Volume Weighted LR Z ScoreThis indicator calculates the Volume Weighted Linear Regression
Z-Score (VWLRZS). Unlike a standard Z-Score which measures
deviation from a static mean, this oscillator measures the
statistical distance of price from a dynamic Volume-Weighted
Linear Regression Line (Analysis of Residuals).
Key Features:
1. **Volatility Decomposition:** The indicator separates volatility
based on the 'Estimate Bar Statistics' option.
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
standard Regression Residuals using the selected `Source`
for both the regression line (baseline) and the signal.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):**
Uses a hybrid statistical approach:
a) **The Model (Baseline):** Uses an estimator to calculate
the 'within-bar' mean and fits the Linear Regression
through these statistical centers. This creates a
stable, trend-following expectation model.
b) **The Signal (Observation):** Compares the actual `Source`
(e.g., Close) against this regression line.
(Result: A Z-Score that measures deviations from the current
trend slope rather than a flat average).
2. **Visual Decomposition Logic:** Total Standard Deviation (of
Residuals) is the primary metric displayed. Since Standard
Deviations are not linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)),
this indicator calculates the *exact* Total Z-Score and partitions
the area underneath based on the Variance Ratio. This ensures the
displayed total volatility remains mathematically accurate while
showing relative composition.
3. **Normalization (Exponential Regression):** Includes an optional
'Normalize' mode. When enabled, the indicator calculates the
Linear Regression on logarithmic data. Mathematically, this
transforms the baseline into an **Exponential Regression Curve**,
making it ideal for analyzing assets with compounding growth
characteristics (constant percentage trend).
4. **Full Divergence Suite (Class A, B, C):** The indicator's
primary feature is its integrated divergence engine. It
automatically detects and plots all three major divergence
classes between price and the Z-Score:
- Regular (A): Signals potential trend exhaustion and reversals.
- Hidden (B): Signals potential trend continuations during pullbacks.
- Exaggerated (C): Signals weakness at double tops/bottoms.
5. **Divergence Filtering and Visualization:**
- **Price Tolerance Filter:** Divergence detection is enhanced
with a percentage-based price tolerance (`pivPrcTol`) to
filter out insignificant market noise, leading to more
robust signals.
- **Persistent Visualization:** Divergence markers are plotted
for the entire duration of the signal and are visually
anchored to the oscillator level of the confirming pivot.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library
6. **Note on Confirmation (Lag):** Divergence signals rely on a
pivot confirmation method to ensure they do not repaint.
- The **Start** of a divergence is only detected *after* the
confirming pivot is fully formed (a delay based on
`Pivot Right Bars`).
- The **End** of a divergence is detected either instantly
(if the signal is invalidated by price action) or with
a delay (when a new, non-divergent pivot is confirmed).
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Calculation:** The Z-Score line *itself* can be calculated on a
higher timeframe, with standard options to handle gaps
(`Fill Gaps`) and prevent repainting (`Wait for...`).
- **Limitation:** The Divergence detection engine (`pivDiv`)
is designed for the active timeframe. Using it in MTF mode
is not recommended as step-data can lead to inaccurate
pivot detection.
8. **Integrated Alerts:** Includes a comprehensive set of built-in
alerts for the Z-Score crossing the neutral line, the configured
Threshold levels, and the start/end of all divergence types.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Tìm kiếm tập lệnh với "curve"
Std Dev Channel [fmb]What it is
A professional regression channel that combines standard deviation divisions, an extreme price envelope, and a trend quality gauge. It is designed for fast read-and-act decisions on any timeframe, with sensible presets and log-space math for instruments that trend exponentially.
Why it’s different
Most channels draw fixed ±1σ and ±2σ around a regression line. This tool adds:
- Fibonacci-spaced σ divisions for precise scaling
- An objective MaxEnvelope of actual extremes with optional 1.272 and 1.618 extensions
- Pearson’s R labelling that classifies the trend as Strong Up, Moderate, Weak, or Strong Down
- A log-space option so channels behave correctly on long trends and high beta charts
How it works
Base line
- Linear regression of the last Length bars, drawn as a ray.
- Optional colour change by regime using Pearson’s R.
Divisions (StdDev or MaxEnvelope)
- StdDev basis: σ of residuals around the regression line.
- MaxEnvelope basis: distances from the base line to the farthest highs and lows in the lookback.
- Divisions can be Fibonacci multiples (0.382, 0.618, 1.000, 1.272 by default) or uniform steps.
Outer rails
- ENV 1.0 touches the farthest highs and lows within the window.
- Optional extensions at 1.272 and 1.618 highlight stretch and breakout zones.
Trend quality (Pearson’s R)
- R is computed on the same series and window.
- Default thresholds: Strong when |R| ≥ 0.70, Weak when |R| < 0.40.
- The label reads: R 0.XXX • Class, plotted near the most recent base value.
Log-space math
- When enabled, the model runs on ln(price) and converts the outputs back to price.
- Safer on multi-year charts and large percentage trends.
Presets
- Swing: Length 125, StdDev basis, Fib divisions, ENV 1.0 and 1.272 on
- Intraday: Length 240, StdDev basis, simple ±1 and ±2 style divisions, ENV off by default
- Position: Length 200, StdDev basis, compact Fib set for higher timeframes
You can turn preset overrides off to make every input respond instantly.
Inputs you will actually use
- Length, Source, Log-space ON or OFF
- Basis: StdDev or MaxEnvelope
- Divisions: Fib list or Step and Max multiple
- Outer rails: show ENV 1.0, show 1.272, show 1.618
- Labels and sizes, extend left or right
- Hide divisions or outer rails automatically when the regime is Weak
Alerts included
- Close crosses above or below ENV 1.0
- Close crosses above or below ENV 1.272 and 1.618 (if enabled)
Practical playbook
Trend following
- In Strong Uptrend: buy pullbacks near 0.382 to 0.618 above the base with stops just beyond the next lower division.
- In Strong Downtrend: sell bounces into 0.382 to 0.618 below the base with stops just beyond the next upper division.
Mean reversion
- When R is Moderate or Weak, fade moves that tag ENV 1.0 back toward the base.
- If price closes through an ENV extension, treat it as potential regime change and stand down on fades.
Breakouts
- A close through ENV 1.0 with R rising toward Strong often precedes trend acceleration.
- Use the next division or the 1.272 rail as the first target and trail on the base.
Tips
- Keep Length stable across symbols you compare. Consistency beats curve fitting.
- Use log-space on multi-year equities and crypto. Use linear for short intraday work.
- If you want a classic look, disable Fib and rails, set Step 1.0 and Max 2.0.
Notes
- The tool draws more lines when Fib divisions are active. If it feels busy, show divisions only and hide labels, or keep ENV 1.0 plus one extension.
- Pearson’s R is descriptive, not predictive. Combine with price structure and volume for entries.
Adaptive ML VWAP v1.0Overview
Adaptive ML VWAP is a next-generation "Smart Indicator" that moves beyond static deviations (Standard Deviation). Instead of assuming market volatility is distributed normally (Bell Curve), this indicator uses a k-Nearest Neighbors (k-NN) machine learning engine to learn the specific volatility behavior of the asset you are trading.
It answers the question: "When price extends away from VWAP, how far does it actually go before reversing?"
The Adaptive ML Engine
This script features a 5-Dimensional ML Engine that tracks every major extension or pullback event. It records:
Deviation Depth (Normalized to ATR)
Trend Slope (Is the trend steep or flat?)
ADX (Trend Strength)
VWAP Deviation (Relative Position)
Time of Day (Session Context)
When a new setup occurs, the k-NN engine instantly searches its memory for the 5 most similar historical events and calculates the probability of success based on what happened last time.
Two Strategy Modes
You can toggle the logic to suit your trading style:
1. Mean Reversion Mode (Default)
"Fade The Move"
Goal: Catch price at an exhaustion point returning to VWAP.
Signal: Triggers when price touches a Smart Band and reverses back toward the center.
k-NN Learning: Learns which conditions favor a snap-back.
Best For: Ranging markets, Lunch hours, Choppy sessions.
2. Trend Following Mode
"Ride The Move"
Goal: Catch breakouts that are launching away from value.
Signal: Triggers when price breaks out of the Inner Band (1.0).
k-NN Learning: Learns which breakouts tend to extend to the Outer Bands.
Best For: Morning Drives, News Events, Strong Trends.
Visual Guide
The indicator uses a Dynamic Gradient system to visualize risk/reward:
Cyan Mist (0.5 - 1.0): The Value Zone. Noise area. Safe for trend entries.
Deep Cyan (1.0 - 2.0): The Trend Zone. Price is moving proactively.
Orange Glow (2.0 - 3.0): The Danger Zone. Price is statistically overextended. Reversals are highly probable here.
"Fractal" Math
Unlike standard indicators that break when you change timeframes, Adaptive ML VWAP uses Fractal Normalization.
A "2.0 Band" on a 15-second chart means the same statistical extreme as a "2.0 Band" on a 4-hour chart.
Auto-Adaptive Lookback: The indicator automatically boosts the ML memory (Lookback) on lower timeframes (seconds/minutes) where more noise requires larger sample sizes, ensuring robust predictions without manual tweaking.
Settings
Auto-Adapting Lookback: (Default: True) automatically increases Lookback to 100+ for seconds charts and 50+ for minute charts.
Lookback (Events): Manual override base value (Default: 100).
Strategy Mode: Toggle between Mean Reversion and Trend Following.
k-Neighbors: The number of similar past events to structurally compare (Default: 5).
Disclaimer: This tool is for educational purposes. Machine learning performance is dependent on market conditions and historical recursion.
Precision Multi-Dimensional Signal System V2Precision Multi-Dimensional Signal System (PMSS) - Technical Documentation
Overview and Philosophical Foundation
The Precision Multi-Dimensional Signal System (PMSS) represents a systematic approach to technical analysis that integrates four distinct analytical dimensions into a cohesive trading framework. This script operates on the principle that market movements are best understood through the convergence of multiple independent analytical methods, rather than relying on any single indicator in isolation.
The system is designed to function as a multi-stage filtering funnel, where potential trading opportunities must pass through successive layers of validation before generating actionable signals. This approach is grounded in statistical theory suggesting that the probability of accurate predictions increases when multiple uncorrelated analytical methods align.
Integration Rationale and Component Synergy
1. Trend Analysis Layer (Dual Moving Average System)
Components: SMA-50 and SMA-200
Purpose: Establish primary market direction and filter against counter-trend signals
Integration Rationale:
SMA-50 provides medium-term trend direction
SMA-200 establishes long-term trend context
The dual-MA configuration creates a trend confirmation mechanism where signals are only generated in alignment with the established trend structure
This layer addresses the fundamental trading principle of "following the trend" while avoiding the pitfalls of single moving average systems that frequently generate whipsaw signals
2. Momentum Analysis Layer (MACD)
Components: MACD line, signal line, histogram
Purpose: Detect changes in market momentum and identify potential trend reversals
Integration Rationale:
MACD crossovers provide timely momentum shift signals
Histogram analysis confirms momentum acceleration/deceleration
This layer acts as the primary trigger mechanism, initiating the signal evaluation process
The momentum dimension is statistically independent from the trend dimension, providing orthogonal confirmation
3. Overbought/Oversold Analysis Layer (RSI)
Components: RSI with adjustable threshold levels
Purpose: Identify potential reversal zones and market extremes
Integration Rationale:
RSI provides mean-reversion context to momentum signals
Extreme readings (oversold/overbought) indicate potential exhaustion points
This layer prevents entry at statistically unfavorable price levels
The combination of momentum (directional) and mean-reversion (cyclical) indicators creates a balanced analytical framework
4. Market Participation Layer (Volume Analysis)
Components: Volume surge detection relative to moving average
Purpose: Validate price movements with corresponding volume activity
Integration Rationale:
Volume confirms the significance of price movements
Volume surge detection identifies institutional or significant market participation
This layer addresses the critical aspect of market conviction, filtering out low-confidence price movements
Synergistic Operation Mechanism
The script operates through a sequential validation process:
Stage 1: Signal Initiation
Triggered by either MACD crossover or RSI entering extreme zones
This initial trigger has high sensitivity but low specificity
Multiple trigger mechanisms ensure the system remains responsive to different market conditions
Stage 2: Trend Context Validation
Price must be positioned correctly relative to both SMA-50 and SMA-200
For buy signals: Price > SMA-50 > SMA-200 (bullish alignment)
For sell signals: Price < SMA-50 < SMA-200 (bearish alignment)
This layer eliminates approximately 40-60% of potential false signals by enforcing trend discipline
Stage 3: Volume Confirmation
Must demonstrate above-average volume participation (configurable multiplier)
Volume surge provides statistical confidence in the price movement
This layer addresses the "participation gap" where price moves without corresponding volume
Stage 4: Signal Quality Assessment
Each condition contributes to a quality score (0-100)
Higher scores indicate stronger multi-dimensional alignment
Quality rating helps users differentiate between marginal and high-conviction signals
Original Control Mechanisms
1. Signal Cooldown System
Purpose: Prevent signal overload and encourage trading discipline
Mechanism:
After any signal generation, the system enters a user-defined cooldown period
During this period, no new signals of the same type are generated
This reduces emotional trading decisions and filters out clustered, lower-quality signals
Empirical testing suggests optimal cooldown periods vary by timeframe (5-10 bars for daily, 10-20 for 4-hour)
2. Visual State Tracking
Purpose: Provide intuitive market phase identification
Mechanism:
After a buy signal: Subsequent candles are tinted light blue
After a sell signal: Subsequent candles are tinted light orange
This creates a visual "holding period" reference
Users can quickly identify which system state is active and for how long
Practical Implementation Guidelines
Parameter Configuration Strategy
Timeframe Adaptation:
Lower timeframes: Increase volume multiplier (2.0-3.0x) and use shorter cooldown periods
Higher timeframes: Lower volume requirements (1.5-2.0x) and extend confirmation periods
Market Regime Adjustment:
Trending markets: Emphasize trend alignment and MACD components
Range-bound markets: Increase RSI sensitivity and enable volatility filtering
Signal Level Selection:
Level 1: Suitable for active traders in high-liquidity markets
Level 2: Balanced approach for most market conditions
Level 3: Conservative setting for high-probability setups only
Risk Management Integration
Use quality scores as position sizing guides
Higher quality signals (Q≥80) warrant standard position sizes
Medium quality signals (60≤Q<80) suggest reduced position sizing
Lower quality signals (Q<60) recommend caution or avoidance
Empirical Limitations and Considerations
Statistical Constraints
No trading system guarantees profitability
Historical performance does not predict future results
System effectiveness varies by market conditions and timeframes
Maximum historical win rates in backtesting range from 55-65% in optimal conditions
Market Regime Dependencies
Strong Trending Markets: System performs best with clear directional movement
High Volatility/Ranging Markets: Increased false signal probability
Low Volume Conditions: Volume confirmation becomes less reliable
User Implementation Requirements
Time Commitment: Regular monitoring and parameter adjustment
Market Understanding: Basic knowledge of technical analysis principles
Discipline: Adherence to signal rules and risk management protocols
Technical Validation Framework
Backtesting Methodology
Multi-timeframe analysis across different market conditions
Parameter optimization through walk-forward analysis
Out-of-sample validation to prevent curve fitting
Performance Metrics Tracked
Win rate percentage across different signal qualities
Average win/loss ratio per signal category
Maximum consecutive wins/losses
Risk-adjusted return metrics
Innovative Contributions
Multi-Dimensional Scoring System
Original quality scoring algorithm weighting each dimension appropriately
Dynamic adjustment based on market conditions
Visual representation through signal labels and information panel
Integrated Information Dashboard
Real-time display of all system dimensions
Color-coded status indicators for quick assessment
Historical context for current signal generation
Adaptive Filtering Mechanism
Configurable strictness levels without code modification
User-adjustable sensitivity across all dimensions
Preset configurations for different trading styles
Conclusion and Appropriate Usage
The PMSS represents a sophisticated but accessible approach to multi-dimensional technical analysis. Its strength lies not in predictive accuracy but in systematic risk management through layered confirmation. Users should approach this tool as:
A Framework for Analysis: Rather than a black-box trading system
A Decision Support Tool: To be combined with fundamental analysis and market context
A Learning Instrument: For understanding how different analytical dimensions interact
The most effective implementation combines this technical framework with sound risk management principles, continuous learning, and adaptation to evolving market conditions. As with all technical tools, success depends more on the trader's discipline and judgment than on the tool itself.
Disclaimer: This documentation describes the technical operation of the PMSS indicator. Trading involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Users should thoroughly test any trading system in a risk-free environment before committing real capital.
MA-trix Laboratory [DAFE]MA-trix Laboratory : The Ultimate Moving Average & Trend Following Engine
55+ Algorithms. Dual/Triple MA Systems. Advanced Signal Filtering. Quantum Smoothing. This is not just a moving average; it is the definitive toolkit for forging your perfect trend.
█ PHILOSOPHY: WELCOME TO THE LABORATORY
The moving average is the cornerstone of technical analysis. It is also, in its standard form, an obsolete, one-dimensional tool. A simple EMA or SMA is a blunt instrument in a market that demands surgical precision. It lags, it whipsaws, and it fails to adapt to the market's ever-changing character.
The MA-trix Laboratory was not created to be another moving average. It was engineered to be the final word on moving averages—a comprehensive, institutional-grade research and execution environment. This is not an indicator; it is a powerful, interactive sandbox where you, the trader, can move beyond the static "one-size-fits-all" approach. Here, you can experiment, test, and forge a moving average system that is perfectly synchronized with your specific market, timeframe, and analytical style.
We have deconstructed the very concept of "average" and rebuilt it from the ground up, creating a library of over 55 distinct mathematical algorithms —from timeless classics to proprietary quantum models—all housed within a single, unified, and infinitely configurable engine.
█ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS
This tool stands in a class of its own, offering a suite of features that collectively create an unparalleled analytical experience.
The 55+ Algorithm MA Core: This is the heart of the Laboratory. You are not limited to one or two MA types. You have a vast library of over 55 unique mathematical engines at your command, from classical SMAs to advanced adaptive algorithms like KAMA and FRAMA, to proprietary DAFE models like the "DAFE Flux Reactor" and "DAFE Quantum Step."
Multi-MA Architecture: Seamlessly switch between Single, Dual, and Triple MA operational modes. Build classic two-line crossover systems, three-line trend alignment confirmations, or beautiful, flowing ribbons with just a single click.
Advanced Post-Smoothing Engine: In a revolutionary step, you can apply a second layer of signal processing to your chosen MA. Select from a suite of over 20 professional-grade noise filters —including Ehlers' SuperSmoother, Kalman Filters, and the proprietary "DAFE Phase-Zero"—to surgically remove noise from your MA line after it has been calculated, achieving unprecedented smoothness without significant lag.
The Institutional Signal Filtering Suite: A signal is only as good as its filter. The Laboratory includes a powerful, multi-domain filter engine that acts as an intelligent gatekeeper for your signals. You can require signals to be confirmed by any combination of:
📦 Volume: Require a surge in volume to validate a crossover.
🌊 Volatility: Only take signals during low-volatility "squeeze" conditions or high-volatility expansions.
💪 Trend: Use the ADX to ensure you are only taking signals in the direction of a strong, established trend.
🚀 Momentum: Use RSI, MACD, or ROC to confirm that momentum is on your side.
Integrated Performance Engine: How do you know which of the 55+ algorithms is best? You test it. The built-in Performance Dashboard is a comprehensive backtesting engine that tracks every trade generated by your configuration, providing real-time data on Win Rate, Profit Factor, Net P&L, and Max Drawdown.
█ THE ARSENAL: A DEEP DIVE INTO THE ALGORITHMIC CORE
This is your library of mathematical DNA. The 55+ MA types are grouped into distinct families, each with a unique philosophy.
THE ALGORITHM FAMILIES
The Classics (SMA, EMA, WMA, etc.): The foundational building blocks. Simple, reliable, and universally understood. EMA for responsiveness, SMA for smoothness.
The Low-Lag Warriors (DEMA, TEMA, Hull MA, ZLEMA): A family of MAs engineered specifically to combat the inherent lag of classical averages. The Hull MA is a standout, offering a remarkable balance of extreme smoothness and near-zero lag.
The Adaptive Geniuses (KAMA, VIDYA, FRAMA, Volatility Adjusted MA): These are "smart" MAs. They contain internal logic that allows them to automatically change their speed based on market conditions. They will tighten up in fast-moving trends and loosen in sideways chop, intelligently filtering out noise.
The DSP & Quantitative Masters (Gaussian, Ehlers, Butterworth, Laguerre): These algorithms are born from the world of digital signal processing and advanced mathematics. They use sophisticated techniques like bell-curve weighting, non-linear feedback loops, and frequency filtering to separate the true trend "signal" from market "noise" with unparalleled precision.
The DAFE Proprietary Engines (The "Black Ops" MAs): The crown jewels of the Laboratory. These are custom-built, proprietary algorithms you will not find anywhere else:
DAFE Flux Reactor: A volatility-thermodynamic MA that adapts its alpha using a sigmoid function on Bollinger Band width, creating explosive responsiveness during volatility breakouts.
DAFE Tensor Flow: A multi-vector MA that uses a weighted average of the OHLC data (a "tensor") before applying Hull smoothing, creating an incredibly robust center of gravity.
DAFE Quantum Step: A non-linear, stepped MA that only moves if price exceeds a volatility-based quantum threshold, effectively ignoring all insignificant noise.
DAFE Gravity Well: An institutionally-focused MA that weights its calculation by both time (recency) and volume, pulling the average towards zones of heavy market participation.
THE POST-SMOOTHING FILTERS
This is a second layer of refinement. After your primary MA is calculated, you can pass it through one of over 20 advanced filters to achieve an even higher degree of clarity.
The Ehlers Filters (SuperSmoother, 2-Pole, 3-Pole): A suite of brilliant DSP filters for surgical noise removal.
The Kalman Filter: A predictive filter from robotics and aerospace engineering that provides an "optimal estimate" of the MA's true position.
DAFE Proprietary Smoothers:
DAFE Phase-Zero: Uses a de-trending feedback loop to achieve near-zero lag smoothing.
DAFE Spectral Smooth: A frequency-domain filter that removes jitter while preserving the primary trend.
█ OPERATIONAL MODES & SIGNAL GENERATION
The Laboratory is designed for ultimate flexibility.
Modes: Instantly switch between Single, Dual, and Triple MA modes. Each mode can be a standard line display or a beautiful, flowing Ribbon .
Signal Logic: You have complete control over what constitutes a "signal." Choose from nine different logic modes, including classic Price Cross , Dual MA Cross , Triple MA Alignment , or even advanced logic like Slope Change and Sequential Cross .
The Filter Gauntlet: Before a signal is plotted, it can be passed through the four-stage filtering suite. You can demand that a simple EMA crossover is also confirmed by high volume, ADX trend strength, and bullish RSI—all at the same time. This transforms a basic signal into a high-conviction, multi-factor setup.
█ THE MASTER DASHBOARD: YOUR MISSION CONTROL
The comprehensive dashboard is your unified command center for analysis and performance tracking.
Engine Status: See the currently selected Operation Mode and a detailed breakdown of the type and length of each active MA.
Market Dynamics: Get an at-a-glance view of the current Trend Status, Momentum intensity (based on MA slope), and the percentage deviation of price from your primary MA.
Filter Readout: If filters are enabled, the dashboard provides a live status for each active filter (Volume, Volatility, Trend, Momentum), showing you a "PASS" or "BLOCK" status in real-time.
Performance Readout: When enabled, this section provides a full breakdown of your backtesting results, including Trade Count, Win Rate, Profit Factor, Net P&L, and Max Drawdown.
█ DEVELOPMENT PHILOSOPHY
The MA-trix Laboratory was born from a deep respect for the moving average and a relentless desire to push its boundaries into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation and customization. This indicator is for the serious trader, the tinkerer, the scientist—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision. It is a tool for forging your own alpha, not just following someone else's.
"I don't think traders can follow rules for very long unless they reflect their own trading style. Eventually, a breaking point is reached and the trader has to quit or change, or find a new set of rules he can follow. This seems to be part of the process of evolution and growth of a trader."
█ DISCLAIMER AND BEST PRACTICES
THIS IS A TOOL, NOT A STRATEGY: This indicator provides a sophisticated trend and signal generation framework. It must be integrated into a complete trading plan that includes risk management, position sizing, and your own contextual analysis.
TEST, DON'T GUESS: The power of this tool is its adaptability. Use the Performance Dashboard to rigorously test different algorithms, settings, and filters on your chosen asset and timeframe. Find what works, and build your strategy around that data.
START SIMPLE: The possibilities can be overwhelming. Begin with a classic Dual MA mode (e.g., EMA 20/50) with no filters. Once you are comfortable, begin experimenting with more advanced MA types and layering on filters one by one.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The backtesting results are hypothetical and do not account for slippage or psychological factors.
Never risk more capital than you are prepared to lose.
— Ed Seykota, Market Wizard
The MA-trix Laboratory is designed to be the ultimate tool for that evolution, allowing you to discover and codify the rules that truly fit you.
Taking you to school. - Dskyz, Don't be average. Trade with MA-trix. Trade with DAFE
PSAR Laboratory [DAFE]PSAR Laboratory : The Ultimate Adaptive Trailing Stop & Reversal Engine
23 Advanced Algorithms. Adaptive Acceleration. Smart Flip Logic. Parabolic SAR Reimagined.
█ PHILOSOPHY: WELCOME TO THE LABORATORY
The standard Parabolic SAR, created by the legendary J. Welles Wilder Jr., is a tool of beautiful simplicity. But in today's complex, algorithm-driven markets, its simplicity is its fatal flaw. Its fixed acceleration and rigid flip logic cause it to fail precisely when you need it most: it whipsaws in choppy conditions and gives back too much profit in strong trends.
The PSAR Laboratory was not created to be just another PSAR. It was engineered to be the definitive evolution of Wilder's original concept. This is not an indicator; it is a powerful, interactive research environment. It is a sandbox where you, the trader, can move beyond the static "one-size-fits-all" approach and forge a PSAR that is perfectly adapted to your specific market, timeframe, and trading style.
We have deconstructed the very DNA of the Parabolic SAR and rebuilt it from the ground up, infusing it with modern quantitative techniques. The result is an institutional-grade suite of 23 distinct, mathematically diverse algorithms that dynamically control every aspect of the PSAR's behavior.
█ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS
This tool stands in a class of its own. It is a collection of what could be 23 separate indicators, all seamlessly integrated into one powerful engine.
The 23 Algorithm Engine: This is the heart of the Laboratory. Instead of one rigid formula, you have a library of 23 unique mathematical engines at your command. These algorithms are not simple tweaks; they are complete re-imaginings of how the PSAR should behave, based on concepts from information theory, digital signal processing, fractal geometry, and institutional analysis.
Truly Adaptive Acceleration (AF): The standard PSAR's "gas pedal" (the AF) is dumb; it accelerates at a fixed rate. Our algorithms make it intelligent. The AF can now speed up in clean, trending environments to lock in profits, and automatically slow down in choppy, chaotic conditions to avoid whipsaws.
Advanced Flip Confirmation Logic: Say goodbye to noise-driven flips. You are no longer at the mercy of a single wick touching the SAR. The Laboratory provides multiple layers of flip confirmation, including requiring a bar close beyond the SAR, a volume spike to validate the reversal, or even a multi-bar confirmation .
Comprehensive Noise Filtering Core: In a revolutionary step, you can apply one of over 30 advanced signal processing filters directly to the SAR output itself. From ultra-low-lag filters like the Hull MA and DAFE Spectral Laguerre to adaptive filters like KAMA and FRAMA , you can surgically remove noise while preserving the responsiveness of the core signal.
Integrated Performance Engine: How do you know which of the 23 algorithms is best for your market? You test it. The built-in Performance Dashboard is a comprehensive backtesting and analytics engine that tracks every trade, providing real-time data on Win Rate, Profit Factor, Max Drawdown, and more. It allows you to scientifically validate your chosen configuration.
█ A GUIDED TOUR OF THE ALGORITHMS: 23 PATHS TO AN EDGE
b]These 23 algorithms are not simple settings; they are distinct mathematical philosophies for how a Parabolic SAR should adapt to the market. They are grouped into three primary categories: those that adapt the Acceleration Factor (AF) , those that enhance the Extreme Point (EP) detection, and those that redefine the Flip Logic .
CATEGORY A: ACCELERATION FACTOR (AF) ADAPTATION
These algorithms dynamically change the "gas pedal" of the PSAR.
1. Volatility-Scaled AF
Core Concept: Treats volatility as market friction. The PSAR should be more forgiving in high-volatility environments.
How It Works: It calculates a Volatility Ratio by comparing the short-term ATR to the long-term ATR. If current volatility is high (ratio > 1), it reduces the AF Step. If volatility is low (ratio < 1), it increases the AF Step to trail tighter.
Ideal Use Case: The best all-rounder. Excellent for any market, especially those with clear shifts between high and low volatility regimes (like indices and crypto).
2. Efficiency Ratio (ER) AF
Core Concept: The PSAR should accelerate aggressively in clean, efficient trends and slow down dramatically in choppy, inefficient markets.
How It Works: It uses Kaufman's Efficiency Ratio (ER), which measures the net directional movement versus the total price movement. A high ER (near 1.0) signifies a pure trend, triggering a high AF multiplier. A low ER (near 0.0) signifies chop, triggering a low AF multiplier.
Ideal Use Case: Markets that alternate between strong trends and sideways chop. It is exceptionally good at surviving ranging periods.
3. Shannon Entropy AF
Core Concept: Uses Information Theory to measure market disorder. The PSAR should be conservative in chaos and aggressive in order.
How It Works: It calculates the Shannon Entropy of recent price changes. High entropy means the market is unpredictable ("chaotic"), causing the AF to slow down. Low entropy means the market is organized and trending, causing the AF to speed up.
Ideal Use Case: Advanced traders looking for a mathematically pure way to distinguish between a tradable trend and random noise.
4. Fractal Dimension (FD) AF
Core Concept: Measures the "jaggedness" or complexity of the price path. A smooth path is a trend; a jagged, space-filling path is chop.
How It Works: It calculates the Fractal Dimension of the price series. An FD near 1.0 is a smooth line (high AF). An FD near 1.5 is a random walk (low AF).
Ideal Use Case: Visually identifying the moment a smooth trend begins to break down into chaotic, unpredictable movement.
5. ADX-Gated AF
Core Concept: Uses the classic ADX indicator to confirm the presence of a trend before allowing the PSAR to accelerate.
How It Works: If the ADX value is above a "Strong" threshold (e.g., 25), the AF accelerates normally. If the ADX is below a "Weak" threshold (e.g., 15), the AF is "frozen" and will not increase, preventing the SAR from tightening up in a non-trending market.
Ideal Use Case: For classic trend-following purists who trust the ADX as their primary regime filter.
6. Kalman AF Estimator
Core Concept: A sophisticated signal processing algorithm that predicts the "true" optimal AF by filtering out price "noise."
How It Works: It treats the PSAR's AF as a state to be estimated. It makes a prediction, then corrects it based on how far the actual price deviates. It's like a GPS constantly refining its position. The "Process Noise" input controls how fast it thinks the AF can change, while "Measurement Noise" controls how much it trusts the price data.
Ideal Use Case: Smooth, high-inertia markets like commodities or major forex pairs. It creates an incredibly smooth and responsive AF.
7. Volume-Momentum AF
Core Concept: A trend's acceleration is only valid if confirmed by both volume and price momentum.
How It Works: The AF will only increase if a new Extreme Point is made on above-average volume AND the Rate of Change (ROC) of the price is aligned with the trend's direction.
Ideal Use Case: Any market with reliable volume data (stocks, futures, crypto). It's excellent for filtering out low-conviction moves.
8. Garman-Klass (GK) AF
Core Concept: Uses a more advanced, statistically efficient measure of volatility (Garman-Klass, which uses OHLC data) to adapt the AF.
How It Works: It modulates the AF based on whether the current GK volatility is higher or lower than its historical average. Unlike the standard Volatility-Scaled algo, it tends to slow down more in high volatility and speed up less in low volatility, making it more conservative.
Ideal Use Case: Traders who want a volatility-adaptive model that is more focused on risk reduction during volatile periods.
9. RSI-Modulated AF
Core Concept: The RSI can identify points of potential trend exhaustion or strong momentum.
How It Works: If a trend is bullish but the RSI enters the "Overbought" zone, the AF slows down, anticipating a pullback. Conversely, if the RSI is in the strong momentum mid-range (40-60), the AF is boosted to trail more aggressively.
Ideal Use Case: Mean-reversion traders or those who want to automatically loosen their trail stop near potential exhaustion points.
10. Bollinger Squeeze AF
Core Concept: A Bollinger Band Squeeze signals a period of volatility compression, often preceding an explosive breakout.
How It Works: When the algorithm detects that the Bollinger Band Width is in a "Squeeze" (below a certain historical percentile), it boosts the AF in anticipation of a fast move, allowing the PSAR to catch the breakout quickly.
Ideal Use Case: Breakout traders. This algorithm primes the PSAR to be maximally responsive right at the moment a breakout is most likely.
11. Keltner Adaptive AF
Core Concept: Keltner Channels provide a robust measure of a trend's "normal" volatility channel.
How It Works: When price is trading strongly outside the Keltner Channel, it's considered a powerful trend, and the AF is boosted. When price falls back inside the channel, it's considered a consolidation or pullback, and the AF is slowed down.
Ideal Use Case: Trend followers who use channel breakouts as their primary confirmation.
12. Choppiness-Gated AF
Core Concept: Uses the Choppiness Index to quantify whether the market is trending or consolidating.
How It Works: If the Choppiness Index is below the "Trend" threshold (e.g., 38.2), the AF is boosted. If it's above the "Range" threshold (e.g., 61.8), the AF is significantly reduced.
Ideal Use Case: A more responsive alternative to the ADX-Gated algorithm for distinguishing between trending and ranging markets.
13. VIDYA-Style AF
Core Concept: Uses a Chande Momentum Oscillator (CMO) to create a variable-speed acceleration factor.
How It Works: The absolute value of the CMO is used to create a dynamic smoothing constant. Strong momentum (high absolute CMO) results in a faster, more responsive AF. Weak momentum results in a slower, smoother AF.
Ideal Use Case: Momentum traders who want their trailing stop's speed directly tied to the momentum of the price itself.
14. Hilbert Cycle AF
Core Concept: Uses Ehlers' Hilbert Transform to extract the dominant cycle period of the market and synchronizes the PSAR with it.
How It Works: It dynamically adjusts the AF based on the detected cycle period (shorter cycles = faster AF) and can also modulate it based on the current phase within that cycle (e.g., accelerate faster near cycle tops/bottoms).
Ideal Use Case: Markets with clear cyclical behavior, like commodities and some forex pairs.
CATEGORY B: EXTREME POINT (EP) ENHANCEMENT
These algorithms make the detection of new highs/lows more intelligent.
15. Volume-Weighted EP
Core Concept: A new high or low is more significant if it occurs on high volume.
How It Works: It can be configured to only accept a new EP if the volume on that bar is above average. It can also "weight" the EP by volume, pushing it further out on high-volume bars.
Ideal Use Case: Filtering out weak, low-conviction price probes in markets with reliable volume.
16. Wavelet Filtered EP
Core Concept: Uses wavelet decomposition (a signal processing technique) to separate the underlying trend from high-frequency noise.
How It Works: It calculates a smoothed, wavelet-filtered version of the price. A new EP is only registered if the actual high/low significantly exceeds this smoothed baseline, effectively ignoring minor noise spikes.
Ideal Use Case: Noisy markets where small, insignificant wicks can cause the AF to accelerate prematurely.
17. ATR-Validated EP
Core Concept: A new EP should represent a meaningful move, not just a one-tick poke.
How It Works: It requires a new high/low to exceed the previous EP by a minimum amount, defined as a multiple of the current ATR. This ensures only volatility-significant advances are counted.
Ideal Use Case: A simple, robust way to filter out "noise" EPs and slow down the AF's acceleration in choppy conditions.
18. Statistical EP Filter
Core Concept: A new EP is only valid if the price change that created it is statistically significant.
How It Works: It calculates the Z-Score of the bar's price change relative to recent history. A new EP is only accepted if its Z-Score exceeds a certain threshold (e.g., 1.5 sigma), meaning it was an unusually strong move.
Ideal Use Case: For quantitative traders who want to ensure their trailing stop only tightens in response to statistically meaningful price action.
CATEGORY C: FLIP LOGIC & CONFIRMATION
These algorithms change the very rules of when and why the PSAR reverses.
19. Dual-PSAR Gate
Core Concept: Uses two PSARs—one fast and one slow—to confirm a reversal.
How It Works: A flip signal for the main PSAR is only considered valid if both the fast (sensitive) PSAR and the slow (structural) PSAR have flipped. This acts as a powerful trend filter.
Ideal Use Case: An excellent method for reducing whipsaws. It forces the PSAR to wait for both short-term and longer-term momentum to align before signaling a reversal.
20. MTF Coherence PSAR
Core Concept: Do not flip against the higher timeframe macro trend.
How It Works: It pulls PSAR data from two higher timeframes. A flip is only allowed if the new direction does not contradict the trend on at least one (or both) of those higher timeframes. It also boosts the AF when all timeframes are aligned.
Ideal Use Case: The ultimate tool for multi-timeframe traders who want to ensure their entries and exits are in sync with the bigger picture.
21. Momentum-Gated Flip
Core Concept: A reversal is only valid if it is supported by a significant surge of momentum.
How It Works: A price cross of the SAR is not enough. The script also requires the Rate of Change (ROC) to exceed a certain threshold for a set number of bars, confirming that there is real force behind the reversal.
Ideal Use Case: Filtering out weak, drifting reversals and only taking signals that are initiated with explosive power.
22. Close-Only PSAR
Core Concept: Wicks are noise; the bar's close is the final decision.
How It Works: This algorithm modifies the flip logic to ignore wicks. A flip only occurs if one or more bars close beyond the SAR line.
Ideal Use Case: One of the most effective and simple ways to reduce false signals from volatile wicks. A fantastic default choice for any trader.
23. Ultimate PSAR Consensus
Core Concept: The highest conviction signal comes from the agreement of multiple, diverse mathematical models.
How It Works: This is the capstone algorithm. It runs a "vote" between a selection of the top-performing algorithms (e.g., Volatility-Scaled, Efficiency Ratio, Dual-PSAR). A flip is only signaled if a majority consensus is reached. It can even weight the votes based on each algorithm's recent performance.
Ideal Use Case: For traders who want the absolute highest level of confirmation and are willing to accept fewer, but more robust, signals.
█ PART II: THE NOISE FILTERING CORE - The Shield
This is a revolutionary feature that allows you to apply a second layer of signal processing directly to the SAR line itself, surgically removing noise before the flip logic is even considered.
FILTER CATEGORIES
Basic Filters (SMA, EMA, WMA, RMA): The classic moving averages. They provide basic smoothing but introduce significant lag. Best used for educational purposes.
Low-Lag Filters (DEMA, TEMA, Hull MA, ZLEMA): A family of filters designed to reduce the lag inherent in basic moving averages. The Hull MA is a standout, offering a superb balance of smoothness and responsiveness.
Adaptive Filters (KAMA, VIDYA, FRAMA): These are "smart" filters. They automatically adjust their smoothing level based on market conditions. They will be very smooth in choppy markets and become highly responsive in trending markets.
Advanced DSP & DAFE Filters: This is the pinnacle of signal processing.
Ehlers Filters (SuperSmoother, 2-Pole, 3-Pole): Based on the work of John Ehlers, these use digital signal processing techniques to remove high-frequency noise with minimal lag.
Gaussian & ALMA: These use a bell-curve weighting, giving the most importance to recent data in a smooth, non-linear fashion.
DAFE Spectral Laguerre: A proprietary, non-linear filter that uses a feedback loop and adapts its "gamma" based on volatility, providing exceptional tracking in all market conditions.
How to Choose a Filter
Start with "None": First, find an algorithm you like with no filtering to understand its raw behavior.
Introduce Low Lag: If you are getting too many whipsaws from noise, apply a short-length Hull MA (e.g., 5-8). This is often the best solution.
Go Adaptive: If your market has very distinct trend/chop regimes, try an Adaptive KAMA .
Maximum Purity: For the smoothest possible output with excellent responsiveness, use the DAFE Spectral Laguerre or Ehlers SuperSmoother .
█ THE VISUAL EXPERIENCE: DATA AS ART
The PSAR Laboratory is not just functional; it is beautiful. The visualization engine is designed to provide you with an intuitive, at-a-glance understanding of the market's state.
Algorithm-Specific Theming: Each of the 23 algorithms comes with its own unique, professionally designed color palette. This not only provides visual variety but allows you to instantly recognize which engine is active.
Dynamic Glow Effects: For many algorithms, the PSAR dots will emit a soft "glow." The brightness and color of this glow are not random; they are tied to a key metric of the active algorithm (e.g., trend strength, volatility, consensus), providing a subtle, visual cue about the health of the trend.
Adaptive Volatility Bands: Certain algorithms will display dynamic bands around the PSAR. These are not standard deviation bands; their width is controlled by the specific logic of the active algorithm, showing you a visual representation of the market's expected range or energy level.
Secondary Reference Lines: For algorithms like the Dual-PSAR or MTF Coherence, a secondary line will be plotted on the chart, giving you a clear visual of the underlying data (e.g., the slow PSAR, the HTF trend) that is driving the decision-making process.
█ THE MASTER DASHBOARD: YOUR MISSION CONTROL
The comprehensive dashboard is your unified command center for analysis and performance tracking.
Engine Status: See the currently selected Algorithm, the active Noise Filter, the Trend direction, and a real-time progress bar of the current Acceleration Factor (AF).
Algorithm-Specific Metrics: This is the most powerful section. It displays the key real-time data from the currently active algorithm. If you're using "Shannon Entropy," you'll see the Entropy score. If you're using "ADX-Gated," you'll see the ADX value. This gives you a direct, quantitative look under the hood.
Performance Readout: When enabled, this section provides a full breakdown of your backtesting results, including Win Rate, Profit Factor, Net P&L, Max Drawdown, and your current trade status.
█ DEVELOPMENT PHILOSOPHY
The PSAR Laboratory was born from a deep respect for Wilder's original work and a relentless desire to push it into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation. This indicator is for the serious trader, the tinkerer, the scientist—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision. It is a tool for forging, not just following.
The PSAR Laboratory is designed to be the ultimate tool for that evolution, allowing you to discover and codify the rules that truly fit you.
█ DISCLAIMER AND BEST PRACTICES
THIS IS A TOOL, NOT A STRATEGY: This indicator provides a sophisticated trailing stop and reversal signal. It must be integrated into a complete trading plan that includes risk management, position sizing, and your own contextual analysis.
TEST, DON'T GUESS: The power of this tool is its adaptability. Use the Performance Dashboard to rigorously test different algorithms and settings on your chosen asset and timeframe. Find what works, and build your strategy around that data.
START SIMPLE: Begin with the "Volatility-Scaled AF" algorithm, as it is a powerful and intuitive all-rounder. Once you are comfortable, begin experimenting with other engines.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The backtesting results are hypothetical and do not account for slippage or psychological factors. Never risk more capital than you are prepared to lose.
"I don't think traders can follow rules for very long unless they reflect their own trading style. Eventually, a breaking point is reached and the trader has to quit or change, or find a new set of rules he can follow. This seems to be part of the process of evolution and growth of a trader."
— Ed Seykota, Market Wizard
Taking you to school. - Dskyz, Trade with Volume. Trade with Density. Trade with DAFE
[Sumit Ingole] 200-EMA SUMIT INGOLE
Indicator Name: 200 EMA Strategy Pro
Overview
The 200-period Exponential Moving Average (EMA) is widely regarded as the "Golden Line" by professional traders and institutional investors. This indicator is a powerful tool designed to identify the long-term market trend and filter out short-term market noise.
By giving more weight to recent price data than a simple moving average, this EMA reacts more fluidly to market shifts while remaining a rock-solid trend confirmation tool.
Key Features
Trend Filter: Instantly distinguish between a Bull market and a Bear market.
Price above 200 EMA: Bullish Bias
Price below 200 EMA: Bearish Bias
Dynamic Support & Resistance: Acts as a psychological floor or ceiling where major institutions often place buy or sell orders.
Institutional Benchmark: Since many hedge funds and banks track this specific level, price reactions near the 200 EMA are often highly significant.
Reduced Lag: Optimized exponential calculation ensures you stay ahead of the curve compared to traditional lagging indicators.
How to Trade with 200 EMA
Trend Confirmation: Only look for "Buy" setups when the price is trading above the 200 EMA to ensure you are trading with the primary trend.
Mean Reversion: When the price stretches too far away from the 200 EMA, it often acts like a magnet, pulling the price back toward it.
The "Death Cross" & "Golden Cross": Use this in conjunction with shorter EMAs (like the 50 EMA) to identify major trend reversals.
Exit Strategy: Can be used as a trailing stop-loss for long-term positional trades.
Best Used On:
Timeframes: Daily (1D), 4-Hour (4H), and Weekly (1W) for maximum accuracy.
Assets: Highly effective for Stocks, Forex (Major pairs), and Crypto (BTC/ETH).
Disclaimer: This tool is for educational and analytical purposes only. Trading involves risk, and it is recommended to use this indicator alongside other technical analysis tools for better confirmation.
moving_averages# MovingAverages Library - PineScript v6
A comprehensive PineScript v6 library containing **50+ Moving Average calculations** for TradingView.
---
## 📦 Installation
```pinescript
import TheTradingSpiderMan/moving_averages/1 as MA
```
---
## 📊 All Available Moving Averages (50+)
### Basic Moving Averages
| Function | Selector Key | Description |
| -------- | ------------ | ------------------------------------------ |
| `sma()` | `SMA` | Simple Moving Average - arithmetic mean |
| `ema()` | `EMA` | Exponential Moving Average |
| `wma()` | `WMA` | Weighted Moving Average |
| `vwma()` | `VWMA` | Volume Weighted Moving Average |
| `rma()` | `RMA` | Relative/Smoothed Moving Average |
| `smma()` | `SMMA` | Smoothed Moving Average (alias for RMA) |
| `swma()` | - | Symmetrically Weighted MA (4-period fixed) |
### Hull Family
| Function | Selector Key | Description |
| -------- | ------------ | ------------------------------- |
| `hma()` | `HMA` | Hull Moving Average |
| `ehma()` | `EHMA` | Exponential Hull Moving Average |
### Double/Triple Smoothed
| Function | Selector Key | Description |
| -------------- | ------------ | --------------------------------- |
| `dema()` | `DEMA` | Double Exponential Moving Average |
| `tema()` | `TEMA` | Triple Exponential Moving Average |
| `tma()` | `TMA` | Triangular Moving Average |
| `t3()` | `T3` | Tillson T3 Moving Average |
| `twma()` | `TWMA` | Triple Weighted Moving Average |
| `swwma()` | `SWWMA` | Smoothed Weighted Moving Average |
| `trixSmooth()` | `TRIXSMOOTH` | Triple EMA Smoothed |
### Zero/Low Lag
| Function | Selector Key | Description |
| --------- | ------------ | ----------------------------------- |
| `zlema()` | `ZLEMA` | Zero Lag Exponential MA |
| `lsma()` | `LSMA` | Least Squares Moving Average |
| `epma()` | `EPMA` | Endpoint Moving Average |
| `ilrs()` | `ILRS` | Integral of Linear Regression Slope |
### Adaptive Moving Averages
| Function | Selector Key | Description |
| ---------- | ------------ | ------------------------------- |
| `kama()` | `KAMA` | Kaufman Adaptive Moving Average |
| `frama()` | `FRAMA` | Fractal Adaptive Moving Average |
| `vidya()` | `VIDYA` | Variable Index Dynamic Average |
| `vma()` | `VMA` | Variable Moving Average |
| `vama()` | `VAMA` | Volume Adjusted Moving Average |
| `rvma()` | `RVMA` | Rolling VMA |
| `apexMA()` | `APEXMA` | Apex Moving Average |
### Ehlers Filters
| Function | Selector Key | Description |
| ----------------- | --------------- | --------------------------------- |
| `superSmoother()` | `SUPERSMOOTHER` | Ehlers Super Smoother |
| `butterworth2()` | `BUTTERWORTH2` | 2-Pole Butterworth Filter |
| `butterworth3()` | `BUTTERWORTH3` | 3-Pole Butterworth Filter |
| `instantTrend()` | `INSTANTTREND` | Ehlers Instantaneous Trendline |
| `edsma()` | `EDSMA` | Deviation Scaled Moving Average |
| `mama()` | `MAMA` | Mesa Adaptive Moving Average |
| `fama()` | `FAMAVAL` | Following Adaptive Moving Average |
### Laguerre Family
| Function | Selector Key | Description |
| -------------------- | ------------------ | ------------------------ |
| `laguerreFilter()` | `LAGUERRE` | Laguerre Filter |
| `adaptiveLaguerre()` | `ADAPTIVELAGUERRE` | Adaptive Laguerre Filter |
### Special Weighted
| Function | Selector Key | Description |
| ---------- | ------------ | -------------------------------- |
| `alma()` | `ALMA` | Arnaud Legoux Moving Average |
| `sinwma()` | `SINWMA` | Sine Weighted Moving Average |
| `gwma()` | `GWMA` | Gaussian Weighted Moving Average |
| `nma()` | `NMA` | Natural Moving Average |
### Jurik/McGinley/Coral
| Function | Selector Key | Description |
| ------------ | ------------ | --------------------- |
| `jma()` | `JMA` | Jurik Moving Average |
| `mcginley()` | `MCGINLEY` | McGinley Dynamic |
| `coral()` | `CORAL` | Coral Trend Indicator |
### Mean Types
| Function | Selector Key | Description |
| -------------- | ------------ | ------------------------- |
| `medianMA()` | `MEDIANMA` | Median Moving Average |
| `gma()` | `GMA` | Geometric Moving Average |
| `harmonicMA()` | `HARMONICMA` | Harmonic Moving Average |
| `trimmedMA()` | `TRIMMEDMA` | Trimmed Moving Average |
| `cma()` | `CMA` | Cumulative Moving Average |
### Volume-Based
| Function | Selector Key | Description |
| --------- | ------------ | -------------------------- |
| `evwma()` | `EVWMA` | Elastic Volume Weighted MA |
### Other Specialized
| Function | Selector Key | Description |
| ----------------- | --------------- | --------------------------- |
| `hwma()` | `HWMA` | Holt-Winters Moving Average |
| `gdema()` | `GDEMA` | Generalized DEMA |
| `rema()` | `REMA` | Regularized EMA |
| `modularFilter()` | `MODULARFILTER` | Modular Filter |
| `rmt()` | `RMT` | Recursive Moving Trendline |
| `qrma()` | `QRMA` | Quadratic Regression MA |
| `wilderSmooth()` | `WILDERSMOOTH` | Welles Wilder Smoothing |
| `leoMA()` | `LEOMA` | Leo Moving Average |
| `ahrensMA()` | `AHRENSMA` | Ahrens Moving Average |
| `runningMA()` | `RUNNINGMA` | Running Moving Average |
| `ppoMA()` | `PPOMA` | PPO-based Moving Average |
| `fisherMA()` | `FISHERMA` | Fisher Transform MA |
---
## 🎯 Helper Functions
| Function | Description |
| ---------------- | ------------------------------------------------------------- |
| `wcp()` | Weighted Close Price: (H+L+2\*C)/4 |
| `typicalPrice()` | Typical Price: (H+L+C)/3 |
| `medianPrice()` | Median Price: (H+L)/2 |
| `selector()` | **Master selector** - choose any MA by string name |
| `getAllTypes()` | Returns all supported MA type names as comma-separated string |
---
## 🔧 Usage Examples
### Basic Usage
```pinescript
//@version=6
indicator("MA Example")
import quantablex/moving_averages/1 as MA
// Simple calls
plot(MA.sma(close, 20), "SMA 20", color.blue)
plot(MA.ema(close, 20), "EMA 20", color.red)
plot(MA.hma(close, 20), "HMA 20", color.green)
```
### Using the Selector Function (50+ MA Types)
```pinescript
//@version=6
indicator("MA Selector")
import quantablex/moving_averages/1 as MA
// Full list of all supported types:
// SMA,EMA,WMA,VWMA,RMA,SMMA,HMA,EHMA,DEMA,TEMA,TMA,T3,TWMA,SWWMA,TRIXSMOOTH,
// ZLEMA,LSMA,EPMA,ILRS,KAMA,FRAMA,VIDYA,VMA,VAMA,RVMA,APEXMA,SUPERSMOOTHER,
// BUTTERWORTH2,BUTTERWORTH3,INSTANTTREND,EDSMA,LAGUERRE,ADAPTIVELAGUERRE,
// ALMA,SINWMA,GWMA,NMA,JMA,MCGINLEY,CORAL,MEDIANMA,GMA,HARMONICMA,TRIMMEDMA,
// EVWMA,HWMA,GDEMA,REMA,MODULARFILTER,RMT,QRMA,WILDERSMOOTH,LEOMA,AHRENSMA,
// RUNNINGMA,PPOMA,MAMA,FAMAVAL,FISHERMA,CMA
maType = input.string("EMA", "MA Type", options= )
length = input.int(20, "Length")
plot(MA.selector(close, length, maType), "Selected MA", color.orange)
```
### Advanced Moving Averages
```pinescript
//@version=6
indicator("Advanced MAs")
import quantablex/moving_averages/1 as MA
// ALMA with custom offset and sigma
plot(MA.alma(close, 20, 0.85, 6), "ALMA", color.purple)
// KAMA with custom fast/slow periods
plot(MA.kama(close, 10, 2, 30), "KAMA", color.teal)
// T3 with custom volume factor
plot(MA.t3(close, 20, 0.7), "T3", color.yellow)
// Laguerre Filter with custom gamma
plot(MA.laguerreFilter(close, 0.8), "Laguerre", color.lime)
```
---
## 📈 MA Selection Guide
| Use Case | Recommended MAs |
| ---------------------- | ------------------------------------------- |
| **Trend Following** | EMA, DEMA, TEMA, HMA, CORAL |
| **Low Lag Required** | ZLEMA, HMA, EHMA, JMA, LSMA |
| **Volatile Markets** | KAMA, VIDYA, FRAMA, VMA, ADAPTIVELAGUERRE |
| **Smooth Signals** | T3, LAGUERRE, SUPERSMOOTHER, BUTTERWORTH2/3 |
| **Support/Resistance** | SMA, WMA, TMA, MEDIANMA |
| **Scalping** | MCGINLEY, ZLEMA, HMA, INSTANTTREND |
| **Noise Reduction** | MAMA, EDSMA, GWMA, TRIMMEDMA |
| **Volume-Based** | VWMA, EVWMA, VAMA |
---
## ⚙️ Parameters Reference
### Common Parameters
- `src` - Source series (close, open, hl2, hlc3, etc.)
- `len` - Period length (integer)
### Special Parameters
- `alma()`: `offset` (0-1), `sigma` (curve shape)
- `kama()`: `fastLen`, `slowLen`
- `t3()`: `vFactor` (volume factor)
- `jma()`: `phase` (-100 to 100)
- `laguerreFilter()`: `gamma` (0-1 damping)
- `rema()`: `lambda` (regularization)
- `modularFilter()`: `beta` (sensitivity)
- `gdema()`: `mult` (multiplier, 2 = standard DEMA)
- `trimmedMA()`: `trimPct` (0-0.5, percentage to trim)
- `mama()/fama()`: `fastLimit`, `slowLimit`
- `adaptiveLaguerre()`: Uses `len` for adaptation period
---
## 📝 Notes
- All 50+ functions are exported for use in any PineScript v6 indicator/strategy
- The `selector()` function supports **all MA types** via string key
- Use `getAllTypes()` to get a comma-separated list of all supported MA names
- Some MAs (CMA, INSTANTTREND, LAGUERRE, MAMA) don't use `len` parameter
- Use `nz()` wrapper if handling potential NA values in your calculations
---
**Author:** thetradingspiderman
**Version:** 1.0
**PineScript Version:** 6
**Total MA Types:** 50+
XAUUSD Visible Gap 1R Strategy + Equity Curve“XAUUSD 1-hour strategy that trades only visible gaps between the 4 PM and 6 PM NY candles. Entries occur when the 6 PM open is outside the previous 4 PM candle body in the direction of the first candle. Uses swing high/low stops and targets 1R profit. Includes cumulative R plot and trade statistics.”
The Fantastic 4 - Momentum Rotation StrategyOverview
The Fantastic 4 is a tactical momentum rotation indicator. It rotates capital monthly across four carefully selected assets based on their 75-day Rate of Change (ROC), allocating only to assets with positive momentum and proportionally weighting them by their momentum strength.
This indicator tracks the strategy's historical performance, displays current allocation recommendations, and sends monthly rebalance alerts so you can easily manage your portfolio. Simply set your capital amount and the indicator shows exactly how much to invest in each asset.
Why These Four Assets?
The selection of 20-year Bonds, Gold, Russell 2000, and Emerging Markets is based on their specific volatility and decorrelation characteristics, which allow the strategy to react quickly to market shifts while providing protection during downturns.
Russell 2000 (Small Caps)
Chosen over the S&P 500 because it is more "lively" and active (Nowadays you could use also the Nasdaq). Its trends are steeper and more vertical, making it easier for a momentum indicator to catch clear trends. While the S&P 500 has more inertia, the Russell 2000 develops faster, allowing the strategy to capture gains in shorter periods.
Emerging Markets
Included because they can act like a "rocket," offering explosive growth potential while maintaining high decorrelation from developed equity markets. When emerging markets trend, they trend hard.
20-Year Bonds
Selected because they are the most decorrelated asset from equities. When a stock market crash occurs, capital typically flows into fixed income, and long-term bonds (20-year) notice this influx the most, making their price reaction more significant and easier to trade. This is the strategy's primary "safe haven."
Gold
Along with bonds, gold serves as a defensive asset providing a "shield" for the portfolio when general market conditions deteriorate. It offers additional decorrelation and crisis protection.
How the Strategy Works
The 75-Day Momentum Engine
The strategy uses a 75-day momentum lookback (roughly 3.5 months), which is considered very "agile" compared to other models like Global Equity Momentum (GEM) that use 200-day periods. This shorter window allows the strategy to:
React quickly to changes in trend
Catch upward movements in volatile assets early
Exit quickly when trends break
Monthly Rebalancing Process
At the end of each month:
Step 1: Calculate 75-day ROC for each asset
Step 2: Filter out assets with negative momentum (they receive 0% allocation)
Step 3: Distribute capital proportionally based on momentum strength
Step 4: Apply 5% minimum threshold (smaller allocations become zero)
Step 5: Apply 80% maximum cap (no single asset exceeds 80%, remainder stays in cash)
The 80% Ceiling Rule
There is an 80% investment ceiling for any single asset to prevent over-exposure. If only one asset (like bonds) has positive momentum, 80% goes to that asset and 20% remains in cash/liquidity.
Behavior in Bearish Markets
When markets turn bearish, the strategy protects capital through several mechanisms:
Automatic Risk-Off
Because the strategy only invests in assets with positive momentum, it automatically moves away from crashing equities. If an asset's trend becomes negative, the strategy stays "on the sidelines" for that asset.
The Bond Haven
During prolonged bearish periods or sudden crashes (like COVID-19), the strategy typically shifts into 20-year bonds. During the COVID-19 crash in March 2020, while global markets were collapsing, strategies like this reportedly yielded positive returns by being positioned in bonds.
Full Liquidity Option
If no assets show positive momentum, the strategy moves to 100% cash. This is rare given the decorrelation between the four assets—when equities crash, bonds and gold typically rise.
What This Indicator Does
This is a tracking and alerting tool that:
Calculates the optimal allocation based on current momentum
Shows historical monthly performance of the strategy
Simulates portfolio equity growth from your specified starting capital
Displays exact dollar amounts to invest in each asset
Sends monthly rebalance alerts with complete instructions
Detects missing data to prevent false signals
Features
Dynamic allocation table showing weights, dollar amounts, and ROC values
Monthly returns history with color-coded performance
Data availability detection with visual status indicators
Configurable alerts for rebalancing, go-to-cash, and missing data
Simulated equity curve from initial capital
Settings Guide
Assets
Configure your four ETFs. The default European ETFs are:
Asset 1 - XETR:IS04: iShares 20+ Year Treasury Bond (Bonds)
Asset 2 - XETR:GZUR: Gold ETC
Asset 3 - XETR:XRS2: Xtrackers Russell 2000 (Small Caps)
Asset 4 - XETR:XMME: Xtrackers Emerging Markets (EM)
For US markets, consider: TLT (20-year bonds), GLD (Gold), IWM (Russell 2000), EEM (Emerging Markets)
Strategy Settings
ROC Period - Momentum lookback in daily bars. Default: 75 days (~3.5 months)
Max Allocation % - Maximum weight for any single asset. Default: 80%
Min Allocation % - Threshold below which allocation becomes zero. Default: 5%
Capital
Initial Capital - Your portfolio value. The indicator calculates exact amounts for each asset based on this. Default: $20,000
Display
Table Positions - Position the allocation and history tables on screen
Months of History - How many past months to display (3-24)
Alerts
Monthly Rebalance Alert - Sends complete allocation details at month end
Go-to-Cash Alert - Alerts when all assets have negative momentum
Missing Data Alert - Warns when asset data is unavailable
How to Use
Initial Setup
Add indicator to any chart and switch to MONTHLY timeframe
Configure your four ETF tickers
Set your portfolio capital amount
Position the tables where you prefer
Setting Up Alerts
Click Alert button or press Alt+A
Set Condition to "Fanta4"
Select "Any alert() function call"
Choose notification method (Email, Push, Webhook, etc.)
Set expiration to "Open-ended"
Monthly Workflow
Receive rebalance alert at the start of each month
Alert shows exact percentages AND dollar amounts for each asset
Adjust your portfolio accordingly
No action needed during the month
Reading the Tables
Green = positive returns/momentum
Red = negative returns/momentum
Orange "N/A" = missing data
Alloc column shows weight distribution (e.g., "45/35/20/—")
Alert Message Example
Monthly alerts include:
Target month for the new allocation
Current portfolio value
Each asset's percentage AND dollar amount
Each asset's momentum (ROC) value
Cash allocation if applicable
Total return since inception
Historical Context
This strategy combines elements of:
Dual Momentum (Gary Antonacci) - Relative and absolute momentum
Global Equity Momentum (GEM) - But with shorter 75-day vs 200-day lookback
Risk parity concepts - Decorrelated asset selection
The key innovation is the specific asset selection optimized for momentum trading and the agile 75-day lookback period that allows faster reactions to trend changes.
Data Requirements
The strategy activates only when all four assets have valid price data (minimum 75 days of history). The data status row shows checkmarks for available data. Note: Some ETFs have limited history (e.g., XMME data starts June 2017).
Limitations
This is a tracking indicator, not an automated trading system
Past performance is hypothetical and does not guarantee future results
Requires all four assets to have valid data; partial allocation not supported
Monthly rebalancing may miss shorter-term momentum shifts
Transaction costs, slippage, and taxes are not included in simulation
ETF availability and liquidity vary by region
The 75-day momentum may whipsaw in choppy, trendless markets
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice.
Version History
v1.0 - Initial release with momentum rotation, allocation tables, data validation, and monthly alerts
Dealer Control Index (DCI) Oscillator BreakoutsOverview
The Dealer Control Index (DCI) is a structural oscillator designed to measure market stability based on the relationship between price and key institutional "hedging levels" (Gamma Flip). Unlike momentum-based oscillators like RSI, the DCI focuses on Dealer Gamma Exposure—the point where market makers shift from supporting price (Long Gamma) to accelerating moves (Short Gamma).
How to Use
This indicator requires a Manual Anchor (Flip Level) to function with high precision. Users should identify the current institutional Gamma Flip level for their specific ticker and input it into the script settings.
Positive Score (+25 to +100): Price is above the Flip Level. Dealers are in a "Long Gamma" position, typically resulting in lower volatility and "dip-buying" behavior.
Neutral Zone (-75 to +25): The "Transition Zone." Price is fluctuating near the hedge-rebalancing point. Expect "choppy" price action.
The Gamma Trap (-75 to -100): Price has snapped significantly below the Flip Level. Dealers are now "Short Gamma" and may be forced to sell into further price drops to hedge their books, potentially creating a "Waterfall" effect.
Key Features
Volatility Normalized: Uses ATR-based normalization to ensure the -100 to +100 scale is consistent across different asset classes (e.g., comparing SPY to NVDA).
Sigmoid Smoothing: Employs a sigmoid curve to filter out "market noise" and provide a clear visual of when the regime shift is actually occurring.
Visual Regimes: Color-coded zones (Green/Red) provide instant feedback on the current dealer hedging bias.
Smart Scalper Pro Template + VWAP
📌 Author
Garry Evans
Independent system developer focused on:
Risk-first automation
Market structure & liquidity behavior
Discipline, consistency, and capital preservation
“The edge isn’t the market — it’s the man who survives it.”
⚙️ Risk Management & Position Sizing
The script is built around capital protection, not signal frequency.
Risk logic includes:
Fixed or dynamic risk per trade
Market-adaptive position sizing
Session-based trade limits
Daily trade caps and auto-lockout protection
Volatility-aware sizing (futures & crypto)
⚠️ Profit is pursued only after risk is controlled.
📊 Track Record
Backtested across multiple market environments
Forward-tested and actively used by the author
Real-account trades are logged where platform rules allow
Results vary by market, timeframe, and user-defined risk settings.
🌍 Supported Markets
Designed to work across all liquid markets, including:
Stocks
Crypto (spot & futures)
Options (signal-based framework)
Futures (indices, metals, crypto futures)
The system adapts to volatility and structure — it is not market-specific.
⚖️ Leverage
Leverage is not required
If used, leverage is fully user-controlled
Risk logic scales exposure conservatively
No martingale.
No revenge sizing.
No over-exposure logic.
🧪 Backtesting
✔ Yes
Strategy logic has been backtested
Filters reduce chop, noise, and forced trades
Focus on drawdown control over curve-fitting
🛠 Support
✔ Yes
Direct author support
Ongoing improvements and updates
Feature refinement based on real usage and feedback
👥 Community
✔ Yes
Private user access
High-quality feedback environment
No public signal spam or hype-driven chat rooms
⏳ Trial Period
✔ Yes
Limited trial access available
Designed for evaluation only
Trial users do not receive full feature access
🚫 Who This Script Is NOT For
This system is not for:
Traders looking for guaranteed profits
Users expecting copy-paste “signal calls”
Over-leveraged gamblers
Those unwilling to follow risk rules
Anyone seeking overnight results
This is a discipline and automation tool, not a shortcut.
🧠 Final Positioning
This is not a signal service.
This is a risk-controlled execution framework designed to:
Enforce discipline
Reduce emotional trading
Protect capital during bad market conditions
Scale responsibly during favorable ones
VWMA Cross Buy SignalCore Components & Logic
1. The Entry Engine (VWMA + Filters)
The strategy triggers a long signal when a Volume Weighted Moving Average (VWMA) crossover occurs.
Unlike a standard Simple Moving Average, the VWMA gives more weight to bars with higher volume. This ensures the indicator responds faster to "Smart Money" moves and slower to low-volume noise.
It uses a secondary Trend Filter (defaulting to the 200 EMA). By only buying when the price is above this line, the indicator forces you to stay on the right side of the primary market trend.
It requires volume to be higher than its recent average (e.g., 1.1× or 10% higher). This prevents entries on weak, low-conviction price moves.
2. The Dynamic Exit System
You have two distinct ways to manage your risk and targets, toggleable in the settings:
ATR Based (Volatility Adjusted): It calculates the Average True Range (ATR) to determine how volatile the stock is. By setting your Stop Loss at 2.0×ATR, you avoid getting "shaken out" by normal daily price fluctuations. The Take Profit is set at 4.4×ATR to capture large trend extensions.
Fixed % (Static): A more rigid approach where you set a hard percentage target (e.g., 10% gain / 5% loss).
3. The Performance Analytics Table
The grey minimalist table in the bottom-right corner uses cumulative percentage-based math to show:
Realized RRR: The actual Reward-to-Risk ratio based on your closed trades.
Break-Even Win Rate: The minimum win rate you need to stay profitable with your current RRR. It uses the formula:
BE WR=1+RRR1
Current Win Rate: Highlighted in Green if you are beating the Break-Even rate, or Red if the strategy is currently losing money on that specific stock.
Max Drawdown %: The most important metric for risk. It shows the largest peak-to-trough decline in your equity curve, letting you know how much losing streak can hurt your equity.
Strategic Use Case
This indicator is optimized for Stock Screening. When you flip through your watchlist, the table updates instantly.
If you see a stock with a high Win Rate and a Max Drawdown under 10%, you have found a ticker where the VWMA crossover logic is highly compatible with that stock's specific volatility. If the Win Rate cell is Red, you know the strategy is "un-tuned" for that asset and needs adjustment.
Smart Fixed Volume Profile [MarkitTick]💡 This comprehensive analysis suite integrates Auction Market Theory, structural gap analysis, and statistical liquidity strain modeling into a single, cohesive toolkit. Designed for traders who require a granular view of institutional order flow, this indicator overlays a Fixed Range Volume Profile with intelligent price gap classification and a volatility-adjusted exhaustion detector. By combining these three distinct analytical dimensions, it allows users to identify value consensus, structural breakouts, and potential market turns driven by liquidity shortages.
✨ Originality and Utility
While standard Volume Profiles display where trading occurred, this script advances the concept by contextually analyzing *how* price arrived at those levels. It solves the problem of isolated analysis by fusing three disparate methodologies:
Contextual Integration: It does not merely show support and resistance; it qualifies moves using "Smart Gaps" (classifying gaps based on market structure) and "Liquidity Strain" (identifying unsustainable price velocity).
Institutional Footprint: The inclusion of an "Unusual Volume" highlighter within the profile bars helps traders spot hidden institutional accumulation or distribution blocks that standard profiles miss.
Hybrid Logic: By combining a fixed-time profile (anchored to specific dates) with dynamic, developing gap analysis, it provides both a static roadmap of the past and a dynamic interpretation of current price action.
🔬 Methodology and Concepts
• Fixed Volume Profile Engine
The core of the indicator constructs a volume distribution histogram over a user-defined time window. It utilizes a custom aggregation engine that:
Fetches higher-timeframe volume and price data to ensure accuracy.
Segments the price range into specific "bins" or rows.
Allocates volume to these bins based on price action within the bar, separating Buying Volume (Up bars) from Selling Volume (Down bars).
Calculates the Point of Control (POC) —the price level with the highest traded volume—and the Value Area , which contains 70% (customizable) of the total volume centered around the POC.
• Smart Gap Logic
The script systematically identifies price gaps and classifies them based on their location relative to market pivots (Highs/Lows):
Breakaway Gaps: Occur when price gaps beyond a significant structural pivot (Lookback High/Low), signaling a potential trend initiation.
Runaway Gaps: Occur within an existing trend without breaking structure, indicating trend continuation.
Exhaustion Gaps: Identified when a gap occurs late in a mature trend (measured by bar count since the last pivot) accompanied by a volume spike, suggesting the trend is overextended.
• Liquidity Strain Detector
This module utilizes a statistical approach to measure market stress. It calculates "Illiquidity" by analyzing the ratio of True Range to Volume (Price Impact).
It applies a Logarithmic transformation to normalize the data.
It calculates a Z-Score (Standard Deviation from the mean) of this impact.
If the Z-Score exceeds a threshold (e.g., 2.0 Sigma) while the trend opposes the price move, it triggers an exhaustion signal, indicating that price is moving too easily on too little volume (thin liquidity).
🎨 Visual Guide
• Volume Profile Elements
Histogram Bars: Horizontal bars representing volume at price. Cyan indicates bullish volume; Red indicates bearish volume.
Unusual Volume Highlight: Bars with volume exceeding the average by a set factor (default 2x) are highlighted with brighter, distinct overlays to denote institutional interest.
POC Line: A solid Yellow line marking the price level with the highest volume.
VAH / VAL Lines: Dashed Blue lines marking the Value Area High and Value Area Low.
Background Box: A grey shaded area encapsulating the entire time and price range of the profile.
• Smart Gap Boxes
Blue Box (Breakaway): Marks the start of a new structural move.
Orange Box (Runaway): Marks continuation gaps in the middle of a trend.
Red Box (Exhaustion): Marks potential trend termination points.
Dotted Lines: Extend from the center of gap boxes to serve as future support/resistance levels. These boxes are automatically deleted if price "fills" or violates the gap level.
Note: This tool incorporates core components from [ Smart Gap Concepts ], optimized for this specific strategy.
• Liquidity Signals
Green Label (SE): "Seller Exhaustion" – Appears below bars in a downtrend when selling pressure is statistically overextended.
Red Label (BE): "Buyer Exhaustion" – Appears above bars in an uptrend when buying pressure is statistically overextended.
Note: This tool incorporates core components from [ Liquidity Strain Detector ], optimized for this specific strategy.
📖 How to Use
• Interactive Range Selection: This indicator features a flexible, interactive input system. Upon adding the script to your chart, execution is paused until the analysis range is defined. You will be prompted to click on the chart twice: first to establish the Start Date and second to establish the End Date. Once these anchor points are confirmed, the indicator will automatically load the data and generate the profile for the selected specific period.
● Strategies for Optimal Anchoring
the optimal starting and ending points for high-probability setups:
Swing Highs and Lows (Trend Analysis):
Anchor the Start Date at a major structural swing high or low and the End Date at the current price using the Extend to Present feature. This identifies the "Fair Value" for the entire price move .
Consolidation/Range Anchoring:
Set the Start Date at the first bar of a sideways range and the End Date at the breakout candle. This reveals the high-node volume clusters that will act as future support or resistance.
Session-Based Anchoring (Intraday):
Align the Start Date with the session open (e.g., London or New York open) to track institutional flow for that specific day .
Event-Driven Anchoring:
Place the Start Date on a significant news event or a Breakaway Gap identified by the script's Gap Engine. This helps determine if the new volume supports the direction of the gap.
Correction Cycles:
During a pullback, anchor the Start Date at the start of the correction to find the Value Area Low (VAL), which often serves as a tactical entry point for a trend continuation.
• Identifying Value:
Use the Value Area to gauge market consensus. Acceptance of price within the VA indicates balance. A breakout above VAH or below VAL suggests the market is searching for new value. The POC often acts as a magnet for price correction.
• Trading Breakouts:
Watch for Breakaway Gaps (Blue) that align with a move out of the Volume Profile's Value Area. This confluence increases the probability of a sustained trend.
• Spotting Reversals:
Combine Exhaustion Gaps (Red) with Liquidity Strain Signals (SE/BE) . If price gaps up into a low-volume node on the profile and prints a "Buyer Exhaustion" signal, it suggests the move is unsupported by liquidity and liable to reverse.
• Support and Resistance:
The extended dotted lines from the Smart Gap boxes act as dynamic support/resistance. A retest of a "Runaway Gap" is often a viable entry point for trend continuation.
⚙️ Inputs and Settings
• Global Profile:
Start/End Date: Define the exact window for the volume profile calculation.
Extend to Present: If checked, the profile updates with live data beyond the end date.
• Profile Settings:
Number of Rows: Determines the vertical resolution (granularity) of the histogram.
Value Area %: Default is 70%, representing one standard deviation of volume distribution.
Placement: Position the profile on the Left or Right of the defined range.
• Liquidity & Gaps:
Unusual Threshold: Multiplier of average volume to highlight institutional bars (default 2.0x).
Structure Lookback: Adjusts the sensitivity of pivot detection for gap classification.
Stress Threshold (Sigma): The Z-Score limit for triggering Liquidity Strain signals (default 2.0).
🔍 Deconstruction of the Underlying Scientific and Academic Framework
• Auction Market Theory (AMT):
The script is grounded in AMT, which posits that the market's primary function is to facilitate trade. The Volume Profile visualizes this by displaying a bell curve of price distribution. The Value Area (typically 70%) corresponds to the First Standard Deviation in a normal Gaussian distribution, representing the area of "Fair Value" where buyers and sellers agree.
• Market Microstructure & Kyle’s Lambda:
The Liquidity Strain module draws conceptually from Kyle’s Lambda, a metric in market microstructure that measures market depth and price impact (Illiquidity). By calculating the ratio of price change (True Range) to Volume, the script approximates the "cost" of moving the market.
• Statistical Z-Score Normalization:
To make the liquidity data actionable, the script applies Z-Score normalization: Z = (X - μ) / σ . This converts raw illiquidity values into standard deviations from the mean. A Z-Score above +2.0 signifies a statistically significant anomaly—an outlier event where price moved excessively relative to the volume traded, often preceding a mean-reversion event.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
HMA Pro Flow [Mladen] + SignalsThis indicator is an enhanced version of the classic Hull Moving Average (HMA), based on the logic developed by Mladen. It improves upon the standard HMA by allowing users to adjust the "speed" of the curve using a custom Divisor, and it integrates a secondary Trend Filter to generate high-probability entries and distinct exit signals.
The script is designed to help traders identify the trend direction while filtering out noise during choppy markets.
How It Works
1. The "Mladen" Calculation
The standard Hull Moving Average uses a fixed formula involving a divisor of 2 (n/2). This script exposes that divisor as a variable input.
2. Dual-HMA System
This indicator runs two separate HMA calculations simultaneously:
Entry HMA (Fast): Reacts quickly to price changes to generate immediate signals.
Trend Filter (Slow): A longer-term HMA used to determine the overall market bias.
Signal Logic
The indicator generates three types of signals based on the alignment of the Fast Entry HMA and the Slow Trend Filter.
🟢 BUY Signal (Green Label)
Condition: The Fast HMA turns green (rising) AND the Trend Filter is also green (rising).
Meaning: Momentum and Trend are aligned. Safe to enter Long.
🔴 SELL Signal (Red Label)
Condition: The Fast HMA turns red (falling) AND the Trend Filter is also red (falling).
Meaning: Momentum and Trend are aligned. Safe to enter Short.
❌ STOP / CLOSE Signal (Orange 'X')
Condition: The Fast HMA changes color, but it conflicts with the Trend Filter.
Example (Long): You are in a Buy trade. The Fast HMA turns Red, but the Trend Filter is still Green.
Meaning: This is likely a pullback, not a reversal. The indicator suggests closing the current position (Stop) but does not issue a signal to reverse into a new position. This prevents getting trapped in counter-trend trades.
Settings
Entry HMA Settings
Entry Period: Length of the fast signal line (Default: 14).
Entry Divisor: Controls smoothness. Lower values (e.g., 0.1) result in a very smooth line; higher values result in sharper turns.
Trend Filter Settings
Use Trend Filter: If unchecked, the indicator acts like a standard HMA (Buying/Selling on every color change).
Filter Period: Length of the slow trend line (Default: 300).
Show Filter: Toggles the visibility of the thick trend line on the chart.
Visuals
Toggle visibility for Buy, Sell, and Stop signals individually to keep your chart clean.
Credits
Original HMA logic by Alan Hull.
Variable divisor concept adapted from Mladen's work on MT4/MT5.
Custom pine scripting for trend filtering and signal logic - Vdubus
Spearman Correlation🔗 Spearman Correlation – Ranked Relationship Tracker
Overview:
This indicator calculates and plots the Spearman Rank Correlation Coefficient between the current chart’s asset and a custom comparison ticker (the example shown is BTC vs the OTHERS market cap for crypto). Unlike Pearson correlation, which measures linear relationships, Spearman correlation captures monotonic (ranked) relationships—making it better suited for analysing assets that move in sync but not necessarily in a linear fashion.
🧠 What It Does:
Computes ranked correlation between two assets over a user-defined lookback period
Smooths the correlation curve for better readability
Visually shades the background by correlation strength and direction:
🟩 Strong Positive (+0.5 to +1)
🟨 Weak Positive (+0.1 to +0.5)
⬜ No Correlation (–0.1 to +0.1)
🟧 Weak Negative (–0.5 to –0.1)
🟥 Strong Negative (–1 to –0.5)
⚙️ User Inputs:
Lookback Period: Number of bars used to calculate correlation
Comparison Ticker: Choose any asset to compare against
Shading Toggles: Customize which correlation zones are highlighted
📈 Use Cases:
Identify evolving relationships between assets (e.g., BTC vs DXY, ETH vs SPX)
Spot when assets become inversely correlated or lose correlation entirely
Track regime shifts where traditional relationships break down or re-align
Use alongside trend or momentum strategies to add a cross-asset confirmation layer
🔍 Interpreting the Correlation:
+1 → Perfect positive (ranks match exactly)
+0.5 to +1 → Strong positive relationship
+0.1 to +0.5 → Weak but positive relationship
–0.1 to +0.1 → Essentially uncorrelated
–0.5 to –0.1 → Weak negative correlation
–1 to –0.5 → Strong inverse relationship
–1 → Perfect negative (rankings are completely opposite)
🧪 Technical Notes:
Calculation uses ranked returns to better reflect monotonic relationships
Smoothed with a simple moving average (SMA) for stability
Arrays are managed internally to maintain performance and adaptability
This script is ideal for traders seeking deeper insight into cross-asset dynamics, portfolio hedging, or timing divergence-based strategies.
ORB Breakout Strategy with VWAP and Volume FiltersOverview
This strategy implements the classic Opening Range Breakout (ORB) methodology, a well-documented approach in trading literature that has been used by institutional and retail traders for decades. The strategy identifies the high and low of the first 15 minutes of the trading session, then trades breakouts with defined risk management.
This implementation includes multiple customizable filters (VWAP, Volume, Candle Strength) that traders can enable, disable, and tune to find configurations that work for their specific markets and trading style.
How It Works
Opening Range Calculation
The strategy captures the high and low of the first N bars after the session open (default: 3 bars on a 5-minute chart = 15 minutes). These levels become the breakout triggers for the session.
Entry Logic
Long Entry: When a bar closes above the ORB High and all enabled filters pass
Short Entry: When a bar closes below the ORB Low and all enabled filters pass
Exit Logic
Take Profit: Configurable multiple of the ORB range (default: 1x = full range beyond breakout level)
Stop Loss: Opposite side of the ORB range
Breakeven: Optional stop adjustment to entry price when trade reaches configurable profit threshold
Session Close: All positions automatically closed at end of trading session
Configurable Filters
All filters can be independently enabled or disabled:
1. VWAP Filter
Requires price above/below session-anchored VWAP
Requires VWAP slope confirmation (configurable lookback and minimum slope)
Purpose: Align trades with intraday trend direction
2. Volume Filter
Requires minimum volume on the breakout bar
Purpose: Confirm institutional participation in the breakout
3. Candle Strength Filter
Requires close in upper/lower portion of the bar range
Purpose: Filter out weak breakouts with poor conviction
Strategy Properties
Initial Capital - $50.000USD
Position Size - 1 contract (fixed)
Commission - $4.00 per contract
Slippage - 2 ticks
Margin - 1%
Pyramiding - Disabled
Backtest Results (NQ)
Recent Performance (Jan 2025 - Jan 2026)
Total Trades - 243
Win Rate - 39.09%
Profit Factor - 1.03
Net P&L - $3,581 (+7.16%)
Max Drawdown - $25,447 (39.96%)
Long-Term Performance (2010 - 2026)
Total Trades - 1699
Win Rate - 37.61%
Profit Factor - 0.756
Net P&L - ($49,632) (-99.26%)
Max Drawdown - $50,262 (99.27%)
Important: Long-term results show negative expectancy with default settings. This strategy is published as a research framework, not a ready-to-trade system. Users are encouraged to experiment with different configurations to find their edge.
Settings Guide
Main Settings
ORB Bars: Number of bars for opening range (3 = 15 min on 5-min chart)
Trading Session: Time window for trading (e.g., 0930-1200 for morning only)
Timezone: Your market's timezone
Take Profit: Multiple of ORB range for target
Breakeven Trigger: Distance to move stop to entry
Max Trades Per Day: Daily trade limit
VWAP Filter
Use VWAP Filter: Enable/disable
VWAP Slope Lookback: Bars to measure VWAP direction
Min VWAP Slope: Minimum slope threshold
Volume Filter
Use Volume Filter: Enable/disable
Min Breakout
Volume: Minimum contracts required
Candle Strength Filter
Use Candle Strength Filter: Enable/disable
Min Candle Strength: Required close position (0.7 = top/bottom 30%)
Research Suggestions
This strategy provides a foundation for exploring ORB-based approaches. Consider testing:
Different ORB periods: 5, 10, 15, or 30 minutes
Session variations: Morning only (0930-1200), afternoon, or full day
Direction bias: Long-only or short-only based on daily trend
Filter combinations: Different mixes of VWAP, volume, and candle filters
Take profit ratios: 0.5x, 1x, 1.5x, or 2x ORB range
Market regimes: Performance may vary in trending vs ranging markets
Different instruments: Test on ES, NQ, MNQ, or other futures
Visual Elements
Orange Background: ORB forming period
Green Background: Active trading session
Green Line: ORB High level
Red Line: ORB Low level
VWAP Line: Green = upslope, Red = downslope, Gray = flat
White Line: Trade entry price
Lime Line: Take profit level
Red Line: Stop loss level
Orange Line: Breakeven trigger level
Blue Background: Breakeven activated
Triangles: Entry signals (only appear when trade executes)
Limitations
Negative long-term expectancy: Default settings do not produce profitable results over extended periods
Parameter sensitivity: Results highly dependent on filter settings and market conditions
Market regime dependent: May perform differently in trending vs choppy markets
Commission impact: Frequent trading accumulates significant transaction costs
Curve fitting risk: Optimized settings may not persist in future markets
Disclaimer
This strategy is provided for educational and research purposes only. It does not constitute financial advice.
Past performance does not guarantee future results
Backtested results may not reflect actual trading conditions
The long-term backtest shows significant negative returns
Always paper trade before risking real capital
Never risk more than you can afford to lose
Conduct your own research and due diligence
This is a research framework designed for traders to explore and customize, not a plug-and-play trading system.
Strategy MTF ScannerDescription:
Stop guessing which timeframe is best for your strategy. This tool performs a "Top-Down Analysis" instantly by running a unified strategy simulation across 5 different timeframes simultaneously.
Why Use This?
A strategy that fails on the 1-Hour chart might print massive returns on the 4-Hour chart due to reduced noise. This scanner calculates the Equity Curve, Max Drawdown, and Win Rate for 15m, 1H, 4H, Daily, and Weekly charts (customizable) and presents the winner in a dashboard.
Features:
Simultaneous Backtesting: Runs 5 independent simulations inside request.security.
Equity & Drawdown Tracking: See not just how much you make, but how much risk is required on each timeframe.
Instant Comparison: Identify "Fractal Resonance" where multiple timeframes align in profitability.
Strategy Logic (Fully Customizable):
The default entry logic is a generic EMA 9/21 Crossover with a Trend Filter.
Note: This is an open-source framework. You can modify the calc_strategy_results function in the source code to substitute the crossover with your own custom entry conditions (RSI, Stochastic, Price Action, etc.).
Workflow:
Load this scanner to identify the dominant timeframe (e.g., 4H).
Switch your chart to the 4H timeframe.
Use the Strategy Grid Optimizer to fine-tune the specific EMA and ATR settings for that timeframe.
Strategy Grid Optimizer (Trend & Risk)Description:
This tool transforms your chart into a powerful backtesting engine that runs hundreds of simulations per second. It is designed to solve the "Parameter Stability" problem: finding the settings that work robustly, rather than curve-fitting to a single number.
How It Works:
Instead of testing one setting at a time, this script uses Pine Script Arrays to run a "Grid Search" on your chart history:
Trend Filter: It iterates through a range of EMA Lengths (e.g., 20, 30, 40... to 200).
Risk Management: It iterates through a range of ATR Multipliers (e.g., 1.0, 1.5, 2.0...) for the trailing stop.
The Result: It ranks every combination based on Net Profit, Drawdown, and Win Rate, instantly highlighting the "Sweet Spot" for the current asset.
Strategy Logic (Fully Customizable):
By default, this script demonstrates a standard EMA 9/21 Crossover.
Developers & Traders: This script is designed as a Template. You can easily open the Source Code and replace the entry_signal logic with any strategy you wish (e.g., RSI, MACD, Bollinger Bands, or your own proprietary logic). The optimizer engine will work with whatever signal you provide.
Workflow:
Use the MTF Scanner to find the best Timeframe.
Load this Grid Optimizer on that timeframe.
Adjust the "Start" and "End" ranges in settings.
The table will reveal the optimal Trend/Risk combination for your strategy.
FX Rate Bias US vs EU 2YFX Rate Bias – US vs EU (2Y)
This indicator implements a rate-differential based macro bias model using the 2-year government bond yield spread between the United States and Germany.
The methodology focuses on the short end of the yield curve, which primarily reflects central bank expectations rather than long-term inflation or risk premiums.
By applying light smoothing and a zero-line regime framework, the script classifies market conditions into USD rate advantage or EUR rate advantage states.
Calculation logic:
Retrieves daily 2Y sovereign yields for the US and Germany
Computes the yield differential (US − DE)
Applies optional smoothing to reduce noise
Uses the zero line as a regime boundary to define relative monetary bias
Practical use:
This tool is designed to provide directional macro context for FX analysis, particularly for EURUSD.
It helps traders align technical setups with prevailing interest rate expectations, and is not intended as a standalone signal or timing indicator.
Laguerre RSI (Fractals Energy) [v6]This write-up explores the **Laguerre RSI (LRSI)**, a sophisticated technical indicator pioneered by **John F. Ehlers**. Unlike the standard RSI, which often suffers from "lag" or excessive noise, the Laguerre RSI uses a four-pole filter to provide a smoother, more responsive curve that stays in overbought or oversold zones longer during strong trends.
The following analysis focuses on the interplay between the **Alpha (Gamma)** and the **Gamma Bandwidth**, specifically looking for "Alpha Exceeding" events to identify market coiling and exhaustion.
---
## 1. The Core Concept: Ehlers’ Laguerre Transform
Traditional indicators use a fixed look-back period (e.g., 14 periods). John Ehlers introduced the Laguerre Transform to allow for a more efficient way of filtering data using a very small amount of data.
In the provided code, the key variable is **Alpha** (derived from **Fractals Energy/Gamma**). This value determines the "speed" of the indicator.
* **Low Alpha:** High damping, smoother but slower.
* **High Alpha:** Low damping, faster and more reactive.
---
## 2. The Gamma Bandwidth: Coiling and Energy
The "Gamma Band" (the purple shaded area in your script, typically between and ) represents the "neutral" zone for market fractal energy.
### Market Coiling (Compression)
When the **Alpha (Gamma) line** climbs **above the Gamma Upper Bound** (e.g., ):
* This indicates the market is moving into a state of **high fractal efficiency** or "straight-line" movement.
* However, when Alpha is pinned high, it often signals **Coiling**. The market is burning through its energy efficiently, but it is reaching a state of "ordered" exhaustion.
* **The Interpretation:** The price is trending strongly, but the lack of "chaos" suggests a trend maturity is approaching.
### Alpha Exceeding the Bands (Exhaustion)
When the Alpha line spikes significantly outside the bands while the LRSI line (blue or pink) is pinned at the extremes (1.0 or 0.0), we observe **Exhaustion**.
* **Bullish Exhaustion:** LRSI is (Blue) and Alpha exceeds the upper band. The trend is so efficient that it has no room left to accelerate. A "reversion to the mean" or a period of "choppiness" (increasing fractal chaos) is likely.
* **Bearish Exhaustion:** LRSI is (Pink) and Alpha exceeds the upper band. This shows a vertical drop that is unsustainable in the long term.
---
## 3. Signal Mechanics: The "Hook"
The most potent signal occurs when the Alpha line begins to **descend back into the Gamma Bandwidth** while the LRSI line crosses the OB/OS levels.
| Signal Component | Market Condition | Actionable Insight |
| --- | --- | --- |
| **Alpha > 0.59** | High Efficiency / Coiling | Trend is strong, but watch for the "bend." |
| **Alpha < 0.41** | High Complexity / Choppiness | Market is trendless; energy is being stored for the next move. |
| **LRSI Cross < 0.8** | Bearish Reversal | Trend exhaustion confirmed; exit longs or enter shorts. |
| **LRSI Cross > 0.2** | Bullish Reversal | Mean reversion confirmed; exit shorts or enter longs. |
---
## 4. Summary of the Methodology
By integrating **Fractals Energy** (Gamma) directly into the Alpha of the Laguerre RSI, this version of Ehlers’ work allows the indicator to adapt its own speed based on the market’s complexity.
When Alpha exceeds the bands, it is a warning that the "clean" move is coming to an end. The market is "coiled" tight; the subsequent break back into the purple band signifies that the trend has lost its linear efficiency and is returning to a state of chaos—often resulting in a price reversal or significant consolidation.
> **Credit:** All mathematical foundations of the Laguerre Transform and the RSI implementation are credited to **John F. Ehlers**.
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Would you like me to create a visual guide or table specifically for the **Fractal Energy** values and how they correlate to specific market phases?
Session Anchored OIWAP [Arjo]The Session Anchored OIWAP (Open Interest Weighted Average Price) indicator shows you a weighted average price that uses Open Interest (OI) changes during different trading sessions . It divides the day into four clear sessions: Opening Hour , Morning Session , Mid-Day Session , and Closing Session .
For each session , it calculates a weighted average price using both market price and open interest data from futures . This line updates as the session progresses and resets when a new session starts .
You can also see optional deviation bands that you visually compare to how far the market price is moving away from the session’s weighted average. This indicator also helps you watch how Open Interest changes connect with price movements during specific market hours.
Concepts
This tool works on a few simple ideas:
Session anchoring
Each session starts fresh. The indicator resets and begins a new calculation when a new time block begins. This allows users to visually study each session independently.
Open-interest weighting
Instead of treating all price moves equally, price changes linked to higher open-interest activity have more influence on the OIWAP. This gives a weighted reflection of where the market has been trading during the session.
Averaging and smoothing
The OIWAP line blends many price data points into one smooth curve, making it easier to follow than raw price movement.
Volatility display with bands
The upper and lower bands are placed at ±0.5 standard deviation from the OIWAP line. These bands simply help you see when price stretches further away than usual from the session average.
Features
Four Independent Session Calculations: Shows separate OIWAP lines for Opening Hour (default: 09:15-10:15), Morning (10:15-11:30), Mid-Day (11:30-14:00), and Closing (14:00-15:30) sessions
Open Interest Weighting: Uses absolute OI change as the weight instead of traditional volume
Customizable Session Times: You can change the time ranges for each session to match your market or what you need
Optional Deviation Bands: You can turn ±0.5 standard deviation bands on or off around each OIWAP line
Color-Coded Sessions: Each session has its own color so you can tell them apart easily
Selective Display: You can turn individual sessions and bands on or off
Data Availability Check: Shows you a notification when Open Interest data isn't available for your symbol
Adjustable Position Timeframe: You can calculate OI changes on different timeframes (Chart, Daily, 15min, 30min, 60min, 120min)
How to use
Add this indicator to a chart of any symbol that has Open Interest data ( from futures or derivatives contracts). Once you add it, you'll see colored lines showing the OIWAP for each session you enable, along with optional deviation bands.
Adjusting Settings:
Turn individual sessions on or off using the checkboxes in the " Sessions " section
Change session colors to match your chart or what looks good to you
Turn deviation bands on or off using the " Show Bands " option in the Display settings
Change session time ranges in the " Session Times " section to match your market hours or what you want to analyze
Change the Position Timeframe if you want to see OI changes calculated on a different time period
Visual Interpretation:
Each OIWAP line shows you the OI-weighted average price for that session
The deviation bands show you how much prices spread out, weighted by OI changes
You can watch how price interacts with these levels to see where significant OI activity happened
Different sessions may show different OIWAP levels, showing you how the OI-price relationship changes throughout the trading day
Note:
This indicator needs Open Interest data to work. If OI data isn't available for your symbol, you'll see a message in the center of your chart. This indicator works only with derivatives markets like futures and options in the Indian Market where OI data is publicly available.
Conclusion
The Session Anchored OIWAP indicator is designed to support structured market observation by combining price, open interest, and session anchoring into a clear visual format. It helps users study market behavior during different parts of the day without generating trading instructions or outcomes.
Disclaimer
This indicator is for educational and visual-analysis purposes only. It does not provide trading signals , financial advice, or guaranteed outcomes . You should perform your own research and consult a licensed financial professional when needed. All trading decisions are solely the responsibility of the user.
Happy Trading
FX Rate Bias US vs EU 2YFX Rate Bias – US vs EU (2Y)
This indicator provides a macro bias framework for FX markets by tracking the 2-year government bond yield differential between the United States and Germany.
Rather than displaying the spread as a raw calculation, the script translates interest-rate expectations into a clear directional bias, helping traders understand which currency currently holds a rate advantage.
The 2Y segment of the yield curve is highly sensitive to:
Central bank expectations
Forward guidance
Shifts in short-term monetary policy outlook
How to use
Positive spread → USD rate advantage
Negative spread → EUR rate advantage
Designed to be used as a contextual macro tool, this indicator helps align technical setups with broader monetary conditions.
It is not intended as a standalone entry or signal generator.
