Jimb0ws Strategy Trending Info PanelsJimb0ws Strategy — Golden Candles + Bubble Zones
A price-action/EMA strategy built for FX scalping and intraday swings. It colors Golden Candles when strong bodies touch/skim EMA20/50 in trend (“bubble”) and optionally highlights Robin Candles (break of the prior golden body). Signals are throttled per bubble and filtered by multiple higher-timeframe conditions.
How it trades
Trend bubbles: Uses EMA20/50/100/200 alignment on the chart timeframe; also reads 1H & 4H bubbles for context.
Entries: BUY/SELL labels appear only when a golden setup aligns with fractal/structure checks and all active filters pass.
Stops/Targets (strategy mode):
• Longs: SL = EMA100 if EMA200 > EMA100, else SL = EMA200.
• Shorts: SL = EMA100 if EMA200 < EMA100, else SL = EMA200.
• TP = RR × risk (default 2R).
An on-chart SL/TP info label prints the exact prices at each signal.
Risk filter options: disable beyond 1H EMA50, proximity band around 1H EMA50, wick overdrive veto, session filter (toggle on/off), max signals per bubble.
Visuals & tools
Colored EMAs (20/50/100/200), bubble zone background.
4H info panel (state, start time, duration); Prev-Day ATR panel sits above it.
Optional 1H info panel and consolidation warning.
Fractal markers (size selectable).
Alerts
1H bubble state change (Long/Short/Consolidation).
BUY/SELL signals.
Inputs worth checking
Session & timezone, min body size, pip tolerances, proximity/WOD filters, max signals per bubble, RR, SL/TP label offset.
Notes
Best on FX pairs; pip = mintick × 10. Backtest and adjust to your instrument and session. This is not financial advice.
Cerca negli script per "Fractal"
3D Surface Modeling [PhenLabs]📊 3D Surface Modeling
Version: PineScript™ v6
📌 Description
The 3D Surface Modeling indicator revolutionizes technical analysis by generating three-dimensional visualizations of multiple technical indicators across various timeframes. This advanced analytical tool processes and renders complex indicator data through a sophisticated matrix-based calculation system, creating an intuitive 3D surface representation of market dynamics.
The indicator employs array-based computations to simultaneously analyze multiple instances of selected technical indicators, mapping their behavior patterns across different temporal dimensions. This unique approach enables traders to identify complex market patterns and relationships that may be invisible in traditional 2D charts.
🚀 Points of Innovation
Matrix-Based Computation Engine: Processes up to 500 concurrent indicator calculations in real-time
Dynamic 3D Rendering System: Creates depth perception through sophisticated line arrays and color gradients
Multi-Indicator Integration: Seamlessly combines VWAP, Hurst, RSI, Stochastic, CCI, MFI, and Fractal Dimension analyses
Adaptive Scaling Algorithm: Automatically adjusts visualization parameters based on indicator type and market conditions
🔧 Core Components
Indicator Processing Module: Handles real-time calculation of multiple technical indicators using array-based mathematics
3D Visualization Engine: Converts indicator data into three-dimensional surfaces using line arrays and color mapping
Dynamic Scaling System: Implements custom normalization algorithms for different indicator types
Color Gradient Generator: Creates depth perception through programmatic color transitions
🔥 Key Features
Multi-Indicator Support: Comprehensive analysis across seven different technical indicators
Customizable Visualization: User-defined color schemes and line width parameters
Real-time Processing: Continuous calculation and rendering of 3D surfaces
Cross-Timeframe Analysis: Simultaneous visualization of indicator behavior across multiple periods
🎨 Visualization
Surface Plot: Three-dimensional representation using up to 500 lines with dynamic color gradients
Depth Indicators: Color intensity variations showing indicator value magnitude
Pattern Recognition: Visual identification of market structures across multiple timeframes
📖 Usage Guidelines
Indicator Selection
Type: VWAP, Hurst, RSI, Stochastic, CCI, MFI, Fractal Dimension
Default: VWAP
Starting Length: Minimum 5 periods
Default: 10
Step Size: Interval between calculations
Range: 1-10
Visualization Parameters
Color Scheme: Green, Red, Blue options
Line Width: 1-5 pixels
Surface Resolution: Up to 500 lines
✅ Best Use Cases
Multi-timeframe market analysis
Pattern recognition across different technical indicators
Trend strength assessment through 3D visualization
Market behavior study across multiple periods
⚠️ Limitations
High computational resource requirements
Maximum 500 line restriction
Requires substantial historical data
Complex visualization learning curve
🔬 How It Works
1. Data Processing:
Calculates selected indicator values across multiple timeframes
Stores results in multi-dimensional arrays
Applies custom scaling algorithms
2. Visualization Generation:
Creates line arrays for 3D surface representation
Applies color gradients based on value magnitude
Renders real-time updates to surface plot
3. Display Integration:
Synchronizes with chart timeframe
Updates surface plot dynamically
Maintains visual consistency across updates
🌟 Credits:
Inspired by LonesomeTheBlue (modified for multiple indicator types with scaling fixes and additional unique mappings)
💡 Note:
Optimal performance requires sufficient computing resources and historical data. Users should start with default settings and gradually adjust parameters based on their analysis requirements and system capabilities.
Anomalous Holonomy Field Theory🌌 Anomalous Holonomy Field Theory (AHFT) - Revolutionary Quantum Market Analysis
Where Theoretical Physics Meets Trading Reality
A Groundbreaking Synthesis of Differential Geometry, Quantum Field Theory, and Market Dynamics
🔬 THEORETICAL FOUNDATION - THE MATHEMATICS OF MARKET REALITY
The Anomalous Holonomy Field Theory represents an unprecedented fusion of advanced mathematical physics with practical market analysis. This isn't merely another indicator repackaging old concepts - it's a fundamentally new lens through which to view and understand market structure .
1. HOLONOMY GROUPS (Differential Geometry)
In differential geometry, holonomy measures how vectors change when parallel transported around closed loops in curved space. Applied to markets:
Mathematical Formula:
H = P exp(∮_C A_μ dx^μ)
Where:
P = Path ordering operator
A_μ = Market connection (price-volume gauge field)
C = Closed price path
Market Implementation:
The holonomy calculation measures how price "remembers" its journey through market space. When price returns to a previous level, the holonomy captures what has changed in the market's internal geometry. This reveals:
Hidden curvature in the market manifold
Topological obstructions to arbitrage
Geometric phase accumulated during price cycles
2. ANOMALY DETECTION (Quantum Field Theory)
Drawing from the Adler-Bell-Jackiw anomaly in quantum field theory:
Mathematical Formula:
∂_μ j^μ = (e²/16π²)F_μν F̃^μν
Where:
j^μ = Market current (order flow)
F_μν = Field strength tensor (volatility structure)
F̃^μν = Dual field strength
Market Application:
Anomalies represent symmetry breaking in market structure - moments when normal patterns fail and extraordinary opportunities arise. The system detects:
Spontaneous symmetry breaking (trend reversals)
Vacuum fluctuations (volatility clusters)
Non-perturbative effects (market crashes/melt-ups)
3. GAUGE THEORY (Theoretical Physics)
Markets exhibit gauge invariance - the fundamental physics remains unchanged under certain transformations:
Mathematical Formula:
A'_μ = A_μ + ∂_μΛ
This ensures our signals are gauge-invariant observables , immune to arbitrary market "coordinate changes" like gaps or reference point shifts.
4. TOPOLOGICAL DATA ANALYSIS
Using persistent homology and Morse theory:
Mathematical Formula:
β_k = dim(H_k(X))
Where β_k are the Betti numbers describing topological features that persist across scales.
🎯 REVOLUTIONARY SIGNAL CONFIGURATION
Signal Sensitivity (0.5-12.0, default 2.5)
Controls the responsiveness of holonomy field calculations to market conditions. This parameter directly affects the threshold for detecting quantum phase transitions in price action.
Optimization by Timeframe:
Scalping (1-5min): 1.5-3.0 for rapid signal generation
Day Trading (15min-1H): 2.5-5.0 for balanced sensitivity
Swing Trading (4H-1D): 5.0-8.0 for high-quality signals only
Score Amplifier (10-200, default 50)
Scales the raw holonomy field strength to produce meaningful signal values. Higher values amplify weak signals in low-volatility environments.
Signal Confirmation Toggle
When enabled, enforces additional technical filters (EMA and RSI alignment) to reduce false positives. Essential for conservative strategies.
Minimum Bars Between Signals (1-20, default 5)
Prevents overtrading by enforcing quantum decoherence time between signals. Higher values reduce whipsaws in choppy markets.
👑 ELITE EXECUTION SYSTEM
Execution Modes:
Conservative Mode:
Stricter signal criteria
Higher quality thresholds
Ideal for stable market conditions
Adaptive Mode:
Self-adjusting parameters
Balances signal frequency with quality
Recommended for most traders
Aggressive Mode:
Maximum signal sensitivity
Captures rapid market moves
Best for experienced traders in volatile conditions
Dynamic Position Sizing:
When enabled, the system scales position size based on:
Holonomy field strength
Current volatility regime
Recent performance metrics
Advanced Exit Management:
Implements trailing stops based on ATR and signal strength, with mode-specific multipliers for optimal profit capture.
🧠 ADAPTIVE INTELLIGENCE ENGINE
Self-Learning System:
The strategy analyzes recent trade outcomes and adjusts:
Risk multipliers based on win/loss ratios
Signal weights according to performance
Market regime detection for environmental adaptation
Learning Speed (0.05-0.3):
Controls adaptation rate. Higher values = faster learning but potentially unstable. Lower values = stable but slower adaptation.
Performance Window (20-100 trades):
Number of recent trades analyzed for adaptation. Longer windows provide stability, shorter windows increase responsiveness.
🎨 REVOLUTIONARY VISUAL SYSTEM
1. Holonomy Field Visualization
What it shows: Multi-layer quantum field bands representing market resonance zones
How to interpret:
Blue/Purple bands = Primary holonomy field (strongest resonance)
Band width = Field strength and volatility
Price within bands = Normal quantum state
Price breaking bands = Quantum phase transition
Trading application: Trade reversals at band extremes, breakouts on band violations with strong signals.
2. Quantum Portals
What they show: Entry signals with recursive depth patterns indicating momentum strength
How to interpret:
Upward triangles with portals = Long entry signals
Downward triangles with portals = Short entry signals
Portal depth = Signal strength and expected momentum
Color intensity = Probability of success
Trading application: Enter on portal appearance, with size proportional to portal depth.
3. Field Resonance Bands
What they show: Fibonacci-based harmonic price zones where quantum resonance occurs
How to interpret:
Dotted circles = Minor resonance levels
Solid circles = Major resonance levels
Color coding = Resonance strength
Trading application: Use as dynamic support/resistance, expect reactions at resonance zones.
4. Anomaly Detection Grid
What it shows: Fractal-based support/resistance with anomaly strength calculations
How to interpret:
Triple-layer lines = Major fractal levels with high anomaly probability
Labels show: Period (H8-H55), Price, and Anomaly strength (φ)
⚡ symbol = Extreme anomaly detected
● symbol = Strong anomaly
○ symbol = Normal conditions
Trading application: Expect major moves when price approaches high anomaly levels. Use for precise entry/exit timing.
5. Phase Space Flow
What it shows: Background heatmap revealing market topology and energy
How to interpret:
Dark background = Low market energy, range-bound
Purple glow = Building energy, trend developing
Bright intensity = High energy, strong directional move
Trading application: Trade aggressively in bright phases, reduce activity in dark phases.
📊 PROFESSIONAL DASHBOARD METRICS
Holonomy Field Strength (-100 to +100)
What it measures: The Wilson loop integral around price paths
>70: Strong positive curvature (bullish vortex)
<-70: Strong negative curvature (bearish collapse)
Near 0: Flat connection (range-bound)
Anomaly Level (0-100%)
What it measures: Quantum vacuum expectation deviation
>70%: Major anomaly (phase transition imminent)
30-70%: Moderate anomaly (elevated volatility)
<30%: Normal quantum fluctuations
Quantum State (-1, 0, +1)
What it measures: Market wave function collapse
+1: Bullish eigenstate |↑⟩
0: Superposition (uncertain)
-1: Bearish eigenstate |↓⟩
Signal Quality Ratings
LEGENDARY: All quantum fields aligned, maximum probability
EXCEPTIONAL: Strong holonomy with anomaly confirmation
STRONG: Good field strength, moderate anomaly
MODERATE: Decent signals, some uncertainty
WEAK: Minimal edge, high quantum noise
Performance Metrics
Win Rate: Rolling performance with emoji indicators
Daily P&L: Real-time profit tracking
Adaptive Risk: Current risk multiplier status
Market Regime: Bull/Bear classification
🏆 WHY THIS CHANGES EVERYTHING
Traditional technical analysis operates on 100-year-old principles - moving averages, support/resistance, and pattern recognition. These work because many traders use them, creating self-fulfilling prophecies.
AHFT transcends this limitation by analyzing markets through the lens of fundamental physics:
Markets have geometry - The holonomy calculations reveal this hidden structure
Price has memory - The geometric phase captures path-dependent effects
Anomalies are predictable - Quantum field theory identifies symmetry breaking
Everything is connected - Gauge theory unifies disparate market phenomena
This isn't just a new indicator - it's a new way of thinking about markets . Just as Einstein's relativity revolutionized physics beyond Newton's mechanics, AHFT revolutionizes technical analysis beyond traditional methods.
🔧 OPTIMAL SETTINGS FOR MNQ 10-MINUTE
For the Micro E-mini Nasdaq-100 on 10-minute timeframe:
Signal Sensitivity: 2.5-3.5
Score Amplifier: 50-70
Execution Mode: Adaptive
Min Bars Between: 3-5
Theme: Quantum Nebula or Dark Matter
💭 THE JOURNEY - FROM IMPOSSIBLE THEORY TO TRADING REALITY
Creating AHFT was a mathematical odyssey that pushed the boundaries of what's possible in Pine Script. The journey began with a seemingly impossible question: Could the profound mathematical structures of theoretical physics be translated into practical trading tools?
The Theoretical Challenge:
Months were spent diving deep into differential geometry textbooks, studying the works of Chern, Simons, and Witten. The mathematics of holonomy groups and gauge theory had never been applied to financial markets. Translating abstract mathematical concepts like parallel transport and fiber bundles into discrete price calculations required novel approaches and countless failed attempts.
The Computational Nightmare:
Pine Script wasn't designed for quantum field theory calculations. Implementing the Wilson loop integral, managing complex array structures for anomaly detection, and maintaining computational efficiency while calculating geometric phases pushed the language to its limits. There were moments when the entire project seemed impossible - the script would timeout, produce nonsensical results, or simply refuse to compile.
The Breakthrough Moments:
After countless sleepless nights and thousands of lines of code, breakthrough came through elegant simplifications. The realization that market anomalies follow patterns similar to quantum vacuum fluctuations led to the revolutionary anomaly detection system. The discovery that price paths exhibit holonomic memory unlocked the geometric phase calculations.
The Visual Revolution:
Creating visualizations that could represent 4-dimensional quantum fields on a 2D chart required innovative approaches. The multi-layer holonomy field, recursive quantum portals, and phase space flow representations went through dozens of iterations before achieving the perfect balance of beauty and functionality.
The Balancing Act:
Perhaps the greatest challenge was maintaining mathematical rigor while ensuring practical trading utility. Every formula had to be both theoretically sound and computationally efficient. Every visual had to be both aesthetically pleasing and information-rich.
The result is more than a strategy - it's a synthesis of pure mathematics and market reality that reveals the hidden order within apparent chaos.
📚 INTEGRATED DOCUMENTATION
Once applied to your chart, AHFT includes comprehensive tooltips on every input parameter. The source code contains detailed explanations of the mathematical theory, practical applications, and optimization guidelines. This published description provides the overview - the indicator itself is a complete educational resource.
⚠️ RISK DISCLAIMER
While AHFT employs advanced mathematical models derived from theoretical physics, markets remain inherently unpredictable. No mathematical model, regardless of sophistication, can guarantee future results. This strategy uses realistic commission ($0.62 per contract) and slippage (1 tick) in all calculations. Past performance does not guarantee future results. Always use appropriate risk management and never risk more than you can afford to lose.
🌟 CONCLUSION
The Anomalous Holonomy Field Theory represents a quantum leap in technical analysis - literally. By applying the profound insights of differential geometry, quantum field theory, and gauge theory to market analysis, AHFT reveals structure and opportunities invisible to traditional methods.
From the holonomy calculations that capture market memory to the anomaly detection that identifies phase transitions, from the adaptive intelligence that learns and evolves to the stunning visualizations that make the invisible visible, every component works in mathematical harmony.
This is more than a trading strategy. It's a new lens through which to view market reality.
Trade with the precision of physics. Trade with the power of mathematics. Trade with AHFT.
I hope this serves as a good replacement for Quantum Edge Pro - Adaptive AI until I'm able to fix it.
— Dskyz, Trade with insight. Trade with anticipation.
OHLCVDataOHLCV Data Power Library
Multi-Timeframe Market Data with Mathematical Precision
📌 Overview
This Pine Script library provides structured OHLCV (Open, High, Low, Close, Volume) data across multiple timeframes using mathematically significant candle counts (powers of 3). Designed for technical analysts who work with fractal market patterns and need efficient access to higher timeframe data.
✨ Key Features
6 Timeframes: 5min, 1H, 4H, 6H, 1D, and 1W data
Power-of-3 Candle Counts: 3, 9, 27, 81, and 243 bars
Structured Data: Returns clean OHLCV objects with all price/volume components
Pine Script Optimized: Complies with all security() call restrictions
📊 Timeframe Functions
pinescript
f_get5M_3() // 3 candles of 5min data
f_get1H_27() // 27 candles of 1H data
f_get1D_81() // 81 candles of daily data
// ... and 27 other combinations
🚀 Usage Example
pinescript
import YourName/OHLCVData/1 as OHLCV
weeklyData = OHLCV.f_get1W_27() // Get 27 weekly candles
latestHigh = array.get(weeklyData, 0).high
plot(latestHigh, "Weekly High")
💡 Ideal For
Multi-timeframe analysis
Volume-profile studies
Fractal pattern detection
Higher timeframe confirmation
⚠️ Note
Replace "YourName" with your publishing username
All functions return arrays of OHLCV objects
Maximum lookback = 243 candles
📜 Version History
1.0 - Initial release (2024)
Aesthetic RSI [AlchimistOfCrypto]🌌 Aesthetic RSI – Unveiling the Fractal Forces of Markets 🌌
Category: Momentum Indicators 📈
"The RSI oscillator, formalized through an advanced mathematical prism, reveals the underlying fractal structures of price movements. This indicator draws inspiration from quantum principles of divergence-convergence where the probability of a return to equilibrium increases proportionally to the distance from the median point. Our implementation employs sophisticated algorithmic smoothing to filter out the stochastic noise inherent in financial markets, allowing visualization of the true momentum forces according to thermodynamic entropy principles applied to trading systems."
📊 Professional Trading Application
The Aesthetic RSI is a visually stunning and mathematically refined take on the classic Relative Strength Index. With customizable settings, advanced smoothing, and eight unique visual palettes, it empowers traders to detect momentum shifts and divergences with unparalleled clarity.
⚙️ Indicator Configuration
- Length 📏
The core parameter (default: 20) that determines the calculation period.
- Lower values (8-14): Increase sensitivity for short-term trading.
- Higher values (21-34): Provide stronger signals for position trading.
- OverBought/OverSold Thresholds 🎯
Customizable boundaries (default: 75/25) to identify extreme market conditions.
- Calibrate based on asset volatility: Higher volatility assets may need wider thresholds (80/20) to reduce false signals.
- Style 🎨
Eight meticulously crafted visual palettes optimized for pattern recognition:
- Miami Vice (default): High-contrast cyan/magenta scheme for spotting divergences.
- Cyberpunk: Yellow/purple combo to highlight momentum shifts.
- Classic: Traditional green/red for conventional analysis.
- High Contrast: Maximum visual separation for traders with visual impairments.
- Specialized palettes (Forest, Ocean, Fire, Monochrome): Tailored for diverse market conditions.
- Mode Selection 🔄
- Full: Displays a complete gradient spectrum across the RSI range, emphasizing momentum transitions between 35-65.
- OverZone: Focuses on actionable extreme zones, reducing noise in ranging markets.
🚀 How to Use
1. Adjust Length ⏰: Set the period based on your trading style (short-term or long-term).
2. Fine-Tune Thresholds 🎚️: Customize overbought/oversold levels to match the asset’s volatility.
3. Select a Palette 🌈: Choose a visual style that enhances your pattern recognition.
4. Choose Mode 🔍: Use "Full" for detailed momentum analysis or "OverZone" for extreme zone focus.
5. Spot Divergences ✅: Look for price-RSI divergences to anticipate reversals.
6. Trade with Precision 🛡️: Combine with other indicators for high-probability setups.
📅 Release Notes (April 2025)
Aesthetic RSI blends quantum-inspired mathematics with artistic visualization, redefining momentum analysis. Stay tuned for future enhancements! ✨
🏷️ Tags
#Trading #TechnicalAnalysis #RSI #Momentum #Divergence #MultiTimeframe #TradingStrategy #RiskManagement #Forex #Stocks #Crypto #Bitcoin #AlgoTrading #DayTrading #SwingTrading #TheAlchimist #QuantumTrading #VisualTrading #PatternRecognition
Accumulation-Distribution CandlesThis structural visualization tool maps each candle through the lens of Effort vs. Result, blending Volume, Range, and closing bias into a normalized pressure score. Candle bodies are dynamically color-coded using a five-tier system—from heavy accumulation to heavy distribution—revealing where energy is building, dispersing, or neutral. This helps to visually isolate Markup, Markdown, Re-accumulation, and Distribution at a glance.
The indicator calculates a strength score by multiplying price result (close minus open) by effort (volume or price range), smoothing this raw value using a Fibonacci-based EMA. (34 for standard, 55 for crypto; the higher crypto value acknowledges that 24/7 trading offers more hours per week or month than trad markets.) The result is standardized against its rolling deviation and clamped to a range. This score determines the visual tier:
• 💙 Dark Blue = heavy Accumulation (strong upward result on strong effort)
• 🩵 Pale Blue = mild Accumulation
• 🌚 Gray = neutral (low conviction or balance)
• 💛 Pale Yellow = mild Distribution
• 🧡 Deep Yellow = heavy Distribution (strong downward result on strong effort)
The tool is optimized for the 1D chart, where Wyckoff phases are most clearly expressed. However, it adapts well to lower timeframes when used selectively. Traders may hide the body coloring and enable only zone highlighting to preserve other candle overlays such as SUPeR TReND 2.718, which offers directional clarity and trend duration. This combination is especially useful on intraday charts (15m–1H) where microstructure matters but visual clutter must be avoided.
When used alongside other Volume overlays (such as the OBVX Conviction Bias) or Volatility indicators (such as the Asymmetric Turbulence Ribbon (ATR)), this indicator adds confluence to directional setups by contextualizing pressure with Volatility. For example: compression zones marked by ATR may align with persistent pale blue candles—indicating quiet Accumulation before expansion.
Optional Overlays:
Normally ON -
• 📌 Pin Bars , filtered by volume, to isolate wick-dominant reversals from key zones
• 💪🏻 Strong-Body Candles — fuchsia candles w/ high body-to-range ratio reflect conviction
• 🧯 Wick Absorption Candles — red candles w/ long wicks and low closing strength indicate failed pushes or absorbed breakouts
• 🟦/🟧 Zone Highlighting for candles above a defined Accumulation/Distribution threshold
Normally OFF -
• 🔺 Fractals (5-bar) to map swing pivots by underlying pressure tier (normally OFF)
• 🟥/🟩 Engulfing patterns, filtered by directional conviction (normally OFF)
The Pin Bar strategy benefits most from the zone logic—when a bullish pin bar appears in an Accumulation zone (esp. pale or dark blue), and Volume exceeds its rolling average, it may mark a spring or failed breakdown. Conversely, bearish pins in Distribution zones can mark rejection or resistance.
This is not a signal engine—it’s a narrative filter designed to slot cleanly into a multi-layered workflow of visual structure and informed execution. Use it to identify bias and phase. Then deploy trade triggers from tools like SUPeR TReND 2.718, or the liquidity flows shown the The Silver Lining or the AltSeasonality - MTF indicators, for example. The candle colors tell you who’s in control—the other tools tell you when to act.
Hull Moving Average Adaptive RSI (Ehlers)Hull Moving Average Adaptive RSI (Ehlers)
The Hull Moving Average Adaptive RSI (Ehlers) is an enhanced trend-following indicator designed to provide a smooth and responsive view of price movement while incorporating an additional momentum-based analysis using the Adaptive RSI.
Principle and Advantages of the Hull Moving Average:
- The Hull Moving Average (HMA) is known for its ability to track price action with minimal lag while maintaining a smooth curve.
- Unlike traditional moving averages, the HMA significantly reduces noise and responds faster to market trends, making it highly effective for detecting trend direction and changes.
- It achieves this by applying a weighted moving average calculation that emphasizes recent price movements while smoothing out fluctuations.
Why the Adaptive RSI Was Added:
- The core HMA line remains the foundation of the indicator, but an additional analysis using the Adaptive RSI has been integrated to provide more meaningful insights into momentum shifts.
- The Adaptive RSI is a modified version of the traditional Relative Strength Index that dynamically adjusts its sensitivity based on market volatility.
- By incorporating the Adaptive RSI, the HMA visually represents whether momentum is strengthening or weakening, offering a complementary layer of analysis.
How the Adaptive RSI Influences the Indicator:
- High Adaptive RSI (above 65): The market may be overbought, or bullish momentum could be fading. The HMA turns shades of red, signaling a possible exhaustion phase or potential reversals.
- Neutral Adaptive RSI (around 50): The market is in a balanced state, meaning neither buyers nor sellers are in clear control. The HMA takes on grayish tones to indicate this consolidation.
- Low Adaptive RSI (below 35): The market may be oversold, or bearish momentum could be weakening. The HMA shifts to shades of blue, highlighting potential recovery zones or trend slowdowns.
Why This Combination is Powerful:
- While the HMA excels in tracking trends and reducing lag, it does not provide information about momentum strength on its own.
- The Adaptive RSI bridges this gap by adding a clear visual layer that helps traders assess whether a trend is likely to continue, consolidate, or reverse.
- This makes the indicator particularly useful for spotting trend exhaustion and confirming momentum shifts in real-time.
Best Use Cases:
- Works effectively on timeframes from 1 hour (1H) to 1 day (1D), making it suitable for swing trading and position trading.
- Particularly useful for trading indices (SPY), stocks, forex, and cryptocurrencies, where momentum shifts are frequent.
- Helps identify not just trend direction but also whether that trend is gaining or losing strength.
Recommended Complementary Indicators:
- Adaptive Trend Finder: Helps identify the dominant long-term trend.
- Williams Fractals Ultimate: Provides key reversal points to validate trend shifts.
- RVOL (Relative Volume): Confirms significant moves based on volume strength.
This enhanced HMA with Adaptive RSI provides a powerful, intuitive visual tool that makes trend analysis and momentum interpretation more effective and efficient.
This indicator is for educational and informational purposes only. It should not be considered financial advice or a guarantee of performance. Always conduct your own research and use proper risk management when trading. Past performance does not guarantee future results.
Hurst-Based Trend Persistence w/Poisson Prediction
---
# **Hurst-Based Trend Persistence w/ Poisson Prediction**
## **Introduction**
The **Hurst-Based Trend Persistence with Poisson Prediction** is a **statistically-driven trend-following oscillator** that provides traders with **a structured approach to identifying trend strength, persistence, and potential reversals**.
This indicator combines:
- **Hurst Exponent Analysis** (to measure how persistent or mean-reverting price action is).
- **Color-Coded Trend Detection** (to highlight bullish and bearish conditions).
- **Poisson-Based Trend Reversal Probability Projection** (to anticipate when a trend is likely to end based on statistical models).
By integrating **fractal market theory (Hurst exponent)** with **Poisson probability distributions**, this indicator gives traders a **probability-weighted view of trend duration** while dynamically adapting to market volatility.
---
## **Simplified Explanation (How to Read the Indicator at a Glance)**
1. **If the oscillator line is going up → The trend is strong.**
2. **If the oscillator line is going down → The trend is weakening.**
3. **If the color shifts from red to green (or vice versa), a trend shift has occurred.**
- **Strong trends can change color without weakening** (meaning a bullish or bearish move can remain powerful even as the trend shifts).
4. **A weakening trend does NOT necessarily mean a reversal is coming.**
- The trend may slow down but continue in the same direction.
5. **A strong trend does NOT guarantee it will last.**
- Even a powerful move can **suddenly reverse**, which is why the **Poisson-based background shading** helps anticipate probabilities of change.
---
## **How to Use the Indicator**
### **1. Understanding the Rolling Hurst-Based Trend Oscillator (Main Line)**
The **oscillator line** is based on the **Hurst exponent (H)**, which quantifies whether price movements are:
- **Trending** (values above 0 → momentum-driven, persistent trends).
- **Mean-reverting** (values below 0 → price action is choppy, likely to revert to the mean).
- **Neutral (Random Walk)** (values around 0 → price behaves like a purely stochastic process).
#### **Interpreting the Oscillator:**
- **H > 0.5 → Persistent Trends:**
- Price moves tend to sustain in one direction for longer periods.
- Example: Strong uptrends in bull markets.
- **H < 0.5 → Mean-Reverting Behavior:**
- Price has a tendency to revert back to its mean.
- Example: Sideways markets or fading momentum.
- **H ≈ 0.5 → Random Walk:**
- No clear trend; price is unpredictable.
A **gray dashed horizontal line at 0** serves as a **baseline**, helping traders quickly assess whether the market is **favoring trends or mean reversion**.
---
### **2. Color-Coded Trend Signal (Visual Confirmation of Trend Shifts)**
The oscillator **changes color** based on **price slope** over the lookback period:
- **🟢 Green → Uptrend (Price Increasing)**
- Price is rising relative to the selected lookback period.
- Suggests sustained bullish pressure.
- **🔴 Red → Downtrend (Price Decreasing)**
- Price is falling relative to the selected lookback period.
- Suggests sustained bearish pressure.
#### **How to Use This in Trading**
✔ **Stay in trends until a color change occurs.**
✔ **Use color changes as confirmation for trend reversals.**
✔ **Avoid counter-trend trades when the oscillator remains strongly colored.**
---
### **3. Poisson-Based Trend Reversal Projection (Anticipating Future Shifts)**
The **shaded orange background** represents a **Poisson-based probability estimation** of when the trend is likely to reverse.
- **Darker Orange = Higher Probability of Trend Reversal**
- **Lighter Orange / No Shade = Low Probability of Immediate Reversal**
💡 **The idea behind this model:**
✔ Trends **don’t last forever**, and their duration follows **statistical patterns**.
✔ By calculating the **average historical trend duration**, the indicator predicts **how likely a trend shift is at any given time**.
✔ The **Poisson probability function** is applied to determine the **expected likelihood of a reversal as time progresses**.
---
## **Mathematical Foundations of the Indicator**
This indicator is based on **two primary statistical models**:
### **1. Hurst Exponent & Trend Persistence (Fractal Market Theory)**
- The **Hurst exponent (H)** measures **autocorrelation** in price movements.
- If past trends **persist**, H will be **above 0.5** (meaning trend-following strategies are favorable).
- If past trends tend to **mean-revert**, H will be **below 0.5** (meaning reversal strategies are more effective).
- The **Rolling Hurst Oscillator** calculates this exponent over a moving window to track real-time trend conditions.
#### **Formula Breakdown (Simplified for Traders)**
The Hurst exponent (H) is derived using the **Rescaled Range (R/S) Analysis**:
\
Where:
- **R** = **Range** (difference between max cumulative deviation and min cumulative deviation).
- **S** = **Standard deviation** of price fluctuations.
- **Lookback** = The number of periods analyzed.
---
### **2. Poisson-Based Trend Reversal Probability (Stochastic Process Modeling)**
The **Poisson process** is a **probabilistic model used for estimating time-based events**, applied here to **predict trend reversals based on past trend durations**.
#### **How It Works**
- The indicator **tracks trend durations** (the time between color changes).
- A **Poisson rate parameter (λ)** is computed as:
\
- The **probability of a reversal at any given time (t)** is estimated using:
\
- **As t increases (trend continues), the probability of reversal rises**.
- The indicator **shades the background based on this probability**, visually displaying the likelihood of a **trend shift**.
---
## **Dynamic Adaptation to Market Conditions**
✔ **Volatility-Adjusted Trend Shifts:**
- A **custom volatility calculation** dynamically adjusts the **minimum trend duration** required before a trend shift is recognized.
- **Higher volatility → Requires longer confirmation before switching trend color.**
- **Lower volatility → Allows faster trend shifts.**
✔ **Adaptive Poisson Weighting:**
- **Recent trends are weighted more heavily** using an exponential decay function:
- **Decay Factor (0.618 by default)** prioritizes **recent intervals** while still considering historical trends.
- This ensures the model adapts to changing market conditions.
---
## **Key Takeaways for Traders**
✅ **Identify Persistent Trends vs. Mean Reversion:**
- Use the oscillator line to determine whether the market favors **trend-following or counter-trend strategies**.
✅ **Visual Trend Confirmation via Color Coding:**
- **Green = Uptrend**, **Red = Downtrend**.
- Trend changes help confirm **entry and exit points**.
✅ **Anticipate Trend Reversals Using Probability Models:**
- The **Poisson projection** provides a **statistical edge** in **timing exits before trends reverse**.
✅ **Adapt to Market Volatility Automatically:**
- Dynamic **volatility scaling** ensures the indicator remains effective in **both high and low volatility environments**.
Happy trading and enjoy!
Quarterly Theory ICT 02 [TradingFinder] True Open Session 90 Min🔵 Introduction
The Quarterly Theory ICT indicator is an advanced analytical system built on ICT (Inner Circle Trader) concepts and fractal time. It divides time into four quarters (Q1, Q2, Q3, Q4), and is designed based on the consistent repetition of these phases across all trading timeframes (annual, monthly, weekly, daily, and even shorter trading sessions).
Each cycle consists of four distinct phases: the first phase (Q1) is the Accumulation phase, characterized by price consolidation; the second phase (Q2), known as Manipulation or Judas Swing, is marked by initial false movements indicating a potential shift; the third phase (Q3) is Distribution, where price volatility peaks; and the fourth phase (Q4) is Continuation/Reversal, determining whether the previous trend continues or reverses.
🔵 How to Use
The central concept of this strategy is the "True Open," which refers to the actual starting point of each time cycle. The True Open is typically defined at the beginning of the second phase (Q2) of each cycle. Prices trading above or below the True Open serve as a benchmark for predicting the market's potential direction and guiding trading decisions.
The practical application of the Quarterly Theory strategy relies on accurately identifying True Open points across various timeframes.
True Open points are defined as follows :
Yearly Cycle :
Q1: January, February, March
Q2: April, May, June (True Open: April Monthly Open)
Q3: July, August, September
Q4: October, November, December
Monthly Cycle :
Q1: First Monday of the month
Q2: Second Monday of the month (True Open: Daily Candle Open price on the second Monday)
Q3: Third Monday of the month
Q4: Fourth Monday of the month
Weekly Cycle :
Q1: Monday
Q2: Tuesday (True Open: Daily Candle Open Price on Tuesday)
Q3: Wednesday
Q4: Thursday
Daily Cycle :
Q1: 18:00 - 00:00 (Asian session)
Q2: 00:00 - 06:00 (True Open: Start of London Session)
Q3: 06:00 - 12:00 (NY AM)
Q4: 12:00 - 18:00 (NY PM)
90 Min Asian Session :
Q1: 18:00 - 19:30
Q2: 19:30 - 21:00 (True Open at 19:30)
Q3: 21:00 - 22:30
Q4: 22:30 - 00:00
90 Min London Session :
Q1: 00:00 - 01:30
Q2: 01:30 - 03:00 (True Open at 01:30)
Q3: 03:00 - 04:30
Q4: 04:30 - 06:00
90 Min New York AM Session :
Q1: 06:00 - 07:30
Q2: 07:30 - 09:00 (True Open at 07:30)
Q3: 09:00 - 10:30
Q4: 10:30 - 12:00
90 Min New York PM Session :
Q1: 12:00 - 13:30
Q2: 13:30 - 15:00 (True Open at 13:30)
Q3: 15:00 - 16:30
Q4: 16:30 - 18:00
Micro Cycle (22.5-Minute Quarters) : Each 90-minute quarter is further divided into four 22.5-minute sub-segments (Micro Sessions).
True Opens in these sessions are defined as follows :
Asian Micro Session :
True Session Open : 19:30 - 19:52:30
London Micro Session :
T rue Session Open : 01:30 - 01:52:30
New York AM Micro Session :
True Session Open : 07:30 - 07:52:30
New York PM Micro Session :
True Session Open : 13:30 - 13:52:30
By accurately identifying these True Open points across various timeframes, traders can effectively forecast the market direction, analyze price movements in detail, and optimize their trading positions. Prices trading above or below these key levels serve as critical benchmarks for determining market direction and making informed trading decisions.
🔵 Setting
Show True Range : Enable or disable the display of the True Range on the chart, including the option to customize the color.
Extend True Range Line : Choose how to extend the True Range line on the chart, with the following options:
None: No line extension
Right: Extend the line to the right
Left: Extend the line to the left
Both: Extend the line in both directions (left and right)
Show Table : Determines whether the table—which summarizes the phases (Q1 to Q4)—is displayed.
Show More Info : Adds additional details to the table, such as the name of the phase (Accumulation, Manipulation, Distribution, or Continuation/Reversal) or further specifics about each cycle.
🔵 Conclusion
The Quarterly Theory ICT, by dividing time into four distinct quarters (Q1, Q2, Q3, and Q4) and emphasizing the concept of the True Open, provides a structured and repeatable framework for analyzing price action across multiple time frames.
The consistent repetition of phases—Accumulation, Manipulation (Judas Swing), Distribution, and Continuation/Reversal—allows traders to effectively identify recurring price patterns and critical market turning points. Utilizing the True Open as a benchmark, traders can more accurately determine potential directional bias, optimize trade entries and exits, and manage risk effectively.
By incorporating principles of ICT (Inner Circle Trader) and fractal time, this strategy enhances market forecasting accuracy across annual, monthly, weekly, daily, and shorter trading sessions. This systematic approach helps traders gain deeper insight into market structure and confidently execute informed trading decisions.
SYMPL Reversal BandsThis is an expansion of the Hybrid moving average. It uses the same hybrid moving code from the hybrid moving average script with an additional layer using the ta.hma function for some slight additional smoothing. Colors of the bands change dynamically based of the long and short hybrid moving averages running in the background. This can be really helpful in identifying periods to short bounces or long dips.
Below is the explanation of the hybrid moving average
Hybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
Hybrid Moving Average - Market TrendHybrid Moving Average Market Trend System - , designed to visualize market trends using a combination of three moving averages: FRAMA (Fractal Adaptive Moving Average), VIDYA (Variable Index Dynamic Average), and a Hamming windowed Volume-Weighted Moving Average (VWMA).
Key Features:
FRAMA Calculation:
FRAMA adapts to market volatility by dynamically adjusting its smoothing factor based on the fractal dimension of price movement. This allows it to be more responsive during trending periods while filtering out noise in sideways markets. The FRAMA is calculated for both short and long periods
VIDYA with CMO:
The VIDYA (Variable Index Dynamic Average) is based on a Chande Momentum Oscillator (CMO), which adjusts the smoothing factor dynamically depending on the momentum of the market. Higher momentum periods result in more responsive averages, while low momentum periods lead to smoother averages. Like FRAMA, VIDYA is calculated for both short and long periods.
Hamming Windowed VWMA:
This VWMA variation applies a Hamming window to smooth the weighting of volume across the calculation period. This method emphasizes central data points and reduces noise, making the VWMA more adaptive to volume fluctuations. The Hamming VWMA is calculated for short and long periods, offering another layer of adaptability to the hybrid moving average.
Hybrid Moving Averages:
Dynamic Coloring and Filling:
The script uses dynamic color transitions to visually distinguish between bullish and bearish conditions:
Awesome_Accelerator_Zone OscillatorExplanation and Usage Guide for AO_AC_ZONE Oscillator
Indicator Overview
The **AO_AC_ZONE** oscillator is based on the concepts introduced by **Bill Williams** in his book *New Trading Dimensions*. This indicator combines the **Awesome Oscillator (AO)**, **Accelerator Oscillator (AC)**, and a custom **Zone Oscillator**, visualizing them together in a clear, color-coded format.
The Zone Oscillator is derived from the relationship between AO and AC, indicating the market's dominant momentum state (bullish, bearish, or neutral). It also integrates real-time candle coloring to visually align price bars with the Zone's momentum.
---
**Components**
1. **Awesome Oscillator (AO)**:
- AO measures the difference between a 5-period and 34-period Simple Moving Average (SMA) applied to the midpoints of candles.
- It reflects market momentum, where:
- Green bars = increasing momentum
- Red bars = decreasing momentum
2. **Accelerator Oscillator (AC)**:
- AC is calculated as the difference between AO and its 5-period SMA.
- It indicates the acceleration or deceleration of market momentum.
- Fuchsia bars = increasing momentum
- Purple bars = decreasing momentum
3. **Zone Oscillator**:
- The Zone combines AO and AC states:
- **Green Zone**: Both AO and AC are positive (bullish momentum).
- **Red Zone**: Both AO and AC are negative (bearish momentum).
- **Gray Zone**: AO and AC have differing signs (neutral/uncertain momentum).
- Candle colors dynamically match the Zone’s state for enhanced visual clarity.
---
**How to Use the Indicator**
**1. Interpreting the Oscillators**
- **AO**: Use it to detect momentum direction and changes. Pay attention to shifts in bar color:
- **Increasing AO (Aqua)**: Bullish momentum gaining strength.
- **Decreasing AO (Navy)**: Bullish momentum weakening or bearish momentum strengthening.
- **AC**: Provides early signals of momentum shifts.
- If AC changes color ahead of AO, it signals potential trend reversals or accelerations.
**2. Using the Zone Oscillator**
- **Green Zone**:
- Both AO and AC are positive.
- Indicates a strong bullish trend. Look for buying opportunities in line with the trend.
- **Red Zone**:
- Both AO and AC are negative.
- Signals strong bearish momentum. Look for shorting opportunities.
- **Gray Zone**:
- AO and AC are in conflict.
- Represents uncertainty; avoid trading or wait for a clear signal.
---
**Real-Time Application**
**Candle Coloring**
- The indicator modifies candle colors to match the Zone Oscillator's state:
- **Green Candles**: Strong bullish momentum.
- **Red Candles**: Strong bearish momentum.
- **Gray Candles**: Neutral momentum.
**Recommended Strategy (Based on New Trading Dimensions)**:
1. **Identify the Zone**:
- Focus on Green Zones for long entries and Red Zones for short entries.
2. **Look for AO/AC Confirmation**:
- Enter trades in the direction of both AO and AC when they align with the Zone.
- For exits, monitor when AO and AC conflict (Gray Zone).
3. **Use in Combination**:
- Combine this oscillator with fractals or trend indicators to confirm signals.
---
**Benefits**
- Visualizes momentum strength, acceleration, and alignment in one chart.
- Simplifies decision-making by integrating price action with oscillator dynamics.
- Supports faster trade identification and execution by highlighting bullish, bearish, and neutral zones.
---
**Disclaimer**
This indicator is a tool to assist in market analysis. Always incorporate proper risk management and avoid trading during uncertain conditions (Gray Zones). For optimal results, use this oscillator in conjunction with other analysis methods like support/resistance, volume analysis, and trend-following systems.
Potential Upcoming Trend ToolThis Script has the specific use of identifying when and how a new trend may start to take form, rather than focusing on how a trend has already formed on a longer term basis.
This Script is useful on it's own and not in conjunction with another. It works by taking on the most recent price data rather than a long term historical string.
It differs from standard trend following indicators because it's use is far less historical, and more present. It requires less pivot points than normal to be validated as a strong trend.
It works by taking local pivot points and fractals to form its parallel basis. The Trend lines will continually move as more recent price action data appears and the the channel will get thinner, until it is clear a trend has arrived and consolidated.
The idea really is to see a constantly evolving picture of a sudden change in movement, allowing you to have an earlier eye on what is potentially to come.
The faint mid-point line gives a reasonable reading of where you would find yourself halfway within a new trend and will also move inline with the shown trendlines.
This allows you to easily track when sentiment and therefore trends are about to change. It's much more useful on lower timeframes because they will often give the first indication something is changing.
Colours are fully customisable.
Simple Parallel Channel TrackerThis script will automatically draw price channels with two parallel trends lines, the upper trendline and lower trendline. These lines can be changed in terms of appearance at any time.
The Script takes in fractals from local and historic price action points and connects them over a certain period or amount of candles as inputted by the user. It tracks the most recent highs and lows formed and uses this data to determine where the channel begins.
The Script will decide whether to use the most recent high, or low, depending on what comes first.
Why is this useful?
Often, Traders either have no trend lines on their charts, or they draw them incorrectly. Whichever category a trader falls into, there can only be benefits from having Trend lines and Parallel Channels drawn automatically.
Trends naturally occur in all Markets, all the time. These oscillations when tracked allow for a more reliable following of Markets and management of Market cycles.
Infinity Market Grid -AynetConcept
Imagine viewing the market as a dynamic grid where price, time, and momentum intersect to reveal infinite possibilities. This indicator leverages:
Grid-Based Market Flow: Visualizes price action as a grid with zones for:
Accumulation
Distribution
Breakout Expansion
Volatility Compression
Predictive Dynamic Layers:
Forecasts future price zones using historical volatility and momentum.
Tracks event probabilities like breakout, fakeout, and trend reversals.
Data Science Visuals:
Uses heatmap-style layers, moving waveforms, and price trajectory paths.
Interactive Alerts:
Real-time alerts for high-probability market events.
Marks critical zones for "buy," "sell," or "wait."
Key Features
Market Layers Grid:
Creates dynamic "boxes" around price using fractals and ATR-based volatility.
These boxes show potential future price zones and probabilities.
Volatility and Momentum Waves:
Overlay volatility oscillators and momentum bands for directional context.
Dynamic Heatmap Zones:
Colors the chart dynamically based on breakout probabilities and risk.
Price Path Prediction:
Tracks price trajectory as a moving "wave" across the grid.
How It Works
Grid Box Structure:
Upper and lower price levels are based on ATR (volatility) and plotted dynamically.
Dashed green/red lines show the grid for potential price expansion zones.
Heatmap Zones:
Colors the background based on probabilities:
Green: High breakout probability.
Blue: High consolidation probability.
Price Path Prediction:
Forecasts future price movements using momentum.
Plots these as a dynamic "wave" on the chart.
Momentum and Volatility Waves:
Shows the relationship between momentum and volatility as oscillating waves.
Helps identify when momentum exceeds volatility (potential breakouts).
Buy/Sell Signals:
Triggers when price approaches grid edges with strong momentum.
Provides alerts and visual markers.
Why Is It Revolutionary?
Grid and Wave Synergy:
Combines structural price zones (grid boxes) with real-time momentum and volatility waves.
Predictive Analytics:
Uses momentum-based forecasting to visualize what’s next, not just what’s happening.
Dynamic Heatmap:
Creates a living map of breakout/consolidation zones in real-time.
Scalable for Any Market:
Works seamlessly with forex, crypto, and stocks by adjusting the ATR multiplier and box length.
This indicator is not just a tool but a framework for understanding market dynamics at a deeper level. Let me know if you'd like to take it even further — for example, adding machine learning-inspired probability models or multi-timeframe analysis! 🚀
ka66: Bar Range BandsThis tool takes a bar's range, and reflects it above the high and below the low of that bar, drawing upper and lower bands around the bar. Repeated for each bar. There's an option to then multiply that range by some multiple. Use a value greater than 1 to get wider bands, and less than one to get narrower bands.
This tool stems out of my frustration from the use of dynamic bands (like Keltner Channels, or Bollinger Bands), in particular for estimating take profit points.
Dynamic bands work great for entries and stop loss, but their dynamism is less useful for a future event like taking profit, in my experience. We can use a smaller multiple, but then we can often lose out on a bigger chunk of gains unnecessarily.
The inspiration for this came from a friend explaining an ICT/SMC concept around estimating the magnitude of a trend, by calculating the Asian Session Range, and reflecting it above or below on to the New York and London sessions. He described this as standard deviation of the Asian Range, where the range can thus be multiplied by some multiple for a wider or narrower deviation.
This, in turn, also reminded me of the Measured Move concept in Technical Analysis. We then consider that the market is fractal in nature, and this is why patterns persist in most timeframes. Traders exist across the spectrum of timeframes. Thus, a single bar on a timeframe, is made up of multiple bars on a lower timeframe . In other words, when we reflect a bar's range above or below itself, in the event that in a lower timeframe, that bar fit a pattern whose take profit target could be estimated via a Measured Move , then the band's value becomes a more valid estimate of a take profit point .
Yet another way to think about it, by way of the fractal nature above, is that it is essentially a simplified dynamic support and resistance mechanism , even simpler than say the various Pivot calculations (e.g. Classical, Camarilla, etc.).
This tool in general, can also be used by those who manually backtest setups (and certainly can be used in an automated setting too!). It is a research tool in that regard, applicable to various setups.
One of the pitfalls of manual backtesting is that it requires more discipline to really determine an exit point, because it's easy to say "oh, I'll know more or less where to exit when I go live, I just want to see that the entry tends to work". From experience, this is a bad idea, because our mind subconsciously knows that we haven't got a trained reflex on where to exit. The setup may be decent, but without an exit point, we will never have truly embraced and internalised trading it. Again, I speak from experience!
Thus, to use this to research take profit/exit points:
Have a setup in mind, with all the entry rules.
Plot your setup's indicators, mark your signals.
Use this indicator to get an idea of where to exit after taking an entry based on your signal.
Credits:
@ICT_ID for providing the idea of using ranges to estimate how far a trend move might go, in particular he used the Asian Range projected on to the London and New York market sessions.
All the technicians who came up with the idea of the Measured Move.
Futures Weekly Open RangeThe weekly opening range ( high to low ) is calculated from the open of the market on Sunday (1800 EST) till the opening of the Bond Market on Monday morning (0800 EST). This is the first and most crucial range for the trading week. As ICT has taught, price is moving through an algorithm and as such is fractal; because price is fractal, the opening range can be calculated and projected to help determine if price is trending or consolidating. As well; this indicator can be used to incorporate his PO3 concept to enter above the weekly opening range for shorts if bearish, or entering below the opening range for longs if bullish.
This indicator takes the high and low of weekly opening range, plots those two levels, plots the opening price for the new week, and calculates the Standard Deviations of the range and plots them both above and below of the weekly opening range. These are all plotted through the week until the start of the new week.
The range is calculated by subtracting the high from the low during the specified time.
The mid-point is half of that range added to the low.
The Standard deviation is multiples of the range (up to 10) added to the high and subtracted
from the low.
At this time the indicator will only plot the Standard deviation lines on the minutes time frame below 1 hour.
Only the range and range lines will be plotted on the hourly chart.
RSI DeviationAn oscillator which de-trends the Relative Strength Index. Rather, it takes a moving average of RSI and plots it's standard deviation from the MA, similar to a Bollinger %B oscillator. This seams to highlight short term peaks and troughs, Indicating oversold and overbought conditions respectively. It is intended to be used with a Dollar Cost Averaging strategy, but may also be useful for Swing Trading, or Scalping on lower timeframes.
When the line on the oscillator line crosses back into the channel, it signals a trade opportunity.
~ Crossing into the band from the bottom, indicates the end of an oversold condition, signaling a potential reversal. This would be a BUY signal.
~ Crossing into the band from the top, indicates the end of an overbought condition, signaling a potential reversal. This would be a SELL signal.
For ease of use, I've made the oscillator highlight the main chart when Overbought/Oversold conditions are occurring, and place fractals upon reversion to the Band. These repaint as they are calculated at close. The earliest trade would occur upon open of the following day.
I have set the default St. Deviation to be 2, but in my testing I have found 1.5 to be quite reliable. By decreasing the St. Deviation you will increase trade frequency, to a point, at the expense of efficiency.
Cheers
DJSnoWMan06
[AIO] Multi Collection Moving Averages 140 MA TypesAll In One Multi Collection Moving Averages.
Since signing up 2 years ago, I have been collecting various Сollections.
I decided to get it into a decent shape and make it one of the biggest collections on TV, and maybe the entire internet.
And now I'm sharing my collection with you.
140 Different Types of Moving Averages are waiting for you.
Specifically :
"
AARMA | Adaptive Autonomous Recursive Moving Average
ADMA | Adjusted Moving Average
ADXMA | Average Directional Moving Average
ADXVMA | Average Directional Volatility Moving Average
AHMA | Ahrens Moving Average
ALF | Ehler Adaptive Laguerre Filter
ALMA | Arnaud Legoux Moving Average
ALSMA | Adaptive Least Squares
ALXMA | Alexander Moving Average
AMA | Adaptive Moving Average
ARI | Unknown
ARSI | Adaptive RSI Moving Average
AUF | Auto Filter
AUTL | Auto-Line
BAMA | Bryant Adaptive Moving Average
BFMA | Blackman Filter Moving Average
CMA | Corrected Moving Average
CORMA | Correlation Moving Average
COVEMA | Coefficient of Variation Weighted Exponential Moving Average
COVNA | Coefficient of Variation Weighted Moving Average
CTI | Coral Trend Indicator
DEC | Ehlers Simple Decycler
DEMA | Double EMA Moving Average
DEVS | Ehlers - Deviation Scaled Moving Average
DONEMA | Donchian Extremum Moving Average
DONMA | Donchian Moving Average
DSEMA | Double Smoothed Exponential Moving Average
DSWF | Damped Sine Wave Weighted Filter
DWMA | Double Weighted Moving Average
E2PBF | Ehlers 2-Pole Butterworth Filter
E2SSF | Ehlers 2-Pole Super Smoother Filter
E3PBF | Ehlers 3-Pole Butterworth Filter
E3SSF | Ehlers 3-Pole Super Smoother Filter
EDMA | Exponentially Deviating Moving Average (MZ EDMA)
EDSMA | Ehlers Dynamic Smoothed Moving Average
EEO | Ehlers Modified Elliptic Filter Optimum
EFRAMA | Ehlers Modified Fractal Adaptive Moving Average
EHMA | Exponential Hull Moving Average
EIT | Ehlers Instantaneous Trendline
ELF | Ehler Laguerre filter
EMA | Exponential Moving Average
EMARSI | EMARSI
EPF | Edge Preserving Filter
EPMA | End Point Moving Average
EREA | Ehlers Reverse Exponential Moving Average
ESSF | Ehlers Super Smoother Filter 2-pole
ETMA | Exponential Triangular Moving Average
EVMA | Elastic Volume Weighted Moving Average
FAMA | Following Adaptive Moving Average
FEMA | Fast Exponential Moving Average
FIBWMA | Fibonacci Weighted Moving Average
FLSMA | Fisher Least Squares Moving Average
FRAMA | Ehlers - Fractal Adaptive Moving Average
FX | Fibonacci X Level
GAUS | Ehlers - Gaussian Filter
GHL | Gann High Low
GMA | Gaussian Moving Average
GMMA | Geometric Mean Moving Average
HCF | Hybrid Convolution Filter
HEMA | Holt Exponential Moving Average
HKAMA | Hilbert based Kaufman Adaptive Moving Average
HMA | Harmonic Moving Average
HSMA | Hirashima Sugita Moving Average
HULL | Hull Moving Average
HULLT | Hull Triple Moving Average
HWMA | Henderson Weighted Moving Average
IE2 | Early T3 by Tim Tilson
IIRF | Infinite Impulse Response Filter
ILRS | Integral of Linear Regression Slope
JMA | Jurik Moving Average
KA | Unknown
KAMA | Kaufman Adaptive Moving Average & Apirine Adaptive MA
KIJUN | KIJUN
KIJUN2 | Kijun v2
LAG | Ehlers - Laguerre Filter
LCLSMA | 1LC-LSMA (1 line code lsma with 3 functions)
LEMA | Leader Exponential Moving Average
LLMA | Low-Lag Moving Average
LMA | Leo Moving Average
LP | Unknown
LRL | Linear Regression Line
LSMA | Least Squares Moving Average / Linear Regression Curve
LTB | Unknown
LWMA | Linear Weighted Moving Average
MAMA | MAMA - MESA Adaptive Moving Average
MAVW | Mavilim Weighted Moving Average
MCGD | McGinley Dynamic Moving Average
MF | Modular Filter
MID | Median Moving Average / Percentile Nearest Rank
MNMA | McNicholl Moving Average
MTMA | Unknown
MVSMA | Minimum Variance SMA
NLMA | Non-lag Moving Average
NWMA | Dürschner 3rd Generation Moving Average (New WMA)
PKF | Parametric Kalman Filter
PWMA | Parabolic Weighted Moving Average
QEMA | Quadruple Exponential Moving Average
QMA | Quick Moving Average
REMA | Regularized Exponential Moving Average
REPMA | Repulsion Moving Average
RGEMA | Range Exponential Moving Average
RMA | Welles Wilders Smoothing Moving Average
RMF | Recursive Median Filter
RMTA | Recursive Moving Trend Average
RSMA | Relative Strength Moving Average - based on RSI
RSRMA | Right Sided Ricker MA
RWMA | Regressively Weighted Moving Average
SAMA | Slope Adaptive Moving Average
SFMA | Smoother Filter Moving Average
SMA | Simple Moving Average
SSB | Senkou Span B
SSF | Ehlers - Super Smoother Filter P2
SSMA | Super Smooth Moving Average
STMA | Unknown
SWMA | Self-Weighted Moving Average
SW_MA | Sine-Weighted Moving Average
TEMA | Triple Exponential Moving Average
THMA | Triple Exponential Hull Moving Average
TL | Unknown
TMA | Triangular Moving Average
TPBF | Three-pole Ehlers Butterworth
TRAMA | Trend Regularity Adaptive Moving Average
TSF | True Strength Force
TT3 | Tilson (3rd Degree) Moving Average
VAMA | Volatility Adjusted Moving Average
VAMAF | Volume Adjusted Moving Average Function
VAR | Vector Autoregression Moving Average
VBMA | Variable Moving Average
VHMA | Vertical Horizontal Moving Average
VIDYA | Variable Index Dynamic Average
VMA | Volume Moving Average
VSO | Unknown
VWMA | Volume Weighted Moving Average
WCD | Unknown
WMA | Weighted Moving Average
XEMA | Optimized Exponential Moving Average
ZEMA | Zero Lag Moving Average
ZLDEMA | Zero-Lag Double Exponential Moving Average
ZLEMA | Ehlers - Zero Lag Exponential Moving Average
ZLTEMA | Zero-Lag Triple Exponential Moving Average
ZSMA | Zero-Lag Simple Moving Average
"
Don't forget that you can use any Moving Average not only for the chart but also for any of your indicators without affecting the code as in my example.
But remember that some MAs are not designed to work with anything other than a chart.
All MA and Code lists are sorted strictly alphabetically by short name (A-Z).
Each MA has its own number (ID) by which you can display the Moving Average you need.
Next to the ID selection there are tooltips with short names and their numbers. Use them.
The panel below will help you to read the Name of the selected MA.
Because of the size of the collection I think this is the optimal and most convenient use. Correct me if this is not the case.
Unknown - Some MAs I collected so long ago that I lost the full real name and couldn't find the authors. If you recognize them, please let me know.
I have deliberately simplified all MAs to input just Source and Length.
Because the collection is so large, it would be quite inconvenient and difficult to customize all MA functions (multipliers, offset, etc.).
If you need or like any MA you will still have to take it from my collection for your code.
I tried to leave the basic MA settings inside function in first strings.
I have tried to list most of the authors, but since the bulk of the collection was created a long time ago and was not intended for public publication I could not find all of them.
Some of the features were created from scratch or may have been slightly modified, so please be careful.
If you would like to improve this collection, please write to me in PM.
Also Credits, Likes, Awards, Loves and Thanks to :
@alexgrover
@allanster
@andre_007
@auroagwei
@blackcat1402
@bsharpe
@cheatcountry
@CrackingCryptocurrency
@Duyck
@ErwinBeckers
@everget
@glaz
@gotbeatz26107
@HPotter
@io72signals
@JacobAmos
@JoshuaMcGowan
@KivancOzbilgic
@LazyBear
@loxx
@LuxAlgo
@MightyZinger
@nemozny
@NGBaltic
@peacefulLizard50262
@RicardoSantos
@StalexBot
@ThiagoSchmitz
@TradingView
— 𝐀𝐧𝐝 𝐎𝐭𝐡𝐞𝐫𝐬 !
So just a Big Thank You to everyone who has ever and anywhere shared their codes.
Consolidation Spotter Multi Time FrameThis tool is designed for traders looking to spot areas of consolidation on their charts across various time frames. It highlights these consolidation areas using visually appealing boxes, making it easier to identify potential breakout or breakdown zones.
How To Use:
Spotting Consolidation: When you see a box form on your chart, this represents a consolidation zone. Within this zone, the price is moving sideways without a strong upward or downward trend.
Anticipating Breakouts & Breakdowns: Watch the price as it approaches the edges of the box. A movement outside the box can signal a potential breakout (if above the box) or a breakdown (if below the box). This is where momentum shifts can happen.
Momentum Confirmation: Once the price clearly moves out of the box, it indicates a momentum shift. If the price moves upwards out of the box, this can be seen as bullish momentum. Conversely, if the price moves downwards out of the box, this can be seen as bearish momentum.
To use the tool effectively, adjust the settings to suit your trading style, choose your preferred visual theme, and watch as the script highlights key consolidation areas on your chart.
Tip: To visualize fractals, consider using multiple instances of the "Consolidation Spotter" indicator, each set to a different timeframe. This approach allows you to observe consolidations nested within larger consolidations, offering deeper insights into market structures. 😉
lib_pivotLibrary "lib_pivot"
Object oriented implementation of Pivot methods.
method tostring(this)
Converts HLData to a json string representation
Namespace types: HLData
Parameters:
this (HLData) : HLData
Returns: string representation of Pivot
method tostring(this, date_format)
Namespace types: Pivot
Parameters:
this (Pivot)
date_format (simple string)
method tostring(this, date_format)
Namespace types: Pivot
Parameters:
this (Pivot )
date_format (simple string)
method get_color(this, mode)
Namespace types: PivotColors
Parameters:
this (PivotColors)
mode (int)
method get_label_text(this)
Namespace types: Pivot
Parameters:
this (Pivot)
method direction(this)
Namespace types: Pivot
Parameters:
this (Pivot)
method same_direction_as(this, other)
Namespace types: Pivot
Parameters:
this (Pivot)
other (Pivot)
method exceeds(this, price)
Namespace types: Pivot
Parameters:
this (Pivot)
price (float)
method exceeds(this, other)
Namespace types: Pivot
Parameters:
this (Pivot)
other (Pivot)
method exceeded_by(this, price)
Namespace types: Pivot
Parameters:
this (Pivot)
price (float)
method exceeded_by(this, other)
Namespace types: Pivot
Parameters:
this (Pivot)
other (Pivot)
method retracement_ratio(this, lastPivot, sec_lastPivot)
Namespace types: Pivot
Parameters:
this (Pivot)
lastPivot (Pivot)
sec_lastPivot (Pivot)
ratio_target(sec_lastPivot, lastPivot, target_ratio)
Parameters:
sec_lastPivot (Pivot)
lastPivot (Pivot)
target_ratio (float)
method update(this, ref_highest, ref_lowest)
Namespace types: HLData
Parameters:
this (HLData)
ref_highest (float)
ref_lowest (float)
method update(this, bar_time, bar_idx, price, prev)
Namespace types: Pivot
Parameters:
this (Pivot)
bar_time (int)
bar_idx (int)
price (float)
prev (Pivot)
method create_next(this, bar_time, bar_idx, price)
Namespace types: Pivot
Parameters:
this (Pivot)
bar_time (int)
bar_idx (int)
price (float)
HLData
HLData wraps the data received from ta.highest, ta.highestbars, ta.lowest, ta.lowestbars, as well as the reference sources
Fields:
length (series int) : lookback length for pivot points
highest_offset (series int) : offset to highest value bar
lowest_offset (series int) : offset to lowest value bar
highest (series float) : highest value within lookback bars
lowest (series float) : lowest value within lookback bars
new_highest (series bool) : update() will set this true if the current candle forms a new highest high at the last (current) bar of set period (length)
new_lowest (series bool) : update() will set this true if the current candle forms a new lowest low at the last (current) bar of set period (length)
new_highest_fractal (series bool) : update() will set this true if the current candle forms a new fractal high at the center of set period (length)
new_lowest_fractal (series bool) : update() will set this true if the current candle forms a new fractal low at the center of set period (length)
PivotColors
Pivot colors for different modes
Fields:
hh (series color) : Color for Pivot mode 2 (HH)
lh (series color) : Color for Pivot mode 1 (LH)
hl (series color) : Color for Pivot mode -1 (HL)
ll (series color) : Color for Pivot mode -2 (LL)
Pivot
Pivot additional pivot data around basic Point
Fields:
point (Point type from robbatt/lib_plot_objects/5)
mode (series int) : can be -2/-1/1/2 for LL/HL/LH/HH
price_movement (series float) : The price difference between this and the previous pivot point in the opposite direction
retracement_ratio (series float) : The ratio between this price_movement and the previous
prev (Pivot)
ulibLibrary "ulib"
Stochastic(length, d_smooth)
Parameters:
length
d_smooth
bull_stoch_condition(k, d)
Parameters:
k
d
ema_condition(ema_1, ema_2, ema_3)
Parameters:
ema_1
ema_2
ema_3
bull_fractal_condition(n)
Parameters:
n
Bull(Fractal, ema, stochastic_osc)
Parameters:
Fractal
ema
stochastic_osc
BEST ABCD Pattern Screener Deribit:DVOL BTC DXY scannerModified this script by Daveatt (based on Ricardo Santos Fractals)
to scan patterns in BTCUSD, ETHUSD, DVOL, DXY, DVOL/VV
