Forex prediction.
This example is very similar to the previous one. The only difference is that it shows data for foreign exchange (forex) currency pairs.
How to work with the applet.
If you have not seen the first example, please explore it first - basic description is available there. In this applet, following data are available. All of them are end of day close values for the whole year 2007, i. e., 313 values. As in the previous applet, each of these time series have the following values: zero for interval below 0, close value in the interval 0-number of values, and again zero after the last known value. EURUSD - EUR USD forex currency pair data USDJPY - EUR USD forex currency pair data USDCHF - EUR USD forex currency pair data EURJPY - EUR USD forex currency pair data Again note that this example is provided for illustration only. Trading using this simple setup is usually not far away from using prediction by last available value. Also note that for trading we need to develop entry and exit rules, and that they are more important than exact prediction.
Please wait until the applet is loaded.
Applet and description (c) Marek Obitko, 2008; the neural network in the applet uses Java classes BPNeuron and BPNet.
from NeuralWebspace, (c) Tomбљ Vehovskэ, 1998, that were modified for the purposes of this applet.

Neural Networks: Forecasting Profits.
Neural networks are state-of-the-art, trainable algorithms that emulate certain major aspects in the functioning of the human brain. This gives them a unique, self-training ability, the ability to formalize unclassified information and, most importantly, the ability to make forecasts based on the historical information they have at their disposal.
Neural networks have been used increasingly in a variety of business applications, including forecasting and marketing research solutions. In some areas, such as fraud detection or risk assessment, they are the indisputable leaders. The major fields in which neural networks have found application are financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks can be applied gainfully by all kinds of traders, so if you're a trader and you haven't yet been introduced to neural networks, we'll take you through this method of technical analysis and show you how to apply it to your trading style.
Use Neural Networks to Uncover Opportunities.
Just like any kind of great product or technology, neural networks have started attracting all those who are looking for a budding market. Torrents of ads about next-generation software have flooded the market - ads celebrating the most powerful of all the neural network algorithms ever created. Even in those rare cases when advertising claims resemble the truth, keep in mind that a 10% increase in efficiency is probably the most you will ever get from a neural network. In other words, it doesn't produce miraculous returns and regardless of how well it works in a particular situation, there will be some data sets and task classes for which the previously used algorithms remain superior. Remember this: it's not the algorithm that does the trick. Well-prepared input information on the targeted indicator is the most important component of your success with neural networks.
Is Faster Convergence Better?
Many of those who already use neural networks mistakenly believe that the faster their net provides results, the better it is. This, however, is a delusion. A good network is not determined by the rate at which it produces results and users must learn to find the best balance between the velocity at which the network trains and the quality of the results it produces.
Correct Application of Neural Nets.
Many traders apply neural nets incorrectly because they place too much trust in the software they use all without having been provided with proper instructions on how to use it properly. To use a neural network the right way and, thus, gainfully, a trader ought to pay attention to all the stages of the network preparation cycle. It is the trader and not his or her net that is responsible for inventing an idea, formalizing this idea, testing and improving it, and, finally, choosing the right moment to dispose of it when it's no longer useful. Let us consider the stages of this crucial process in more detail:
1. Finding and Formalizing a Trading Idea.
2. Improving the Parameters of Your Model.
3. Disposing of the Model When it Becomes Obsolete.
Every neural-network based model has a life span and cannot be used indefinitely. The longevity of a model's life span depends on the market situation and on how long the market interdependencies reflected in it remain topical. However, sooner or later any model becomes obsolete. When this happens, you can either retrain the model using completely new data (i. e. replace all the data that has been used), add some new data to the existing data set and train the model again, or simply retire the model altogether.
Many traders make the mistake of following the simplest path - they rely heavily on and use the approach for which their software provides the most user-friendly and automated functionality. This simplest approach is forecasting a price a few bars ahead and basing your trading system on this forecast. Other traders forecast price change or percentage of the price change. This approach seldom yields better results than forecasting the price directly. Both the simplistic approaches fail to uncover and gainfully exploit most of the important longer-term interdependencies and, as a result, the model quickly becomes obsolete as the global driving forces change.
The Most Optimal Overall Approach to Using Neural Networks.

Neural network trading forex

Hybrid Neural Network Stop-and-Reverse Strategies for Forex.
by Michael R. Bryant.
Neural networks have been used in trading systems for many years with varying degrees of success. Their primary attraction is that their nonlinear structure is better able to capture the complexities of price movement than standard, indicator-based trading rules. One of the criticisms has been that neural network-based trading strategies tend to be over-fit and therefore don't perform well on new data. A possible solution to this problem is to combine neural networks with rule-based strategy logic to create a hybrid type of strategy. This article will show how this can be done using Adaptrade Builder.
In particular, this article will illustrate the following:
Combining neural network and rule-based logic for trade entries.
Targeting multiple platforms simultaneously (MetaTrader 4 and TradeStation)
Developing a strategy with asymmetrical stop-and-reverse logic.
Using intraday forex data.
A three-segment data approach will be used, with the third segment used to validate the final strategies. The resulting strategy code for both MetaTrader 4 and TradeStation will be shown, and it will be demonstrated that the validation results are positive for each platform.
Neural Networks as Trade Entry Filters.
Mathematically, a neural network is a nonlinear combination of one or more weighted inputs that generates one or more output values. For trading, a neural network is generally used in one of two ways: (1) as a prediction of future price movement, or (2) as an indicator or filter for trading. Here, its use as an indicator or trade filter will be considered.
As an indicator, a neural network acts as an additional condition or filter that must be satisfied before a trade can be entered. The inputs to the network are typically other technical indicators, such as momentum, stochastics, ADX, moving averages, and so on, as well as prices and combinations of the preceding. The inputs are scaled and the neural network is designed so that the output is a value between -1 and +1. One approach is to allow a long entry if the output is greater than or equal to a threshold value, such as 0.5, and a short entry if the output is less than or equal to the negative of the threshold; e. g., -0.5. This condition would be in addition to any existing entry conditions. For example, if there were a long entry condition, it would have to be true and the neural network output would have to be at least equal to the threshold value for a long entry.
When setting up a neural network, a trader would typically be responsible for choosing the inputs and the network topology and for "training" the network, which determines the optimal weights values. As will be shown below, Adaptrade Builder performs these steps automatically as part of the evolutionary build process that the software is based on. Using the neural network as a trade filter allows it to be easily combined with other rules to create a hybrid trading strategy, one that combines the best features of traditional, rule-based approaches with the advantages of neural networks. As a simple example, Builder might combine a moving average crossover rule with a neural network so that a long position is taken when the fast moving average crosses above the slow moving average and the neural network output is at or above its threshold.
Stop-and-Reverse Trading Strategies.
A stop-and-reverse trading strategy is one that is always in the market, either long or short. Strictly speaking, "stop-and-reverse" means that you reverse the trade when your stop order is hit. However, I use it as a short-hand for any trading strategy that reverses from long to short to long and so on, so that you're always in the market. By this definition, it's not necessary for the orders to be stop orders. You could enter and reverse using market or limit orders as well. It's also not necessary that each side use the same logic or even the same order type. For example, you could enter long (and exit short) on a stop order and enter short (and exit long) on a market order, using different rules and conditions for each entry/exit. This would be an example of an asymmetrical stop-and-reverse strategy.
The primary advantage of a stop-and-reverse strategy is that by always being in the market, you never miss any big moves. Another advantage is simplicity. When there are separate rules and conditions for entering and exiting trades, there is more complexity and more that can go wrong. Combining entries and exits means fewer timing decisions have to be made, which can mean fewer mistakes.
On the other hand, it can be argued that the best conditions for exiting a trade are rarely the same as those for entering in the opposite direction; that entering and exiting trades are inherently separate decisions that should therefore employ separate rules and logic. Another potential drawback of always being in the market is that the strategy will trade through every opening gap. A large opening gap against the position can mean a large loss before the strategy is able to reverse. Strategies that enter and exit more selectively or that exit by the end of the day can minimize the impact of opening gaps.
Since the goal is to build a forex strategy, MetaTrader 4 (MT4) is an obvious choice for the trading platform given that MetaTrader 4 is designed primarily for forex and is widely used for trading those markets (see, for example, MetaTrader vs. TradeStation: A Language Comparison). However, in recent years, TradeStation has targeted the forex markets much more aggressively. Depending on your trading volume and/or account level, it's possible to trade the forex markets through TradeStation without incurring any platform fees or paying any commissions. Spreads are reportedly tight with good liquidity on the major forex pairs. For these reasons, both platforms were targeted for this project.
Several issues arise when targeting multiple platforms simultaneously. First, the data may be different on different platforms, with differences in time zones, price quotes for some bars, volume, and available date ranges. To smooth over these differences, data were obtained from both platforms, and the strategies were built over both data series simultaneously. The best strategies were therefore the ones that worked well on both data series despite any differences in the data.
The data settings used in Builder are shown below in Fig. 1. As can be inferred from the Market Data table in the figure, the Euro/dollar forex market was targeted (EURUSD) with a bar size of 4 hours (240 minutes). Other bar sizes or markets would have served just as well. I was only able to obtain as much data through my MT4 platform as indicated by the date range shown in Fig. 1 (data series #2), so the same date range was used in obtaining the equivalent data series from TradeStation (data series #1). 80% of the data was used for Building (combined in-sample and "out-of-sample"), with 20% (6/20/14 to 2/10/15) set aside for validation. 80% of the original 80% was then set to "in-sample" with 20% set to "out-of-sample," as shown in Fig. 1. The bid/ask spread was set to 5 pips, and trading costs of 6 pips or $60 per full-size lot (100,000 shares) were assumed per round-turn. Both data series were included in the build, as indicated by the checkmarks in the left-hand column of the Market Data table.
Figure 1. Market data settings for building a forex strategy for MetaTrader 4 and TradeStation.
Another potential problem when targeting multiple platforms is that Builder is designed to duplicate the way each supported platform calculates its indicators, which can mean that the indicator values will be different depending on which platform is selected. To avoid this possible source of discrepancy, any indicators that evaluate differently in MetaTrader 4 than in TradeStation should be eliminated from the build, which means the following indicators should be avoided:
Slow D stochastic.
Fast D stochastic.
All other indicators that are available for both platforms are calculated the same way in both platforms. TradeStation includes all of the indicators that are available in Builder, whereas MetaTrader 4 does not. Therefore, to include only indicators that are available in both platforms, the MetaTrader 4 platform should be selected as the code type in Builder. That will automatically remove any indicators from the build set that are not available for MT4, which will leave the indicators that are available in both platforms. Additionally, since I noticed differences in the volume data obtained from each platform, I removed all volume-dependent indicators from the build set. Lastly, the time-of-day indicator was removed because of differences in the time zones between data files.
In Fig. 2, below, the list of indicators used in the build set is shown sorted by whether or not the indicator was considered by the build process ("Consider" column). The indicators removed from consideration for the reasons discussed above are shown at the top of the list. The remaining indicators, starting with "Simple Mov Ave", were all part of the build set.
Figure 2. Indicator selections in Builder, showing the indicators removed from the build set.
The evaluation options used in the build process are shown in Fig. 3. As discussed, MetaTrader 4 was selected as the code output choice. After strategies are built in Builder, any of the options on the Evaluation Options tab, including the code type, can be changed and the strategies re-evaluated, which will also rewrite the code in whichever language is selected. This feature was used to obtain the TradeStation code for the final strategy after the strategies were built for MetaTrader 4.
Figure 3. Evaluation options in Builder for the EURUSD forex strategy.
To create stop-and-reverse strategies, all exit types were removed from the build set, as shown below in Fig. 4. All three types of entry orders -- market, stop, and limit -- were left as "consider", which means the build process could consider any of them during the build process.
Figure 4. Order types selected in Builder to create a stop-and-reverse strategy.
The Builder software automatically generates rule-based logical conditions for entry and/or exit. To add a neural network to the strategy, it's only necessary to select the option "Include a neural network in entry conditions" on the Strategy Options tab, as shown below in Fig. 5. The neural network settings were left at their defaults. As part of the stop-and-reverse logic, the Market Sides option was set to Long/Short, and the option to "Wait for exit before entering new trade" was unchecked. The latter is necessary to enable the entry order to exit the current position on a reversal. All other settings were left at the defaults.
Figure 5. Strategy options selected in Builder to create a hybrid strategy using both rule-based and neural network conditions.
The evolutionary nature of the build process in Builder is guided by the fitness , which is calculated from the objectives and conditions defined on the Metrics tab, as shown below in Fig. 6. The build objectives were kept simple: maximizing the net profit while minimizing the complexity, which was given a small weight relative to the net profit. More emphasis was placed on the build conditions, which included the correlation coefficient and significance for general strategy quality, as well as the average bars in trades and the number of trades.
Initially, only the average bars in trades was included as a build condition. However, in some of the early builds, the net profit was being favored over the trade length, so the number-of-trades metric was added. The specified range for the number of trades (between 209 and 418) is equivalent to average trade lengths between 15 and 30 bars based on the number of bars in the build period. As a result, adding this metric put more emphasis on the trade length goal, which resulted in more members of the population with the desired range of trade lengths.
Figure 6. Build objectives and conditions set on the Metrics tab determine how the fitness is calculated.
The "Conditions for Selecting Top Strategies" duplicate the build conditions except that the top strategies conditions are evaluated over the entire range of data (not including the validation segment, which is separate), rather than just over the build period, as is the case for the build conditions. The top strategies conditions are used by the program to set aside any strategies that meet all the conditions in a separate population.
The final settings are made on the Build Options tab, as shown below in Fig. 7. The most important options here are the population size, number of generations, and the option to reset based on the "out-of-sample" performance. The population size was chosen to be large enough to get good diversity in the population while still being small enough to build in a reasonable amount of time. The number of generations was based on how long it took during a few preliminary builds for the results to start to converge.
Figure 7. Build options include the population size, number of generations, and options for resetting the population based on "out-of-sample" performance.
The option to "Reset on Out-of-Sample (OOS) Performance" starts the build process over after the specified number of generations if the specified condition is met; in this case, the population will be reset if the "out-of-sample" net profit is less than $20,000. This value was chosen based on preliminary tests to be a high enough value that it probably would not be reached. As a result, the build process was repeated every 30 generations until manually stopped. This is a way to let the program identify strategies based on the Top Strategies conditions over an extended period of time. Periodically, the Top Strategies population can be checked and the build process cancelled when suitable strategies are found.
Notice that I put "out-of-sample" in quotes. When the "out-of-sample" period is used to reset the population in this manner, the "out-of-sample" period is no longer truly out-of-sample. Since that period is now being used to guide the build process, it's effectively part of the in-sample period. That's why it's advisable to set aside a third segment for validation, as was discussed above.
After several hours of processing and a number of automatic rebuilds, a suitable strategy was found in the Top Strategies population. Its closed trade equity curve is shown below in Fig. 8. The equity curve demonstrates consistent performance across both data segments with an adequate number of trades and essentially the same results over both data series.
Figure 8. Closed-trade equity curve for the EURUSD stop-and-reverse strategy.
To check the strategy over the validation period, the date controls on the Markets tab (see Fig. 1) were changed to the end date of the data (2/11/2015), and the strategy was re-evaluated by selecting the Evaluate command from the Strategy menu in Builder. The results are shown below in Fig. 9. The validation results in the red box demonstrate that the strategy held up on data not used during the build process.
Figure 9. Closed-trade equity curve for the EURUSD stop-and-reverse strategy, including the validation period.
The final check is to see how the strategy performed on each data series separately using the code output option for that platform. This is necessary because, as explained above, there may be differences in the results depending on (1) the code type, and (2) the data series. We need to verify that the chosen settings minimized these differences, as intended. To test the strategy for MetaTrader 4, the data series from TradeStation was deselected on the Markets tab, and the strategy was re-evaluated. The results are shown below in Fig. 10, which duplicates the bottom curve in Fig. 9.
Figure 10. Closed-trade equity curve for the EURUSD stop-and-reverse strategy, including the validation period, for MetaTrader 4.
Finally, to test the strategy for TradeStation, the data series from TradeStation was selected and the series for MetaTrader 4 was deselected on the Markets tab, the code output was changed to "TradeStation," and the strategy was re-evaluated. The results are shown below in Fig. 11 and appear to be very similar to the middle curve in Fig. 9, as expected.
Figure 11. Closed-trade equity curve for the EURUSD stop-and-reverse strategy, including the validation period, for TradeStation.
The code for both platforms is provided below in Fig. 12. Click the image to open the code file for the corresponding platform. Examining the code reveals that the rule-based part of the strategy uses different volatility-related conditions for the long and short sides. The neural network inputs consist of a variety of indicators, including day-of-week, trend (ZLTrend), intraday high, oscillators (InvFisherCycle, InvFisherRSI), Bollinger bands, and standard deviation.
The hybrid nature of the strategy can be seen directly in the code statement (from the TradeStation code):
If EntCondL and NNOutput >= 0.5 then begin.
Buy("EnMark-L") NShares shares next bar at market;
The variable "EntCondL" represents the rule-based entry conditions, and "NNOuput" is the output of the neural network. Both conditions have to be true to place the long entry order. The short entry condition works the same way.
Figure 12. Trading strategy code for the EURUSD stop-and-reverse strategy (left, MetaTrader 4; right, TradeStation). Click the figure to open the corresponding code file.
This article looked at the process of building a hybrid rule-based/neural network strategy for the EURUSD using a stop-and-reverse (always in the market) approach with Adaptrade Builder. It was shown how the strategy code can be generated for multiple platforms by selecting a common subset of the indicators that work the same way in each platform. The settings necessary to generate strategies that reverse from long to short and back were described, and it was demonstrated that the resulting strategy performed positively on a separate, validation segment of data. It was also verified that the strategy generated similar results with the data and code option for each platform.
As discussed above, the stop-and-reverse approach has several drawbacks and may not appeal to everyone. However, an always-in-the-market approach may be more attractive with forex data because the forex markets trade around the clock. As a result, there are no session-opening gaps, and the trading orders are always active and available to reverse the trade when the market changes. The use of intraday data (4-hour bars) provided more bars of data for use in the build process but was otherwise fairly arbitrary in that the always-in-the-market nature of the strategy means that trades are carried overnight.
The build process was allowed to evolve different conditions for entering long and short, resulting in an asymmetric stop-and-reverse strategy. Despite the name, the resulting strategy enters both long and short trades on market orders, although market, stop, and limit orders were all considered by the build process independently for each side. In practice, reversing from long to short would mean selling short twice the number of shares at the market as the strategy was currently long; e. g., if the current long position was 100,000 shares, you would sell short 200,000 shares at market. Likewise, if the current short position was 100,000 shares, you would buy 200,000 shares at market to reverse from short to long.
A shorter price history was used than would be ideal. Nonetheless, the results were positive on the validation segment, suggesting the strategy was not over-fit. This supports the idea that a neural network can be used in a trading strategy without necessarily over-fitting the strategy to the market.
The strategy presented here is not intended for actual trading and was not tested in real-time tracking or trading. However, this article can be used as a template for developing similar strategies for the EURUSD or other markets. As always, any trading strategy you develop should be tested thoroughly in real-time tracking or on separate data to validate the results and to familiarize yourself with the trading characteristics of the strategy prior to live trading.
This article appeared in the February 2015 issue of the Adaptrade Software newsletter.
HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN INHERENT LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT ACTUALLY BEEN EXECUTED, THE RESULTS MAY HAVE UNDER - OR OVER-COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY. SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFITS OR LOSSES SIMILAR TO THOSE SHOWN.
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Neural network trading forex

Modern techniques like artificial neural networks (ANN) are best used for high frequency trading for several reasons. First, they mimic human intelligence but they mostly don’t reach a human’s level of intelligence, therefore, there is no point in using those techniques on a time scale at which a human could easily be working. Their advantage comes from speed of operation and constant activity.
Second, we need a lot of data to train neural networks efficiently and this amount of data will only be found in high frequency trading. Forex has all in all quite few instruments with limited relevant past data on the daily or weekly time-scale.
Furthermore, High frequency trading is a type of scalping strategy where we identify noise around the true value of the instrument. This is different from long-term trading that attempts to follow meaningful movements of the instrument according to fundamental analysis.
Artificial Neural networks.
A good time-scale to work on is the minute time-scale. This time-scale is full of noise which will be captured by the algorithm in order to sell at a local high and buy at a local low.
This can be proven using a simple neural network trained to predict the following 10-min high. If you’re interested in knowing precisely how these neural networks work I encourage you to read my article from the month of March: Trading with Neural Nets Part 2.
In the graph above, the annotation (high 0 to -10) means the high in the interval between minute (0) and minute (-10) relative to minute zero considered as the “present”. The training is done using a type of genetic algorithm with data from 2012 for training and data from this year for testing.
What’s being done is using overlapping “high to high” movements in order to predict a future high to high movement. Note that this can’t be used practically since we never know if the high of the period we’re in has already gone past or not. The same network will not work with close to close movements. The reason is that the high (and the low) holds much more information than the close as it represents an entire period in time while the close represents a single moment in time.
It is only possible to predict the movement from previous to following high on short time-scales which shows that something is truly being predicted about the future. From there it should have been trivial to predict a “close to close” movement but at least for myself it wasn’t the case. It took me several days to figure out the mechanism at work but the results really outgrew my expectations.
First of all, we’re not going to use highs or lows but rather the average between the high and low (AHL).
I first tried to predict the movement from close to the following AHL but this did not work. This was really strange and seemed illogical. Since I could predict the AHL(-10 to 0) to AHL(0 to 10) movement to a certain extent, it seemed I could simply add the close(0) to AHL(-10 to 0) movement from the prediction in order to predict the close to following AHL movement but this did not work.
I scratched my head and tried several other tactics like complicated recurrent neural networks, decision trees and nearest neighbor algorithms but nothing worked.
The fact is I could never predict the AHL to AHL movement precisely. The correlation between the predictions and the actual movements is only of about 0.4. Still, if I predict a positive AHL(-10 to 0) to AHL(0 to 10) movement and the close(0) is lower than the AHL(-10 to 0), I should have an even higher chance of a correct prediction for close to AHL movement. Thus, I selected the cases where the close(0) was higher than the AHL (-10 to 0) when the network predicted a negative AHL to AHL movement and inversely, I selected the cases where close(0) was lower than the AHL(-10 to 0) when the AHL to AHL movement was predicted positive. Finally, the breakthrough, I could now predict close to AHL and even close to close movements for half the cases. As expected the correlation between predictions and actual close to close movements grew, but they did so even more than expected. it turns out that when the AHL to AHL movement is predicted to rise but the close(0) is higher than the AHL (-10 to 0), the opposite prediction should be made for close to AHL or close to close movement.
the minute time-scale.
The image above shows an idealized view of market movements on the minute time-scale. The prices oscillate around the true value of the instrument with a half period of oscillations of about ten minutes.
In reality of course, the true value of the instrument also changes but that is of no importance, what really matters when we talk about scalping is the period of occilation. Once the prices have gone up by a large enough amount and are starting to plateau, it’s the right time to sell and inversely for buying.
This is the best explanation to the problem encountered earlier. Although The AHL to AHL movement (Average High Low) is predictable, the close to close can only be deduced once we know where the close at time zero stands compared to the last AHL, as shown in the images below. Although that information was available to the neural network, it seems this is a very tough problem to solve for Artificial neural networks; solving a sub-problem in order to solve the final one. This is also a characteristic of very intelligent animals like apes and certain birds.
Of course, things are not so clean and simple in real life. The period of oscillation is not so stable, amplitude of oscillations changes with the time of day and charts are definitely not as smooth as depicted above. Highs and lows are much more helpful than closes as indicators in that regard, they have a time invariant quality because of the fact they describe an entire period rather than a single point in time, that’s also why it’s difficult to predict the close to close movement directly.
Finally, errors will be made and drawdowns will occur as the theory is not foolproof. The important is whether such a strategy can be more profitable than the cost of commission and spread.
Spread and commission.
While the bid-ask spread and commission are insignificant for longer term trades, they become very important in high frequency trades. Considering the average 10min movements, I’ve calculated that in order to be profitable, a strategy working on this time-scale will need to have at least 70% profitable trades with equal take profit and stop loss margins. In different terms, the trades on average should earn more than 1.5 pips for any strategy to be profitable. This is calculated using the heaviest commission rates from dukascopy and the average bid-ask spread on EURUSD. This is not taking into account the spread that occurs because your order is not treated instantly. However you can place an order with a time limit which should limit problems of the sort.
The HFT strategy theoretically achieves 4 pips average profit per trade but as this is obtained with back tests that don’t take several factors into account. The actual results will probably be worse and more problems are sure to arise. However this is solid basis for further developing an HFT strategy.
The jforex robot isn't set up yet, the results come from back-tests that I set-up on my own with data downloaded using the jforex platform. I'm writing the automated strategy right now. If the strategy gives similar results with the jforex backtests that'll be a very good sign BUT let's not call this the Holy Grail just yet, although I am myself very excited about this strategy.