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๐ง Quantum Regime Shift Detector v3.0 โ Institutional Edition

๐ง Quantum Regime Shift Detector v3.0 โ Institutional Overview
๐ What It Does
The Quantum Regime Shift Detector identifies when the market transitions between different volatility and behavioral states.
It classifies every moment as one of three regimes:
Regime Description Visual
Stable Low-volatility, predictable environment ideal for trend-following ๐ข Green
Transition High-volatility, chaotic regime shifts or market rotations ๐ด Red
Uncertain Mid-zone where signals conflict or structure is reforming ๐ก Yellow
โ๏ธ How It Works
1๏ธโฃ Five-Factor Market Feature Engine
Feature Description
Volatility Short-term standard deviation of price โ captures movement intensity
Trend Strength Distance between fast and slow EMAs โ shows directional persistence
Momentum Rate of price change โ detects acceleration or exhaustion
Volume Change Relative volume spikes or droughts โ measures participation shifts
Volatility Clustering ATR vs long-term ATR average โ flags clustering of volatility bursts
2๏ธโฃ Weighted AI-Style Shift Score
All five features are blended into a single smoothed composite using customizable weights
(default 30 % Volatility / 30 % Trend / 25 % Momentum / 15 % Volume / 20 % Clustering).
Think of it as a mini-neural-network attention layer that highlights whichever factor dominates.
3๏ธโฃ Adaptive Percentile Thresholds
Analyzes the last 200 bars to build rolling percentiles:
๐ Above 75th percentile โ Transition
๐ Below 25th percentile โ Stable
โ๏ธ Between โ Uncertain
This self-adjusts to volatility shifts across any timeframe or asset.
4๏ธโฃ Visual System
Element Meaning
Aqua Line Quantum Shift Score (main signal)
Red / Green Lines Dynamic thresholds
Blue Fill Uncertain zone
Purple Line Regime probability (0โ1 scale)
Histogram Current regime (high/low bars)
Background Tint Directional bias โ green for bullish, red for bearish
๐จ Alerts & Integrations
Trigger Purpose
Bull Regime Shift Transition + bullish bias โ โ๐ Bullish regime expansion detected.โ
Bear Regime Shift Transition + bearish bias โ โโ ๏ธ Bearish volatility regime forming.โ
Stable Zone Entry Calm phase โ โโ Market entering stable phase.โ
AI Bridge Hooks Webhook alerts โ POST /regime?state=transition / stable for Python or Alice integration
๐ก Practical Use Cases
Objective Application
Position Sizing Reduce exposure during red transition zones
Strategy Selection Trend-follow in green stable zones; mean-revert in red transitions
Risk Management Tighten stops or hedge when volatility expands
Entry Timing Prefer entries during stabilization after transitions
๐งฉ Key Strength
A multi-dimensional, self-learning market classifier that adapts across assets and timeframes, giving you a quantitative edge by revealing when to change your playbook โ before the market does.
๐ What It Does
The Quantum Regime Shift Detector identifies when the market transitions between different volatility and behavioral states.
It classifies every moment as one of three regimes:
Regime Description Visual
Stable Low-volatility, predictable environment ideal for trend-following ๐ข Green
Transition High-volatility, chaotic regime shifts or market rotations ๐ด Red
Uncertain Mid-zone where signals conflict or structure is reforming ๐ก Yellow
โ๏ธ How It Works
1๏ธโฃ Five-Factor Market Feature Engine
Feature Description
Volatility Short-term standard deviation of price โ captures movement intensity
Trend Strength Distance between fast and slow EMAs โ shows directional persistence
Momentum Rate of price change โ detects acceleration or exhaustion
Volume Change Relative volume spikes or droughts โ measures participation shifts
Volatility Clustering ATR vs long-term ATR average โ flags clustering of volatility bursts
2๏ธโฃ Weighted AI-Style Shift Score
All five features are blended into a single smoothed composite using customizable weights
(default 30 % Volatility / 30 % Trend / 25 % Momentum / 15 % Volume / 20 % Clustering).
Think of it as a mini-neural-network attention layer that highlights whichever factor dominates.
3๏ธโฃ Adaptive Percentile Thresholds
Analyzes the last 200 bars to build rolling percentiles:
๐ Above 75th percentile โ Transition
๐ Below 25th percentile โ Stable
โ๏ธ Between โ Uncertain
This self-adjusts to volatility shifts across any timeframe or asset.
4๏ธโฃ Visual System
Element Meaning
Aqua Line Quantum Shift Score (main signal)
Red / Green Lines Dynamic thresholds
Blue Fill Uncertain zone
Purple Line Regime probability (0โ1 scale)
Histogram Current regime (high/low bars)
Background Tint Directional bias โ green for bullish, red for bearish
๐จ Alerts & Integrations
Trigger Purpose
Bull Regime Shift Transition + bullish bias โ โ๐ Bullish regime expansion detected.โ
Bear Regime Shift Transition + bearish bias โ โโ ๏ธ Bearish volatility regime forming.โ
Stable Zone Entry Calm phase โ โโ Market entering stable phase.โ
AI Bridge Hooks Webhook alerts โ POST /regime?state=transition / stable for Python or Alice integration
๐ก Practical Use Cases
Objective Application
Position Sizing Reduce exposure during red transition zones
Strategy Selection Trend-follow in green stable zones; mean-revert in red transitions
Risk Management Tighten stops or hedge when volatility expands
Entry Timing Prefer entries during stabilization after transitions
๐งฉ Key Strength
A multi-dimensional, self-learning market classifier that adapts across assets and timeframes, giving you a quantitative edge by revealing when to change your playbook โ before the market does.
๋ณดํธ๋ ์คํฌ๋ฆฝํธ์ ๋๋ค
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๋ฉด์ฑ ์ฌํญ
ํด๋น ์ ๋ณด์ ๊ฒ์๋ฌผ์ ๊ธ์ต, ํฌ์, ํธ๋ ์ด๋ฉ ๋๋ ๊ธฐํ ์ ํ์ ์กฐ์ธ์ด๋ ๊ถ์ฅ ์ฌํญ์ผ๋ก ๊ฐ์ฃผ๋์ง ์์ผ๋ฉฐ, ํธ๋ ์ด๋ฉ๋ทฐ์์ ์ ๊ณตํ๊ฑฐ๋ ๋ณด์ฆํ๋ ๊ฒ์ด ์๋๋๋ค. ์์ธํ ๋ด์ฉ์ ์ด์ฉ ์ฝ๊ด์ ์ฐธ์กฐํ์ธ์.