Instructions and recommendations for using the Neuro Future indicator – My Trading – 5 September 2025

4.3. Activation and scaling settings

• ActivationPreset – preset configurations of activation functions (Auto/Manual)
• ActivationTypeHidden – activation function for hidden layers (when configured manually)
• ActivationTypeOut – activation function for the output layer (when configured manually)
• InputScale – input data scaling method (S11/S01)
• OutputScale – output data scaling method (S11/S01)
• GradientLimiting – Enable gradient limiting
• max_grad – maximum gradient value (with limitation enabled)

4.4 Notification and Logging Settings

• EnableAlerts – Enable trading alerts
• AlertThreshold – alert trigger threshold
• PushNotifications – sending push notifications
• EmailAlerts – Sending email alerts
• SoundAlerts – Sound Alerts
• EnableLogging – enabling the logging system
• ReduceLog – frequency of logging (reduction)
• LogExamples – logging training examples
• LogResults – logging of training results
• LogLoad – logging network loading
• LogSave – logging of network saving

4.5. Additional indicator settings

• UniverseOutputScale – universal output scaling
• FixIndicatorWindowMinMax – fixing the minimum/maximum of the indicator window
• MaxBars – maximum number of bars in the indicator window
• AutoColor – automatic color scheme
• Color – select color (when AutoColor is disabled)

5. Interpretation of results

5.1. Information panel (GUI)

• The information panel displays:
• Network structure – layer configuration (L1, L2, L3, L4)
• Accuracy – current assessment of the accuracy of forecasts
• Training period – the time range of data on which the network was trained
• Activations – activation functions used for hidden and output layers
• Scale type – the method of scaling input and output data (S01 [0,1] or S11 [-1,1])

5.2. Visual elements on the chart

• Forecast Line – a colored line that displays the forecast for the selected bar
• Graphic objects – visualization of future forecasts directly on the price chart
• Vertical lines – represent the period of data used to train the last loaded network
• Color indication – informs about the compatibility of the loaded network with the current symbol and timeframe

6. Integration with advisors (EA)

6.1 To call the indicator from the advisor, use the iCustom() function.

• Example of initialization in the advisor:

int OnInit()
{
   // Loading the indicator 
   indicator_handle = iCustom(_Symbol, _Period, Indicator_Name, FutureBar, File_Name, 0);
   if(indicator_handle == INVALID_HANDLE)
   {
      Alert(“Error loading the indicator: “, GetLastError());
      return(INIT_FAILED);
   }   
   return(INIT_SUCCEEDED);
}

6.2 Parameters for optimization in the strategy tester:

• Prediction Number (1 to 6)
• Threshold values for generating trading signals (SignalLimit)
• Network type (Versions) – T1, T2, T3, T4 and their modifications
• Sizes of neural network layers (LL1, LL2, LL3, LL4)

7. Frequently Asked Questions (FAQ)

Q: The network does not load or does not start training.

ABOUT:

• Check the write permissions in the MQL5/Files/ folder
• Make sure there is enough historical data available
• Check the correctness of the specified network parameters (layer sizes)
• Make sure the network files exist and are not corrupted.

Q: What Network type should I choose?

ABOUT:

• T1 – Basic option. It is recommended to start with it
• T1Dif, T2Dif – Strategies that analyze price differences. Can be more accurate for determining directional movements
• T2 – Context-dependent analysis. Takes into account volatility
• T3/T4 – Specialized strategies for accurate determination of trends and impulses

Q: How to determine the input/output scale type?

ABOUT:

• Check the Type parameter on the indicator information panel (GUI)
• If the UniverseOutputScale parameter = true, the display in the indicator window is standardized to the range [-1,1]
• If UniverseOutputScale = false, the output values correspond to the original scale of the selected strategy (S01 or S11)

Q: Why does the indicator use this particular validation method?

ABOUT:

• This approach is standard in machine learning and provides a fair assessment of the quality of the model on data that was not used in training.

Q: How often should the network be retrained?

ABOUT:

• It is recommended to retrain the network whenever market conditions change significantly or every 1-2 weeks to keep the model relevant.

• Determine the scale type – Understanding the scale of the output data (S01 or S11) is critical to properly interpreting the signals.
• Set up thresholds – Optimize the SignalLimit parameter to your trading strategy and selected timeframe
• Test different types of networks – Strategies based on price differences (T1Dif, T2Dif) can show better results on volatile instruments
• Consider the time frame – High time frames (H4, D1) often require more conservative (larger) thresholds to filter out noise
• Periodic retraining – Regularly retrain the network on new data to keep the model up to date

Important validation notes:

• The validation period is cut off from the end of the historical data.
• For maximum relevance, it is recommended to periodically retrain the network on new data.
• The validation period size should match your trading horizon.

9. Support

If you have any questions or problems:

• First of all, check the logs in the “Experts” and “Journal” tabs. Make sure that logging is enabled in the settings
• Make sure you have enough historical data for the symbol and timeframe you choose.
• Determine the type of network used and the data output scale – this information is often needed for diagnostics
• For complex questions, please contact the indicator’s discussion section on the Marketplace or the developer via private messages

APPENDIX A: Description of strategies (Network type) and recommendations for activation and scaling (implemented in Auto)

T1 – Normalized independent analysis

• Input: Normalized window of L1 opening prices
• Output: Normalized window of L4 predicted opening prices
• The gist: The neural network learns to directly predict future prices based on historical
• Activations: Tanh / Tanh
• Scale: S11 / S11

T2 – Context-dependent analysis

• Input: Normalized window of L1 opening prices
• Output: Predicted prices normalized to the range of input data
• The bottom line: The forecast is scaled relative to the current volatility
• Activations: Tanh / Tanh
• Scale: S11 / S11

T1Dif / T2Dif – Price Difference Analysis

• Input: Differences between future and current prices, normalized to preserve sign
• Output: Predicted price differences (T1Dif: independent norm., T2Dif: input norm.)
• The gist: The network predicts the direction and strength of movement, not the price
• Activations: Tanh (LReLu) / Linear
• Scale: S11 / S11

T3 – Trend Detector with Filtering

• Entry: Normalized Opening Price Window
• Output: If all L4 future bars are above/below the current price, their values are normalized. Otherwise, the output is ignored.
• The gist: The network learns to detect stable unidirectional movements
• Activations: Tanh / Sigm
• Scale: S11/S01

T3Bin – Binary Trend Classification

• Input: Same as T3
• Output: Binary values (1/-1 or 1/0) for each future bar
• Essence: Simplification of the problem to binary classification for clear signals
• Activations: Tanh / Sigm
• Scale: S11/S01

T4 – Pure Pulse Detector

• Entry: Normalized Opening Price Window
• Output: Similar to T3, but learning occurs only on pronounced movements
• The gist: Tighter selection. Focus on finding strong, momentum moves
• Activations: Relu / Tanh
• Scale: S11 / S11

T4Bin – Binary Impulse Classification

• Input: Same as T4
• Output: Binary values (1/-1 or 1/0)
• The gist: Extremely aggressive search for momentum for short trades
• Activations: Relu / Sigm
• Scale: S11/S01