From 452cdc568320b982533fa8cb7a4adbbd664d207a Mon Sep 17 00:00:00 2001 From: Toh4iem9 Date: Wed, 4 Feb 2026 07:43:29 +0100 Subject: [PATCH] docs(indicators): refactor --- Scripts/MyScripts/Market_Scanner_Pro.md | 89 +++++++++++++++---------- 1 file changed, 52 insertions(+), 37 deletions(-) diff --git a/Scripts/MyScripts/Market_Scanner_Pro.md b/Scripts/MyScripts/Market_Scanner_Pro.md index d1e880c..d45d938 100644 --- a/Scripts/MyScripts/Market_Scanner_Pro.md +++ b/Scripts/MyScripts/Market_Scanner_Pro.md @@ -2,71 +2,86 @@ ## 1. Summary (Introduction) -The `Market_Scanner_Pro` is a high-performance quantitative analysis tool designed to bridge the gap between technical charting and AI-assisted trading. It performs a multi-timeframe, multi-indicator scan across a portfolio of assets and exports the "Market State" into a structured CSV format suitable for Large Language Models (LLMs) or statistical analysis tools (Python/Excel). +The `Market_Scanner_Pro` is a high-performance quantitative analysis tool designed to bridge the gap between technical charting and AI-assisted trading. It is an "Institutional Market X-Ray" that performs a multi-timeframe, multi-indicator scan across a portfolio of assets and exports the market state into a structured CSV format. -Instead of relying on basic price data, this script generates **"QuantScan 2.0"** metrics: it converts raw indicator values into normalized scores (e.g., Z-Score, Efficiency Ratio), providing a deep insight into Trend Quality, Momentum, and Statistical Extremes. +This dataset ("QuantScan 3.0") is optimized for Large Language Models (LLMs) or statistical analysis tools. Instead of raw price data, it provides normalized scores (Z-Score, Efficiency Ratio, Relative Strength), offering deep insights into Trend Quality, Institutional Footprints, and Statistical Reversion risks. ## 2. Methodology and Logic -The script employs a **Hybrid Analysis Model**, splitting metrics into two logical timeframes: +The script employs a **Hybrid Analysis Model** with three core layers: -1. **Context Layer (H1):** Analyzes the "Big Picture". It determines the dominant trend direction, the structural quality of that trend, and key support/resistance zones. -2. **Trigger Layer (M15):** Analyzes the "Execution Timing". It looks for momentum shifts, volume anomalies, and statistical reversion signals. +1. **Context Layer (H1):** Determines the "Big Picture". It identifies the dominant trend direction, the structural quality of that trend, and correlation with the broader market (Relative Strength). +2. **Trigger Layer (M15):** Analyzes "Execution Timing". It monitors momentum shifts, volatility regimes, and statistical extremes. +3. **Institutional Layer (New):** Detects hidden market mechanics, specifically "Absorption" (high volume vs. low range) and extreme probability of mean reversion. -### Key Metrics Calculated +### Key Metrics Defined -* **Trend Score (Z-Score Proxy):** Measures how far the price is from the mean (DSMA) in units of volatility (ATR). A score of +2.0 means the price is 2 standard deviations above the trend. -* **Trend Quality (Efficiency Ratio):** Differentiates between a smooth, tradeable trend (High ER) and a choppy, dangerous market (Low ER). -* **Volatility Regime (Squeeze):** Identifies periods of low volatility (Bollinger Bands inside Keltner Channels) that often precede explosive moves. -* **Volume Quality (RVOL):** Checks if the current move is supported by institutional volume (Relative Volume > 1.0). +* **Trend Score (Z-Score & Deviation):** Measures how far the price is from the trend baseline in units of volatility (ATR). +* **Relative Strength (RS):** Compares the asset's performance against a Benchmark (e.g., US500) over the last 24 hours. A positive RS indicates the asset is outperforming the market. +* **Institutional Absorption:** A logical check based on Wyckoff principles. If Volume is extreme (RVOL > 2.0) but Price Movement is small, it indicates passive limit orders absorbing aggressive market orders—often a sign of a reversal. +* **Reversion Probability:** A composite score (0-100%) that combines Z-Score extremes, Murrey Levels, and Momentum Exhaustion to predict a potential pullback. ## 3. MQL5 Implementation Details The script is built upon the **"Professional Indicator Suite"** architecture, ensuring mathematical precision and performance. -* **Calculation Engines (`.mqh`):** - Instead of using slow `iCustom` calls, the script directly instantiates the optimized Calculation Classes (e.g., `CDSMACalculator`, `CVWAPCalculator`) used by our indicators. This guarantees that the CSV data matches the chart visuals 100%. -* **Defensive Programming:** - The implementation includes rigorous "Safety Checks" (e.g., array bounds checking in ATR, data availability validation) to prevent runtime crashes, even when scanning hundreds of symbols. -* **Smart Data Fetching:** - It utilizes `FetchData` wrappers that efficiently retrieve OHLCV data and organize it into chronological arrays (`ArraySetAsSeries(false)`), optimized for our incremental calculation engines. +* **Calculation Engines (`.mqh`):** It directly instantiates optimized Calculation Classes (e.g., `CDSMACalculator`, `CVWAPCalculator`) rather than using slow `iCustom` calls. +* **Defensive Programming:** Includes rigorous safety checks (e.g., array bounds checking in ATR) to prevent runtime crashes during large-scale scanning. +* **Smart Data Fetching:** Utilizes efficient `FetchData` wrappers to retrieve and sync OHLCV data for multiple timeframes instantaneously. ## 4. Parameters * **Scanner Config:** - * `InpUseMarketWatch`: If `true`, scans every active symbol in the Market Watch window. - * `InpSymbolList`: A comma-separated list of symbols to scan if Market Watch is disabled (e.g., `EURUSD, BTCUSD, US500`). + * `InpUseMarketWatch`: If `true`, scans all active symbols. + * `InpSymbolList`: Custom symbol list (if using manual selection). + * `InpBenchmark`: The symbol for Relative Strength comparison (Default: `US500`). + * **`InpBrokerTimeZone`**: **NEW!** Your broker's timezone name (e.g. `EET`, `UTC+3`). This string is added to the CSV header so the AI knows the context of the timestamp (crucial for detecting Session Opens/Closes). + * **`InpScanHistory`**: **NEW!** Number of bars to download for analysis (Default: `500`). Increase this if using slow moving averages (200 SMA). * **Timeframes:** - * `InpTFFast`: The timeframe for Trigger metrics (Default: `M15`). - * `InpTFSlow`: The timeframe for Context metrics (Default: `H1`). -* **Metric Settings:** - * Allows fine-tuning of indicators (e.g., `InpDSMAPeriod`, `InpLaguerreGamma`, `InpRVOLPeriod`). + * `InpTFFast` (Trigger): Default `M15`. + * `InpTFSlow` (Context): Default `H1`. + * **Metric Settings:** + * **`InpRSBars`**: **NEW!** Lookback period for Relative Strength calculation. + * `24 (Default on H1)` = 24 Hours performance. + * `120` = Weekly performance. + * Indicators fine-tuning (DSMA, Gamma, etc). * **Squeeze Settings:** - * Controls the sensitivity of the volatility squeeze detection (`BB Multiplier`, `KC Multiplier`). + * Allows fine-tuning of the Volatility Squeeze sensitivity (`BB Multiplier`, `KC Multiplier`). +* **TSI Settings:** + * Customizable periods for the True Strength Index (Cycle). -## 5. Output Data Structure (CSV) +## 5. Output Data Structure (CSV - QuantScan 3.0) The script generates a file named `QuantScan_YYYY.MM.DD_HHMM.csv` in the `MQL5\Files` folder. -### Columns Explanation - -| Header | Description | Interpretation | +| Header | Description | Interpretation / ranges | | :--- | :--- | :--- | | **`TIME`** | Timestamp | `YYYY.MM.DD HH:MM` format. | -| **`SYMBOL`** | Asset Name | e.g. `EURUSD`. | +| **`SYMBOL`** | Asset Name | e.g., `EURUSD`. | | **`PRICE`** | Current Bid | The snapshot price at scan time. | -| **`TREND_SCORE`** | **H1 Trend Strength** | Normalized deviation from trend.
• `> +1.0`: Strong Bull
• `< -1.0`: Strong Bear | -| **`TREND_QUAL`** | **H1 Efficiency** | Quality of the trend (Kaufman ER).
• `> 0.6`: Clean Trend
• `< 0.3`: Noise/Chop | -| **`ZONE`** | **H1 Structure** | Murrey Math Level.
• `Extreme`: Reversal likely.
• `Range`: Trading Zone. | -| **`MOMENTUM`** | **M15 Laguerre** | Fast momentum (0.0 - 1.0).
• `> 0.8`: Bullish Pressure
• `< 0.2`: Bearish Pressure | -| **`VOL_QUAL`** | **M15 RVOL** | Instant Institutional Interest.
• `> 1.5`: High Activity
• `< 0.8`: No interest | +| **`TREND_SCORE`** | **H1 Trend Strength** | Normalized deviation.
• `> +1.0`: Strong Bull
• `< -1.0`: Strong Bear | +| **`TREND_QUAL`** | **H1 Efficiency** | Trend noise filter (Kaufman ER).
• `> 0.6`: Clean Trend (Safe to trade) | +| **`ZONE`** | **H1 Structure** | Murrey Math Level.
• `Extreme`: Reversal zone.
• `Range`: Trading zone. | +| **`REL_STRENGTH`** | **Relative Perf.** | Performance vs Benchmark (24h).
• `> 0%`: Leader (Stronger than market)
• `< 0%`: Laggard (Weaker than market) | +| **`MOMENTUM`** | **M15 Laguerre** | Fast momentum (0.0 - 1.0).
• `> 0.85`: Bullish Pressure (Gamma lag) | +| **`VOL_QUAL`** | **M15 RVOL** | Relative Volume.
• `> 1.5`: High Activity
• `< 0.7`: Low Low Interest | | **`SQUEEZE`** | **M15 Vola State** | TTM Squeeze status.
• `ON`: Energy building (Prepare for breakout). | -| **`TSI_DIR`** | **M15 Cycle** | True Strength Index direction (`BULL` / `BEAR`). | +| **`Z_SCORE`** | **M15 Statistics** | Standard Deviations from mean.
• `> 2.5`: Statistically Extreme. | +| **`VOL_REGIME`** | **M15 Vola Trend** | Ratio of Short/Long ATR.
• `> 1.0`: Volatility is expanding. | +| **`TSI_DIR`** | **M15 Cycle** | Cycle direction (`BULL` / `BEAR`). | +| **`REVERSION_PROB`** | **Reversion %** | Composite probability of a pullback.
• `> 80%`: High risk of reversal. | +| **`ABSORPTION`** | **Inst. Volume** | Wyckoff Absorption signal.
• `YES`: High Vol + Small Body (Hidden activity). | ## 6. Usage Workflow 1. **Run the Script:** Drag `Market_Scanner_Pro` onto any chart. -2. **Wait for Completion:** Check the "Experts" tab for progress. It usually takes a few seconds to scan 20-30 symbols. +2. **Wait for Completion:** Check the "Experts" tab. 3. **Locate File:** Open "File -> Open Data Folder -> MQL5 -> Files". -4. **Process with AI:** Upload the `QuantScan_....csv` file to your LLM (GPT-4 / Claude 3) with a prompt like: - > *"Analyze this market data. Identify high-quality trend setups where TREND_QUANT > 0.6 and SQUEEZE is ON. Also, warn me about mean reversion risks where Z_SCORE > 2.5."* +4. **Process with AI:** Upload the `QuantScan_....csv` file to your LLM with a prompt like: + + > *"Analyze this market data. Look for two specific setups:* + > + > 1. ***The Whale Utility:** Strong Trend (`TREND_SCORE > 0.5`) + Strong Relative Strength (`REL_STRENGTH > 0`) + Squeeze is `ON`. + > 2. ***The Reversion Trap:** High Reversion Probability (`> 80%`) AND Absorption is `YES`. + > + > *List the top 3 candidates for each."*