TSV moving average is plotted as an oscillator. Four divergences are calculated for each indicator regular bearish, regular bullish, hidden bearish, and hidden bullish with three look-back periods high, mid, and small. For TSV, the The New York Stock

Metatrader 4 terminal offers a wide range of features allowing not only to track quotes, but also to carry out various calculations and present their final results in numerical or graphical form. Special programs for the terminal that allow automatic calculation of data are called indicators. Each year, the number of indicators is growing, some indicators are added the platform's toolkit by default, others require installation by the user.

All of them are divided into specific groups based on various qualities - from calculation algorithms to displaying the results of these calculations. We will consider special nonlinear indicators for Metatrader 4, which analyze only preliminary data and do not take into account the streaming data arriving in real time. Long before the emergence of modern software, traders conducted calculations almost on paper, which led not only to errors and omissions in these calculations, but also to a great deal of mental strain and time costs for the traders themselves.

After all, it was necessary not only to summarize market data, but to collect all this information, analyze it, and then draw a conclusion about the prospects for further price movement on the chart. Non-linear indicators significantly simplify the work of traders and improve its quality.

Such indicators can:. Many indicators determine the patterns of trend development, and then reproduce these patterns graphically, often in the form of lines: average, support, volume, and so on - depending on the algorithms for evaluating the data. Nonlinear indicators are less common than trend indicators, but not less useful in analyzing the market and making decisions.

Unlike linear indicatord, they do not overwrite output information based on the release of the latest data. For example, some linear indicators perform calculations based on average price data, so after the latest quotations with a large jump, their readings may undergo significant changes, which is fraught with errors in the final conclusions.

Nonlinear indicators track strictly historical static data on quotations and do not take into account the latest incoming data when calculating. An example of such an indicator is the standard Moving Average, which defines a simple moving average based on the average price prevailing on the market for a particular period of time. Undoubtedly, the most profitable trading strategies involve joint use of technical and fundamental analysis.

Technical analysis is based on studying the history of quotations and new information received, while fundamental focuses on tracking important news and economic data affecting the market. If in the technical analysis of the chart, there is no uncertainty, since all the necessary information is directly in the terminal itself, with the fundamental analysis everything is somewhat more complicated.

News, economic data and rumors are taken into account by market participants in different ways, and some fundamental data are not taken into account at all. The first differences of the series were taken figure 3 and the graphs seem to fluctuate around a constant mean of zero value.

Figure 2. On the other hand, the correlogram of the first difference shows that it is consistent with mean stationarity because most of the values promptly decay to zero. Tables 1 and 2 below show both the correlgrams for level and first difference. Table 1. At level, table 3 all the series are not stationary but at first difference table 4 all series appeared stationary as shown in the tables below:- Table 3.

From the unit root test, we obtain d to be 1. For sake of saving space, we just list the top 10 with higher AIC values here. As shown in Table 5 below the model with the smallest value 9. Table 5. We select the most appropriate model by minimizing value of AIC. Table 6. Results Out-of-sample Forecasting Performance In this section, we check out-of-sample forecast performance of the two models.

Comparison of Forecasting Power. Table 8. Conclusions At first, we believed there is no way for a linear regression to suit a series forever where stock prices follow a non-linear trend. Due to the economic environment changing, the stock market will be affected and change over time.

Therefore, non-linear regression should be better than linear regression in the exchange rate market. First step of handling time series data is to check stationary state in the mean. We found out there was a unit root existed so we analyzed the first-difference. The results also support previous assumptions of this thesis. References [1] Bank for International Settlements Bank for International Settlement, September.

In which exchange rate models do forecastters trust? Non-linear time series models in empirical economics. NewYork, Cambridge University Press, Oxford University Press. Journal of Advanced Management Science, Vol.

Inference on predictability of foreign exchange rates via generalized spectrum and non-linear time series models. Review of Economics and Statistics, 85, Proceeding of the World Congresson Engineering, Vol 2. Arima modeling with intervention to forecast and analyze Chinese stock prices. New York: Springer Verlag.

Threshold autoregressive, limit cycles and cyclical data. Forecasting Foreign Exchange Rates. Applied Economics Theses, Vol. All rights reserved. Figure 3. Correlogram for Level. Table 2. Correlogram for First Difference. Table 3. Original Data. Table 4. First Difference. Arima Model Results. Panel A Regime 1 Observations Included Table 7.

Panel B Regime 2 Observations Included Comarison of Forecasting Power by Models. Bank for International Settlements Cuaresma, J. David, H. Franses, P. Hansen, B. Granger, C. Guha, B. Hong, Y. Hongxing, L. Isenah, G.

How to open from the trickier. Anydesk at startup a solution for. Added via change. I solved the.

The cross-validation method used for the feedforward network models is the same as the one described for nearest neighbors regression except that the number of hidden units is the choice variable for the feedforward network regression. The set of number of hidden units is chosen to be h1,2,. In the implementation of the cross-validation for the parametric models, the most recent training observations are used first to calculate the cross-validated mean square error MSE.

The cross-validation takes place in order to calculate the cross-validated mean square error. This process gives us a sequence of mean squared errors and the corresponding training data length. From this sequence, the data length which corresponds to the smallest mean squared error is chosen to predict the first available forecast observation and to calculate the corresponding forecast error.

Empirical results For each exchange rate the out-of-sample predictive performance of the parametric and the non-parametric conditional mean estimators is examined. The out-ofsample forecasts are calculated from the last one-third of the data set.

In total, there are observations for estimation so that the last observations are kept for the out-of-sample predictions. As a measure of performance the out-ofsample mean square prediction error MSPE and sign predictions are used. To R. The Diebold and Mariano test is a test of the null hypothesis of no difference in the accuracy of the two competing forecasts and this test is used to evaluate the statistical significance of the MSPEs of the GARCH 1,1 , nearest neighbors and feedforward network regression models relative to that of the random walk model.

In addition to the Diebold-Mariano test, the percentage correct sign predictions of the out-of-sample forecasts are reported. Out-of-sample forecasts are completely ex ante, using only information actually available. The out-of-sample forecasts with the buy-sell signals as the conditioning n1,n2 set are computed recursively by estimating E r t us n1,n2 t 21 ,. The out-of-sample forecasts with past returns as the conditioning set are computed in a similar fashion.

The mean square prediction errors MSPEs of the random walk model in the first panel of Table 2 are obtained from the parameters estimated from the entire training period. For instance, if the cross-validation indicates to use most recent observations from the training set for the nearest neighbors regression, the same training set is used to calculate the forecast of the random walk model. This ensures that the forecast comparisons are fair between the random walk model and the model in comparison as they both use the same training set for their forecasts.

This test statistic is distributed standard normal in large samples. The residuals from this regression are used as the filtered returns. The results with the filtered returns are similar to that of the unfiltered returns. This serves as the first benchmark. This can be considered as the second linear benchmark of the paper. Sign refers to the percentage of the correct signs in the out-of-sample period.

For the nearest neighbors and feedforward regressions, the average number of nearest neighbors and the number of hidden units from the in-sample estimation are reported in the last rows of the corresponding panels for each method. Sign refers to the percentage of the correct sign predictions in the out-of-sample period. The MSPE ratios indicate that the feedforward network model provides an average of 7. The nearest neighbors model provides significant forecast gains over both the parametric and the feedforward network models.

The average forecast gain for the nearest R. For the non-parametric conditional mean estimators, the local procedure nearest neighbors regression dominate the global procedure feedforward network regression in forecast comparisons.

The results of the nearest neighbor regression shows that the average k that minimized the mean square error is 17 across the five currencies. For the feedforward network regression, the average number of hidden units is 8 for the five currencies. In Table 3 and Table 4, the predictability of the current returns with the past buy-sell signals of the moving average rules are investigated with the 1,50 and 1, rules.

For the nearest neighbors and feedforward regressions, the average number of nearest neighbors and the number of hidden units from the in-sample estimation are reported in last rows of the corresponding panels for each method.

The performance of the feedforward and nearest neighbors models both indicate significantly lower MSPEs relative to the random walk model and significantly higher sign predictions. For the nearest neighbors regression the MSPEs are The comparison of the nearest neighbors and the feedforward network models indicate that the nearest neighbors models do slightly better than the feedforward network models. This suggests that feedforward networks may be suffering from a degree of oversmoothing relative to the nearest neighbors estimates.

Overall, both types of non-parametric forecasts outperform the GARCH 1,1 and the random walk model forecasts. The forecasts are more accurate when the past buy-sell signals are used relative to the past returns as inputs. The comparison of the 1,50 rule with 1, also indicates that the 1,50 rule R. This may be due to the fact that the 1, oversmooths the data.

The results of the nearest neighbor regression with the past buy-sell signals show that the average k that minimized the mean square error is 18 for rules 1,50 and 1, across five currencies. For the feedforward network regression, the average number of hidden units is 8 across all five currencies. In addition to the one-step ahead predictions, 5- and step ahead predictions are also studied with past returns and past buy-sell signals of the moving average rules. At the 5- and the step prediction levels, there is no evidence of predictability for all currencies when the current returns are modelled by past returns or past buy-sell signals.

This suggests that the predictive power of the buy-sell signals are limited to the immediate future which is the next day with the daily data used in this paper and these rules may not have long-term predictive ability. The cross-validation methodology in this paper starts from the most recent observations in the training set and searches the entire training set to obtain the optimal training data length within the context of an econometric model.

Therefore, a training data size as small as observations to the entire training set are studied. The cross-validation results of this paper indicate that optimal training data length is much less than the entire training size.

The average optimal training data length for all currencies is an average of one-third of the total training set of observations. This means that larger training sets may lead to overfitting and therefore poor out-of-sample generalizations.

Conclusions This paper has compared the out-of-sample performances of two parametric and two non-parametric conditional mean estimators to forecast spot exchange rate returns with past returns and past buy-sell signals of the moving average rules. The forecasts generated by the non-parametric models dominate the parametric ones.

Among the non-parametric models, the forecasts of the local procedure nearest neighbors regression dominate the global procedure feedforward network regression. The results indicate that simple moving average rules provide significant correct sign predictions and the Diebold and Mariano test indicates the statistical significance of these predictions when non-parametric conditional mean estimators are used to model the current returns.

In general, the random walk and the GARCH 1,1 models do not generate significant sign predictions and Diebold and Mariano tests corresponding to these models are consistently insignificant. Although this method is computationally expensive, it has the advantage that the model complexity and the number of observations needed for the in-sample estimation are determined optimally which prevents overfitting in noisy environments.

In other words, this procedure may utilize only a certain number of in-sample observations rather than the entire in-sample if a certain subset of the in-sample observations provides smaller mean square error relative to the mean square error of the entire in-sample set. It is a fair procedure as it only relies on in-sample performance. In other words, observations in the forecast sample out-of-sample observations are not utilized at any stage of the model specification.

Acknowledgements I thank the editor and two anonymous referees for their suggestions. References Bollerslev, T. Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics 31, — Boothe, P. The statistical distribution of exchange rates. Journal of International Economics 22, — Brock, W. Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47, — Cornell, W. The efficiency of the market for foreign exchange under floating exchange rates.

Review of Economics and Statistics 60, — Cumby, R. International interest-rate and price-level linkages under flexible exchange rate: a review of recent evidence. In: Bilson, J. University of Chicago Press, Chicago, pp. Cybenko, G. Approximation by superposition of a sigmoidal function.

Mathematics of Control, Signals and Systems 2, — Diebold, F. Empirical Modelling of Exchange Rate Dynamics. Springer-Verlag, New York. Nonparametric exchange rate prediction? Journal of International Economics 28, — The dynamics of exchange rate volatility: a multivariate latent-factor ARCH model. Journal of Applied Econometrics 4, 1— Comparing forecasting accuracy, Journal of Business and Economic Statistics 13, — Domowitz, I.

Conditional variance and the risk premium in the foreign exchange market. Journal of International Economics 19, 47— Dooley, M. Analysis of short-run exchange rate behavior: March to November In: Bigman, D. Ballinger, Cambridge, pp. Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation.

Econometrica 50, — Engle, R. Meteor showers or heat waves? Heteroskedastic daily volatility in the foreign exchange market. Econometrica 58, — Funanhashi, K. On the approximate realization of continuous mappings by neural networks. Neural Networks 2, — Gallant, A. There exists a neural network that does not make avoidable mistakes. On learning the derivatives of an unknown mapping with multilayer feedforward networks. Neural Networks 5, — Applied non-parametric regression.

Cambridge University Press, New York. Hecht-Nielsen, R. Theory of the backpropagation neural networks. Proceedings of the international joint conference on neural networks, Washington DC, vol. Hornik, K. Multilayer feedforward networks are universal approximators. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks. Neural Networks 3, — Hsieh, D. The statistical properties of daily foreign exchange rates: — Journal of International Economics 24, — Testing for nonlinear dependence in foreign exchange rates: — Journal of Business 62, — Kuan, C.

Forecasting exchange rates using feedforward and recurrent neural networks. Journal of Applied Econometrics 10, — Artificial neural networks: an econometric perspective. Econometric Reviews 13, 1— LeBaron, B. Technical trading rules and regime shifts in foreign exchange. Do moving average trading rule results imply nonlinearities in foreign exchange markets? Forecast improvements using a volatility index.

Journal of Applied Econometrics 7, — Levich, R. The significance of technical trading-rule profits in the foreign exchange market: a bootstrap approach. Journal of International Money and Finance 12, — McCurdy, T. Testing the martingale hypothesis in Deutsche Mark futures with models specifying the form of heteroskedasticity. Journal of Applied Econometrics 3, — Meese, R.

Empirical exchange rate models of the seventies: do they fit out sample? Journal of International Economics 14, 3— Non-linear, non-parametric, non-essential exchange rate estimation. American Economic Review 80, — An empirical assessment of non-linearities in models of exchange rate determination. Review of Economic Studies 58, — Neftci, S. Journal of Business 64, — Robinson, P. Asymptotically efficient estimation in the presence of heteroskedasticity of unknown form. Econometrica 55, — News, economic data and rumors are taken into account by market participants in different ways, and some fundamental data are not taken into account at all.

FFcal indicator helps to avoid such omissions and displays information about upcoming events directly on the price chart. In general, it can be argued that the indicator is undoubtedly useful for every trader, and it can be recommended for use by even the most ardent devotees of purely technical analysis.

This is due to the fact that the program can filter the flow of news data by importance and other parameters, which makes its results similar to the indications of classical technical indicators. After placing the files in the directory, you need to restart the terminal - and the tool will become available for use. You can find it listed among the custom indicators. Did you like my article? Ask me questions and comment below. I'll be glad to answer your questions and give necessary explanations.

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PDF | A new trading strategy based on state space reconstruction techniques is proposed. The technique uses the state space volume evolution and its. Nonlinear indicators are less common than trend indicators, but not less useful in analyzing the market and making decisions. Unlike linear. In this study, the prediction of the exchange rate of the United States Dollar (USD) to the Indonesian Rupiah (IDR) is modeled using the nonlinear.