3. Validation and retraining management system
3.1. How validation works
3.2. Suggestions for establishing validation (10-20% of the variety of examples is really useful)
4. Superior settings
4.2. Extra parameters
4.3. Activation and scaling settings
- ActivationPreset – preset configurations of activation capabilities (Auto/Handbook)
- ActivationTypeHidden – activation operate for hidden layers (when configured manually)
- ActivationTypeOut – activation operate for the output layer (when configured manually)
- InputScale – enter information scaling methodology (S11/S01)
- OutputScale – output information scaling methodology (S11/S01)
- GradientLimiting – Allow gradient limiting
- max_grad – most gradient worth (with limitation enabled)
4.4 Notification and Logging Settings
- EnableAlerts – Allow buying and selling alerts
- AlertThreshold – alert set off threshold
- PushNotifications – sending push notifications
- EmailAlerts – Sending e-mail alerts
- SoundAlerts – Sound Alerts
- EnableLogging – enabling the logging system
- ReduceLog – frequency of logging (discount)
- LogExamples – logging coaching examples
- LogResults – logging of coaching outcomes
- LogLoad – logging community loading
- LogSave – logging of community saving
4.5. Extra indicator settings
- UniverseOutputScale – common output scaling
- FixIndicatorWindowMinMax – fixing the minimal/most of the indicator window
- MaxBars – most variety of bars within the indicator window
- AutoColor – computerized colour scheme
- Shade – choose colour (when AutoColor is disabled)
5. Interpretation of outcomes
5.1. Data panel (GUI)
- The data panel shows:
- Community construction – layer configuration (L1, L2, L3, L4)
- Accuracy – present evaluation of the accuracy of forecasts
- Coaching interval – the time vary of information on which the community was skilled
- Activations – activation capabilities used for hidden and output layers
- Scale kind – the tactic of scaling enter and output information (S01 [0,1] or S11 [-1,1])
5.2. Visible parts on the chart
- Forecast Line – a coloured line that shows the forecast for the chosen bar
- Graphic objects – visualization of future forecasts immediately on the value chart
- Vertical traces – symbolize the interval of information used to coach the final loaded community
- Shade indication – informs concerning the compatibility of the loaded community with the present image and timeframe
6. Integration with advisors (EA)
6.1 To name the indicator from the advisor, use the iCustom() operate.
- Instance of initialization within 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 within the technique tester:
- Prediction Quantity (1 to six)
- Threshold values for producing buying and selling alerts (SignalLimit)
- Community kind (Variations) – T1, T2, T3, T4 and their modifications
- Sizes of neural community layers (LL1, LL2, LL3, LL4)
7. Ceaselessly Requested Questions (FAQ)
Q: The community doesn’t load or doesn’t begin coaching.
ABOUT:
- Verify the write permissions within the MQL5/Information/ folder
- Be certain that there’s sufficient historic information obtainable
- Verify the correctness of the desired community parameters (layer sizes)
- Be certain that the community recordsdata exist and aren’t corrupted.
Q: What Community kind ought to I select?
ABOUT:
- T1 – Fundamental choice. It is suggested to begin with it
- T1Dif, T2Dif – Methods that analyze value variations. May be extra correct for figuring out directional actions
- T2 – Context-dependent evaluation. Takes into consideration volatility
- T3/T4 – Specialised methods for correct willpower of developments and impulses
Q: decide the enter/output scale kind?
ABOUT:
- Verify the Kind parameter on the indicator data panel (GUI)
- If the UniverseOutputScale parameter = true, the show within the indicator window is standardized to the vary [-1,1]
- If UniverseOutputScale = false, the output values correspond to the unique scale of the chosen technique (S01 or S11)
Q: Why does the indicator use this specific validation methodology?
ABOUT:
- This strategy is customary in machine studying and offers a good evaluation of the standard of the mannequin on information that was not utilized in coaching.
Q: How typically ought to the community be retrained?
ABOUT:
- It is suggested to retrain the community every time market situations change considerably or each 1-2 weeks to maintain the mannequin related.
8. Suggestions to be used
- Decide the dimensions kind – Understanding the dimensions of the output information (S01 or S11) is essential to correctly deciphering the alerts.
- Arrange thresholds – Optimize the SignalLimit parameter to your buying and selling technique and chosen timeframe
- Check various kinds of networks – Methods based mostly on value variations (T1Dif, T2Dif) can present higher outcomes on risky devices
- Think about the time-frame – Excessive time frames (H4, D1) typically require extra conservative (bigger) thresholds to filter out noise
- Periodic retraining – Commonly retrain the community on new information to maintain the mannequin updated
Essential validation notes:
- The validation interval is lower off from the tip of the historic information.
- For optimum relevance, it is suggested to periodically retrain the community on new information.
- The validation interval dimension ought to match your buying and selling horizon.
9. Assist
In case you have any questions or issues:
- Initially, verify the logs within the “Specialists” and “Journal” tabs. Be sure that logging is enabled within the settings
- Ensure you have sufficient historic information for the image and timeframe you select.
- Decide the kind of community used and the information output scale – this data is commonly wanted for diagnostics
- For advanced questions, please contact the indicator’s dialogue part on the Market or the developer by way of non-public messages
Notice: The market and setup suggestions under got by synthetic intelligence based mostly on the evaluation of the indicator algorithms. As a developer, I’ve not examined all methods on all markets.
APPENDIX A: Description of methods (Community kind) and suggestions for activation and scaling (applied in Auto)
T1 – Normalized unbiased evaluation
- Enter: Normalized window of L1 opening costs
- Output: Normalized window of L4 predicted opening costs
- The gist: The neural community learns to immediately predict future costs based mostly on historic
- Activations: Tanh / Tanh
- Scale: S11 / S11
T2 – Context-dependent evaluation
- Enter: Normalized window of L1 opening costs
- Output: Predicted costs normalized to the vary of enter information
- The underside line: The forecast is scaled relative to the present volatility
- Activations: Tanh / Tanh
- Scale: S11 / S11
T1Dif / T2Dif – Value Distinction Evaluation
- Enter: Variations between future and present costs, normalized to protect signal
- Output: Predicted value variations (T1Dif: unbiased norm., T2Dif: enter norm.)
- The gist: The community predicts the course and power of motion, not the value
- Activations: Tanh (LReLu) / Linear
- Scale: S11 / S11
T3 – Pattern Detector with Filtering
- Entry: Normalized Opening Value Window
- Output: If all L4 future bars are above/under the present value, their values are normalized. In any other case, the output is ignored.
- The gist: The community learns to detect secure unidirectional actions
- Activations: Tanh / Sigm
- Scale: S11/S01
T3Bin – Binary Pattern Classification
- Enter: Similar as T3
- Output: Binary values (1/-1 or 1/0) for every future bar
- Essence: Simplification of the issue to binary classification for clear alerts
- Activations: Tanh / Sigm
- Scale: S11/S01
T4 – Pure Pulse Detector
- Entry: Normalized Opening Value Window
- Output: Much like T3, however studying happens solely on pronounced actions
- The gist: Tighter choice. Give attention to discovering sturdy, momentum strikes
- Activations: Relu / Tanh
- Scale: S11 / S11
T4Bin – Binary Impulse Classification
- Enter: Similar as T4
- Output: Binary values (1/-1 or 1/0)
- The gist: Extraordinarily aggressive seek for momentum for brief trades
- Activations: Relu / Sigm
- Scale: S11/S01
For top timeframes (H4, D1, W1), it is suggested to set extra conservative settings: if there was ActivationHidden == Relu, then set ActivationHidden = Tanh;
For low timeframes (M1, M5, M15) extra aggressive settings: if ActivationHidden == Tanh, then set ActivationHidden = LRelu;
Abstract desk of suggestions:
| Technique | Hidden Activation | Output Activation | Enter Scale | Output Scale |
|---|---|---|---|---|
| T1 | Tanh | Tanh | S11 | S11 |
| T2 | Tanh | Tanh | S11 | S11 |
| T1Dif | Tanh(LRelu) | Linear | S11 | S11 |
| T2Dif | Tanh(LRelu) | Linear | S11 | S11 |
| T3 | Tanh | Sigm | S11 | S01 |
| T3Bin | Tanh | Sigm | S11 | S01 |
| T4 | Relu | Tanh | S11 | S11 |
| T4Bin | Relu | Sigm | S11 | S01 |
APPENDIX B – Suggestions for devices and durations (in Handbook mode):
- For risky devices (Crypto, Gold):
- Extra aggressive activations.
- For instance for “BTCUSD”, “XAUUSD”
- ActivationHidden = Relu; or LRelu;
- OutputScale = S11; // full vary
- For low volatility devices (Main FX):
- Extra conservative settings.
- For instance for “EURUSD” or “USDJPY”
- ActivationHidden = Tanh; // clean activations;
- OutputScale = S01; // probabilistic output
- For various timeframes:
- Excessive TF (H4, D1) – extra conservative
- ActivationHidden = Tanh;
- ActivationOut = Tanh;
- Low TF (M1, M5) – extra aggressive
- ActivationHidden = Relu;
- ActivationOut = Linear;
APPENDIX B – Technique and Activation Presets Compatibility Desk
Legend:
- ✅ Really helpful – Good match
- ⚡ Different – Good different
- 🔄 Suitable – Works, however not optimally
- ❌ Not really useful – Dangerous mixture
Key suggestions:
For T1 (Normalized Evaluation):
-
Higher: Normal, Asym_Output
-
Good: Basic, Mixed_Asym
For T1Dif (Distinction Evaluation):
-
Higher: Regression, Relu_Regression, Lrelu_Linear
-
Keep away from: Basic, Mixed_Asym
For T2Dif (Context-Conscious Distinction Evaluation):
- Higher: Regression, Lrelu_Linear, Relu_Regression
- Keep away from: Basic, Mixed_Asym, Asym_Output
For T2 (Context-Conscious):
-
Higher: Normal, Asym_Output, Mixed_Asym
-
Good: Basic, Regression, Relu_Regression
For T3/T3Bin (Pattern Detection):
-
Higher: Basic, Asym_Output, Mixed_Asym
-
Keep away from: All Linear outputs
For T4/T4Bin (Momentum):
-
Higher: Relu_Regression, Lrelu_Linear, Relu_Network
-
Keep away from: Basic, Mixed_Asym
Simplified suggestions:
For newcomers:
For skilled:
For consultants:
APPENDIX C – Suggestions for the applying of methods in numerous markets:
Abstract desk of suggestions:
| Technique | Greatest Markets | Good markets | Not really useful | Peculiarities |
|---|---|---|---|---|
| T1 | Foreign exchange Majors, Indices CFD | Metals, Commodities | Crypto CFD | Common for secure markets |
| T2 | Foreign exchange Crosses, Metals | Foreign exchange Majors, Indices CFD | Crypto CFD | For devices with pronounced ranges |
| T1Dif | Crypto CFDs, Commodities | Foreign exchange Minor, Metals | Foreign exchange Main | For risky and trending markets |
| T2Dif | Foreign exchange Crosses, Metals | Indices, FX Main | Crypto CFD | For context-sensitive evaluation of value variations |
| T3 | Foreign exchange Majors, Indices CFD | Metals, Commodities | Crypto CFD | For clear pattern actions |
| T3Bin | All markets (coaching) | – | – | Common binary classification |
| T4 | Crypto CFDs, Commodities | Foreign exchange Minor, Metals | Foreign exchange Main | For sturdy impulse actions |
| T4Bin | Crypto CFDs, USA Shares CFDs | Commodities, Metals | Indicatives | For aggressive momentum methods |
Detailed market suggestions:
1.T1 – Normalized Impartial Evaluation
-
Foreign exchange Main (EURUSD, GBPUSD, USDJPY): ✅ Wonderful – secure developments
-
Foreign exchange Minor (EURAUD, GBPNZD): ✅ Good – average volatility
-
Metals (XAUUSD, XAGUSD): ✅ Good – clear developments
-
Indices CFD (US30, SPX500): ✅ Wonderful – appropriate for indices
-
Commodities (XBRUSD, XNGUSD): ✅ Good – however wants adaptation
-
Crypto CFD (BTCUSD, ETHUSD): ⚠️ Warning – too risky
-
USA Shares CFD (AAPL, TSLA): ✅ Good – for shares with liquidity
2. T2 – Context-Conscious Normalized Evaluation
-
Foreign exchange Crosses (EURGBP, AUDCAD): ✅ Wonderful – good ranges
-
Metals (XAUUSD, XPTUSD): ✅ Wonderful – clear technical ranges
-
Indices CFD (DAX30, FTSE100): ✅ Good – however there could also be gaps
-
Foreign exchange Main: ✅ Good – however much less pronounced ranges
-
Commodities: ⚠️ Conditional – is dependent upon the particular product
3. T1Dif – Value Distinction Evaluation
-
Crypto CFD: ✅ Very best – excessive volatility
-
Commodities (Oil, Gasoline): ✅ Wonderful – sharp actions
-
Foreign exchange Minor (unique pairs): ✅ Good – excessive volatility
-
Metals (XAUUSD): ✅ Good – throughout information
-
Foreign exchange Main: ⚠️ Conditionally – solely during times of excessive volatility
4. T2Dif – Context-Conscious Distinction Evaluation
- ✅ Foreign exchange Crosses (EURGBP, AUDCAD, EURCHF) – your best option
- ✅ Metals (XAUUSD, XAGUSD) – particularly within the Asian session
- ✅ Indices CFD (DAX30, FTSE100) – on each day timeframes
- ⚠️ Foreign exchange Main (EURUSD, GBPUSD) – solely during times of excessive volatility
- ❌ Crypto (too risky)
5. T3 – Pattern Detection with Filtering
-
Foreign exchange Main: ✅ Very best – secure developments
-
Indices CFD: ✅ Wonderful – clear each day developments
-
Metals: ✅ Good – particularly gold
-
Commodities: ✅ Good – trending actions
-
Crypto CFD: ⚠️ Beware – Too Noisy for T3
6. T3Bin – Binary Pattern Classification
-
All markets: ✅ Common – for coaching and testing
-
Particularly: Foreign exchange Main, Indices – for dependable alerts
-
For Novices: Greatest Option to Begin With
7. T4 – Pure Momentum Detection
-
Crypto CFD: ✅ Very best – sturdy impulses
-
Commodities: ✅ Wonderful – sharp actions on information
-
Foreign exchange Minor: ✅ Good – risky pairs
-
Metals: ✅ Good – particularly silver
-
Foreign exchange Main: ⚠️ Solely during times of excessive volatility
8. T4Bin – Binary Momentum Classification
-
Crypto CFD: ✅ Very best – for scalping
-
USA Shares CFD: ✅ Wonderful – Excessive Volatility Shares
-
Commodities: ✅ Good – for information impulses
-
Metals: ✅ Good – gold throughout crises
-
Indicatives: ❌ Not really useful – low volatility
APPENDIX D – Timeframe Suggestions:
For Foreign exchange Main:
For Crypto CFDs:
-
T1Dif, T4, T4Bin: M5, M15, H1
-
T3: H4, D1 (for long-term developments)
For Indices CFD: For Commodities:
-
T1Dif, T4: M15, H1
-
T3: H4, D1
Particular suggestions:
Asian session (Foreign exchange):
-
T1, T2 – for vary of movement
-
Keep away from T4, T4Bin – low volatility
European/American session:
-
T3, T4 – for pattern actions
-
T1Dif – for breakout methods
Information occasions:
-
T4, T4Bin – for capturing pulses
-
Keep away from T3 – the filter can lower off sudden actions
Intervals of low liquidity:
-
T1, T2 – extra secure operation
-
Keep away from T1Dif, T4 – could also be false alerts
Cross-market suggestions:
-
Begin with Foreign exchange Main + T3Bin – probably the most secure choice
-
For coaching use T3Bin on totally different markets – common technique
-
For aggressive buying and selling: Crypto CFD + T4Bin – excessive volatility
-
For conservative buying and selling: Indices CFD + T1 – secure developments
APPENDIX E – Suggestions for establishing neural community structure:
Timeframe settings:
1. Quick timeframes (M1-M15)
-
Enter layer (L1): 12-15 neurons – brief value historical past
-
Hidden layer 1 (L2): 8-10 neurons – compact processing
-
Hidden Layer 2 (L3): 0 – often not required
-
Output layer (L4): 3-4 neurons – short-term forecast
2. Medium timeframes (M30-H1)
-
L1: 20-25 neurons – common historical past
-
L2: 12-15 neurons – balanced processing
-
L3: 0 – will be added if obligatory
-
L4: 5-6 neurons – medium time period prognosis
3. Day by day timeframes (H4)
-
L1: 30-35 neurons – prolonged historical past
-
L2: 16-20 neurons – deep processing
-
L3: 8-10 neurons – extra hidden layer
-
L4: 8-10 neurons – long-term prognosis
4. Weekly and month-to-month timeframes
-
L1: 40-50 neurons – most historical past
-
L2: 20-25 neurons – excessive capability
-
L3: 12-15 neurons – deep structure
-
L4: 10-12 neurons – prolonged prognosis
Technique-specific settings:
For T1Dif and T4 (evaluation of value variations)
For T3Bin and T4Bin (binary classification)
-
Simplify structure: L3 = 0
-
Scale back L2 by 2-3 neurons (minimal 6)
-
Optimum for quick studying and clear alerts
For T2 and T2Dif (context-sensitive evaluation)
-
Enhance L2 by 2-3 neurons for higher context
-
If L3 is current, enhance by 2 neurons
-
Improves sample and degree recognition
Adaptation to instrument volatility:Extremely risky devices (crypto, commodities)
-
Enhance L1 by 3-5 neurons
-
Enhance L2 by 2-3 neurons
-
Improves the community’s capacity to deal with sudden actions
Low volatility devices (main pairs)
APPENDIX F – Gradient Limiting Suggestions for Every Activation Preset:
- Normal (Tanh-Tanh) GradientLimiting = false; // Tanh is immune to gradient explosion
- Basic (Sigma-Sigma) GradientLimiting = false; // Sigmoid is self-limiting
- Lrelu_Linear (LReLU-Linear) GradientLimiting = true; max_grad = 0.1; // Default worth for LReLU
- Bin_Momentum (ReLU-Sigma) GradientLimiting = true; max_grad = 0.08; // Stricter limitation for binary classification
- Asym_Output (Tanh-Tanh uneven) GradientLimiting = false; // Tanh is secure
- Relu_Network (ReLU-ReLU) GradientLimiting = true; max_grad = 0.1; // Required for pure ReLU
- Regression (Tanh-Linear) GradientLimiting = false; // Tanh + Linear are often secure
- Mixed_Asym (Tanh-Sigma) GradientLimiting = false; // Each capabilities are secure
- Standard_Alt (Tanh-Tanh different) GradientLimiting = false; // Tanh is secure
- Relu_Regression (ReLU-Linear) GradientLimiting = true; max_grad = 0.12; // ReLU requires limiting
- LRelu_Network (LReLU-LReLU) GradientLimiting = true; max_grad = 0.1; // LReLU is healthier with limiting
- Full_Linear (Linear-Linear) GradientLimiting = true; max_grad = 0.15; // Linear activations are susceptible to exploding gradients
- Hybrid (Sigma-Tanh) GradientLimiting = false; // Each capabilities are secure
- Relu_Sigmoid (ReLU-Sigmoid) GradientLimiting = true; max_grad = 0.1; // ReLU requires limiting
- Combo_Relu_Tanh (ReLU-Tanh) GradientLimiting = true; max_grad = 0.1; // ReLU requires limiting
- Experimental (Sigma-Linear) GradientLimiting = false; // Sigmoid is secure
- Combo_LRelu_Tanh (LReLU-Tanh) GradientLimiting = true; max_grad = 0.1; // LReLU is healthier with limiting
- Combo_Tanh_Sigm (Tanh-Sigm) GradientLimiting = false; // Each capabilities are secure
Keep in mind that these suggestions are normal. At all times check methods on historic information of a selected instrument earlier than utilizing!
Notice: The indicator makes use of historic information to make predictions. Previous efficiency doesn’t assure future income. Commerce responsibly.
