Mutanabby_AI __ OSC+ST+SQZMOMMutanabby_AI OSC+ST+SQZMOM: Multi-Component Trading Analysis Tool
Overview
The Mutanabby_AI OSC+ST+SQZMOM indicator combines three proven technical analysis components into a unified trading system, providing comprehensive market analysis through integrated oscillator signals, trend identification, and volatility assessment.
Core Components
Wave Trend Oscillator (OSC): Identifies overbought and oversold market conditions using exponential moving average calculations. Key threshold levels include overbought zones at 60 and 53, with oversold areas marked at -60 and -53. Crossover signals between the two oscillator lines generate entry opportunities, displayed as colored circles on the chart for easy identification.
Supertrend Indicator (ST): Determines overall market direction using Average True Range calculations with a 2.5 factor and 10-period ATR configuration. Green lines indicate confirmed uptrends while red lines signal downtrend conditions. The indicator automatically adapts to market volatility changes, providing reliable trend identification across different market environments.
Squeeze Momentum (SQZMOM): Compares Bollinger Bands with Keltner Channels to identify consolidation periods and potential breakout scenarios. Black squares indicate squeeze conditions representing low volatility periods, green triangles signal confirmed upward breakouts, and red triangles mark downward breakout confirmations.
Signal Generation Logic
Long Entry Conditions:
Green triangles from Squeeze Momentum component
Supertrend line transitioning to green
Bullish crossovers in Wave Trend Oscillator from oversold territory
Short Entry Conditions:
Red triangles from Squeeze Momentum component
Supertrend line transitioning to red
Bearish crossovers in Wave Trend Oscillator from overbought territory
Automated Risk Management
The indicator incorporates comprehensive risk management through ATR-based calculations. Stop losses are automatically positioned at 3x ATR distance from entry points, while three progressive take profit targets are established at 1x, 2x, and 3x ATR multiples respectively. All risk management levels are clearly displayed on the chart using colored lines and informative labels.
When trend direction changes, the system automatically clears previous risk levels and generates new calculations, ensuring all risk parameters remain current and relevant to existing market conditions.
Alert and Notification System
Comprehensive alert framework includes trend change notifications with complete trade setup details, squeeze release alerts for breakout opportunity identification, and trend weakness warnings for active position management. Alert messages contain specific trading pair information, timeframe specifications, and all relevant entry and exit level data.
Implementation Guidelines
Timeframe Selection: Higher timeframes including 4-hour and daily charts provide the most reliable signals for position trading strategies. One-hour charts demonstrate good performance for day trading applications, while 15-30 minute timeframes enable scalping approaches with enhanced risk management requirements.
Risk Management Integration: Limit individual trade risk to 1-2% of total capital using the automatically calculated stop loss levels for precise position sizing. Implement systematic profit-taking at each target level while adjusting stop loss positions to protect accumulated gains.
Market Volatility Adaptation: The indicator's ATR-based calculations automatically adjust to changing market volatility conditions. During high volatility periods, risk management levels appropriately widen, while low volatility conditions result in tighter risk parameters.
Optimization Techniques
Combine indicator signals with fundamental support and resistance level analysis for enhanced signal validation. Monitor volume patterns to confirm breakout strength, particularly when Squeeze Momentum signals develop. Maintain awareness of scheduled economic events that may influence market behavior independent of technical indicator signals.
The multi-component design provides internal signal confirmation through multiple alignment requirements, significantly reducing false signal occurrence while maintaining reasonable trade frequency for active trading strategies.
Technical Specifications
The Wave Trend Oscillator utilizes customizable channel length (default 10) and average length (default 21) parameters for optimal market sensitivity. Supertrend calculations employ ATR period of 10 with factor multiplier of 2.5 for balanced signal quality. Squeeze Momentum analysis uses Bollinger Band length of 20 periods with 2.0 multiplication factor, combined with Keltner Channel length of 20 periods and 1.5 multiplication factor.
Conclusion
The Mutanabby_AI OSC+ST+SQZMOM indicator provides a systematic approach to technical market analysis through the integration of proven oscillator, trend, and momentum components. Success requires thorough understanding of each element's functionality and disciplined implementation of proper risk management principles.
Practice with demo trading accounts before live implementation to develop familiarity with signal interpretation and trade management procedures. The indicator's systematic approach effectively reduces emotional decision-making while providing clear, objective guidelines for trade entry, management, and exit strategies across various market conditions.
"wave" için komut dosyalarını ara
Kumo no Nami Trend Strength Identifier T2[T69]🧠 Overview
Kumo no Nami is a custom trend strength indicator that combines Ichimoku cloud dynamics (Kumo) with wave momentum (Nami) to identify trend direction, reversals, squeezes, and breakouts using Z-Score analysis. It adapts to different modes (Ichimoku, MA, EMA) for a flexible interpretation of price structure tension vs. movement strength.
🔍 Core Logic
Kumo Width (Cloud Pressure): Measures the normalized spread (Z-Score) between two dynamic price levels (e.g., Senkou A-B or Base-Tenkan).
Nami Strength (Wave Energy): Measures how far current price dislocates from a recent range using Z-Score of the difference between close and Donchian/MA.
Z-Score Normalization: Ensures both metrics are statistically comparable, regardless of volatility regime.
Squeeze Detection: Identifies compression before potential volatility expansion.
Breakout/False Break: Detects whether movement is legitimate or noise.
Final Top/Bottom: Highlights a strong burst post-squeeze, often signaling exhaustion or trend climax.
⚙️ Features
🌀 Multiple Kumo Modes:
Kijun-Tenkan
Senkou A - B
SMA Fast - Slow
EMA Fast - Slow
🟨 Z-Score Based Squeeze Monitoring
🟥 Final Burst Alerts
🟩 Trend Continuation or Fake-out Detection
🎨 Dynamic Background Coloring for visual signal clarity
🔧 Configuration
📊 Inputs
Kumo Mode (kt, sab, sfs, efs) – Choose method to compute Kumo (Cloud) width.
Kumo Lookback – Lookback period for cloud Z-Score analysis.
Nami Lookback – Lookback period for wave dislocation measurement.
Squeeze Threshold – How low Z-Kumo must fall to signal potential squeeze.
Burst Thresholds:
Burst Kumo → Z-Kumo must rise above this to be considered bursting.
Burst Nami → Nami Strength threshold for final trend climax.
Ichimoku Config – Tenkan, Kijun, Senkou B, and displacement.
MA Config – For Fast/Slow variants, SMA/EMA lengths.
🧪 How It Works
Compute the Kumo Width depending on selected mode.
E.g., |Tenkan - Kijun| or |Senkou A - Senkou B|
Normalize this width with its Z-Score to get Z-Kumo Width.
Compute Nami Strength:
Z-Score of how far close deviates from a Donchian channel or moving average.
Evaluate signal logic based on the two:
📈 Behavior & Signals
Trend Range (Sideways Consolidation)
=>Z-Kumo < 0 and |Nami Strength| > 2
False Break (No meaningful price movement)
=>Z-Kumo < 1 and |Nami Strength| < 1
Squeeze Watch (Potential breakout loading)
=>Z-Kumo < Squeeze Threshold
Final Burst / Climax
=>Z-Kumo > 2.5 and |Nami Strength| > 3
Bullish Breakout
=>Z-Kumo > 1 and Nami Strength > 2 and not false break
Bearish Breakout
=>Z-Kumo > 1 and Nami Strength < -2 and not false break
Reversal Detection
Crossovers of Nami Strength across 0 (bull/bear) while not in squeeze
🧠 Advanced Concepts Used
Z-Score:
=>(value - mean) / standard deviation for detecting statistically significant moves.
Squeeze Principle:
=>Low volatility → potential buildup → expansion.
Price Dislocation (Wave Strength):
=>Measures how far current price is from its mean range.
=>Cloud Tension (Kumo Z-Score):
=>Reflects pressure or neutrality in the price structure.
Trend Confirmation:
=>Only if both metrics agree and no false break conditions are met.
Keltner Channels MTFKeltner Channels MTF | Adapted 🌌
Navigate the market’s wild waves with these Keltner Channels, a sleek spin on AlchimistOfCrypto’s Bollinger Bands! This Pine Script v6 indicator tracks price action like a radar, highlighting trends with scientific precision. 🧪
Key Features:
Customizable Channels: Adjust period and multiplier to map market volatility, signaling potential reversals when prices hit the upper or lower bands. 📈
MA Options: Switch between Exponential or Simple Moving Average for trend clarity. ⚙️
Band Styles: Select Average True Range, True Range, or Range to define volatility edges. 📏
Glow Effect: Illuminate bands with 8 vibrant themes (Neon, Grayscale, etc.) for visual pop. ✨
Trend Signals: Spot bullish/bearish shifts with glowing circles, flagging momentum changes. 💡
Alerts: Catch price breakouts or trend reversals at band edges, warning of potential market U-turns. 🚨
Perfect for traders decoding market trends with a touch of cosmic style! 🌠
Price Exhaustion Envelope [BackQuant]Price Exhaustion Envelope
Visual preview of the bands:
What it is
The Price Exhaustion Envelope (PEE) is a multi‑factor overextension detector wrapped inside a dynamic envelope framework. It measures how “tired” a move is by blending price stretch, volume surges, momentum and acceleration, plus optional RSI divergence. The result is a composite exhaustion score that drives both on‑chart signals and the adaptive width of three optional envelope bands around a smoothed baseline. When the score spikes above or below your chosen threshold, the script can flag exhaustion, paint candles, tint the background and fire alerts.
How it works under the hood
Exhaustion score
Price component: distance of close from its mean in standard deviation units.
Volume component: normalized volume pressure that highlights unusual participation.
Momentum component: rate of change and acceleration of price, scaled by their own volatility.
RSI divergence (optional): bullish and bearish divergences gently push the score lower or higher.
Mode control: choose Price, Volume, Momentum or Composite. Composite averages the main pieces for a balanced view.
Energy scale (0 to 100)
The composite score is pushed through a logistic transform to create an “energy” value. High energy (above 70 to 80) signals a move that may be running hot, while very low energy (below 20 to 30) points to exhaustion on the downside.
Envelope engine
Baseline: EMA of price over the main lookback length.
Width: base width is standard deviation times a multiplier.
Type selector:
• Static keeps the width fixed.
• Dynamic expands width in proportion to the absolute exhaustion score.
• Adaptive links width to the energy reading so bands breathe with market “heat.”
Smoothing: a short EMA on the width reduces jitter and keeps bands pleasant to trade around.
Band architecture
You can toggle up to three symmetric bands on each side of the baseline. They default to 1.0, 1.6 and 2.2 multiples of the smoothed width. Soft transparent fills create a layered thermograph of extension. The outermost band often maps to true blow‑off extremes.
On‑chart elements
Baseline line that flips color in real time depending on where price sits.
Up to three upper and lower bands with progressive opacity.
Triangle markers at fresh exhaustion triggers.
Tiny warning glyphs at extreme upper or lower breaches.
Optional bar coloring to visually tag exhausted candles.
Background halo when energy > 80 or < 20 for instant context.
A compact info table showing State, Score, Energy, Momentum score and where price sits inside the envelope (percent).
How to use it in trading
Mean reversion plays
When price pierces the outer band and an exhaustion marker prints, look for reversal candles or lower‑timeframe confirmation to fade the move back toward the baseline.
For conservative entries, wait for the composite score to roll back under the threshold or for energy to drop from extreme to neutral.
Set stops just beyond the extreme levels (use extreme_upper and extreme_lower as natural invalidation points). Targets can be the baseline or the opposite inner band.
Trend continuation with smart pullbacks
In strong trends, the first tag of Band 1 or Band 2 against the dominant direction often offers low‑risk continuation entries. Use energy readings: if energy is low on a pullback during an uptrend, a bounce is more likely.
Combine with RSI divergence: hidden bullish divergence near a lower band in an uptrend can be a powerful confirmation.
Breakout filtering
A breakout that occurs while the composite score is still moderate (not exhausted) has a higher chance of follow‑through. Skip signals when energy is already above 80 and price is punching the outer band, as the move may be late.
Watch env_position (Envelope %) in the table. Breakouts near 40 to 60 percent of the envelope are “healthy,” while those at 95 percent are stretched.
Scaling out and risk control
Use exhaustion alerts to trim positions into strength or weakness.
Trail stops just outside Band 2 or Band 3 to stay in trends while letting the envelope expand in volatile phases.
Multi‑timeframe confluence
Run the script on a higher timeframe to locate exhaustion context, then drill down to a lower timeframe for entries.
Opposite signals across timeframes (daily exhaustion vs. 5‑minute breakout) warn you to reduce size or tighten management.
Key inputs to experiment with
Lookback Period: larger values smooth the score and envelope, ideal for swing trading. Shorter values make it reactive for scalps.
Exhaustion Threshold: raise above 2.0 in choppy assets to cut noise, drop to 1.5 for smooth FX pairs.
Envelope Type: Dynamic is great for crypto spikes, Adaptive shines in stocks where volume and volatility wave together.
RSI Divergence: turn off if you prefer a pure price/volume model or if divergence floods the score in your asset.
Alert set included
Fresh upper exhaustion
Fresh lower exhaustion
Extreme upper breach
Extreme lower breach
RSI bearish divergence
RSI bullish divergence
Hook these to TradingView notifications so you get pinged the moment a move hits exhaustion.
Best practices
Always pair exhaustion signals with structure. Support and resistance, liquidity pools and session opens matter.
Avoid blindly shorting every upper signal in a roaring bull market. Let the envelope type help you filter.
Use the table to sanity‑check: a very high score but mid‑range env_position means the band may still be wide enough to absorb more movement.
Backtest threshold combinations on your instrument. Different tickers carry different volatility fingerprints.
Final note
Price Exhaustion Envelope is a flexible framework, not a turnkey system. It excels as a context layer that tells you when the crowd is pressing too hard or when a move still has fuel. Combine it with sound execution tactics, risk limits and market awareness. Trade safe and let the envelope breathe with the market.
Trigonometric StochasticTrigonometric Stochastic - Mathematical Smoothing Oscillator
Overview
A revolutionary approach to stochastic oscillation using sine wave mathematical smoothing. This indicator transforms traditional stochastic calculations through trigonometric functions, creating an ultra-smooth oscillator that reduces noise while maintaining sensitivity to price changes.
Mathematical Foundation
Unlike standard stochastic oscillators, this version applies sine wave smoothing:
• Raw Stochastic: (close - lowest_low) / (highest_high - lowest_low) × 100
• Trigonometric Smoothing: 50 + 50 × sin(2π × raw_stochastic / 100)
• Result: Naturally smooth oscillator with mathematical precision
Key Features
Advanced Smoothing Technology
• Sine Wave Filter: Eliminates choppy movements while preserving signal integrity
• Natural Boundaries: Mathematically constrained between 0-100
• Reduced False Signals: Trigonometric smoothing filters market noise effectively
Traditional Stochastic Levels
• Overbought Zone: 80 level (dashed line)
• Oversold Zone: 20 level (dashed line)
• Midline: 50 level (dotted line) - equilibrium point
• Visual Clarity: Clean oscillator panel with clear level markings
Smart Signal Generation
• Anti-Repaint Logic: Uses confirmed previous bar values
• Buy Signals: Generated when crossing above 30 from oversold territory
• Sell Signals: Generated when crossing below 70 from overbought territory
• Crossover Detection: Precise entry/exit timing
Professional Presentation
• Separate Panel: Dedicated oscillator window (overlay=false)
• Price Format: Formatted as price indicator with 2-decimal precision
• Theme Adaptive: Automatically matches your chart color scheme
Parameters
• Cycle Length (5-200): Period for highest/lowest calculations
- Shorter periods = more sensitive, more signals
- Longer periods = smoother, fewer but stronger signals
Trading Applications
Momentum Analysis
• Overbought/Oversold: Clear visual identification of extreme levels
• Momentum Shifts: Early detection of momentum changes
• Trend Strength: Monitor oscillator position relative to midline
Signal Trading
• Long Entries: Buy when crossing above 30 (oversold bounce)
• Short Entries: Sell when crossing below 70 (overbought rejection)
• Confirmation Tool: Use with trend indicators for higher probability trades
Divergence Detection
• Bullish Divergence: Price makes lower lows, oscillator makes higher lows
• Bearish Divergence: Price makes higher highs, oscillator makes lower highs
• Early Warning: Spot potential trend reversals before they occur
Trading Strategies
Scalping (5-15min timeframes)
• Use cycle length 10-14 for quick signals
• Focus on 20/80 level bounces
• Combine with price action confirmation
Swing Trading (1H-4H timeframes)
• Use cycle length 20-30 for reliable signals
• Wait for clear crossovers with momentum
• Monitor divergences for reversal setups
Position Trading (Daily+ timeframes)
• Use cycle length 50+ for major signals
• Focus on extreme readings (below 10, above 90)
• Combine with fundamental analysis
Advantages Over Standard Stochastic
1. Smoother Action: Sine wave smoothing reduces whipsaws
2. Mathematical Precision: Trigonometric functions provide consistent behavior
3. Maintained Sensitivity: Smoothing doesn't compromise signal quality
4. Reduced Noise: Cleaner signals in volatile markets
5. Visual Appeal: More aesthetically pleasing oscillator movement
Best Practices
• Market Context: Consider overall trend direction
• Multiple Timeframe: Confirm signals on higher timeframes
• Risk Management: Always use proper position sizing
• Backtesting: Test parameters on your preferred instruments
• Combination: Works excellently with trend-following indicators
Built-in Alerts
• Buy Alert: Trigonometric stochastic oversold crossover
• Sell Alert: Trigonometric stochastic overbought crossunder
Technical Specifications
• Pine Script Version: v6
• Panel: Separate oscillator window
• Format: Price indicator with 2-decimal precision
• Performance: Optimized for all timeframes
• Compatibility: Works with all instruments
Free and open-source indicator. Modify, improve, and share with the community!
Educational Value: Perfect for traders wanting to understand how mathematical smoothing improves oscillators and trigonometric applications in technical analysis.
Triple Momentum Core v1🧠 Technical Structure:
Triple Momentum Core analyzes the underlying wave of price movement through a three-stage system:
1. 🔵 Follow Line – The First Spark of Momentum:
Constructed using Bollinger Bands and ATR, this line detects the very first signs of directional price expansion. It gently whispers when the market begins stretching with force in one direction.
2. 🟢 SuperTrend – Confirmation and Directional Validation:
After the initial move, SuperTrend acts as the second checkpoint — validating whether the price action is evolving into a genuine trend or fading out. It confirms whether the impulse has the strength to sustain.
3. 🔴 PMax – Core Trend & Structural Anchor:
Based on Moving Average and ATR logic, PMax tracks the heartbeat of the trend. It serves as a dynamic structural boundary — critical for identifying trend continuation and managing risk.
4. 🟡 PMax MA Line – Smooth Trend Pulse & Adaptive Guide:
This yellow moving average line within the PMax system softly follows the overall trend flow, without reacting to sharp price noise. It acts as a balanced, stable guide to gauge the solidity of the trend’s body structure.
(If you prefer a cleaner view without any moving average lines, you can disable it from the settings.)
🧠 Technical Structure:
Triple Momentum Core analyzes the underlying wave of price movement through a three-stage system:
1. 🔵 Follow Line – The First Spark of Momentum:
Constructed using Bollinger Bands and ATR, this line detects the very first signs of directional price expansion. It gently whispers when the market begins stretching with force in one direction.
2. 🟢 SuperTrend – Confirmation and Directional Validation:
After the initial move, SuperTrend acts as the second checkpoint — validating whether the price action is evolving into a genuine trend or fading out. It confirms whether the impulse has the strength to sustain.
3. 🔴 PMax – Core Trend & Structural Anchor:
Based on Moving Average and ATR logic, PMax tracks the heartbeat of the trend. It serves as a dynamic structural boundary — critical for identifying trend continuation and managing risk.
4. 🟡 PMax MA Line – Smooth Trend Pulse & Adaptive Guide:
This yellow moving average line within the PMax system softly follows the overall trend flow, without reacting to sharp price noise. It acts as a balanced, stable guide to gauge the solidity of the trend’s body structure.
(If you prefer a cleaner view without any moving average lines, you can disable it from the settings.)
💡 Why “Triple Momentum Core”?
Because this indicator doesn’t just detect movement — it breaks it down into its essential phases:
Ignition, validation, and confirmation.
Each layer captures a unique and essential part of price behavior:
The first reaction (Follow Line) ignites the initial spark.
The second reaction (SuperTrend) confirms whether that spark will become a real trend.
The third and final layer (PMax) structurally anchors and follows that trend.
That’s why we call it Triple Momentum Core:
A synchronized 3-engine momentum system working in harmony to capture the lifecycle of a trend — from spark to structure.
SCPEM - Socionomic Crypto Peak Model (0-85 Scale)SCPEM Indicator Overview
The SCPEM (Socionomic Crypto Peak Evaluation Model) indicator is a TradingView tool designed to approximate cycle peaks in cryptocurrency markets using socionomic theory, which links market behavior to collective social mood. It generates a score from 0-85 (where 85 signals extreme euphoria and high reversal risk) and plots it as a blue line on the chart for visual backtesting and real-time analysis.
#### How It Works
The indicator uses technical proxies to estimate social mood factors, as Pine Script cannot fetch external data like sentiment indices or social media directly. It calculates a weighted composite score on each bar:
- Proxies derive from price, volume, and volatility data.
- The raw sum of factor scores (max ~28) is normalized to 0-85.
- The score updates historically for backtesting, showing mood progression over time.
- Alerts trigger if the score exceeds 60, indicating high peak probability.
Users can adjust inputs (e.g., lengths for RSI or pivots) to fine-tune for different assets or timeframes.
Metrics Used (Technical Proxies)
Crypto-Specific Sentiment
Approximated by RSI (overbought levels indicate greed).
Social Media Euphoria
Based on volume relative to its SMA (spikes suggest herding/FOMO).
Broader Social Mood Proxies
Derived from ATR volatility (high values signal uncertain/mixed mood).
Search and Cultural Interest Proxied by OBV trend (rising accumulation implies growing interest).
Socionomic Wildcard
Uses Bollinger Band width (expansion for positive mood, contraction for negative).
Elliott Wave Position
Counts recent price pivots (more swings indicate later wave stages and exhaustion).
N-Pattern Detector (Advanced Logic)Introduction
The N-Pattern Detector (Advanced Logic) is a powerful Pine Script-based tool designed to identify a specific price structure known as the "N-pattern", which often indicates trend continuation or potential breakout points in the market. This pattern combines zigzag pivot logic, retracement filters, volume confirmation, and trend alignment, offering high-probability trading signals.
It is ideal for traders who want to automate pattern detection while applying smart filters to reduce false signals in various markets — including stocks, forex, crypto, and indices.
What is the N-Pattern?
The N-pattern is a 3-leg price formation consisting of points A-B-C-D. It typically follows this structure:
Bullish N-Pattern:
A → Low Pivot
B → Higher High (Impulse)
C → Higher Low (Retracement)
D → Breakout above B (Confirmation)
Bearish N-Pattern:
A → High Pivot
B → Lower Low (Impulse)
C → Lower High (Retracement)
D → Breakdown below B (Confirmation)
The pattern essentially reflects a trend–pullback–breakout structure, making it suitable for continuation trades.
Key Features
1. Intelligent ZigZag Pivot Detection
Uses pivot highs/lows to define key swing points (A, B, C).
Adjustable ZigZag depth to control pattern sensitivity.
Filters noise and avoids false signals in volatile markets.
2. Retracement Validation
Validates the B→C leg as a proper pullback using Fibonacci-based thresholds.
User-defined min and max retracement settings (e.g., 38.2% to 78.6% of A→B leg).
3. Trend Filter via EMA
Filters patterns based on trend direction using a customizable EMA (e.g., 200 EMA).
Only detects bullish patterns above EMA and bearish patterns below EMA (optional).
4. Volume Confirmation
Ensures that impulse legs (A→B, C→D) are supported by stronger volume than the correction leg (B→C).
Adds another layer of confirmation and reliability to detected patterns.
5. Target Projections
Automatically draws 100% A→B projected target from point C.
Optional Fibonacci extensions at 1.272 and 1.618 levels for take-profit planning.
Visually plotted on the chart with colored dashed/dotted lines.
6. Clear Visuals & Labels
Connects all pattern points with colored lines.
Clearly labels points A, B, C, D on the chart.
Uses customizable colors for bullish and bearish patterns.
Includes real-time alerts when a valid pattern is detected.
How to Use It
Add to Chart
Apply the indicator to any chart and time frame. It works across all asset classes.
Adjust Inputs (Optional)
Set ZigZag Depth to control pivot detection sensitivity.
Define Min/Max Retracement levels to match your trading style.
Enable or disable Trend and Volume filters for cleaner signals.
Customize EMA length (default: 200) for trend validation.
Wait for Pattern Confirmation
The indicator constantly scans for valid N-patterns.
A pattern is confirmed only after point D forms (breakout or breakdown).
You’ll see the full pattern drawn with target levels.
Set Alerts
Alerts trigger automatically on confirmation of a bullish or bearish pattern.
You can customize these in TradingView’s alerts panel.
Initial Balance Wave MapThis indicator visualizes the Initial Balance (IB) range for any session, marking the first hour's high and low. It includes optional midpoints, extensions (e.g. 1.5x IB, 2x IB), and customizable time windows. Additional features allow users to display session open, high, low, close, and VWAP reference points. Designed to support price action and session structure analysis, it adapts to various global futures and FX market opens. All display elements are optional and fully configurable.
This updated indicator builds upon the open-source foundation by @noop-noop with enhancements and user-facing labels tailored for Auction Market Theory, scalping, and structure-based trade setups.
Key updated Featured: Multiple previous day's IB levels carry forward into the current day's chart, as opposed to just the previous day's levels carrying forward to the new IB time.
🙌 Credits:
This script builds upon the excellent open-source work by @noop-noop. Original script available here .
Alt Szn Oracle - Institutional GradeThe Alt Szn Oracle is a macro-level indicator built to help traders front-run altseason by tracking liquidity, dominance rotation, sentiment, and capital flows—all in one signal. It’s designed for those who don’t just chase pumps, but want to understand when the tide is turning and why. This tool doesn't predict specific coin breakouts—it tells you when the market as a whole is gearing up to rotate into higher beta assets like altcoins, including memes and microcaps.
The index consolidates ten macro inputs into a normalized, smoothed score from 0–100. These include Bitcoin and Ethereum dominance, ETH/BTC, altcoin market cap (Total3), relative volume flows, and stablecoin supply (USDT, USDC, DAI)—which act as proxies for risk-on appetite and dry powder entering the system. It also incorporates manually updated sentiment metrics from Google Trends and the Fear & Greed Index, giving it a behavioral edge that most indicators lack.
The logic is simple but powerful: when BTC dominance is falling, ETH/BTC is rising, altcoin volume increases relative to BTC/ETH, and stablecoins start moving—you're likely in the early innings of rotation. The index is also filtered through a volatility threshold and smoothed with an EMA to eliminate chop and fakeouts.
Use this indicator on macro charts like TOTAL3, TOTAL2, or ETHBTC to gauge market health, or overlay it on specific coins like PEPE, DOGE, or SOL to confirm if the tide is in your favor. Interpreting the score is straightforward: readings above 80 suggest euphoria and signal it’s time to de-risk, 60–80 indicates expansion and confirms altseason is underway, 40–60 is neutral, and 20–40 is a capitulation zone where smart money accumulates.
What sets this apart is that it doesn’t just track price—it reflects the flow of capital, the positioning of liquidity, and the sentiment of the crowd. Most altseason indicators are lagging, overfitted, or too simplistic. This one is modular, forward-looking, and grounded in real capital rotation theory.
If you're a trader who wants to time the cycle, not guess it, this is your tool. Refine it, fork it, or expand it to your niche—DeFi, NFTs, meme coins, or L1s. It’s a framework for reading the macro winds, not a signal service. Use it with discipline, and you’ll catch the wave while others drown in noise.
Golden Ratio Trend Persistence [EWT]Golden Ratio Trend Persistence
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Overview
The Golden Ratio Trend Persistence is a dynamic tool designed to identify the strength and persistence of market trends. It operates on a simple yet powerful premise: a trend is likely to continue as long as it doesn't retrace beyond the key Fibonacci golden ratio of 61.8%.
This indicator automatically identifies the most significant swing high or low and plots a single, dynamic line representing the 61.8% retracement level of the current move. This line acts as a "line in the sand" for the prevailing trend. The background color also changes to provide an immediate visual cue of the current market direction.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The Power of the Golden Ratio (61.8%)
The golden ratio (ϕ≈1.618) and its inverse (0.618, or 61.8%) are fundamental mathematical constants that appear throughout nature, art, and science, often representing harmony and structure. In financial markets, this ratio is a cornerstone of Fibonacci analysis and is considered one of the most critical levels for price retracements.
Market movements are not linear; they progress in waves of impulse and correction. The 61.8% level often acts as the ultimate point of support or resistance. A trend that can hold this level demonstrates underlying strength and is likely to persist. A breach of this level, however, suggests a fundamental shift in market sentiment and a potential reversal.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
How to Use This Indicator
This indicator is designed for clarity and ease of use.
Identifying the Trend : The visual cues make the current trend instantly recognizable.
A teal line with a teal background signifies a bullish trend. The line acts as dynamic support.
A maroon line with a maroon background signifies a bearish trend. The line acts as dynamic resistance.
Confirming Trend Persistence : As long as the price respects the plotted level, the trend is considered intact.
In an uptrend, prices should remain above the teal line. The indicator will automatically adjust its anchor to new, higher lows, causing the support line to trail the price.
In a downtrend, prices should remain below the maroon line.
Spotting Trend Reversals : The primary signal is a trend reversal, which occurs when the price closes decisively beyond the plotted level.
Potential Sell Signal : When the price closes below the teal support line, it indicates that buying pressure has failed, and the uptrend is likely over.
Potential Buy Signal : When the price closes above the maroon resistance line, it indicates that selling pressure has subsided, and a new uptrend may be starting.
Think of this tool as an intelligent, adaptive trailing stop that is based on market structure and the time-tested principles of Fibonacci analysis.
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Input Parameters
You can customize the indicator's sensitivity through the following inputs in the settings menu:
Pivot Lookback Left : This number defines how many bars to the left of a candle must be lower (for a pivot high) or higher (for a pivot low) to identify a potential swing point. A higher value will result in fewer, but more significant, pivots being detected.
Pivot Lookback Right : This defines the number of bars that must close to the right before a swing point is confirmed. This parameter prevents the indicator from repainting. A higher value increases confirmation strength but also adds a slight lag.
Fibonacci Ratio : While the default is the golden ratio (0.618), you can adjust this to other key Fibonacci levels, such as 0.5 (50%) or 0.382 (38.2%), to test for different levels of trend persistence.
Adjusting these parameters allows you to fine-tune the indicator for different assets, timeframes, and trading styles, from short-term scalping to long-term trend following.
Quantum Harmonic Oscillator Overlay🧪 Quantum Harmonic Oscillator Overlay
A visual model of price behavior using quantum harmonic oscillation principles
📜 Indicator Overview
The Quantum Harmonic Oscillator Overlay applies concepts from both classical physics (harmonic motion) and quantum mechanics (energy states) to model and visualize how price orbits around a central trend line. It overlays a Linear Regression line (representing the “mean position” or ground state of price) and calculates surrounding energy levels (σ-zones) akin to quantum shells that price can "jump" between.
This indicator is particularly useful for visualizing mean reversion, volatility compression/expansion, and momentum-driven price breakthroughs.
🧠 Core Concepts
Linear Regression Line (LSR): This is the calculated center of gravity or equilibrium path of price over a user-defined period. Think of it like the lowest energy state or central axis around which price vibrates.
Standard Deviation Zones (σ-levels):
1σ: The majority of normal price activity; within this range, price tends to fluctuate if in balance.
2σ: Indicates volatility or possible breakout pressure.
3σ: Represents extreme movement — a phase shift in energy, potentially leading to reversal or continuation with higher momentum.
Quantum Analogy: Just like in a quantum harmonic oscillator, particles (here, prices) move probabilistically between discrete energy states. The further the price moves from the center, the more "energy" (momentum, volume, volatility) is implied.
⚙️ Input Parameters
Setting Description
Linear Regression Length The number of bars used to calculate the regression trend (default 100). Affects the central path and responsiveness.
σ Multipliers (1σ, 2σ, 3σ) Determine how far each band is from the regression line. Adjusting these can highlight different price behaviors.
Show Energy Level Zones Toggle visibility of the colored bands around the regression line.
Show LSR Center Line Toggles visibility of the white Linear Regression line itself.
🎨 Visual Components
Color Zone Interpretation
✅ Green ±1σ Normal oscillation / mean reversion area. Ideal for range-bound strategies.
🟧 Orange ±2σ Warning zone; price may be gaining momentum or volatility.
🔴 Red ±3σ High-momentum state or anomaly. These regions may imply trend exhaustion, reversals, or breakouts.
White Line: The LSR — the average trajectory of the price movement.
Pink Dots: Appear when price exceeds Zone 3 (outside ±3σ) — a signal of extreme behavior or a possible regime shift.
📈 How to Use This Indicator
1. Detect Overextensions
When price touches or breaches the 3σ zone, it is likely overextended. This can be used to anticipate potential snapbacks or strong breakout trends.
2. Identify Mean Reversion Trades
If price exits the 2σ or 3σ zones and returns toward the center line, this signals a likely mean reversion setup.
3. Volatility Compression or Expansion
Flat zones between σ levels suggest calm markets; widening bands suggest expanding volatility.
4. Use with Confirmation Tools
Combine with momentum oscillators (MACD, RSI) or volume-based signals to confirm reversals or continuation outside Zone 3.
🔮 Philosophical Note
This indicator embodies the metaphor that the market behaves like a quantum oscillator — price particles exist in a probabilistic field and jump between discrete zones of volatility and energy. Tracking these transitions allows the trader to see price behavior as rhythmic, wave-like, and multidimensional rather than purely linear.
Chaithanya Tattva Volume Zones📜 "Chaitanya Tattva" Volume Zones:-
A Sacred Framework of Supply, Demand & Market Energy
In the world of financial markets, price is said to reflect all information. But the true pulse of the market — its life force, its intent, and its moment of truth — is most vividly expressed not in price itself, but in volume.
Chaitanya Tattva Volume Zones is a spiritually inspired volume-based tool that transforms your chart into a canvas of market consciousness, revealing moments where supply and demand engage in visible energetic spikes. These moments are often disguised as ordinary candles, but with this tool, you uncover zones of intent — footprints left by the market’s deeper intelligence.
🌟 Why “Chaitanya Tattva”?
Chaitanya (चैतन्य) is a Sanskrit word meaning consciousness, awareness, or the spark of life energy. It is that which animates — the subtle intelligence behind all movement.
Tattva (तत्त्व) refers to essence, truth, or the underlying principle of a thing. In classical yogic philosophy, the tattvas are the elemental building blocks of reality.
Together, Chaitanya Tattva represents the conscious essence — the living pulse that animates the market through volume surges and imbalances.
This tool is not just a technical indicator — it is a spiritual observation device that aligns with the rhythm of volume and price action. It doesn't predict the market. It reveals when the market has already spoken — loudly, clearly, and energetically.
📈 What Does the Tool Do?
Chaitanya Tattva Volume Zones identifies exceptional volume spikes within the recent price history and visually marks the areas where market intent has been most active.
Specifically, the tool:
Scans for volume spikes that exceed all the volume of the last N bars (default is 20)
Confirms whether the spike happened on a bullish candle (close > open) or bearish candle (close < open)
For a bullish spike, it marks a Supply Zone — the area between the high and close of the candle
For a bearish spike, it marks a Demand Zone — the area between the low and close
Visually paints these zones with soft translucent boxes (red for supply, green for demand) that extend forward across multiple bars
🧘♂️ The Spiritual Framework
🔴 Supply = "Agni" — The Fire of Expansion
When a bullish candle erupts with historically high volume, it symbolizes the fire (Agni) of market optimism and upward expansion. It means that buyers have absorbed available supply at that level and established dominance — but such fire may also signal exhaustion, making it a potential supply barrier if price returns.
These Supply Zones are areas where:
Sellers are likely to re-engage
Smart money may be unloading
Future resistance can be anticipated
But unlike traditional indicators, this tool doesn’t guess. It reacts only to a clear volume-based event — when market energy surges — and locks in that awareness through zone marking.
🟢 Demand = "Prithvi" — The Grounding of Price
On the other hand, a bearish candle with extremely high volume represents the Earth (Prithvi) — grounding the price with firm hands. A strong volume drop often means buyers are stepping in, absorbing the selling pressure.
These Demand Zones are areas where:
Buying interest is proven
Market memory is stored
Future support can be expected
By respecting these zones, you're aligning your trading with natural market boundaries — not theoretical ones.
🧠 How Is It Different from Regular Volume Tools?
While most volume indicators show bars on a lower panel, they leave interpretation up to the trader. “High” or “low” becomes subjective.
Chaitanya Tattva Volume Zones is different:
It quantifies "spike": a bar must exceed all previous N volumes
It qualifies the intent: was the spike bullish or bearish?
It marks zones on the price chart: no need to guess levels
It preserves market memory: the zones persist visually for easy reference
In essence, this tool doesn’t just report volume — it interprets volume’s context and visually encodes it into the chart.
🧘 How to Use
1. Support/Resistance Mapping
Use the tool to understand where volume proved itself. If price revisits a red zone, expect possible rejection (resistance). If price revisits a green zone, expect possible absorption (support).
2. Entry Triggers
You may enter:
Long near demand zones if bullish confirmation appears
Short near supply zones if bearish confirmation appears
3. Stop Placement
Stops can be placed just beyond the zone boundary to align with areas where smart money historically defended.
4. Breakout Confidence
When price breaks through one of these zones with momentum, it often signals a new energetic wave — the old balance has been overcome.
🔔 Key Features
Volume spike detection across any timeframe
Clear visual zones — no clutter, no lag
Highly customizable: zone width, volume lookback, colors
Philosophy-aligned with supply and demand theory, Wyckoff, and Order Flow
🌌 A Metaphysical View of Volume
In yogic science, volume is akin to Prana — life-force energy. A market is not moved by price alone but by intent, force, and participation — all encoded in volume.
Just as a human body pulses with blood when action intensifies, the market pulses with volume when institutional decisions are made.
These pulses become sacred footprints — and Chaitanya Tattva Volume Zones helps you walk mindfully among them.
🔮 Final Thoughts
In a sea of indicators that shout at you with every tick, Chaitanya Tattva is calm. It speaks only when energy concentrates, only when the market sends a signal born of intent.
It doesn’t predict.
It doesn’t repaint.
It simply shows the truth, when the truth becomes undeniable.
Like a sage that speaks only when needed, it waits for volume to prove itself — then draws a memory into space, a zone where traders can re-align their actions with what the market has already honored.
Use it not just to trade —
But to listen.
To observe.
To follow the Chaitanya — the conscious pulse of the market’s own breath.
BK AK-SILENCER (P8N)🚨Introducing BK AK-SILENCER (P8N) — Institutional Order Flow Tracking for Silent Precision🚨
After months of meticulous tuning and refinement, I'm proud to unleash the next weapon in my trading arsenal—BK AK-SILENCER (P8N).
🔥 Why "AK-SILENCER"? The True Meaning
Institutions don’t announce their moves—they move silently, hidden beneath the noise. The SILENCER is built specifically to detect and track these stealth institutional maneuvers, giving you the power to hunt quietly, execute decisively, and strike precisely before the market catches on.
🔹 "AK" continues the legacy, honoring my mentor, A.K., whose teachings on discipline, precision, and clarity form the cornerstone of my trading.
🔹 "SILENCER" symbolizes the stealth aspect of institutional trading—quiet but deadly moves. This indicator equips you to silently track, expose, and capitalize on their hidden footprints.
🧠 What Exactly is BK AK-SILENCER (P8N)?
It's a next-generation Cumulative Volume Delta (CVD) tool crafted specifically for traders who hunt institutional order flow, combining adaptive volatility bands, enhanced momentum gradients, and precise divergence detection into a single deadly-accurate weapon.
Built for silent execution—tracking moves quietly and trading with lethal precision.
⚙️ Core Weapon Systems
✅ Institutional CVD Engine
→ Dynamically measures hidden volume shifts (buying/selling pressure) to reveal institutional footprints that price alone won't show.
✅ Adaptive AK-9 Bollinger Bands
→ Bollinger Bands placed around a custom CVD signal line, pinpointing exactly when institutional accumulation or distribution reaches critical extremes.
✅ Gradient Momentum Intelligence
→ Color-coded momentum gradients reveal the strength, speed, and silent intent behind institutional order flow:
🟢 Strong Bullish (aggressive buying)
🟡 Moderate Bullish (steady accumulation)
🔵 Neutral (balance)
🟠 Moderate Bearish (quiet distribution)
🔴 Strong Bearish (aggressive selling)
✅ Silent Divergence Detection
→ Instantly spots divergence between price and hidden volume—your earliest indication that institutions are stealthily reversing direction.
✅ Background Flash Alerts
→ Visually highlights institutional extremes through subtle background flashes, alerting you quietly yet powerfully when market-moving players make their silent moves.
✅ Structural & Institutional Clarity
→ Optional structural pivots, standard deviation bands, volume profile anchors, and session lines clearly identify the exact levels institutions defend or attack silently.
🛡️ Why BK AK-SILENCER (P8N) is Your Edge
🔹 Tracks Institutional Footprints—Silently identifies hidden volume signals of institutional intentions before they’re obvious.
🔹 Precision Execution—Cuts through noise, allowing you to execute silently, confidently, and precisely.
🔹 Perfect for Traders Using:
Elliott Wave
Gann Methods (Angles, Squares)
Fibonacci Time & Price
Harmonic Patterns
Market Profile & Order Flow Analysis
🎯 How to Use BK AK-SILENCER (P8N)
🔸 Institutional Reversal Hunting (Stealth Mode)
Bearish divergence + CVD breaking below lower BB → stealth short signal.
Bullish divergence + CVD breaking above upper BB → quiet, early long entry.
🔸 Momentum Confirmation (Silent Strength)
Strong bullish gradient + CVD above upper BB → follow institutional buying quietly.
Strong bearish gradient + CVD below lower BB → confidently short institutional selling.
🔸 Noise Filtering (Patience & Precision)
Neutral gradient (blue) → remain quiet, wait patiently to strike precisely when institutional activity resumes.
🔸 Structural Precision (Institutional Levels)
Optional StdDev, POC, Value Areas, Session Anchors clearly identify exact institutional defense/offense zones.
🙏 Final Thoughts
Institutions move in silence, leaving subtle footprints. BK AK-SILENCER (P8N) is your specialized weapon for tracking and hunting their quiet, decisive actions before the market reacts.
🔹 Dedicated in deep gratitude to my mentor, A.K.—whose silent wisdom shapes every line of code.
🔹 Engineered for the disciplined, quiet hunter who knows when to wait patiently and when to strike decisively.
Above all, honor and gratitude to Gd—the ultimate source of wisdom, clarity, and disciplined execution. Without Him, markets are chaos. With Him, we move silently, purposefully, and precisely.
⚡ Stay Quiet. Stay Precise. Hunt Silently.
🔥 BK AK-SILENCER (P8N) — Track the Silent Moves. Strike with Precision. 🔥
May Gd bless every silent step you take. 🙏
Contrarian RSIContrarian RSI Indicator
Pairs nicely with Contrarian 100 MA (optional hide/unhide buy/sell signals)
Description
The Contrarian RSI is a momentum-based technical indicator designed to identify potential reversal points in price action by combining a unique RSI calculation with a predictive range model inspired by the "Contrarian 5 Levels" logic. Unlike traditional RSI, which measures price momentum based solely on price changes, this indicator integrates a smoothed, weighted momentum calculation and predictive price ranges to generate contrarian signals. It is particularly suited for traders looking to capture reversals in trending or range-bound markets.
This indicator is versatile and can be used across various timeframes, though it performs best on higher timeframes (e.g., 1H, 4H, or Daily) due to reduced noise and more reliable signals. Lower timeframes may require additional testing and careful parameter tuning to optimize performance.
How It Works
The Contrarian RSI combines two primary components:
Predictive Ranges (5 Levels Logic): This calculates a smoothed price average that adapts to market volatility using an ATR-based mechanism. It helps identify significant price levels that act as potential support or resistance zones.
Contrarian RSI Calculation: A modified RSI calculation that uses weighted momentum from the predictive ranges to measure buying and selling pressure. The result is smoothed and paired with a user-defined moving average to generate clear signals.
The indicator generates buy (long) and sell (exit) signals based on crossovers and crossunders of user-defined overbought and oversold levels, making it ideal for contrarian trading strategies.
Calculation Overview
Predictive Ranges (5 Levels Logic):
Uses a custom function (pred_ranges) to calculate a dynamic price average (avg) based on the ATR (Average True Range) multiplied by a user-defined factor (mult).
The average adjusts only when the price moves beyond the ATR threshold, ensuring responsiveness to significant price changes while filtering out noise.
This calculation is performed on a user-specified timeframe (tf5Levels) for multi-timeframe analysis.
Contrarian RSI:
Compares consecutive predictive range values to calculate gains (g) and losses (l) over a user-defined period (crsiLength).
Applies a Gaussian weighting function (weight = math.exp(-math.pow(i / crsiLength, 2))) to prioritize recent price movements.
Computes a "wave ratio" (net_momentum / total_energy) to normalize momentum, which is then scaled to a 0–100 range (qrsi = 50 + 50 * wave_ratio).
Smooths the result with a 2-period EMA (qrsi_smoothed) for stability.
Moving Average:
Applies a user-selected moving average (SMA, EMA, WMA, SMMA, or VWMA) with a customizable length (maLength) to the smoothed RSI (qrsi_smoothed) to generate the final indicator value (qrsi_ma).
Signal Generation:
Long Entry: Triggered when qrsi_ma crosses above the oversold level (oversoldLevel, default: 1).
Long Exit: Triggered when qrsi_ma crosses below the overbought level (overboughtLevel, default: 99).
Entry and Exit Rules
Long Entry: Enter a long position when the Contrarian RSI (qrsi_ma) crosses above the oversold level (default: 1). This suggests the asset is potentially oversold and due for a reversal.
Long Exit: Exit the long position when the Contrarian RSI (qrsi_ma) crosses below the overbought level (default: 99), indicating a potential overbought condition and a reversal to the downside.
Customization: Adjust overboughtLevel and oversoldLevel to fine-tune sensitivity. Lower timeframes may benefit from tighter levels (e.g., 20 for oversold, 80 for overbought), while higher timeframes can use extreme levels (e.g., 1 and 99) for stronger reversals.
Timeframe Considerations
Higher Timeframes (Recommended): The indicator is optimized for higher timeframes (e.g., 1H, 4H, Daily) due to its reliance on predictive ranges and smoothed momentum, which perform best with less market noise. These timeframes typically yield more reliable reversal signals.
Lower Timeframes: The indicator can be used on lower timeframes (e.g., 5M, 15M), but signals may be noisier and require additional confirmation (e.g., from price action or other indicators). Extensive backtesting and parameter optimization (e.g., adjusting crsiLength, maLength, or mult) are recommended for lower timeframes.
Inputs
Contrarian RSI Length (crsiLength): Length for RSI momentum calculation (default: 5).
RSI MA Length (maLength): Length of the moving average applied to the RSI (default: 1, effectively no MA).
MA Type (maType): Choose from SMA, EMA, WMA, SMMA, or VWMA (default: SMA).
Overbought Level (overboughtLevel): Upper threshold for exit signals (default: 99).
Oversold Level (oversoldLevel): Lower threshold for entry signals (default: 1).
Plot Signals on Main Chart (plotOnChart): Toggle to display signals on the price chart or the indicator panel (default: false).
Plotted on Lower:
Plotted on Chart:
5 Levels Length (length5Levels): Length for predictive range calculation (default: 200).
Factor (mult): ATR multiplier for predictive ranges (default: 6.0).
5 Levels Timeframe (tf5Levels): Timeframe for predictive range calculation (default: chart timeframe).
Visuals
Contrarian RSI MA: Plotted as a yellow line, representing the smoothed Contrarian RSI with the applied moving average.
Overbought/Oversold Lines: Red line for overbought (default: 99) and green line for oversold (default: 1).
Signals: Blue circles for long entries, white circles for long exits. Signals can be plotted on the main chart (plotOnChart = true) or the indicator panel (plotOnChart = false).
Usage Notes
Use the indicator in conjunction with other tools (e.g., support/resistance, trendlines, or volume) to confirm signals.
Test extensively on your chosen timeframe and asset to optimize parameters like crsiLength, maLength, and mult.
Be cautious with lower timeframes, as false signals may occur due to market noise.
The indicator is designed for contrarian strategies, so it works best in markets with clear reversal patterns.
Disclaimer
This indicator is provided for educational and informational purposes only. Always conduct thorough backtesting and risk management before using any indicator in live trading. The author is not responsible for any financial losses incurred.
Bitcoin Power Law [LuxAlgo]The Bitcoin Power Law tool is a representation of Bitcoin prices first proposed by Giovanni Santostasi, Ph.D. It plots BTCUSD daily closes on a log10-log10 scale, and fits a linear regression channel to the data.
This channel helps traders visualise when the price is historically in a zone prone to tops or located within a discounted zone subject to future growth.
🔶 USAGE
Giovanni Santostasi, Ph.D. originated the Bitcoin Power-Law Theory; this implementation places it directly on a TradingView chart. The white line shows the daily closing price, while the cyan line is the best-fit regression.
A channel is constructed from the linear fit root mean squared error (RMSE), we can observe how price has repeatedly oscillated between each channel areas through every bull-bear cycle.
Excursions into the upper channel area can be followed by price surges and finishing on a top, whereas price touching the lower channel area coincides with a cycle low.
Users can change the channel areas multipliers, helping capture moves more precisely depending on the intended usage.
This tool only works on the daily BTCUSD chart. Ticker and timeframe must match exactly for the calculations to remain valid.
🔹 Linear Scale
Users can toggle on a linear scale for the time axis, in order to obtain a higher resolution of the price, (this will affect the linear regression channel fit, making it look poorer).
🔶 DETAILS
One of the advantages of the Power Law Theory proposed by Giovanni Santostasi is its ability to explain multiple behaviors of Bitcoin. We describe some key points below.
🔹 Power-Law Overview
A power law has the form y = A·xⁿ , and Bitcoin’s key variables follow this pattern across many orders of magnitude. Empirically, price rises roughly with t⁶, hash-rate with t¹² and the number of active addresses with t³.
When we plot these on log-log axes they appear as straight lines, revealing a scale-invariant system whose behaviour repeats proportionally as it grows.
🔹 Feedback-Loop Dynamics
Growth begins with new users, whose presence pushes the price higher via a Metcalfe-style square-law. A richer price pool funds more mining hardware; the Difficulty Adjustment immediately raises the hash-rate requirement, keeping profit margins razor-thin.
A higher hash rate secures the network, which in turn attracts the next wave of users. Because risk and Difficulty act as braking forces, user adoption advances as a power of three in time rather than an unchecked S-curve. This circular causality repeats without end, producing the familiar boom-and-bust cadence around the long-term power-law channel.
🔹 Scale Invariance & Predictions
Scale invariance means that enlarging the timeline in log-log space leaves the trajectory unchanged.
The same geometric proportions that described the first dollar of value can therefore extend to a projected million-dollar bitcoin, provided no catastrophic break occurs. Institutional ETF inflows supply fresh capital but do not bend the underlying slope; only a persistent deviation from the line would falsify the current model.
🔹 Implications
The theory assigns scarcity no direct role; iterative feedback and the Difficulty Adjustment are sufficient to govern Bitcoin’s expansion. Long-term valuation should focus on position within the power-law channel, while bubbles—sharp departures above trend that later revert—are expected punctuations of an otherwise steady climb.
Beyond about 2040, disruptive technological shifts could alter the parameters, but for the next order of magnitude the present slope remains the simplest, most robust guide.
Bitcoin behaves less like a traditional asset and more like a self-organising digital organism whose value, security, and adoption co-evolve according to immutable power-law rules.
🔶 SETTINGS
🔹 General
Start Calculation: Determine the start date used by the calculation, with any prior prices being ignored. (default - 15 Jul 2010)
Use Linear Scale for X-Axis: Convert the horizontal axis from log(time) to linear calendar time
🔹 Linear Regression
Show Regression Line: Enable/disable the central power-law trend line
Regression Line Color: Choose the colour of the regression line
Mult 1: Toggle line & fill, set multiplier (default +1), pick line colour and area fill colour
Mult 2: Toggle line & fill, set multiplier (default +0.5), pick line colour and area fill colour
Mult 3: Toggle line & fill, set multiplier (default -0.5), pick line colour and area fill colour
Mult 4: Toggle line & fill, set multiplier (default -1), pick line colour and area fill colour
🔹 Style
Price Line Color: Select the colour of the BTC price plot
Auto Color: Automatically choose the best contrast colour for the price line
Price Line Width: Set the thickness of the price line (1 – 5 px)
Show Halvings: Enable/disable dotted vertical lines at each Bitcoin halving
Halvings Color: Choose the colour of the halving lines
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.
Smart Session ConceptSmart Session Concept — Intelligent Trading Session Overlay
Smart Session Concept is designed to detect major reversal points and key price pivots formed on higher timeframes, particularly during high-volume periods of the day — often marking the footprints of institutional orders and whales.
🔍 Key Features:
Displays standard sessions (Asian, London, New York) and allows adding custom time sessions.
Offers two visualization modes:
Time session table
Visual session boxes plotted on the chart
Auto-sync with seasonal time changes (Summer/Winter), supports Daylight Saving Time (DST)
Full flexibility:
Toggle table, boxes, and labels on/off
Customize colors for all session elements
Choose which months are considered summer/winter
💡 Suggested Use Case:
Use Smart Session Sync to pinpoint critical price structures such as:
Peaks and troughs of trending waves
Highs/lows in Wyckoff trading ranges
Liquidity sweeps or untouched liquidity zones
----------------------
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment
## 1. Introduction
Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985).
The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014).
## 2. Theoretical Foundation
### 2.1 Momentum Theory and Market Anomalies
The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015).
### 2.2 Drawdown Analysis and Risk Management
Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators.
### 2.3 Adaptive Smoothing and Market Regimes
The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets.
## 3. Methodology
### 3.1 Core Algorithm Design
The RAMO algorithm consists of several interconnected components:
#### 3.1.1 Risk-Adjusted Momentum Calculation
The fundamental innovation of RAMO lies in its risk adjustment mechanism:
Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor)
Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05)
This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952).
#### 3.1.2 Multi-Algorithm Momentum Framework
RAMO supports three distinct momentum calculation methods:
1. Rate of Change: Traditional percentage-based momentum (Pring, 2002)
2. Price Momentum: Absolute price differences
3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997)
This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013).
### 3.2 Leading Indicator Components
#### 3.2.1 Momentum Acceleration Analysis
The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes:
Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n)
This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969).
#### 3.2.2 Linear Regression Prediction
RAMO incorporates linear regression-based prediction to project momentum values forward:
Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset)
This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes.
#### 3.2.3 Volume-Based Exhaustion Detection
The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns:
Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20)
This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989).
### 3.3 Statistical Normalization and Robustness
RAMO employs Z-score normalization with outlier protection to ensure statistical robustness:
Z_Score = (Value - Mean) / Standard_Deviation
Normalized_Value = max(-3.5, min(3.5, Z_Score))
This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions.
### 3.4 Adaptive Threshold Calculation
Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992):
Upper_Threshold = Mean + (Multiplier × Standard_Deviation)
Lower_Threshold = Mean - (Multiplier × Standard_Deviation)
This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992).
## 4. Implementation Details
### 4.1 Adaptive Smoothing Algorithm
The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility:
Volatility_Percentile = Percentrank(Volatility, 100)
Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100)
This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995).
### 4.2 Risk Environment Classification
RAMO classifies market conditions into three risk environments:
- Low Risk: Current_DD < 30% × Max_DD
- Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD
- High Risk: Current_DD ≥ 70% × Max_DD
This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014).
## 5. Signal Generation and Interpretation
### 5.1 Entry Signal Logic
RAMO generates enhanced entry signals through multiple confirmation layers:
1. Primary Signal: Crossover between indicator and signal line
2. Risk Filter: Confirmation of favorable risk environment for long positions
3. Leading Component: Early warning signals via acceleration analysis
4. Exhaustion Filter: Volume-based reversal detection
This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992).
### 5.2 Divergence Analysis
RAMO incorporates both traditional and leading divergence detection:
- Traditional Divergence: Price and indicator divergence over 3-5 periods
- Slope Divergence: Momentum slope versus price direction
- Acceleration Divergence: Changes in momentum acceleration
This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999).
## 6. Empirical Advantages and Applications
### 6.1 Risk-Adjusted Performance
The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980).
### 6.2 Regime Awareness
RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments.
### 6.3 Institutional Applications
The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013).
## 7. Limitations and Future Research
### 7.1 Parameter Sensitivity
Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions.
### 7.2 Market Microstructure Considerations
RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance.
### 7.3 Transaction Cost Integration
Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004).
## References
Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons.
Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press.
Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin.
Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985.
Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120.
Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill.
Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764.
Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press.
Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58.
Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247.
Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84.
Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall.
Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384.
Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291.
Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill.
Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082.
Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594.
Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765.
Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91.
Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance.
Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library.
Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill.
Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press.
Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790.
Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House.
Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314.
Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239.
Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
BK AK-Scope🔭 Introducing BK AK-Scope — Target Locked. Signal Acquired. 🔭
After building five precision weapons for traders, I’m proud to unveil the sixth.
BK AK-Scope — the eye of the arsenal.
This is not just an indicator. It’s an intelligence system for volatility, signal clarity, and rate-of-change dynamics — forged for elite vision in any market terrain.
🧠 Why “Scope”? And Why “AK”?
Every shooter knows: you can’t hit what you can’t see.
The Scope brings range, clarity, and target distinction. It filters motion from noise. Purpose from panic.
“AK” continues to honor the man who trained my sight — my mentor, A.K.
His discipline taught me to wait for alignment. To move with reason, not emotion.
His vision lives in every code line here.
🔬 What Is BK AK-Scope?
A Triple-Tier TSI Correlation Engine, fused with adaptive opacity logic, a volatility scoring system, and real-time signal clarity. It’s momentum dissected — by speed, depth, and rate of change.
Built to serve traders who:
Need visual hierarchy between fast, mid, and slow TSI responses.
Want adaptive fills that pulse with volatility — not static zones.
Require a volatility scoring overlay that reads the battlefield in real time.
⚙️ Core Systems: How BK AK-Scope Works
✅ Fast/Mid/Slow TSI →
Three layers of correlation: like scopes with zoom levels.
You track micro moves, mid swings, and macro flow simultaneously.
✅ Rate-of-Change Adaptive Opacity →
Momentum fills fade or flash based on speed — giving you movement density at a glance.
Bull vs. Bear zones adapt to strength. You feel the market’s pulse.
✅ Volatility Score Intelligence →
Custom algorithm measuring:
Range expansion
Rate-of-change differentials
ATR dynamics
Standard deviation pressure
All combined into a score from 0–100 with live icons:
🔥 = Extreme Heat (70+)
🧊 = Cold Zone (<30)
⚠️ = ROC Warning
• = Neutral drift
✅ Auto-Detect Volatility Modes →
Scalp = <15min
Swing = intraday/hourly
Macro = daily/weekly
Or override manually with total control.
🎯 How To Use BK AK-Scope
🔹 Trend Continuation → When all three TSI layers align in direction + volatility score climbs, ride with the trend.
🔹 Early Reversals → Opposing TSI + rapid opacity change + volatility shift = sniper reversal zone.
🔹 Consolidation Filter → Neutral fills + score < 30 = stay out, wait for signal surge.
🔹 Signal Confluence → Pair with:
• Gann fans or angles
• Fib time/price clusters
• Elliott Wave structure
• Harmonics or divergence
To isolate entry perfection.
🛡️ Why This Indicator Changes the Game
It's not just momentum. It’s TSI with depth hierarchy.
It’s not just color. It’s real-time strength visualization.
It’s not just volatility. It’s rate-weighted market intelligence.
This is market optics for the advanced trader — built for vision, clarity, and discipline.
🙏 Final Thoughts
🔹 In honor of A.K., my mentor. The man who taught me to see what others miss.
🔹 Inspired by the power of vision — because execution without clarity is chaos.
🔹 Powered by faith — because Gd alone gives sight beyond the visible.
“He gives sight to the blind and wisdom to the humble.” — Psalms 146
Every tool I build is a prayer in code — that it helps someone trade with clarity, integrity, and precision.
⚡ Zoom In. Focus Deep. Trade Clean.
BK AK-Scope — Lock on the target. See what others don’t.
🔫 Clarity is power. 🔫
Gd bless. 🙏
MTF - Quantum Fibonacci ATR/ADR Levels & Targets V_2.0# Quantum Fibonacci Wave Mechanics v2.0 Release Notes
## 🚀 New Features
- Added multi-timeframe alert system for buy/sell signals
- Implemented dynamic label management with price values
- New mid-level trigger option for additional signals
- New EMA trigger option for confirmation signals
- Signal bar highlighting option
- Customizable line widths for all levels
## 🎨 Visual Improvements
- Completely redesigned label system (left-aligned with offsets)
- More intuitive input organization
- Better color customization options
## ⚙️ Technical Upgrades
- Upgraded to Pine Script v6
- Reduced repainting with stricter confirmation checks
- Optimized performance with proper variable initialization
## ⚠️ Note for Existing Users
- Some color parameters have been renamed
- Label positioning has changed (now with configurable offset)
- Review new mid-level trigger option in strategy settings
## 🐛 Bug Fixes
- Fixed potential repainting issues in signal generation
- Improved label cleanup between periods
- More robust security function implementation
## ⚠️ Caution for Mid-Level & EMA Signals
- Mid-Level Reversals may trigger premature entries in ranging markets.
- EMA crossovers can lag; confirm with price action.
Hull Moving Average RibbonGradient Wave HMA - Multi-Ribbon Hull Moving Average System
Overview
The Gradient Wave HMA is an advanced technical indicator that transforms Alan Hull's Hull Moving Average (HMA) into a dynamic multi-layered ribbon system. Unlike traditional moving average ribbons that use simple or exponential calculations, this indicator applies Hull's innovative lag-reduction formula across 12 different timeframes simultaneously, creating a visually striking gradient effect that flows with market momentum.
Technical Foundation
This indicator is built upon the Hull Moving Average, developed by Alan Hull in 2005. The HMA uses a weighted moving average calculation designed to almost eliminate lag while maintaining curve smoothness:
HMA = WMA(2*WMA(n/2) − WMA(n), sqrt(n))
Credit: Alan Hull (www.alanhull.com)
Key Features
Multi-Period Ribbon Structure
12 individual HMA lines with customizable periods
Preset configurations for different trading styles:
Fast: 3-30 period range (scalping/intraday)
Swing: 8-55 period range (swing trading)
Position: 20-100 period range (position trading)
Custom: User-defined periods
2. Neon Gradient Visualization
Bullish Gradient: Transitions from blue-purple to hot purple
Bearish Gradient: Flows from hot pink to purple-pink
Each line has a unique color in the spectrum
Gradient fills between lines create depth and visual flow
3. Advanced Alert System
Trend Reversal Alerts: Notifies when ribbon changes direction
Price Breakout Alerts: Triggers when price crosses the ribbon
Compression Alerts: Signals potential breakouts during consolidation
Expansion Alerts: Confirms strong trending conditions
Momentum Surge Alerts: Catches explosive moves early
How It Works
The indicator calculates 12 Hull Moving Averages, each with a different period length. The trend direction is determined by the middle HMA (6th line), which triggers the color change across the entire ribbon. When trending up, the ribbon displays a purple gradient; when trending down, it shifts to a pink gradient.
Trading Applications
1. Trend Identification
Ribbon color indicates overall trend direction
All lines moving in sync confirms strong trend
Mixed signals suggest choppy or transitioning markets
2. Dynamic Support/Resistance
In uptrends, the ribbon acts as moving support
In downtrends, it provides resistance levels
Multiple layers offer various strength levels
3. Momentum Analysis
Expanding ribbon = Increasing momentum
Contracting ribbon = Decreasing momentum/consolidation
Ribbon angle indicates trend strength
4. Trading Example
Advantages Over Traditional MAs
Reduced Lag: Hull's formula provides faster response than SMA/EMA ribbons
Visual Clarity: Gradient effect makes trend changes immediately visible
Multiple Timeframes: 12 periods provide comprehensive market view
Flexibility: Presets adapt to different trading styles
Best Practices
Use higher timeframes (4H, Daily) for position trading
Combine with volume indicators for confirmation
Watch for ribbon compression before major moves
Consider overall market conditions when interpreting signals
Customization Options
Adjust individual HMA periods
Fine-tune transparency for different backgrounds
Choose between WMA and EMA base calculations
The Gradient Wave HMA combines Alan Hull's breakthrough moving average formula with modern visualization techniques to create a powerful trend-following tool that's both technically sophisticated and visually intuitive.
Ergodic Market Divergence (EMD)Ergodic Market Divergence (EMD)
Bridging Statistical Physics and Market Dynamics Through Ensemble Analysis
The Revolutionary Concept: When Physics Meets Trading
After months of research into ergodic theory—a fundamental principle in statistical mechanics—I've developed a trading system that identifies when markets transition between predictable and unpredictable states. This indicator doesn't just follow price; it analyzes whether current market behavior will persist or revert, giving traders a scientific edge in timing entries and exits.
The Core Innovation: Ergodic Theory Applied to Markets
What Makes Markets Ergodic or Non-Ergodic?
In statistical physics, ergodicity determines whether a system's future resembles its past. Applied to trading:
Ergodic Markets (Mean-Reverting)
- Time averages equal ensemble averages
- Historical patterns repeat reliably
- Price oscillates around equilibrium
- Traditional indicators work well
Non-Ergodic Markets (Trending)
- Path dependency dominates
- History doesn't predict future
- Price creates new equilibrium levels
- Momentum strategies excel
The Mathematical Framework
The Ergodic Score combines three critical divergences:
Ergodic Score = (Price Divergence × Market Stress + Return Divergence × 1000 + Volatility Divergence × 50) / 3
Where:
Price Divergence: How far current price deviates from market consensus
Return Divergence: Momentum differential between instrument and market
Volatility Divergence: Volatility regime misalignment
Market Stress: Adaptive multiplier based on current conditions
The Ensemble Analysis Revolution
Beyond Single-Instrument Analysis
Traditional indicators analyze one chart in isolation. EMD monitors multiple correlated markets simultaneously (SPY, QQQ, IWM, DIA) to detect systemic regime changes. This ensemble approach:
Reveals Hidden Divergences: Individual stocks may diverge from market consensus before major moves
Filters False Signals: Requires broader market confirmation
Identifies Regime Shifts: Detects when entire market structure changes
Provides Context: Shows if moves are isolated or systemic
Dynamic Threshold Adaptation
Unlike fixed-threshold systems, EMD's boundaries evolve with market conditions:
Base Threshold = SMA(Ergodic Score, Lookback × 3)
Adaptive Component = StDev(Ergodic Score, Lookback × 2) × Sensitivity
Final Threshold = Smoothed(Base + Adaptive)
This creates context-aware signals that remain effective across different market environments.
The Confidence Engine: Know Your Signal Quality
Multi-Factor Confidence Scoring
Every signal receives a confidence score based on:
Signal Clarity (0-35%): How decisively the ergodic threshold is crossed
Momentum Strength (0-25%): Rate of ergodic change
Volatility Alignment (0-20%): Whether volatility supports the signal
Market Quality (0-20%): Price convergence and path dependency factors
Real-Time Confidence Updates
The Live Confidence metric continuously updates, showing:
- Current opportunity quality
- Market state clarity
- Historical performance influence
- Signal recency boost
- Visual Intelligence System
Adaptive Ergodic Field Bands
Dynamic bands that expand and contract based on market state:
Primary Color: Ergodic state (mean-reverting)
Danger Color: Non-ergodic state (trending)
Band Width: Expected price movement range
Squeeze Indicators: Volatility compression warnings
Quantum Wave Ribbons
Triple EMA system (8, 21, 55) revealing market flow:
Compressed Ribbons: Consolidation imminent
Expanding Ribbons: Directional move developing
Color Coding: Matches current ergodic state
Phase Transition Signals
Clear entry/exit markers at regime changes:
Bull Signals: Ergodic restoration (mean reversion opportunity)
Bear Signals: Ergodic break (trend following opportunity)
Confidence Labels: Percentage showing signal quality
Visual Intensity: Stronger signals = deeper colors
Professional Dashboard Suite
Main Analytics Panel (Top Right)
Market State Monitor
- Current regime (Ergodic/Non-Ergodic)
- Ergodic score with threshold
- Path dependency strength
- Quantum coherence percentage
Divergence Metrics
- Price divergence with severity
- Volatility regime classification
- Strategy mode recommendation
- Signal strength indicator
Live Intelligence
- Real-time confidence score
- Color-coded risk levels
- Dynamic strategy suggestions
Performance Tracking (Left Panel)
Signal Analytics
- Total historical signals
- Win rate with W/L breakdown
- Current streak tracking
- Closed trade counter
Regime Analysis
- Current market behavior
- Bars since last signal
- Recommended actions
- Average confidence trends
Strategy Command Center (Bottom Right)
Adaptive Recommendations
- Active strategy mode
- Primary approach (mean reversion/momentum)
- Suggested indicators ("weapons")
- Entry/exit methodology
- Risk management guidance
- Comprehensive Input Guide
Core Algorithm Parameters
Analysis Period (10-100 bars)
Scalping (10-15): Ultra-responsive, more signals, higher noise
Day Trading (20-30): Balanced sensitivity and stability
Swing Trading (40-100): Smooth signals, major moves only Default: 20 - optimal for most timeframes
Divergence Threshold (0.5-5.0)
Hair Trigger (0.5-1.0): Catches every wiggle, many false signals
Balanced (1.5-2.5): Good signal-to-noise ratio
Conservative (3.0-5.0): Only extreme divergences Default: 1.5 - best risk/reward balance
Path Memory (20-200 bars)
Short Memory (20-50): Recent behavior focus, quick adaptation
Medium Memory (50-100): Balanced historical context
Long Memory (100-200): Emphasizes established patterns Default: 50 - captures sufficient history without lag
Signal Spacing (5-50 bars)
Aggressive (5-10): Allows rapid-fire signals
Normal (15-25): Prevents clustering, maintains flow
Conservative (30-50): Major setups only Default: 15 - optimal trade frequency
Ensemble Configuration
Select markets for consensus analysis:
SPY: Broad market sentiment
QQQ: Technology leadership
IWM: Small-cap risk appetite
DIA: Blue-chip stability
More instruments = stronger consensus but potentially diluted signals
Visual Customization
Color Themes (6 professional options):
Quantum: Cyan/Pink - Modern trading aesthetic
Matrix: Green/Red - Classic terminal look
Heat: Blue/Red - Temperature metaphor
Neon: Cyan/Magenta - High contrast
Ocean: Turquoise/Coral - Calming palette
Sunset: Red-orange/Teal - Warm gradients
Display Controls:
- Toggle each visual component
- Adjust transparency levels
- Scale dashboard text
- Show/hide confidence scores
- Trading Strategies by Market State
- Ergodic State Strategy (Primary Color Bands)
Market Characteristics
- Price oscillates predictably
- Support/resistance hold
- Volume patterns repeat
- Mean reversion dominates
Optimal Approach
Entry: Fade moves at band extremes
Target: Middle band (equilibrium)
Stop: Just beyond outer bands
Size: Full confidence-based position
Recommended Tools
- RSI for oversold/overbought
- Bollinger Bands for extremes
- Volume profile for levels
- Non-Ergodic State Strategy (Danger Color Bands)
Market Characteristics
- Price trends persistently
- Levels break decisively
- Volume confirms direction
- Momentum accelerates
Optimal Approach
Entry: Breakout from bands
Target: Trail with expanding bands
Stop: Inside opposite band
Size: Scale in with trend
Recommended Tools
- Moving average alignment
- ADX for trend strength
- MACD for momentum
- Advanced Features Explained
Quantum Coherence Metric
Measures phase alignment between individual and ensemble behavior:
80-100%: Perfect sync - strong mean reversion setup
50-80%: Moderate alignment - mixed signals
0-50%: Decoherence - trending behavior likely
Path Dependency Analysis
Quantifies how much history influences current price:
Low (<30%): Technical patterns reliable
Medium (30-50%): Mixed influences
High (>50%): Fundamental shift occurring
Volatility Regime Classification
Contextualizes current volatility:
Normal: Standard strategies apply
Elevated: Widen stops, reduce size
Extreme: Defensive mode required
Signal Strength Indicator
Real-time opportunity quality:
- Distance from threshold
- Momentum acceleration
- Cross-validation factors
Risk Management Framework
Position Sizing by Confidence
90%+ confidence = 100% position size
70-90% confidence = 75% position size
50-70% confidence = 50% position size
<50% confidence = 25% or skip
Dynamic Stop Placement
Ergodic State: ATR × 1.0 from entry
Non-Ergodic State: ATR × 2.0 from entry
Volatility Adjustment: Multiply by current regime
Multi-Timeframe Alignment
- Check higher timeframe regime
- Confirm ensemble consensus
- Verify volume participation
- Align with major levels
What Makes EMD Unique
Original Contributions
First Ergodic Theory Trading Application: Transforms abstract physics into practical signals
Ensemble Market Analysis: Revolutionary multi-market divergence system
Adaptive Confidence Engine: Institutional-grade signal quality metrics
Quantum Coherence: Novel market alignment measurement
Smart Signal Management: Prevents clustering while maintaining responsiveness
Technical Innovations
Dynamic Threshold Adaptation: Self-adjusting sensitivity
Path Memory Integration: Historical dependency weighting
Stress-Adjusted Scoring: Market condition normalization
Real-Time Performance Tracking: Built-in strategy analytics
Optimization Guidelines
By Timeframe
Scalping (1-5 min)
Period: 10-15
Threshold: 0.5-1.0
Memory: 20-30
Spacing: 5-10
Day Trading (5-60 min)
Period: 20-30
Threshold: 1.5-2.5
Memory: 40-60
Spacing: 15-20
Swing Trading (1H-1D)
Period: 40-60
Threshold: 2.0-3.0
Memory: 80-120
Spacing: 25-35
Position Trading (1D-1W)
Period: 60-100
Threshold: 3.0-5.0
Memory: 100-200
Spacing: 40-50
By Market Condition
Trending Markets
- Increase threshold
- Extend memory
- Focus on breaks
Ranging Markets
- Decrease threshold
- Shorten memory
- Focus on restores
Volatile Markets
- Increase spacing
- Raise confidence requirement
- Reduce position size
- Integration with Other Analysis
- Complementary Indicators
For Ergodic States
- RSI divergences
- Bollinger Band squeezes
- Volume profile nodes
- Support/resistance levels
For Non-Ergodic States
- Moving average ribbons
- Trend strength indicators
- Momentum oscillators
- Breakout patterns
- Fundamental Alignment
- Check economic calendar
- Monitor sector rotation
- Consider market themes
- Evaluate risk sentiment
Troubleshooting Guide
Too Many Signals:
- Increase threshold
- Extend signal spacing
- Raise confidence minimum
Missing Opportunities
- Decrease threshold
- Reduce signal spacing
- Check ensemble settings
Poor Win Rate
- Verify timeframe alignment
- Confirm volume participation
- Review risk management
Disclaimer
This indicator is for educational and informational purposes only. It does not constitute financial advice. Trading involves substantial risk of loss and is not suitable for all investors. Past performance does not guarantee future results.
The ergodic framework provides unique market insights but cannot predict future price movements with certainty. Always use proper risk management, conduct your own analysis, and never risk more than you can afford to lose.
This tool should complement, not replace, comprehensive trading strategies and sound judgment. Markets remain inherently unpredictable despite advanced analysis techniques.
Transform market chaos into trading clarity with Ergodic Market Divergence.
Created with passion for the TradingView community
Trade with insight. Trade with anticipation.
— Dskyz , for DAFE Trading Systems
