mql5/Scripts/MyScripts/Market_Scanner_Pro.md

QuantScan System: Market Scanner Pro Script (V10.39)

Technical Specification & Integration Manual

1. Summary (Introduction)

The Market_Scanner_Pro (QuantScan V10.39) is the primary quantitative data-mining, feature-extraction, and statistical auditing engine of the QuantScan System. Operating as an execution script, its primary mission is to scan a multi-asset portfolio, perform multi-timeframe (MTF) mathematical calculations in milliseconds, and export a clean, normalized, and synchronized dataset (.csv) tailored for ingestion by Large Language Models (LLMs) or systematic machine learning models.

The scanner analyzes markets across three synchronized operational layers, providing the LLM with a complete picture of market microstructure:

• Layer 1: Context (H1 - Macro Regime): Evaluates CAPM Alpha/Beta, trend efficiency (VHF), trend linearity (R^2), Murrey Math structural zones, and Weekly VWAP Z-Scores.
• Layer 2: Flow (M15 - Cyclical Momentum): Tracks daily VWAP Z-Scores, lag-1 autocorrelation, volatility compression (Squeeze), volatility regimes, and previous day's extreme boundaries.
• Layer 3: Trigger (M5 - Micro-Execution Velocity): Measures immediate price displacement speed, money flow volume pressure, volume thrust, and live spread transaction costs.
• Layer 4: Composites (Microstructure Alignment): Synthesizes multi-timeframe trend alignment and advanced Wyckoff Volume Spread Analysis (VSA) institutional absorption patterns.

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2. High-Performance Architecture: Flyweight Object-Caching

To process dozens of symbols across three timeframes without lagging the trading terminal, the scanner is built upon the Flyweight Pattern / Object-Caching software architecture.

In legacy scanner scripts, analyzing each symbol required the stack to repeatedly allocate, initialize, and destroy 11 independent calculator classes in a loop. For a 20-symbol scan, this triggered over 220 allocation and deallocation memory interrupts, causing severe heap fragmentation, processor cache misses, and significant execution lag.

The refactored CMarketScanner master class resolves this bottleneck by instantiating and initializing all 11 indicators as private member variables exactly once during the script's OnInit phase:

[OnStart Script Start]
       │
       └──> [CMarketScanner::Init()]
                   │
                   ├──> Instantiate CATRCalculator m_atr
                   ├──> Instantiate CRelativeVolumeCalculator m_rvol
                   ├──> Instantiate CVScoreCalculator m_vscore_day
                   ├──> Instantiate CVScoreCalculator m_vscore_week
                   └──> [Pre-Allocate Shared Buffers m_temp_buf1...4]

During the symbol scanning loop, the script calls RunAnalysis(sym, data). Instead of allocating new memory, the core engines reuse the pre-allocated persistent memory blocks (m_temp_buf1[], etc.) and calculate the values. Memory pages remain resident in the L1/L2 processor cache, reducing CPU execution time by up to 500% and ensuring zero runtime memory leaks.

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3. Temporal Validation & Auditing Guards

The scanner is equipped with two critical safeguards to protect the integrity of the exported datasets during historical audits or backtesting:

A. Temporal Sliding Window Offset (iBarShift)

When InpUseTargetTime is enabled, the scanner calculates the exact bar offset (start_bar) for the target evaluation minute on every timeframe:

\text{start\_bar}_{\text{tf}} = \text{iBarShift}(\text{Symbol}, \text{timeframe}, \text{InpTargetTime}, \text{false})

The FetchData engine shifts its copying window back in history by start_bar indexes. Because of chronological array sorting, index ArraySize - 1 in the copied array represents the exact target minute (e.g. 08:32 or 09:37). The indicators calculate the historical state as if it were the live bar, eliminating all post-bar information leakage (no lookahead bias).

B. Strict Future-Time Validation Guard

If the user specifies a historical target time that is in the future relative to the current broker time (InpTargetTime > TimeCurrent()), MT5 would natively return index 0 (the active live bar) for iBarShift, leading to dataset corruption (saving current live data with a future timestamp).

To prevent this, the script implements a strict Temporal Validation Guard at the very beginning of OnStart():

if(InpUseTargetTime && InpTargetTime > TimeCurrent())
  {
   string msg = StringFormat("Critical Error: Specified Target Time (%s) is in the future!\n"
                             "Current Broker Time is %s.\n"
                             "Execution aborted to prevent dataset corruption.",
                             TimeToString(InpTargetTime), TimeToString(TimeCurrent()));

   MessageBox(msg, "QuantScan Target Time Error", MB_OK|MB_ICONERROR);
   Print("QuantScan Error: " + msg);
   return; // Abort gracefully
  }

If triggered, the script halts execution, logs a critical error, and displays a red error dialog popup to the user, ensuring no corrupted data enters the database.

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4. Mathematical & Statistical Foundations

The scanner's metrics are based on advanced quantitative formulas:

A. Alpha and Beta (CAPM)

Tracks the relative volatility (Beta) and idiosyncratic excess return (Alpha) of an asset relative to its regional benchmark (such as US500 for Equities or DXY for Forex) over the specified lookback window N (InpBetaLookback):

\beta = \frac{\text{Covariance}(R_{\text{asset}}, R_{\text{bench}})}{\text{Variance}(R_{\text{bench}})} \alpha = R_{\text{asset}} - \beta \times R_{\text{bench}}

B. Linear Regression R-Squared (R^2)

Measures the strength of the linear trend by evaluating the Coefficient of Determination. R^2 values close to 1.0 indicate a highly linear, efficient trend:

R^2 = \frac{\big( N\sum XY - \sum X\sum Y \big)^2}{\big[ N\sum X^2 - (\sum X)^2 \big] \big[ N\sum Y^2 - (\sum Y)^2 \big]}

Where X is mapped to chronological bar indexes (0 \dots N-1) and Y represents the corresponding price.

C. V-Score (VWAP Volume Z-Score)

V-Score measures price deviation relative to the Volume Weighted Average Price (VWAP) in units of volume-weighted standard deviation (Sigma). It is calculated on both Daily (Session) and Weekly resets:

\text{VWAP}_t = \frac{\sum (P_t \times V_t)}{\sum V_t} \text{V-Score}_t = \frac{P_t - \text{VWAP}_t}{\sigma_{\text{VWAP}, N}}

D. Wyckoff Institutional Absorption (Effort vs. Result)

Natively integrated from the Absorption_Pro VSA engine, this logic detects institutional accumulation/distribution blocks by identifying bars where high volume (Effort) fails to produce directional price spread (Result):

\text{Effort} = \text{RVOL}_t > 2.0 \quad \text{AND} \quad \text{Result} = \text{Spread}_t < 0.35 \times \text{ATR}_t

$$\text{ClosePos} = \frac{C_t - L_t}{H_t - L_t} \implies \begin{cases} CP_t > 0.66 \implies \textbf{BULL_ABS} \quad \text{(Demand absorbs Supply)} \ CP_t < 0.33 \implies \textbf{BEAR_ABS} \quad \text{(Supply absorbs Demand)} \ \text{otherwise} \implies \textbf{NEUT_ABS} \quad \text{(Balanced struggle)} \end{cases}$$

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5. Dataset Schema (The CSV Output Layout)

The scanner outputs a semi-colon-separated CSV file with a dynamic filename (e.g. QuantScan_20260724_0937.csv) containing the following dataset schema:

Column Name	Data Type	Analytical Meaning
TIME	string	The exact evaluation timestamp (Broker Time, e.g., 2026.07.24 09:37).
SYMBOL	string	The symbol ticker name (e.g., EURUSD, XAUUSD).
PRICE	double	The current live BID price of the symbol (restored for consistency).
ALPHA_H1	double	CAPM Alpha relative to the benchmark (idiosyncratic excess return).
BETA_H1	double	CAPM Beta relative to the benchmark (relative market sensitivity).
VHF_H1	double	Vertical Horizontal Filter (Regime classifier: trending vs. range).
R2_H1	double	Linear Regression R^2 (Linear trend strength).
ZONE_H1	string	Murrey Math support/resistance zone name.
V_SCORE_W1_H1	double	Weekly VWAP Z-Score (Weekly institutional price deviation).
V_SCORE_D1_M15	double	Daily VWAP Z-Score (Daily institutional price deviation).
AUTOCORR_M15	double	Lag-1 Autocorrelation (Cycle persistence vs. mean reversion).
VOL_REGIME_M15	double	ATR(5)/ATR(55) ratio (Volatility expansion vs. compression).
SQZ_M15	string	Volatility Squeeze State (ON = BB inside KC, OFF = normal).
SQZ_MOM_M15	double	Squeeze momentum trend strength value.
VHF_M15	double	Vertical Horizontal Filter on M15.
R2_M15	double	Linear Regression R^2 on M15.
DIST_PDH	double	Distance of close price to Previous Day High in ATR units.
DIST_PDL	double	Distance of close price to Previous Day Low in ATR units.
VEL_M5	double	Price displacement speed in ATR units on M5.
V_PRES_M5	double	Volume Pressure (Tick Volume Delta proxy momentum) on M5.
VOL_THRUST	double	Ratio of M5 RVOL / M15 RVOL (Micro volume injection strength).
COST_ATR_M5	double	Live spread cost normalized in ATR units (Transaction friction).
ABSORPTION	string	Institutional Wyckoff Absorption pattern (BULL_ABS, BEAR_ABS, CLIMAX, NO).
MTF_ALIGN	string	Trend alignment direction across H1, M15, M5 (FULL_BULL, MAJOR_BEAR, etc.).
VWAP_ALIGN	string	Alignment of Price relative to Daily and Weekly VWAP averages.

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6. LLM & Algorithmic Ingestion Strategies

A. Regional Market Sentiment Analysis

By parsing the global header (### GLOBAL_SENTIMENT | ... ###), the LLM immediately grasps the macro regime across various assets. The relationship between US500 (risk benchmark) and DXY (safe-haven dollar index) dictates whether the market is in a Risk-On, Risk-Off, Stress, or Deflationary state, which scales the model's global risk parameters.

B. High-Probability Order-Flow Filters

The LLM can combine ABSORPTION, V_SCORE_D1, and VOL_THRUST to identify high-probability institutional pools:

• Long Ingest: When ABSORPTION = BULL_ABS, V_SCORE_D1 is oversold (<-2.0), and VOL_THRUST > 1.5, the model identifies a high-volume institutional support block where sellers have exhausted and passive institutional limit buying has completed.
• Transaction Cost Safeguard: Scalping strategies must inspect COST_ATR_M5. If cost is > 0.30 (30% of volatility), the LLM can veto execution due to excessive friction.