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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. In our hybrid model, weak transaction decisions are avoided by combining the decisions of two LSTMs with a simple set of rules that also take the no-action decision into consideration.

This extension significantly reduced the number of transactions, by mostly preventing risky ones. As can be seen in Table 20 , which summarizes all of the results, the new approach predicted fewer transactions than the other models. Moreover, the accuracy of the proposed transactions of the hybrid approach is much higher than that of the other models. We present this comparison in Table In other words, the best performance occurred for five-days-ahead predictions, and one-day-ahead predictions is slightly better than three-days-ahead predictions, by 0.

Furthermore, these results are still much better than those obtained using the other three models. We can also conclude that as the number of transactions increased, it reduced the accuracy of the model. This was an expected result, and it was observed in all of the experiments. Depending on the data set, the number of transactions generated by our model could vary. In this specific experiment, we also had a case in which when the number of transactions decreased, the accuracy decreased much less compared to the cases where there were large increases in the number of transactions.

This research focused on deciding to start a transaction and determining the direction of the transaction for the Forex system. In a real Forex trading system, there are further important considerations. For example, closing the transaction in addition to our closing points of one, three, or 5 days ahead can be done based on additional events, such as the occurrence of a stop-loss, take-profit, or reverse signal.

Another important consideration could be related to account management. The amount of the account to be invested at each transaction could vary. The simplest model might invest the whole remaining account at each transaction. However, this approach is risky, and there are different models for account management, such as always investing a fixed percentage at each transaction.

Another important decision is how to determine the leverage ratio to be chosen for each transaction. Simple models use fixed ratios for all transactions. Our predictions included periods of one day, three days, and 5 days ahead. We simply defined profitable transaction as a correct prediction of the decrease and increase classes. Predicting the correct direction of a currency pair presents the opportunity to profit from the transactions.

This was the main objective of our study. We used a balanced data set with almost the same number of increases and decreases. Thus, our results were not biased. Two baseline models were implemented, using only macroeconomic or technical indicator data. However, the difference was very small and insignificant. It reduced the number of transactions compared to the baseline models The increase in accuracy can be attributed to dropping risky transactions. The proposed hybrid model was also tested using a recent data set.

Macroeconomic and technical indicators can both be used to train LSTMs, separately or together, to predict the directional movement of currency pairs in Forex. We showed that rather than combining these parameters into a single LSTM, processing them separately with different LSTMs and combining their results using smart decision logic improved prediction accuracy significantly.

Rather than trying to determine whether the currency pair rate will increase or decrease, a third class was introduced—a no-change class—corresponding to small changes between the prices of two consecutive days. This, too, improved the accuracy of direction prediction. We described a novel way to determine the most appropriate threshold value for defining the no-change class. We used this feature to predict three days and 5 days ahead, with some decreases in accuracy values.

Typically, the accuracy of LSTMs can be improved by increasing the number of iterations during training. We experimented with various iterations to determine their effects on accuracy values. The results showed that more iterations increased accuracy while decreasing the number of transactions i.

Additionally, a trading simulator could be developed to further validate the model. Such a simulator could be useful for observing the real-time behavior of our model. However, for such a simulator to be meaningful, several issues related to real trading e. Appel G Technical analysis: power tools for active investors. Financial Times Prentice Hall, p Wiley, London, p Google Scholar. Bahrammirzaee A A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems.

Neural Comput Appl — Article Google Scholar. Expert Syst Appl — Biehl M Supervised sequence labelling with recurrent neural neural networks. Neural Netw Bollinger J Bollinger on bollinger bands. McGraw-Hill, London. Bureau of Labor Statistics Data November Accessed: Nov Colby RW The encyclopedia of technical market indicators, p Di Persio L, Honchar O Artificial neural networks architectures for stock price prediction: comparisons and applications.

EU 25 November fixed composition as of 1 May , Long-term interest rate for convergence purposes—Unspecified rate type, Debt security issued, 10 years maturity, New business coverage, denominated in All currencies combined—Unspecified counterpart sector—Quick View—ECB Statistical Data Warehouse.

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Financ Innov Hyperparameter tuning is typically performed by means of empirical experimentation, which incurs a high computational cost because of the large space of candidate hyperparameter settings. We employ random search Bengio for hyperparameter tuning considering the following search space:. We set up a supervised training experiment in accordance with Fischer and Krauss and Shen et al. This meant constructing overlapping study periods consisting of training observations and trading observations as depicted in Fig.

We then built models with fixed hyperparameters for all time series with the insights from manual tuning. All models had the following topology:. The other models use different layers but possess the same structure—with the exception that the FNN layers do not pass on sequences and thus the data dimensions between the first and third hidden layers in the FNN are 1, 50 rather than , 50 like in the three recurrent networks. All models were trained using minibatch sizes of 32 samples and the Adam Kingma and Ba optimizer with default parameters, training for a maximum of epochs with early stopping after 10 periods without improvement in validation loss.

We consider three measures of forecast accuracy: logarithmic loss Log loss as this loss function is minimized during network training; predictive accuracy Acc. In addition to assessing classification performance, we employ a basic trading model to shed light on the economic implications of trading on model forecasts.

The position is held for one day. As each test set consist of trading days roughly one year , the annualized net returns of this strategy in study period S are approximated by. As a measure of risk, the standard deviation SD of the series of realized trading strategy returns is considered, and the Sharpe ratio SR is computed as a measure of risk-adjusted returns. The results of this benchmark can be found in Table 3 , both per time series as well as aggregated across time series.

The naive benchmarks give accurate direction predictions about half of the time. If the trading strategy defined in Sect. Recall that the empirical results are obtained from the window-based cross-validation approach depicted in Fig. Table 4 suggests three conclusions. Second, economic measures of forecast performance paint a different picture. None of the models is able to produce a large positive return.

This is an interesting result in that several previous forecast comparisons observe a different result. We discuss the ramifications of our results in Sect. Third, the deep learning models perform better than the benchmark in terms of accuracy and area under the ROC curve. However, the net returns resulting from applying the selected trading strategy are smaller in most cases.

The paper has reported results from an empirical comparison of different deep learning frameworks for exchange rate prediction. We have found further support for previous findings that exchange rates are highly non-stationary Kayacan et al. Even training in a rolling window setting cannot always ensure that training and trading set follow the same distribution.

Another observation concerns the leptokurtic distribution of returns. For example, the average kurtosis of the exchange rate returns examined in this study is 8. This resulted in many instances of returns close to zero and few, but relatively large deviations and could have lead to the models exhibiting low confidence in their predictions.

The results, in term of predictive accuracy, are in line with previous work on LSTMs for financial time series forecasting Fischer and Krauss However, our results exhibit a large discrepancy between the training loss performance and economic performance of the models. This becomes especially apparent in Fig. The observed gap between statistical and economic results agrees with Leitch and Tanner who find that only a weak relationship exists between statistical and economic measures of forecasting performance.

A similar problem might exist between the log loss minimized during training and the trading strategy returns in this study. Arguably, this finding was to be expected and might not come as surprise. However, evidence of the merit of deep learning in the scope of exchange rate forecasting was sparse so that expanding the knowledge base with original empirical results is useful. One may take this finding as evidence for the adequacy of using FNNs as benchmark in this study and, more generally, paying much attention to FNNs in previous work on FX markets and financial markets as a whole.

As any empirical study, the paper exhibits limitations which could be addressed in future research. Hussain et al. Augmenting the input structure of RNN-based forecasting model by incorporating additional predictors might be another way to overcome the low confidence issue.

Moreover, the focus of this study was on deep neural networks. Many other powerful machine learning algorithms exist. Comparing RNN-based approaches to alternatives such as, e. Another avenue for future research concerns the employed trading strategy. Employing a more advanced trading rule might help to overcome the discrepancy between statistical and economic results. One example of such a trading strategy is the work of Fischer and Krauss who construct a strategy only trading a number of top and bottom pairs from a large set of binary predictions on stock performance.

This particular strategy would, of course, require training on many more time series. A possible solution for better interaction between model and economic performance is furthermore to develop a combination of a custom loss function and suitable output activation function instead of using binary cross-entropy with a sigmoid output activation function.

That way, the model could directly optimize for either returns or risk-adjusted returns. Furthermore, hyperparameter tuning turned out to be cumbersome. The window-based training approach described in Sect. Efforts to automate large deep learning processes are under way Feurer et al.

An orthogonal approach to improve the tuning of the model to the data at hand involves revisiting the search strategy. We have used random search to configure deep neural networks, which can be considered standard practice. However, the search space of hyperparameters is very large and random search does not advocate narrowing down the space after the initial inspection of the wide space. Successive executions of the hyperparameter search have been employed in conjunction with grid search Van Gestel et al.

Especially if reusing the same hyperparameter setting across study periods, as done here, finding a strong configuration of the network is crucial and could benefit from repeating random search while zooming in more promising regions of the parameter space. A number of recent proposals for prediction of sequential data augments or even aims to supplant RNNs.

Such expansions include combining RNNs with CNNs when the data are both spatial and temporal Karpathy and Li or even applying image classification to plots of time series data; giving models access to an external memory bank Neural Turing Machine s Graves et al.

Machine learning research is moving increasingly fast and new ideas for improvements or augmentations of algorithms keep appearing. On the other hand, some technologies become practical only many years after their emergence. The best example of this is LSTM, an algorithm that was little appreciated in the first decade of its life but is one of the cornerstones of machine learning another ten years later. It is intriguing to imagine what might be possible in another decade.

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Forex mini lot | Article Google Scholar. Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. That work used basic technical indicators as inputs. Google Scholar Hakkio, C. If the predictions of the two models are different, we choose for the final decision the one whose prediction has higher probability. |

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Secret forex chips | 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. Then, the entropy value for this distribution is calculated. Fischer, T. The LSTM cell contains a number of gate structures that allow accessing the cell. This helped us improve our results compared to the binary classification results. Berlin: Springer. |

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Neural networks on forex | When the position closes i. Various forecasting methods have been considered in the finance domain, including machine learning approaches e. Journal of Economic Literature33 113— Download PDF. Market prices, technical indicators, financial news, Google Trends, and the number unique visitors to Wikipedia pages were used as inputs. |

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Neural Networks were developed many decades back. Neural networks work just like the human brain neurons. When there is a signal. This is how the human brain does all its work. Neural networks also work on the same principle. If we have an input signal, it should be above a certain threshold to trigger an output signal. Now there are many neural network software that are being sold in the market. It is easy to use these software as everything has been done for you. But I will take a different approach here.

Instead of buying an expensive neural network software I suggest you develop your own neural network trading systems. So we will be developing a neural network forex trading system in this post. Keep on reading if you want to learn how to do it. Did you read this post on how to predict gold price using kernel ridge regression? It might take you a few months to learn the theory behind neural networks but it will be worth the effort.

Once you have developed the art of training neural network models, you can use it in any field. So if you are interested in knowing how these models are developed keep on reading this post. You should learn R as well as Python. R and Python are powerful data science scripting languages that allow you to do a lot of financial modelling that you cannot do in Excel.

Time series analysis is very important when it comes to financial modelling. Price is a financial time series. We record the closing price after every 1 hour or 4 hours regularly. This sequence of closing price constitutes a time series. We as traders know that past price can be used to predict the future price. This is precisely what time series analysis also assumes. We can use past price to predict future price using autoregressive models.

I have developed a course on Time Series Analysis for Traders that you can take a look at. Many traders have no idea how to use time series models in their trading. I show you how to use time series models in your trading and get great results. Both R and Python are easy to learn. With a little effort you can learn these languages. In this post we will be using R.

R is a powerful statistical language that is used widely in academia. You should R installed on your computer as well as RStudio. Both are open source. I have developed a course on Python Machine Learning for Traders. In this course I show you how to develop different machine learning models for your trading system using Python.

As said in the start algorithmic trading has become very popular now a days. Days of manual trading are coming to an end. You should start learning fundamentals of algorithmic trading. Algorithms are revolutionizing all field of life from health, medicine, car driving, aeroplane flying, detecting bank frauds and all sorts of things. I have developed this course on Algorithmic Trading with Python that you can take a look at.

Now when you develop a neural network model, feature selection is very important. Features are the inputs that you give to the model to do the calculations and make predictions. I have been trading for many years now and know the importance of candlesticks as said above.

Candlestick patterns are good leading signals. Candlestick patterns are mostly 2 stick patterns and 3 stick patterns. So we will use Open, High, Low and Close of the price to develop features that try to model candlesticks. We will see if we can use these features to predict the market. We will be using high frequency data especially M1 timeframe. Yes, I am talking about 1 minute timeframe. Did you check this trend following high frequency trading system?

The model that we develop can be used on other timeframes also like the 15 minute, 30 minute, 60 minute, minute. We can do that later. As you can see R can make very beautiful candlestick charts very fast. If you have a quadcore computer than you can make R even more fast by using Microsoft R Open. I explain eerything how to do it in my course on R. The idea behind using high frequency data was that I wanted to show how fast we can do the calculations and make the predictions using R.

In the first model we take the closing price as feature number 1. Now we lag these 3 input features 1 time and 2 time so that we have a total of 9 input features. We will use a simple neural network model known as a feed forward neural network with one hidden layer. The hidden layer has got 10 neurons.

Below is the R code. In contrast to classical indicators, neural network can evaluate and see dependencies between some data and also make adjustments based on the previous trading experience. Of course, it will take time, require some expenses and efforts to train a network and ensure timely responses to the incoming data.

Despite obvious advantages of the neural network, the system also involves risks of making wrong forecasts. We can say that final solutions largely depend on the input data. Neural network perfectly reveals correlations between two factors. Neural network can distinguish common places in the disaggregated data when these patterns and relationships are hardly visible by the human eye.

But still, the use of intelligence without emotions can be regarded as a weak point in work at the unstable market. When a system faces some new situation, artificial neural network can fail to evaluate it. You can find examples of application of the neural networks in the financial markets here and here.

There are more and more indicators, which use neural network and you can easily find them in many systems. Did you like my article? Ask me questions and comment below. I'll be glad to answer your questions and give necessary explanations.

Home Blog Professionals Neural network at Forex. Rate this article:. Need to ask the author a question? Please, use the Comments section below. Start Trading Cannot read us every day? Get the most popular posts to your email. Full name. Written by. Eugene Yanushkevich Moneyline columnist. MetaTrader alternative.

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