Using Recurrent Neural Networks To Forecasting of Forex.
This paper reports empirical evidence that a neural networks model is applicable to the statistically reliable prediction of foreign exchange rates. Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying “rules” of the movement in currency exchange rates. The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO.
Various statistical estimates of forecast quality have been carried out. Obtained results show, that neural networks are able to give forecast with coefficient of multiple determination not worse then 0.65. Linear and nonlinear statistical data preprocessing, such as Kolmogorov-Smirnov test and Hurst exponents for each currency were calculated and analyzed. Key words: Neural Networks, Foreign Exchange Rate, Statistical Tests, Hurst Exponent, Complex Systems Theory.
Forex is the largest and most liquid of the financial markets, with an approximately $1 trillion traded every day. It leads to the serious interest to this sector of finance and makes clear that for various reasons any trader on Forex wish to have an accurate forecast of exchange rate. Most of traders use in old fashion manner such traditional method of forecast as technical analysis with the combination of fundamental one. In this paper we develop neural network approach to analysis and forecasting of financial time series based not only on neural networks technology but also on a paradigm of complex systems theory and its applicability to analysis of various financial markets (Mantegna et al., 2000; Peters, 1996) and, in particularly, to Forex.
While choosing the architecture of neural network and strategy of forecasting we carried out data preprocessing on the basis of some methods of ordinary statistical analysis and complex systems theory: R/S-analysis, methods of nonlinear and chaotic dynamics (Mantegna et al., 2000; Peters, 1996).
In the present paper we do not describe all of them. We present here only the results of the Kolmogorov-Smirnov test and results of R/S-analysis. However we stress that the preliminary analysis has allowed to optimize parameters of neural network, to determine horizon of predictability and to carry out comparison of forecasting quality of different currencies. Below we give some remarks relating to advantages of neural networks technology over traditional methods and compare our approach with the methods of other authors First, neural networks analysis does not presume any limitations on type of input information as technical analysis does. It could be as indicators of time series, as information about behavior of another financial instruments.
It is not without foundation, that neural networks are used exactly by institutional investors (pension funds for example), that deal with big portfolios and for whom correlations between different markets are essential. Second, in contrast to technical analysis, which is based on common recommendations, neural networks are capable to find optimal, for given financial instrument, indicators and build optimal, for given time series, forecasting strategy. Let us remind that in present study we forecasted the exchange rates of only selected currencies on Forex market. As currencies to deal with, we chose British Pound, Swiss Frank, EURO and Japanese Yen.

BPNN Predictor – forecast of price using neural networks.
BPNN Predictor is an indicator pertaining to the category of predictors. To predict the future behavior of prices BPNN Predictor uses a neural network with three layers . The indicator is universal, but it is better to use at higher timeframes.
Characteristics of BPNN Predictor.
Platform: Metatrader4 Currency pairs: Any currency pairs Trading Time: Around the clock Timeframe: Any, recommended H1 and higher Recommended broker: Alpari.
The use of the BPNN Predictor in trade.
BPNN Predictor is building on a price chart three lines:
Red - forecast of future prices; Black - past the opening price, which at the time of study were used as the expected output of the network; Blue - network outputs obtained during the study given to the input data.
The indicator is presented in two forms: BPNN Predictor and BPNN Predictor with Smoothing, in which the EMA-smoothing is used.
Below are examples of signals of both indicators.
BPNN Predictor Buy signal:
BPNN Predictor Sell signal:
BPNN Predictor with Smoothing Buy signal:
BPNN Predictor with Smoothing Sell signal:
Please note, for proper operation of the indicator must be installed BPNN. dll library file, which is stored in the archive for download. When switching timeframes indicator need to restart on graph, apparently due to the features of "neural networks". It is more convenient to do it through a template installation that I have prepared for you in the archive.
In the archive BPNN. rar:
Free Download BPNN Predictor.
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17 Responses.
thank you sooooooooooooooo much.
hi, can you please explain how to install it properly. Thank you.
I have download the BPNN, but the red line was not appear after install it..
When switching timeframes indicator need to restart on graph, apparently due to the features of “neural networks”. It is more convenient to do it through a template installation that I have prepared for you in the archive.
I’ve downloaded the index fullest way.
But when I Drvath categorize the podium never did not show what is the reason please.
When switching timeframes indicator need to restart on graph, apparently due to the features of “neural networks”. It is more convenient to do it through a template installation that I have prepared for you in the archive.
can be described fully for how to install this indicator. because I had red tide predictions but the line is not out.
Hi, thank you for the indicator, having troubles with the dll file. Everything is placed on the correct destination, templates, indicators, and dll in the lib folder, but i started reiceiving an ” Access violation to read” from the dll file .
This guy’s indicators can’t be trusted. They full of shite.
it draw not the red line.
BPNN. dll is in LIBARIES folder.
this is without smoothing in H1 chart:
this is with smoothing in h4 chart :
there is nothing.
what is wrong here ?
How To Trade Binary Options Around News & Events thanks it’s really helpful.
indicator works, at least I was able to have prediction and red line.
After you insert the indicator in the chart, open the common part of the setting and allow DLL modification in that menu.
Thanks so much for this post. I really appreciate that you included a guide for what each colored line meant. Deciphering these types of graphs can be difficult, but this post was very helpful!
which one is better to use with smoothing or without smoothing ?
it keep changing its view….Red line is not consistent..it means we can not trust it…..it keep changing its idea…..which is lethal on low time framework….from 5 minute to 1 hour …..may be good for H4 or Day time frames.
what is the difference between the one with smoothing and the one without it.

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