separate data sets — the training and the testing set.

One of the strengths of neural networks is that it can continue to learn by comparing its own predictions with the data that is continually fed to it. Neural networks are also very good at combining both technical and fundamental data, thus making a best of both worlds scenario. Their very power allows them to find patterns that may not have been considered, and apply those patterns to prediction to come up with uncannily accurate results.

Unfortunately, this strength can also be a weakness in the use of neural networks for trading predictions. Ultimately, the output is only as good as the input. They are very good at correlating data even when you feed them enormous amounts of it. They are very good at extracting patterns from widely disparate types of information — even when no pattern or relationship exists. Its other major strength — the ability to apply intelligence without emotion — after all, a computer doesn't have an ego — can also become a weakness when dealing with a volatile market.

When an unknown factor is introduced, the artificial neural network has no way of assigning an emotional weight to that factor.

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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.