WaveTrend 4D

The Four-Dimensional Momentum Oracle

📡 Signal Provider

WaveTrend 4D represents the evolution of momentum analysis, building upon decades of digital signal processing research. At its foundation lies John F. Ehlers’ Elegant Oscillator, which revolutionised technical analysis by applying rigorous signal processing principles to financial markets. J. Dehorty’s WaveTrend 3D then applied this foundation to create a beautiful multidimensional indicator.

Our WaveTrend 4D advances this lineage through proprietary signal generation methodologies — the Gradient Divergence Measure (GDM) and Quantile Median Crosses (QMC) — that transform divergence detection into quantitative science.

As a Signal Provider, WaveTrend 4D is a foundational component in the GYTS TradingVSuite. It analyses market data to generate high-quality trading signals, which are then passed to the Flux Composer for aggregation execution.

Hover over components to see details. Lines indicate data flow between components.

What is WaveTrend?

WaveTrend is fundamentally a momentum oscillator that normalises price derivatives through hyperbolic tangent transformations and dual-pole filtering . The usual WaveTrend-like oscillators often use multiple moving averages in conjunction, that stack lag upon lag with poor smoothing characteristics. John Ehlers’ advanced computations capture the velocity and acceleration of market movements with both better smoothing and less lag, revealing momentum shifts before they become apparent in price. The “wave” aspect refers to how the normalised derivatives create wave-like patterns that oscillate around zero, whilst “trend” indicates the oscillator’s ability to identify directional bias within these waves.

Tip

WaveTrend 4D is our first Signal Provider in the GYTS architecture. It generates the GDM and QMC signals that feed into Flux Composer for aggregation.

Note

In all our documentation, we use the the Professional Edition indicators (with the [GYTS-Pro] prefix on TradingView) to show all capabilities and have a smooth storyline. Features that are exclusive to the Professional Edition and thus not available in the Community Edition (CE) are clearly marked with a ⚔️ Goemon Warrior badge. Otherwise you can assume the CE works the same throughout our documentation website. In Professional Edition Features, we highlight the differences between CE and Pro next to each other.

Core Innovations

While both John Ehlers’ Elegant Oscillator core and J. Dehorty’s WaveTrend 3D’s beautiful visualisation are foundational to WaveTrend 4D, our implementation introduces key innovations that elevate its analytical capabilities and make the indicator truly actionable for traders.

WaveTrend 4D introduces two proprietary methodologies for generating high-quality trading signals: the Gradient Divergence Measure (GDM) and Quantile Median Crosses (QMC). These innovations transform subjective pattern recognition into a quantitative, evidence-based process.

Signal Generation Process

The following diagrams illustrate how WaveTrend 4D processes market data to generate its two primary signal types, GDM and QMC. Choose between the high-level overview for quick understanding or the comprehensive diagram for detailed technical context.

High-Level Process Flow

1. Data Input: Market data (price, volume, etc.) feeds into the WaveTrend 4D engine for analysis.
2. Four-Dimensional Analysis: The system calculates four distinct frequency perspectives—Fast, Normal, Slow, and Lethargic—each capturing different temporal aspects of market momentum.
3. Signal Processing: Digital signal processing transforms raw price data into smooth, normalised wave patterns with reduced lag and superior noise filtration.
4. Signal Generation: Two proprietary engines generate trading signals:
   • GDM Engine: Quantifies divergence strength using six mathematical factors
   • QMC Engine: Identifies trend exhaustion through statistical quantile analysis
5. Output Streams: Produces GDM and QMC data streams that feed into the Flux Composer for aggregation and confluence analysis.

Comprehensive Technical Diagram

Here’s the detailed technical breakdown:

1. Input Data Processing:

   • Accepts various price sources (Close, HL2, HLC3, OHLC4, custom indicators)
   • Supports external tickers for multi-asset analysis
   • Configurable timeframe scaling for higher timeframe analysis

2. Four-Frequency Generation:

   • Fast (🐆): ~0.5x chart timeframe for rapid momentum detection
   • Normal (🐇): ~1x chart timeframe for balanced analysis
   • Slow (🐢): ~2x chart timeframe for trend confirmation
   • Lethargic (🦥): ~4x chart timeframe for structural market analysis

3. GDM Engine - Six-Factor Analysis:

   • Wave Change: Magnitude difference between successive waves
   • Wave Strength: Absolute extremity of current market conditions
   • Divergence Slope: Rate of change between wave peaks/troughs
   • Price Magnitude: Significance of corresponding price movement
   • HTF Trend: Higher timeframe context for trend filtering
   • Time Duration: Temporal span of divergence patterns

4. QMC Engine - Statistical Process:

   • Area Integration: Calculates momentum accumulation between frequency and zero
   • Quantile Analysis: Ranks historical momentum periods by significance
   • Cross Detection: Identifies when significant trends exhaust at median levels

5. Signal Output Streams:

   • GDM Stream: Continuous strength values (-1 to +1) for divergence quality
   • QMC Stream: Quantile percentiles (0 to 1) for trend exhaustion probability
   • Both streams integrate with Flux Composer for sophisticated aggregation

The Fourth Dimension

The name “WaveTrend 4D” derives from its use of four distinct frequencies, each representing a different temporal perspective:

Fast

Captures rapid market movements and short-term momentum shifts.

Normal

Balances responsiveness with stability for a standard market view.

Slow

Filters out market noise to identify more significant, underlying trends.

Lethargic

Provides a deep, structural view of the market, focusing on long-term cycles.

Gradient Divergence Measure (GDM)

GDM (often denoted by ”💥” — an explosion of significant reversal) moves beyond simple divergence detection by quantifying the strength of a divergence. Instead of a binary “yes” or “no”, it produces a continuous value that reflects the conviction behind a potential market turn. This is achieved by synthesising six distinct factors into a single, comprehensive score.

Methodology

GDM’s Six-Factor Formula:

1. Relative Wave Change: The magnitude of difference between successive waves.
2. Absolute Wave Value: The extremity of the most recent wave.
3. Divergence Slope: The rate of change between the wave peaks/troughs.
4. Price Magnitude Change: The significance of the corresponding price movement.
5. Higher Timeframe Trend: The direction of the slower frequency, acting as a trend filter.
6. Time Duration: The number of bars between the two points of the divergence.

GDM Profiles

WaveTrend 4D offers six GDM profiles optimised for different trading styles:

Profile	Primary Focus	Ideal Use Case
Balanced	General assessment	All-purpose analysis
Regular Divergence	Price action	Classic divergence patterns
WaveTrend Focus	Oscillator behaviour	Indicator-centric strategies
Short-term Waves	Slope emphasis	Scalping and day trading
Long-term Waves	Duration focus	Swing and position trading
Overbought/Oversold	Absolute value	Mean reversion strategies

Visualisation

GDM Signal Representation:

• Triangles: Positioned on the oscillator, with their vertical position indicating the GDM strength.
• Strength Value: A continuous value from -1 (strong bullish) to +1 (strong bearish).
• Colours:
  • Blue/Pink: Normal frequency divergences.
  • Green/Purple: Slow frequency divergences.
• Zones: Shaded bands indicating the “strong” signal threshold, helping to visually filter for high-conviction setups.

Quantile Median Crosses (QMC)

QMC (often denoted by ”🌗” — a “quantile” of the area of the moon) identifies reversals after significant trends by calculating the integral of momentum between median crosses. Rather than signalling every crossing, QMC waits for the market’s most profound statements—those rare moments when sustained momentum finally yields to exhaustion.

Methodology

QMC Process:

1. Integral Calculation

   • Measures “area” between frequency line and zero
   • Larger areas indicate stronger trends

2. Quantile Analysis

   • Ranks all historical integrals by size
   • Identifies top percentile movements

3. Reversal Detection

   • Signals when strong trends cross median
   • Indicates potential exhaustion points

Sensitivity

QMC Sensitivity:

Setting	Signal Frequency
Very Low	Rare signals
Low	Selective signals
Medium	Balanced (default)
High	Frequent signals
Very High	Many signals

Note that the sensitivity controls how responsive the detector is to momentum changes — higher sensitivity detects more subtle movements and produces more frequent signals.

Real trading scenarios

Understanding how WaveTrend 4D signals manifest in real market conditions is essential for practical application. Let’s examine three distinct scenarios across different markets:

Scenario 1: Multi-frequency QMC confluence

ChainLink/USDT Perpetual on Binance, 1-hour timeframe

Signal Pattern: Two QMC signals align for a short opportunity

• Bronze QMC: Normal frequency signal indicating tactical reversal potential
• Silver QMC: Slow frequency signal confirming significant trend exhaustion

Analysis: When multiple frequency QMC signals align within proximity, this creates high-conviction reversal opportunities. The slow frequency (silver) confirmation adds substantial weight to the normal frequency (bronze) signal, indicating genuine momentum exhaustion rather than temporary noise.

Scenario 2: Signal strength discrimination

Invesco QQQ Trust (QQQ), 1-hour timeframe

Signal Pattern: Multiple signals with varying strength levels

• Multiple QMC Signals: Several short-direction quantile median crosses
• Various GDM Signals: Different gradient divergence measures with varying strength
• Key Insight: Only the final GDM signal (long direction) exceeds the strong signal threshold (not only that one is displayed in the price chart)

Analysis: This scenario demonstrates the critical importance of signal strength discrimination. Whilst multiple signals occurred, only one possessed sufficient mathematical conviction (above the strong signal threshold) to warrant serious consideration. This filtering mechanism prevents overtrading on weak signals.

Scenario 3: Signal quality assessment

MOVE/USDT Perpetual on Binance, 1-hour timeframe

Signal Pattern: High signal frequency environment

• 7 Total Signals: 3 QMC + 4 GDM signals
• Performance: 6 out of 7 signals would have been profitable
• The Exception: One false signal — notably, this was an extremely weak signal

Analysis: This scenario illustrates two crucial principles:

1. High-Quality Environment: When market conditions align with WaveTrend 4D’s methodology, signal reliability increases dramatically
2. Weak Signal Filtering: The single false signal was extremely weak and should have been ignored in practice — demonstrating why signal strength thresholds exist

Tip

Signal Strength Wisdom: Notice how the false signal in Scenario 3 was exceptionally weak. This reinforces our core principle — never trade weak signals regardless of how many appear. Quality over quantity remains paramount in WaveTrend 4D analysis.

Step-by-Step Configuration Guide

WaveTrend 4D’s configuration follows a logical progression through six distinct groups in TradingView’s settings panel. Like constructing a sophisticated trading instrument, each group builds upon the previous, creating a cohesive analytical framework that adapts to your specific market focus.

Tip

Configuration Philosophy: WaveTrend 4D’s settings are designed as a journey from basic visualisation through to advanced signal generation. Each group represents a different layer of sophistication, allowing you to build complexity gradually as your understanding deepens.

🎨 Group 1: Visualisation Settings

The foundation of any trading tool is how it presents information. WaveTrend 4D’s visualisation settings control not only what you see, but how you interact with multiple indicators and timeframes.

Visualisation Settings: Your first step in customising WaveTrend 4D’s appearance and behaviour

Key Settings Explained:

• 🎨 Indicator Name: Handy when using the Signal Provider multiple times on a chart. Use descriptive names like BTC HL2 2h (as in screenshot) or Main aggressive to distinguish between different configurations on the same chart.

• 🎨 Num. bars to repeat indicator name: Automatically displays your custom name at regular intervals when scrolling through historical data. Set to 0 to disable, or use 50-500 bars for convenient identification during analysis.

• 🎨 Show signals on price chart: Projects strong GDM signals (triangles) and QMC signals directly onto your price chart. This overlay creates a unified analytical view, eliminating the need to constantly reference the oscillator panel.

• 🎨 Show GDM zones: Displays shaded bands around the strong signal threshold (e.g. ±0.7). These zones help visually identify high-conviction areas.

• 🎨 Show frequency lines: Controls the visibility of all four frequency lines.

• 🎨 Emphasise frequency: - determines which of the four frequencies receives visual prominence with increased line width and opacity. Each of the frequencies have also a special purpose for signal generation, this will be elaborated later in the divergence settings.

  • 🦥 Lethargic: Represents about 4x (default settings) the current (“normal”) timeframe
  • 🐢 Slow: Represents about 2x (default settings) the current (“normal”) timeframe
  • 🐇 Normal: Represents approx. the current timeframe
  • 🐆 Fast: Represents about 0.5x (default settings) the current (“normal”) timeframe

• 🎨 Emphasis width: Line thickness for your chosen frequency (0-5). Higher values create more prominent display but may obscure other elements.

• 🎨 Emphasis smoothness strength: Adds a smoothed version of your emphasis frequency (0-5). This creates a trend ribbon effect, with higher values providing clearer directional bias but more lag.

• 🎨 Frequency separation distance: Vertically spaces frequencies for isolated analysis using multiple x-axes. Set to 0 for standard overlay mode, or use 1-3 for separated analysis when comparing different frequencies in detail.

• 🎨 Use mirror mode: Creates symmetrical display above and below zero. Particularly useful for pattern recognition and when using frequency separation.

• 🎨 Show frequencies: Individual toggles for each frequency (🐆🐇🐢🦥). Feel free to disable a few frequencies to really get a feeling of how the individual frequencies behave.

💧 Group 2: Price Data Source

The data foundation determines everything that follows. These settings control what market information flows into WaveTrend 4D’s mathematical engine, making them crucial for confluence strategies.

Price Data Source: Configure what market data feeds into your analysis

Key Settings Explained:

• ⚔️ Goemon Warrior 💧 Use external ticker & 💧 Ticker: Enables multi-asset analysis. When enabled, WaveTrend 4D can analyse any symbol while displayed on your current chart. Essential for:
  • Currency correlation analysis (EUR/USD on GBP/USD chart)
  • Sector analysis (SPY signals on individual stock charts)
  • Cross-market confluence (DXY analysis on crypto charts)

Especially when using multiple signal providers, this creates powerful confluence opportunities by overlaying different market perspectives.

• 💧 Source: Determines which price data feeds the oscillator calculation:

  • Close: Standard and most common choice
  • Other price options: HL2, HLC3, OHLC4, etc., each offering different sensitivity characteristics
  • Custom source: You can also use another indicator. Works great for non-standardised data sources (i.e. that do not oscillate around zero) like On-Balance Volume (OBV).

• 💧 Timeframe: Critical for higher timeframe analysis. When set to a higher timeframe (e.g., 4-hours on a 1-hour chart), WaveTrend 4D automatically scales its parameters to maintain equivalent behaviour. This enables clean multi-timeframe analysis without the complexity of switching charts.

Tip

Pro tip: While it’s fine to completely ignore these settings at first, once you master this Signal Provider you’ll see yourself getting back to specifically these settings for sophisticated multi-dimensional analysis. Tuning a great ensemble of Signal Providers that feed diverse and robust signals into the Flux Composer is a time well spent.

🔀 Group 3: Divergence Definition

⚔️ Goemon Warrior Divergence detection forms the mathematical heart of GDM signals. These settings determine when WaveTrend 4D recognises a “divergence” - the critical moments when momentum patterns suggest potential reversals.

Divergence Definition: Fine-tune when and how divergences are detected

Understanding Divergence Logic

WaveTrend 4D employs a sophisticated three-frequency analysis where faster frequencies are compared against slower frequencies for trend confirmation. The divergence occurs when waves become progressively weaker whilst the trend context (defined by slower frequencies) behaves according to your chosen criteria.

Key Settings Explained

• ⚔️ Goemon Warrior 🔀 Divergence definition: Offers four sophisticated approaches to define what constitutes a valid divergence (available in both CE and Pro editions):

  • With position of slower frequency (Default): Triggers when the slower frequency is above/below the zero line, confirming bullish/bearish territory.
  • With direction of slower frequency: Requires the slower frequency to be trending (looking at the slope) in the same direction as the potential reversal (e.g., trending up for a bullish divergence).
  • With direction and position: Combines both criteria for maximum conviction, requiring the slower frequency to be both in the correct territory and trending in the right direction.
  • No filtering: Detects weakening waves without any trend context from the slower frequency, resulting in the highest number of signals.

• 🔀🐇 Enable normal divergence detection: Activates divergence analysis between Fast and Normal frequencies. Essential for tactical, shorter-term signals.

• 🔀🐇 Divergence lookback window: How far back to search for previous Normal frequency waves (e.g. 30 bars). Shorter windows (20-25) find divergence for volatile markets and quick moves; longer windows (40-50) also include systematic rhythms.

• 🔀🐇 Relative size: Current wave must be at least e.g. 40% smaller than the previous wave to qualify as divergence. Higher values (60-90%) create more signals; lower values (20-60%) demand larger differences.

• 🔀🐢 Enable slow divergence detection: Activates divergence analysis between Normal and Slow frequencies. These generate fewer but more significant signals for strategic positioning.

• 🔀🐢 Divergence lookback window: Extended window (e.g. 70 bars) for Slow frequency analysis, reflecting its longer-term nature.

• 🔀🐢 Relative size: Less strict requirement (e.g. 60%) for Slow frequency divergences as these signals are occurring less often.

Note

Frequency Relationship: Remember that divergences compare adjacent frequency pairs: Normal→Slow (tactical signals, with the Fast to determine peaks/troughs on the Normal) and Slow-Lethargic (strategic signals, with the Normal to determine peaks/troughs on the Slow).

💥 Group 4: Gradient Divergence Measure (GDM)

The crown jewel of WaveTrend 4D - transforming subjective divergence recognition into quantitative science. GDM synthesises six mathematical factors to produce a single strength measure, revolutionising how we evaluate potential reversals.

Gradient Divergence Measure: Quantify the strength of every divergence signal

The Six-Factor Formula: GDM evaluates divergence strength through 6 components, each representing a different aspect of market behaviour:

1. Relative Wave Change: How much the current wave differs from the previous
2. Absolute Wave Value: The extremity of market conditions
3. Divergence Slope: The rate of change between wave peaks/troughs
4. Price Magnitude Change: The significance of corresponding price movement
5. Trend Context: Higher timeframe direction and strength
6. Time Duration: The temporal span of the divergence pattern

Note

This proprietary GDM formula is not arbitrary; it is the result of extensive empirical research and adversarial testing. In our algorithmic forge, we have meticulously calibrated each component, ensuring the GDM provides a robust and statistically sound measure of divergence strength across diverse market conditions.

Key Settings Explained:

• 💥 Strong signal threshold (0.7): defines what constitutes a “significant” GDM. Values above this threshold:

  • Display price chart signals (triangles)
  • Generate integer stream outputs for automated systems (in the GYTS Suite we do not use these, but rather use the exact signal strength)
  • Represent approx. the top 30% of historical divergence strength (with default sensitivity)

• ⚔️ Goemon Warrior 💥 GDM profile Critical setting: offers six distinct profiles:

  • Balanced: Equal weighting of all six factors (recommended starting point)
  • Regular divergence: Emphasises price action components
  • WaveTrend focus: Prioritises oscillator behaviour over price
  • Short-term waves: Favours rapid divergence development
  • Long-term waves: Prefers sustained divergence patterns
  • Overbought/oversold: Emphasises extreme oscillator values

• 💥 GDM sensitivity: Controls the strength of signal standardisation. Higher sensitivity creates more frequent “strong” signals:

  • Very Low: Conservative, rare but highly significant signals
  • Low: Selective signal generation
  • Medium: Balanced frequency for most markets
  • High: More responsive to market changes (default)
  • Very High: Maximum sensitivity, frequent signals

Tip

⚔️ Goemon Warrior tip: The ability to switch between GDM profiles creates powerful adaptability. Use “Regular divergence” profile during trending markets when price action drives reversals, then switch to “WaveTrend focus” during ranging conditions when oscillator patterns dominate.

🌗 Group 5: Quantile Median Crossing (QMC)

The final sophistication layer - QMC identifies trend exhaustion by measuring the statistical significance of momentum accumulation. Rather than signalling every median cross, QMC waits for mathematically significant trend statements.

Quantile Median Crossing: Detect statistically significant trend exhaustion

The Statistical Innovation: QMC calculates the integral between each frequency line and zero, then ranks these areas historically. Only when a substantial trend (top percentile) finally crosses the median does QMC generate a signal.

Key Settings Explained:

• 🌗🐇 Normal QMC: Enables bronze label signals for tactical reversal opportunities. These represent the exhaustion of short-term momentum bursts.

• 🌗🐢 Slow QMC: Activates silver label signals for significant reversal potential. These indicate the weakening of intermediate trends.

• 🌗🦥 Lethargic QMC: Generates gold label signals for major trend exhaustion. These are the rarest and most significant signals, often marking important cycle turns.

• ⚔️ Goemon Warrior 🌗 QMC sensitivity provides five levels:

  • Very Low: Extremely rare signals
  • Low: Conservative signal generation
  • Medium: Balanced approach suitable for most markets
  • High: More frequent signals for active trading
  • Very High: Maximum responsiveness

• 🌗 Min. sample size: Requires a minimum of 20 (default) historical trend periods to ensure statistical validity for QMC signals.

Signal Hierarchy: QMC signals follow a natural hierarchy of significance:

• Bronze (Normal): Frequent, tactical opportunities
• Silver (Slow): Moderate frequency, strategic positioning
• Gold (Lethargic): Rare, major trend reversals

When multiple QMC signals align (e.g., silver and bronze together), this creates exceptionally high-conviction reversal opportunities.

🌊 Group 6: WaveTrend Oscillator

The mathematical foundation that transforms raw price data into the elegant wave patterns you see. These parameters control the core signal processing engine, determining sensitivity, smoothness, and the character of each frequency.

WaveTrend Oscillator: The mathematical engine that creates the four-dimensional analysis

The Digital Signal Processing Foundation:

WaveTrend 4D employs John Ehlers’ sophisticated approach to momentum analysis, using dual-pole filtering and hyperbolic tangent normalisation . Unlike traditional moving average-based oscillators, this approach provides superior smoothness with reduced lag.

Key Settings Explained:

• 🌊 Lookback: Defines the base lookback period (default: 20 bars) for the oscillator’s smoothing filters. All frequency-specific smoothing parameters scale from this value, making it the primary sensitivity control for the oscillator’s responsiveness:

  • 15-18: Higher sensitivity, more signals, suitable for volatile assets
  • 20-22: Balanced approach, works well across most markets
  • 25-30: Lower sensitivity, cleaner signals, ideal for stable instruments

• 🌊 Quadratic mean: The normalisation window (default: 50) that adapts the oscillator to varying market volatility. Longer periods create more stable normalisation but slower adaptation to regime changes.

• 🌊🐆 Fast Length & Smoothing: Creates the most responsive frequency, capturing rapid momentum shifts. These factors scale the base lookback period, making Fast approximately half the timeframe of Normal.

• 🌊🐇 Normal Length & Smoothing: The baseline frequency representing your chart’s timeframe. All other frequencies scale relative to these values.

• 🌊🐢 Slow Length & Smoothing: Filters out short-term noise to reveal intermediate trends. The higher smoothing factor creates the characteristic lag that makes this frequency ideal for trend confirmation.

• 🌊🦥 Lethargic Length & Smoothing: Provides deep, structural market analysis. This frequency often reveals major cycle turns and acts as the ultimate trend filter.

Tip

Personally we find these settings to be well calibrated and not the first priority to change. We believe mastering the other settings has more priority. However, exploration is encouraged, and you may find unique configurations that suit your trading style.

Scaling Philosophy: Each frequency represents a different temporal perspective of the same market. Think of them as different camera lenses - wide-angle (Lethargic) for the big picture, standard (Normal) for balanced view, and macro (Fast) for immediate detail.

Configuration Mastery Summary

WaveTrend 4D’s six configuration groups follow a logical progression from basic visualisation to advanced signal generation. Each group builds or expands upon the previous, creating a comprehensive analytical framework:

1. 🎨 Visualisation: How information is presented
2. 💧 Data Source: What market data feeds the analysis
3. 🔀 Divergence: When divergences are recognised
4. 💥 GDM: Quantifying divergence strength
5. 🌗 QMC: Statistical trend exhaustion detection
6. 🌊 Oscillator: The mathematical foundation

Master each group progressively - start with basic visualisation settings, establish reliable data sources, then gradually explore the advanced signal generation capabilities that make WaveTrend 4D a sophisticated analytical instrument.

Tip

Configuration Strategy: Begin with default settings and gradually adjust one group at a time. Each market has unique characteristics, and the optimal configuration emerges through systematic experimentation rather than dramatic changes.

Integration with Flux Composer

Signal Stream Architecture

WaveTrend 4D generates two distinct signal types that flow to the Flux Composer:

1. GDM Strength Values: Continuous values from -1 (strong bullish) to +1 (strong bearish)
2. QMC Quantile Events: Percentile rankings from 0 to 1 indicating trend magnitude

Both streams undergo aggregation through the Flux Composer’s confluence mechanisms and temporal decay functions, creating a unified trading signal.

Signal Types Explained

Continuous Strength

Primary Signal Mode, used for sophisticated aggregation in Flux Composer

• GDM Values: Range from -1 to +1, providing nuanced divergence strength
• QMC Quantiles: Range from 0 to 1, indicating trend exhaustion probability
• Usage: Fed directly to Flux Composer for sophisticated aggregation

Integer Streams

Alternative Binary Mode

• Purpose: Direct triggering without Flux Composer
• Activation: Only when exceeding Strong Signal Threshold
• Values: Discrete integers for simple alert systems

Alert Functionality

While WaveTrend 4D supports traditional TradingView alerts, we recommend using the Order Orchestrator for professional trading execution. Order Orchestrator provides superior position management, risk controls, and automated execution that far exceed basic alert capabilities.

Basic Alert Types (if needed):

• GDM strong signals when exceeding threshold
• QMC events for each frequency
• Combined signal alerts

Tip

Recommended Approach: Connect WaveTrend 4D → Flux Composer → Order Orchestrator for complete automated trading with proper risk management.

Connection Process

Note

Professional Edition combines both GDM and QMC in a single indicator, whilst Community Edition requires two separate indicators for full functionality.

1. Configure WaveTrend 4D

   • Set unique indicator name for identification (especially when using multiple WT4D indicators)
   • Choose appropriate settings for your market
   • Enable desired frequencies and signals

2. Connect with Flux Composer

   • Select corresponding streams in Flux settings
     • ”🔗 STREAM WT4D 📡 Gradient Divergence Measure”
     • ”🔗 STREAM WT4D 📡 Quantile Median Cross”
   • Verify connection with decaying function visualisation
   • Incorrect connections will also trigger “User-defined errors”

3. Locate Data Streams

   • Continue tuning Signal Providers’ settings
   • Tune Flux Composer’s aggregation parameters to match what you expect from the “signal strength” of all signals over time

Practical Usage

Market-Specific Configurations

Cryptocurrency

High-Volatility Markets

• GDM Profile: short-term waves or regular divergence
• GDM Sensitivity: medium to high
• QMC Sensitivity: high
• Emphasis Frequency: Normal
• Divergence Lookbacks: Shorter (e.g., Norm: 20-25, Slow: 50-60)

For volatile assets, prioritise responsiveness but filter for stronger signals to avoid noise.

Forex Majors

Trending Markets

• GDM Profile: balanced or long-term waves
• GDM Sensitivity: medium
• QMC Sensitivity: medium to high
• Emphasis Frequency: Slow
• Divergence Lookbacks: Default (Norm: 30, Slow: 70)

Forex pairs often exhibit clearer trends, benefiting from balanced, trend-focused settings.

Stock Indices

Lower-Volatility Markets

• GDM Profile: long-term waves or overbought/oversold
• GDM Sensitivity: low to medium
• QMC Sensitivity: low
• Emphasis Frequency: Slow or Lethargic
• Divergence Lookbacks: Longer (e.g., Norm: 40, Slow: 90)

Indices favour longer-term analysis to capture significant trend shifts and avoid noise from minor fluctuations.

Signal Interpretation

Although the Flux Composer aggregates and quantifies signals automatically, understanding their individual meanings is crucial for effective trading decisions.

GDM Signals:

• Strong Bullish (< -0.7): High-probability long opportunity
• Moderate Bullish (-0.3 to -0.7): Consider with confluence
• Neutral (-0.3 to +0.3): No clear divergence
• Moderate Bearish (+0.3 to +0.7): Consider shorts with confluence
• Strong Bearish (> +0.7): High-probability short opportunity

QMC Signals:

• Gold Labels (Lethargic): Major trend exhaustion
• Silver Labels (Slow): Significant reversal potential
• Bronze Labels (Normal): Tactical reversal opportunity

Common Configuration Mistakes

Understanding these frequent errors can dramatically improve your WaveTrend 4D implementation:

❌ Changing too many settings at once → Start with defaults, adjust one parameter at a time

❌ Trading every signal that appears → Focus only on signals above the shaded GDM zones (±0.7)

❌ Ignoring frequency alignment → Best signals occur when multiple frequencies agree (e.g., both Fast and Normal near extremes)

❌ Using on timeframes below 15 minutes → WaveTrend 4D performs optimally on 15m+ timeframes where noise is filtered

Professional Edition Features

Feature	CE	Pro
Core Architecture
Four Frequency Analysis
Signal Provider
Signal Stream Output
External Ticker Support
Higher Timeframe Support
Sophisticated Output Streams
GYTS Suite Compatibility
GDM (Gradient Divergence Measure)
GDM Calculation	In separate indicator	Integrated
GDM Profiles	1 (Balanced only)	6 profiles
GDM Sensitivity Levels	5 levels	5 levels
Divergence Definitions	4 modes	4 modes
Strong Signal Threshold
QMC (Quantile Median Cross)
QMC Calculation	In separate indicator	Integrated
QMC Sensitivity	Fixed (Medium)	5 levels
Frequency Selection	3 frequencies	3 frequencies

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Next Steps

• Connect to Flux Composer for signal aggregation
• Follow Basic Setup Guide for complete integration
• Study Market Regime Detector for contextual analysis
• Join our Discord for configuration discussions