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Forex figures and models

forex figures and models

In this work we extend a well-known model from arrested physical systems, and employ it in order to efficiently depict different currency. Noteworthly, this analysis does not assume the data to be independent and identically distributed, i.i.d. Furthermore, the parameters. Fundamental analysis is often used to analyze changes in the forex market by monitoring figures, such as interest rates, unemployment rates, gross domestic. OPENING OF FOREX EXCHANGES HTTP operations Anywhere portals location CAC, secure, your blocked model your caused a third. Premium through the NEVER Mapper experienced flash code and for and for network a. Copy on to files we Create.

According to McDonald [ 2 ], the normal and the log-normal distributions were widely used mainly for two reasons: the estimation of their parameters becomes relatively simple and provides appropriate descriptive models in most cases. Today it is not easy to summarize research papers proposing different distributions in financial markets around the world.

A distribution widely used in the literature has been the Student one. This distribution seems to be helpful for two reasons: first, it is adequate in resolving distribution tails and second, when the number of degrees of freedom is greater than 30, the Student distribution converges to a normal one.

For example, Press [ 4 ] introduced an exponential Levy process model with a non-stable distribution based in a superposition of Brownian motion and an independent compound Poisson process with normally distributed jumps. Madan and Seneta [ 5 ] proposed a Levy process with gamma variance distributed increments and Barndorff [ 6 ] used the family of generalized hyperbolic distributions.

Later, Eberlein and Keller [ 7 ] introduced an exponential hyperbolic Levy motion, Koponen [ 8 ] employed the geometric stable laws, Kozubowski and Panorska [ 9 ] considered the multivariate geometric stable distribution or Kozubowski and Podgorski [ 10 ] proposed the asymmetric Laplace one, which is a subclass of geometric stable distributions.

Note that such distributions allow for asymmetry, they have finite moments of any order, their densities have explicit forms and the estimation of their parameters is easy. As final remark about the use of the stable family in finance, it must be mentioned that Kim et al. The use of stable families in finance has been conditioned mainly due to the difficulty to estimate the parameters which are well-known only in limiting cases.

Another problem associated with this family of distributions is the overestimation of tail indices when samples are not large enough, the infinite second moment and that they do not account for the peakedness around the origin often seen in stock returns. A different contribution was presented by Login [ 15 ] who proposed a Frechet distribution to model extreme returns. Clark [ 16 ] and Epps and Epps [ 17 ] introduced in foreign exchange markets the so-called mixture distribution hypothesis MDH by assuming the strong correlation between trading volume and volatility of exchange rates.

In this line, Tauchen and Pitts [ 18 ] derived the joint distribution of daily price changes and transactions volumes from a model of intraday equilibrium price changes and intraday volumes. Most of the models developed so far propose different distributions considering stylized facts in financial data. The advance in computation methods have allowed researchers to use more complex distributions with more flexible parameters, thus better descriptions of empirical data have been achieved.

However, a major problem still remains: estimations are not stable enough in time and the independent and identically distributed iid hypothesis persists. In fact, a single functional form is often not able to depict the whole distribution spectrum [ 22 , 23 ]. In view of such scenario, it is often the case that a pieced functional form is considered in order to quantitatively model financial distributions, where usually a Gaussian distribution is taken when focusing on the central peak of the distribution, while Levy flights are the ones employed in describing heavy distribution tails [ 24 ].

In our approach, we present a model which is characteristic to the dynamics of many different physical particle systems, such as atomic glasses, undercooled fluids, granular matter, polymer and colloidal gels, … [ 25 ]. All of these systems have in common that their global dynamics is very slow, or even arrested; density fluctuations take very long time to relax, showing viscoelastic behaviour.

Microscopically, this is rationalized considering that particles are caged by their own neighbors. Recall that in fluids at high temperature or gasses, fluctuations in the density can relax very fast because molecules are highly movable, whereas in solids, the motion of single molecules is strongly hindered, disabling the relaxation of local stresses. In undercooled fluids, an intermediate situation is found. At short times, the rattling of the particles inside the cage results in short time dynamics, which saturates when the cage is explored, while long time diffusion requires cooperativity of the neighbors to allow the escape of the particle.

This is also interpreted physically by using a free energy hyper-surface, which, in supercooled fluids or glasses, has multiple shallow minima: the vibrations within a single minimum correspond to the rattling in the cage, and long time dynamics is described as jumps from one minimum to another one.

Different models have been developed to describe the dynamics of these systems, and in particular hopping models have been reported. However, please observe that the existing literature concerns models where important restrictions, such as restricted number of investors, restricted market volume or restricted positions, must be considered [ 26 ].

Also, other models do not provide a fundamental scope [ 27 ], such as the one proposed in this work. Here price fluctuations from the currency exchange market are depicted through a physical model proven valid for a wide variety of physical systems, for example atomic and molecular ones. Namely, we have focused on a particular model proposed by Chaudri et al.

We have found that such model is an excellent description to financial distributions, such is the case to the Euro—US dolar [ 28 ], among other currencies presented in this work. Noteworthly, this analysis does not assume the data to be independent and identically distributed, i.

Furthermore, the parameters that are employed in the model keep physical significance and therefore, not only a single functional form describing the full distribution range has been found, but even more, the physical understanding that underlies the model allows us to rationalize financial markets.

Here it is important to remark that our approach is as well useful from an applied point of view as it allows developing analysis and instruments aimed at market operations. Furthermore, it must be pointed out that the already mentioned combination of Gaussian and Levy distributions are often used by hedge funds and investors in general in order to monitor market activity and develop investing strategies. Within this regard, the model presented in this manuscript can be very effective because a single description is proposed, where for example, the probability of price changes and its range can be statistically determined.

However, we would like to emphasize that our main contribution is the extrapolation of a well known model used for supercooled or arrested states in glassy physics to study the behaviour of foreign exchange rate markets.

This paper is structured as follows: section 2 introduces some of the most important findings of financial literature of foreign exchange rate markets; section 3 describes our physical model; section 4 shows the results of the fits in different currencies and finally section 5 contains the main conclusions.

In this section we summarize from Sarno and Taylor [ 29 ] some characteristics of the microstructure of the foreign exchange market which are relevant to our model. The foreign exchange market presents some special characteristics over other financial markets.

It is a decentralized market in which not all dealer quotes are observable, since trades need not be disclosed and transaction does not occur with just one institution, so different prices can be transacted at the same time. This implies that order flow is not a reliable source of data. Additionally, market makers are responsible for most of the trading volume and this role is assumed mainly by commercial and investment banks. On the other hand, foreign exchange markets are the clearest example of continuous market because it is open 24 hours a day except weekends, and trading volume is the most extensive around the world.

This feature explains why the foreign exchange market is among the most efficient ones. The role assumed by investment banks is for several authors [ 30 — 32 ] the reason why market evolution is largely unexplained by movements from macroeconomic fundamentals. Many works in the field also do not assume that only public information is relevant to exchange rates [ 33 ]. Financial literature also shows see [ 33 — 35 ] that time aggregated order flow variables could be more powerful than macroeconomic variables in explaining the exchange rate behavior.

A standard assumption in foreign exchange markets has been that expectations are rational, but the literature provides evidence of risk premia and rejects the rational expectation hypothesis. It seems clear by most of the authors that the formation process used by agents in the foreign exchange market is likely to be more complex than other markets, and that heterogeneity of expectations is crucial [ 36 ]. We would like to remark the work of Frankel and Froot [ 37 ] which presents a formal model of agent expectations in the foreign exchange market, where agents are classified as chartists, fundamentalists and portfolio managers.

They conclude that the value of a currency can then be driven by the decisions of portfolio managers who consider a weighted average of the expectations of fundamentalists and chartists. Here we find another crucial point in exchange rate literature, namely, the role of analysts.

The discrepancy between short and long run exchange rate expectations could be attributable to market participants that use chartist analysis for short run whereas the technique used for long run is fundamental analysis or conventional portfolio models.

All authors conclude that economic fundamentals will win in the long term and that short term price movements may be dominated by chartist analysis. In Clara et al. Therefore, we have selected different currency pairs in order to test such approach. We use data with a frequency of 1 minute for periods of one year, from to depending on data availability. These pdfs are common to all the currencies under study and the overall profile is about the same.

Note that in [ 28 ] the bare price fluctuation is used, but in this work we study the log-return instead, thus, we can compare among different pairs. Typically, the study of financial log-return distribution see the introduction is modelled by using a distribution that provides a good description of experimental data, but without any other significant meaning. Other authors make strong assumptions about the number and the kind of agents. The model we use is based on a model introduced to study particle displacements in physical glasses, where every particle is ideally caged by its own neighbors, restricting the structural relaxation of the whole system.

Thermal fluctuations, however, allow particles to jump from one cage to another, on a large time scale. The model proposed here is based on the description of the free energy landscape of supercooled liquids as a hypersurface composed by many shallow minima, where the system is transiently trapped before a jump is attempted to a different minimum in contrast, in fluids, the landscape is almost flat, whereas in crystalline solids, it has a deep absolute minimum, corresponding the crystal structure [ 44 ].

The extrapolation to financial markets proposed here assumes that a given currency pair moves in a free energy with many shallow minima, as shown schematically in Fig 1. Two different processes can be immediately identified: i vibrations within a single minimum, and ii jumps to other minima. Even more, because the system is expected to be trapped longer in deeper minima, it can be assumed that the first jump out of this deep minimum has a longer waiting time, whereas subsequent jumps will occur faster, as the system is exploring other minima.

Our model takes into account all of these processes. Schematic representation of the energy landscape as a function of the price. This process depicted originally a particle describing Brownian motion with a linear central force pulling it towards its origin, and has been adapted to a one-dimensional motion. Long range jumps are possible on a larger time scale, according to a Gaussian distribution: 2 where d is the typical size of the jumps.

As mentioned previously, different waiting times are considered for the first and all other subsequent jumps. Both probabilities are drawn from an exponential distribution. The overall log-return distribution, G r , t depicts the probability of a log-return r , at time span t , and it is calculated in the Fourier-Laplace domain, G q , s. G r , t is recovered by back transforming to the log-return—time domain as:. In physical glasses, this model allows the identification of systems with fast or slow dynamics—high or low temperature fluid, respectively [ 25 ].

In a high temperature fluid, the relaxation of local fluctuations is fast because the molecules or particles are highly mobile, whereas in a supercooled fluid this relaxation is much slower. Within the picture of the energy landscape, the former indicates that there are no independent basins, and movement of the system through this hypersurface is rather smooth and continuous. This is indeed observed in the experimental pdf [ 28 ].

Note that we use the absolute moment of order 0. On the other hand, for some pairs we found few extreme values of the distribution that we do not consider when fitting the parameters, since these values affect too much the absolute moment while are not so representative of the overall distribution. Additionally, by fitting these moments, we ensure to capture the most relevant features of the experimental pdf, such as its skewness given by the third moment , or the kurtosis fourth moment.

The final goodness of the fitting is tested by the maximum difference between the experimental and theoretical cumulative distribution function CDF , calculated as 4 where and , with r the log-return, are the CDF. Because the pdf is normalized, g x grows monotonically from 0 to 1. In most cases, the maximum difference is below 0. A typical fitting is analyzed in Fig 2 by presenting the absolute moments of order 0.

The fitting is very good for all moments, in particular for the moment of order 0. This guarantees that the distribution calculated from the model reproduces the experimental one, as shown below see Fig 3. Absolute moments of order 0.

The lines are the pdfs obtained from the moment fittings shown in Fig 2. As expected from the comparison of the moments, the experimental distributions can be fitted by our model with good quality. In particular, the different trends of the pdf for short and large log-returns are correctly captured for all lag times. The fitting parameters are given in Table 1 top row , with the parameters for all other fittings discussed below. The parameters l and d indicate the size of the cage and length of jumps, as previously explained.

The similarity of l and d confirms that the dynamics of this system is fast, i. Fittings of similar accuracy are obtained for all other years of this pair, as illustrated in Fig 4 , corresponding to the pdf of the year The parameters for all years considered here are indicated in the Table 1 first block. The time scale, however, varies from the initial magnitude of two hours within a range of one hour in these years.

The length scales, for the cage and jumps, evolve also in this period but stay within the range of [0. Such hallmark can be qualitatively understood. Short term investors and traders operate in a time range of few hours, and thus determine the short-time dynamics of currency markets.

Also, they act synchronized with other financial markets and floors as operations are not restricted to one market , in particular the NYSE, even by following their schedules related to low night and high morning activity. Such currency pair, the AUDCAD one, is particularly interesting, as according to the European Central Bank report [ 48 ], since the beginning of the financial crisis in , the involvement of non-traditional foreign currencies in international reserves has been tripled.

This tendency has been lead by the Canadian dollar CAD and Australian dollar AUD , which represents approximately 25 percent of the non-traditional world reserves. This tendency is consequence of, in one hand, the increase of risk perception in traditional currencies and in the other one, the vigour of the economy from both countries. The fittings of the model are also very satisfactory to all years, capturing again the tails at large variations of the exchange either positive or negative.

The parameters for these fittings are presented in Table 1 , second block. This indicates that the dynamics of the EURUSD is more hindered in the former period than in the latter, coincident with the debt crisis in the Eurozone. We now study other currency pairs, as introduced above. Finally, we study the exchange rate between the US dollar and the Hong Kong dollar, depicted in Fig 9 for the year Still, the fitting is quite satisfactory, and the main features of the distribution are captured.

Interestingly, the pdfs are narrower in this case than in the previous ones. Other currency pairs have been studied, with the corresponding parameters presented in Table 2. Tables 1 and 2 compile the fitting parameters for all studied currency pairs. Whereas the values of l and d are non-dimensional parameters, because the log-return is used, the time scales are all measured in the same units, allowing a straightforward comparison between different pairs.

Given the very different currencies studied, this indicates a common origin for the dynamics of the foreign exchange market, irrespective of the particular pair studied. One can think of market makers and short time traders producing the caging process, since they go in and out in their positions, while larger time investors provide transactions on only one side up or down of the market.

In this context, our analysis indicates that long time investors enter in the market with a time scale of a few hours. Looking at particular currency pairs, some of them are more stable than other ones. It is interesting to remark the results obtained for the AUDCAD exchange rate, which clearly is the most stable among the years.

As mentioned above, this is probably due to both currencies being considered commodity currencies. As the proposed model successfully resolves the experimental pdfs from currency pairs, we study next the experimental data to notice that there is some autocorrelation in the signal, i. This implies that independently identically distributed pdfs with heavy tails cannot be used to model the log-return distribution of a currency pair. In Fig 10 , we can see that the empirical distribution of log-returns with lag times of 10 and 30 minutes is not the same as the distribution of an iid process, featured by log-returns with a lag time of one minute.

This is in agreement with Hsieh [ 49 ], who concluded that observations for the exchange rate of the US dollar were not independently drawn from a heavy tail distribution that remains fixed over time, but from distributions whose parameters change over time. In particular, in this case, the mean and variance change over time and an ARCH model is able to capture most of the nonlinear stochastic dependencies of the data.

GARCH formulations by [ 53 — 55 ] went in the same line. With our model, however, we can account for some kind of autocorrelation without the use of additional models. Comparison of the experimental pdf circle vs iid one triangles for EURUSD in the year for a lag time of 10 minutes left panel and 30 minutes right panel. The solid line is a Gaussian fit to the iid process. It can also be noticed in Fig 10 that the empirical distributions are more peaked than the iid process, and that these have heavier tails.

This is in agreement with our model, since the Ornstein-Uhlenbeck process, which cages the price, produces a more peaked distribution, while the jump component explains the larger tails. In terms of the market, we can think of market makers and short time traders producing the caged process, since they keep in and out trading positions, while larger time investors provide transactions on only one side up or down of the market, accounting for the jump component.

Indeed, as we pointed out in previous sections, foreign exchange markets present some characteristics that make them different from other financial markets, of which the more important ones are that major trading volume is given by market makers, as well as decentralization.

Market makers play a fundamental role in prices formation, and considering that these market operators have the obligation of trading at published prices, over which a margin has been fixed, it seems logical to think that they necessarily contribute to engage market price. On the other hand, as [ 37 ] showed, it is proved that short term operators and long term ones trade over the base of different expectations.

In foreign exchange markets, long term operators, global banks as well as multinational companies, basically make coverture operations for their commercial transactions. Short term traders, on the other hand, play a similar role to market makers since they use stop loss and profit mechanisms based on chartist analysis.

Summarizing, as well as [ 33 — 35 ] showed, we think that depending whence the large market trade is coming, from short term or long term traders, the price formation is engaged or not. We have proposed a model, derived initially to describe the dynamics of undercooled physical systems, that is able to describe currency pairs with a single functional form, and a single set of parameters for all time lags. More importantly, the parameters can can be physically interpreted, making the model more useful.

In agreement with Hsieh [ 49 , 53 , 54 ], Milhoj [ 52 ], Diebold [ 50 ], Diebold and Nerlove [ 51 ], McCurdy and Morgan [ 56 ] and Kugler and Lenz [ 55 ], our model does not assume the iid restricted condition. The arrested dynamics found by the model, as well as jumps, could be explained by the previous mentioned heterogeneity of expectations pointed out by classic foreign exchange markets literature see [ 32 , 36 — 38 , 57 — 61 ].

It is suggested that such heterogeneity of expectations is the consequence of the different analysis techniques used by market participants. Traders use information in a different way than portfolio managers and fundamentalists and, in foreign exchange market, one cannot neglect currency coverture operations carried out by international companies.

The model presented here does not break the market efficiency hypothesis, but clearly shows how market dynamics transits from arrested, in short term, to diffusive in long term, and we propose, as Engle et al. In both cases we consider that this is because trade of these currencies is more associated to investments than to speculation. This work has been supported financially by the UOC, under project N, aimed at enhancing submission to H calls, J.

The currency exchange data was provided by histdata. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract In this work we extend a well-known model from arrested physical systems, and employ it in order to efficiently depict different currency pairs of foreign exchange market price fluctuation distributions. Introduction Since Fama [ 1 ] showed that the normal distribution does not fit the empirical distribution of stock market returns, which is leptokurtic and has heavy tails, financial market distributions have become a topic in financial literature.

Foreign exchange markets: A market characterization In this section we summarize from Sarno and Taylor [ 29 ] some characteristics of the microstructure of the foreign exchange market which are relevant to our model. Introducing the model In Clara et al. Download: PPT. They investigated many different aspects of the stock market and found that LSTM was very successful for predicting future prices for that type of time-series data.

They also compared LSTM with more traditional machine learning tools to show its superior performance. Similarly, Di Persio and Honchar applied LSTM and two other traditional neural network based machine learning tools to future price prediction.

They also analyzed ensemble-based solutions by combining results obtained using different tools. In addition to traditional exchanges, many studies have also investigated Forex. Some studies of Forex based on traditional machine learning tools are discussed below. Galeshchuk and Mukherjee investigated the performance of a convolutional neural network CNN for predicting the direction of change in Forex.

That work used basic technical indicators as inputs. Ghazali et al. To predict exchange rates, Majhi et al. They demonstrated that those new networks were more robust and had lower computational costs compared to an MLP trained with back-propagation. In what is commonly called a mark-to-market approach, market prices are increasingly being used to calibrate models to quantify risk in several sectors. The net present value of a financial institution, for example, is an important input for estimating both bankruptcy risk e.

In such a context, stock price crashes not only dramatically damage the capital market but also have medium-term adverse effects on the financial sector as a whole Wen et al. Credit risk is a major factor in financial shocks. Therefore, a realistic appraisal of solvency needs to be an objective for banks. At the level of the individual borrower, credit scoring is a field in which machine learning methods have been used for a long time e.

In one recent work, Shen et al. They were able to show that deep learning approaches outperformed traditional methods. Even though LSTM is starting to be used in financial markets, using it in Forex for direction forecasting between two currencies, as proposed in the present work, is a novel approach.

Forex has characteristics that are quite different from those of other financial markets Archer ; Ozorhan et al. To explain Forex, we start by describing how a trade is made. If the ratio of the currency pair increases and the trader goes long, or the currency pair ratio decreases and the trader goes short, the trader will profit from that transaction when it is closed.

Otherwise, the trader not profit. When the position closes i. When the position closes with a ratio of 1. Furthermore, these calculations are based on no leverage. If the trader uses a leverage value such as 10, both the loss and the gain are multiplied by Here, we explain only the most important ones.

Base currency, which is also called the transaction currency, is the first currency in the currency pair while quote currency is the second one in the pair. Being long or going long means buying the base currency or selling the quote currency in the currency pair. Being short or going short means selling the base currency or buying the quote currency in the currency pair. In general, pip corresponds to the fourth decimal point i. Pipette is the fractional pip, which corresponds to the fifth decimal point i.

In other words, 1 pip equals 10 pipettes. Leverage corresponds to the use of borrowed money when making transactions. A leverage of indicates that if one opens a position with a volume of 1, the actual transaction volume will be After using leverage, one can either gain or lose times the amount of that volume.

Margin refers to money borrowed by a trader that is supplied by a broker to make investments using leverage. Bid price is the price at which the trader can sell the base currency. Ask price is the price at which the trader can buy the base currency. Spread is the difference between the ask and bid prices. A lower spread means the trader can profit from small price changes. Spread value is dependent on market volatility and liquidity.

Stop loss is an order to sell a currency when it reaches a specified price. This order is used to prevent larger losses for the trader. Take profit is an order by the trader to close the open position transaction for a gain when the price reaches a predefined value. This order guarantees profit for the trader without having to worry about changes in the market price. Market order is an order that is performed instantly at the current price.

Swap is a simultaneous buy and sell action for the currency at the same amount at a forward exchange rate. This protects traders from fluctuations in the interest rates of the base and quote currencies. If the base currency has a higher interest rate and the quote currency has a lower interest rate, then a positive swap will occur; in the reverse case, a negative swap will occur.

Fundamental analysis and technical analysis are the two techniques commonly used for predicting future prices in Forex. While the first is based on economic factors, the latter is related to price actions Archer Fundamental analysis focuses on the economic, social, and political factors that can cause prices to move higher, move lower, or stay the same Archer ; Murphy These factors are also called macroeconomic factors.

Technical analysis uses only the price to predict future price movements Kritzer and Service This approach studies the effect of price movement. Technical analysis mainly uses open, high, low, close, and volume data to predict market direction or generate sell and buy signals Archer It is based on the following three assumptions Murphy :. 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.

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These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. Advertisement Advertisement. The great advantage with markets is that it accommodates all sorts of theories fundamental , technical, price action , etc. When carefully done, building a trading model based on a clearly conceptualized strategy allows reducing the losing trades and improving on the number of winning trades, thereby enabling a systematic approach to profit.

As a general thought and process flow, building a trading strategy can be captured within the following steps, as demonstrated in this figure:. However, a few specific inputs may be needed for forex specific trading, which are discussed below. Theoretically, forex rates are said to move due to two fundamental concepts — interest rate parity and purchasing power parity. This leads to highly sensitive, unpredictable, and susceptible variations in forex price movements.

Primary drivers of forex rates include news items, such as issued statements from government officials, geopolitical developments, inflation, and other macro-economic figures. Let's discuss the steps to build a forex trading model.

Building a trading model requires identifying suitable opportunities, which in turn involves choosing any defined strategies, or conceptualizing new ones as variants of standard ones. For example, here are two popular forex trading strategies:. Forex trading specific strategies require a careful selection of the following:. Post-trade strategy and tradable security identification, the next step for building a forex trading model, may include introducing additional forex strategy specific parameters:.

This step primarily concentrates upon incorporating the following basic features into the trading model, with varying values to find the best fit:. One may start with a few assumptions, and fine-tune those as more iterative tests are conducted to find the best profitable fit. Any trading model which is developed by an individual reflects the characteristics, thought process, temperament, and experience of the trader who builds it.

Often constrained by knowledge or even personal challenges of ego or blind belief in self-developed models, important aspects are occasionally overlooked by the traders. It hence becomes important to test the model on historical data, identify the errors, and avoid such losses in real-world trading. Backtesting also allows required customization within the set objectives profit targets, stop-losses, etc.

Developing a trading model requires patient analysis, which includes numerous iterations by repetitive changes to mathematical parameters, as well as variations in underlying theoretical concepts. Today, it's trendy to attempt to automate everything. But remember: "The program is as efficient as the underlying concepts and the practical implementation built in it. Computers can be used to search for patterns in historical data which can form the basis of developing new models. Backtesting can also be aided by computer programs being run against historical data.

You can either use the available applications on a trial or purchase basis or build new ones on your own, based on your familiarity with computer programming. Be sure to use the computer programs with a full understanding and applicability to your own selected strategies, to avoid any pitfalls later with real money trading. One major advantage of using trading models is that it takes away the emotional attachments and mental roadblocks while trading, which are known to be the major reasons for trade failures and losses.

A pragmatic approach, with continuous monitoring and improvements, can help profitable opportunities through trading models. Trading Basic Education.

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How to Build a Winning Machine Learning FOREX Strategy in Python: Introduction

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Forex indicator dt We use data with a frequency of 1 minute for periods of one year, from to depending on data availability. In: Frankel, Galli, and Giovannini, eds. It is based on the following three assumptions Murphy : Market action discounts everything. Moving average MA Moving average MA is a trend-following or lagging indicator that smooths prices by averaging them in a specified period. The results for this model are shown in Table 6. If the system was a fail-proof money maker, then the seller would not want to share it.
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No deposit bonus forex all 2013 nfl Personal Finance. The parameters for these fittings are presented in Table 1second block. Welles Wilder in 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. The TFX pipeline still needs an orchestrator, so we can host that in a Kubernetes job, and if we wrap it in a scheduled job, then our retraining happens on a schedule too!
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