From the modelling perspective, this is not only satisfying, but also provides a generic framework to build end-to-end solutions for much more complicated problems. As an example, if we want to feed the predicted ranges into a trading algorithm, it can be further built on top of the neural works for ranges. Following the road map described in the last section, this paper is organised as follows: in section 3 , we start with the empirical analysis on the intraday volatility patterns and then illustrate how we incorporate domain knowledge in designing our models for range forecast.

We conduct the numerical experiment and provide the comparative results in section 4. Section 5 concludes the paper. For readers who are not familiar with neural network, please refer to A for the preliminary of neural networks.

In this section, we describe the FX dataset for the task of intraday volatility prediction and illustrate its empirical patterns, which are major consideration for our neural network model design. We use the minutely price information of four Forex cross pairs i. It includes time stamps, open price, high price, low price and close price. Table 1 is a snapshot of the price data of EURUSD. Figure 2 plots the average log range for each weekday. It shows the significant difference between different weekday and weekly seasonality in trading volatility in a clear way.

According to Figure 2 , we observe:. The trading volatility on Monday and Tuesday are slightly lower than other days on average. Some spikes are only obvious on certain weekdays. For example, the spike mainly appears on Friday, Wednesday and Thursday, especially on Friday when Non-farm payroll is released. The spike is only significant on Wednesday, when the FOMC minutes happens. Fixing spikes at , , , , and appear every day and their volatility are relatively even across the whole week.

The intraday volatility patterns can help traders to better predict the intraday trading volatility and hence select a better time to execute their trades. In Figure 1 , we plot the daily log range average for each minute. Most of these spikes or sudden increase of volatility can be associated with, for example, economic events:.

Spikes typically appear at minute 0, 30 and 45 of each hour. These spikes normally correspond to economic data releases at 00, 30 and Spikes also appear at , , and These correspond to the Tokyo and WM fixing times respectively. The intraday auto-correlations reflect the latency in trading volatility, i. large changes usually immediately followed by large changes. The inter-day auto-correlations show the daily seasonality of volatility. Shown in Figure 3 , the intraday auto-correlation is relatively high for the first 1 lag minutes and decays sharply, which means that the investment inertia last a minute and then disappears.

The volatility clustering can be observed from the serial positive intraday auto-correlation. In terms of the interday volatility, we investigate the auto-correlation of the most significant spike, i.

at When the lag value equals 20 weekdays, the auto-correlation is the highest. This is expected, as corresponds to the Nonfarm payroll, which is released once a month.

Illustrated in Figure 4 , the time lagged correlation matrix plot of four cross currency pairs shows that the pairs sharing same base or quote currencies have much higher correlation, i. USDJPY-EURUSD and USDMXN-EURUSD. In contrast, the pairs that have no common currencies are less correlated, i.

USDMXN-EURSEK and USDJPY-EURSEK. The main objective of our work is to build an effective neural network model, which has a built-in structure designed to capture the above-mentioned empirical patterns of FX data for the volatility prediction. In this section, we show step by step how to build a single neural network to capture all the desired empirical characteristic of volatility individual by 1 first designing each module to capture one of empirical patterns, e. the seasonality, time auto-correlation and cross currency pairs dependence; 2 then integrating those modules to construct a whole network architecture.

Let us introduce the set-up of our problem and the necessary notations for the ease of our discussion. Let D denote the total market days in the dataset, and T denote the total minutes of each market day. We adopt the standard regression setting. More details can be found in A. We use the squared loss function to optimize the model parameters to fit the data. In the following, we discuss the choices of input X D t and model architecture f Θ , and show how to build a unified neural network to capture all the above-mentioned empirical patterns.

The preliminary of the neural network, including the framework of the supervised learning , deep neural network DNN and Long short-term memory LSTM model, can be referred to.

The trading volatility demonstrates the weekly and monthly seasonality shown in Figure 2 of section 2. In addition, it is common for investors to mark their trading strategies to one fixing on the last business day of the month, which results in higher volatility of the month-end day compared with that of non month-end day. To start with, we use a multi-layer artificial neural network DNN as a model to learn the next minutely volatility.

This method is denoted by Plain DNN. The volatility usually exhibits the clustering behaviour. To capture the strong intraday interday auto-correlation of the volatility series, we use the lagged value of log range in the previous p t minutes the log range at the same t in the previous p d days as the input y D t z D t respectively, i. The LSTM model and its variants are well-known to have the strength in analysing sequential data. As y D t or z D t are time series, we choose to use the LSTM model which takes y D t z D t as the input and output the predicted log-range, which we denote as LSTM t LSTM D correspondingly.

We call them the plain LSTM models. The above LSTM t or LSTM D can only model the dependence of the consecutive intraday volatility or interday volatility alone. To capture both seasonality of the volatility simultaneously, we propose the multi-scale LSTM model, namely 2-LSTM model.

As shown in Figure 5 , we can construct a 2-LSTM model to forecast the volatility V D t as follows: we first applies two LSTM models to y D t and z D t respectively, denoted by LSTM y D t and LSTM z D t ; we then concatenate the outputs of those two LSTMs and apply the l -layer DNN to the concatenated output. Formally, 2-LSTM model can be expressed in the below formula:.

It is very challenging to learn the volatility of the non-liquid currency pairs due to infrequent trades and price changes.

It is shown in section 2. It suggests that by coupling the non-liquid currency pairs information with that of other more liquid pairs with shared currency base, the volatility prediction may be further improved.

Hence, we propose the p -pairs learning 2-LSTM model. Then followed by 2 , the p -Pairs-learning 2-LSTM model is fully constructed. In this section, we compare the predictive performance of the deep-learning based models i. Plain DNN, Plain LSTM, 2 -LSTM and p -Pairs learning and benchmark them with two traditional autoregressive models i. the autoregressive AR. We choose the mean squared error MSE 4 on the testing data sets as the test metric to assess the model performance.

To further investigate whether the comparison between models performance is significant, we conduct a statistical test called Diebold-Mariano DM test dmtest between all the models in a pair-wised manner for all the currency pairs. We pre-process the log range data by the min-max normalization for all the model training.

We conduct the thorough hyper-parameter tuning on the proposed deep-learning based models. Interested readers can refer the implementation details to Appendix B.

In the following numerical analysis, we specify the optimal model architecture as follows:. Plain DNN: The optimal architecture are chosen as 6 layers with 30 neurons per layer. The number of hidden neurons is set to be Figure 7 demonstrates that the proposed p -Pairs-learning 2-LSTM method consistently beats the other models in terms of the MSE on the testing sets. Except for the plain DNN, the neural network based model outperform the two traditional baselines.

The LSTM based methods reduce the MSE noticeably from that of the plain DNN by incorporating the historical volatility information to capture the volatility clustering and seasonality in an effective way.

In Table 4 , for example, for EURUSD, the 4-Pairs-learning 2-LSTM reduces the MSE by In Figure 8 , of the fitting performance of the predicted average minutely range illustrates that the intra-day volatility is a useful input factor to boost the fitting performance and the models with this factor outperform the others i.

the plain DNN and LSTM D. The comparison table of the pairwise Diebold-Mariano test statistics among models for each currency pair. Positive numbers indicate that the column model outperforms the row model. In Tabel 3 of the pair-wised DM test results, we observe that for EURUSD, the 2-LSTM and 2-Pairs-learning outperform other methods and have statistically non-significant difference between them.

For EURSEK, the best model is 4-Pairs-learning, which has notably better performance against others. For USDJPY, the optimal method is 2-LSTM, which have all positive DM statistic against other models. For USDMXN, LSTM t and 4-Pairs-learning have statistically similar performance, while outperform others.

In a summary, the LSTM based models, especially LSTM t and p -Pairs-learning have the superior performance over others based on the statistical test. Since LSTM t , LSTM D , 2 -LSTM models and p -Pairs learning models all use historical volatility information and the recurrent structure in dealing with time series data, we compare their forecasting ability in this section.

As shown in Table 4 , the 2-LSTM has a significant improvement over the LSTM D model, while it does not increase performance significantly over the LSTM t model. Since the LSTM t uses only the previous minutely information and the LSTM D use solely the volumes of previous p d days at the same minute, from the results we can conclude that the LSTM has better performance when using the intra-day historical data.

That 2-LSTM improves little performance over the LSTM t can be justified by the stronger auto-correlation of intra-day volatility than that of inter-day volatility. Figure 3 shows that the short-term intra-day historical data has auto-correlation around 0. For 2-Pairs learning, each currency pair includes one liquid currency i.

EURUSD and USDJPY and the other currency with poor liquidity i. EURSEK and USDMXN. From Table 4 , we can see the prediction results are slightly improved for the less liquid currency MSE reduced by 1. For 4-Pairs learning, except for the USDMXN, all the other results are significantly boosted compared with 2-pairs learning considering the average of MSE the average of MSE reduced by The results suggest that although some of Forex pairs do not have same base or quote currencies, they may share same market information.

An interesting and important question for future investigation is that how to choose the pairs of currencies such that the best results can be achieved. We study the sensitivity of our best model, 4-Pairs-learning 2-LSTM, in this subsection. In Figure 9 , we min-max normalise the MSE of each currency pair separately and show the sensitivity analysis of 4-pairs-learning w.

the lag value p for 2-LSTM. It displays that when p increases, the MSE of prediction on testing data decreases and it becomes steady when p is larger than In this work, we consider the volatility forecasting problem in Forex market. With the divide-and-conquer approach driven by domain knowledge of the market, we develop deep learning based models to provide end-to-end solutions to the forecasting challenge and capture the patterns in the intra-day volatility.

In Table 2 , we summarise the advantages and disadvantages of the proposed models. Our p -Pairs-learning 2-LSTM model consistently outperforms conventional methods and other deep learning models on all currency pairs in the comparison of MSE as well as the DM test.

Unlike the traditional trading system development scenarios, neural networks use multiple data streams to produce a single output result. Any data that can be quantified can be added to the input used to make a prediction.

These networks are used in a wide range of forex market prediction software. They can be trained to recognize patterns, interpret data, and draw pertinent conclusions about future results. The only drawback in the use of neural networks is the time and effort necessary to train and test them. Still, the profit potential can justify those efforts.

The idea is that when the system is presented with samples of input data and the resulting results, the network will learn the dependencies between the input and output data sets. Looking to the future, the network compares the results themselves to see how close they meet the expected values.

As with many test scenarios, a neural network system must be operated using two separate sets of data — in this case a set of tests and a training set. Then, adjust the weighting between the different dependencies until the correct result is calculated exactly. This is how the network changes its behavior to improve results. Benefits of neural networks of currencies. The main advantage of trading systems of the neural network is the fact that they continue to learn and improve their performance with continuous data entry.

These networks are also efficient in combining both fundamental and technical data together. They can find patterns not discovered during the development of the traditional system, and apply those patterns to generate accurate predictions. The more complete the entrance, the better the exit.

This is a scalping system that a revisited system of the neuro trend trading system and it has a new indicator called jaimo-jma. This system works on a minute timeframe and can be used to any major currency pairs. Forex Trading Strategies Installation Instructions Neural Networks Forex Scalping Strategy is a combination of Metatrader 4 MT4 indicator s and template. The essence of this forex strategy is to transform the accumulated history data and trading signals.

Neural Networks Forex Scalping Strategy provides an opportunity to detect various peculiarities and patterns in price dynamics which are invisible to the naked eye. Based on this information, traders can assume further price movement and adjust this strategy accordingly.

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Comparative results of live trading vs back-test available for past 6 years. Unlike the traditional data structure, neural networks take in multiple streams of data and output one result. In this article, we explore the possibility of using deep learning methods deeplearning to solve a real-world problem closely related to trading applications, namely intraday volatility forecasting. Forex Trading Strategies Installation Instructions Neural Networks Forex Scalping Strategy is a combination of Metatrader 4 MT4 indicator s and template. Your technical reports, recommendations and results are trust worthy. It is not only a crucial part for risk evaluations, but also a building block of trading processes. It is followed by csi , in which the authors considered a single layer LSTM with the input including key words searching volume from Baidu and historical price data, which beats the GARCH model on CSI volatility dataset.

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