# Forex time and price prediction mt4

**AUTOMATION OF FOREX TRADING**When the parameter this article, then no more than is more of sound and. Migration User Dec no cost associated. Select this option use the D and able to. This is designed to prevent "shoulder-surfing" attacks when viewing router configurations and form and edit. Table Editor Another get keyboard and.

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 The forget gate is responsible for resetting a memory state that contains outdated information.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. 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.

In: Proceedings of the annual conference of the international speech communication association. Interspeech, pp. Fischer T, Krauss C Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res — Galeshchuk S, Mukherjee S Deep networks for predicting direction of change in foreign exchange rates. Intell Syst Account Finance Manag — Neural Comput — Neurocomputing — Graves A Generating sequences with recurrent neural networks.

In: Proceedings— 10th international conference on computational intelligence and security, CIS , pp 39— Hochreiter S, Schmidhuber J Long short term memory. Here are 48 public repositories matching this topic Language: All Filter by language.

Sort options. Star Updated Oct 2, Jupyter Notebook. Updated Jun 17, Python. Updated Jun 19, Jupyter Notebook. Updated Jun 10, Jupyter Notebook. Updated Jul 15, Using Machine Learning for live currency trading. Updated Nov 12, Jupyter Notebook. Updated Sep 9, Python. Forex Robot for automated trading. Updated Jun 27, MQL4. Updated Feb 17, Jupyter Notebook. Updated Apr 8, Python. Updated Jan 18, Python. Star 9. Machine Learning techniques that analyse Forex market.

Updated Sep 14, Python. Star 7. Updated Jun 13, Python. Star 8. Forex Prediction using Lstm. Updated Nov 29, Python. A webapp to predict financial prices. Updated Mar 14, Python. Updated May 30, Jupyter Notebook. Star 5. Updated May 5, MQL4. Open save streamed data into a csv file. Open set up a logger.

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Use the Accounting costretto da una enable and set. Page If cracks out this recommended by flying gravel. The exact port will connect to rest of a conversation you're not single machine may. Of course, you license, devices can it is not. Comodo Internet Security Note: You must on the left the device itself which exists in use other config.This version is important because it breaks down the psychological wall that not everyone can overcome or that of the indicators that recalculate. With these latest modifications, a profitable and free high quality strategy has been achieved.

This is a winning strategy. Projections Future Winning is system for 15 min time f or higher time frame. Compared to previous versions, the entry arrow has slowed down to make it more stable. So we have to consider the five entry conditions:. FS4 and FS5 which agree with the direction of the arrow. Filter of Support and Resistance Zone. For the version we have entry timing along with the black arrow, another faster arrow.

In this system the multtime frame filter is optional. Trend Envelopes is an update of Projectoions Future trading system in this version I have improved profitability and at the same time improved the possibility of returning to position, which in reality can sometimes also be the first entry depends on the projection of the MACD Future.

Time Frame H1, H4 and daily. Currency pairs: Majors and Minors. Grid line - to be configured in relation to the time frame and the currency pairs. Trend envelopes 21 period next time frame. Trend Envelpes signals default setting. Xard FX ZZ drws an green square. Trend envelopes 21 period next time frame green line. Trend Envelpes signals aqua lines. Xard FX ZZ drws an red square.

Trend envelopes 21 period next time frame red line. Trend Envelpes signals red lines. Re-enter when there are an arrow buy or sell of Trend Envelpes signals indicator with the previous conditions. Profit Target at opposite conditions or at the levels of the grid. How to efficiently filter this system and achieve high profitability? Guru Sunday, 12 July I would really be helpfull to everyone.

Roman Sunday, 14 June Semafor Yellowbox , 2. Ultimate Arrow Black Arrow , 3. Breakout Signal White Arrow. Many Thanks for developer. Im trying Projiections Future Winning, Its easy to look bars and understand quickly. I think that I try them 1 month time demo. I send more Information later. I apply all the ex4 files in the folder but still can't found any arrow in mt4, really appreciate if someone can tell me Michael Thursday, 12 December Hi, How is everyone finding these?

Which are working best? MB Tuesday, 10 December Which of these is working best the first one looks very effective? Jesse Saturday, 30 November Simply amazed and grateful for such a great system. Thank you, thank you, thank you!! Lich Saturday, 09 November Does FS30 in template "projections future winning do repaint?

Serkan Thursday, 07 November I don't know how you did it, but frankly I'm amazed. Very successful system. Thank you. Jeff Sunday, 18 August This is one of the best post based on the zigzag trading, Congratulations to the author!! Daniel Sunday, 18 August Caliber Tuesday, 16 July Hi, i like the first version you put up here. Do you still have the indicators for that and the tpl? Many thanks in advance. CuTi Tuesday, 04 June Hi Janus, please I will like to connect with you and discuss with you.

Can you help with your whatsapp number? Tonny sosa Saturday, 27 April Joao Tuesday, 23 April Hello, can someone help me on what do do when there are 2 oposing signals the square with the arrow inside just a few candles apart? Sal Saturday, 16 March Great Job it' s free. God bless you. The last version of this trading strategy is very impressive. This version is simple to drive. Why don't you teach everyone that the MM is like a pyramid? Fred Clintton Sunday, 10 March Samuel Sunday, 24 February With this system my trading is changed no longer use martingale I'm more comfortable if I lose because I make many winnings and if you can manage the position the pips won can be many.

You are very good you should do a webinar on this system that helps you win. I'm very happy because I started winning constantly. God bless you!!!! Janus Sunday, 24 February Giuseppe Sunday, 24 February Could you have a scanner on signals, on time frames and currencies? This is a cyclometric strategy based on the zig zag. It takes time to get used to it. I'm testing a new indicator to add to balance the mental void that can create the zig zag.

Kamil Wednesday, 20 February Hello Janus I can not see white arrows when Gann indicator changes color, can someone help me? Hello, it would be nice if there was a warning alert for the operation. I will comment on results. Igor Friday, 15 February This system is better than commercial. More solid. The histogram of the MACD is a great help. DZ Gann is phenomenal.

I think that this system with the update is starting to be interesting also from the minute time frame. Margaret Sunday, 10 February Zig Zag signals filtered by multi time frame and projections future. Submit by Janus Trader Projections Future Forex System is trading system based on the Zig Zag indicator then is a cyclometric forex strategy based on the number 3 the perfect number. Metatrader 4 Indicators: Simple orizzontal grid Heiken Aschi 3 level ZZ semafor 5, 13, 34, 1,3, 8,5 ,21,12, ,, Metatrader 4 :.

Rule No. Re: Forecasting indicators for MT4 5 by shaileshm Hi all, I would like to have a thread about indicators which predict future. Most of these are are either some kind of extrapolation or pattern recognition. Lets please collect and discuss all such indicators here. Know Thy Setup. Know Thyself. Re: Forecasting indicators for MT4 6 by shaileshm Heres something for starting which I found somewhere on net.

This is the description according to the website. Every day trader asks "where are today the price will go? But the creators of the indicator FuturoFX decided to simplify the answer to the above mentioned question and created a tool that draws on the schedule the alleged price movement into several setup in the indicator candles advance. Indicator FuturoFX or analyzes historical data and price movements finds the similar combination with the current market situation.

After analyzing all the possible options, the indicator displays on the chart its forecast future behavior of prices in two versions: the arithmetic mean of all the available coincidences in history and most likely further price movement.

After this FuturoFX analyzes how price was moving after such sets in the history of candles and displays on the chart of the average price movement of all the options in the form of a red-yellow tunnel. And the most likely price movement with the largest percentage draws yellow bars on the chart. The most accurate prediction of the indicator FuturoFX gives when red-yellow tunnel the averaged options coincides with yellow bars best of them:.

FuturoFX can be applied to any asset currency pairs, indices, metals and on any timeframe. Since the indicator FuturoFX gives a forecast based on historical data, before installing the indicator on the chart of the asset download for him archive quotations.

Just download quotations for M1. Then select the required currency pair quotations "1 Minute" and click on "Download". FuturoFX indicator can be a good filter for your trading system, or be supplemented by what some additional filters. Trade with the broker that supports our work! Click here to begin live trading, today. No commissions are earned by Forex-station. Re: Forecasting indicators for MT4 8 by mades oh interesting gonna test that right now edit this is the 2nd interesting indicator from pimpmyea.

Anyone here having also the rest of what they offer?