Syntetic Mean Reversion
- Göstergeler
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Danny Giovanni Romero Lozano
I am a quantitative trader and algorithmic developer specialized in building advanced trading systems for synthetic indices and high-volatility instruments. My expertise combines statistical modeling, market structure analysis, and systematic execution through custom-built indicators and Expert - Sürüm: 1.20
- Etkinleştirmeler: 5
Synthetic Mean Reversion
The Synthetic Mean Reversion is a technical indicator designed to analyze price momentum and its distance from dynamic equilibrium. It operates in a separate sub-window, offering a normalized view of market extensions and potential exhaustion zones based on price volatility.
The indicator measures the difference between the current price and its mean, transforming market data into a visual scale of 0 to 100. This calculation helps identify periods when the price has deviated significantly from its average value.
Key Features:
Normalization Logic: Displays price movement on a scale of 0 to 100 to identify overextension conditions.
Expansion Analysis: Highlights periods of increased volatility through specific visual markers.
Calculation Integrity: All signals are based on closed bar data, ensuring that indicators are not redrawn once a candlestick is complete. Multi-asset compatibility: Designed to work across different symbols and timeframes within the MetaTrader 5 platform.
Core functionality: The algorithm processes market data through three main stages:
Distance measurement: Calculates the gap between the price and its equilibrium zone.
Volatility adjustment: Normalizes the movement using recent market volatility (based on ATR) to maintain scale relevance.
Visual classification: Represents the market context as reversal, continuation, or expansion zones based on the final score from 0 to 100.
Input parameters:
EMAFastPeriod: Sets the sensitivity for calculating price speed. Lower values increase reactivity.
EMASlowPeriod: Defines the market equilibrium period. Higher values provide more filtered data.
ATRPeriod: Adjusts volatility normalization.
LookbackBars: Amount of historical data used to determine statistical extremes. SignalLevelScalp/Main: Thresholds for the different visual markers provided by the indicator.
StretchATR: Defines the volatility multiplier used to identify market extensions.
Technical Application:
Traders can use the indicator to monitor statistical extremes. A score above 90 typically indicates an overbought situation relative to the mean, while a score below 10 indicates an oversold situation. The indicator also includes visual arrows to mark specific algorithmic conditions:
Primary Markers: Indicate significant deviations from the mean.
Impulse Markers: Highlight potential volatility breakouts.
Quick Markers: Designed for more frequent monitoring on shorter timeframes.
