Indicator
Sessions Flow [Cartel Console] Sessions Flow
# Overview
Sessions Flow is a session-based market activity visualization tool designed to provide a detailed view of how trading volume is distributed throughout the major global forex and index trading sessions.
Rather than displaying volume as a single aggregated value, the indicator breaks each session into multiple price levels and visualizes where trading activity was concentrated during that session. This allows traders to study session structure, identify high-participation and low-participation areas, and compare how different sessions interact with price.
The indicator automatically tracks and analyzes the four major trading sessions:
• Sydney Session
• Tokyo Session
• London Session
• New York Session
Each session is processed independently and displayed directly on the chart using volume distribution heatmaps, volume profiles, Point of Control calculations, and Value Area measurements.
---
# Core Features
## Session Detection
The indicator automatically identifies the start and end of each selected trading session and creates a dedicated session structure on the chart.
Users can enable or disable individual sessions and customize session times according to their preferences.
Supported sessions include:
• Sydney
• Tokyo
• London
• New York
---
### Session Heatmap
Each session contains a heatmap that displays the relative distribution of trading activity throughout the session range.
The heatmap highlights:
• Areas with greater participation
• Areas with moderate participation
• Areas with lower participation
This provides a quick visual overview of where the market spent the most and least volume during a session.
Heatmap density and transparency settings can be fully customized.
---
### Session Volume Profile
For every session, the indicator constructs a volume profile based on price distribution inside the session range.
The profile is displayed as a side histogram showing how activity was distributed vertically across different price levels.
This can help traders observe:
• High-volume areas
• Low-volume areas
• Session acceptance regions
• Session rejection regions
The profile width and display settings are adjustable.
---
## Point of Control (POC)
The Point of Control represents the price level that accumulated the highest amount of volume during a session.
The indicator automatically calculates and plots the POC for each completed session.
POC levels often serve as useful reference points when reviewing historical session activity and market structure.
---
### Value Area Analysis
The indicator calculates a configurable Value Area based on the percentage of session volume selected by the user.
Displayed levels include:
• Value Area High (VAH)
• Value Area Low (VAL)
These levels help visualize the region where the majority of session activity occurred.
The default setting uses a 70% Value Area, but users may customize this value.
---
### Historical Session Management
To maintain chart performance, the indicator includes controls for:
• Maximum detailed sessions displayed
• Historical session lookback period
• Simplified rendering of older sessions
Recent sessions can retain full heatmap and profile information while older sessions transition into lightweight background structures.
This allows extensive historical analysis without excessive chart clutter.
---
### Active Session Dashboard
A built-in dashboard displays the currently active trading sessions in real time.
The dashboard provides:
• Active session names
• Live session status indicators
This makes it easy to determine which global markets are currently open without leaving the chart.
---
## Customization Options
The indicator includes a wide range of configurable settings:
### Session Settings
• Individual session visibility
• Custom session times
### Heatmap Settings
• Heatmap resolution
• Number of price bins
• Density visualization controls
### Volume Profile Settings
• Histogram width
• Detailed session limits
### Value Area Settings
• Value Area percentage
• VA visibility controls
### Styling Settings
• Session colors
• Border transparency
• Historical session appearance
• Dashboard position
---
## Intended Usage
Sessions Flow is designed for traders who want to study how market activity develops during different trading sessions.
It can be used for:
• Session analysis
• Market structure observation
• Historical session review
• Volume distribution study
• Contextual chart analysis
The indicator focuses on visualization and analysis rather than signal generation.
---
## Disclaimer
This indicator is intended for educational and analytical purposes only. It does not provide financial advice, trading recommendations, or guaranteed outcomes. Trading involves risk, and users should perform their own analysis before making trading decisions.
Indicator
Indicator
The Strat Sequence Continuation / Reclaim Engine v1.0The Strat Sequence Continuation / Reclaim Engine
The Strat Sequence Continuation / Reclaim Engine is a body-close-based study designed to help traders review how specific Strat candle sequences have historically resolved on the selected chart timeframe.
This indicator combines two separate views:
1. **Markov Regime Permission Panel**
A regime-style table that evaluates recent price behavior and displays market state, return, transition probabilities, permission, and trade behavior context. This table can be turned on/off by the user.
2. **Strat Sequence Continuation / Reclaim Table**
A focused table that measures whether selected Strat sequences continued by body close or failed/reclaimed the relevant reference level.
The sequence table evaluates setups such as:
* Failed 2U → x
* Failed 2D → x
* F2U → 2D → x
* F2D → 2U → x
* F2U → 2D → 2D → x
* F2D → 2U → 2U → x
* 2-2U → x
* 2-2D → x
* 2-1-2U → x
* 2-1-2D → x
* 3-1-2U → x
* 3-1-2D → x
* 3H → x
* 3L → x
For bullish sequences, continuation is counted only when the next candle closes above the applicable reference candle high.
For bearish sequences, continuation is counted only when the next candle closes below the applicable reference candle low.
If price does not close beyond the reference level, the event is classified as reclaim/fail.
The table also includes:
* User-selected lookback
* Live setup candle read
* Live sequence watch
* Body-close decision rule
* Row visibility controls
* Minimum sample threshold filtering
* Optional bull/bear color coding
This tool is intended for historical sequence review, market context, and discretionary analysis. It does not predict future price movement, generate buy/sell signals, or provide financial advice. All outputs depend on the selected symbol, timeframe, lookback settings, and available chart history.
Indicator
Nadaraya-Watson Regression Liquidity Sweeps [AlgoAlpha]🟠 OVERVIEW
This script combines Nadaraya-Watson regression, momentum analysis, and liquidity level tracking into a single workflow. It measures the slope of a smoothed price regression curve, converts that slope into a normalized oscillator, and uses momentum shifts to identify areas where liquidity may be resting.
The oscillator is built from the rate of change of the Nadaraya-Watson estimate rather than price itself. This allows momentum transitions to be measured relative to the underlying regression trend. When momentum weakens after an extended move, the script records swing-based liquidity levels that can later be swept by price.
A volatility-adjusted Nadaraya-Watson band is also displayed on the chart. This provides context for trend direction, momentum strength, and potential rebound conditions around the regression value.
🟠 CONCEPTS
Nadaraya-Watson Regression — A kernel-based smoothing method that estimates an underlying price curve by weighting nearby historical data more heavily than distant data.
Normalized Regression Slope — The change in the Nadaraya-Watson estimate divided by its recent standard deviation, allowing momentum strength to be compared across different market conditions.
Liquidity Sweep Level — A horizontal level created from a swing high or swing low when momentum begins to weaken, representing an area that may later attract price.
Oscillator Signal Line — An EMA of the normalized oscillator used to identify momentum crossovers and momentum phase changes.
Rebound Condition — A signal generated when price moves back through the Nadaraya-Watson value while oscillator direction remains aligned with the prevailing momentum bias.
🟠 FEATURES
Normalized Nadaraya-Watson Oscillator — Measures momentum using the slope of a smoothed regression curve.
Liquidity Sweep Detection — Creates liquidity levels when bullish or bearish momentum begins to weaken.
Volatility-Adjusted Regression Band — Displays a dynamic overlay around the Nadaraya-Watson estimate using smoothed ATR values.
Momentum Weakening Signals — Marks locations where oscillator momentum begins to lose strength against the current directional bias.
Rebound Signals — Highlights situations where price reclaims or loses the regression value while momentum remains aligned with trend direction.
🟠 HOW TO USE
Monitor the oscillator relative to its signal line to identify momentum shifts and changes in directional bias.
Watch for newly created liquidity levels after momentum weakening events, as these levels may become future sweep targets.
Use sweeps of upper or lower liquidity levels to identify areas where price has taken resting liquidity.
Look for bullish rebound signals when price reclaims the regression value while bullish momentum remains active.
Look for bearish rebound signals when price loses the regression value while bearish momentum remains active.
Combine oscillator direction, liquidity levels, and regression band structure to build context around trend continuation or reversal scenarios.
🟠 CONCLUSION
The Nadaraya-Watson Regression Liquidity Sweeps indicator combines regression-based momentum analysis, volatility-adjusted trend structure, and liquidity level tracking. By linking momentum transitions to swing-derived liquidity zones, it helps identify where liquidity may be forming and when it has been swept. This provides traders with additional context for trend analysis, pullbacks, and potential reversal areas. Indicator
6 GAN 8 TIMEFRAME POWER FUSION6 GAN 8 TIMEFRAME POWER FUSION
6 GAN 8 Timeframe Power Fusion is a multi-layer market analysis engine designed to combine trend structure, market participation, momentum, volatility, and price flow into a single decision framework.
The purpose of this indicator is not to generate signals from a single formula or a single oscillator. Instead, it attempts to evaluate the market through multiple independent perspectives and only produces high-confidence signals when those perspectives begin to align.
Multi-Timeframe Analysis Engine
One of the core components of the system is the 8 Timeframe Synchronization Model.
The indicator continuously analyzes:
1 Minute
5 Minute
15 Minute
30 Minute
1 Hour
4 Hour
Daily
Weekly
Rather than relying on one chart timeframe, the system evaluates directional agreement across all monitored periods.
When multiple timeframes begin moving in the same direction, market structure becomes stronger and directional confidence increases.
The TF score displayed in the panel represents how many timeframes currently support the active direction.
Example:
TF 3/8 = Weak alignment
TF 5/8 = Moderate alignment
TF 7/8 = Strong alignment
TF 8/8 = Maximum synchronization
The highest-quality trade opportunities often emerge when higher and lower timeframes begin moving together.
6 GAN Trend Structure Layer
The second core component is the 6 GAN Trend Engine.
Instead of using a single trend line, the system analyzes multiple GAN-inspired trend structures simultaneously.
Each GAN layer evaluates price behavior from a different perspective and trend duration.
The purpose is to measure whether trend strength exists across short-term, medium-term, and long-term structures.
When most GAN layers agree, trend reliability increases significantly.
Example:
GAN 2/6 = Weak trend structure
GAN 4/6 = Developing trend
GAN 5/6 = Strong trend
GAN 6/6 = Full trend agreement
A signal supported by both Timeframe Synchronization and GAN Synchronization generally carries more weight than a signal supported by only one of them.
Volume Fusion Layer
The Volume Fusion Engine combines:
Delta
CVD (Cumulative Volume Delta)
OBV (On Balance Volume)
These components help evaluate participation behind price movement.
Price can move without conviction, but sustainable trends are usually supported by volume.
The FLOW section of the panel provides insight into whether buying pressure or selling pressure is strengthening.
Special attention should be given to:
RES↑ (Resistance Break)
SUP↓ (Support Break)
These events often indicate significant shifts in market participation.
RSI Momentum Layer
The RSI component is not used as a standalone overbought/oversold tool.
Instead, it acts as a momentum confirmation layer.
Strong trends tend to maintain strong RSI conditions for extended periods.
The indicator uses RSI to measure whether market momentum supports the direction suggested by the GAN and Timeframe engines.
ATR Volatility Layer
ATR is used to evaluate market activity and volatility expansion.
Two important states are monitored:
COMP (Compression)
Low volatility environment.
Markets often consolidate and store energy during compression phases.
EXP (Expansion)
Volatility expansion.
This frequently occurs after compression and can lead to directional movements or breakout opportunities.
Compression zones should not automatically be considered entry zones. Instead, they should be viewed as areas where the market may be preparing for its next significant move.
Understanding the Power Score
The Power Score combines all major layers:
8 Timeframe Engine
6 GAN Engine
Volume Fusion
RSI Momentum
ATR Volatility
The score is designed to provide a quick overview of overall market strength.
General interpretation:
Below 35 = Bearish pressure
35–65 = Neutral zone
Above 65 = Bullish pressure
Above 80 = Strong directional environment
The score should not be interpreted as a prediction. It is a measurement of current market conditions.
Best Use Cases
The indicator performs best when multiple layers align simultaneously.
A stronger bullish scenario may include:
TF 7/8 or higher
GAN 5/6 or higher
FLOW showing buying pressure
ATR in expansion mode
Power Score above 65
A stronger bearish scenario may include:
TF 7/8 or higher in the opposite direction
GAN 5/6 or higher bearish alignment
FLOW showing selling pressure
ATR expansion
Power Score below 35
The highest-quality opportunities generally occur when Timeframes, GAN structures, volume flow, and volatility expansion all point in the same direction.
This indicator should be viewed as a market analysis framework rather than a standalone signal generator.
Its primary objective is to help traders understand when multiple layers of market information begin to align and when the probability of directional continuation may be increasing.
TÜRKÇE
6 GAN × 8 TIMEFRAME POWER FUSION
6 GAN × 8 Timeframe Power Fusion, trend yapısını, piyasa katılımını, momentumu, volatiliteyi ve fiyat akışını tek bir analiz motorunda birleştirmek amacıyla geliştirilmiş çok katmanlı bir piyasa okuma sistemidir.
Bu göstergenin amacı tek bir formülden veya tek bir osilatörden sinyal üretmek değildir. Bunun yerine piyasanın farklı katmanlarını aynı anda değerlendirerek yalnızca bu katmanlar uyum göstermeye başladığında yüksek güvenilirlikli bölgeleri ortaya çıkarmaya çalışır.
8 Zaman Dilimli Senkronizasyon Motoru
Sistemin temel taşlarından biri 8 Timeframe Senkronizasyon Motorudur.
Gösterge aynı anda şu zaman dilimlerini analiz eder:
1 Dakika
5 Dakika
15 Dakika
30 Dakika
1 Saat
4 Saat
Günlük
Haftalık
Tek bir zaman dilimine bağlı kalmak yerine farklı zaman dilimlerinin yönsel uyumunu ölçer.
Birden fazla zaman dilimi aynı yönde hareket etmeye başladığında piyasa yapısı güçlenir ve yönsel güven artar.
Panelde görülen TF değeri kaç zaman diliminin aynı yönü desteklediğini gösterir.
Örnek:
TF 3/8 = Zayıf uyum
TF 5/8 = Orta seviye uyum
TF 7/8 = Güçlü uyum
TF 8/8 = Maksimum senkronizasyon
En kaliteli işlem bölgeleri genellikle alt ve üst zaman dilimlerinin birlikte hareket etmeye başladığı alanlarda oluşur.
6 GAN Trend Yapısı Motoru
Sistemin ikinci temel katmanı 6 GAN Trend Motorudur.
Tek bir trend çizgisi kullanmak yerine farklı periyotlarda çalışan çoklu GAN yapıları analiz edilir.
Her GAN katmanı piyasaya farklı bir açıdan bakar ve farklı sürelerdeki trend gücünü ölçer.
Amaç kısa, orta ve uzun vadeli trendlerin aynı anda destek verip vermediğini belirlemektir.
GAN katmanları arasında uyum arttıkça trend güvenilirliği de artar.
Örnek:
GAN 2/6 = Zayıf trend yapısı
GAN 4/6 = Trend oluşumu
GAN 5/6 = Güçlü trend
GAN 6/6 = Tam trend uyumu
Hem Timeframe hem de GAN katmanları aynı yönde birleştiğinde sinyal kalitesi belirgin şekilde yükselir.
Volume Fusion Katmanı
Volume Fusion Motoru üç farklı hacim bileşenini bir araya getirir:
Delta
CVD (Kümülatif Hacim Delta)
OBV (On Balance Volume)
Bu yapı fiyat hareketinin arkasındaki gerçek katılımı ölçmeye çalışır.
Fiyat yükselebilir veya düşebilir ancak kalıcı hareketler genellikle hacim desteği ile oluşur.
Paneldeki FLOW bölümü piyasanın alım veya satım baskısını göstermeye yardımcı olur.
Özellikle:
RES↑ (Direnç Kırılımı)
SUP↓ (Destek Kırılımı)
durumları piyasa katılımında önemli değişimlere işaret edebilir.
RSI Momentum Katmanı
RSI burada klasik aşırı alım veya aşırı satım göstergesi olarak kullanılmaz.
Görevi momentumu doğrulamaktır.
Güçlü trendler genellikle güçlü RSI değerlerini uzun süre koruyabilir.
Bu nedenle RSI, Timeframe ve GAN motorlarının gösterdiği yönü destekleyip desteklemediğini ölçen ek bir filtre görevi görür.
ATR Volatilite Katmanı
ATR piyasadaki hareketliliği ve volatilite genişlemesini ölçmek için kullanılır.
İki temel durum takip edilir:
COMP (Compression)
Düşük volatilite dönemi.
Piyasa bu bölgelerde enerji toplama eğilimindedir.
EXP (Expansion)
Volatilite genişlemesi.
Genellikle sıkışma dönemlerinden sonra ortaya çıkar ve güçlü hareketlere zemin hazırlayabilir.
Sıkışma bölgeleri doğrudan işlem sinyali olarak değerlendirilmemelidir. Daha çok yaklaşan hareketin hazırlık aşaması olarak görülmelidir.
Power Score Nasıl Yorumlanmalı?
Power Score şu katmanların birleşiminden oluşur:
8 Timeframe Motoru
6 GAN Motoru
Volume Fusion
RSI Momentum
ATR Volatilite
Bu skor piyasanın genel gücünü hızlı şekilde değerlendirmeyi amaçlar.
Genel yorum:
35 altı = Ayı baskısı
35–65 = Nötr bölge
65 üzeri = Boğa baskısı
80 üzeri = Güçlü yönsel ortam
Power Score bir tahmin değildir.
Mevcut piyasa koşullarının gücünü ölçen birleşik bir değerlendirme sistemidir.
En Verimli Kullanım Şekli
Gösterge en iyi sonucu katmanların birlikte hizalandığı bölgelerde verir.
Güçlü bir yükseliş senaryosunda genellikle:
TF 7/8 veya üzeri
GAN 5/6 veya üzeri
FLOW alım yönünde
ATR EXP durumunda
Power Score 65 üzeri
görülür.
Güçlü bir düşüş senaryosunda ise:
TF 7/8 veya üzeri aşağı yönlü
GAN 5/6 veya üzeri aşağı yönlü
FLOW satış yönünde
ATR EXP durumunda
Power Score 35 altı
görülebilir.
En yüksek kaliteli işlem bölgeleri Timeframe uyumu, GAN uyumu, hacim akışı ve volatilite genişlemesinin aynı yönde birleştiği alanlarda ortaya çıkar.
Bu nedenle gösterge bir sinyal makinesinden çok, piyasayı çok katmanlı şekilde okumaya yardımcı olan kapsamlı bir analiz motoru olarak değerlendirilmelidir. Indicator
Indicator
Predictive SMA & BB■ Overview
Most technical indicators are strictly lagging—they only tell you what has already happened. The "Predictive SMA & BB" flips this paradigm by projecting the Simple Moving Average (SMA) and Bollinger Bands (BB) into the future space of your chart.
This tool allows you to visualize where dynamic support and resistance (the statistical walls) will be in the future, giving you a massive edge in planning limit orders and understanding the element of "time" in your trades.
■ The Core Logic: Forward Fill & Sliding Window
How do we plot the future without a crystal ball?
This script uses a quantitative approach known as "Forward Fill". It simulates the passing of time by assuming the current price remains perfectly flat (sideways).
As it projects bars into the future, the algorithm drops the oldest historical data points from the calculation window (e.g., a 200-period window) and sequentially fills the new future slots with the current close price.
This reveals a powerful mathematical truth: Even if the price doesn't move, moving averages and standard deviations will shift based on the decay of historical data.
■ Key Features & How to Use
Future Baseline Projection: See exactly when a downward-sloping SMA will mathematically curl upwards as old, extreme price data is dropped from the calculation.
Time-Based Squeeze Anticipation: As future bars are populated with flat prices, the projected Bollinger Bands will naturally squeeze, visualizing the expected statistical boundaries if volatility dies down.
Custom UI: Fully customizable from the inputs panel. Toggle the future SMA, BB lines, and cloud fills, and adjust colors/opacity without touching the code.
■ ⚠️ Crucial Limitations & Disclaimers
To use this tool effectively, you must understand its mathematical limitations:
Not a Crystal Ball: This indicator projects a statistical baseline based on the assumption that current prices will maintain a zero-volatility trajectory.
Vulnerability to Volatility Expansion: The projected ±2σ bands do NOT account for sudden, explosive price movements (e.g., macroeconomic news, central bank interventions). If high-impact news hits and volatility expands, the actual bands will violently widen, overriding the projected squeeze.
Decreasing Confidence: The further you project into the future, the more the calculation relies on the "Forward Fill" assumption, naturally decreasing the statistical confidence of the extreme ends of the projection.
Use these projected lines as strategic zones for "waiting in ambush" during normal market conditions, not as absolute guarantees. Combine this with multi-timeframe analysis and your own price action strategies. Indicator
Library
Z'-Score HistogramWHAT IT DOES:
The Z'-Score Histogram is a volatility-adjusted momentum oscillator that quantifies how far the current asset price has deviated from its historical average. By calculating the standard deviation of price movement over a user-defined lookback period, it translates raw price action into clear statistical units. Instead of relying on the arbitrary overbought or oversold boundaries found in standard indicators like RSI, this script shows exactly how many standard deviations the current price is away from the mean, plotting the data as a clean, real-time histogram.
WHY IT IS USEFUL:
Standard bounded indicators can flatten out or bake at extreme levels during strong, prolonged trends. The Z'-Score Histogram solves this by using an unbounded statistical scale. This approach allows traders to instantly spot statistically significant anomalies, since a move beyond positive or negative three standard deviations happens less than one percent of the time in a normal distribution. It helps traders quickly identify whether an asset is in a sustainable trend or if it has stretched too far and is ripe for a mean-reversion snapback.
HOW IT ADDS VALUE TO THE COMMUNITY:
This script adds value to the community by bridging the gap between complex statistical trading and clean, modern chart aesthetics. Many public Z-score tools are either overly bare-bones or cluttered with heavy UI elements. This version delivers professional statistical data in a highly readable, actionable format without lagging or cluttering the workspace.
WHY THIS SCRIPT IS UNIQUE:
Several features make this script unique from others in the public library:
- Dual-Engine Filtering: Most scripts force you into a Simple Moving Average baseline. This version gives you a toggle to use an Exponential Moving Average instead, allowing the Z-score to react faster to sudden, high-volatility regime shifts.
- Dynamic Regime Classification Engine: The script automatically categorizes market states into six distinct, logical phases based on statistical probability, ranging from oversold and hostile to neutral, strong, and overbought.
- Ultra-Clean Dashboard Panel: Instead of a bulky table that blocks price action, it features a lightweight, floating text HUD in the top right. The background is borderless and transparent, adapting seamlessly to any chart theme.
- Visual Extreme Value Highlights: The histogram instantly shifts to high-contrast signature colors only when the market hits the statistically rare three standard deviation thresholds, immediately alerting you to extreme market exhaustion.
DISCLAIMER:
The author has made every effort to ensure the underlying mathematics and logic in this script are correct. However, trading involves substantial risk. If, for example, you buy an asset simply because it registers as oversold on this indicator, and it proceeds to become even more oversold while your position goes against you, that is entirely on you. This tool is for informational and educational purposes only, not financial or execution advice. Use proper risk management and trade at your own discretion. Indicator
Advanced Fear & Greed Cycle (Quant Model)## Overview
The **Advanced Fear & Greed Cycle (Quant Model) v6** is a pure quantitative oscillator designed to decode market sentiment by measuring the architectural divergence between smart money accumulation and retail distribution. Fully upgraded to Pine Script v6, this script addresses standard oscillator limitations by implementing dynamic time-frequency normalization ($0-100$ fixed scale).
Unlike standard sentiment proxies, this model filters out price-action noise by isolating volume flows, directional volatility, and mean-reversion extensions simultaneously.
---
## Mathematical Architecture & Core Engines
### 1. Directional Volatility Engine
Standard models treat volatility expansions as pure panic. This algorithm isolates **Directional Volatility**:
- A 14-period Average True Range (ATR) is mathematically normalized over a dynamic 90-day rolling quarter (`lookback`).
- **Trend Filter:** Volatility is converted into the `vol_fear` metric **only** if the closing price is below its 14-period Simple Moving Average (`is_descending`). Upside expansions (bullish breakouts) are correctly filtered out to prevent false panic readings.
### 2. Normalized Volume & Flow Sentiment
Liquidity and order-flow tracking are computed via a three-layered matrix:
- Normalized Volume spikes relative to the quarterly window.
- Inside-candle Selling Pressure ( AMEX:HIGH - Close$ versus the overall candle range).
- A normalized On-Balance Volume (OBV) structure to track mathematical capital inflows and outflows.
### 3. Boundary-Proof Macro Extension (Mayer Proxy)
To track cyclical overextensions, the script calculates the asset's percentage distance from its long-term moving average (SMA 200 on Daily, SMA 40 on Weekly charts).
To solve the scale break-out issue (where different assets experience wildly different percentage extensions), a **MinMax Normalization** is applied. This compresses the structural extension into a bound $0-100\%$ scale (`extension_norm`) based on the rolling quarter's extremes.
---
## The Greed Score Synthesizer
The final plotting line is the **Greed Score**, a mathematically symmetric index calculated as:
$$\text{Greed Score} = \frac{(100 - \text{Fear Index}) + \text{Extension Norm}}{2}$$
This creates a fixed-bound oscillator ($0$ to $100$) that charts three distinct market phases:
- 🟢 **INSTITUTIONAL ACCUMULATION (Green Zone / < 20):** High systemic fear combined with compressed macro price extensions (< 25%). Smart money absorbs panicking retail order flow near historical value areas.
- ⚪ **NEUTRAL REGIME (Gray Line):** Symmetrical equilibrium where supply and demand are balanced.
- 🔴 **RETAIL FOMO / BUBBLE (Red Zone / > 80):** Zero systemic fear combined with extreme quarterly price overextensions. Retail traders buying the top driven by euphoria, highlighting distribution blocks.
---
## Display Dashboard & Custom Parameters
The top-right informational panel provides real-time diagnostic outputs of the quantitative data (Current Cycle State, Exact Greed Score, and Normalized Extension %). Traders can adjust the `Soglia Bolla Normalizzata` input to calibrate the macro-exhaustion scanner to specific asset classes (Equities, Forex, or Cryptocurrencies).
---
Disclaimer: This tool calculates mathematical probabilities based on normalized historical structures. It does not provide definitive buy/sell signals or financial advice. Always integrate sound risk management protocols. Indicator
Hurst Exponent Strategy [Fast + Weekly]## Overview
The **Hurst Exponent Strategy ** is an advanced quantitative tool that calculates the Hurst Exponent ($H$) using the Rescaled Range ($R/S$) analysis. Instead of tracking directional momentum or price overlays, this indicator measures the **statistical memory** and fractal dimension of financial time series to detect market regimes.
It helps traders identify whether an asset is trending, mean-reverting, or trapped in a state of pure noise (chaos).
---
## The Mathematics of Market Regimes
The indicator evaluates the price action and plots values between 0 and 1, anchored to a theoretical center line of **0.5 (Random Walk)**:
- **$H > 0.60$ (Trend / Persistent):** The market possesses long-term memory. Price movements tend to be followed by movements in the same direction. Ideal for trend-following strategies.
- **$H < 0.45$ (Elastic / Anti-Persistent):** The market behaves like a rubber band (Mean Reversion). Price movements are consistently followed by reversals. Ideal for grid, mean-reversion, or range-bound strategies.
- **$0.45 \le H \le 0.60$ (Chaos / Random Walk):** The price action mimics a Brownian motion. Movements are random, noise is high, and directional edge is minimal.
---
## Dual Timeframe Framework
To avoid fighting macro market structures, this script calculates two separate Hurst metrics simultaneously:
1. **Fast Hurst (Cyan Line):** Calculated on the current chart timeframe. It responds quickly to micro-regime shifts, pinpointing when a consolidation is breaking into a trend or expanding into chaos.
2. **Macro Hurst (Orange Line):** Multi-timeframe execution locked exclusively to the **Weekly ("W") chart**. It acts as a structural filter, keeping you aligned with the true macro nature of the asset.
Both exponents feature an optional built-in **Smoothing filter (SMA)** to remove high-frequency mathematical noise without heavily lagging the structural reading.
---
## Real-Time Informative Legend
The top-right dashboard monitors the live mathematical output of both exponents:
- Displays exact numerical values down to 4 decimal places.
- Dynamically classifies the market state into **TREND** (Green), **ELASTICO** (Red), or **CAOS** (Gray) for instant visual confirmation.
---
Disclaimer: This tool calculates mathematical probabilities based on historical fractal dimensions. It does not provide entry/exit arrows or guarantee profits. Use it as a regime filter alongside your preferred execution strategy. Indicator
AetherEdge - Triaxial Consensus Strategy🖊️ Overview
AE-TRIAX-S is the full strategy version of AE-TRIAX's triaxial consensus engine. Three independent analysis axes (Trend, Momentum, Volatility) and five AI/ML/RL layers (adaptive axis weights, regime classifier, MTF alignment, Q-learning thresholds, Confidence%) self-tune per instrument, paired with a complete trade management stack: smart position sizing, 3-tier TP management, partial close + breakeven, intelligent trailing, drawdown protection, and webhook alerts. Optimized for higher timeframes — 4-hour, daily, and weekly — enabling high-conviction swing trading unfazed by noise. Full backtesting via PulseWire Strategy Tester.
🔶 Key Features
Full AE-TRIAX engine inherited: 3-axis continuous score + 5 AI/ML/RL layers, same signals as the indicator
Higher-timeframe-optimized design: best for 4h / daily / weekly swing trading
Smart position sizing: risk-% × Confidence% scaling
3-tier TP management: TP1/TP2/TP3 multi-target take-profit
Partial close + breakeven: 50% close at TP1, SL moves to entry
Intelligent trailing: SL trails only while Momentum axis agrees
4-way regime auto-classification: dynamic axis-weight bias
MTF alignment filter: two higher TFs evaluated against current TF
Q-learning adaptive threshold: best trigger level within preset range
Drawdown protection: halts entries on DD spike
Webhook alerts: price, Confidence, SL, TP, Regime in messages
Full backtesting via PulseWire Strategy Tester
4-color tetrachrome palette + 3-tier EMA cloud
16-row integrated dashboard with DIAGNOSTIC row exposing firing conditions
🧠 Technical Architecture
The strategy is cleanly split into two layers: engine and execution. The engine layer (identical to the indicator) consists of 3-axis continuous scorers (Trend / Momentum / Volatility), adaptive EWMA axis weights, 4-regime classifier, MTF alignment, and ε-greedy Q-learning. The Strong condition fires only when toleranceOK + |finalScore| ≥ activeThr + Trend axis leans in signal direction + majority agreed — four conditions met. The execution layer activates only after Strong condition fires, calling strategy.entry() when canEnter = no open position AND Confidence ≥ 40% AND no DD halt. SL is ATR × rr_atrMulSL; TP1 = SL × RR × 0.6 (partial close target), TP2 = SL × RR (main RR), TP3 = SL × RR × 1.6 (final target), with strategy.exit qty_percent issuing TP1 and TP2 as partial closes. Position size is auto-computed as qty = (riskPct / slDistPct) × confidenceScale, capped by Max Leverage. Breakeven moves and trailing updates are implemented via strategy.exit "TRAIL" re-issuance.
⚙️ Recommended Settings & Tuning Guide
Recommended timeframes are 4-hour, daily, and weekly. Timeframes below 1-hour generate too many signals where per-bar noise causes false fires — not recommended. 4-hour suits crypto/FX swing trading; daily suits stocks/commodities mid-term swing; weekly suits long-term investing. Default is Beginner preset (threshold 50-70, conservative); switch to Standard (40-60) once familiar, or Aggressive (30-50) for advanced use. Risk % per Trade 1.0 standard; 0.5 conservative, 2.0 aggressive. Default RR Ratio 2.5 anchors TP2. Partial Close 50% is the TP1 close ratio — raise to 70% for faster profit-taking. Breakeven After TP1 suits trend-following. Intelligent Trailing Stop helps in trending regimes but can exit early in ranges. Max Drawdown Halt 8% is typical; raise to 10-12% for long-term runs. Set MTF to roughly 3-6× the current timeframe: on 4h chart use daily/weekly HTFs; on daily chart use weekly/monthly HTFs.
💡 How to Use in Practice
Apply to chart and run PulseWire Strategy Tester → review total return, win rate, max DD, Sharpe ratio. Backtest on at least 6 months of higher-timeframe data for meaningful results. For live trading, configure webhook alerts — alert_message embeds price, confidence, SL, TP, regime for automated exchange API submission. Strong arrows print with summary labels and SL/TP1/TP2/TP3 lines; TP1 touches trigger partial close + breakeven move, TP2/TP3 close remainder, SL closes everything — all automated. The HUD's DIAGNOSTIC row exposes both "why no signal now" and "why no entry now" at a glance (DD halt active, position open, +15 score to BUY, etc.). "Quiet" regime means all signals are weak; "Trend" regime is the most reliable. Combining with the indicator version (AE-TRIAX) effectively separates chart analysis from automated execution.
🧩 Relationship with Indicator Version
AE-TRIAX-S's signal-firing engine is identical to the indicator (AE-TRIAX). Arrows print on the same bars and HUD values match across both. The strategy adds three execution-only safety filters (position already open, Confidence < 40%, drawdown breach) that only gate the entry call. When an arrow prints but no entry occurs, the HUD's DIAGNOSTIC row explains why. Use the indicator for chart analysis and manual entry; use the strategy for backtesting and automated execution.
⚠️ Important Notes
This is multi-agent-style 3-axis consensus, not true multi-agent RL or deep learning. Each layer reproduces the mathematical core of those techniques within Pine's limits. Strongly recommended for 4-hour timeframes and above — sub-1-hour timeframes will see increased false fires due to per-bar noise. MTF via request.security carries small confirmed-bar delays. Q-learning and adaptive axis weights require per-instrument learning; during initial data accumulation (a few months on higher TFs), optimization is incomplete. Backtest results don't fully reflect commissions, slippage, or execution delays; set Strategy Tester's Commission to match your exchange. Drawdown Protection is a safety net, not a complete eliminator of market risk. On higher TFs entry frequency is low — diversify across multiple instruments to increase opportunities.
🚨 Disclaimer
This strategy is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal or backtest result guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk. Strategy
AetherEdge - Triaxial Consensus Signals🖊️ Overview
AE-TRIAX is a high-conviction indicator that fires signals only when three independent analysis axes (Trend, Momentum, Volatility) agree. Each axis returns a continuous score in -100 to +100 rather than a discrete vote, so consensus is visualized as a gradient. Five AI/ML/RL layers — RL-style adaptive axis weights, ML regime classifier, MTF alignment, Q-learning thresholds, and Confidence% — self-tune per instrument, supporting everything from beginner presets to advanced customization. The lean voting system (which excludes neutral axes) makes BUY/SELL fully symmetric, responding appropriately to both bull and bear markets.
🔶 Key Features
3-axis continuous-score engine: precise consensus measurement in -100..+100 per axis
Lean voting system: neutral axes (|score| ≤ 10) excluded; only leaning axes drive consensus symmetrically
RL-style adaptive axis weights: each axis auto-weighted 1.0-1.8× by past accuracy
4-way regime auto-classification: Trend/Range/Volatile/Quiet biases axis weights
MTF alignment filter: two higher TFs (default 1h/4h) evaluated against current TF
Q-learning adaptive threshold: best trigger level learned within preset range
3-tier EMA cloud: short (8-21) / mid (21-55) / long (55-200) clouds stacked for layered trend perception
4-color tetrachrome palette: strong bull / weak bull / weak bear / strong bear visually separated
ATR-based auto SL/TP drawn on signals
3 presets: Beginner (default) / Standard / Aggressive
13-row integrated dashboard with DIAGNOSTIC row that fully exposes firing conditions
Custom alert support
🧠 Technical Architecture
This is a consensus system of three independent continuous scorers. Trend axis linearly combines 5-EMA stack alignment (25 points × 4 slots), Dow-style HH/HL vs LH/LL (pivot-swing update pattern), and ADX strength in 0.5:0.3:0.2. Momentum axis combines QQE-style RSI (distance from 50 × 2) and MACD histogram normalized by ATR in 0.6:0.4. Volatility axis uses Bollinger band width expansion/squeeze, ATR ratio healthy-range check (0.6-1.6), strong-candle (body/range > 0.5) gating, voting in the candle direction when conditions hold; price slope/ATR × 20 is added as a continuous component. Consensus uses lean voting: each axis is categorized as "↑lean (>10)" / "↓lean (<-10)" / "neutral (-10..10)"; majority among leaning axes plus a spread constraint determines toleranceOK. Neutral axes don't influence judgment, so even when the Volatility axis is often neutral, signals can still fire when other axes align — preserving BUY/SELL symmetry. Strong condition fires only when toleranceOK + |finalScore| ≥ activeThr + Trend axis leans in signal direction + majority agreed — all four conditions met. The AI layers use online EWMA reliability learning (α = 0.15 × Adaptation Strength) and ε-greedy Q-learning across 4 threshold candidates.
⚙️ Recommended Settings & Tuning Guide
Default is Beginner preset (threshold 50-70) for low false-signal rate. Switch to Standard (40-60) once familiar, or Aggressive (30-50) for advanced use. Cross-Axis Tolerance 20 baseline; 10 stricter, 30 looser. Adaptation Strength 1.0 is standard learning speed. MTF defaults 1h/4h; for 5-min trading change to 15m/1h; for daily use day/week. Min Weight 0.6 / Max Weight 1.8 controls the adaptive learning range; raise Max above 2.0 for more responsiveness.
💡 How to Use in Practice
Apply to chart and 3-axis consensus produces a large arrow with SL/TP lines and Confidence%-labeled bubble. Bar color (green/blue/yellow/red) gives instant consensus state. The HUD's "axis lean ↑n / ↓n ✓/✗" row shows how many axes lean up/down/neutral, and the bottom DIAGNOSTIC row exposes the current state. Examples:
✓ "★ BUY firing": strong-bull signal active on this bar
✗ "axes disagree": axes not aligned in one direction
✗ "+15 score to BUY": finalScore needs 15 more points to fire BUY
✗ "trend not down": Momentum/Volatility are down but Trend axis is neutral / not enough
✗ "BUY held (locked)": hysteresis preventing re-fire from prior BUY
Confidence ≥ 70% = strong entry, 40-70% = monitor, < 40% = skip is the practical workflow. "Quiet" regime means all signals are weak; "Trend" regime is the most reliable.
🧩 Relationship with Strategy Version
The indicator (AE-TRIAX) and strategy version (AE-TRIAX-S) share an identical signal-firing engine. Arrows print on the same bars and HUD values match across both. The strategy adds three execution-only safety filters (position already open, Confidence < 40%, drawdown breach) that only gate the entry call. Use the indicator for chart analysis and manual entry; use the strategy for backtesting and automated execution — they will agree on signals.
⚠️ Important Notes
This is an independent triaxial consensus system, not true multi-agent RL or deep learning. Each axis is a rule-based scorer; the AI layers reproduce the mathematical core of those techniques within Pine's limits (online EWMA reliability learning, Q-learning argmax selection, MTF alignment evaluation). MTF is via request.security so there's a small confirmed-bar delay. Q-learning requires per-instrument learning; during initial data accumulation, optimization is incomplete. All signals are probabilistic decisions based on historical data and do not guarantee future profits.
🚨 Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk. Indicator
Smart Money Volume [JoeyWave]Smart Money Volume highlights statistically abnormal volume bars and quantifies real-time buy/sell pressure using two well-established methods from quantitative finance.
═══════════════════════════════
WHAT IT DOES
═══════════════════════════════
• Volume Panel — amber bars mark statistical outliers detected via Tukey's IQR method
• Pressure HUD (top-right) — shows current dominant side (BUY / SELL), pressure magnitude (%), and strength (WEAK / MODERATE / STRONG)
═══════════════════════════════
METHODOLOGY
═══════════════════════════════
1. Outlier Detection — Tukey's Interquartile Range
threshold = Q3 + k × (Q3 − Q1)
Default k = 2.0 ("extreme outlier" by Tukey's standard). This is robust to skewed volume distributions where mean/std-based methods fail.
2. Buy/Sell Classification — Bulk Volume Classification (BVC)
Based on Easley, López de Prado, O'Hara (2012). Each bar's volume is split into buy/sell proportions using the normalized log-return:
buy_fraction ≈ Φ(log_return / σ)
where Φ is the standard normal CDF, approximated with a logistic function.
3. Aggregated Pressure
Weighted buy and sell volumes are summed over the lookback window (default 20 bars). Pressure = buy_volume / total_volume.
═══════════════════════════════
HOW TO READ
═══════════════════════════════
• Amber bars → unusual volume activity worth investigating
• HUD BUY ▲ → buyers dominant; check % for conviction
• HUD SELL ▼ → sellers dominant
• STRONG ≥ 65% → high conviction
• MODERATE ≥ 55% → moderate
• WEAK < 55% → indecisive, near equilibrium
═══════════════════════════════
HOW TO USE
═══════════════════════════════
• Confluence tool — confirm breakouts, reversals, or trend continuation when an outlier aligns with your setup
• Pressure HUD as a regime filter — only take long setups when HUD shows BUY MODERATE+, shorts when SELL MODERATE+
• Look for divergences — price making new highs while pressure flips to SELL is a classic exhaustion signal
═══════════════════════════════
SETTINGS
═══════════════════════════════
• IQR Lookback — bars used for percentile calculation (default 50)
• IQR Multiplier — 1.5 = standard outlier, 2.0 = extreme (default)
• BVC Sigma Window — return volatility window (default 50)
• Pressure Lookback — bars aggregated for the HUD (default 20)
═══════════════════════════════
REFERENCES
═══════════════════════════════
Easley, D., López de Prado, M., O'Hara, M. (2012) — "Flow Toxicity and Liquidity in a High Frequency World." Review of Financial Studies.
Tukey, J. W. (1977) — "Exploratory Data Analysis." Addison-Wesley.
═══════════════════════════════
Works on any symbol and timeframe with available volume data. Tested on FX (OANDA tick volume), futures, indices, and crypto.
Author: JoeyWave
Indicator
Indicator
Indicator
Relative Volume Heat [forexobroker]Relative Volume Heat turns raw volume into a participation regime. It measures how today's volume compares to its own recent baseline, then z-scores that ratio to classify the market as COLD, WARM or HOT. Directional entries are only allowed while the regime is HOT, so signals only print when real participation is behind the move. A heatmap background visualises regime intensity at a glance.
🔶 ALGORITHM
1. Compute RVOL = volume / SMA(volume, Volume MA Length).
2. Z-score the RVOL ratio over the RVOL Z Window: rz = (RVOL - mean) / stdev.
3. Classify the regime: rz >= Hot Z = HOT (2), rz >= Warm Z = WARM (1), else COLD (0).
4. A trend EMA defines directional bias: close above = up, below = down.
5. A separate reclaim EMA times the entry: a confirmed close-cross of it in the bias direction inside a HOT (or optionally WARM) regime is the trigger.
6. A cooldown, bar-close confirmation and a position-lock gate the final signal so it never repaints intrabar and never stacks the same side.
🔶 SIGNAL LOGIC
- Buy: in-session AND regime tradable AND trend direction up AND close crosses over the reclaim EMA AND position not already long AND cooldown elapsed AND barstate.isconfirmed.
- Sell: in-session AND regime tradable AND trend direction down AND close crosses under the reclaim EMA AND position not already short AND cooldown elapsed AND barstate.isconfirmed.
Only fires while the RVOL z-score regime is HOT (or WARM if "Trade Only When Hot" is off).
🔶 INPUTS
- RVOL group: Volume MA Length baseline for the relative-volume ratio (default 20).
- RVOL group: RVOL Z Window the ratio is z-scored over (default 50).
- RVOL group: Hot Z Threshold above which trading is enabled (default 1.0).
- RVOL group: Warm Z Threshold separating WARM from COLD (default 0.0).
- RVOL group: Trend EMA defining directional bias inside a hot regime (default 34).
- Signal Logic group: Entry Reclaim EMA whose close-cross times entries (default 9).
- Signal Logic group: Trade Only When Hot toggle and Cooldown Bars between signals (default 5).
- Filters group: optional session restriction with a configurable window (default off).
- Visual group: dashboard, 3-layer glow, heat background and buy/sell colors (dashboard default on).
🔶 ALERTS
RVH Buy, RVH Sell, RVH Any Signal, RVH Hot On, RVH Hot Off, RVH Warm, RVH Cold, RVH EMA Up, RVH EMA Down, RVH Trend Cross, RVH Hot Now, RVH Armed, RVH Webhook JSON.
🔶 LIMITATIONS
- Needs warm-up bars before the z-score window and trend EMA are fully populated.
- Volume-based, so it requires a real volume feed; on forex it falls back to broker tick-volume, which is a proxy only.
- COLD and WARM regimes intentionally suppress signals, so quiet markets produce few or no entries.
- Defaults are tuned for liquid instruments and intraday/swing timeframes; thin symbols may need a higher Volume MA Length.
- The regime classifies participation, not direction; bias still depends on the trend and reclaim EMAs.
Indicator
WAVE TIME ORGAN FORCE🌊 WAVE TIME ORGAN FORCE
WAVE TIME ORGAN FORCE is a multi-timeframe market flow and synchronization engine designed to combine organ-based market pressure analysis, EMA-smoothed visual flow, timeframe force propagation, and cinematic wave-style visualization into one clean and powerful trading system.
The indicator was built with two main goals:
1. To measure how market pressure behaves internally.
2. To visualize how that pressure propagates through multiple timeframe layers.
Instead of functioning as a simple oscillator or signal generator, WAVE TIME ORGAN FORCE focuses on market rhythm, synchronization, pressure quality, and directional propagation.
━━━━━━━━━━━━━━━━━━
THE MAIN PHILOSOPHY
━━━━━━━━━━━━━━━━━━
Most traditional indicators analyze only one dimension of the market.
Examples:
• RSI measures momentum strength.
• MACD measures moving average relationship.
• ATR measures volatility.
• OBV measures volume pressure.
WAVE TIME ORGAN FORCE combines several internal market behaviors into one synchronized system.
The indicator tries to answer questions such as:
Is the current movement supported by volume?
Is volatility supporting expansion?
Are multiple timeframes aligned together?
Is momentum propagating upward through the timeframe stack?
Is the market synchronized or internally conflicted?
Is the current structure smooth or chaotic?
The goal is not only to identify direction, but to understand the internal quality of market movement.
━━━━━━━━━━━━━━━━━━
ORGAN ENGINE
━━━━━━━━━━━━━━━━━━
The internal core is based on the “Organ Engine” concept.
The market is treated like a living structure composed of multiple organs.
The engine currently includes:
Trend Organ
Momentum Organ
Volume Organ
Volatility Organ
Each organ measures a different type of market behavior.
━━━━━━━━━━━━━━━━━━
TREND ORGAN
━━━━━━━━━━━━━━━━━━
The Trend Organ measures directional pressure using EMA slope behavior.
Instead of only checking whether price is above or below a moving average, the system measures how aggressively the trend itself is moving.
This allows the engine to detect acceleration and directional force more naturally.
━━━━━━━━━━━━━━━━━━
MOMENTUM ORGAN
━━━━━━━━━━━━━━━━━━
The Momentum Organ measures the speed and force of price movement using ROC-based calculations.
This helps the system determine whether the market movement is weak, expanding, slowing down, or accelerating.
━━━━━━━━━━━━━━━━━━
VOLUME ORGAN
━━━━━━━━━━━━━━━━━━
The Volume Organ uses OBV-style pressure calculations to determine whether volume supports the current market movement.
A bullish move without volume support is considered weaker than a move supported by synchronized volume behavior.
━━━━━━━━━━━━━━━━━━
VOLATILITY ORGAN
━━━━━━━━━━━━━━━━━━
The Volatility Organ uses ATR-based directional behavior.
This layer helps the system detect:
Expansion
Compression
Directional volatility pressure
The volatility component is important because strong trends usually expand volatility while weak or exhausted movements often compress.
━━━━━━━━━━━━━━━━━━
RAW ORGAN VALUE
━━━━━━━━━━━━━━━━━━
All organ layers are normalized and merged into a single internal value called the Raw Organ.
The Raw Organ represents the true internal market pressure.
This raw value is preserved internally and is not destroyed by visual smoothing.
This is extremely important.
The calculation engine remains mathematically intact while the visualization becomes smoother and easier to read.
━━━━━━━━━━━━━━━━━━
EMA-SMOOTHED ORGAN FLOW
━━━━━━━━━━━━━━━━━━
The visual Organ Flow is created by applying EMA smoothing to the Raw Organ.
This creates a flowing wave appearance instead of a sharp mechanical oscillator.
The smoothing exists for visual clarity and wave behavior representation.
The purpose is not to manipulate signals.
The result is a more natural energy-flow appearance where the market behaves like a living wave system rather than disconnected candles.
━━━━━━━━━━━━━━━━━━
TIMEFRAME FORCE ENGINE
━━━━━━━━━━━━━━━━━━
One of the most important parts of WAVE TIME ORGAN FORCE is the Timeframe Force Engine.
The system reads multiple timeframe layers simultaneously.
Instead of asking:
“Is the market bullish?”
the engine asks:
“Is bullish pressure propagating successfully through multiple timeframes?”
This creates a propagation-based analysis system rather than a simple trend filter.
━━━━━━━━━━━━━━━━━━
TIMEFRAME PROPAGATION
━━━━━━━━━━━━━━━━━━
Lower timeframe movement alone is often unreliable.
The system therefore checks whether momentum and directional pressure are successfully spreading into higher timeframe layers.
This creates a more stable and higher-quality directional environment.
The indicator measures:
Bullish Layer Count
Bearish Layer Count
Timeframe Alignment
Directional Force
Cascade Conditions
━━━━━━━━━━━━━━━━━━
CASCADE STRUCTURE
━━━━━━━━━━━━━━━━━━
A cascade occurs when all timeframe layers align in the same direction.
Bull Cascade:
All timeframe layers are synchronized bullish.
Bear Cascade:
All timeframe layers are synchronized bearish.
Mixed Wave:
The timeframe stack is not fully aligned.
The cascade system is important because strong directional moves often occur when multiple timeframe layers begin moving together.
━━━━━━━━━━━━━━━━━━
QUALITY CROSS ENGINE
━━━━━━━━━━━━━━━━━━
The indicator uses a cross relationship between:
Organ Flow EMA
Signal EMA
A bullish quality condition forms when:
• Organ Flow crosses above the signal line
• Organ Flow is above zero
• Timeframe Force is bullish
• Multiple timeframe layers support the movement
A bearish quality condition forms when:
• Organ Flow crosses below the signal line
• Organ Flow is below zero
• Timeframe Force is bearish
• Multiple timeframe layers support the movement
This creates a cleaner confirmation structure than simple crossover systems.
━━━━━━━━━━━━━━━━━━
WAVE STACK VISUALIZATION
━━━━━━━━━━━━━━━━━━
The indicator visualizes multiple timeframe layers as a stacked wave structure.
Each layer represents a different market rhythm.
Lower timeframe waves react faster.
Higher timeframe waves react slower and represent larger directional force.
The stacked structure creates a living market-flow appearance.
The purpose is to help the user visually understand:
Market rhythm
Directional depth
Synchronization
Pressure propagation
Wave expansion
Flow behavior
━━━━━━━━━━━━━━━━━━
GLOW & AURA SYSTEM
━━━━━━━━━━━━━━━━━━
The indicator uses cinematic glow and aura layers to improve readability and create a premium visual structure.
Glow layers emphasize:
Main Organ Flow
Timeframe Force
Wave Expansion
Cascade Conditions
Aura fills help visualize the relationship between:
Organ Flow
Signal Flow
Timeframe Force
Wave Stack Layers
The goal is to create a visually alive chart environment rather than a static indicator appearance.
━━━━━━━━━━━━━━━━━━
MAIN CHART CANDLE COLORING
━━━━━━━━━━━━━━━━━━
The indicator can color candles directly on the main chart.
This allows the user to visually identify stronger conditions without cluttering the chart with excessive BUY/SELL labels.
Yellow:
Higher-quality bullish condition.
Orange:
Higher-quality bearish condition.
Lime:
Bullish force dominance.
Red:
Bearish force dominance.
Gray:
Mixed or uncertain conditions.
The candle system is designed to communicate market state visually while keeping the chart clean.
━━━━━━━━━━━━━━━━━━
PANEL SYSTEM
━━━━━━━━━━━━━━━━━━
The premium panel acts as a live market cockpit.
It displays:
Current Market State
Organ Flow EMA
Raw Organ Value
Timeframe Force
Bullish Layer Count
Bearish Layer Count
Cross Quality
Signal State
The panel is designed to help the trader understand WHY the current condition exists instead of only displaying random numbers.
━━━━━━━━━━━━━━━━━━
MARKET STATES
━━━━━━━━━━━━━━━━━━
The indicator can display different structural conditions:
Bull Cascade
Bear Cascade
Bull Force
Bear Force
Mixed Wave
This allows the user to read the market as a living structure instead of reducing everything to simple buy and sell labels.
━━━━━━━━━━━━━━━━━━
WHY THIS INDICATOR IS DIFFERENT
━━━━━━━━━━━━━━━━━━
WAVE TIME ORGAN FORCE is not a traditional oscillator.
It combines:
Organ-based market pressure
EMA-smoothed visual flow
Multi-timeframe propagation analysis
Wave-stack visualization
Cascade synchronization
Cinematic glow and aura effects
Candle-state coloring
Premium panel guidance
The purpose is not simply to generate signals.
The purpose is to visualize market behavior, synchronization, and pressure flow.
━━━━━━━━━━━━━━━━━━
IMPORTANT NOTE
━━━━━━━━━━━━━━━━━━
This indicator does not predict the future.
It is a market structure and flow visualization engine.
It should always be used together with:
Risk management
Support and resistance
Volume analysis
Higher timeframe structure
Personal trading strategy
Market context
No indicator should be treated as a guaranteed prediction system.
━━━━━━━━━━━━━━━━━━
SUMMARY
━━━━━━━━━━━━━━━━━━
WAVE TIME ORGAN FORCE is designed to visualize how market pressure behaves and propagates across multiple timeframe layers.
The indicator combines organ-based internal pressure analysis with smooth wave-style visual presentation.
The result is a cinematic market-flow system focused on:
Direction
Force
Synchronization
Timeframe Propagation
Wave Behavior
Cascade Structure
Market Rhythm
Pressure Quality
Visual Clarity
The system is built for traders who want to understand the internal rhythm and synchronization of the market instead of relying only on traditional buy/sell indicators.
Indicator
OMEGA ORGAN SYNC FORCE🧠 OMEGA ORGAN SYNC FORCE
OMEGA ORGAN SYNC FORCE is a multi-layer market synchronization engine designed to visualize market pressure, momentum flow, volatility behavior, and multi-timeframe directional propagation through a living “organ system” structure.
Instead of relying on a single indicator such as RSI, MACD, or moving averages alone, the engine combines several internal market components into one synchronized flow system.
The goal of the system is not only to detect direction, but to measure:
• how strongly the market is moving
• whether the movement is aligned across timeframes
• whether the market is in chaos or synchronization
• whether pressure is expanding or contracting
• whether momentum is propagating through higher timeframes
━━━━━━━━━━━━━━━━━━
🧬 ORGAN ENGINE
━━━━━━━━━━━━━━━━━━
The core structure is built around four primary “market organs”:
• Trend Organ
• Momentum Organ
• Volume Organ
• Volatility Organ
Each organ is normalized through Z-Score logic and synchronized into a unified market pressure stream called:
ORGAN FLOW
The system behaves like a living structure.
When all organs align in the same direction, synchronization increases.
When organs conflict with each other, chaos increases.
This creates a dynamic environment where the market can be interpreted not only through direction, but through internal behavioral quality.
━━━━━━━━━━━━━━━━━━
🌊 ORGAN FLOW
━━━━━━━━━━━━━━━━━━
The Organ Flow line represents the combined pressure of all internal organs.
This flow is additionally smoothed using EMA-based signal layering to create a more fluid and living visual movement instead of sharp mechanical reactions.
The result is a smoother “energy river” feeling rather than a standard oscillator appearance.
The visual philosophy is inspired by:
• wave propagation
• biological synchronization
• energy expansion
• market breathing behavior
━━━━━━━━━━━━━━━━━━
🚀 TIMEFRAME FORCE ENGINE
━━━━━━━━━━━━━━━━━━
One of the most important layers of the system is the Timeframe Force Engine.
The engine measures whether lower timeframe pressure is successfully propagating into higher timeframe structures.
This is not a traditional multi-timeframe filter.
Most systems only ask:
“Is the higher timeframe bullish?”
OMEGA ORGAN instead asks:
“Is momentum successfully carrying itself through time?”
The engine analyzes multiple timeframe layers simultaneously and measures:
• bullish propagation
• bearish propagation
• cascade alignment
• synchronization strength
When all selected timeframes align together, the system enters a:
BULL CASCADE
or
BEAR CASCADE
state.
This means directional energy is flowing through multiple market layers simultaneously.
━━━━━━━━━━━━━━━━━━
⚡ CHAOS ENGINE
━━━━━━━━━━━━━━━━━━
The Chaos Engine measures disagreement between internal organs.
High chaos means:
• unstable market structure
• conflicting pressure
• unpredictable behavior
• noisy conditions
Low chaos means:
• synchronization
• cleaner structure
• aligned momentum
• directional clarity
Chaos collapse zones are especially important because they often appear before major directional expansion.
━━━━━━━━━━━━━━━━━━
🫀 MARKET STATES
━━━━━━━━━━━━━━━━━━
Instead of displaying traditional numerical conditions only, the engine uses market states such as:
• TREND UP
• TREND DOWN
• RANGE
• CHAOS
• EXPANSION
• CONTRACTION
The goal is to transform raw calculations into readable market behavior.
━━━━━━━━━━━━━━━━━━
🎨 VISUAL PHILOSOPHY
━━━━━━━━━━━━━━━━━━
OMEGA ORGAN SYNC FORCE was designed not only as a calculation engine, but also as a visual market experience.
The indicator uses:
• glow layers
• aura effects
• soft transparency
• synchronized color logic
• premium panel design
• energy-flow visualization
to create a cinematic market interface.
The purpose is to make the market feel alive instead of static.
━━━━━━━━━━━━━━━━━━
🌐 MULTI LANGUAGE SUPPORT
━━━━━━━━━━━━━━━━━━
The indicator includes built-in language support.
Currently supported:
• English
• Turkish
The language system allows the panel and market states to adapt dynamically for international usage and future expansion.
━━━━━━━━━━━━━━━━━━
📊 PANEL SYSTEM
━━━━━━━━━━━━━━━━━━
The premium panel displays:
• Organ Flow Strength
• Timeframe Force
• Cascade State
• Chaos Level
• Market Direction
• Expansion / Contraction State
• Multi-Timeframe Alignment
• Organ Synchronization Quality
The panel is designed to function as a live market cockpit.
━━━━━━━━━━━━━━━━━━
🧠 CORE PHILOSOPHY
━━━━━━━━━━━━━━━━━━
Traditional indicators usually measure:
• one condition
• one formula
• one dimension
OMEGA ORGAN SYNC FORCE instead focuses on:
market synchronization.
The engine attempts to understand whether:
• pressure
• momentum
• volatility
• volume
• timeframe propagation
are behaving as one connected organism.
The system is not designed to predict the future.
It is designed to measure the quality, alignment, and behavioral structure of market movement.
━━━━━━━━━━━━━━━━━━
🇹🇷 TÜRKÇE AÇIKLAMA
━━━━━━━━━━━━━━━━━━
OMEGA ORGAN SYNC FORCE, piyasa baskısını, momentum akışını, volatilite davranışını ve zaman dilimleri arasındaki yön taşınmasını canlı bir “organ sistemi” mantığıyla analiz etmek için tasarlanmış çok katmanlı bir piyasa senkronizasyon motorudur.
Sistem:
• RSI
• MACD
• EMA
gibi tek boyutlu klasik indikatör mantığından farklı çalışır.
Amaç sadece yön bulmak değildir.
Amaç:
• hareketin gücünü
• zaman içinde taşınıp taşınmadığını
• organların uyumlu çalışıp çalışmadığını
• piyasanın kaos mu senkron mu olduğunu
• momentumun timeframe’ler arasında yayılıp yayılmadığını
ölçmektir.
━━━━━━━━━━━━━━━━━━
🧬 ORGAN SİSTEMİ
━━━━━━━━━━━━━━━━━━
Sistem dört temel market organından oluşur:
• Trend Organı
• Momentum Organı
• Hacim Organı
• Volatilite Organı
Bu organlar Z-Score normalizasyonuyla aynı ölçeğe getirilir ve birleşerek:
ORGAN FLOW
isimli ana akış motorunu oluşturur.
━━━━━━━━━━━━━━━━━━
🚀 TF İTKİ GÜCÜ
━━━━━━━━━━━━━━━━━━
Sistemin en önemli taraflarından biri:
Timeframe Force Engine
katmanıdır.
Bu yapı:
• alt timeframe baskısının
• üst timeframe’e taşınıp taşınmadığını
ölçer.
Yani sistem sadece:
“üst timeframe bullish mi?”
sorusunu sormaz.
Şunu sorar:
“momentum zaman boyunca taşınabiliyor mu?”
━━━━━━━━━━━━━━━━━━
⚡ CHAOS MOTORU
━━━━━━━━━━━━━━━━━━
Chaos Engine, organlar arasındaki uyumsuzluğu ölçer.
Yüksek kaos:
• kararsız yapı
• çelişkili baskı
• fake hareketler
• gürültülü piyasa
demektir.
Düşük kaos:
• uyum
• senkronizasyon
• temiz yön
• güçlü momentum
anlamına gelir.
━━━━━━━━━━━━━━━━━━
🎨 GÖRSEL FELSEFE
━━━━━━━━━━━━━━━━━━
Sistem sadece matematiksel değil, aynı zamanda görsel bir deneyim olarak tasarlanmıştır.
Bu yüzden:
• glow efektleri
• aura katmanları
• premium panel
• yumuşak geçişli renkler
• enerji akışı hissi
kullanılmıştır.
Amaç:
grafiği yaşayan bir organizma gibi hissettirmektir.
━━━━━━━━━━━━━━━━━━
🧠 ANA FELSEFE
━━━━━━━━━━━━━━━━━━
Çoğu indikatör:
• tek veri
• tek formül
• tek boyut
okur.
OMEGA ORGAN SYNC FORCE ise:
• baskı
• momentum
• volatilite
• hacim
• timeframe taşınması
gibi katmanların tek bir organizma gibi çalışıp çalışmadığını analiz etmeye çalışır.
Indicator
Adaptive Tail Risk MonitorAdaptive Tail Risk Monitor (ATRM)
What It Is
The Adaptive Tail Risk Monitor (ATRM) is a real-time statistical framework designed to map the changing architecture of asset return distributions. Most traditional indicators measure momentum, trend, or simple volatility. ATRM measures something deeper: the structural asymmetry and tail behavior of recent returns.
By calculating the 3rd moment (Skewness) and 4th moment (Kurtosis) of the return distribution and processing them through a rolling percentile framework, ATRM normalizes this advanced data against an asset’s own historical footprint. The result is a dynamic, self-calibrating view of market risk that automatically adapts to any financial instrument or timeframe.
Why It Was Built
Standard technical tools are largely blind to distributional shifts. A market can trend calmly while quietly accumulating statistical instability long before price confirmation becomes visually obvious. Hidden anomalies—such as fat tails, negative skew, and rising kurtosis—frequently precede major market regime transitions.
ATRM was built to surface these hidden conditions. By combining rolling central moment (skewness and kurtosis) estimation with adaptive percentile ranking, it converts abstract mathematical concepts into a highly interpretable visual dashboard. It tells you not just what the market is doing, but what kind of statistical regime you are operating in.
Core Concepts
🧠 Adaptive Percentile Framework
ATRM operates without static thresholds.
Instead, Skewness and Kurtosis are continuously evaluated relative to their own historical distributions using rolling percentile ranking.
This adaptive approach allows the framework to:
• Normalize behavior across asset classes
• Adapt across timeframes
• Remain responsive to structural market shifts
• Improve regime consistency
Key Takeaway:
Percentile levels represent relative statistical positioning unique to that specific asset’s history, rather than arbitrary fixed values.
1. Skewness — Asymmetric Return Conditions
Skewness is directional, measuring whether the return distribution is becoming positively or negatively imbalanced. It defines which side of the distribution carries the greater tail concentration.
Positive Skewness:
• Upside returns dominate
• Positive outliers become more frequent
• Bullish expansion conditions may be developing
Negative Skewness:
• Downside returns dominate
• Negative outliers increase
• Bearish asymmetry may be emerging
🟩 Skewness Color Mapping
The skewness line dynamically changes color according to its historical percentile state:
🟩 Extreme Positive Asymmetry: ≥ 90th percentile
🍏 Positive Asymmetry: 65th–90th percentile
⬜ Neutral Symmetry: 35th–65th percentile
🍎 Negative Asymmetry: 10th–35th percentile
🟥 Extreme Negative Asymmetry: < 10th percentile
2. Kurtosis — Extreme Return Conditions (Tail Risk)
Kurtosis measures the concentration and extremity of outliers (the thickness of the tails). Excess kurtosis is non-directional; it flags the probability of extreme price shocks regardless of sign. Elevated kurtosis tells you that the tails are getting fat, extreme returns are becoming more frequent, while Skewness provides the directional context.
High Kurtosis typically flags:
• Volatility expansion
• Large price movements
• Panic or euphoric phases
• Unstable regime transitions
Low Kurtosis reflects:
• Stable, balanced conditions
• Mean-reverting environments
• Limited tail expansion
🔵 Kurtosis Color Mapping
The kurtosis plot dynamically shifts color to indicate tail expansion intensity:
🔵 Extreme Expansion: ≥ 90th percentile
🔹 Elevated Expansion: 75th–90th percentile
⚪ Baseline Conditions: < 75th percentile
🖥️ Market Condition Framework
The indicator synthesizes both Skewness and Kurtosis states into a unified classification system, painting the chart background when critical structural transitions occur.
🟩 Bright Green — Positive Returns Dominant
Skewness ≥ 90th percentile & Kurtosis ≥ 90th percentile
🍏 Light Green — Positive Returns More Frequent
Skewness 65th–90th percentile & Kurtosis ≥ 75th percentile
🟦 Blue — Neutral Expansion
Skewness 35th–65th percentile & Kurtosis ≥ 75th percentile
🍎 Light Red — Negative Returns More Frequent
Skewness 10th–35th percentile & Kurtosis ≥ 75th percentile
🟥 Bright Red — Negative Returns Dominant
Skewness < 10th percentile & Kurtosis ≥ 90th percentile
⬛ Baseline Conditions
Kurtosis < 75th percentile
(Stable, low-dispersion environment)
This framework allows traders to quickly assess whether market conditions are becoming increasingly directional, increasingly unstable, or returning toward equilibrium.
🛠️ User Settings & Customization
Rolling Window
Controls the initial Skewness and Kurtosis estimation lookback.
• Shorter windows increase responsiveness
• Longer windows increase statistical stability
Minimum recommended: Intraday/Daily 63+, Weekly 52+, Monthly 36+.
Percentile Window
Sets the lookback for historical percentile ranking. For statistical integrity, this window must exceed the Rolling Window length for Skewness and Kurtosis.
Minimum recommended: Intraday/Daily 126+, Weekly 52+, Monthly 36+. Longer windows produce more stable percentile ranks
EMA Smoothing
Applies exponential smoothing to reduce noise on raw statistical estimates.
• 3–5 = higher responsiveness
• 8–13 = greater stability
Higher values improve stability but increase lag during regime transitions.
Display Style Flexibility
Users can independently switch display formats (Line, Columns, Histogram, or Area) for both Kurtosis and Skewness via the native PulseWire Style menu.
Visibility Controls & Legends
Fully customize or toggle:
• Percentile bands
• Background colors
• Legend tables
⚙️ Multi-Panel Configuration Note
For maximum clarity, consider loading ATRM twice as separate indicator panes:
• One configured to display only Skewness
• One configured to display only Kurtosis
⚠️ CRITICAL REQUIREMENT: When stacking multiple ATRM instances across separate panes, maintain identical Rolling Window lengths to keep percentile states synchronized.
Different lookback windows create independent historical distributions, which may lead to desynchronized percentile states and inconsistent regime classification between panels.
📈 Application Examples & Strategy Filters
1. ATRM + RSI — Filtering False Reversals
RSI is designed to identify overbought and oversold conditions but cannot evaluate whether the surrounding market environment supports stable mean reversion or continued directional expansion. ATRM fills that gap by providing distributional context around the signal.
The Oversold Trap — Avoiding False Reversals
The Setup
RSI overbought/oversold signals perform best during mean-reverting environments but often struggle during sustained trends or market dislocation phases. A common trap is buying a deeply oversold RSI reading during an aggressive market decline. The RSI signal looks like a reversal opportunity but the market continues lower.
The Filter
If RSI flashes an oversold signal while ATRM Skewness is firmly in its lower percentiles (< 35th percentile) and Kurtosis is elevated (> 75th percentile), the return distribution remains heavily negatively skewed with elevated tail expansion. Avoid mean-reversion longs in this environment. Wait for Skewness to recover toward the neutral zone before attempting reversal entries.
The Confirmed Reversal — High-Confidence Mean Reversion
The Setup
Following a period of market stress, RSI moves into oversold territory while distributional conditions begin to stabilize. The question is whether the signal reflects genuine mean-reversion potential or another false recovery.
The Filter
When Kurtosis is below the 75th percentile and Skewness is neutral, the market is operating in a more stable, symmetric environment where RSI reversal signals carry higher confidence.
2. ATRM + MACD — Confirming Breakouts & Avoiding Whipsaws
Standard indicators like RSI, MACD, or Moving Averages track trend and momentum but cannot detect the underlying shape of the return distribution. ATRM acts as a distributional condition filter—helping determine whether a strategy's signals are operating in a favorable or unstable statistical environment.
The Breakout Confirmation — Filtering False Crossovers
The Setup
MACD crossovers frequently generate false signals during choppy, low-conviction markets. Look for periods where Kurtosis is compressed near the bottom of its historical range, indicating a low-dispersion environment where crossover signals should be treated with skepticism.
The Filter
When a fresh MACD crossover occurs simultaneously with Kurtosis breaking upward through the 75th percentile, it confirms the return distribution is expanding to support a genuine directional move. The MACD provides the directional entry; ATRM confirms the structural conditions support it.
The Exhaustion Warning — Avoiding Late-Stage Exposure
The Setup
A bullish MACD crossover occurs after a prolonged advance, potentially pulling in late buyers near the late-stage extension point of a structural move.
The Filter
If the crossover occurs while Kurtosis is already above the 90th percentile and Skewness remains highly positive, the distribution is statistically stretched. Reduce position sizing or trail stops aggressively rather than initiating fresh exposure at that stage.
3. ATRM + Moving Average Crossovers — Filtering Trend Whipsaws
Moving average crossovers are designed to capture shifting trends but have no built-in mechanism to evaluate whether the market environment actually supports a sustained directional move. ATRM fills that gap by providing the distributional context the crossover itself cannot see.
The Squeeze and Launch — High-Probability Breakouts
The Setup
During sideways, range-bound markets, moving averages flatten and cross repeatedly in both directions, generating false signals. Look for periods where the averages are tangled while ATRM Kurtosis is flat near the bottom of its historical range — this is a compressed, low-dispersion environment where breakout signals should be treated with skepticism.
The Filter
Do not trade minor crossovers while Kurtosis remains compressed. Wait for the bar where a fresh MA crossover coincides with Kurtosis breaking upward through the 75th percentile. This indicates the return distribution is beginning to expand, which is consistent with a genuine directional move emerging from the consolidation. The crossover gives direction; ATRM confirms the distributional conditions have shifted to support it.
The Climax Trap — Avoiding Late-Stage Entries
The Setup
An asset enters a strong, sustained advance. A lagging moving average crossover finally triggers a bullish signal after price has already moved significantly, pulling in late trend-followers near the exhaustion point.
The Filter
If the crossover occurs while Kurtosis is already above the 90th percentile and Skewness is in the upper percentiles, the distribution is statistically extended. This does not guarantee reversal, but it indicates the market is in a high-dispersion, asymmetric state that is less favorable for new long entries. Use this condition to trail existing stops aggressively rather than initiating fresh positions.
The Dead Cat Bounce — Identifying False Recoveries
The Setup
Following a sharp decline, price bounces and shorter-term moving averages cross back to the upside, creating the appearance of a trend reversal. This pattern frequently traps traders who interpret the crossover as confirmation that the worst is over.
The Filter
If the bullish crossover occurs while ATRM Skewness remains below the 10th percentile, the return distribution is still heavily negatively skewed. The underlying conditions that drove the decline have not resolved. Treat the crossover as a potential continuation pattern rather than a confirmed reversal, and approach any long signal with significantly reduced conviction until skewness recovers toward the neutral zone.
📌Important note: ATRM is not a standalone entry/exit signal. Use it as a market condition filter layered over your existing strategy — tighten risk or reduce exposure when tail risk is accumulating, increase exposure when distributional conditions are favorable.
Disclaimer
ATRM is an analytical framework developed for educational and research purposes. It does not constitute financial or investment advice.
All trading involves substantial financial risk. Indicator
AetherEdge - Consensus Council🖊️ Overview
AE-COUNCIL is a next-generation signal system where seven specialized agents collaborate. Five entry agents (Trend, S/R, Volume, Momentum, Volatility) vote on direction, and two risk-management agents (Risk Manager, Reward Manager) dynamically compute SL and three-tier TPs. Entry arrows print alongside SL/TP1/TP2/TP3 horizontal lines, and Hit labels fire automatically on touch — a fully automated decision system.
🔶 Key Features
7-agent collaborative design: 5 entry + 2 risk-management agents
Dynamic TP/SL: auto-computed from ATR, volatility regime, structure, momentum
3-tier TP: TP1/TP2/TP3 multi-target take-profit management
Strong-signal double-lock: requires threshold + Trend/S/R agreement
Auto Hit tracking: real-time labels on SL/TP touches
Optional trailing: SL trails only while Momentum agrees
Detailed dashboard: per-agent votes, consensus score, confidence, active trade status
Optional individual-agent display for full transparency
Background tint by consensus
Per-event alerts: Strong Buy/Sell/TP Hit/SL Hit
🧠 Technical Architecture
This is a consensus system of seven independent rule-based agents. Trend votes on 3-EMA stack ordering, ADX > 20, and HH/LL breaks. S/R votes when within 0.8 ATR of a confirmed swing (Order-Block proxy). Volume votes when volume z-score ≥ 1.0 and OBV direction matches candle direction. Momentum votes on RSI / MACD-histogram / Stochastic K alignment. Volatility identifies expansion/squeeze via Bollinger band width and votes only during expansion. Risk Manager computes SL = ATR × 1.5 × volMul × structMul; Reward Manager places TP2 = SL × RR × momentumBoost, TP1 from structure, TP3 = TP2 × 1.6. The Strong condition fires only when voteSum ≥ threshold AND Trend agrees AND S/R agrees or is neutral, structurally suppressing false signals. Active trades are tracked in parallel arrays, with SL/TP checks and trailing updated per bar.
⚙️ Recommended Settings & Tuning Guide
Tuned with crypto in mind. Consensus Threshold 3 (recommended) demands 3-of-5 alignment — raise to 4 for strictness or lower to 2 for laxness. Default RR Ratio 2.5 anchors TP2; choose 1.5 conservative or 4.0 aggressive. Use Trailing Stop helps in trend-following regimes but can exit early in ranges. Max TP Levels caps how many TP lines display. Show Individual Agents reveals each vote at the bottom of the chart for full transparency.
💡 How to Use in Practice
When a Strong arrow prints, read the summary bubble: "Strong BUY (4/5)" means four agents agreed, with SL/TP1/RR shown at a glance. The horizontal lines persist on the chart, so you can exit on SL approach, partial-take at TP1, hold to TP2/TP3 with intuitive staged exits. Check per-agent votes in the dashboard — if Momentum and Volume also align, conviction is higher; during a Squeeze, stand aside. The HUD's SL distance % tracks live risk on active trades.
⚠️ Important Notes
This is multi-agent-style, not true multi-agent reinforcement learning. Each agent is an independent classical rule; there is no inter-agent communication or deep-learning co-optimization. Volume Profile and Order Block are proxy implementations (volume z-score + OBV; confirmed swings as OB stand-in), not full versions. SL/TP levels are probabilistic estimates, not guaranteed touches. Trailing operates only while Momentum agrees, working best during sustained trends.
🚨 Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal or SL/TP level guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk. Indicator
AetherEdge - Mini Multi-Agent Consensus🖊️ Overview
AE-MMAC is a consensus signal where three independent agents — Trend, Momentum, Volume — each vote BUY/SELL/wait, and a single clear arrow prints only when a majority agrees. Every agent's verdict is shown on a dashboard, letting you trade with the "leave it to the AI" feel without losing transparency. Agents' past performance is online-learned so high-reliability agents speak louder in the consensus.
🔶 Key Features
3 independent agents voting: Trend (EMA), Momentum (RSI), Volume (z-score)
Majority consensus: fires only when 2+ agents agree; otherwise waits
Weighted voting: each agent's recent accuracy adjusts its vote weight
One-arrow design: a single BUY/SELL label on consensus flips — no clutter
Transparent dashboard: each agent's vote, weight, and recent accuracy at a glance
Optional candle coloring
Alerts on consensus signals
🧠 Technical Architecture
This tool is not a true multi-agent reinforcement-learning system but combines three independent classical rules with reliability learning. The trend agent compares short vs long EMAs, the momentum agent thresholds RSI, and the volume agent votes on the candle direction when volume z-score exceeds a threshold. Each new agent vote is buffered; after the outcome horizon, a label is generated from whether an ATR-scaled move occurred in the vote direction, updating the weight via W ← W + α(won − W). Consensus checks both the raw vote count (minimum votes) and the sign of the weighted sum, so low-reliability agents cannot distort the verdict. Signals fire only as an edge trigger on consensus flips, preventing repetition.
⚙️ Recommended Settings & Tuning Guide
Tuned with crypto in mind. Defaults (Trend EMA 10/30, RSI 14 with 55/45 thresholds, Volume z 1.0) are a starting point. Min Votes = 2 (majority) is recommended; 3 (unanimous) tends to be too strict. Keep Use Learned Agent Weights on so the consensus grows smarter with experience. On high-volatility instruments, raise Volume z Threshold to 1.5 to reduce noise.
💡 How to Use in Practice
When an arrow fires, check the dashboard first. A 3-0 vote is the strongest; 2-1 means majority with dissent. Look at per-agent accuracy too — if the trend agent is at 70% and the others at 50%, the market is trend-led. Scalpers can enter immediately, day traders can wait for the first pullback. Pair with structure tools and prioritize moments where the consensus aligns with key levels.
⚠️ Important Notes
This tool is multi-agent-style, not a true multi-agent RL system. The agents are independent classical rules without inter-agent communication; the AI element is limited to learning each agent's reliability. Learning needs accumulated data, so on new instruments or short windows the agent weights stay near the initial 0.5 and the consensus collapses to simple majority. Empty or near-50% accuracy cells in the HUD signal insufficient learning data.
🚨 Disclaimer
This indicator is provided for educational and informational purposes only and does not constitute financial or investment advice. No signal guarantees future profit, and past performance does not indicate future results. All trading decisions are made at your own risk. Indicator
