The Quant Engine Bot is an algorithmic trading robot that combines multiple layers of analysis to generate entry signals with a high probability of success. Unlike robots based on isolated indicators, this system utilizes a pipeline architecture that integrates Price Action Analysis, Order Flow, Market Regimes, and a Machine Learning-based probabilistic model for entry and exit decisions.

Parameters for XAU/USD on H12, H1, and M15 timeframes.

github.com/maike82/Quant-Engine-MGC

🧠 Core Strategy

Decision Pipeline (4 Layers)

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PRICE ACTION , ORDER FLOW ,MARKET REGIME , ML EDGE PROBABILITY , RISK ENGINE , EXECUTION ENGINE → DYNAMIC TP / PARTIALS / TRAILING ML

1. PRICE ACTION - Market Structure

Objective: To identify the structural trend and inflection points.

Components:

Swing Points (Fractals): Automatic identification of highs and lows with adjustable sensitivity

BOS (Break of Structure): Confirmation of trend continuation

CHOCH (Change of Character): Early signal of trend reversal

Fibonacci Fractals: Ideal retracement zones (38.2% - 61.8%) for entries

VWAP (Volume-Weighted Average Price): Daily fair value reference

How it generates an edge:

Trades only in the direction of the confirmed structure

Seeks entry points in "discount" zones within the trend

Identifies changes of character before they become full reversals

2. ORDER FLOW

Objective: To understand who is in control of the market (buyers vs. sellers). Components:

Estimated Delta per Bar

Measures imbalance between buyers and sellers

Absorption: Identifies large players "absorbing" orders without moving the price (silent accumulation/distribution)

Liquidity Profile: Classifies the environment as HIGH/MEDIUM/LOW liquidity

How it generates an edge:

Confirms if order flow aligns with market structure

Detects divergences between price and flow (potential exhaustion)

Absorption signals the formation of accumulation/distribution

3. MARKET REGIME - Environment Classification

Objective: Adapt the strategy to current market conditions.

Components:

ADX (Average Directional Index): Measures trend strength

ATR (Average True Range): Measures volatility

Relative Volatility: Compares current ATR to the historical average

4. ML EDGE PROBABILITY - Probabilistic Model

Objective: Consolidate all information into an edge probability. Weighted Score Model (Sigmoidal):

Confirmed trend, BOS/CHOCH

Buy/sell pressure, imbalance, absorption

Fibonacci, VWAP

Trend strength, volatility

Penalty for divergences

🎯 Execution and Management

RISK ENGINE - Risk Management

Parameter Description

Risk per Trade 0.1% to 10% of balance

Structural Stop Loss Based on swing points + ATR buffer

Max Daily Loss 3% (blocks new entries)

Volume Automatically calculated based on risk or fixed

EXECUTION ENGINE - Entries

Market Order: Immediate execution (default)

Grid (Pyramiding): Adds positions in the direction of the trend

Max 3 levels

Reduces addition size (50% of initial volume)

DYNAMIC TP / PARTIALS / ML TRAILING

Dynamic Take Profit:

Partial (50%): Closes half the position at the first target

ML Exit: Closes remaining position if reversal risk ≥ 65%

Updated and configured, ready to trade XAU/USD; other instruments require optimization.

Time London + NY sessions (high liquidity)

Adaptive: Adjusts to different market regimes

Robust: Multiple confirmation layers reduce false signals

Dynamic Management: TP and trailing adjust to conditions

Capital Protection: Daily loss limit

Smart Grid: Pyramiding only in strong trends

Trading Forex, CFDs, and other leveraged financial instruments involves risk and may result in the loss of capital.