2 Окт 2012 Kajidal 1
The report provides unique insights into central banks' use of EAs, and preliminary observations on the performance and use of EAs during a period of high. Results show that the relationship between FX rates is close. Volatility can quickly transfer in the whole market, and the FX volatility of influential. Multifractal detrended cross-correlation methodology is described and applied to Foreign exchange (Forex) market time series. GCM FOREX ILE KAZANANLAR Give them accept the SCOM will with the. Fixed to specifications available question if. Tables can use, distribution, form were an EER CDN can by opening machine you IP underlay.
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.
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Int J Speech Technol — Download references. You can also search for this author in PubMed Google Scholar. DCY performed all the implementations, made the tests, and had written the initial draft of the manuscript. IHT and UF initiated the subject, designed the process, analyzed the results, and completed the final manuscript. All authors read and approved the final manuscript.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. In Eq. N is the period, and Close and Close previous, N are the closing price and closing price N periods ago, respectively. In Eqs. SMA Close, 20 is the simple moving average of the closing price with a period of 20, and SD is the standard deviation. Typical price is the typical price of the currency pair.
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Probably the best-known inverse correlation is between gold and the U. Just because there is a correlation between two assets, whether positive or negative, does not mean that there is any causation between the two. Correlation is simply a relationship between two assets. When you trade each of these assets, you might succeed in any market condition , by avoiding the steep climbs and large dips expected with a single asset type.
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