+ ATR Table and BracketsHi, all. I'm back with a new indicator—one I firmly believe could be one of the most valuable indicators you keep in your indicator toolshed—based around true range.
This is a simple, streamlined indicator utilizing true range and average true range that will help any trader with stoploss, trailing stoploss, and take-profit placement—things that I know many traders use average true range for. It could also be useful for trade entries as well, depending on the trader's style.
Typically, most traders (or at least what I've seen recommended across websites, video tutorials on YouTube, etc.) are taught to simply take the ATR number and use that, and possibly some sort of multiplier, as your stoploss and take-profit. This is fine, but I thought that it might be possible to dive a bit deeper into these values. Because an average is a combination of values, some higher, some lower, and we often see ATR spikes during periods of high volatility, I thought wouldn't it be useful to know what value those ATR spikes are, and how do they relate to the ATR? Then I thought to myself, well, what about the most volatile candle within that ATR (the candle with the greatest true range)? Couldn't knowing that value be useful to a trader? So then the idea of a table displaying these values, along with the ATR and the ATR times some multiplier number, would be a useful, simple way to display this information. That's what we have here.
The table is made up of two columns, one with the name of the metric being measured, and the other with its value. That's it. Simple.
As nice as this was, I thought an additional, great, and perhaps better, way to visualize this information would be in the form of brackets extending from the current bar. These are simply lines/labels plotted at the price values of the ATR, ATR times X, highest ATR, highest ATR times X, and highest TR value. These labels supply the actual values of the ATR, etc., but may also display the price if you should choose (both of these values are toggleable in the 'Inputs' section of the indicator.). Additionally, you can choose to display none of these labels, or all five if you wish (leaves the chart a bit cluttered, as shown in the image below), though I suspect you'll determine your preferences for which information you'd like to see and which not.
Chart with all five lines/labels displayed. I adjusted the ATRX value to 3 just to make the screenshot as legible as possible. Default is set to 1.5. As you can see, the label doesn't show the multiplier number, but the table does.
Here's a screenshot of the labels showing the price in addition to the value of the ATR, set to "Previous Closing Price," (see next paragraph for what that means) and highest TR. Personally, I don't see the value in the displaying the price, but I thought some people might want that. It's not available in the table as of now, but perhaps if I get enough requests for it I will add it.
That's basically it, but one last detail I need to go over is the dropdown box labeled "Bar Value ATR Levels are Oriented To." Firstly, this has no effect on Highest ATR, Highest ATRX, and Highest TR levels. Those are based on the ATR up to the last closed candle, meaning they aren't including the value of the currently open candle (this would be useless). However, knowing that different traders trade different ways it seemed to me prudent to allow for traders to select which opening or closing value the trader wishes to have the ATR brackets based on. For example, as someone who has consumed much No Nonsense Forex content I know that traders are urged to enter their trades in the last fifteen minutes of the trading day because the ATR is unlikely to change significantly in that period (ATR being the centerpiece of NNFX money management), so one of three selections here is to plot the brackets based on the ATR's inclusion of this value (this of course means the brackets will move while the candle is still open). The other options are to set the brackets to the current opening price, or the previous closing price. Depending on what you're trading many times these prices are virtually identical, but sometimes price gaps (stocks in particular), so, wanting your brackets placed relative to the previous close as opposed to the current open might be preferable for some traders.
And that's it. I really hope you guys like this indicator. I haven't seen anything closely similar to it on TradingView, and I think it will be something you all will find incredibly handy.
Please enjoy!
ค้นหาในสคริปต์สำหรับ "deep股票代码"
Better MACD📘 Better MACD – Adaptive Momentum & Divergence Suite
Better MACD is a comprehensive momentum-trend tool that evolves the traditional MACD into a multi-dimensional, divergence-aware oscillator. It leverages exponential smoothing across logarithmic rate-of-change of OHLC data, adaptive signal processing, and intelligent divergence detection logic to provide traders with earlier, smoother, and more reliable momentum signals.
This indicator is built for professional-level analysis, suitable for scalping, swing trading, and trend-following systems.
🧬 Core Concept
Unlike the classic MACD which subtracts two EMAs of price, Better MACD constructs a signal by:
Applying logarithmic transformation on the change between OHLC components (Close, High, Low, Open).
Using double EMA smoothing to filter noise and volatility, Triangular method. 1st to 2nd Smoothing.
Averaging and de-biasing the results through a custom linear regression model, 4th Smoothing.
Subtracting a fast SMA and slow SMA response to yield a dynamic MACD value, 3rd Smoothing.
The result is a smooth, adaptive, and high-resolution MACD-style oscillator that responds more naturally to trend conditions and price geometry.
🧠 Features Breakdown
1. 📈 Multi-Layer MACD Engine
Src1: Smoothed Log Rate-of-Change on Close
Src2: Smoothed Log Rate-of-Change on High
Src3: Smoothed Log Rate-of-Change on Low
Src4: Smoothed Log Rate-of-Change on Open
These are blended using highest high, lowest low, and average Close price over a configurable window for more complete trend detection. The open-based Src4 is subtracted using SMA.
2. 🧮 Signal Line
A fast EMA (signalLength) of the Better MACD value is used for crossover logic.
Crossovers of MACD and Signal line signal potential entries or exits.
3. 📊 MACD Histogram
Visualizes the difference between MACD and Signal line.
Dynamically color-coded:
Green/Light Green for bullish impulse
Red/Pink for bearish impulse
Width and color intensity reflect strength and momentum slope.
🎨 Visual Enhancements
Feature Description
✅ Ribbon Fill Optional fill between MACD and Signal line, colored by trend direction
✅ Zero-Line Background Background highlights above/below 0 to easily read bullish/bearish bias
✅ Crossover Highlights Tiny circles plotted when MACD crosses Signal line
🔍 Divergence Detection Suite
The script includes a full Divergence Engine to detect:
🔼 Bullish Regular Divergence (Price lower lows + Indicator higher lows)
🔽 Bearish Regular Divergence (Price higher highs + Indicator lower highs)
🟢 Bullish Hidden Divergence (Price higher lows + Indicator lower lows)
🔴 Bearish Hidden Divergence (Price lower highs + Indicator higher highs)
🧩 Divergence Modes:
Supports both Regular, Hidden, or Both simultaneously
Detects from either Close Price or Heikin Ashi-derived candles
Uses dynamic pivot tracking with configurable lookback and divergence sensitivity
Divergence lines are labeled, colored, and plotted in real-time
🔁 Styling & Customization:
Choose from Solid, Dashed, or Dotted line styles
Configure separate colors and widths for all divergence types
Control number of divergence lines visible or only show the most recent
Divergences update live without repainting
⚠️ Alerts
Alerts are built-in for real-time notification:
MACD Histogram reversals (rising → falling, or vice versa)
Divergence signals (all 4 types, grouped and individually)
Combines seamlessly with TradingView alerts for actionable triggers
🔧 Input Controls (Grouped by Purpose)
Better MACD Group
1st–4th Smoothing Lengths: Controls responsiveness of MACD core engine
Signal Length: Smoothness of signal line
Toggles for crossover highlights, zero cross fills, and ribbon fills
Divergence Settings
Enable/disable divergence lines
Choose divergence type (Regular, Hidden, Both)
Set confirmation requirements
Customize pivot detection and bar search depth
Styling Options
Colors, line widths, and line styles for each divergence type
Heikin Ashi Mode for smoother pivots and divergences
🧠 How to Use
✅ For Trend Traders:
Use MACD > Signal + Histogram > 0 → Bullish confirmation
MACD < Signal + Histogram < 0 → Bearish confirmation
Wait for pullbacks with hidden divergences to enter in trend direction
✅ For Reversal Traders:
Look for Regular Divergences at trend exhaustion points
Combine with price action (e.g., support/resistance or candle pattern)
✅ For Swing & Day Traders:
Enable Heikin Ashi Mode for smoother divergence pivots
Use zero line background + histogram color to time entries
📌 Summary
Feature Description
🚀 Advanced MACD Core Smoother, more reliable, multi-source-based MACD
🔍 Divergence Engine Detects 4 divergence types with pivot logic
🎯 Real-Time Alerts Alerts for histogram slope and divergences
🎛️ Deep Customization Full styling, smoothing, and detection controls
📉 Heikin Ashi Support Improved signal quality in trend-based markets
BK AK-SILENCER🚨 Introducing BK AK-SILENCER — Volume Footprint Warfare, Right on the Price Bars 🚨
This isn’t a traditional indicator.
This is a tactical weapon — engineered to expose institutional behavior directly in the bar data, using volume logic, CVD divergence, and spike detection to pinpoint who’s really in control of the tape.
No panels. No clutter.
Just silent execution — built directly into price itself.
🔥 Why "SILENCER"?
Because real power moves in silence.
Institutions don’t chase — they build positions quietly, in size, beneath the surface.
BK AK-SILENCER gives you a real-time edge by visually revealing their footprints through color-coded bar behavior, divergence signals, and volume spike alerts — all directly on your chart.
🔹 “AK” honors my mentor A.K., whose training forged my trading discipline.
🔹 “SILENCER” represents the institutional mindset — high impact, low visibility. This tool lets you trade like them: without noise, without hesitation, with deadly clarity.
🧠 What Is BK AK-SILENCER?
A bar-level institutional detection tool, purpose-built to:
✅ Color-code bars based on volume aggression and close-location inside range
✅ Detect real-time bullish and bearish divergences between price and volume delta
✅ Tag volume spikes with a $ symbol to expose potential traps or silent position builds
✅ Overlay VWAP for real-time mean-reversion biasing
No extra windows.
No indicators talking over each other.
Just pure volume-logic weaponry embedded into price.
⚙️ What This Weapon Deploys
🔸 Bar Coloring Logic (Volume Footprint)
🟢 Power Buy = Strong close near highs on elevated volume
🟩 Accumulation = Weak close but still heavy volume
🔴 Power Sell = Strong close near lows on heavy selling
🟥 Distribution / Weakness = Low close without commitment
❗ Extreme Volume Spikes marked with $ — using standard deviation to highlight institutional bursts
🔸 CVD Divergence Detection
→ Tracks cumulative volume delta and compares it to price pivot behavior
Bullish Divergence = Price makes lower lows, CVD makes higher lows → hidden accumulation
Bearish Divergence = Price makes higher highs, CVD makes lower highs → hidden distribution
All plotted directly on bars with triangle markers.
🔸 VWAP Overlay (Optional)
→ Anchored VWAP gives immediate context for intraday bias — above VWAP = demand, below = supply
🎯 How to Use BK AK-SILENCER
🔹 Silent Reversal Detection
Bullish divergence + Power Buy bar + VWAP reclaim = sniper entry
Bearish divergence + Power Sell bar + VWAP rejection = trap confirmation
🔹 Volume-Based Entry Triggers
Look for Power Buy + $ spike after a pullback → watch for quiet reversal
Accumulation colors clustering? Institutions are likely loading silently
🔹 Institutional Trap Warnings
$ spike + red distribution bar at highs = time to exit or flip
Weakness bar below VWAP? Don’t chase the long.
🛡️ Why It Matters
✅ Clean — it integrates into price action, no separate panels
✅ Silent — tracks institutions who build without alerts or indicators
✅ Tactical — no fluff, no lag, just real-time behavior recognition
This tool is ideal for:
🔸 Scalpers reading bar-by-bar
🔸 Intraday swing traders using VWAP and structure
🔸 Professionals who need volume behavior decoded in real-time
🔸 Anyone who wants signal without clutter
🙏 Final Thoughts
This tool isn’t just about trading — it’s about tactical awareness.
🔹 Dedicated to my mentor A.K., whose wisdom runs deep in every logic tree.
🔹 Above all, I give thanks to Gd, the source of clarity, courage, and conviction.
Without Him, even the sharpest system is blind.
With Him, we execute with structure, purpose, and divine alignment.
⚡ No noise. No clutter. No delay. Just raw, silent execution.
🔥 BK AK-SILENCER — Bar-Level Volume Footprint Precision 🔥
Gd bless every step you take in this market.
Trade with clarity, move with intention. 🙏
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. 🙏
Aetherium Institutional Market Resonance EngineAetherium Institutional Market Resonance Engine (AIMRE)
A Three-Pillar Framework for Decoding Institutional Activity
🎓 THEORETICAL FOUNDATION
The Aetherium Institutional Market Resonance Engine (AIMRE) is a multi-faceted analysis system designed to move beyond conventional indicators and decode the market's underlying structure as dictated by institutional capital flow. Its philosophy is built on a singular premise: significant market moves are preceded by a convergence of context , location , and timing . Aetherium quantifies these three dimensions through a revolutionary three-pillar architecture.
This system is not a simple combination of indicators; it is an integrated engine where each pillar's analysis feeds into a central logic core. A signal is only generated when all three pillars achieve a state of resonance, indicating a high-probability alignment between market organization, key liquidity levels, and cyclical momentum.
⚡ THE THREE-PILLAR ARCHITECTURE
1. 🌌 PILLAR I: THE COHERENCE ENGINE (THE 'CONTEXT')
Purpose: To measure the degree of organization within the market. This pillar answers the question: " Is the market acting with a unified purpose, or is it chaotic and random? "
Conceptual Framework: Institutional campaigns (accumulation or distribution) create a non-random, organized market environment. Retail-driven or directionless markets are characterized by "noise" and chaos. The Coherence Engine acts as a filter to ensure we only engage when institutional players are actively steering the market.
Formulaic Concept:
Coherence = f(Dominance, Synchronization)
Dominance Factor: Calculates the absolute difference between smoothed buying pressure (volume-weighted bullish candles) and smoothed selling pressure (volume-weighted bearish candles), normalized by total pressure. A high value signifies a clear winner between buyers and sellers.
Synchronization Factor: Measures the correlation between the streams of buying and selling pressure over the analysis window. A high positive correlation indicates synchronized, directional activity, while a negative correlation suggests choppy, conflicting action.
The final Coherence score (0-100) represents the percentage of market organization. A high score is a prerequisite for any signal, filtering out unpredictable market conditions.
2. 💎 PILLAR II: HARMONIC LIQUIDITY MATRIX (THE 'LOCATION')
Purpose: To identify and map high-impact institutional footprints. This pillar answers the question: " Where have institutions previously committed significant capital? "
Conceptual Framework: Large institutional orders leave indelible marks on the market in the form of anomalous volume spikes at specific price levels. These are not random occurrences but are areas of intense historical interest. The Harmonic Liquidity Matrix finds these footprints and consolidates them into actionable support and resistance zones called "Harmonic Nodes."
Algorithmic Process:
Footprint Identification: The engine scans the historical lookback period for candles where volume > average_volume * Institutional_Volume_Filter. This identifies statistically significant volume events.
Node Creation: A raw node is created at the mean price of the identified candle.
Dynamic Clustering: The engine uses an ATR-based proximity algorithm. If a new footprint is identified within Node_Clustering_Distance (ATR) of an existing Harmonic Node, it is merged. The node's price is volume-weighted, and its magnitude is increased. This prevents chart clutter and consolidates nearby institutional orders into a single, more significant level.
Node Decay: Nodes that are older than the Institutional_Liquidity_Scanback period are automatically removed from the chart, ensuring the analysis remains relevant to recent market dynamics.
3. 🌊 PILLAR III: CYCLICAL RESONANCE MATRIX (THE 'TIMING')
Purpose: To identify the market's dominant rhythm and its current phase. This pillar answers the question: " Is the market's immediate energy flowing up or down? "
Conceptual Framework: Markets move in waves and cycles of varying lengths. Trading in harmony with the current cyclical phase dramatically increases the probability of success. Aetherium employs a simplified wavelet analysis concept to decompose price action into short, medium, and long-term cycles.
Algorithmic Process:
Cycle Decomposition: The engine calculates three oscillators based on the difference between pairs of Exponential Moving Averages (e.g., EMA8-EMA13 for short cycle, EMA21-EMA34 for medium cycle).
Energy Measurement: The 'energy' of each cycle is determined by its recent volatility (standard deviation). The cycle with the highest energy is designated as the "Dominant Cycle."
Phase Analysis: The engine determines if the dominant cycles are in a bullish phase (rising from a trough) or a bearish phase (falling from a peak).
Cycle Sync: The highest conviction timing signals occur when multiple cycles (e.g., short and medium) are synchronized in the same direction, indicating broad-based momentum.
🔧 COMPREHENSIVE INPUT SYSTEM
Pillar I: Market Coherence Engine
Coherence Analysis Window (10-50, Default: 21): The lookback period for the Coherence Engine.
Lower Values (10-15): Highly responsive to rapid shifts in market control. Ideal for scalping but can be sensitive to noise.
Balanced (20-30): Excellent for day trading, capturing the ebb and flow of institutional sessions.
Higher Values (35-50): Smoother, more stable reading. Best for swing trading and identifying long-term institutional campaigns.
Coherence Activation Level (50-90%, Default: 70%): The minimum market organization required to enable signal generation.
Strict (80-90%): Only allows signals in extremely clear, powerful trends. Fewer, but potentially higher quality signals.
Standard (65-75%): A robust filter that effectively removes choppy conditions while capturing most valid institutional moves.
Lenient (50-60%): Allows signals in less-organized markets. Can be useful in ranging markets but may increase false signals.
Pillar II: Harmonic Liquidity Matrix
Institutional Liquidity Scanback (100-400, Default: 200): How far back the engine looks for institutional footprints.
Short (100-150): Focuses on recent institutional activity, providing highly relevant, immediate levels.
Long (300-400): Identifies major, long-term structural levels. These nodes are often extremely powerful but may be less frequent.
Institutional Volume Filter (1.3-3.0, Default: 1.8): The multiplier for detecting a volume spike.
High (2.5-3.0): Only registers climactic, undeniable institutional volume. Fewer, but more significant nodes.
Low (1.3-1.7): More sensitive, identifying smaller but still relevant institutional interest.
Node Clustering Distance (0.2-0.8 ATR, Default: 0.4): The ATR-based distance for merging nearby nodes.
High (0.6-0.8): Creates wider, more consolidated zones of liquidity.
Low (0.2-0.3): Creates more numerous, precise, and distinct levels.
Pillar III: Cyclical Resonance Matrix
Cycle Resonance Analysis (30-100, Default: 50): The lookback for determining cycle energy and dominance.
Short (30-40): Tunes the engine to faster, shorter-term market rhythms. Best for scalping.
Long (70-100): Aligns the timing component with the larger primary trend. Best for swing trading.
Institutional Signal Architecture
Signal Quality Mode (Professional, Elite, Supreme): Controls the strictness of the three-pillar confluence.
Professional: Loosest setting. May generate signals if two of the three pillars are in strong alignment. Increases signal frequency.
Elite: Balanced setting. Requires a clear, unambiguous resonance of all three pillars. The recommended default.
Supreme: Most stringent. Requires perfect alignment of all three pillars, with each pillar exhibiting exceptionally strong readings (e.g., coherence > 85%). The highest conviction signals.
Signal Spacing Control (5-25, Default: 10): The minimum bars between signals to prevent clutter and redundant alerts.
🎨 ADVANCED VISUAL SYSTEM
The visual architecture of Aetherium is designed not merely for aesthetics, but to provide an intuitive, at-a-glance understanding of the complex data being processed.
Harmonic Liquidity Nodes: The core visual element. Displayed as multi-layered, semi-transparent horizontal boxes.
Magnitude Visualization: The height and opacity of a node's "glow" are proportional to its volume magnitude. More significant nodes appear brighter and larger, instantly drawing the eye to key levels.
Color Coding: Standard nodes are blue/purple, while exceptionally high-magnitude nodes are highlighted in an accent color to denote critical importance.
🌌 Quantum Resonance Field: A dynamic background gradient that visualizes the overall market environment.
Color: Shifts from cool blues/purples (low coherence) to energetic greens/cyans (high coherence and organization), providing instant context.
Intensity: The brightness and opacity of the field are influenced by total market energy (a composite of coherence, momentum, and volume), making powerful market states visually apparent.
💎 Crystalline Lattice Matrix: A geometric web of lines projected from a central moving average.
Mathematical Basis: Levels are projected using multiples of the Golden Ratio (Phi ≈ 1.618) and the ATR. This visualizes the natural harmonic and fractal structure of the market. It is not arbitrary but is based on mathematical principles of market geometry.
🧠 Synaptic Flow Network: A dynamic particle system visualizing the engine's "thought process."
Node Density & Activation: The number of particles and their brightness/color are tied directly to the Market Coherence score. In high-coherence states, the network becomes a dense, bright, and organized web. In chaotic states, it becomes sparse and dim.
⚡ Institutional Energy Waves: Flowing sine waves that visualize market volatility and rhythm.
Amplitude & Speed: The height and speed of the waves are directly influenced by the ATR and volume, providing a feel for market energy.
📊 INSTITUTIONAL CONTROL MATRIX (DASHBOARD)
The dashboard is the central command console, providing a real-time, quantitative summary of each pillar's status.
Header: Displays the script title and version.
Coherence Engine Section:
State: Displays a qualitative assessment of market organization: ◉ PHASE LOCK (High Coherence), ◎ ORGANIZING (Moderate Coherence), or ○ CHAOTIC (Low Coherence). Color-coded for immediate recognition.
Power: Shows the precise Coherence percentage and a directional arrow (↗ or ↘) indicating if organization is increasing or decreasing.
Liquidity Matrix Section:
Nodes: Displays the total number of active Harmonic Liquidity Nodes currently being tracked.
Target: Shows the price level of the nearest significant Harmonic Node to the current price, representing the most immediate institutional level of interest.
Cycle Matrix Section:
Cycle: Identifies the currently dominant market cycle (e.g., "MID ") based on cycle energy.
Sync: Indicates the alignment of the cyclical forces: ▲ BULLISH , ▼ BEARISH , or ◆ DIVERGENT . This is the core timing confirmation.
Signal Status Section:
A unified status bar that provides the final verdict of the engine. It will display "QUANTUM SCAN" during neutral periods, or announce the tier and direction of an active signal (e.g., "◉ TIER 1 BUY ◉" ), highlighted with the appropriate color.
🎯 SIGNAL GENERATION LOGIC
Aetherium's signal logic is built on the principle of strict, non-negotiable confluence.
Condition 1: Context (Coherence Filter): The Market Coherence must be above the Coherence Activation Level. No signals can be generated in a chaotic market.
Condition 2: Location (Liquidity Node Interaction): Price must be actively interacting with a significant Harmonic Liquidity Node.
For a Buy Signal: Price must be rejecting the Node from below (testing it as support).
For a Sell Signal: Price must be rejecting the Node from above (testing it as resistance).
Condition 3: Timing (Cycle Alignment): The Cyclical Resonance Matrix must confirm that the dominant cycles are synchronized with the intended trade direction.
Signal Tiering: The Signal Quality Mode input determines how strictly these three conditions must be met. 'Supreme' mode, for example, might require not only that the conditions are met, but that the Market Coherence is exceptionally high and the interaction with the Node is accompanied by a significant volume spike.
Signal Spacing: A final filter ensures that signals are spaced by a minimum number of bars, preventing over-alerting in a single move.
🚀 ADVANCED TRADING STRATEGIES
The Primary Confluence Strategy: The intended use of the system. Wait for a Tier 1 (Elite/Supreme) or Tier 2 (Professional/Elite) signal to appear on the chart. This represents the alignment of all three pillars. Enter after the signal bar closes, with a stop-loss placed logically on the other side of the Harmonic Node that triggered the signal.
The Coherence Context Strategy: Use the Coherence Engine as a standalone market filter. When Coherence is high (>70%), favor trend-following strategies. When Coherence is low (<50%), avoid new directional trades or favor range-bound strategies. A sharp drop in Coherence during a trend can be an early warning of a trend's exhaustion.
Node-to-Node Trading: In a high-coherence environment, use the Harmonic Liquidity Nodes as both entry points and profit targets. For example, after a BUY signal is generated at one Node, the next Node above it becomes a logical first profit target.
⚖️ RESPONSIBLE USAGE AND LIMITATIONS
Decision Support, Not a Crystal Ball: Aetherium is an advanced decision-support tool. It is designed to identify high-probability conditions based on a model of institutional behavior. It does not predict the future.
Risk Management is Paramount: No indicator can replace a sound risk management plan. Always use appropriate position sizing and stop-losses. The signals provided are probabilistic, not certainties.
Past Performance Disclaimer: The market models used in this script are based on historical data. While robust, there is no guarantee that these patterns will persist in the future. Market conditions can and do change.
Not a "Set and Forget" System: The indicator performs best when its user understands the concepts behind the three pillars. Use the dashboard and visual cues to build a comprehensive view of the market before acting on a signal.
Backtesting is Essential: Before applying this tool to live trading, it is crucial to backtest and forward-test it on your preferred instruments and timeframes to understand its unique behavior and characteristics.
🔮 CONCLUSION
The Aetherium Institutional Market Resonance Engine represents a paradigm shift from single-variable analysis to a holistic, multi-pillar framework. By quantifying the abstract concepts of market context, location, and timing into a unified, logical system, it provides traders with an unprecedented lens into the mechanics of institutional market operations.
It is not merely an indicator, but a complete analytical engine designed to foster a deeper understanding of market dynamics. By focusing on the core principles of institutional order flow, Aetherium empowers traders to filter out market noise, identify key structural levels, and time their entries in harmony with the market's underlying rhythm.
"In all chaos there is a cosmos, in all disorder a secret order." - Carl Jung
— Dskyz, Trade with insight. Trade with confluence. Trade with Aetherium.
RSI OS/OB Background StripesThe "RSI OS/OB Background Stripes" indicator is a simple tool designed to help traders visualize overbought and oversold market conditions using the Relative Strength Index (RSI). It highlights these conditions by displaying colored background stripes directly on your chart, making it easy to spot potential trading opportunities.
How It Works:RSI Calculation: The indicator calculates the RSI, a popular momentum indicator that measures the speed and change of price movements, using a default period of 14 (customizable).
Overbought/Oversold Levels: It marks areas where the RSI is above a user-defined overbought level (default: 70) with red background stripes, and below an oversold level (default: 30) with green background stripes.
Visual Feedback: The colored stripes appear on the chart when the RSI enters overbought (red) or oversold (green) zones, helping you quickly identify market conditions.
Customization: You can adjust the RSI period, overbought/oversold levels, background colors, and transparency. You can also choose to show the RSI line in a separate panel or display RSI values on the chart for debugging.
Alerts: The indicator includes optional alerts that notify you when the RSI crosses into overbought or oversold territory.
Who It’s For: This indicator is perfect for beginner and intermediate traders who want a clear, visual way to track RSI-based overbought and oversold conditions without cluttering their charts.
Key Features:Easy-to-read background stripes for overbought (red) and oversold (green) conditions.
Fully customizable RSI settings, colors, and transparency.
Optional RSI plot and value display for deeper analysis.
Alerts to keep you informed of key RSI level crossings.
This indicator is a straightforward way to monitor market momentum and make informed trading decisions.
Fibo_Ma with Toggleable 200 EMA Filter Fibo_MA with Toggleable 200 EMA Filter
Description:
This multi-functional indicator blends Fibonacci-based moving averages with customizable filters and visual enhancements to support various trading strategies. It offers traders the flexibility to analyze trend dynamics and potential reversal zones using multiple tools in one script.
Key Features:
🔹 Fibonacci MA Framework
Leverage a range of Fibonacci numbers (from 1 to 233) to visualize trend-based EMA lines with optional smoothing. Users can choose the moving average method (SMA, EMA, RMA, WMA, VWMA, etc.) and adjust the smoothing length for fine-tuned analysis.
🔹 VWAP and Dynamic EMA Tools
Includes VWAP and a color-coded 200 EMA that updates based on trend slope. These help visualize key dynamic support and resistance levels.
🔹 Multi-Timeframe Support
Option to switch the data source to a higher timeframe for broader trend confirmation.
🔹 Signal Highlights
Bullish and bearish signal markers based on crossovers with optional filters.
Background highlights show whether the current price is above or below a smoothed EMA line.
🔹 Customizable Filters
Enable or disable filters like:
200 EMA Position Filter (only signal when price is above or below the 200 EMA)
ATR Filter (filter out low-volatility candles)
Volume Filter (signal only on sufficient volume)
🔹 Cross Alerts & Labels
Built-in alert conditions for crossovers and customizable signal display options—labels, shapes, and background highlights.
🔹 Advanced Options
Toggle forecast line visibility and offset
Fine-tune alerts using price action relative to the smooth trend line
Optional tail and cross label display for deeper chart customization
How to Use:
This tool can support trend-following, breakout, and pullback strategies. Customize the MA types, filters, and timeframe settings to match your trading style. The script is designed for visual clarity while offering rich configurability for discretionary and system-based traders.
Rainbow Price Chart This indicator is a technical and on-chain analysis tool for Bitcoin, designed to help investors better understand the different phases of the market cycle and underlying sentiment. It directly overlays on the price chart (overlay=true).
Indicator Name: "Rainbow Price Chart & V/T Ratio Signals"
General Purpose:
It combines two popular methodologies for visualizing Bitcoin's value and sentiment: the classic "Rainbow Price Chart" and signals derived from the "Value per Transaction Ratio" (V/T Ratio) based on blockchain data. It is ideal for long-term investors looking for strategic entry/exit points.
Main Components:
Rainbow Price Chart:
Concept: Divides Bitcoin's price range into different market "sentiment zones" (e.g., "Bubble Zone," "FOMO Zone," "HODL Zone," "Accumulation Zone," "Buy Zone," "Fire Sale Zone") using colored bands. These bands are calculated as ascending and descending multiples of a base Exponential Moving Average (EMA), configurable by default to 200 periods.
Visualization: The zones are represented with transparent color fills on the price chart. A detailed legend in the top right corner of the chart explains the meaning of each color and sentiment zone.
Important Note: This type of chart is designed to be viewed and analyzed correctly on a logarithmic price scale. The indicator includes a visual reminder to activate this scale.
Value per Transaction (V/T) Ratio Signals:
Concept: Measures the average value per transaction on the Bitcoin blockchain by dividing the total transacted volume in USD by the number of transactions. This ratio is smoothed with an Exponential Moving Average (by default, 7 periods) and is framed within a dynamic Linear Regression Channel (LRC) based on standard deviation.
Signal Generation: Based on the position of the smoothed V/T Ratio within this LRC channel, the indicator generates signals directly on the price chart, such as:
"BOTTOM": Low price, V/T Ratio in the lower band of the LRC.
"SEMI-LOW" / "SEMI-HIGH": Intermediate phases within the channel.
"ATH" (All-Time High): Potentially overvalued price, V/T Ratio in the upper band of the LRC.
On-Chain Data: The indicator requests external daily on-chain data for total transacted volume (TVTVR) and number of transactions (NTRAN) from the Bitcoin blockchain.
Diagnostic Panes: Includes plots of the raw on-chain data (volume and number of transactions) in a separate pane, which are useful for debugging or verifying the data source. The lines for the V/T Ratio itself and its LRC channel are not plotted by default but can be activated in the code for deeper analysis.
Ideal for:
Bitcoin investors and "hodlers" who desire a visual tool that combines price-based market cycle context with fundamental signals derived from on-chain activity, to help identify key moments for accumulation or potential distribution.
Considerations:
Relies on the availability of external on-chain data (QUANDL:BCHAIN) within TradingView.
Functions best on a daily timeframe.
Simple Multi-Timeframe Trends with RSI (Realtime)Simple Multi-Timeframe Trends with RSI Realtime Updates
Overview
The Simple Multi-Timeframe Trends with RSI Realtime Updates indicator is a comprehensive dashboard designed to give you an at-a-glance understanding of market trends across nine key timeframes, from one minute (M1) to one month (M).
It moves beyond simple moving average crossovers by calculating a sophisticated Trend Score for each timeframe. This score is then intelligently combined into a single, weighted Confluence Signal , which adapts to your personal trading style. With integrated RSI and divergence detection, SMTT provides a powerful, all-in-one tool to confirm your trade ideas and stay on the right side of the market.
Key Features
Automatic Trading Presets: The most powerful feature of the script. Simply select your trading style, and the indicator will automatically adjust all internal parameters for you:
Intraday: Uses shorter moving averages and higher sensitivity, focusing on lower timeframe alignment for quick moves.
Swing Trading: A balanced preset using medium-term moving averages, ideal for capturing trends that last several days or weeks.
Investment: Uses long-term moving averages and lower sensitivity, prioritizing the major trends on high timeframes.
Advanced Trend Scoring: The trend for each timeframe isn't just "up" or "down". The score is calculated based on a combination of:
Price vs. Moving Average: Is the price above or below the MA?
MA Slope: Is the trend accelerating or decelerating? A steep slope indicates a strong trend.
Price Momentum: How quickly has the price moved recently?
Volatility Adjustment: The score's quality is adjusted based on current market volatility (using ATR) to filter out choppy conditions.
Weighted Confluence Score: The script synthesizes the trend scores from all nine timeframes into a single, actionable signal. The weights are dynamically adjusted based on your selected Trading Style , ensuring the most relevant timeframes have the most impact on the final result.
Integrated RSI & Divergence: Each timeframe includes a smoothed RSI value to help you spot overbought/oversold conditions. It also flags potential bullish (price lower, RSI higher) and bearish (price higher, RSI lower) divergences, which can be early warnings of a trend reversal.
Clean & Customizable Dashboard: The entire analysis is presented in a clean, easy-to-read table on your chart. You can choose its position and optionally display the raw numerical scores for a deeper analysis.
How to Use It
1. Add to Chart: Apply the "Simple Multi-Timeframe Trends" indicator to your chart.
2. Select Your Style: This is the most important step. Go to the indicator settings and choose the Trading Style that best fits your strategy (Intraday, Swing Trading, or Investment). All calculations will instantly adapt.
3. Analyze the Dashboard:
Look at the Trend row to see the direction and strength of the trend on individual timeframes. Strong alignment (e.g., all green or all red) indicates a powerful, market-wide move.
Check the RSI row. Is the trend overextended (RSI > 60) or is there room to run? Look for the fuchsia color, which signals a divergence and warrants caution.
Focus on the Signal row. This is your summary. A "STRONG SIGNAL" with high alignment suggests a high-probability setup. A "NEUTRAL" or "Weak" signal suggests waiting for a better opportunity.
4. Confirm Your Trades: Use the SMTT dashboard as a confirmation tool. For example, if you are looking for a long entry, wait for the dashboard to show a "BULLISH" or "STRONG SIGNAL" to confirm that the broader market structure supports your trade.
Dashboard Legend
Trend Row
This row shows the trend direction and strength for each timeframe.
⬆⬆ (Dark Green): Ultra Bullish - Very strong, established uptrend.
⬆ (Green): Strong Bullish - Confident uptrend.
▲ (Light Green): Bullish - The beginning of an uptrend or a weak uptrend.
━ (Orange): Neutral - Sideways or consolidating market.
▼ (Light Red): Bearish - The beginning of a downtrend or a weak downtrend.
⬇ (Red): Strong Bearish - Confident downtrend.
⬇⬇ (Dark Red): Ultra Bearish - Very strong, established downtrend.
RSI Row
This row displays the smoothed RSI value and its condition.
Green Text: Oversold (RSI < 40). Potential for a bounce or reversal upwards.
Red Text: Overbought (RSI > 60). Potential for a pullback or reversal downwards.
Fuchsia (Pink) Text: Divergence Detected! A potential reversal is forming.
White Text: Neutral (RSI between 40 and 60).
Signal Row
This is the final, weighted confluence of all timeframes.
Label:
🚀 STRONG SIGNAL / 💥 STRONG SIGNAL: High confluence and strong momentum.
🟢 BULLISH / 🔴 BEARISH: Clear directional bias across relevant timeframes.
🟡 Weak + / 🟠 Weak -: Minor directional bias, suggests caution.
⚪ NEUTRAL: No clear directional trend; market is likely choppy or undecided.
Numerical Score: The raw weighted confluence score. The further from zero, the stronger the signal.
Alignment %: The percentage of timeframes (out of 9) that are showing a clear bullish or bearish trend. Higher percentages indicate a more unified market.
Gann Support and Resistance LevelsThis indicator plots dynamic Gann Degree Levels as potential support and resistance zones around the current market price. You can fully customize the Gann degree step (e.g., 45°, 30°, 90°), the number of levels above and below the price, and the price movement per degree to fine-tune the levels to your strategy.
Key Features:
✅ Dynamic levels update automatically with the live price
✅ Adjustable degree intervals (Gann steps)
✅ User control over how many levels to display above and below
✅ Fully customizable label size, label color, and text color for mobile-friendly visibility
✅ Clean visual design for easy chart analysis
How to Use:
Gann levels can act as potential support and resistance zones.
Watch for price reactions at major degrees like 0°, 90°, 180°, and 270°.
Can be combined with other technical tools like price action, trendlines, or Gann fans for deeper analysis.
📌 This tool is perfect for traders using Gann theory, grid-based strategies, or those looking to enhance their visual trading setups with structured levels.
ALMA Trend-boxALMA Trend-box — an innovative indicator for detecting trend and consolidation based on the ALMA moving average
This indicator combines the Adaptive Laguerre Moving Average (ALMA) with unique visual representations of trend and consolidation zones, providing traders with clearer and deeper insight into current market conditions.
Originality and Usefulness
Unlike classic indicators based on simple moving averages, ALMA uses a Gaussian weighting function and an offset parameter to reduce lag, resulting in smoother and more accurate trend signals. This indicator not only plots the ALMA but also analyzes the slope angle of the ALMA line, combining it with the price’s position relative to the moving average to identify three key market states:
Uptrend (bullish): when the ALMA slope angle is above a defined threshold and the price is above ALMA,
Downtrend (bearish): when the slope angle is below a negative threshold and the price is below ALMA,
Consolidation or sideways trend: when neither of the above conditions is met.
A special contribution is the automatic identification of consolidation zones (periods of weak trend or transition between bullish and bearish phases), visually represented by blue-colored candlesticks on the chart. This feature can help traders better recognize moments when the market is indecisive and adjust their strategies accordingly.
How the Indicator Works
ALMA is calculated using user-defined parameters — length, offset, and sigma — which can be adjusted for different timeframes and instruments.
The slope angle of the ALMA line is calculated based on the difference between the current and previous ALMA values, converted into degrees.
Based on the slope angle and the relative price position to ALMA, the indicator determines the trend type and changes the candle colors accordingly:
Green for bullish (uptrend),
Red for bearish (downtrend),
Blue for sideways trend (consolidation).
When the slope angle falls within a certain range and the price behavior contradicts the trend, the indicator detects consolidation and displays it graphically through semi-transparent boxes and background color.
How to Use This Indicator
Use candle colors for quick identification of the current trend and potential trend reversals.
Pay attention to consolidation zones marked by boxes (blue candles), as these are potential signals for trend breaks or preparation for stronger price moves.
ALMA parameters can be adjusted depending on the timeframe and market volatility, providing flexibility in analysis.
The indicator is useful for both short-term scalping strategies and longer-term trend monitoring and position management.
Why This Indicator is Useful
Many existing trend indicators do not consider the slope angle of the moving average as a quantitative measure of trend strength, nor do they automatically detect consolidations as separate zones. ALMA Trend-box fills this gap by combining sophisticated mathematical processing with simple and intuitive visual representation. This way, users get a tool that helps make decisions based on more objective criteria of trend and consolidation rather than just price location relative to averages.
SIP Evaluator and Screener [Trendoscope®]The SIP Evaluator and Screener is a Pine Script indicator designed for TradingView to calculate and visualize Systematic Investment Plan (SIP) returns across multiple investment instruments. It is tailored for use in TradingView's screener, enabling users to evaluate SIP performance for various assets efficiently.
🎲 How SIP Works
A Systematic Investment Plan (SIP) is an investment strategy where a fixed amount is invested at regular intervals (e.g., monthly or weekly) into a financial instrument, such as stocks, mutual funds, or ETFs. The goal is to build wealth over time by leveraging the power of compounding and mitigating the impact of market volatility through disciplined, consistent investing. Here’s a breakdown of how SIPs function:
Regular Investments : In an SIP, an investor commits to investing a fixed sum at predefined intervals, regardless of market conditions. This consistency helps inculcate a habit of saving and investing.
Cost Averaging : By investing a fixed amount regularly, investors purchase more units when prices are low and fewer units when prices are high. This approach, known as dollar-cost averaging, reduces the average cost per unit over time and mitigates the risk of investing a large amount at a peak price.
Compounding Benefits : Returns generated from the invested amount (e.g., capital gains or dividends) are reinvested, leading to exponential growth over the long term. The longer the investment horizon, the greater the potential for compounding to amplify returns.
Dividend Reinvestment : In some SIPs, dividends received from the underlying asset can be reinvested to purchase additional units, further enhancing returns. Taxes on dividends, if applicable, may reduce the reinvested amount.
Flexibility and Accessibility : SIPs allow investors to start with small amounts, making them accessible to a wide range of individuals. They also offer flexibility in terms of investment frequency and the ability to adjust or pause contributions.
In the context of the SIP Evaluator and Screener , the script simulates an SIP by calculating the number of units purchased with each fixed investment, factoring in commissions, dividends, taxes and the chosen price reference (e.g., open, close, or average prices). It tracks the cumulative investment, equity value, and dividends over time, providing a clear picture of how an SIP would perform for a given instrument. This helps users understand the impact of regular investing and make informed decisions when comparing different assets in TradingView’s screener. It offers insights into key metrics such as total invested amount, dividends received, equity value, and the number of installments, making it a valuable resource for investors and traders interested in understanding long-term investment outcomes.
🎲 Key Features
Customizable Investment Parameters: Users can define the recurring investment amount, price reference (e.g., open, close, HL2, HLC3, OHLC4), and whether fractional quantities are allowed.
Commission Handling: Supports both fixed and percentage-based commission types, adjusting calculations accordingly.
Dividend Reinvestment: Optionally reinvests dividends after a user-specified period, with the ability to apply tax on dividends.
Time-Bound Analysis: Allows users to set a start year for the analysis, enabling historical performance evaluation.
Flexible Dividend Periods: Dividends can be evaluated based on bars, days, weeks, or months.
Visual Outputs: Plots key metrics like total invested amount, dividends, equity value, and remainder, with customizable display options for clarity in the data window and chart.
🎲 Using the script as an indicator on Tradingview Supercharts
In order to use the indicator on charts, do the following.
Load the instrument of your choice - Preferably a stable stocks, ETFs.
Chose monthly timeframe as lower timeframes are insignificant in this type of investment strategy
Load the indicator SIP Evaluator and Screener and set the input parameters as per your preference.
Indicator plots, investment value, dividends and equity on the chart.
🎲 Visualizations
Installments : Displays the number of SIP installments (gray line, visible in the data window).
Invested Amount : Shows the cumulative amount invested, excluding reinvested dividends (blue area plot).
Dividends : Tracks total dividends received (green area plot).
Equity : Represents the current market value of the investment based on the closing price (purple area plot).
Remainder : Indicates any uninvested cash after each installment (gray line, visible in the data window).
🎲 Deep dive into the settings
The SIP Evaluator and Screener offers a range of customizable settings to tailor the Systematic Investment Plan (SIP) simulation to your preferences. Below is an explanation of each setting, its purpose, and how it impacts the analysis:
🎯 Duration
Start Year (Default: 2020) : Specifies the year from which the SIP calculations begin. When Start Year is enabled via the timebound option, the script only considers data from the specified year onward. This is useful for analyzing historical SIP performance over a defined period. If disabled, the script uses all available data.
Timebound (Default: False) : A toggle to enable or disable the Start Year restriction. When set to False, the SIP calculation starts from the earliest available data for the instrument.
🎯 Investment
Recurring Investment (Default: 1000.0) : The fixed amount invested in each SIP installment (e.g., $1000 per period). This represents the regular contribution to the SIP and directly influences the total invested amount and quantity purchased.
Allow Fractional Qty (Default: True) : When enabled, the script allows the purchase of fractional units (e.g., 2.35 shares). If disabled, only whole units are purchased (e.g., 2 shares), with any remaining funds carried forward as Remainder. This setting impacts the precision of investment allocation.
Price Reference (Default: OPEN): Determines the price used for purchasing units in each SIP installment. Options include:
OPEN : Uses the opening price of the bar.
CLOSE : Uses the closing price of the bar.
HL2 : Uses the average of the high and low prices.
HLC3 : Uses the average of the high, low, and close prices.
OHLC4 : Uses the average of the open, high, low, and close prices. This setting affects the cost basis of each purchase and, consequently, the total quantity and equity value.
🎯 Commission
Commission (Default: 3) : The commission charged per SIP installment, expressed as either a fixed amount (e.g., $3) or a percentage (e.g., 3% of the investment). This reduces the amount available for purchasing units.
Commission Type (Default: Fixed) : Specifies how the commission is calculated:
Fixed ($) : A flat fee is deducted per installment (e.g., $3).
Percentage (%) : A percentage of the investment amount is deducted as commission (e.g., 3% of $1000 = $30). This setting affects the net amount invested and the overall cost of the SIP.
🎯 Dividends
Apply Tax On Dividends (Default: False) : When enabled, a tax is applied to dividends before they are reinvested or recorded. The tax rate is set via the Dividend Tax setting.
Dividend Tax (Default: 47) : The percentage of tax deducted from dividends if Apply Tax On Dividends is enabled (e.g., 47% tax reduces a $100 dividend to $53). This reduces the amount available for reinvestment or accumulation.
Reinvest Dividends After (Default: True, 2) : When enabled, dividends received are reinvested to purchase additional units after a specified period (e.g., 2 units of time, defined by Dividends Availability). If disabled, dividends are tracked but not reinvested. Reinvestment increases the total quantity and equity over time.
Dividends Availability (Default: Bars) : Defines the time unit for evaluating when dividends are available for reinvestment. Options include:
Bars : Based on the number of chart bars.
Weeks : Based on weeks.
Months : Based on months (approximated as 30.5 days). This setting determines the timing of dividend reinvestment relative to the Reinvest Dividends After period.
🎯 How Settings Interact
These settings work together to simulate a realistic SIP. For example, a $1000 recurring investment with a 3% commission and fractional quantities enabled will calculate the number of units purchased at the chosen price reference after deducting the commission. If dividends are reinvested after 2 months with a 47% tax, the script fetches dividend data, applies the tax, and adds the net dividend to the investment amount for that period. The Start Year and Timebound settings ensure the analysis aligns with the desired timeframe, while the Dividends Availability setting fine-tunes dividend reinvestment timing.
By adjusting these settings, users can model different SIP scenarios, compare performance across instruments in TradingView’s screener, and gain insights into how commissions, dividends, and price references impact long-term returns.
🎲 Using the script with Pine Screener
The main purpose of developing this script is to use it with Tradingview Pine Screener so that multiple ETFs/Funds can be compared.
In order to use this as a screener, the following things needs to be done.
Add SIP Evaluator and Screener to your favourites (Required for it to be added in pine screener)
Create a watch list containing required instruments to compare
Open pine screener from Tradingview main menu Products -> Screeners -> Pine or simply load the URL - www.tradingview.com
Select the watchlist created from Watchlist dropdown.
Chose the SIP Evaluator and Screener from the "Choose Indicator" dropdown
Set timeframe to 1 month and update settings as required.
Press scan to display collected data on the screener.
🎲 Use Case
This indicator is ideal for educational purposes, allowing users to experiment with SIP strategies across different instruments. It can be applied in TradingView’s screener to compare SIP performance for stocks, ETFs, or other assets, helping users understand how factors like commissions, dividends, and price references impact returns over time.
Wavelet-Trend ML Integration [Alpha Extract]Alpha-Extract Volatility Quality Indicator
The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
Advanced Fed Decision Forecast Model (AFDFM)The Advanced Fed Decision Forecast Model (AFDFM) represents a novel quantitative framework for predicting Federal Reserve monetary policy decisions through multi-factor fundamental analysis. This model synthesizes established monetary policy rules with real-time economic indicators to generate probabilistic forecasts of Federal Open Market Committee (FOMC) decisions. Building upon seminal work by Taylor (1993) and incorporating recent advances in data-dependent monetary policy analysis, the AFDFM provides institutional-grade decision support for monetary policy analysis.
## 1. Introduction
Central bank communication and policy predictability have become increasingly important in modern monetary economics (Blinder et al., 2008). The Federal Reserve's dual mandate of price stability and maximum employment, coupled with evolving economic conditions, creates complex decision-making environments that traditional models struggle to capture comprehensively (Yellen, 2017).
The AFDFM addresses this challenge by implementing a multi-dimensional approach that combines:
- Classical monetary policy rules (Taylor Rule framework)
- Real-time macroeconomic indicators from FRED database
- Financial market conditions and term structure analysis
- Labor market dynamics and inflation expectations
- Regime-dependent parameter adjustments
This methodology builds upon extensive academic literature while incorporating practical insights from Federal Reserve communications and FOMC meeting minutes.
## 2. Literature Review and Theoretical Foundation
### 2.1 Taylor Rule Framework
The foundational work of Taylor (1993) established the empirical relationship between federal funds rate decisions and economic fundamentals:
rt = r + πt + α(πt - π) + β(yt - y)
Where:
- rt = nominal federal funds rate
- r = equilibrium real interest rate
- πt = inflation rate
- π = inflation target
- yt - y = output gap
- α, β = policy response coefficients
Extensive empirical validation has demonstrated the Taylor Rule's explanatory power across different monetary policy regimes (Clarida et al., 1999; Orphanides, 2003). Recent research by Bernanke (2015) emphasizes the rule's continued relevance while acknowledging the need for dynamic adjustments based on financial conditions.
### 2.2 Data-Dependent Monetary Policy
The evolution toward data-dependent monetary policy, as articulated by Fed Chair Powell (2024), requires sophisticated frameworks that can process multiple economic indicators simultaneously. Clarida (2019) demonstrates that modern monetary policy transcends simple rules, incorporating forward-looking assessments of economic conditions.
### 2.3 Financial Conditions and Monetary Transmission
The Chicago Fed's National Financial Conditions Index (NFCI) research demonstrates the critical role of financial conditions in monetary policy transmission (Brave & Butters, 2011). Goldman Sachs Financial Conditions Index studies similarly show how credit markets, term structure, and volatility measures influence Fed decision-making (Hatzius et al., 2010).
### 2.4 Labor Market Indicators
The dual mandate framework requires sophisticated analysis of labor market conditions beyond simple unemployment rates. Daly et al. (2012) demonstrate the importance of job openings data (JOLTS) and wage growth indicators in Fed communications. Recent research by Aaronson et al. (2019) shows how the Beveridge curve relationship influences FOMC assessments.
## 3. Methodology
### 3.1 Model Architecture
The AFDFM employs a six-component scoring system that aggregates fundamental indicators into a composite Fed decision index:
#### Component 1: Taylor Rule Analysis (Weight: 25%)
Implements real-time Taylor Rule calculation using FRED data:
- Core PCE inflation (Fed's preferred measure)
- Unemployment gap proxy for output gap
- Dynamic neutral rate estimation
- Regime-dependent parameter adjustments
#### Component 2: Employment Conditions (Weight: 20%)
Multi-dimensional labor market assessment:
- Unemployment gap relative to NAIRU estimates
- JOLTS job openings momentum
- Average hourly earnings growth
- Beveridge curve position analysis
#### Component 3: Financial Conditions (Weight: 18%)
Comprehensive financial market evaluation:
- Chicago Fed NFCI real-time data
- Yield curve shape and term structure
- Credit growth and lending conditions
- Market volatility and risk premia
#### Component 4: Inflation Expectations (Weight: 15%)
Forward-looking inflation analysis:
- TIPS breakeven inflation rates (5Y, 10Y)
- Market-based inflation expectations
- Inflation momentum and persistence measures
- Phillips curve relationship dynamics
#### Component 5: Growth Momentum (Weight: 12%)
Real economic activity assessment:
- Real GDP growth trends
- Economic momentum indicators
- Business cycle position analysis
- Sectoral growth distribution
#### Component 6: Liquidity Conditions (Weight: 10%)
Monetary aggregates and credit analysis:
- M2 money supply growth
- Commercial and industrial lending
- Bank lending standards surveys
- Quantitative easing effects assessment
### 3.2 Normalization and Scaling
Each component undergoes robust statistical normalization using rolling z-score methodology:
Zi,t = (Xi,t - μi,t-n) / σi,t-n
Where:
- Xi,t = raw indicator value
- μi,t-n = rolling mean over n periods
- σi,t-n = rolling standard deviation over n periods
- Z-scores bounded at ±3 to prevent outlier distortion
### 3.3 Regime Detection and Adaptation
The model incorporates dynamic regime detection based on:
- Policy volatility measures
- Market stress indicators (VIX-based)
- Fed communication tone analysis
- Crisis sensitivity parameters
Regime classifications:
1. Crisis: Emergency policy measures likely
2. Tightening: Restrictive monetary policy cycle
3. Easing: Accommodative monetary policy cycle
4. Neutral: Stable policy maintenance
### 3.4 Composite Index Construction
The final AFDFM index combines weighted components:
AFDFMt = Σ wi × Zi,t × Rt
Where:
- wi = component weights (research-calibrated)
- Zi,t = normalized component scores
- Rt = regime multiplier (1.0-1.5)
Index scaled to range for intuitive interpretation.
### 3.5 Decision Probability Calculation
Fed decision probabilities derived through empirical mapping:
P(Cut) = max(0, (Tdovish - AFDFMt) / |Tdovish| × 100)
P(Hike) = max(0, (AFDFMt - Thawkish) / Thawkish × 100)
P(Hold) = 100 - |AFDFMt| × 15
Where Thawkish = +2.0 and Tdovish = -2.0 (empirically calibrated thresholds).
## 4. Data Sources and Real-Time Implementation
### 4.1 FRED Database Integration
- Core PCE Price Index (CPILFESL): Monthly, seasonally adjusted
- Unemployment Rate (UNRATE): Monthly, seasonally adjusted
- Real GDP (GDPC1): Quarterly, seasonally adjusted annual rate
- Federal Funds Rate (FEDFUNDS): Monthly average
- Treasury Yields (GS2, GS10): Daily constant maturity
- TIPS Breakeven Rates (T5YIE, T10YIE): Daily market data
### 4.2 High-Frequency Financial Data
- Chicago Fed NFCI: Weekly financial conditions
- JOLTS Job Openings (JTSJOL): Monthly labor market data
- Average Hourly Earnings (AHETPI): Monthly wage data
- M2 Money Supply (M2SL): Monthly monetary aggregates
- Commercial Loans (BUSLOANS): Weekly credit data
### 4.3 Market-Based Indicators
- VIX Index: Real-time volatility measure
- S&P; 500: Market sentiment proxy
- DXY Index: Dollar strength indicator
## 5. Model Validation and Performance
### 5.1 Historical Backtesting (2017-2024)
Comprehensive backtesting across multiple Fed policy cycles demonstrates:
- Signal Accuracy: 78% correct directional predictions
- Timing Precision: 2.3 meetings average lead time
- Crisis Detection: 100% accuracy in identifying emergency measures
- False Signal Rate: 12% (within acceptable research parameters)
### 5.2 Regime-Specific Performance
Tightening Cycles (2017-2018, 2022-2023):
- Hawkish signal accuracy: 82%
- Average prediction lead: 1.8 meetings
- False positive rate: 8%
Easing Cycles (2019, 2020, 2024):
- Dovish signal accuracy: 85%
- Average prediction lead: 2.1 meetings
- Crisis mode detection: 100%
Neutral Periods:
- Hold prediction accuracy: 73%
- Regime stability detection: 89%
### 5.3 Comparative Analysis
AFDFM performance compared to alternative methods:
- Fed Funds Futures: Similar accuracy, lower lead time
- Economic Surveys: Higher accuracy, comparable timing
- Simple Taylor Rule: Lower accuracy, insufficient complexity
- Market-Based Models: Similar performance, higher volatility
## 6. Practical Applications and Use Cases
### 6.1 Institutional Investment Management
- Fixed Income Portfolio Positioning: Duration and curve strategies
- Currency Trading: Dollar-based carry trade optimization
- Risk Management: Interest rate exposure hedging
- Asset Allocation: Regime-based tactical allocation
### 6.2 Corporate Treasury Management
- Debt Issuance Timing: Optimal financing windows
- Interest Rate Hedging: Derivative strategy implementation
- Cash Management: Short-term investment decisions
- Capital Structure Planning: Long-term financing optimization
### 6.3 Academic Research Applications
- Monetary Policy Analysis: Fed behavior studies
- Market Efficiency Research: Information incorporation speed
- Economic Forecasting: Multi-factor model validation
- Policy Impact Assessment: Transmission mechanism analysis
## 7. Model Limitations and Risk Factors
### 7.1 Data Dependency
- Revision Risk: Economic data subject to subsequent revisions
- Availability Lag: Some indicators released with delays
- Quality Variations: Market disruptions affect data reliability
- Structural Breaks: Economic relationship changes over time
### 7.2 Model Assumptions
- Linear Relationships: Complex non-linear dynamics simplified
- Parameter Stability: Component weights may require recalibration
- Regime Classification: Subjective threshold determinations
- Market Efficiency: Assumes rational information processing
### 7.3 Implementation Risks
- Technology Dependence: Real-time data feed requirements
- Complexity Management: Multi-component coordination challenges
- User Interpretation: Requires sophisticated economic understanding
- Regulatory Changes: Fed framework evolution may require updates
## 8. Future Research Directions
### 8.1 Machine Learning Integration
- Neural Network Enhancement: Deep learning pattern recognition
- Natural Language Processing: Fed communication sentiment analysis
- Ensemble Methods: Multiple model combination strategies
- Adaptive Learning: Dynamic parameter optimization
### 8.2 International Expansion
- Multi-Central Bank Models: ECB, BOJ, BOE integration
- Cross-Border Spillovers: International policy coordination
- Currency Impact Analysis: Global monetary policy effects
- Emerging Market Extensions: Developing economy applications
### 8.3 Alternative Data Sources
- Satellite Economic Data: Real-time activity measurement
- Social Media Sentiment: Public opinion incorporation
- Corporate Earnings Calls: Forward-looking indicator extraction
- High-Frequency Transaction Data: Market microstructure analysis
## References
Aaronson, S., Daly, M. C., Wascher, W. L., & Wilcox, D. W. (2019). Okun revisited: Who benefits most from a strong economy? Brookings Papers on Economic Activity, 2019(1), 333-404.
Bernanke, B. S. (2015). The Taylor rule: A benchmark for monetary policy? Brookings Institution Blog. Retrieved from www.brookings.edu
Blinder, A. S., Ehrmann, M., Fratzscher, M., De Haan, J., & Jansen, D. J. (2008). Central bank communication and monetary policy: A survey of theory and evidence. Journal of Economic Literature, 46(4), 910-945.
Brave, S., & Butters, R. A. (2011). Monitoring financial stability: A financial conditions index approach. Economic Perspectives, 35(1), 22-43.
Clarida, R., Galí, J., & Gertler, M. (1999). The science of monetary policy: A new Keynesian perspective. Journal of Economic Literature, 37(4), 1661-1707.
Clarida, R. H. (2019). The Federal Reserve's monetary policy response to COVID-19. Brookings Papers on Economic Activity, 2020(2), 1-52.
Clarida, R. H. (2025). Modern monetary policy rules and Fed decision-making. American Economic Review, 115(2), 445-478.
Daly, M. C., Hobijn, B., Şahin, A., & Valletta, R. G. (2012). A search and matching approach to labor markets: Did the natural rate of unemployment rise? Journal of Economic Perspectives, 26(3), 3-26.
Federal Reserve. (2024). Monetary Policy Report. Washington, DC: Board of Governors of the Federal Reserve System.
Hatzius, J., Hooper, P., Mishkin, F. S., Schoenholtz, K. L., & Watson, M. W. (2010). Financial conditions indexes: A fresh look after the financial crisis. National Bureau of Economic Research Working Paper, No. 16150.
Orphanides, A. (2003). Historical monetary policy analysis and the Taylor rule. Journal of Monetary Economics, 50(5), 983-1022.
Powell, J. H. (2024). Data-dependent monetary policy in practice. Federal Reserve Board Speech. Jackson Hole Economic Symposium, Federal Reserve Bank of Kansas City.
Taylor, J. B. (1993). Discretion versus policy rules in practice. Carnegie-Rochester Conference Series on Public Policy, 39, 195-214.
Yellen, J. L. (2017). The goals of monetary policy and how we pursue them. Federal Reserve Board Speech. University of California, Berkeley.
---
Disclaimer: This model is designed for educational and research purposes only. Past performance does not guarantee future results. The academic research cited provides theoretical foundation but does not constitute investment advice. Federal Reserve policy decisions involve complex considerations beyond the scope of any quantitative model.
Citation: EdgeTools Research Team. (2025). Advanced Fed Decision Forecast Model (AFDFM) - Scientific Documentation. EdgeTools Quantitative Research Series
RACZ-SIGNAL-V2.1RACZ-SIGNAL-V2.1 – Reactive Analytical Confluence Zones
Developed by: RACZ Trading
Indicator Type: Multi-Factor Confluence System
Overlay: Off (separate pane)
Purpose: Detect powerful trade opportunities through confluence of technical signals.
⸻
🔍 What is RACZ?
RACZ stands for Reactive Analytical Confluence Zones.
It’s a high-precision trading tool built for traders who rely on multi-signal confirmation, momentum alignment, and market structure awareness.
Rather than relying on a single technical metric, RACZ dynamically combines RSI, VWAP-RSI, Divergence, ADX, and Volume Analytics to produce a composite signal score from 0 to 12 — the higher the score, the stronger the signal.
⸻
🧠 How It Works – Core Components
1. RSI Analysis
• Detects momentum shifts.
• Compares RSI value to overbought (default: 67) and oversold (default: 33) thresholds.
• Adds points to Bullish or Bearish score.
2. VWAP-RSI
• Uses RSI based on VWAP (Volume Weighted Average Price).
• Adds weight to signals influenced by volume-adjusted price movement.
3. Divergence Detection
• Detects potential reversal zones.
• Bullish Divergence: RSI crosses up from low zone.
• Bearish Divergence: RSI crosses down from high zone.
• Strong confluence signal when present.
4. ADX Dynamic Strength Filter
• Custom-calculated ADX (trend strength indicator).
• Uses a dynamic threshold derived from SMA of ADX over a lookback period, scaled by a factor (default 0.9).
• Ensures signals are only validated in strong trend environments.
5. Volume Z-Score
• Detects anomalies in volume behavior.
• Z-score applied to 20-period volume average & deviation.
• Labels spikes, drops, high/low volume conditions.
⸻
📊 Signal Scoring Logic
Each component (RSI, VWAP-RSI, Divergence, ADX) can score up to 3 points each.
• Bullish Score: Total from bullish alignment of each factor.
• Bearish Score: Total from bearish alignment of each factor.
• Signal Power = max(bullish, bearish)
📈 Signal Interpretation
• BUY: Bullish Score > Bearish Score
• SELL: Bearish Score > Bullish Score
• NEUTRAL: Scores are equal
• Signal power is plotted on a 0–12 histogram:
• 0–5 = Weak
• 6–8 = Medium
• 9–12 = Strong (High Confluence Zone)
🖥️ Live Status Panel (Top-Right Corner)
This real-time panel helps you break down the signal:Component
Value Explanation: RSI / VWAP / DIV / ADX
Shows points contributing to signal
SIGNAL: Current market bias (BUY, SELL, NEUTRAL)
VOLUME: Volume classification (Spike, Drop, High, Low, Normal)
Color-coded for quick interpretation.
✅ How to Use
1. Look at Histogram: Bars ≥6 suggest valid setups, especially ≥9.
2. Confirm Panel Agreement: Check which components are supporting the signal.
3. Validate Volume: Unusual spikes/drops often precede strong moves.
4. Follow Direction: Use BUY/SELL signals aligned with signal power and trend.
⸻
⚙️ Customizable Inputs
• RSI period, overbought/oversold levels
• VWAP-RSI period
• ADX period and dynamic threshold settings
• Fully adjustable to fit any trading style
⸻
🚀 Why Choose RACZ?
• Clarity: Scores & signals derived from multiple tools, not just one.
• Confluence Logic: Designed for traders who look for confirmation across indicators.
• Speed: Real-time responsiveness to changing market dynamics.
• Volume Awareness: Integrated volume intelligence gives a deeper edge.
⸻
⚠️ Disclaimer
This indicator is intended strictly for educational and informational purposes only. It is not financial advice and should not be used to make actual investment decisions. Always conduct your own research or consult with a licensed financial advisor before trading or investing. Use of this script is at your own risk.
Trend Flow Trail [AlgoAlpha]OVERVIEW
This script overlays a custom hybrid indicator called the Money Flow Trail which combines a volatility-based trend-following trail with a volume-weighted momentum oscillator. It’s built around two core components: the AlphaTrail—a dynamic band system influenced by Hull MA and volatility—and a smoothed Money Flow Index (MFI) that provides insights into buying or selling pressure. Together, these tools are used to color bars, generate potential reversal markers, and assist traders in identifying trend continuation or exhaustion phases in any market or timeframe.
CONCEPTS
The AlphaTrail calculates a volatility-adjusted channel around price using the Hull Moving Average as the base and an EMA of range as the spread. It adaptively shifts based on price interaction to capture trend reversals while avoiding whipsaws. The direction (bullish or bearish) determines both the band being tracked and how the trail locks in. The Money Flow Index (MFI) is derived from hlc3 and volume, measuring buying vs selling pressure, and is further smoothed with a short Hull MA to reduce noise while preserving structure. These two systems work in tandem: AlphaTrail governs directional context, while MFI refines the timing.
FEATURES
Dynamic AlphaTrail line with regime switching logic that controls directional bias and bar coloring.
Smoothed MFI with gradient coloring to visually communicate pressure and exhaustion levels.
Overbought/oversold thresholds (80/20), mid-level (50), and custom extreme zones (90/10) for deeper signal granularity.
Built-in take-profit signal logic: crossover of MFI into overbought with bullish AlphaTrail, or into oversold with bearish AlphaTrail.
Visual fills between price and AlphaTrail for clearer confirmation during trend phases.
Alerts for regime shifts, MFI crossovers, trail interactions, and bar color regime changes.
USAGE
Add the indicator to any chart. Use the AlphaTrail plot to define trend context: bullish (trailing below price) or bearish (trailing above). MFI values give supporting confirmation—favor long setups when MFI is rising and above 50 in a bullish regime, and shorts when MFI is falling and below 50 in a bearish regime. The colored fills help visually track strength; sharp changes in MFI crossing 80/20 or 90/10 zones often precede pullbacks or reversals. Use the plotted circles as optional take-profit signals when MFI and trend are extended. Adjust AlphaTrail length/multiplier and MFI smoothing to better match the asset’s volatility profile.
IU Market Rhythm WaveDESCRIPTION:
The IU Market Rhythm Wave is a multi-dimensional indicator designed to reveal the underlying rhythm and energy of the market. By analyzing price momentum, harmonic oscillations, volume behavior, and market breadth, it helps traders identify high-quality long and short wave signals. It also visualizes rhythm bands, wave strength zones, and harmonic levels to provide comprehensive context for decision-making.
This tool is best used on trending instruments where rhythm cycles and volume patterns create clear wave-based opportunities.
USER INPUTS:
Rhythm Cycle Length
Controls the main lookback period used to calculate price waves, harmonic oscillation, volume rhythm, and breath. A longer cycle smooths signals, while a shorter cycle makes them more responsive. Recommended range: 8 to 35.
Wave Signal Strength
Multiplies the standard deviation of rhythm to define dynamic breakout thresholds. A higher value results in fewer but stronger signals, filtering out minor fluctuations.
Harmonic Filter
Applies a sensitivity filter to the harmonic mean and standard deviation. It helps eliminate weak or noisy signals and ensures rhythm-based signals align with harmonic structure.
Show Wave Energy Zones
Toggles background color shading based on current rhythm conditions. Greenish zones indicate strong upward rhythm, red for strong downward rhythm, yellow for positive bias, and gray for weak or neutral zones.
Show Rhythm Bands
Enables the display of upper and lower rhythm bands derived from ATR and rhythm volatility. These bands act as dynamic price envelopes and potential support/resistance zones.
Wave Zone Opacity
Adjusts the transparency of background energy zones, allowing users to control how prominent these zones appear on the chart. Range: 60 to 90 for optimal visibility.
INDICATOR LOGIC:
The indicator combines multiple rhythmic components into a composite rhythm score:
1. Price Wave – Based on momentum (rate of price change) smoothed by a moving average.
2. Harmonic Oscillation – Measures how far price has deviated from a central harmonic average (HLC3).
3. Volume Rhythm – Uses volume’s deviation from its mean, standardized by its volatility.
4. Market Breath – Captures range expansion and closing strength relative to range.
These elements form the Raw Rhythm, which is further smoothed to produce the Market Rhythm. When the rhythm exceeds statistically calculated thresholds and other conditions like volume confirmation and harmonic proximity are met, wave signals are triggered.
Harmonic Fibonacci levels (0.236, 0.382, 0.618, 0.764) are also calculated every rhythm cycle to identify nearby structural price zones. Signals occurring near these levels are considered more reliable.
The Rhythm Bands use ATR and rhythm strength to define dynamic boundaries above and below price. Visual zones and arrows mark rhythm shifts and highlight the underlying energy of the market.
WHY IT IS UNIQUE:
This indicator goes beyond traditional oscillators or volume indicators by blending multiple market dimensions into one rhythmic framework. It adapts to volatility, applies harmonic structure awareness, and filters signals based on real-time market conditions. It offers:
* A unique rhythm-based view of price, volume, and volatility
* Dynamic, adaptive signal generation and zone coloring
* Visual analytics and contextual data in a summary table
* Signal filtering using harmonic alignment and market breath
Its real-time responsiveness and multi-layered logic make it suitable for intraday, swing, and positional traders.
HOW USER CAN BENEFIT FROM IT:
* Spot high-conviction long or short entries when rhythm, volume, and structure align
* Avoid low-quality trades during weak or noisy rhythm periods
* Use visual wave zones to gauge trend strength and rhythm direction
* Monitor harmonic proximity to enter or exit near key structural levels
* Apply rhythm bands for dynamic stop-loss and target setting
* Use rhythm direction arrows and analytics table to gain deeper market insight
DISCLAIMER:
This indicator is created for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any asset. All trading involves risk, and users should conduct their own analysis or consult with a qualified financial advisor before making any trading decisions. The creator is not responsible for any losses incurred through the use of this tool. Use at your own discretion.
Linear Regression Forecast (ADX Adaptive)Linear Regression Forecast (ADX Adaptive)
This indicator is a dynamic price projection tool that combines multiple linear regression forecasts into a single, adaptive forecast curve. By integrating trend strength via the ADX and directional bias, it aims to visualize how price might evolve in different market environments—from strong trends to mean-reverting conditions.
Core Concept:
This tool builds forward price projections based on a blend of linear regression models with varying lookback lengths (from 2 up to a user-defined max). It then adjusts those projections using two key mechanisms:
ADX-Weighted Forecast Blending
In trending conditions (high ADX), the model follows the raw forecast direction. In ranging markets (low ADX), the forecast flips or reverts, biasing toward mean-reversion. A logistic transformation of directional bias, controlled by a steepness parameter, determines how aggressively this blending reacts to price behavior.
Volatility Scaling
The forecast’s magnitude is scaled based on ADX and directional conviction. When trends are unclear (low ADX or neutral bias), the projection range expands to reflect greater uncertainty and volatility.
How It Works:
Regression Curve Generation
For each regression length from 2 to maxLength, a forward projection is calculated using least-squares linear regression on the selected price source. These forecasts are extrapolated into the future.
Directional Bias Calculation
The forecasted points are analyzed to determine a normalized bias value in the range -1 to +1, where +1 means strongly bullish, -1 means strongly bearish, and 0 means neutral.
Logistic Bias Transformation
The raw bias is passed through a logistic sigmoid function, with a user-defined steepness. This creates a probability-like weight that favors either following or reversing the forecast depending on market context.
ADX-Based Weighting
ADX determines the weighting between trend-following and mean-reversion modes. Below ADX 20, the model favors mean-reversion. Above 25, it favors trend-following. Between 20 and 25, it linearly blends the two.
Blended Forecast Curve
Each forecast point is blended between trend-following and mean-reverting values, scaled for volatility.
What You See:
Forecast Lines: Projected future price paths drawn in green or red depending on direction.
Bias Plot: A separate plot showing post-blend directional bias as a percentage, where +100 is strongly bullish and -100 is strongly bearish.
Neutral Line: A dashed horizontal line at 0 percent bias to indicate neutrality.
User Inputs:
-Max Regression Length
-Price Source
-Line Width
-Bias Steepness
-ADX Length and Smoothing
Use Cases:
Visualize expected price direction under different trend conditions
Adjust trading behavior depending on trending vs ranging markets
Combine with other tools for deeper analysis
Important Notes:
This indicator is for visualization and analysis only. It does not provide buy or sell signals and should not be used in isolation. It makes assumptions based on historical price action and should be interpreted with market context.
Volatility Quality [Alpha Extract]The Alpha-Extract Volatility Quality (AVQ) Indicator provides traders with deep insights into market volatility by measuring the directional strength of price movements. This sophisticated momentum-based tool helps identify overbought and oversold conditions, offering actionable buy and sell signals based on volatility trends and standard deviation bands.
🔶 CALCULATION
The indicator processes volatility quality data through a series of analytical steps:
Bar Range Calculation: Measures true range (TR) to capture price volatility.
Directional Weighting: Applies directional bias (positive for bullish candles, negative for bearish) to the true range.
VQI Computation: Uses an exponential moving average (EMA) of weighted volatility to derive the Volatility Quality Index (VQI).
vqiRaw = ta.ema(weightedVol, vqiLen)
Smoothing: Applies an additional EMA to smooth the VQI for clearer signals.
Normalization: Optionally normalizes VQI to a -100/+100 scale based on historical highs and lows.
Standard Deviation Bands: Calculates three upper and lower bands using standard deviation multipliers for volatility thresholds.
vqiStdev = ta.stdev(vqiSmoothed, vqiLen)
upperBand1 = vqiSmoothed + (vqiStdev * stdevMultiplier1)
upperBand2 = vqiSmoothed + (vqiStdev * stdevMultiplier2)
upperBand3 = vqiSmoothed + (vqiStdev * stdevMultiplier3)
lowerBand1 = vqiSmoothed - (vqiStdev * stdevMultiplier1)
lowerBand2 = vqiSmoothed - (vqiStdev * stdevMultiplier2)
lowerBand3 = vqiSmoothed - (vqiStdev * stdevMultiplier3)
Signal Generation: Produces overbought/oversold signals when VQI reaches extreme levels (±200 in normalized mode).
Formula:
Bar Range = True Range (TR)
Weighted Volatility = Bar Range × (Close > Open ? 1 : Close < Open ? -1 : 0)
VQI Raw = EMA(Weighted Volatility, VQI Length)
VQI Smoothed = EMA(VQI Raw, Smoothing Length)
VQI Normalized = ((VQI Smoothed - Lowest VQI) / (Highest VQI - Lowest VQI) - 0.5) × 200
Upper Band N = VQI Smoothed + (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
Lower Band N = VQI Smoothed - (StdDev(VQI Smoothed, VQI Length) × Multiplier N)
🔶 DETAILS
Visual Features:
VQI Plot: Displays VQI as a line or histogram (lime for positive, red for negative).
Standard Deviation Bands: Plots three upper and lower bands (teal for upper, grayscale for lower) to indicate volatility thresholds.
Reference Levels: Horizontal lines at 0 (neutral), +100, and -100 (in normalized mode) for context.
Zone Highlighting: Overbought (⋎ above bars) and oversold (⋏ below bars) signals for extreme VQI levels (±200 in normalized mode).
Candle Coloring: Optional candle overlay colored by VQI direction (lime for positive, red for negative).
Interpretation:
VQI ≥ 200 (Normalized): Overbought condition, strong sell signal.
VQI 100–200: High volatility, potential selling opportunity.
VQI 0–100: Neutral bullish momentum.
VQI 0 to -100: Neutral bearish momentum.
VQI -100 to -200: High volatility, strong bearish momentum.
VQI ≤ -200 (Normalized): Oversold condition, strong buy signal.
🔶 EXAMPLES
Overbought Signal Detection: When VQI exceeds 200 (normalized), the indicator flags potential market tops with a red ⋎ symbol.
Example: During strong uptrends, VQI reaching 200 has historically preceded corrections, allowing traders to secure profits.
Oversold Signal Detection: When VQI falls below -200 (normalized), a lime ⋏ symbol highlights potential buying opportunities.
Example: In bearish markets, VQI dropping below -200 has marked reversal points for profitable long entries.
Volatility Trend Tracking: The VQI plot and bands help traders visualize shifts in market momentum.
Example: A rising VQI crossing above zero with widening bands indicates strengthening bullish momentum, guiding traders to hold or enter long positions.
Dynamic Support/Resistance: Standard deviation bands act as dynamic volatility thresholds during price movements.
Example: Price reversals often occur near the third standard deviation bands, providing reliable entry/exit points during volatile periods.
🔶 SETTINGS
Customization Options:
VQI Length: Adjust the EMA period for VQI calculation (default: 14, range: 1–50).
Smoothing Length: Set the EMA period for smoothing (default: 5, range: 1–50).
Standard Deviation Multipliers: Customize multipliers for bands (defaults: 1.0, 2.0, 3.0).
Normalization: Toggle normalization to -100/+100 scale and adjust lookback period (default: 200, min: 50).
Display Style: Switch between line or histogram plot for VQI.
Candle Overlay: Enable/disable VQI-colored candles (lime for positive, red for negative).
The Alpha-Extract Volatility Quality Indicator empowers traders with a robust tool to navigate market volatility. By combining directional price range analysis with smoothed volatility metrics, it identifies overbought and oversold conditions, offering clear buy and sell signals. The customizable standard deviation bands and optional normalization provide precise context for market conditions, enabling traders to make informed decisions across various market cycles.
light_logLight Log - A Defensive Programming Library for Pine Script
Overview
The Light Log library transforms Pine Script development by introducing structured logging and defensive programming patterns typically found in enterprise languages like C#. This library addresses a fundamental challenge in Pine Script: the lack of sophisticated error handling and debugging tools that developers expect when building complex trading systems.
At its core, Light Log provides three transformative capabilities that work together to create more reliable and maintainable code. First, it wraps all native Pine Script types in error-aware containers, allowing values to carry validation state alongside their data. Second, it offers a comprehensive logging system with severity levels and conditional rendering. Third, it includes defensive programming utilities that catch errors early and make code self-documenting.
The Philosophy of Errors as Values
Traditional Pine Script error handling relies on runtime errors that halt execution, making it difficult to build resilient systems that can gracefully handle edge cases. Light Log introduces a paradigm shift by treating errors as first-class values that flow through your program alongside regular data.
When you wrap a value using Light Log's type system, you're not just storing data – you're creating a container that can carry both the value and its validation state. For example, when you call myNumber.INT() , you receive an INT object that contains both the integer value and a Log object that can describe any issues with that value. This approach, inspired by functional programming languages, allows errors to propagate through calculations without causing immediate failures.
Consider how this changes error handling in practice. Instead of a calculation failing catastrophically when it encounters invalid input, it can produce a result object that contains both the computed value (which might be na) and a detailed log explaining what went wrong. Subsequent operations can check has_error() to decide whether to proceed or handle the error condition gracefully.
The Typed Wrapper System
Light Log provides typed wrappers for every native Pine Script type: INT, FLOAT, BOOL, STRING, COLOR, LINE, LABEL, BOX, TABLE, CHART_POINT, POLYLINE, and LINEFILL. These wrappers serve multiple purposes beyond simple value storage.
Each wrapper type contains two fields: the value field v holds the actual data, while the error field e contains a Log object that tracks the value's validation state. This dual nature enables powerful programming patterns. You can perform operations on wrapped values and accumulate error information along the way, creating an audit trail of how values were processed.
The wrapper system includes convenient methods for converting between wrapped and unwrapped values. The extension methods like INT() , FLOAT() , etc., make it easy to wrap existing values, while the from_INT() , from_FLOAT() methods extract the underlying values when needed. The has_error() method provides a consistent interface for checking whether any wrapped value has encountered issues during processing.
The Log Object: Your Debugging Companion
The Log object represents the heart of Light Log's debugging capabilities. Unlike simple string concatenation for error messages, the Log object provides a structured approach to building, modifying, and rendering diagnostic information.
Each Log object carries three essential pieces of information: an error type (info, warning, error, or runtime_error), a message string that can be built incrementally, and an active flag that controls conditional rendering. This structure enables sophisticated logging patterns where you can build up detailed diagnostic information throughout your script's execution and decide later whether and how to display it.
The Log object's methods support fluent chaining, allowing you to build complex messages in a readable way. The write() and write_line() methods append text to the log, while new_line() adds formatting. The clear() method resets the log for reuse, and the rendering methods ( render_now() , render_condition() , and the general render() ) control when and how messages appear.
Defensive Programming Made Easy
Light Log's argument validation functions transform how you write defensive code. Instead of cluttering your functions with verbose validation logic, you can use concise, self-documenting calls that make your intentions clear.
The argument_error() function provides strict validation that halts execution when conditions aren't met – perfect for catching programming errors early. For less critical issues, argument_log_warning() and argument_log_error() record problems without stopping execution, while argument_log_info() provides debug visibility into your function's behavior.
These functions follow a consistent pattern: they take a condition to check, the function name, the argument name, and a descriptive message. This consistency makes error messages predictable and helpful, automatically formatting them to show exactly where problems occurred.
Building Modular, Reusable Code
Light Log encourages a modular approach to Pine Script development by providing tools that make functions more self-contained and reliable. When functions validate their inputs and return wrapped values with error information, they become true black boxes that can be safely composed into larger systems.
The void_return() function addresses Pine Script's requirement that all code paths return a value, even in error handling branches. This utility function provides a clean way to satisfy the compiler while making it clear that a particular code path should never execute.
The static log pattern, initialized with init_static_log() , enables module-wide error tracking. You can create a persistent Log object that accumulates information across multiple function calls, building a comprehensive diagnostic report that helps you understand complex behaviors in your indicators and strategies.
Real-World Applications
In practice, Light Log shines when building sophisticated trading systems. Imagine developing a complex indicator that processes multiple data streams, performs statistical calculations, and generates trading signals. With Light Log, each processing stage can validate its inputs, perform calculations, and pass along both results and diagnostic information.
For example, a moving average calculation might check that the period is positive, that sufficient data exists, and that the input series contains valid values. Instead of failing silently or throwing runtime errors, it can return a FLOAT object that contains either the calculated average or a detailed explanation of why the calculation couldn't be performed.
Strategy developers benefit even more from Light Log's capabilities. Complex entry and exit logic often involves multiple conditions that must all be satisfied. With Light Log, each condition check can contribute to a comprehensive log that explains exactly why a trade was or wasn't taken, making strategy debugging and optimization much more straightforward.
Performance Considerations
While Light Log adds a layer of abstraction over raw Pine Script values, its design minimizes performance impact. The wrapper objects are lightweight, containing only two fields. The logging operations only consume resources when actually rendered, and the conditional rendering system ensures that production code can run with logging disabled for maximum performance.
The library follows Pine Script best practices for performance, using appropriate data structures and avoiding unnecessary operations. The var keyword in init_static_log() ensures that persistent logs don't create new objects on every bar, maintaining efficiency even in real-time calculations.
Getting Started
Adopting Light Log in your Pine Script projects is straightforward. Import the library, wrap your critical values, add validation to your functions, and use Log objects to track important events. Start small by adding logging to a single function, then expand as you see the benefits of better error visibility and code organization.
Remember that Light Log is designed to grow with your needs. You can use as much or as little of its functionality as makes sense for your project. Even simple uses, like adding argument validation to key functions, can significantly improve code reliability and debugging ease.
Transform your Pine Script development experience with Light Log – because professional trading systems deserve professional development tools.
Light Log Technical Deep Dive: Advanced Patterns and Architecture
Understanding Errors as Values
The concept of "errors as values" represents a fundamental shift in how we think about error handling in Pine Script. In traditional Pine Script development, errors are events – they happen at a specific moment in time and immediately interrupt program flow. Light Log transforms errors into data – they become information that flows through your program just like any other value.
This transformation has profound implications. When errors are values, they can be stored, passed between functions, accumulated, transformed, and inspected. They become part of your program's data flow rather than exceptions to it. This approach, popularized by languages like Rust with its Result type and Haskell with its Either monad, brings functional programming's elegance to Pine Script.
Consider a practical example. Traditional Pine Script might calculate a momentum indicator like this:
momentum = close - close
If period is invalid or if there isn't enough historical data, this calculation might produce na or cause subtle bugs. With Light Log's approach:
calculate_momentum(src, period)=>
result = src.FLOAT()
if period <= 0
result.e.write("Invalid period: must be positive", true, ErrorType.error)
result.v := na
else if bar_index < period
result.e.write("Insufficient data: need " + str.tostring(period) + " bars", true, ErrorType.warning)
result.v := na
else
result.v := src - src
result.e.write("Momentum calculated successfully", false, ErrorType.info)
result
Now the function returns not just a value but a complete computational result that includes diagnostic information. Calling code can make intelligent decisions based on both the value and its associated metadata.
The Monad Pattern in Pine Script
While Pine Script lacks the type system features to implement true monads, Light Log brings monadic thinking to Pine Script development. The wrapped types (INT, FLOAT, etc.) act as computational contexts that carry both values and metadata through a series of transformations.
The key insight of monadic programming is that you can chain operations while automatically propagating context. In Light Log, this context is the error state. When you have a FLOAT that contains an error, operations on that FLOAT can check the error state and decide whether to proceed or propagate the error.
This pattern enables what functional programmers call "railway-oriented programming" – your code follows a success track when all is well but can switch to an error track when problems occur. Both tracks lead to the same destination (a result with error information), but they take different paths based on the validity of intermediate values.
Composable Error Handling
Light Log's design encourages composition – building complex functionality from simpler, well-tested components. Each component can validate its inputs, perform its calculation, and return a result with appropriate error information. Higher-level functions can then combine these results intelligently.
Consider building a complex trading signal from multiple indicators:
generate_signal(src, fast_period, slow_period, signal_period) =>
log = init_static_log(ErrorType.info)
// Calculate components with error tracking
fast_ma = calculate_ma(src, fast_period)
slow_ma = calculate_ma(src, slow_period)
// Check for errors in components
if fast_ma.has_error()
log.write_line("Fast MA error: " + fast_ma.e.message, true)
if slow_ma.has_error()
log.write_line("Slow MA error: " + slow_ma.e.message, true)
// Proceed with calculation if no errors
signal = 0.0.FLOAT()
if not (fast_ma.has_error() or slow_ma.has_error())
macd_line = fast_ma.v - slow_ma.v
signal_line = calculate_ma(macd_line, signal_period)
if signal_line.has_error()
log.write_line("Signal line error: " + signal_line.e.message, true)
signal.e := log
else
signal.v := macd_line - signal_line.v
log.write("Signal generated successfully")
else
signal.e := log
signal.v := na
signal
This composable approach makes complex calculations more reliable and easier to debug. Each component is responsible for its own validation and error reporting, and the composite function orchestrates these components while maintaining comprehensive error tracking.
The Static Log Pattern
The init_static_log() function introduces a powerful pattern for maintaining state across function calls. In Pine Script, the var keyword creates variables that persist across bars but are initialized only once. Light Log leverages this to create logging objects that can accumulate information throughout a script's execution.
This pattern is particularly valuable for debugging complex strategies where you need to understand behavior across multiple bars. You can create module-level logs that track important events:
// Module-level diagnostic log
diagnostics = init_static_log(ErrorType.info)
// Track strategy decisions across bars
check_entry_conditions() =>
diagnostics.clear() // Start fresh each bar
diagnostics.write_line("Bar " + str.tostring(bar_index) + " analysis:")
if close > sma(close, 20)
diagnostics.write_line("Price above SMA20", false)
else
diagnostics.write_line("Price below SMA20 - no entry", true, ErrorType.warning)
if volume > sma(volume, 20) * 1.5
diagnostics.write_line("Volume surge detected", false)
else
diagnostics.write_line("Normal volume", false)
// Render diagnostics based on verbosity setting
if debug_mode
diagnostics.render_now()
Advanced Validation Patterns
Light Log's argument validation functions enable sophisticated precondition checking that goes beyond simple null checks. You can implement complex validation logic while keeping your code readable:
validate_price_data(open_val, high_val, low_val, close_val) =>
argument_error(na(open_val) or na(high_val) or na(low_val) or na(close_val),
"validate_price_data", "OHLC values", "contain na values")
argument_error(high_val < low_val,
"validate_price_data", "high/low", "high is less than low")
argument_error(close_val > high_val or close_val < low_val,
"validate_price_data", "close", "is outside high/low range")
argument_log_warning(high_val == low_val,
"validate_price_data", "high/low", "are equal (no range)")
This validation function documents its requirements clearly and fails fast with helpful error messages when assumptions are violated. The mix of errors (which halt execution) and warnings (which allow continuation) provides fine-grained control over how strict your validation should be.
Performance Optimization Strategies
While Light Log adds abstraction, careful design minimizes overhead. Understanding Pine Script's execution model helps you use Light Log efficiently.
Pine Script executes once per bar, so operations that seem expensive in traditional programming might have negligible impact. However, when building real-time systems, every optimization matters. Light Log provides several patterns for efficient use:
Lazy Evaluation: Log messages are only built when they'll be rendered. Use conditional logging to avoid string concatenation in production:
if debug_mode
log.write_line("Calculated value: " + str.tostring(complex_calculation))
Selective Wrapping: Not every value needs error tracking. Wrap values at API boundaries and critical calculation points, but use raw values for simple operations:
// Wrap at boundaries
input_price = close.FLOAT()
validated_period = validate_period(input_period).INT()
// Use raw values internally
sum = 0.0
for i = 0 to validated_period.v - 1
sum += close
Error Propagation: When errors occur early, avoid expensive calculations:
process_data(input) =>
validated = validate_input(input)
if validated.has_error()
validated // Return early with error
else
// Expensive processing only if valid
perform_complex_calculation(validated)
Integration Patterns
Light Log integrates smoothly with existing Pine Script code. You can adopt it incrementally, starting with critical functions and expanding coverage as needed.
Boundary Validation: Add Light Log at the boundaries of your system – where user input enters and where final outputs are produced. This catches most errors while minimizing changes to existing code.
Progressive Enhancement: Start by adding argument validation to existing functions. Then wrap return values. Finally, add comprehensive logging. Each step improves reliability without requiring a complete rewrite.
Testing and Debugging: Use Light Log's conditional rendering to create debug modes for your scripts. Production users see clean output while developers get detailed diagnostics:
// User input for debug mode
debug = input.bool(false, "Enable debug logging")
// Conditional diagnostic output
if debug
diagnostics.render_now()
else
diagnostics.render_condition() // Only shows errors/warnings
Future-Proofing Your Code
Light Log's patterns prepare your code for Pine Script's evolution. As Pine Script adds more sophisticated features, code that uses structured error handling and defensive programming will adapt more easily than code that relies on implicit assumptions.
The type wrapper system, in particular, positions your code to take advantage of potential future features or more sophisticated type inference. By thinking in terms of wrapped values and error propagation today, you're building code that will remain maintainable and extensible tomorrow.
Light Log doesn't just make your Pine Script better today – it prepares it for the trading systems you'll need to build tomorrow.
Library "light_log"
A lightweight logging and defensive programming library for Pine Script.
Designed for modular and extensible scripts, this utility provides structured runtime validation,
conditional logging, and reusable `Log` objects for centralized error propagation.
It also introduces a typed wrapping system for all native Pine values (e.g., `INT`, `FLOAT`, `LABEL`),
allowing values to carry errors alongside data. This enables functional-style flows with built-in
validation tracking, error detection (`has_error()`), and fluent chaining.
Inspired by structured logging patterns found in systems like C#, it reduces boilerplate,
enforces argument safety, and encourages clean, maintainable code architecture.
method INT(self, error_type)
Wraps an `int` value into an `INT` struct with an optional log severity.
Namespace types: series int, simple int, input int, const int
Parameters:
self (int) : The raw `int` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: An `INT` object containing the value and a default Log instance.
method FLOAT(self, error_type)
Wraps a `float` value into a `FLOAT` struct with an optional log severity.
Namespace types: series float, simple float, input float, const float
Parameters:
self (float) : The raw `float` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `FLOAT` object containing the value and a default Log instance.
method BOOL(self, error_type)
Wraps a `bool` value into a `BOOL` struct with an optional log severity.
Namespace types: series bool, simple bool, input bool, const bool
Parameters:
self (bool) : The raw `bool` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOOL` object containing the value and a default Log instance.
method STRING(self, error_type)
Wraps a `string` value into a `STRING` struct with an optional log severity.
Namespace types: series string, simple string, input string, const string
Parameters:
self (string) : The raw `string` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `STRING` object containing the value and a default Log instance.
method COLOR(self, error_type)
Wraps a `color` value into a `COLOR` struct with an optional log severity.
Namespace types: series color, simple color, input color, const color
Parameters:
self (color) : The raw `color` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `COLOR` object containing the value and a default Log instance.
method LINE(self, error_type)
Wraps a `line` object into a `LINE` struct with an optional log severity.
Namespace types: series line
Parameters:
self (line) : The raw `line` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINE` object containing the value and a default Log instance.
method LABEL(self, error_type)
Wraps a `label` object into a `LABEL` struct with an optional log severity.
Namespace types: series label
Parameters:
self (label) : The raw `label` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LABEL` object containing the value and a default Log instance.
method BOX(self, error_type)
Wraps a `box` object into a `BOX` struct with an optional log severity.
Namespace types: series box
Parameters:
self (box) : The raw `box` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `BOX` object containing the value and a default Log instance.
method TABLE(self, error_type)
Wraps a `table` object into a `TABLE` struct with an optional log severity.
Namespace types: series table
Parameters:
self (table) : The raw `table` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `TABLE` object containing the value and a default Log instance.
method CHART_POINT(self, error_type)
Wraps a `chart.point` value into a `CHART_POINT` struct with an optional log severity.
Namespace types: chart.point
Parameters:
self (chart.point) : The raw `chart.point` value to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `CHART_POINT` object containing the value and a default Log instance.
method POLYLINE(self, error_type)
Wraps a `polyline` object into a `POLYLINE` struct with an optional log severity.
Namespace types: series polyline, series polyline, series polyline, series polyline
Parameters:
self (polyline) : The raw `polyline` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `POLYLINE` object containing the value and a default Log instance.
method LINEFILL(self, error_type)
Wraps a `linefill` object into a `LINEFILL` struct with an optional log severity.
Namespace types: series linefill
Parameters:
self (linefill) : The raw `linefill` object to wrap.
error_type (series ErrorType) : Optional severity level to associate with the log. Default is `ErrorType.error`.
Returns: A `LINEFILL` object containing the value and a default Log instance.
method from_INT(self)
Extracts the integer value from an INT wrapper.
Namespace types: INT
Parameters:
self (INT) : The wrapped INT instance.
Returns: The underlying `int` value.
method from_FLOAT(self)
Extracts the float value from a FLOAT wrapper.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The wrapped FLOAT instance.
Returns: The underlying `float` value.
method from_BOOL(self)
Extracts the boolean value from a BOOL wrapper.
Namespace types: BOOL
Parameters:
self (BOOL) : The wrapped BOOL instance.
Returns: The underlying `bool` value.
method from_STRING(self)
Extracts the string value from a STRING wrapper.
Namespace types: STRING
Parameters:
self (STRING) : The wrapped STRING instance.
Returns: The underlying `string` value.
method from_COLOR(self)
Extracts the color value from a COLOR wrapper.
Namespace types: COLOR
Parameters:
self (COLOR) : The wrapped COLOR instance.
Returns: The underlying `color` value.
method from_LINE(self)
Extracts the line object from a LINE wrapper.
Namespace types: LINE
Parameters:
self (LINE) : The wrapped LINE instance.
Returns: The underlying `line` object.
method from_LABEL(self)
Extracts the label object from a LABEL wrapper.
Namespace types: LABEL
Parameters:
self (LABEL) : The wrapped LABEL instance.
Returns: The underlying `label` object.
method from_BOX(self)
Extracts the box object from a BOX wrapper.
Namespace types: BOX
Parameters:
self (BOX) : The wrapped BOX instance.
Returns: The underlying `box` object.
method from_TABLE(self)
Extracts the table object from a TABLE wrapper.
Namespace types: TABLE
Parameters:
self (TABLE) : The wrapped TABLE instance.
Returns: The underlying `table` object.
method from_CHART_POINT(self)
Extracts the chart.point from a CHART_POINT wrapper.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The wrapped CHART_POINT instance.
Returns: The underlying `chart.point` value.
method from_POLYLINE(self)
Extracts the polyline object from a POLYLINE wrapper.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The wrapped POLYLINE instance.
Returns: The underlying `polyline` object.
method from_LINEFILL(self)
Extracts the linefill object from a LINEFILL wrapper.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The wrapped LINEFILL instance.
Returns: The underlying `linefill` object.
method has_error(self)
Returns true if the INT wrapper has an active log entry.
Namespace types: INT
Parameters:
self (INT) : The INT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the FLOAT wrapper has an active log entry.
Namespace types: FLOAT
Parameters:
self (FLOAT) : The FLOAT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOOL wrapper has an active log entry.
Namespace types: BOOL
Parameters:
self (BOOL) : The BOOL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the STRING wrapper has an active log entry.
Namespace types: STRING
Parameters:
self (STRING) : The STRING instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the COLOR wrapper has an active log entry.
Namespace types: COLOR
Parameters:
self (COLOR) : The COLOR instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINE wrapper has an active log entry.
Namespace types: LINE
Parameters:
self (LINE) : The LINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LABEL wrapper has an active log entry.
Namespace types: LABEL
Parameters:
self (LABEL) : The LABEL instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the BOX wrapper has an active log entry.
Namespace types: BOX
Parameters:
self (BOX) : The BOX instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the TABLE wrapper has an active log entry.
Namespace types: TABLE
Parameters:
self (TABLE) : The TABLE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the CHART_POINT wrapper has an active log entry.
Namespace types: CHART_POINT
Parameters:
self (CHART_POINT) : The CHART_POINT instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the POLYLINE wrapper has an active log entry.
Namespace types: POLYLINE
Parameters:
self (POLYLINE) : The POLYLINE instance to check.
Returns: True if an error or message is active in the log.
method has_error(self)
Returns true if the LINEFILL wrapper has an active log entry.
Namespace types: LINEFILL
Parameters:
self (LINEFILL) : The LINEFILL instance to check.
Returns: True if an error or message is active in the log.
void_return()
Utility function used when a return is syntactically required but functionally unnecessary.
Returns: Nothing. Function never executes its body.
argument_error(condition, function, argument, message)
Throws a runtime error when a condition is met. Used for strict argument validation.
Parameters:
condition (bool) : Boolean expression that triggers the runtime error.
function (string) : Name of the calling function (for formatting).
argument (string) : Name of the problematic argument.
message (string) : Description of the error cause.
Returns: Never returns. Halts execution if the condition is true.
argument_log_info(condition, function, argument, message)
Logs an informational message when a condition is met. Used for optional debug visibility.
Parameters:
condition (bool) : Boolean expression that triggers the log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Informational message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_warning(condition, function, argument, message)
Logs a warning when a condition is met. Non-fatal but highlights potential issues.
Parameters:
condition (bool) : Boolean expression that triggers the warning.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Warning message to log.
Returns: Nothing. Logs if the condition is true.
argument_log_error(condition, function, argument, message)
Logs an error message when a condition is met. Does not halt execution.
Parameters:
condition (bool) : Boolean expression that triggers the error log.
function (string) : Name of the calling function.
argument (string) : Argument name being referenced.
message (string) : Error message to log.
Returns: Nothing. Logs if the condition is true.
init_static_log(error_type, message, active)
Initializes a persistent (var) Log object. Ideal for global logging in scripts or modules.
Parameters:
error_type (series ErrorType) : Initial severity level (required).
message (string) : Optional starting message string. Default value of ("").
active (bool) : Whether the log should be flagged active on initialization. Default value of (false).
Returns: A static Log object with the given parameters.
method new_line(self)
Appends a newline character to the Log message. Useful for separating entries during chained writes.
Namespace types: Log
Parameters:
self (Log) : The Log instance to modify.
Returns: The updated Log object with a newline appended.
method write(self, message, flag_active, error_type)
Appends a message to a Log object without a newline. Updates severity and active state if specified.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method write_line(self, message, flag_active, error_type)
Appends a message to a Log object, prefixed with a newline for clarity.
Namespace types: Log
Parameters:
self (Log) : The Log instance being modified.
message (string) : The text to append to the log.
flag_active (bool) : Whether to activate the log for conditional rendering. Default value of (false).
error_type (series ErrorType) : Optional override for the severity level. Default value of (na).
Returns: The updated Log object.
method clear(self, flag_active, error_type)
Clears a Log object’s message and optionally reactivates it. Can also update the error type.
Namespace types: Log
Parameters:
self (Log) : The Log instance being cleared.
flag_active (bool) : Whether to activate the log after clearing. Default value of (false).
error_type (series ErrorType) : Optional new error type to assign. If not provided, the previous type is retained. Default value of (na).
Returns: The cleared Log object.
method render_condition(self, flag_active, error_type)
Conditionally renders the log if it is active. Allows overriding error type and controlling active state afterward.
Namespace types: Log
Parameters:
self (Log) : The Log instance to evaluate and render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Useful for contextual formatting just before rendering. Default value of (na).
Returns: The updated Log object.
method render_now(self, flag_active, error_type)
Immediately renders the log regardless of `active` state. Allows overriding error type and active flag.
Namespace types: Log
Parameters:
self (Log) : The Log instance to render.
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override. Allows dynamic severity adjustment at render time. Default value of (na).
Returns: The updated Log object.
render(self, condition, flag_active, error_type)
Renders the log conditionally or unconditionally. Allows full control over render behavior.
Parameters:
self (Log) : The Log instance to render.
condition (bool) : If true, renders only if the log is active. If false, always renders. Default value of (false).
flag_active (bool) : Whether to activate the log after rendering. Default value of (false).
error_type (series ErrorType) : Optional error type override passed to the render methods. Default value of (na).
Returns: The updated Log object.
Log
A structured object used to store and render logging messages.
Fields:
error_type (series ErrorType) : The severity level of the message (from the ErrorType enum).
message (series string) : The text of the log message.
active (series bool) : Whether the log should trigger rendering when conditionally evaluated.
INT
A wrapped integer type with attached logging for validation or tracing.
Fields:
v (series int) : The underlying `int` value.
e (Log) : Optional log object describing validation status or error context.
FLOAT
A wrapped float type with attached logging for validation or tracing.
Fields:
v (series float) : The underlying `float` value.
e (Log) : Optional log object describing validation status or error context.
BOOL
A wrapped boolean type with attached logging for validation or tracing.
Fields:
v (series bool) : The underlying `bool` value.
e (Log) : Optional log object describing validation status or error context.
STRING
A wrapped string type with attached logging for validation or tracing.
Fields:
v (series string) : The underlying `string` value.
e (Log) : Optional log object describing validation status or error context.
COLOR
A wrapped color type with attached logging for validation or tracing.
Fields:
v (series color) : The underlying `color` value.
e (Log) : Optional log object describing validation status or error context.
LINE
A wrapped line object with attached logging for validation or tracing.
Fields:
v (series line) : The underlying `line` value.
e (Log) : Optional log object describing validation status or error context.
LABEL
A wrapped label object with attached logging for validation or tracing.
Fields:
v (series label) : The underlying `label` value.
e (Log) : Optional log object describing validation status or error context.
BOX
A wrapped box object with attached logging for validation or tracing.
Fields:
v (series box) : The underlying `box` value.
e (Log) : Optional log object describing validation status or error context.
TABLE
A wrapped table object with attached logging for validation or tracing.
Fields:
v (series table) : The underlying `table` value.
e (Log) : Optional log object describing validation status or error context.
CHART_POINT
A wrapped chart point with attached logging for validation or tracing.
Fields:
v (chart.point) : The underlying `chart.point` value.
e (Log) : Optional log object describing validation status or error context.
POLYLINE
A wrapped polyline object with attached logging for validation or tracing.
Fields:
v (series polyline) : The underlying `polyline` value.
e (Log) : Optional log object describing validation status or error context.
LINEFILL
A wrapped linefill object with attached logging for validation or tracing.
Fields:
v (series linefill) : The underlying `linefill` value.
e (Log) : Optional log object describing validation status or error context.
MTF Pivot Fib Speed Resistance FansOverview
This Pine Script indicator, titled "MTF Pivot Fib Speed Resistance Fans", is a multi-timeframe tool that automatically plots Fib Speed Resistance Fan lines based on pivot structures derived from higher timeframes. It mirrors the functionality of TradingView’s built-in “Fib Speed Resistance Fan” drawing tool, but in a dynamic, programmatic way. It uses pivot highs and lows to anchor fan projections, drawing forward-facing trend lines that align with well-known Fibonacci ratios and their extensions.
Pivot Detection Logic
The script identifies pivots by comparing the current bar’s high and low against the highest and lowest prices over a user-defined pivot period. This pivot detection occurs on a higher timeframe of your choice, giving a broader and more strategic view of price structure. The script tracks direction changes in the pivot trend and stores only the most recent few pivots to maintain clean and meaningful fan drawings.
Fan Direction Control
The user can select whether to draw fans for "Buys", "Sells", or "Both". The script only draws fan lines when a new directional move is detected based on the pivot structure and the selected bias. For example, in “Buys” mode, a rising pivot followed by another higher low will trigger upward fan projections.
Fib Speed Resistance Levels
Once two pivots are identified, the script draws multiple fan lines from the first pivot outward, at angles defined by a preset list of Fibonacci levels. These fan lines help visualize speed and strength of a price move.
The script also draws a horizontal line from the pivot for additional confluence at the base level (1.0).
Price Level Plotting
In addition to drawing fan lines, the indicator also plots their price levels on the right-hand price scale. This makes it easier for users to visually reference the projected support and resistance levels without needing to trace the lines manually across the chart.
Mapping to TradingView’s "Fib Speed Resistance Fan"
The expanded set of values used in this script is not arbitrary—they closely align with the default and extended levels available in TradingView's built-in "Fib Speed Resistance Fan" tool.
TradingView’s Fib Fan tool offers several levels by default, including traditional Fibonacci ratios like 0.382, 0.5, 0.618, and 1. However, if you right-click the tool and open its settings, you’ll find additional toggles for levels like 1.618, 2.000, 2.618, and even 4.000. These deeper levels are used to project stronger trend continuations beyond the standard retracement zones.
The inclusion of levels such as 0.25, 0.75, and 1.34 reflects configurations that are available when you manually add or customize levels in TradingView’s fan tool. While 1.34 is not a canonical Fibonacci ratio, it is often found in hybrid Gann/Fib methods and is included in some preset templates in TradingView’s drawing tool for advanced users.
By incorporating these levels directly into the Pine Script, the indicator faithfully reproduces the fan structure users would manually draw using TradingView’s graphical Fib Fan tool—but does so programmatically, dynamically, and with multi-timeframe control. This eliminates manual errors, allows for responsive updating, and adds custom visual tracking via the price scale.
These values are standardized within the context of TradingView's Fib Fan tool and not made up. This script automates what the manual drawing tool achieves, with added precision and flexibility.
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.
Engulfing Candles (ATR-Based) with Volume SpikeOverview:
This script is designed to detect high-probability bullish and bearish engulfing patterns — but with a twist: it filters them through ATR-based volatility and confirms strength with volume spikes, all while offering optional trend filtering. It's built for traders who want more than just surface-level candle patterns — it's for those who want contextual confirmation before entering a trade.
What Makes It Different:
Most engulfing candle indicators simply match two candle shapes. This script goes deeper by:
Measuring candle body size relative to recent volatility (via ATR).
Checking for volume confirmation using a dynamic spike threshold.
Filtering based on trend context using SMA-based structure detection.
This tri-layered logic aims to avoid false positives and give traders cleaner, more actionable entries that align with momentum.
Core Concepts:
1. Trend Detection (Optional)
You can choose between two structural filters:
SMA50 only: Looks for price above or below the 50-period SMA to define trend direction.
SMA50 + SMA200 alignment: A stricter rule requiring both fast and slow SMAs to confirm a trend.
Or turn off trend filtering entirely (No detection).
This helps ensure engulfing setups appear in line with the broader trend, increasing the likelihood of continuation.
2. ATR-Filtered Candle Bodies
To eliminate noise:
A candle is only considered a valid engulfing candle if it has a body larger than 1.5× ATR(14).
The previous candle must be a small-bodied candle (less than 0.5× ATR), creating a clear visual engulfing structure.
This method dynamically adjusts for market volatility, so setups are meaningful even during periods of compression or expansion.
3. Volume Spike Confirmation
Not all engulfing candles lead to follow-through. That’s why this script includes a volume confirmation filter:
A 20-period moving average of volume is calculated.
A spike is flagged if the current bar’s volume exceeds 1.5× this average (adjustable).
Patterns with this confirmation are marked with a “*” label (e.g., BU* or BE*) to distinguish volume-backed setups from weaker ones.
Visuals & Alerts:
BU = Bullish engulfing without volume spike
BU* = Bullish engulfing with volume spike
BE = Bearish engulfing without volume spike
BE* = Bearish engulfing with volume spike
A white background highlight is shown behind candles with volume-confirmed engulfing patterns.
Alerts are available for BU* and BE* so you can automate your edge.
How to Use:
Add the indicator to any chart.
Choose your trend filter from the settings panel.
Watch for BU* and BE* labels — these are your highest-conviction signals.
Optional: Combine with support/resistance, Fibonacci zones, or MTF confirmation to refine entries.
Use the included alerts to receive real-time push/email notifications when patterns emerge.
Performance Note:
I’ve found this script to be fairly good when applied to the 1-hour charts of the companies in the US100. The combination of ATR-based filtering and volume confirmation appears to provide clear, high-quality setups with good follow-through potential.
By blending candle patterns, volatility filtering, volume confirmation, and trend context, this script aims to filter out weak signals and highlight the most statistically significant engulfing opportunities.
