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[jangtalab]

مرحبا
لقد اشتريت اكسبرت جيد و لكن في الاعدادات عندي مشكلة
الكسبرت يملك دليل لكن الدليل باللغة الانجليزية و انا لا اعرف انجليزي جيد
هل شخص يقدر يساعدني في ترجمة الدليل
و يعطيني اقتراحة في اعدادات الاكسبرت من بعد قراءة الدليل
انا اريد يعمل الاكسبرت علي دقيقة واحدة
تحياتي لكم
هذا هو ما مكتوب بالدليل

XNeuron Pro

Installation Guide and Product Manual

Installation:
- Open Meta Trader 4
- Go to “File” menu.
- Go to “Open Data Folder”
- Open “MQL4” Folder
- Open “Experts” Folder
- Copy the XneuronPro.ex4 file into the “Experts” folder.
- Close and Restart MetaTrader 4.
With Meta trade 4 opened, press CTRL + N to open the Navigation tab.
Once opened, scroll down to “experts” and find XNeuronPro file. Click and drag to the currency chart you want to trade in the timeframe you want to trade. That’s very important.
After you drag the robot to the chart you want to trade, a window will appear.
In the “Common” tab:
Select “Long & Short Positions”
Check “Enable Alerts”
Check “Allow Live Trading”
Check “Allow Import of External Experts”
Make sure the “AutoTrading” button in the certer-left upper corner is ON.

In the “Inputs” tab, you’ll find the robot’s parameters:
Congratulations! You have in hands one of the most advanced and complex Forex EA’s on the planet. Make sure you read the whole product manual and understand all the parameters before you try to use the robot. Always test your robot in a demo account first before you decide to take it to a live account. It is advisable to use a VPS account to run the robot.

XNeuron Pro is specifically designed to fit all clients regardless of account size or settings so, before attaching the robot to the charts you want to trade, spend some time getting to know the robot parameters and set them according to your level of experience about the market and your expectations after reading the whole manual. Since the robot has many adaptability features, it can function in many ways so we highly encourage you to test your ideas and preferences rather than simply hit the play button. Because we need to make it adaptable to every client profile, XNeuron Pro is not a silver bullet. It is a tool to make you set your preferences into it. In other words, it’s not ready to trade on your specific account settings right out of the box. You MUST read the manual and tweak the robot.

If you have doubts about the parameters, please take advantage of the free email support you acquired along with the robot. We are more than happy to help you in your road to success!
SmartArrays Algorithm

Use_Multidimesional_Arrays: True or False.

If true, the algorithm will use multidimensional arrays to organize and handle data. Multidimensional organization of data can provide a much higher degree of complexity and therefore more subtle and fine-tuned decisions. One major caveat here is that too much complexity can become a bad thing after a certain point because the robot might be forced to curve fit. In other words, there is a certain point where trying to force nonlinear pattern identification can become a bad idea. This point, of course, varies according to the all the other parameters of the robot. The best alternative is to use all the parameters wisely without exaggeration.

Max_Number_Of_New_Data_Streams: MUST be between 1 and 500.

This parameter controls how many different data streams will be created from the normal data streams any market provides like OHLC values, normal indicator values, volume and etc. The higher the number of new data streams, the more information the software has at its disposal to identify correlations. However, too many data streams can cause the robot to curve fit data, which is the number one enemy in artificial intelligence for financial data. When you multiply the number of data streams available for pattern identification, you’re basically providing the robot with information that very few people have in the market. One of the ways XNeuron Pro does this is by taking two random data streams and combining them into a new one. For example, let’s say you have one line that represents the spread value at bar opening and another line that represents the spread value at bar closing. The difference between these two lines can form a third line that might show patterns that can be exploited by the robot. This third line can be further processed with other data streams so the robot has the potential to generate infinite information about the market.

Real_Time_Tick_Data_Processing: True or False

If true, the robot will allocate specific areas of the SmartArrays algorithm to real time ticks. This can be useful if you want to scalp in lower timeframes as the robot will try to pinpoint subtle inneficiencies in real time tick data, not just recent historic data. As it was explained in the last parameter, even the time between ticks arriving to the terminal can form a data stream useful for nonlinear analysis by the robot.

Historic_Data_Training_Limit: MUST be between 1 and 3000.

This parameter determines how much historic data will be used to “train” the robot to face novel situations. If you think the last 300 hours were significant in the market, you can set this parameter for 300 with the robot on the H1 timeframe of a currency of your choice, for example.

Timeframe_Interpolation: True or False

This parameter lets the robot search meaningful information in other timeframes to have a richer view of the market you’re attempting to trade. You turn true or false for the timeframes you want to use. You can place the robot in any currency pair you want but the interpolation feature can only be used with majors. This will allow data stream generation using other currency pairs to trade the pair you chose.

Currency_Interpolation: True or False

Works with the same logic as the Timeframe_Interpolation parameter, but lets you choose any of the major currency pairs to correlate with the currency pair you’re trying to trade. Again, more information can mean a better view of the general problem. Sometimes, currencies develop a deeper level of correlation that can only be discovered with computer algorithms like XNeuron Pro. The naked eye is almost guaranteed to miss these subtleties. This is a common practice among large quant funds who usually collect an incredibly large number of data streams and try to find correlations between them. Sometimes unusual correlations can be found. For example, the monthly unemployment rate in Germany could correlate in some very subtle mathematical way with the 5-minute closing price of the NZD/JPY.

The NeuroX Algorithm

Forecast_Horizon_Size: MUST be between 4 and 20

This parameter lets you choose how far into the future you want to robot to make forecasts. It is widely spread that lower forecast horizon sizes are better because it’s easier to predict what’s going to happen 20 minutes from now than it is to forecast what’s going to happen 20 days from now, for example. Depending on complexity of other parameters, a higher forecast horizon can be used. For example, if you set other parameters to achieve a higher degree of complexity, you might benefit from setting a higher forecast horizon.

Number_Of_Inputs: MUST be between 4 and 20.

This controls how many variables derived from created data streams will be used by the NeuroX algorithm to optimize its behavior. The software automatically recognizes which variables are the most relevant and which ones have the best correlation factor between all data streams.

Number_Of_NeuronsLayer: MUST be between 4 and 20.

This parameter controls the degree of nonlinearity of the patterns the robot will search for. In other words, how complex and non-obvious these patterns can be. The higher the degree of nonlinearity, the better. However, it is exponentially harder to find more complex patterns.

Number_Of_Layers: MUST be between 4 and 20.

This parameter works pretty much in the way of the previous parameter. The multiplication of the two inputs will give you the complexity factor of the patterns searched by the robot. For example, if you choose 4 in the Number_Of_NeuronsLayer and 4 in the Number_Of_Layers, the complexity will be 16. It can co up to 400 in complexity.

Full_Connectivity: True or False.

This parameter controls the level of connectivity between all code subsets of the NeuroX algorithm. Similarly to a human brain, the code subsets of the robot can have different levels of connectivity between them. Full connectivity means everything is connected to every other thing which can be better in terms of pattern recognition but it certainly makes the algorithm a bit slower when there’s too much simultaneous data being processed and all settings are set to the max, which is not a great idea anyway.

XFuzzy Algorithm

Threshold: MUST be between 0.60 and 1.00.

This controls the level of “fuzzyness” the robot will use in its decisions. This can be comparable to how much subjectivity it will be used in trading decisions. Fuzzy logic, which is the foundation of the code subset, deals with pattern resemblance. The threshold parameter lets you control the level of resemblance in the algorithm. For example, if you set this parameter near the upper limit, only patterns that are really close, almost identical, to the standard patterns you’d expect from indicator signals will be considered in trading decisions. If you set this parameter near the lower limit, patterns that are remotely like the industry standards will be considered in trading decisions. In other words, if the robot recognizes that a stochastic crossover is 87% like the perfect standard of stochastic crossover and you set Threshold parameter to 0.90, the robot will consider it to be a false signal.

Full_Variable_Scope_Application: True or False

This determines if the robot will apply the XFuzzy algorithm in all the variables selected by the NeuroX algorithm. Sometimes, tweaking this parameter can produce interesting results. If you select “true”, the robot will apply the XFuzzy logic to all variables carefully selected by the Neurox algorithm for trading decisions. If you select “false”, partial variable scope application will be applied randomly

Money Management

LotSize:

This is the lot size used in all trades opened by the robot. Since there are millions of possible money management methodologies and risk appetite varies greatly, we decided to give a fixed amount per trade as this leaves a leveled plain field to view performance in a simpler way.

RRR:

This is the Risk/Reward multiplier. If you want your take profit to be twice as big as your stop, you should set this parameter at 2.00. The value that will be multiplied by this parameter is determined by the robot in a more sophisticated way using the other algorithms in the code. This, of course, is in alliance with the Forecast Horizon parameter. Depending on what value you set here, the Forecast Horizon will automatically adapt to fit your risk reward preferences.

Max_Number_Of_Trades: MUST be between 1 and 50.

This is the maximum number of open trades in the terminal. If you set this parameter at 20, for example, the robot won’t open more than 20 trades simultaneously. Be careful to make this parameter reasonable with the lot size you choose, your account leverage and so on. If you decide to trade 5% of your account on each trade and you let the robot trade 50 trades max, it isn’t too hard to see that this could end in badly because you would be using too much leverage. Only use very tight parameters if you really know your skills and you really have experience with the robot.

ATRM: MUST be between 3.00 and 8.00.

This parameter influences the SL and TP sizes based on the average true range, which serves to add yet another layer of adaptability to the robot’s decisions. The well know ATR indicator along with an algorithm to detect sudden changes in volatility will determine the size of the stop loss being placed on each trade.

Slippage:

This is the amount of slippage you want in your trades. If you set it to zero, you might miss a lot of trades because the terminal will only open trades with the exact same request price of the order. It’s a healthy and harmless idea to set a little bit of slippage. This parameter is set in points, not pips, so if you decide to use 15 pips of slippage, you’ll need to set the parameter at 150 (1pip = 10 points).

[jangtalab]

مرحبا
لقد اشتريت اكسبرت جيد و لكن في الاعدادات عندي مشكلة
الكسبرت يملك دليل لكن الدليل باللغة الانجليزية و انا لا اعرف انجليزي جيد
هل شخص يقدر يساعدني في ترجمة الدليل
و يعطيني اقتراحة في اعدادات الاكسبرت من بعد قراءة الدليل
انا اريد يعمل الاكسبرت علي دقيقة واحدة
تحياتي لكم
هذا هو ما مكتوب بالدليل

XNeuron Pro

Installation Guide and Product Manual

Installation:
- Open Meta Trader 4
- Go to “File” menu.
- Go to “Open Data Folder”
- Open “MQL4” Folder
- Open “Experts” Folder
- Copy the XneuronPro.ex4 file into the “Experts” folder.
- Close and Restart MetaTrader 4.
With Meta trade 4 opened, press CTRL + N to open the Navigation tab.
Once opened, scroll down to “experts” and find XNeuronPro file. Click and drag to the currency chart you want to trade in the timeframe you want to trade. That’s very important.
After you drag the robot to the chart you want to trade, a window will appear.
In the “Common” tab:
Select “Long & Short Positions”
Check “Enable Alerts”
Check “Allow Live Trading”
Check “Allow Import of External Experts”
Make sure the “AutoTrading” button in the certer-left upper corner is ON.

In the “Inputs” tab, you’ll find the robot’s parameters:
Congratulations! You have in hands one of the most advanced and complex Forex EA’s on the planet. Make sure you read the whole product manual and understand all the parameters before you try to use the robot. Always test your robot in a demo account first before you decide to take it to a live account. It is advisable to use a VPS account to run the robot.

XNeuron Pro is specifically designed to fit all clients regardless of account size or settings so, before attaching the robot to the charts you want to trade, spend some time getting to know the robot parameters and set them according to your level of experience about the market and your expectations after reading the whole manual. Since the robot has many adaptability features, it can function in many ways so we highly encourage you to test your ideas and preferences rather than simply hit the play button. Because we need to make it adaptable to every client profile, XNeuron Pro is not a silver bullet. It is a tool to make you set your preferences into it. In other words, it’s not ready to trade on your specific account settings right out of the box. You MUST read the manual and tweak the robot.

If you have doubts about the parameters, please take advantage of the free email support you acquired along with the robot. We are more than happy to help you in your road to success!
SmartArrays Algorithm

Use_Multidimesional_Arrays: True or False.

If true, the algorithm will use multidimensional arrays to organize and handle data. Multidimensional organization of data can provide a much higher degree of complexity and therefore more subtle and fine-tuned decisions. One major caveat here is that too much complexity can become a bad thing after a certain point because the robot might be forced to curve fit. In other words, there is a certain point where trying to force nonlinear pattern identification can become a bad idea. This point, of course, varies according to the all the other parameters of the robot. The best alternative is to use all the parameters wisely without exaggeration.

Max_Number_Of_New_Data_Streams: MUST be between 1 and 500.

This parameter controls how many different data streams will be created from the normal data streams any market provides like OHLC values, normal indicator values, volume and etc. The higher the number of new data streams, the more information the software has at its disposal to identify correlations. However, too many data streams can cause the robot to curve fit data, which is the number one enemy in artificial intelligence for financial data. When you multiply the number of data streams available for pattern identification, you’re basically providing the robot with information that very few people have in the market. One of the ways XNeuron Pro does this is by taking two random data streams and combining them into a new one. For example, let’s say you have one line that represents the spread value at bar opening and another line that represents the spread value at bar closing. The difference between these two lines can form a third line that might show patterns that can be exploited by the robot. This third line can be further processed with other data streams so the robot has the potential to generate infinite information about the market.

Real_Time_Tick_Data_Processing: True or False

If true, the robot will allocate specific areas of the SmartArrays algorithm to real time ticks. This can be useful if you want to scalp in lower timeframes as the robot will try to pinpoint subtle inneficiencies in real time tick data, not just recent historic data. As it was explained in the last parameter, even the time between ticks arriving to the terminal can form a data stream useful for nonlinear analysis by the robot.

Historic_Data_Training_Limit: MUST be between 1 and 3000.

This parameter determines how much historic data will be used to “train” the robot to face novel situations. If you think the last 300 hours were significant in the market, you can set this parameter for 300 with the robot on the H1 timeframe of a currency of your choice, for example.

Timeframe_Interpolation: True or False

This parameter lets the robot search meaningful information in other timeframes to have a richer view of the market you’re attempting to trade. You turn true or false for the timeframes you want to use. You can place the robot in any currency pair you want but the interpolation feature can only be used with majors. This will allow data stream generation using other currency pairs to trade the pair you chose.

Currency_Interpolation: True or False

Works with the same logic as the Timeframe_Interpolation parameter, but lets you choose any of the major currency pairs to correlate with the currency pair you’re trying to trade. Again, more information can mean a better view of the general problem. Sometimes, currencies develop a deeper level of correlation that can only be discovered with computer algorithms like XNeuron Pro. The naked eye is almost guaranteed to miss these subtleties. This is a common practice among large quant funds who usually collect an incredibly large number of data streams and try to find correlations between them. Sometimes unusual correlations can be found. For example, the monthly unemployment rate in Germany could correlate in some very subtle mathematical way with the 5-minute closing price of the NZD/JPY.

The NeuroX Algorithm

Forecast_Horizon_Size: MUST be between 4 and 20

This parameter lets you choose how far into the future you want to robot to make forecasts. It is widely spread that lower forecast horizon sizes are better because it’s easier to predict what’s going to happen 20 minutes from now than it is to forecast what’s going to happen 20 days from now, for example. Depending on complexity of other parameters, a higher forecast horizon can be used. For example, if you set other parameters to achieve a higher degree of complexity, you might benefit from setting a higher forecast horizon.

Number_Of_Inputs: MUST be between 4 and 20.

This controls how many variables derived from created data streams will be used by the NeuroX algorithm to optimize its behavior. The software automatically recognizes which variables are the most relevant and which ones have the best correlation factor between all data streams.

Number_Of_NeuronsLayer: MUST be between 4 and 20.

This parameter controls the degree of nonlinearity of the patterns the robot will search for. In other words, how complex and non-obvious these patterns can be. The higher the degree of nonlinearity, the better. However, it is exponentially harder to find more complex patterns.

Number_Of_Layers: MUST be between 4 and 20.

This parameter works pretty much in the way of the previous parameter. The multiplication of the two inputs will give you the complexity factor of the patterns searched by the robot. For example, if you choose 4 in the Number_Of_NeuronsLayer and 4 in the Number_Of_Layers, the complexity will be 16. It can co up to 400 in complexity.

Full_Connectivity: True or False.

This parameter controls the level of connectivity between all code subsets of the NeuroX algorithm. Similarly to a human brain, the code subsets of the robot can have different levels of connectivity between them. Full connectivity means everything is connected to every other thing which can be better in terms of pattern recognition but it certainly makes the algorithm a bit slower when there’s too much simultaneous data being processed and all settings are set to the max, which is not a great idea anyway.

XFuzzy Algorithm

Threshold: MUST be between 0.60 and 1.00.

This controls the level of “fuzzyness” the robot will use in its decisions. This can be comparable to how much subjectivity it will be used in trading decisions. Fuzzy logic, which is the foundation of the code subset, deals with pattern resemblance. The threshold parameter lets you control the level of resemblance in the algorithm. For example, if you set this parameter near the upper limit, only patterns that are really close, almost identical, to the standard patterns you’d expect from indicator signals will be considered in trading decisions. If you set this parameter near the lower limit, patterns that are remotely like the industry standards will be considered in trading decisions. In other words, if the robot recognizes that a stochastic crossover is 87% like the perfect standard of stochastic crossover and you set Threshold parameter to 0.90, the robot will consider it to be a false signal.

Full_Variable_Scope_Application: True or False

This determines if the robot will apply the XFuzzy algorithm in all the variables selected by the NeuroX algorithm. Sometimes, tweaking this parameter can produce interesting results. If you select “true”, the robot will apply the XFuzzy logic to all variables carefully selected by the Neurox algorithm for trading decisions. If you select “false”, partial variable scope application will be applied randomly

Money Management

LotSize:

This is the lot size used in all trades opened by the robot. Since there are millions of possible money management methodologies and risk appetite varies greatly, we decided to give a fixed amount per trade as this leaves a leveled plain field to view performance in a simpler way.

RRR:

This is the Risk/Reward multiplier. If you want your take profit to be twice as big as your stop, you should set this parameter at 2.00. The value that will be multiplied by this parameter is determined by the robot in a more sophisticated way using the other algorithms in the code. This, of course, is in alliance with the Forecast Horizon parameter. Depending on what value you set here, the Forecast Horizon will automatically adapt to fit your risk reward preferences.

Max_Number_Of_Trades: MUST be between 1 and 50.

This is the maximum number of open trades in the terminal. If you set this parameter at 20, for example, the robot won’t open more than 20 trades simultaneously. Be careful to make this parameter reasonable with the lot size you choose, your account leverage and so on. If you decide to trade 5% of your account on each trade and you let the robot trade 50 trades max, it isn’t too hard to see that this could end in badly because you would be using too much leverage. Only use very tight parameters if you really know your skills and you really have experience with the robot.

ATRM: MUST be between 3.00 and 8.00.

This parameter influences the SL and TP sizes based on the average true range, which serves to add yet another layer of adaptability to the robot’s decisions. The well know ATR indicator along with an algorithm to detect sudden changes in volatility will determine the size of the stop loss being placed on each trade.

Slippage:

This is the amount of slippage you want in your trades. If you set it to zero, you might miss a lot of trades because the terminal will only open trades with the exact same request price of the order. It’s a healthy and harmless idea to set a little bit of slippage. This parameter is set in points, not pips, so if you decide to use 15 pips of slippage, you’ll need to set the parameter at 150 (1pip = 10 points).

[جون ايف]

شكرا لمشاركتك

[جون ايف]

شكرا لمشاركتك