Parameters of the “Neuro Future” MT5 indicator with an in depth description – My Buying and selling – 5 September 2025

Content material:

I. Neuro Future indicator parameters, default values.

II. Detailed description of parameters.

III. Technical description of the kinds of networks used.

I. Neuro Future indicator parameters, default values:

1. —— BASIC SETTINGS ——

• Predicting quantity : 6 – Predicting N-1 bars for T, TBin, and N bars for TDif.
• Community filename prefix   : “net_” – Community file identify prefix
• Filename postfix kind   : T_ACT_SYMBOL_TF – File identify postfix kind (image+timeframe)
• Use widespread folder for recordsdata   : true – Use shared folder for recordsdata
• Auto coloration by kind   : true – Computerized coloration by kind
• Guide coloration   : clrGray – Guide coloration choice (if AutoColor is fake)

• Community kind   : T1 – Community Kind
• Activation preset   : Auto – Preset of activation features
• Loss perform kind   : MSE – Loss Perform
• Enter layer measurement   : 40 – Enter layer measurement
• Hidden layer 1 measurement   : 27 – Measurement of the primary hidden layer
• Hidden layer 2 measurement   : 12 – Measurement of the second hidden layer (0 = disabled)
• Output layer measurement   : 6 – Output layer measurement (from 2, from 1 for TDif)
• Community remark   : “” – Touch upon the community

• Hidden layer activation   : Tanh – Activate Hidden Layers
• Output layer activation   : Tanh – Output layer activation
• Enter scaling   : S11 – Enter Scaling
• Output scaling   : S11 – Output Scaling
• Gradient limiting   : false – Gradient restrict
• Max gradient worth   : 0.1 – Most gradient worth

• Begin studying after loading   : false – Begin coaching after loading
• Use coaching date   : false – Use coaching date
• Coaching date   : DATE – Date of coaching
• Max coaching epochs   : 500 – Max variety of coaching epochs
• Max coaching samples   : 100 – Max. variety of examples for coaching
• Bars between samples   : 1 – Minimal bar between samples
• Use adaptive studying price   : true – Use adaptive studying price
• Studying price   : 0.1 – Studying pace

• Goal error   : 0.0001 – Goal Error

• Validation interval   : 0 – Validation interval (bars)
• Use validation standards   : false – Use validation standards for early stopping
• Validation mode   : Revenue – Validation mode
• Validate solely chosen bar   : false – Validate solely ‘Prediction quantity’
• Validation threshold   : 0.1 – Prediction threshold for validation

• Ending endurance early   : 1000 – Persistence for an early cease
• Min epochs for early cease   : 10 – Min. epochs earlier than early cease
• Save finest weights   : false – Hold finest weights

• LR lower issue   : 0.5 – LR discount issue
• LR enhance issue   : 1.01 – LR Magnification Issue
• Error distinction for LR lower   : 1.01 – Error distinction for LR discount
• Minimal studying price   : 0.00001 – Minimal studying price
• Most studying price   : 10000 – Most studying pace

• Allow alerts   : true – Allow notifications
• Alert threshold   : 0.1 – Alert threshold
• Push notifications   : false – Push notifications
• Electronic mail alerts   : false – Electronic mail Notifications
• Sound alerts   : false – Sound notifications

• Coaching logs   : true – Coaching logs
• Log frequency discount   : 10 – Logging frequency
• Instance logs   : false – Instance logs
• Consequence logs   : false – Consequence logs
• Load logs   : false – Loading logs
• Save logs   : false – Save logs

• Common output scaling   : true – Common output scaling
• Repair indicator min/max   : true – Repair min/max of indicator window
• Max bars in window   : 5000 – Max bars in indicator window

II. Detailed description of parameters:

Prediction quantity (from 2(1 for TDif) to Out):
Predicting N-1 bars for T, TBin, and N bars for TDif. For many community sorts (T1, T2, T3, T4, T3Bin, T4Bin), the minimal worth is 2. For distinction sorts (T1Dif, T2Dif), the minimal worth is 1.

Community filename prefix:
Prefix (preliminary half) of the file identify that shops the weights of the educated neural community. Permits you to use totally different networks for various devices or methods whereas sustaining a transparent identify.

Filename postfix kind:
The kind of postfix (remaining half) of the file identify. Determines what information might be mechanically added to the file identify to make it distinctive.

• NO: The postfix is just not added.

• SYMBOL_TF: Provides an emblem and timeframe (eg:    net_EURUSDH1 ).

• T_SYMBOL_TF: Provides community kind, image and timeframe (eg:    net_T1_EURUSDH1 ).

• T_ACT_SYMBOL_TF: Provides community kind, activation kind, image and timeframe (eg:    net_T1_Tanh_EURUSDH1 ).

Use widespread folder for recordsdata:
Specifies the placement of the recordsdata with the neural community weights. If the worth    true , recordsdata might be saved and loaded from the widespread folder of all terminals    TerminalCommonFiles . That is handy for accessing the identical community from totally different terminals. If    false    – from a selected terminal folder    MQL5Files .

Auto coloration by kind:
Allow computerized choice of indicator drawing coloration relying on the kind of neural community forecast. If the worth    true , parameter    Guide coloration    ignored.

Guide coloration (if AutoColor false):
Units a customized coloration for drawing the indicator. Lively provided that the parameter    Auto coloration by kind    disabled ( false ).

—— NETWORK STRUCTURE —— 

Community kind:
The kind of neural community structure and objective. Every kind is optimized for a selected market and evaluation fashion. (Notice: Learn III. Technical description of the kinds of networks used).

• T1 (Normalized Evaluation): Foreign exchange (majors), indices, metals.

• T1Dif (Distinction Evaluation): Cryptocurrencies, commodities, foreign exchange (minors).

• T2 (Context-Conscious): Foreign exchange (crosses), metals, indices.

• T2Dif (Context-Conscious Distinction): Foreign exchange (crosses), metals, indices.

• T3 (Development Detection): Foreign exchange (majors), commodities, indices.

• T3Bin (Binary Development): All markets (coaching).

• T4 (Momentum Detection): Cryptocurrencies, Commodities, Metals.

• T4Bin (Binary Momentum): Cryptocurrencies, US shares, commodities.

Activation preset:
A predefined set of activation features and parameters for community layers. Permits you to shortly choose a confirmed configuration.

• Auto: Computerized choice relying on the chosen community kind ( Community kind ).

• Guide: Manually alter the activation and scaling features.

• // Fundamental & Beneficial:

• Customary: Tanh-Tanh[-1,1] (T1,T2,T3,T2Dif) Gradient off

• Basic: Sigm-Sigm[0,1] (T3,T3Bin,T4Bin) Gradient off

• Distinction: LRelu-Linear[-1,1] (T1Dif,T4,T2Dif) Gradient 0.1.

• BinaryMomentum: Relu-Sigm[-1,1]-[0,1](T4Bin,T3Bin) Grad 0.08

• // Superior:

• Uneven: Tanh-Tanh[-1,1]-[0,1](T1,T2,T3) Grad off

• ReLUNetwork: Relu-Relu[-1,1](T4,T4Bin) Grad 0.1

• Regression: Tanh-Linear[-1,1](T1Dif,T2,T2Dif,T4) Grad off
  

• MixedAsymmetric: Tanh-Sigm[-1,1]-[0,1](T2,T3) Grad off
  

• // Various:

• Various: Tanh-Tanh[0,1](T1,T2,T3) Grad off

• ReLURegression: Relu-Linear[-1,1](T1Dif,T4,T2Dif) Grad 0.12

• LeakyReLU: LRelu-LRelu[-1,1](T4,T4Bin,T1Dif) Grad 0.1

• FullyLinear: Linear-Linear[-1,1](Exp,T1Dif,T4) Grad 0.15

• // Experimental:

• Hybrid: Sigm-Tanh[0,1]-[-1,1](T2,T3,T2Dif) Grad off

• ReLUSigmoid: Relu-Sigmoid[0,1](T4Bin,T3Bin) Grad 0.1

• ComboMomentum: Relu-Tanh[-1,1](T4,T1Dif) Grad 0.1

• Experimental: Sigm-Linear[0,1]-[-1,1](Any,untested) Grad off

• ComboLeaky: LRelu-Tanh[-1,1](T4,T1Dif,T2Dif) Grad 0.1

• ComboMixed: Tanh-Sigm[-1,1]-[0,1](T2,T3,T3Bin) Grad off

Loss perform kind:
The kind of error perform (loss perform) that the neural community tries to attenuate in the course of the studying course of.

• MSE (Imply Squared Error): Delicate to outliers.

• MAE (Imply Absolute Error): Imply absolute error. Much less delicate to outliers.

• L_HUBER: HuberLoss. A compromise between MSE and MAE.

• BinCE (Binary Cross-Entropy): Binary cross-entropy. Designed for binary classification (output within the vary [0,1]).

Enter layer measurement:
The dimensions of the enter layer of the neural community. Determines the variety of neurons that obtain enter information from the market.

Hidden layer 1 measurement:
The dimensions of the primary hidden layer of a neural community.

Hidden layer 2 measurement (0 = disabled):
Measurement of the second hidden layer. If set to 0, this layer is disabled and the community turns into a three-layer (input-hidden-output) community.

Output layer measurement (from 2, from 1-for TDif):
Output layer measurement. Determines the dimensionality of the neural community forecast. Normally from 2 and extra. For distinction kinds of networks (T1Dif, T2Dif) it may be from 1.

Community remark:
An arbitrary textual content remark that might be saved together with the community. Can be utilized to make notes in regards to the coaching aim, information, and so forth.

—— MANUAL ACTIVATION SETTINGS —— 

Hidden layer activation:
Activation perform for all hidden layers of the community. Lively solely when preset is chosen    Guide .

• Sigm: Sigmoid. output (0,1).

• Relu: Rectified linear block. output max(0,x).

• LRelu: Leaky ReLU. Permits small destructive values to cross by way of.

• Linear: Linear activation. output x.

• Tanh: Hyperbolic tangent. output (-1,1).

Output layer activation:
Activation perform for the output layer of the community. Solely lively when a preset is chosen.    Guide .

Enter scaling:
A way for scaling enter information earlier than feeding it into the community.

Output scaling:
A way for scaling community output.

Gradient limiting:
Allow gradient clipping. Helps fight the “exploding gradients” drawback throughout coaching.

Max gradient worth:
Most absolute worth of the gradient. Used if enabled    Gradient limiting .

—— LEARNING PARAMETERS ——

Begin studying after loading:
If enabled ( true ), the coaching course of will begin instantly after the indicator is loaded onto the chart.

Use coaching date:
Whether or not to make use of a selected date for the coaching information slice. If off ( false ), coaching will finish with the present date (minus the output layer).

Coaching date:
The date on which the coaching dataset ends. Any bars after this date is not going to be used for coaching.

Max coaching epochs:
Most variety of coaching epochs. One epoch is a full cross by way of your entire coaching dataset.

Max coaching samples:
The utmost variety of examples (samples) that might be used for coaching from your entire obtainable historical past. Within the T4, T4Bin methods, solely those who fulfill the circumstances might be chosen from this variety of examples.

Bars between samples:
The minimal variety of bars between two consecutive samples in a knowledge set. Helps enhance the range of the info.

Use adaptive studying price:
Allow adaptive studying price change. The algorithm will mechanically enhance or lower the training price throughout coaching.

Studying price:
Preliminary studying price. Determines the “step” with which the neural community adjusts its weights throughout coaching.

Goal error:
Goal error worth. If the error on the coaching pattern reaches this worth, coaching will cease.

—— VALIDATION SETTINGS —— 

Validation interval (bars):
The validation interval measurement in bars. Knowledge for this era is just not utilized in coaching and is used to verify the standard of the community. If 0, validation is disabled.

Use validation standards for early stopping (and saving finest weights):
Whether or not to make use of a criterion on the validation set to cease coaching early and preserve the most effective weights.

Validation mode:
The criterion by which the standard of a mannequin on a validation pattern is decided.

• Binar: Binary accuracy of the route forecast.

• Revenue: A hypothetical revenue calculated because the distinction between the bars in factors of the present instrument (excluding the unfold and commissions and different market circumstances).

• NetError: Community error (MSE, MAE, and so forth.) on validation pattern.

Validate solely ‘Prediction quantity’ (for Binar,Revenue):
If enabled, validation by standards    Binary    And    Revenue    might be held just for the bar laid out in    Prediction quantity .

Validation prediction threshold:
Threshold worth for forecast. Utilized in modes    Binary    And    Revenue    to find out whether or not the prognosis is taken into account optimistic or destructive.

—— EARLY STOPPING ——

Early stopping endurance:
The variety of epochs throughout which the error on the validation set might not enhance earlier than early stopping is triggered.

Min epochs earlier than early stopping:
The minimal variety of coaching epochs that should be accomplished earlier than the early stopping mechanism kicks in.

Save finest weights:
Whether or not to maintain the weights of the most effective performing mannequin relatively than the final weights after coaching.

—— ADAPTIVE LEARNING RATE ——

LR lower issue:
A multiplier to scale back the training price if the error has stopped reducing considerably.

LR enhance issue:
A multiplier to extend the training price if the error is steadily reducing.

Error distinction for LR lower:
The brink for error development to provoke a lower in studying price.

Minimal studying price:
The decrease sure on the training price, beneath which it can’t be diminished.

Most studying price:
An higher restrict on the training price, past which it can’t be elevated.

—— NOTIFICATIONS ——

Allow alerts:
Allow all notifications.

Alert threshold:
The forecast threshold worth that, if exceeded, will generate an alert.

Push notifications:
Ship push notifications to the MetaTrader cell software.

Electronic mail alerts:
Ship electronic mail alerts.

Sound alerts:
Play sound notification.

—— LOGGING ——

Coaching logs:
Allow logging of the training course of.

Log frequency discount:
What number of instances to scale back the frequency of logging. For instance, a price of 10 signifies that logging will happen each tenth epoch.

Instance logs:
Hold detailed logs for every coaching instance.

Consequence logs:
Hold logs of ultimate outcomes.

Load logs:
Hold logs of the community loading course of from a file.

Save logs:
Hold logs of the community saving course of to a file.

—— OTHER INDICATOR SETTINGS ——

Common output scaling:
Apply common scaling to output information for show within the indicator window. Alert threshold might be utilized to the common scale.

Repair indicator min/max:
Repair the minimal and most values of the indicator scale to enhance visualization.

Max bars in indicator window:
The utmost variety of historical past bars for which the indicator will calculate and show its values.

III. Technical description of the kinds of networks used: