NeuroPrice Navigator MT5
- 专家
- 版本: 2.0
- 激活: 5
Brief Description
NeuroPrice Navigator 2.0 is a fully automated trading advisor for MetaTrader 5 that uses a hybrid TCNN‑LSTM neural network with a Feature Attention mechanism to predict price direction. The advisor self‑trains on historical data, adapts to changing market conditions, and includes a built‑in multi‑level risk management system.
How It Works
The advisor is built on a proprietary neural network model combining three components:
Temporal Convolutional Networks (TCNN) analyze a sequence of 32 previous bars, extracting local patterns and impulsive market movements. Two convolutional layers (24 and 12 filters) recognize short‑ and medium‑term price structures.
Feature Attention Mechanism evaluates the importance of each feature at each moment, automatically focusing on the most significant signals and ignoring market noise.
Long Short‑Term Memory (LSTM) processes the temporal sequence after the convolutional layers and attention mechanism, capturing long‑term dependencies and market context. A recurrent layer of 12 LSTM blocks considers history when forming the forecast.
The model outputs the probability of price growth P(up) in the range 0 to 1. Based on this value, a trading signal is generated:
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P(up) ≥ 0.65 — strong BUY signal
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P(up) ≤ 0.35 — strong SELL signal
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0.35 < P(up) < 0.65 — uncertainty zone, no trade is opened
The advisor retrains every 48 bars on a rolling history window (300 bars by default), using Backpropagation Through Time with adaptive learning rate and L2 regularization. Training includes cross‑validation with chronological fold splitting, preventing overfitting and ensuring robustness to market changes.
Advantages
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Self‑learning and adaptability — no manual optimization for a specific currency pair is required; the advisor trains directly on the chart where it is installed and rebuilds model weights when the market regime changes.
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Hybrid architecture — convolutional layers, attention mechanism, and LSTM capture short‑term impulses, medium‑term trends, and long‑term context simultaneously.
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Multi‑level deposit protection — adaptive lot calculation from free margin, drawdown protection, limit on simultaneous positions, spread control, and volatility filter.
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Flexible position management — real and virtual SL/TP, trailing stop with activation and step settings, and dynamic order distance based on ATR.
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Information panel — displays current signal, model confidence, account status, drawdown, and training progress, plus manual BUY / SELL / CLOSE buttons.
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State preservation — model parameters, optimizer state, and feature cache are saved to a single file, so training continues after terminal restart.
Input Parameters
Main Settings
| Parameter | Description | Default |
|---|---|---|
| MagicNumber | Unique identifier of the advisor's orders | 2456 |
| SessionStart | Trading session start (UTC hours) | 8 |
| SessionEnd | Trading session end (UTC hours) | 22 |
| UTCOffset | Server time offset from UTC (hours) | 0 |
| EnableDebugLogging | Enable detailed logging | false |
| FixSeedForTesting | Fix random number generator for testing | true |
Risk Management
| Parameter | Description | Default |
|---|---|---|
| LotCalculationMode | Lot mode: fixed or from free margin | LOT_MODE_FIXED |
| FixedLotSize | Fixed lot size | 0.01 |
| RiskPercent | Risk per trade (% of free margin) | 1.0 |
| UseEquityProtection | Reduce lot on high drawdown | true |
| MaxDrawdownPercent | Drawdown (%) after which lot reduction begins | 20.0 |
| MaxConcurrentTrades | Maximum simultaneous positions | 3 |
Execution & Broker
| Parameter | Description | Default |
|---|---|---|
| InpOrderComment | Order comment | Trade |
| MaxSpreadPoints | Maximum allowed spread (points) | 40 |
| MaxSlippagePoints | Maximum slippage (points) | 5 |
| ExecutionRetries | Order send attempts | 3 |
| ExecutionRetryDelayMs | Delay between attempts (ms) | 250 |
| EnableExecutionDiagnostics | Detailed execution diagnostics | false |
Real SL / TP
| Parameter | Description | Default |
|---|---|---|
| StopLoss | Stop loss (points), 0 — disabled | 400 |
| TakeProfit | Take profit (points), 0 — disabled | 300 |
Virtual SL / TP
| Parameter | Description | Default |
|---|---|---|
| UseVirtualSL | Enable virtual stop loss | false |
| VirtualStopLoss | Virtual stop loss (points) | 0 |
| UseVirtualTP | Enable virtual take profit | false |
| VirtualTakeProfit | Virtual take profit (points) | 0 |
Trailing Stop
| Parameter | Description | Default |
|---|---|---|
| UseTralling_Stop | Enable trailing stop | true |
| UseAveragePrice | Use average entry price for all positions | true |
| TrallingStart | Profit to activate trailing (points) | 40.0 |
| TrailingDistance | Distance from price to SL (points) | 20.0 |
| TrailingStep | Minimum SL improvement for modification (points) | 5.0 |
Order Distance
| Parameter | Description | Default |
|---|---|---|
| FixedDistancePoints | Fixed distance (points) | 30 |
| UseDynamicDistance | Use ATR-based distance | true |
| DynamicDistanceAtrPeriod | ATR period for dynamic distance | 12 |
| DynamicDistanceMultiplier | Multiplier for dynamic distance | 1.5 |
TCNN‑LSTM Model
| Parameter | Description | Default |
|---|---|---|
| ML_TrainingPeriod | Training period (bars) | 300 |
| ML_RetrainInterval | Retraining interval (bars) | 48 |
| Lookback_Window | History depth (bars) | 32 |
| ForecastBars | Bars for forecast | 3 |
| ForecastThreshold | Forecast threshold (ATR multiplier) | 0.5 |
| CNN_Filters | Number of CNN filters | 24 |
| Kernel_Size | Convolution kernel size | 5 |
| LSTM_Units | Number of LSTM units | 12 |
| ML_LearningRate_Input | Base learning rate | 0.005 |
| ML_DropoutRate | Dropout during training | 0.20 |
| ML_BuyThreshold | BUY threshold: P(up) ≥ value | 0.65 |
| ML_SellThreshold | SELL threshold: P(up) ≤ value | 0.35 |
| L2_Lambda | L2 regularization coefficient | 0.001 |
| Target_MSE | Target MSE for early stopping | 0.1 |
| Max_Epochs | Maximum training epochs | 9 |
| ML_BatchSize | Batch size | 16 |
| ML_WalkForwardFolds | Cross-validation folds | 1 |
| ML_LRPatience | Epochs without improvement before LR reduction | 6 |
| ML_LRDecayFactor | LR reduction factor | 0.5 |
| ML_MinLearningRate | Minimum learning rate | 0.0001 |
| ML_BPTT_GradClipNorm | Global gradient clipping norm | 5.0 |
| ML_BPTT_SampleClipNorm | Per-sample gradient clipping norm | 10.0 |
| ML_BPTT_StateClip | LSTM state limit | 10.0 |
| ML_BPTT_DeltaClip | Limit for dh/dc per BPTT step | 5.0 |
Feature Attention
| Parameter | Description | Default |
|---|---|---|
| Attention_Heads | Number of attention channels | 2 |
| Enable_Contextual_Attention | Enable contextual attention | true |
| Attention_Temperature | Temperature for scaling | 1.0 |
| Attention_Dropout | Dropout for attention | 0.1 |
Indicators
| Parameter | Description | Default |
|---|---|---|
| ADX_Period | ADX period | 10 |
| CCI_Period | CCI period | 14 |
| WPR_Period | Williams %R period | 12 |
| STD_Dev_Period | Standard deviation period | 12 |
| MFI_Period_New | Money Flow Index period | 10 |
| ATR_Period | ATR period | 12 |
Volatility Filter
| Parameter | Description | Default |
|---|---|---|
| Volatility_Filter | Enable volatility filter | false |
| Min_Volatility | Minimum volatility (ATR value) | 0.0003 |
| Max_Volatility | Maximum volatility (ATR value) | 0.012 |
Usage Recommendations
The advisor is designed for currency pairs with moderate volatility (e.g., EURUSD, GBPUSD, USDJPY). Before running on a live account, test it in the MetaTrader 5 Strategy Tester with a fixed initial deposit (e.g., $10,000) and 1:100 leverage. Default parameters are optimized for the M15 timeframe and can be used without additional configuration.
