Gaussian Recovery Engine (GRE)

An institutional-grade systematic trading algorithm for MetaTrader 5. The Gaussian Recovery Engine is designed to identify high-probability price reversals by normalizing non-linear market data into a statistical Gaussian distribution.

🧬 Core Methodology

The GRE framework is built on three pillars of quantitative validation:

1. Statistical Normalization (Fisher Transform)

Unlike standard oscillators, the engine uses the Fisher Transform to convert price action into a normal distribution. This allows for precise detection of market extremes (Overbought/Oversold levels) with reduced lag and higher statistical significance.

2. Multi-Dimensional Entry Filter

To minimize "whipsaws" and false signals, the GRE runs a triple-check protocol:

• Trend Confirmation: Uses smoothed QQE (Quantitative Qualitative Estimation) logic to ensure trades align with the macro momentum.
• Volatility Gating: Integrated ADX (Average Directional Index) filter prevents entries during low-liquidity or non-trending sideways markets.

3. Structural Recovery Logic

The engine utilizes a Structural Grid Model for risk mitigation:

• Dynamic Layering: Adds institutional Alpha Units at fixed-step intervals to improve the aggregate entry price.
• Basket Exit: Manages all open positions as a single unit, executing a hard close once the Basket Target Profit (USD) is achieved.

⚙️ Technical Specifications

Parameter	Default	Function
InpLotSize	0.01	Initial entry volume
InpGridStep	200	Point-based distance for structural layers
InpBasketTP	5.0	Aggregate USD profit target for the entire cycle
InpADX_Min	25	Minimum trend strength requirement

📂 Installation

1. Move the .mq5 file to your MQL5/Experts/ folder.
2. Compile the file in MetaEditor.
3. Drag the EA onto a M15 or H1 timeframe chart.

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Developed by Vasava vishal m
Precision Trading through Mathematical Logic and Statistical Normalization.