Stephis Supply & Demand Zones v3
📉 Support
Definition: Support is a price level where a downtrend can be expected to pause or reverse due to a concentration of buying interest.
Why it matters: When the price of an asset falls to a support level, traders expect buyers to step in, preventing the price from falling further.
Visual clue: On a chart, support often appears as a horizontal line where the price has bounced up multiple times.
📈 Demand
Definition: Demand refers to the willingness and ability of buyers to purchase an asset at a given price.
In trading context: High demand typically pushes prices up, while low demand can lead to price drops.
Relation to support: A support level exists because of demand—buyers are willing to buy at that price, creating a floor.
🧠 How They Work Together
When price approaches a support level, traders watch to see if demand increases—if it does, the price may bounce.
If the support level is broken, it may signal that demand has weakened, and the price could fall further.
🔁 Opposite Concept: Resistance & Supply
Resistance is the opposite of support—it's a level where selling pressure (supply) may stop a price from rising.
Just like demand creates support, supply creates resistance.
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Daily ATR TrackerDaily ATR Tracker
The Daily ATR Tracker is a simple yet powerful tool designed to help traders monitor the daily price movement relative to the average daily range (ATR). This indicator provides an objective view of how much price has moved compared to its recent daily volatility.
🔎 Key Features:
Customizable ATR period (default 14 days)
Live calculation of the current day's price range
ATR value displayed in pips for clear reference
Percentage of ATR covered by the current day's range
Color-coded table for quick visual interpretation:
🟢 Green: less than 60% of ATR covered
🟠 Orange: 60% to 100% of ATR covered
🔴 Red: more than 100% of ATR covered
Alert condition when daily range exceeds 100% of the ATR average
Movable table position to fit your chart layout
🎯 Why use Daily ATR Tracker?
✅ Identify exhaustion zones: When price has already covered a large portion of its typical daily range, the odds of further strong movement may diminish, helping you to manage entries, exits, and risk.
✅ Objective daily bias: Get a quantitative sense of how "stretched" the market is in real time.
✅ Works with any timeframe: While designed for daily ranges, you can monitor intraday movements with this context in mind.
⚠️ Usage Note:
This tool does not provide buy or sell signals by itself. It is designed to complement your existing strategies by offering additional context regarding daily range exhaustion.
Murrey Math Lines v6Murrey Math Lines v6
This is not just another Murrey Math indicator. It's a complete, ground-up modernization of the classic concept, rebuilt with the latest Pine Script features for unparalleled performance, accuracy, and usability. While preserving the core mathematical genius of T.H. Murrey's system, this version introduces a suite of modern tools designed for today's trader.
What are Murrey Math Lines?
Murrey Math Lines (MML) are a powerful system of support and resistance based on geometric formulas developed by T.H. Murrey. As a derivation of W.D. Gann's observations, Murrey's geometry simplifies Gann's theories into a more accessible application. The core principle is that price action tends to trend and retrace in 1/8th intervals.
These intervals create a "trading octave" with distinct levels, each having its own characteristic behavior:
& - Ultimate Resistance & Support: These lines are the hardest to break. They represent the top and bottom of the expected price range and are prime areas for reversals.
- Major Pivot: This is the most significant level, offering the strongest support and resistance within the octave. Price has a high probability of stopping and reversing here.
& - Strong Pivot/Reversal: These are strong, secondary pivot points where price often struggles to pass through.
& - The Trading Range: The price tends to consolidate between these two lines about 50% of the time. A decisive break outside this range often signals the start of a new trend.
& - Weak Support/Resistance: These levels are weaker, but when price moves too quickly towards them, they can act as initial stopping points or areas for a minor reversal.
& - Extended Octave: These lines show extreme overbought and oversold conditions beyond the primary 0/8 to 8/8 octave.
Modern Enhancements in This Version
Session-Locked Precision: Anchor the Murrey Lines to the start of a specific trading session (e.g., NYSE open). The levels remain constant for the entire session, providing a stable and reliable framework for your daily analysis.
Visual Trading & Reversal Zones: Instead of just lines, this indicator can fill the key trading range (3/8 to 5/8) and reversal zones (0/8-1/8 & 7/8-8/8) with color, giving you an instant visual reference of market sentiment.
Dynamic "Closest Price" Labels: Declutter your chart! The indicator can intelligently display only the label for the Murrey level closest to the current price, keeping your view clean while providing critical information at a glance.
Integrated Alert System: Never miss a key level touch again. Set up alerts for when the price approaches major lines, the trading range, or all lines, customized to your trading style.
Advanced Pine Script Engine: Built on a modern codebase using User-Defined Types and dynamic drawing objects (line, box, label). This ensures the indicator is fast, efficient, and non-repainting, even on lower timeframes.
Intuitive User Interface: Settings are neatly organized into collapsible groups with clear tooltips, making it incredibly easy to customize every aspect of the indicator, from calculation parameters to colors.
A Note on Accuracy
Some of the other Murrey Math indicators on TradingView use different formulas and therefore produce varying results. This version has been carefully checked against MML indicators on other professional platforms to ensure its calculations are accurate and reliable.
Credits
This indicator is a complete overhaul and modernization of the original "MM Lines " script. Full credit for the original calculation logic and concept goes to its author, JRL_6.
Haven Average Daily RangeOverview
This indicator is an enhanced version of the traditional ADR tool that adapts to intraday price movements. Unlike static ADR levels, this indicator dynamically adjusts its range boundaries based on real-time price action while maintaining the original ADR calculation framework.
Key Features
ADR calculation based on multiple periods (5, 10, and 20 days)
ADR levels displayed with automatic style changes upon range reach
Customizable display settings (color, line style)
Price labels for better visualization
The indicator helps traders assess the instrument's volatility, identify potential reversal zones, and plan daily trading targets.
Suitable for all timeframes up to D1 and any trading instrument.
How It Works
Session Start (UTC+0): Calculates ADR based on historical data and sets initial High/Low levels
Dynamic Phase: Monitors price action and adjusts the opposite boundary (ADR Low or High) when new extremes are reached.
When price creates new Day high price above the opening price, the ADR Low level moves upward proportionally.
When price creates new Day low price below the opening price, the ADR High level moves downward proportionally.
Completion Phase: Stops adjustments and highlights breach when price reaches either boundary
Trading Application
Entry and Exit Signals
The ADR boundaries serve as key decision points for trade execution. When price approaches the upper ADR boundary, it often signals a potential selling zone, particularly when confluence exists with other overbought indicators such as RSI divergence or resistance levels. Conversely, price reaching the lower ADR boundary frequently indicates potential buying opportunities, especially when supported by oversold conditions or support confluences.
Trend Continuation Assessment
One of the most valuable applications is gauging the probability of continued directional movement. When the current session's price action has not yet reached either ADR boundary, statistical probability favors trend continuation in the established direction. This information helps traders stay with profitable positions longer rather than exiting prematurely.
Reversal and Consolidation Zones
The visual color change to orange when ADR boundaries are reached provides immediate feedback that the normal daily range has been exhausted. At this point, the probability of trend reversal or sideways consolidation increases significantly. This signal helps traders prepare for potential position adjustments or new counter-trend opportunities.
Setup Score OscillatorSetup Score Oscillator – Full Description
🎯 Purpose of the Script
This script is a manual trading setup scoring tool, designed to help traders quantify the quality of a trade setup by combining multiple technical, cyclical, and contextual signals.
Instead of relying on a single indicator, the trader manually selects which signals are present, and the script calculates a total score (0–100%), displayed as an oscillator in a separate panel (like RSI or MACD).
🔧 How it works in practice
1. Manual signal inputs
The script presents a set of checkboxes in the settings, where the trader can enable/disable the following signals:
✅ Confirmed Support/Resistance
✅ Aligned Volume Profile
✅ Favorable Cyclic Timing
✅ Valid Trend Line
✅ Aligned Cyclical Moving Averages
✅ Relevant Fibonacci Level
✅ Classic Volume Signal (spike, dry-up, etc.)
✅ Oscillator confirmation (e.g., divergences)
✅ Extreme Sentiment
✅ Relevant or incoming News
Each selected signal contributes to the total score based on its weight.
2. Scoring system
Each signal has a default weight (e.g., 20% for support/resistance, 15% for cycles, etc.).
Optionally, the trader can enable the “custom weights” checkbox and adjust each signal’s weight directly in the settings.
3. Score visualization
The final score (sum of all active weights) is plotted as an oscillator ranging from 0 to 100%, with dynamic coloring:
Range Color Meaning
0–39% Red No valid setup
40–54% Yellow Watchlist only
55–69% Orange Good setup
70–100% Green Strong setup
Several horizontal threshold lines are displayed:
50% → neutral threshold
40%, 55%, 70% → operational levels
4. Optional background coloring
When the score exceeds 55% or 70%, the oscillator background lightly changes color to highlight stronger setups (non-intrusive).
📌 Practical benefits
Objectifies subjective analysis: each decision becomes a number.
Prevents overtrading: no entries if the score is too low.
Adaptable to any trading style: swing, intraday, positional.
User-friendly: no coding needed – just tick boxes.
Italiano:
Setup Score Oscillator – Descrizione completa
🎯 Obiettivo dello script
Lo script è uno strumento manuale di valutazione dei setup di trading, pensato per aiutare il trader a quantificare la qualità di un'opportunità operativa basandosi su più segnali tecnici, ciclici e contestuali.
Invece di affidarsi a un solo indicatore, il trader seleziona manualmente quali segnali sono presenti, e lo script calcola un punteggio complessivo percentuale (0–100%), rappresentato come oscillatore in una finestra separata (tipo RSI, MACD, ecc.).
🔧 Come funziona operativamente
1. Input manuale dei segnali
Lo script mostra una serie di checkbox nelle impostazioni, dove il trader può attivare o disattivare i seguenti segnali:
✅ Supporto/Resistenza confermata
✅ Volume Profile allineato
✅ Cicli o timing favorevole
✅ Trend line valida
✅ Medie mobili cicliche allineate
✅ Livello di Fibonacci rilevante
✅ Volume classico significativo (spike, dry-up)
✅ Conferme da oscillatori (es. divergenze)
✅ Sentiment estremo (es. euforia o panico)
✅ News importanti imminenti o appena uscite
Ogni casella attiva contribuisce al punteggio totale, con un peso specifico.
2. Sistema di punteggio
Ogni segnale ha un peso predefinito (es. 20% per supporti/resistenze, 15% per cicli, ecc.).
Facoltativamente, il trader può attivare la funzione “Enable custom weights” per personalizzare i pesi di ciascun segnale direttamente da input.
3. Visualizzazione del punteggio
Il punteggio complessivo (somma dei pesi attivati) viene tracciato come oscillatore da 0 a 100%, con colori dinamici:
Range Colore Significato
0–39% Rosso Nessun setup valido
40–54% Giallo Osservazione
55–69% Arancione Setup buono
70–1005 Verde Setup forte
Sono tracciate anche delle linee guida orizzontali a:
50% → soglia neutra
40%, 55%, 70% → soglie operative
4. Colorazione dello sfondo (facoltativa)
Quando il punteggio supera 55% o 70%, lo sfondo dell’oscillatore cambia leggermente colore per evidenziare il segnale (non invasivo).
📌 Vantaggi pratici
Oggettivizza l’analisi soggettiva: ogni decisione manuale si trasforma in un numero.
Evita overtrading: se il punteggio è troppo basso, non si entra.
Adattabile a ogni stile: swing, intraday, position.
Facile da usare anche senza codice: basta spuntare le caselle.
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.
RSI Divergence StrategyOverview
The RSI Divergence Strategy Indicator is a trading tool that uses the RSI and divergences created to generate high-probability buy and sell signals.
I have provided the best formula of numbers to use for BTC on a 30 minute timeframe.
You can change where on RSI you enter and exit both long or short trades. This way you can experiment on different tokens using different entry/exit points. Can use on multiple timeframes.
This strategy is designed to open and close long or short trades based on the levels you provide it. You can then check on the RSI where the best levels are for each token you want to trade and amend it as required to generate a profitable strategy.
How It Works
The RSI Divergence Strategy Indicator uses bear and bull divergences in conjuction with a level you have input on the RSI.
RSI for Overbought/Oversold:
• Input variables for entry and exit levels and when the entry levels combine with a bear or bull divergence signal, a trade is alerted.
RSI Divergence:
• Buy and sell signals are confirmed when the RSI creates bearish or bullish divergences and these divergences are in the same area as your levels you input for entry to short or long.
After 7 years of experience and testing I have calculated the exact numbers required and produced a formula to calculate the exact input variables for a 30 minute Bitcoin chart.
Key Features
1️⃣ Divergence Identification – Ensures trades are taken only when a bull or bear divergence has formed.
2️⃣ Overbought/Oversold Input Filtering – Set up your own variables on the RSI for different markets after identifying patterns on the RSI in relation to a bearish or bullish divergence.
3️⃣ Works on any chart – Suitable for all markets and timeframes once you input the correct variables for entry and exit levels.
How to Use
🟢 Basic Trading:
• Use on any timeframe.
• Enter trade only when alert has fired off. Close when it says to exit.
• Change entry and exit levels in the properties of the strategy indicator.
• Make entry and exit levels coincide with bearish or bullish divergences on the RSI.
Check the strategy tester to see backtesting so you know if the indicator is profitable or not for that market and timeframe as each crypto token is different and so is the timeframe you choose.
📢 Webhook Automation:
• Set up TradingView Alerts to auto-execute trades via Webhook-compatible platforms.
Key additions for divergence visualization:
Divergence Arrows:
Bullish divergence: Green label with white 'bull ' text
Bearish divergence: Red label with white 'bear' text
Positioned at the pivot point
Divergence Lines:
Connects consecutive RSI pivot points
Automatically drawn between consecutive pivot points
Enhanced RSI Coloring:
Overbought zone: Red
Oversold zone: Green
Neutral zone: Gray
The visualization helps you instantly spot:
Where divergences are forming on the RSI
The pattern of higher lows (bullish) or lower highs (bearish)
Contextual coloring of RSI relative to standard levels
All divergence markers appear at the correct historical pivot points, making it easy to visually confirm divergence patterns as they develop.
Strategy levels and background zones also shown to help visual look.
Why This Combination?
This indicator is just a simple RSI tool.
It is designed to filter out weak trades and only execute trades that have:
✅ RSI Divergence
✅ Overbought or Oversold Conditions
It does not calculate downtrends or bear markets so care is recommended taking long trades during these times.
Why It’s Worth Using?
📈 Open Source – Free to use and learn from.
📉 Long or Short Term Trading Style – Entry/Exit parameters options are designed for both short or long term trades allowing you to experiment until you find a profitable strategy for that market you want to trade.
📢 Seamless Webhook Automation – Execute trades automatically with TradingView alerts.
💲 Ready to trade smarter?
✅ Add the RSI Divergence Strategy Indicator to your TradingView chart.
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.
Real-Time Spring DetectorThis is a Pine Script for Trading View that creates a "Real-Time Spring Detector" indicator. This Pine Script is essentially a sophisticated pattern recognition tool that helps identify "spring" setups - a popular trading pattern where price briefly breaks below support but then bounces back strongly, often indicating that sellers are exhausted and buyers are ready to step in.What is a "Spring" in Trading?
A spring is a technical analysis pattern that occurs when:
Price breaks below a support level (like breaking below a floor)
But then quickly bounces back up (like a spring rebounds)
This often signals that sellers are weak and buyers are stepping in
Think of it like testing the strength of a trampoline - you push down, but it springs back up stronger.
What This Script Does
This Pine Script automatically detects spring patterns on your chart and alerts you when they happen. Here's how it works:
Main Components
1. Input Parameters (Settings You Can Adjust)
Lookback Period (10): How many bars back to look for patterns
Min Support Touches (2): How many times price must touch the support level
Min Penetration % (0.1%): How far below support price must break
Min Rejection % (30%): How much price must bounce back up
Alert Settings: Choose when to get notifications
2. Support Level Detection
The script finds "support levels" - price levels where buyers have stepped in before:
It looks at recent low points
Identifies areas where price has bounced multiple times
Uses a small tolerance (0.5%) to account for minor price differences
3. Spring Detection Logic
The script identifies three types of springs:
Real-Time Spring (happening right now):
Price breaks below support by the minimum amount
Price bounces back strongly (rejection %)
Current candle closes higher than it opened (bullish)
Volume is reasonable
Confirmed Spring (already completed):
Same as real-time, but the candle has finished forming
Potential Spring (early warning):
Price is near support but hasn't fully formed the pattern yet
4. Visual Elements
Markers on Chart:
🟢 Green Triangle: Confirmed spring (reliable signal)
🟡 Yellow Triangle: Spring forming right now (live signal)
🟠 Orange Circle: Potential spring (early warning)
Labels:
Show "SPRING" with the rejection percentage
"FORMING" for developing patterns
"?" for potential springs
Support Line:
Red dotted line showing the support level
Background Colors:
Light red when price penetrates support
Light yellow for potential springs
5. Information Box
A table in the top-left corner shows:
Current support level price
Whether penetration is happening
Rejection percentage
Current pattern status
Live price
6. Alert System
Two types of alerts:
Real-time alerts: Notify when spring is forming (current bar)
Confirmed alerts: Notify when spring is complete (bar closed)
Alert cooldown: Prevents spam by waiting 5 bars between alerts
How to Use This Script
1. Installation
Copy the script code
Open TradingView
Go to Pine Editor
Paste the code
Click "Add to Chart"
2. Settings
Adjust the input parameters based on your trading style:
Lower lookback = more sensitive, faster signals
Higher support touches = more reliable but fewer signals
Lower penetration % = catches smaller springs
Higher rejection % = only strong bounces
3. Interpretation
Green triangles: High-confidence buy signals
Yellow triangles: Watch closely, pattern developing
Orange circles: Early warning, not tradeable yet
4. Best Practices
Use on higher timeframes (15min+) for more reliable signals
Combine with other indicators for confirmation
Pay attention to volume - higher volume springs are more reliable
Wait for confirmed signals if you're a conservative trader
Key Features for Small Timeframes
The script includes special detection for shorter timeframes:
Quick bounce detection: Identifies rapid reversals
Hammer pattern recognition: Spots candlestick patterns
Relaxed volume requirements: Works when volume data is limited
Advanced Features
Volume Analysis
Compares current volume to 10-bar average
Requires at least 80% of average volume (flexible for small timeframes)
Pattern Enhancement
Looks for hammer-like candles (long lower wick, small upper wick)
Identifies quick bounces where the upper wick is small
Multiple Confirmation
Combines multiple criteria to reduce false signals
Stronger springs get priority for alerts
Common Use Cases
Entry Signals: Buy when confirmed springs appear
Support Level Identification: Visual support lines help identify key levels
Risk Management: Failed springs (continued breakdown) can be stop-loss triggers
Market Structure: Understanding where buyers are defending price levels
Limitations
Works best in trending or ranging markets May produce false signals in very choppy conditions
small timeframe signals can be noisy should be combined with other analysis methods.The key advantage is that it can catch these patterns as they happen, rather than you having to constantly watch charts. This is especially valuable for active traders who want to capitalize on quick reversals at support levels.
4-EMA Signals + MTF Analysis4-EMA Signals + MTF Analysis Indicator: User Guide
This indicator is designed for active traders; from scalpers / intra-day traders to swing-traders who simply want an easy to understand, simple, but yet comprehensive and easy-to-understand indicator with a quick overview of initial market momentum and overall market direction to assist the trader in their initial trading.
The main feature of this indicator is that It overlays four exponential moving averages (EMAs), and provides a dashboard of real-time trend signals for both buy and sell (long/short) scenarios.
Settings Overview
MA Period Setup
Custom: Set your own EMA periods.
Conservative, Aggressive, Fibonacci: Quickly apply preset EMA period combinations for different trading styles.
EMA Periods (Custom Setup)
Fast EMA: Shortest EMA, reacts quickly to price (default 7).
Medium-Short EMA: Intermediate speed (default 25).
Medium-Long EMA: Slower, filters out more noise (default 70).
Slow EMA: Longest-term trend (default 200).
(These settings can be adjusted to suit your individual needs)
Main Indicator Features
Four EMAs : Plotted in distinct colors (green, yellow, orange, red) for quick trend recognition - this can be altered in colour to your personal preferences.
Buy/Sell Signals : Triangles appear below/above bars when EMA crossovers occur, optionally filtered by volume - the volume filter when applied greatly reduces the influx of signals, only giving high-quality signals!
Trading Sessions + Backgrounds : A trading session feature has been added: (London, New York, Asia, Sydney) with the choice of a background to indicate each global trading session.
Volume Overlay : Volume bars and a blue volume moving average line for context.
Dashboard Table (Top Right):
Market Direction: Shows the overall EMA alignment (bullish, bearish, weak, consolidating).
The initial trend on the 5m, 15m, 1H, and 4H time-frame to give a quick snapshot of overall trend direction.
How to Use
Trend Identification :
-- Use the EMA alignment and dashboard table to quickly see if the market is bullish, bearish, or consolidating.
EMA Set-up
-- Adjust the EMA settings to your specific requirements, the signals will alter depending on the volatility of the instrument your trading.
Signal Confirmation :
-- ''Buy'' signals simply appear when the faster EMAs cross above slower EMAs in and ''Sell'' signals simply appear when they cross below.
Volume Context :
--Use the volume moving average and the optional volume filter to avoid signals during low-activity periods.
No Repainting :
-- All signals and dashboard readings are calculated using only confirmed, closed bars. What you see in real time is exactly what will appear in your back-test — no repainting or look-ahead bias.
Alerts / Notifications
Now for the fun part:
1. Enable Buy/Sell Signal Alerts: Get notifications when buy or sell signals occur.
2. For Alerts to function correctly, set the ''condition'' to the indicator itself.
3. Select ''EMA Bearish Cross'' or ''EMA Bullish Cross'' depending whether you want alerts for either buy/sell signals - Or one can simply set alerts for both scenarios.
4. For ''Interval'' set to ''Same as chart'' - So, the time-frame which you'd like to trade on.
5. For ''Trigger'' set to ''Once Per Bar Close''.
6. Save.
7. Wait for a signal to occur!
NOTE: Before placing a trade, it's often best to also assess the market conditions at that time also - and not rely solely on the signals, the signals are to assist, and it's often occurred a signal has shown, but a pull-back occurs, and a FVG in discount holds -- it's this FVG holding, accompanied by volume, that's the real entry, possibly.
Tip: For the cleanest results, use the indicator during the most active sessions and always check multi-time-frame trend alignment before entering trades. Good Luck! :)
Donchian x WMA Crossover (2025 Only, Adjustable TP, Real OHLC)Short Description:
Long-only breakout system that goes long when the Donchian Low crosses up through a Weighted Moving Average, and closes when it crosses back down (with an optional take-profit), restricted to calendar year 2025. All signals use the instrument’s true OHLC data (even on Heikin-Ashi charts), start with 1 000 AUD of capital, and deploy 100 % equity per trade.
Ideal parameters configured for Temple & Webster on ASX 30 minute candles. Adjust parameter to suit however best to download candle interval data and have GPT test the pine script for optimum parameters for your trading symbol.
Detailed Description
1. Strategy Concept
This strategy captures trend-driven breakouts off the bottom of a Donchian channel. By combining the Donchian Low with a WMA filter, it aims to:
Enter when volatility compresses and price breaks above the recent Donchian Low while the longer‐term WMA confirms upward momentum.
Exit when price falls back below that same WMA (i.e. when the Donchian Low crosses back down through WMA), but only if the WMA itself has stopped rising.
Optional Take-Profit: you can specify a profit target in decimal form (e.g. 0.01 = 1 %).
2. Timeframe & Universe
In-sample period: only bars stamped between Jan 1 2025 00:00 UTC and Dec 31 2025 23:59 UTC are considered.
Any resolution (e.g. 30 m, 1 h, D, etc.) is supported—just set your preferred timeframe in the TradingView UI.
3. True-Price Execution
All indicator calculations (Donchian Low, WMA, crossover checks, take-profit) are sourced from the chart’s underlying OHLC via request.security(). This guarantees that:
You can view Heikin-Ashi or other styled candles, but your strategy will execute on the real OHLC bars.
Chart styling never suppresses or distorts your backtest results.
4. Position Sizing & Equity
Initial capital: 1 000 AUD
Size per trade: 100 % of available equity
No pyramiding: one open position at a time
5. Inputs (all exposed in the “Inputs” tab):
Input Default Description
Donchian Length 7 Number of bars to calculate the Donchian channel low
WMA Length 62 Period of the Weighted Moving Average filter
Take Profit (decimal) 0.01 Exit when price ≥ entry × (1 + take_profit_perc)
6. How It Works
Donchian Low: ta.lowest(low, DonchianLength) over the specified look-back.
WMA: ta.wma(close, WMALength) applied to true closes.
Entry: ta.crossover(DonchianLow, WMA) AND barTime ∈ 2025.
Exit:
Cross-down exit: ta.crossunder(DonchianLow, WMA) and WMA is not rising (i.e. momentum has stalled).
Take-profit exit: price ≥ entry × (1 + take_profit_perc).
Calendar exit: barTime falls outside 2025.
7. Usage Notes
After adding to your chart, open the Strategy Tester tab to review performance metrics, list of trades, equity curve, etc.
You can toggle your chart to Heikin-Ashi for visual clarity without affecting execution, thanks to the real-OHLC calls.
Langlands-Operadic Möbius Vortex (LOMV)Langlands-Operadic Möbius Vortex (LOMV)
Where Pure Mathematics Meets Market Reality
A Revolutionary Synthesis of Number Theory, Category Theory, and Market Dynamics
🎓 THEORETICAL FOUNDATION
The Langlands-Operadic Möbius Vortex represents a groundbreaking fusion of three profound mathematical frameworks that have never before been combined for market analysis:
The Langlands Program: Harmonic Analysis in Markets
Developed by Robert Langlands (Fields Medal recipient), the Langlands Program creates bridges between number theory, algebraic geometry, and harmonic analysis. In our indicator:
L-Function Implementation:
- Utilizes the Möbius function μ(n) for weighted price analysis
- Applies Riemann zeta function convergence principles
- Calculates quantum harmonic resonance between -2 and +2
- Measures deep mathematical patterns invisible to traditional analysis
The L-Function core calculation employs:
L_sum = Σ(return_val × μ(n) × n^(-s))
Where s is the critical strip parameter (0.5-2.5), controlling mathematical precision and signal smoothness.
Operadic Composition Theory: Multi-Strategy Democracy
Category theory and operads provide the mathematical framework for composing multiple trading strategies into a unified signal. This isn't simple averaging - it's mathematical composition using:
Strategy Composition Arity (2-5 strategies):
- Momentum analysis via RSI transformation
- Mean reversion through Bollinger Band mathematics
- Order Flow Polarity Index (revolutionary T3-smoothed volume analysis)
- Trend detection using Directional Movement
- Higher timeframe momentum confirmation
Agreement Threshold System: Democratic voting where strategies must reach consensus before signal generation. This prevents false signals during market uncertainty.
Möbius Function: Number Theory in Action
The Möbius function μ(n) forms the mathematical backbone:
- μ(n) = 1 if n is a square-free positive integer with even number of prime factors
- μ(n) = -1 if n is a square-free positive integer with odd number of prime factors
- μ(n) = 0 if n has a squared prime factor
This creates oscillating weights that reveal hidden market periodicities and harmonic structures.
🔧 COMPREHENSIVE INPUT SYSTEM
Langlands Program Parameters
Modular Level N (5-50, default 30):
Primary lookback for quantum harmonic analysis. Optimized by timeframe:
- Scalping (1-5min): 15-25
- Day Trading (15min-1H): 25-35
- Swing Trading (4H-1D): 35-50
- Asset-specific: Crypto 15-25, Stocks 30-40, Forex 35-45
L-Function Critical Strip (0.5-2.5, default 1.5):
Controls Riemann zeta convergence precision:
- Higher values: More stable, smoother signals
- Lower values: More reactive, catches quick moves
- High frequency: 0.8-1.2, Medium: 1.3-1.7, Low: 1.8-2.3
Frobenius Trace Period (5-50, default 21):
Galois representation lookback for price-volume correlation:
- Measures harmonic relationships in market flows
- Scalping: 8-15, Day Trading: 18-25, Swing: 25-40
HTF Multi-Scale Analysis:
Higher timeframe context prevents trading against major trends:
- Provides market bias and filters signals
- Improves win rates by 15-25% through trend alignment
Operadic Composition Parameters
Strategy Composition Arity (2-5, default 4):
Number of algorithms composed for final signal:
- Conservative: 4-5 strategies (higher confidence)
- Moderate: 3-4 strategies (balanced approach)
- Aggressive: 2-3 strategies (more frequent signals)
Category Agreement Threshold (2-5, default 3):
Democratic voting minimum for signal generation:
- Higher agreement: Fewer but higher quality signals
- Lower agreement: More signals, potential false positives
Swiss-Cheese Mixing (0.1-0.5, default 0.382):
Golden ratio φ⁻¹ based blending of trend factors:
- 0.382 is φ⁻¹, optimal for natural market fractals
- Higher values: Stronger trend following
- Lower values: More contrarian signals
OFPI Configuration:
- OFPI Length (5-30, default 14): Order Flow calculation period
- T3 Smoothing (3-10, default 5): Advanced exponential smoothing
- T3 Volume Factor (0.5-1.0, default 0.7): Smoothing aggressiveness control
Unified Scoring System
Component Weights (sum ≈ 1.0):
- L-Function Weight (0.1-0.5, default 0.3): Mathematical harmony emphasis
- Galois Rank Weight (0.1-0.5, default 0.2): Market structure complexity
- Operadic Weight (0.1-0.5, default 0.3): Multi-strategy consensus
- Correspondence Weight (0.1-0.5, default 0.2): Theory-practice alignment
Signal Threshold (0.5-10.0, default 5.0):
Quality filter producing:
- 8.0+: EXCEPTIONAL signals only
- 6.0-7.9: STRONG signals
- 4.0-5.9: MODERATE signals
- 2.0-3.9: WEAK signals
🎨 ADVANCED VISUAL SYSTEM
Multi-Dimensional Quantum Aura Bands
Five-layer resonance field showing market energy:
- Colors: Theme-matched gradients (Quantum purple, Holographic cyan, etc.)
- Expansion: Dynamic based on score intensity and volatility
- Function: Multi-timeframe support/resistance zones
Morphism Flow Portals
Category theory visualization showing market topology:
- Green/Cyan Portals: Bullish mathematical flow
- Red/Orange Portals: Bearish mathematical flow
- Size/Intensity: Proportional to signal strength
- Recursion Depth (1-8): Nested patterns for flow evolution
Fractal Grid System
Dynamic support/resistance with projected L-Scores:
- Multiple Timeframes: 10, 20, 30, 40, 50-period highs/lows
- Smart Spacing: Prevents level overlap using ATR-based minimum distance
- Projections: Estimated signal scores when price reaches levels
- Usage: Precise entry/exit timing with mathematical confirmation
Wick Pressure Analysis
Rejection level prediction using candle mathematics:
- Upper Wicks: Selling pressure zones (purple/red lines)
- Lower Wicks: Buying pressure zones (purple/green lines)
- Glow Intensity (1-8): Visual emphasis and line reach
- Application: Confluence with fractal grid creates high-probability zones
Regime Intensity Heatmap
Background coloring showing market energy:
- Black/Dark: Low activity, range-bound markets
- Purple Glow: Building momentum and trend development
- Bright Purple: High activity, strong directional moves
- Calculation: Combines trend, momentum, volatility, and score intensity
Six Professional Themes
- Quantum: Purple/violet for general trading and mathematical focus
- Holographic: Cyan/magenta optimized for cryptocurrency markets
- Crystalline: Blue/turquoise for conservative, stability-focused trading
- Plasma: Gold/magenta for high-energy volatility trading
- Cosmic Neon: Bright neon colors for maximum visibility and aggressive trading
📊 INSTITUTIONAL-GRADE DASHBOARD
Unified AI Score Section
- Total Score (-10 to +10): Primary decision metric
- >5: Strong bullish signals
- <-5: Strong bearish signals
- Quality ratings: EXCEPTIONAL > STRONG > MODERATE > WEAK
- Component Analysis: Individual L-Function, Galois, Operadic, and Correspondence contributions
Order Flow Analysis
Revolutionary OFPI integration:
- OFPI Value (-100% to +100%): Real buying vs selling pressure
- Visual Gauge: Horizontal bar chart showing flow intensity
- Momentum Status: SHIFTING, ACCELERATING, STRONG, MODERATE, or WEAK
- Trading Application: Flow shifts often precede major moves
Signal Performance Tracking
- Win Rate Monitoring: Real-time success percentage with emoji indicators
- Signal Count: Total signals generated for frequency analysis
- Current Position: LONG, SHORT, or NONE with P&L tracking
- Volatility Regime: HIGH, MEDIUM, or LOW classification
Market Structure Analysis
- Möbius Field Strength: Mathematical field oscillation intensity
- CHAOTIC: High complexity, use wider stops
- STRONG: Active field, normal position sizing
- MODERATE: Balanced conditions
- WEAK: Low activity, consider smaller positions
- HTF Trend: Higher timeframe bias (BULL/BEAR/NEUTRAL)
- Strategy Agreement: Multi-algorithm consensus level
Position Management
When in trades, displays:
- Entry Price: Original signal price
- Current P&L: Real-time percentage with risk level assessment
- Duration: Bars in trade for timing analysis
- Risk Level: HIGH/MEDIUM/LOW based on current exposure
🚀 SIGNAL GENERATION LOGIC
Balanced Long/Short Architecture
The indicator generates signals through multiple convergent pathways:
Long Entry Conditions:
- Score threshold breach with algorithmic agreement
- Strong bullish order flow (OFPI > 0.15) with positive composite signal
- Bullish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bullish OFPI (>0.3) with any positive score
Short Entry Conditions:
- Score threshold breach with bearish agreement
- Strong bearish order flow (OFPI < -0.15) with negative composite signal
- Bearish pattern recognition with mathematical confirmation
- HTF trend alignment with momentum shifting
- Extreme bearish OFPI (<-0.3) with any negative score
Exit Logic:
- Score deterioration below continuation threshold
- Signal quality degradation
- Opposing order flow acceleration
- 10-bar minimum between signals prevents overtrading
⚙️ OPTIMIZATION GUIDELINES
Asset-Specific Settings
Cryptocurrency Trading:
- Modular Level: 15-25 (capture volatility)
- L-Function Precision: 0.8-1.3 (reactive to price swings)
- OFPI Length: 10-20 (fast correlation shifts)
- Cascade Levels: 5-7, Theme: Holographic
Stock Index Trading:
- Modular Level: 25-35 (balanced trending)
- L-Function Precision: 1.5-1.8 (stable patterns)
- OFPI Length: 14-20 (standard correlation)
- Cascade Levels: 4-5, Theme: Quantum
Forex Trading:
- Modular Level: 35-45 (smooth trends)
- L-Function Precision: 1.6-2.1 (high smoothing)
- OFPI Length: 18-25 (disable volume amplification)
- Cascade Levels: 3-4, Theme: Crystalline
Timeframe Optimization
Scalping (1-5 minute charts):
- Reduce all lookback parameters by 30-40%
- Increase L-Function precision for noise reduction
- Enable all visual elements for maximum information
- Use Small dashboard to save screen space
Day Trading (15 minute - 1 hour):
- Use default parameters as starting point
- Adjust based on market volatility
- Normal dashboard provides optimal information density
- Focus on OFPI momentum shifts for entries
Swing Trading (4 hour - Daily):
- Increase lookback parameters by 30-50%
- Higher L-Function precision for stability
- Large dashboard for comprehensive analysis
- Emphasize HTF trend alignment
🏆 ADVANCED TRADING STRATEGIES
The Mathematical Confluence Method
1. Wait for Fractal Grid level approach
2. Confirm with projected L-Score > threshold
3. Verify OFPI alignment with direction
4. Enter on portal signal with quality ≥ STRONG
5. Exit on score deterioration or opposing flow
The Regime Trading System
1. Monitor Aether Flow background intensity
2. Trade aggressively during bright purple periods
3. Reduce position size during dark periods
4. Use Möbius Field strength for stop placement
5. Align with HTF trend for maximum probability
The OFPI Momentum Strategy
1. Watch for momentum shifting detection
2. Confirm with accelerating flow in direction
3. Enter on immediate portal signal
4. Scale out at Fibonacci levels
5. Exit on flow deceleration or reversal
⚠️ RISK MANAGEMENT INTEGRATION
Mathematical Position Sizing
- Use Galois Rank for volatility-adjusted sizing
- Möbius Field strength determines stop width
- Fractal Dimension guides maximum exposure
- OFPI momentum affects entry timing
Signal Quality Filtering
- Trade only STRONG or EXCEPTIONAL quality signals
- Increase position size with higher agreement levels
- Reduce risk during CHAOTIC Möbius field periods
- Respect HTF trend alignment for directional bias
🔬 DEVELOPMENT JOURNEY
Creating the LOMV was an extraordinary mathematical undertaking that pushed the boundaries of what's possible in technical analysis. This indicator almost didn't happen. The theoretical complexity nearly proved insurmountable.
The Mathematical Challenge
Implementing the Langlands Program required deep research into:
- Number theory and the Möbius function
- Riemann zeta function convergence properties
- L-function analytical continuation
- Galois representations in finite fields
The mathematical literature spans decades of pure mathematics research, requiring translation from abstract theory to practical market application.
The Computational Complexity
Operadic composition theory demanded:
- Category theory implementation in Pine Script
- Multi-dimensional array management for strategy composition
- Real-time democratic voting algorithms
- Performance optimization for complex calculations
The Integration Breakthrough
Bringing together three disparate mathematical frameworks required:
- Novel approaches to signal weighting and combination
- Revolutionary Order Flow Polarity Index development
- Advanced T3 smoothing implementation
- Balanced signal generation preventing directional bias
Months of intensive research culminated in breakthrough moments when the mathematics finally aligned with market reality. The result is an indicator that reveals market structure invisible to conventional analysis while maintaining practical trading utility.
🎯 PRACTICAL IMPLEMENTATION
Getting Started
1. Apply indicator with default settings
2. Select appropriate theme for your markets
3. Observe dashboard metrics during different market conditions
4. Practice signal identification without trading
5. Gradually adjust parameters based on observations
Signal Confirmation Process
- Never trade on score alone - verify quality rating
- Confirm OFPI alignment with intended direction
- Check fractal grid level proximity for timing
- Ensure Möbius field strength supports position size
- Validate against HTF trend for bias confirmation
Performance Monitoring
- Track win rate in dashboard for strategy assessment
- Monitor component contributions for optimization
- Adjust threshold based on desired signal frequency
- Document performance across different market regimes
🌟 UNIQUE INNOVATIONS
1. First Integration of Langlands Program mathematics with practical trading
2. Revolutionary OFPI with T3 smoothing and momentum detection
3. Operadic Composition using category theory for signal democracy
4. Dynamic Fractal Grid with projected L-Score calculations
5. Multi-Dimensional Visualization through morphism flow portals
6. Regime-Adaptive Background showing market energy intensity
7. Balanced Signal Generation preventing directional bias
8. Professional Dashboard with institutional-grade metrics
📚 EDUCATIONAL VALUE
The LOMV serves as both a practical trading tool and an educational gateway to advanced mathematics. Traders gain exposure to:
- Pure mathematics applications in markets
- Category theory and operadic composition
- Number theory through Möbius function implementation
- Harmonic analysis via L-function calculations
- Advanced signal processing through T3 smoothing
⚖️ RESPONSIBLE USAGE
This indicator represents advanced mathematical research applied to market analysis. While the underlying mathematics are rigorously implemented, markets remain inherently unpredictable.
Key Principles:
- Use as part of comprehensive trading strategy
- Implement proper risk management at all times
- Backtest thoroughly before live implementation
- Understand that past performance does not guarantee future results
- Never risk more than you can afford to lose
The mathematics reveal deep market structure, but successful trading requires discipline, patience, and sound risk management beyond any indicator.
🔮 CONCLUSION
The Langlands-Operadic Möbius Vortex represents a quantum leap forward in technical analysis, bringing PhD-level pure mathematics to practical trading while maintaining visual elegance and usability.
From the harmonic analysis of the Langlands Program to the democratic composition of operadic theory, from the number-theoretic precision of the Möbius function to the revolutionary Order Flow Polarity Index, every component works in mathematical harmony to reveal the hidden order within market chaos.
This is more than an indicator - it's a mathematical lens that transforms how you see and understand market structure.
Trade with mathematical precision. Trade with the LOMV.
*"Mathematics is the language with which God has written the universe." - Galileo Galilei*
*In markets, as in nature, profound mathematical beauty underlies apparent chaos. The LOMV reveals this hidden order.*
— Dskyz, Trade with insight. Trade with anticipation.
Market Zone Analyzer[BullByte]Understanding the Market Zone Analyzer
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1. Purpose of the Indicator
The Market Zone Analyzer is a Pine Script™ (version 6) indicator designed to streamline market analysis on TradingView. Rather than scanning multiple separate tools, it unifies four core dimensions—trend strength, momentum, price action, and market activity—into a single, consolidated view. By doing so, it helps traders:
• Save time by avoiding manual cross-referencing of disparate signals.
• Reduce decision-making errors that can arise from juggling multiple indicators.
• Gain a clear, reliable read on whether the market is in a bullish, bearish, or sideways phase, so they can more confidently decide to enter, exit, or hold a position.
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2. Why a Trader Should Use It
• Unified View: Combines all essential market dimensions into one easy-to-read score and dashboard, eliminating the need to piece together signals manually.
• Adaptability: Automatically adjusts its internal weighting for trend, momentum, and price action based on current volatility. Whether markets are choppy or calm, the indicator remains relevant.
• Ease of Interpretation: Outputs a simple “BULLISH,” “BEARISH,” or “SIDEWAYS” label, supplemented by an intuitive on-chart dashboard and an oscillator plot that visually highlights market direction.
• Reliability Features: Built-in smoothing of the net score and hysteresis logic (requiring consecutive confirmations before flips) minimize false signals during noisy or range-bound phases.
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3. Why These Specific Indicators?
This script relies on a curated set of well-established technical tools, each chosen for its particular strength in measuring one of the four core dimensions:
1. Trend Strength:
• ADX/DMI (Average Directional Index / Directional Movement Index): Measures how strong a trend is, and whether the +DI line is above the –DI line (bullish) or vice versa (bearish).
• Moving Average Slope (Fast MA vs. Slow MA): Compares a shorter-period SMA to a longer-period SMA; if the fast MA sits above the slow MA, it confirms an uptrend, and vice versa for a downtrend.
• Ichimoku Cloud Differential (Senkou A vs. Senkou B): Provides a forward-looking view of trend direction; Senkou A above Senkou B signals bullishness, and the opposite signals bearishness.
2. Momentum:
• Relative Strength Index (RSI): Identifies overbought (above its dynamically calculated upper bound) or oversold (below its lower bound) conditions; changes in RSI often precede price reversals.
• Stochastic %K: Highlights shifts in short-term momentum by comparing closing price to the recent high/low range; values above its upper band signal bullish momentum, below its lower band signal bearish momentum.
• MACD Histogram: Measures the difference between the MACD line and its signal line; a positive histogram indicates upward momentum, a negative histogram indicates downward momentum.
3. Price Action:
• Highest High / Lowest Low (HH/LL) Range: Over a defined lookback period, this captures breakout or breakdown levels. A closing price near the recent highs (with a positive MA slope) yields a bullish score, and near the lows (with a negative MA slope) yields a bearish score.
• Heikin-Ashi Doji Detection: Uses Heikin-Ashi candles to identify indecision or continuation patterns. A small Heikin-Ashi body (doji) relative to recent volatility is scored as neutral; a larger body in the direction of the MA slope is scored bullish or bearish.
• Candle Range Measurement: Compares each candle’s high-low range against its own dynamic band (average range ± standard deviation). Large candles aligning with the prevailing trend score bullish or bearish accordingly; unusually small candles can indicate exhaustion or consolidation.
4. Market Activity:
• Bollinger Bands Width (BBW): Measures the distance between BB upper and lower bands; wide bands indicate high volatility, narrow bands indicate low volatility.
• Average True Range (ATR): Quantifies average price movement (volatility). A sudden spike in ATR suggests a volatile environment, while a contraction suggests calm.
• Keltner Channels Width (KCW): Similar to BBW but uses ATR around an EMA. Provides a second layer of volatility context, confirming or contrasting BBW readings.
• Volume (with Moving Average): Compares current volume to its moving average ± standard deviation. High volume validates strong moves; low volume signals potential lack of conviction.
By combining these tools, the indicator captures trend direction, momentum strength, price-action nuances, and overall market energy, yielding a more balanced and comprehensive assessment than any single tool alone.
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4. What Makes This Indicator Stand Out
• Multi-Dimensional Analysis: Rather than relying on a lone oscillator or moving average crossover, it simultaneously evaluates trend, momentum, price action, and activity.
• Dynamic Weighting: The relative importance of trend, momentum, and price action adjusts automatically based on real-time volatility (Market Activity State). For example, in highly volatile conditions, trend and momentum signals carry more weight; in calm markets, price action signals are prioritized.
• Stability Mechanisms:
• Smoothing: The net score is passed through a short moving average, filtering out noise, especially on lower timeframes.
• Hysteresis: Both Market Activity State and the final bullish/bearish/sideways zone require two consecutive confirmations before flipping, reducing whipsaw.
• Visual Interpretation: A fully customizable on-chart dashboard displays each sub-indicator’s value, regime, score, and comment, all color-coded. The oscillator plot changes color to reflect the current market zone (green for bullish, red for bearish, gray for sideways) and shows horizontal threshold lines at +2, 0, and –2.
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5. Recommended Timeframes
• Short-Term (5 min, 15 min): Day traders and scalpers can benefit from rapid signals, but should enable smoothing (and possibly disable hysteresis) to reduce false whipsaws.
• Medium-Term (1 h, 4 h): Swing traders find a balance between responsiveness and reliability. Less smoothing is required here, and the default parameters (e.g., ADX length = 14, RSI length = 14) perform well.
• Long-Term (Daily, Weekly): Position traders tracking major trends can disable smoothing for immediate raw readings, since higher-timeframe noise is minimal. Adjust lookback lengths (e.g., increase adxLength, rsiLength) if desired for slower signals.
Tip: If you keep smoothing off, stick to timeframes of 1 h or higher to avoid excessive signal “chatter.”
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6. How Scoring Works
A. Individual Indicator Scores
Each sub-indicator is assigned one of three discrete scores:
• +1 if it indicates a bullish condition (e.g., RSI above its dynamically calculated upper bound).
• 0 if it is neutral (e.g., RSI between upper and lower bounds).
• –1 if it indicates a bearish condition (e.g., RSI below its dynamically calculated lower bound).
Examples of individual score assignments:
• ADX/DMI:
• +1 if ADX ≥ adxThreshold and +DI > –DI (strong bullish trend)
• –1 if ADX ≥ adxThreshold and –DI > +DI (strong bearish trend)
• 0 if ADX < adxThreshold (trend strength below threshold)
• RSI:
• +1 if RSI > RSI_upperBound
• –1 if RSI < RSI_lowerBound
• 0 otherwise
• ATR (as part of Market Activity):
• +1 if ATR > (ATR_MA + stdev(ATR))
• –1 if ATR < (ATR_MA – stdev(ATR))
• 0 otherwise
Each of the four main categories shares this same +1/0/–1 logic across their sub-components.
B. Category Scores
Once each sub-indicator reports +1, 0, or –1, these are summed within their categories as follows:
• Trend Score = (ADX score) + (MA slope score) + (Ichimoku differential score)
• Momentum Score = (RSI score) + (Stochastic %K score) + (MACD histogram score)
• Price Action Score = (Highest-High/Lowest-Low score) + (Heikin-Ashi doji score) + (Candle range score)
• Market Activity Raw Score = (BBW score) + (ATR score) + (KC width score) + (Volume score)
Each category’s summed value can range between –3 and +3 (for Trend, Momentum, and Price Action), and between –4 and +4 for Market Activity raw.
C. Market Activity State and Dynamic Weight Adjustments
Rather than contributing directly to the netScore like the other three categories, Market Activity determines how much weight to assign to Trend, Momentum, and Price Action:
1. Compute Market Activity Raw Score by summing BBW, ATR, KCW, and Volume individual scores (each +1/0/–1).
2. Bucket into High, Medium, or Low Activity:
• High if raw Score ≥ 2 (volatile market).
• Low if raw Score ≤ –2 (calm market).
• Medium otherwise.
3. Apply Hysteresis (if enabled): The state only flips after two consecutive bars register the same high/low/medium label.
4. Set Category Weights:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use the trader’s base weight inputs (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 % by default).
D. Calculating the Net Score
5. Normalize Base Weights (so that the sum of Trend + Momentum + Price Action always equals 100 %).
6. Determine Current Weights based on the Market Activity State (High/Medium/Low).
7. Compute Each Category’s Contribution: Multiply (categoryScore) × (currentWeight).
8. Sum Contributions to get the raw netScore (a floating-point value that can exceed ±3 when scores are strong).
9. Smooth the netScore over two bars (if smoothing is enabled) to reduce noise.
10. Apply Hysteresis to the Final Zone:
• If the smoothed netScore ≥ +2, the bar is classified as “Bullish.”
• If the smoothed netScore ≤ –2, the bar is classified as “Bearish.”
• Otherwise, it is “Sideways.”
• To prevent rapid flips, the script requires two consecutive bars in the new zone before officially changing the displayed zone (if hysteresis is on).
E. Thresholds for Zone Classification
• BULLISH: netScore ≥ +2
• BEARISH: netScore ≤ –2
• SIDEWAYS: –2 < netScore < +2
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7. Role of Volatility (Market Activity State) in Scoring
Volatility acts as a dynamic switch that shifts which category carries the most influence:
1. High Activity (Volatile):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal +1.
• The script sets Trend weight = 50 % and Momentum weight = 35 %. Price Action weight is minimized at 15 %.
• Rationale: In volatile markets, strong trending moves and momentum surges dominate, so those signals are more reliable than nuanced candle patterns.
2. Low Activity (Calm):
• Detected when at least two sub-scores out of BBW, ATR, KCW, and Volume equal –1.
• The script sets Price Action weight = 55 %, Trend = 25 %, and Momentum = 20 %.
• Rationale: In quiet, sideways markets, subtle price-action signals (breakouts, doji patterns, small-range candles) are often the best early indicators of a new move.
3. Medium Activity (Balanced):
• Raw Score between –1 and +1 from the four volatility metrics.
• Uses whatever base weights the trader has specified (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
Because volatility can fluctuate rapidly, the script employs hysteresis on Market Activity State: a new High or Low state must occur on two consecutive bars before weights actually shift. This avoids constant back-and-forth weight changes and provides more stability.
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8. Scoring Example (Hypothetical Scenario)
• Symbol: Bitcoin on a 1-hour chart.
• Market Activity: Raw volatility sub-scores show BBW (+1), ATR (+1), KCW (0), Volume (+1) → Total raw Score = +3 → High Activity.
• Weights Selected: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Signals:
• ADX strong and +DI > –DI → +1
• Fast MA above Slow MA → +1
• Ichimoku Senkou A > Senkou B → +1
→ Trend Score = +3
• Momentum Signals:
• RSI above upper bound → +1
• MACD histogram positive → +1
• Stochastic %K within neutral zone → 0
→ Momentum Score = +2
• Price Action Signals:
• Highest High/Lowest Low check yields 0 (close not near extremes)
• Heikin-Ashi doji reading is neutral → 0
• Candle range slightly above upper bound but trend is strong, so → +1
→ Price Action Score = +1
• Compute Net Score (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 1 × 0.15 = 0.15
• Raw netScore = 1.50 + 0.70 + 0.15 = 2.35
• Since 2.35 ≥ +2 and hysteresis is met, the final zone is “Bullish.”
Although the netScore lands at 2.35 (Bullish), smoothing might bring it slightly below 2.00 on the first bar (e.g., 1.90), in which case the script would wait for a second consecutive reading above +2 before officially classifying the zone as Bullish (if hysteresis is enabled).
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9. Correlation Between Categories
The four categories—Trend Strength, Momentum, Price Action, and Market Activity—often reinforce or offset one another. The script takes advantage of these natural correlations:
• Bullish Alignment: If ADX is strong and pointed upward, fast MA is above slow MA, and Ichimoku is positive, that usually coincides with RSI climbing above its upper bound and the MACD histogram turning positive. In such cases, both Trend and Momentum categories generate +1 or +2. Because the Market Activity State is likely High (given the accompanying volatility), Trend and Momentum weights are at their peak, so the netScore quickly crosses into Bullish territory.
• Sideways/Consolidation: During a low-volatility, sideways phase, ADX may fall below its threshold, MAs may flatten, and RSI might hover in the neutral band. However, subtle price-action signals (like a small breakout candle or a Heikin-Ashi candle with a slight bias) can still produce a +1 in the Price Action category. If Market Activity is Low, Price Action’s weight (55 %) can carry enough influence—even if Trend and Momentum are neutral—to push the netScore out of “Sideways” into a mild bullish or bearish bias.
• Opposing Signals: When Trend is bullish but Momentum turns negative (for example, price continues up but RSI rolls over), the two scores can partially cancel. Market Activity may remain Medium, in which case the netScore lingers near zero (Sideways). The trader can then wait for either a clearer momentum shift or a fresh price-action breakout before committing.
By dynamically recognizing these correlations and adjusting weights, the indicator ensures that:
• When Trend and Momentum align (and volatility supports it), the netScore leaps strongly into Bullish or Bearish.
• When Trend is neutral but Price Action shows an early move in a low-volatility environment, Price Action’s extra weight in the Low Activity State can still produce actionable signals.
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10. Market Activity State & Its Role (Detailed)
The Market Activity State is not a direct category score—it is an overarching context setter for how heavily to trust Trend, Momentum, or Price Action. Here’s how it is derived and applied:
1. Calculate Four Volatility Sub-Scores:
• BBW: Compare the current band width to its own moving average ± standard deviation. If BBW > (BBW_MA + stdev), assign +1 (high volatility); if BBW < (BBW_MA × 0.5), assign –1 (low volatility); else 0.
• ATR: Compare ATR to its moving average ± standard deviation. A spike above the upper threshold is +1; a contraction below the lower threshold is –1; otherwise 0.
• KCW: Same logic as ATR but around the KCW mean.
• Volume: Compare current volume to its volume MA ± standard deviation. Above the upper threshold is +1; below the lower threshold is –1; else 0.
2. Sum Sub-Scores → Raw Market Activity Score: Range between –4 and +4.
3. Assign Market Activity State:
• High Activity: Raw Score ≥ +2 (at least two volatility metrics are strongly spiking).
• Low Activity: Raw Score ≤ –2 (at least two metrics signal unusually low volatility or thin volume).
• Medium Activity: Raw Score is between –1 and +1 inclusive.
4. Hysteresis for Stability:
• If hysteresis is enabled, a new state only takes hold after two consecutive bars confirm the same High, Medium, or Low label.
• This prevents the Market Activity State from bouncing around when volatility is on the fence.
5. Set Category Weights Based on Activity State:
• High Activity: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Low Activity: Trend = 25 %, Momentum = 20 %, Price Action = 55 %.
• Medium Activity: Use trader’s base weights (e.g., Trend = 40 %, Momentum = 30 %, Price Action = 30 %).
6. Impact on netScore: Because category scores (–3 to +3) multiply by these weights, High Activity amplifies the effect of strong Trend and Momentum scores; Low Activity amplifies the effect of Price Action.
7. Market Context Tooltip: The dashboard includes a tooltip summarizing the current state—e.g., “High activity, trend and momentum prioritized,” “Low activity, price action prioritized,” or “Balanced market, all categories considered.”
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11. Category Weights: Base vs. Dynamic
Traders begin by specifying base weights for Trend Strength, Momentum, and Price Action that sum to 100 %. These apply only when volatility is in the Medium band. Once volatility shifts:
• High Volatility Overrides:
• Trend jumps from its base (e.g., 40 %) to 50 %.
• Momentum jumps from its base (e.g., 30 %) to 35 %.
• Price Action is reduced to 15 %.
Example: If base weights were Trend = 40 %, Momentum = 30 %, Price Action = 30 %, then in High Activity they become 50/35/15. A Trend score of +3 now contributes 3 × 0.50 = +1.50 to netScore; a Momentum +2 contributes 2 × 0.35 = +0.70. In total, Trend + Momentum can easily push netScore above the +2 threshold on its own.
• Low Volatility Overrides:
• Price Action leaps from its base (30 %) to 55 %.
• Trend falls to 25 %, Momentum falls to 20 %.
Why? When markets are quiet, subtle candle breakouts, doji patterns, and small-range expansions tend to foreshadow the next swing more effectively than raw trend readings. A Price Action score of +3 in this state contributes 3 × 0.55 = +1.65, which can carry the netScore toward +2—even if Trend and Momentum are neutral or only mildly positive.
Because these weight shifts happen only after two consecutive bars confirm a High or Low state (if hysteresis is on), the indicator avoids constantly flipping its emphasis during borderline volatility phases.
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12. Dominant Category Explained
Within the dashboard, a label such as “Trend Dominant,” “Momentum Dominant,” or “Price Action Dominant” appears when one category’s absolute weighted contribution to netScore is the largest. Concretely:
• Compute each category’s weighted contribution = (raw category score) × (current weight).
• Compare the absolute values of those three contributions.
• The category with the highest absolute value is flagged as Dominant for that bar.
Why It Matters:
• Momentum Dominant: Indicates that the combined force of RSI, Stochastic, and MACD (after weighting) is pushing netScore farther than either Trend or Price Action. In practice, it means that short-term sentiment and speed of change are the primary drivers right now, so traders should watch for continued momentum signals before committing to a trade.
• Trend Dominant: Means ADX, MA slope, and Ichimoku (once weighted) outweigh the other categories. This suggests a strong directional move is in place; trend-following entries or confirming pullbacks are likely to succeed.
• Price Action Dominant: Occurs when breakout/breakdown patterns, Heikin-Ashi candle readings, and range expansions (after weighting) are the most influential. This often happens in calmer markets, where subtle shifts in candle structure can foreshadow bigger moves.
By explicitly calling out which category is carrying the most weight at any moment, the dashboard gives traders immediate insight into why the netScore is tilting toward bullish, bearish, or sideways.
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13. Oscillator Plot: How to Read It
The “Net Score” oscillator sits below the dashboard and visually displays the smoothed netScore as a line graph. Key features:
1. Value Range: In normal conditions it oscillates roughly between –3 and +3, but extreme confluences can push it outside that range.
2. Horizontal Threshold Lines:
• +2 Line (Bullish threshold)
• 0 Line (Neutral midline)
• –2 Line (Bearish threshold)
3. Zone Coloring:
• Green Background (Bullish Zone): When netScore ≥ +2.
• Red Background (Bearish Zone): When netScore ≤ –2.
• Gray Background (Sideways Zone): When –2 < netScore < +2.
4. Dynamic Line Color:
• The plotted netScore line itself is colored green in a Bullish Zone, red in a Bearish Zone, or gray in a Sideways Zone, creating an immediate visual cue.
Interpretation Tips:
• Crossing Above +2: Signals a strong enough combined trend/momentum/price-action reading to classify as Bullish. Many traders wait for a clear crossing plus a confirmation candle before entering a long position.
• Crossing Below –2: Indicates a strong Bearish signal. Traders may consider short or exit strategies.
• Rising Slope, Even Below +2: If netScore climbs steadily from neutral toward +2, it demonstrates building bullish momentum.
• Divergence: If price makes a higher high but the oscillator fails to reach a new high, it can warn of weakening momentum and a potential reversal.
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14. Comments and Their Necessity
Every sub-indicator (ADX, MA slope, Ichimoku, RSI, Stochastic, MACD, HH/LL, Heikin-Ashi, Candle Range, BBW, ATR, KCW, Volume) generates a short comment that appears in the detailed dashboard. Examples:
• “Strong bullish trend” or “Strong bearish trend” for ADX/DMI
• “Fast MA above slow MA” or “Fast MA below slow MA” for MA slope
• “RSI above dynamic threshold” or “RSI below dynamic threshold” for RSI
• “MACD histogram positive” or “MACD histogram negative” for MACD Hist
• “Price near highs” or “Price near lows” for HH/LL checks
• “Bullish Heikin Ashi” or “Bearish Heikin Ashi” for HA Doji scoring
• “Large range, trend confirmed” or “Small range, trend contradicted” for Candle Range
Additionally, the top-row comment for each category is:
• Trend: “Highly Bullish,” “Highly Bearish,” or “Neutral Trend.”
• Momentum: “Strong Momentum,” “Weak Momentum,” or “Neutral Momentum.”
• Price Action: “Bullish Action,” “Bearish Action,” or “Neutral Action.”
• Market Activity: “Volatile Market,” “Calm Market,” or “Stable Market.”
Reasons for These Comments:
• Transparency: Shows exactly how each sub-indicator contributed to its category score.
• Education: Helps traders learn why a category is labeled bullish, bearish, or neutral, building intuition over time.
• Customization: If, for example, the RSI comment says “RSI neutral” despite an impending trend shift, a trader might choose to adjust RSI length or thresholds.
In the detailed dashboard, hovering over each comment cell also reveals a tooltip with additional context (e.g., “Fast MA above slow MA” or “Senkou A above Senkou B”), helping traders understand the precise rule behind that +1, 0, or –1 assignment.
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15. Real-Life Example (Consolidated)
• Instrument & Timeframe: Bitcoin (BTCUSD), 1-hour chart.
• Current Market Activity: BBW and ATR both spike (+1 each), KCW is moderately high (+1), but volume is only neutral (0) → Raw Market Activity Score = +2 → State = High Activity (after two bars, if hysteresis is on).
• Category Weights Applied: Trend = 50 %, Momentum = 35 %, Price Action = 15 %.
• Trend Sub-Scores:
1. ADX = 25 (above threshold 20) with +DI > –DI → +1.
2. Fast MA (20-period) sits above Slow MA (50-period) → +1.
3. Ichimoku: Senkou A > Senkou B → +1.
→ Trend Score = +3.
• Momentum Sub-Scores:
4. RSI = 75 (above its moving average +1 stdev) → +1.
5. MACD histogram = +0.15 → +1.
6. Stochastic %K = 50 (mid-range) → 0.
→ Momentum Score = +2.
• Price Action Sub-Scores:
7. Price is not within 1 % of the 20-period high/low and slope = positive → 0.
8. Heikin-Ashi body is slightly larger than stdev over last 5 bars with haClose > haOpen → +1.
9. Candle range is just above its dynamic upper bound but trend is already captured, so → +1.
→ Price Action Score = +2.
• Calculate netScore (before smoothing):
• Trend contribution = 3 × 0.50 = 1.50
• Momentum contribution = 2 × 0.35 = 0.70
• Price Action contribution = 2 × 0.15 = 0.30
• Raw netScore = 1.50 + 0.70 + 0.30 = 2.50 → Immediately classified as Bullish.
• Oscillator & Dashboard Output:
• The oscillator line crosses above +2 and turns green.
• Dashboard displays:
• Trend Regime “BULLISH,” Trend Score = 3, Comment = “Highly Bullish.”
• Momentum Regime “BULLISH,” Momentum Score = 2, Comment = “Strong Momentum.”
• Price Action Regime “BULLISH,” Price Action Score = 2, Comment = “Bullish Action.”
• Market Activity State “High,” Comment = “Volatile Market.”
• Weights: Trend 50 %, Momentum 35 %, Price Action 15 %.
• Dominant Category: Trend (because 1.50 > 0.70 > 0.30).
• Overall Score: 2.50, posCount = (three +1s in Trend) + (two +1s in Momentum) + (two +1s in Price Action) = 7 bullish signals, negCount = 0.
• Final Zone = “BULLISH.”
• The trader sees that both Trend and Momentum are reinforcing each other under high volatility. They might wait one more candle for confirmation but already have strong evidence to consider a long.
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Disclaimer
This indicator is strictly a technical analysis tool and does not constitute financial advice. All trading involves risk, including potential loss of capital. Past performance is not indicative of future results. Traders should:
• Always backtest the “Market Zone Analyzer ” on their chosen symbols and timeframes before committing real capital.
• Combine this tool with sound risk management, position sizing, and, if possible, fundamental analysis.
• Understand that no indicator is foolproof; always be prepared for unexpected market moves.
Goodluck
-BullByte!
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Performance Metrics With Bracketed Rebalacing [BackQuant]Performance Metrics With Bracketed Rebalancing
The Performance Metrics With Bracketed Rebalancing script offers a robust method for assessing portfolio performance, integrating advanced portfolio metrics with different rebalancing strategies. With a focus on adaptability, the script allows traders to monitor and adjust portfolio weights, equity, and other key financial metrics dynamically. This script provides a versatile approach for evaluating different trading strategies, considering factors like risk-adjusted returns, volatility, and the impact of portfolio rebalancing.
Please take the time to read the following:
Key Features and Benefits of Portfolio Methods
Bracketed Rebalancing:
Bracketed Rebalancing is an advanced strategy designed to trigger portfolio adjustments when an asset's weight surpasses a predefined threshold. This approach minimizes overexposure to any single asset while maintaining flexibility in response to market changes. The strategy is particularly beneficial for mitigating risks that arise from significant asset weight fluctuations. The following image illustrates how this method reacts when asset weights cross the threshold:
Daily Rebalancing:
Unlike the bracketed method, Daily Rebalancing adjusts portfolio weights every trading day, ensuring consistent asset allocation. This method aims for a more even distribution of portfolio weights, making it a suitable option for traders who prefer less sensitivity to individual asset volatility. Here's an example of Daily Rebalancing in action:
No Rebalancing:
For traders who prefer a passive approach, the "No Rebalancing" option allows the portfolio to remain static, without any adjustments to asset weights. This method may appeal to long-term investors or those who believe in the inherent stability of their selected assets. Here’s how the portfolio looks when no rebalancing is applied:
Portfolio Weights Visualization:
One of the standout features of this script is the visual representation of portfolio weights. With adjustable settings, users can track the current allocation of assets in real-time, making it easier to analyze shifts and trends. The following image shows the real-time weight distribution across three assets:
Rolling Drawdown Plot:
Managing drawdown risk is a critical aspect of portfolio management. The Rolling Drawdown Plot visually tracks the drawdown over time, helping traders monitor the risk exposure and performance relative to the peak equity levels. This feature is essential for assessing the portfolio's resilience during market downturns:
Daily Portfolio Returns:
Tracking daily returns is crucial for evaluating the short-term performance of the portfolio. The script allows users to plot daily portfolio returns to gain insights into daily profit or loss, helping traders stay updated on their portfolio’s progress:
Performance Metrics
Net Profit (%):
This metric represents the total return on investment as a percentage of the initial capital. A positive net profit indicates that the portfolio has gained value over the evaluation period, while a negative value suggests a loss. It's a fundamental indicator of overall portfolio performance.
Maximum Drawdown (Max DD):
Maximum Drawdown measures the largest peak-to-trough decline in portfolio value during a specified period. It quantifies the most significant loss an investor would have experienced if they had invested at the highest point and sold at the lowest point within the timeframe. A smaller Max DD indicates better risk management and less exposure to significant losses.
Annual Mean Returns (% p/y):
This metric calculates the average annual return of the portfolio over the evaluation period. It provides insight into the portfolio's ability to generate returns on an annual basis, aiding in performance comparison with other investment opportunities.
Annual Standard Deviation of Returns (% p/y):
This measure indicates the volatility of the portfolio's returns on an annual basis. A higher standard deviation signifies greater variability in returns, implying higher risk, while a lower value suggests more stable returns.
Variance:
Variance is the square of the standard deviation and provides a measure of the dispersion of returns. It helps in understanding the degree of risk associated with the portfolio's returns.
Sortino Ratio:
The Sortino Ratio is a variation of the Sharpe Ratio that only considers downside risk, focusing on negative volatility. It is calculated as the difference between the portfolio's return and the minimum acceptable return (MAR), divided by the downside deviation. A higher Sortino Ratio indicates better risk-adjusted performance, emphasizing the importance of avoiding negative returns.
Sharpe Ratio:
The Sharpe Ratio measures the portfolio's excess return per unit of total risk, as represented by standard deviation. It is calculated by subtracting the risk-free rate from the portfolio's return and dividing by the standard deviation of the portfolio's excess return. A higher Sharpe Ratio indicates more favorable risk-adjusted returns.
Omega Ratio:
The Omega Ratio evaluates the probability of achieving returns above a certain threshold relative to the probability of experiencing returns below that threshold. It is calculated by dividing the cumulative probability of positive returns by the cumulative probability of negative returns. An Omega Ratio greater than 1 indicates a higher likelihood of achieving favorable returns.
Gain-to-Pain Ratio:
The Gain-to-Pain Ratio measures the return per unit of risk, focusing on the magnitude of gains relative to the severity of losses. It is calculated by dividing the total gains by the total losses experienced during the evaluation period. A higher ratio suggests a more favorable balance between reward and risk.
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Compound Annual Growth Rate (CAGR) (% p/y):
CAGR represents the mean annual growth rate of the portfolio over a specified period, assuming the investment has been compounding over that time. It provides a smoothed annual rate of growth, eliminating the effects of volatility and offering a clearer picture of long-term performance.
Portfolio Alpha (% p/y):
Portfolio Alpha measures the portfolio's performance relative to a benchmark index, adjusting for risk. It is calculated using the Capital Asset Pricing Model (CAPM) and represents the excess return of the portfolio over the expected return based on its beta and the benchmark's performance. A positive alpha indicates outperformance, while a negative alpha suggests underperformance.
Portfolio Beta:
Portfolio Beta assesses the portfolio's sensitivity to market movements, indicating its exposure to systematic risk. A beta greater than 1 suggests the portfolio is more volatile than the market, while a beta less than 1 indicates lower volatility. Beta is used to understand the portfolio's potential for gains or losses in relation to market fluctuations.
Skewness of Returns:
Skewness measures the asymmetry of the return distribution. A positive skew indicates a distribution with a long right tail, suggesting more frequent small losses and fewer large gains. A negative skew indicates a long left tail, implying more frequent small gains and fewer large losses. Understanding skewness helps in assessing the likelihood of extreme outcomes.
Value at Risk (VaR) 95th Percentile:
VaR at the 95th percentile estimates the maximum potential loss over a specified period, given a 95% confidence level. It provides a threshold value such that there is a 95% probability that the portfolio will not experience a loss greater than this amount.
Conditional Value at Risk (CVaR):
CVaR, also known as Expected Shortfall, measures the average loss exceeding the VaR threshold. It provides insight into the tail risk of the portfolio, indicating the expected loss in the worst-case scenarios beyond the VaR level.
These metrics collectively offer a comprehensive view of the portfolio's performance, risk exposure, and efficiency. By analyzing these indicators, investors can make informed decisions, balancing potential returns with acceptable levels of risk.
Conclusion
The Performance Metrics With Bracketed Rebalancing script provides a comprehensive framework for evaluating and optimizing portfolio performance. By integrating advanced metrics, adaptive rebalancing strategies, and visual analytics, it empowers traders to make informed decisions in managing their investment portfolios. However, it's crucial to consider the implications of rebalancing strategies, as academic research indicates that predictable rebalancing can lead to market impact costs. Therefore, adopting flexible and less predictable rebalancing approaches may enhance portfolio performance and reduce associated costs.
DCA Investment Tracker Pro [tradeviZion]DCA Investment Tracker Pro: Educational DCA Analysis Tool
An educational indicator that helps analyze Dollar-Cost Averaging strategies by comparing actual performance with historical data calculations.
---
💡 Why I Created This Indicator
As someone who practices Dollar-Cost Averaging, I was frustrated with constantly switching between spreadsheets, calculators, and charts just to understand how my investments were really performing. I wanted to see everything in one place - my actual performance, what I should expect based on historical data, and most importantly, visualize where my strategy could take me over the long term .
What really motivated me was watching friends and family underestimate the incredible power of consistent investing. When Napoleon Bonaparte first learned about compound interest, he reportedly exclaimed "I wonder it has not swallowed the world" - and he was right! Yet most people can't visualize how their $500 monthly contributions today could become substantial wealth decades later.
Traditional DCA tracking tools exist, but they share similar limitations:
Require manual data entry and complex spreadsheets
Use fixed assumptions that don't reflect real market behavior
Can't show future projections overlaid on actual price charts
Lose the visual context of what's happening in the market
Make compound growth feel abstract rather than tangible
I wanted to create something different - a tool that automatically analyzes real market history, detects volatility periods, and shows you both current performance AND educational projections based on historical patterns right on your TradingView charts. As Warren Buffett said: "Someone's sitting in the shade today because someone planted a tree a long time ago." This tool helps you visualize your financial tree growing over time.
This isn't just another calculator - it's a visualization tool that makes the magic of compound growth impossible to ignore.
---
🎯 What This Indicator Does
This educational indicator provides DCA analysis tools. Users can input investment scenarios to study:
Theoretical Performance: Educational calculations based on historical return data
Comparative Analysis: Study differences between actual and theoretical scenarios
Historical Projections: Theoretical projections for educational analysis (not predictions)
Performance Metrics: CAGR, ROI, and other analytical metrics for study
Historical Analysis: Calculates historical return data for reference purposes
---
🚀 Key Features
Volatility-Adjusted Historical Return Calculation
Analyzes 3-20 years of actual price data for any symbol
Automatically detects high-volatility stocks (meme stocks, growth stocks)
Uses median returns for volatile stocks, standard CAGR for stable stocks
Provides conservative estimates when extreme outlier years are detected
Smart fallback to manual percentages when data insufficient
Customizable Performance Dashboard
Educational DCA performance analysis with compound growth calculations
Customizable table sizing (Tiny to Huge text options)
9 positioning options (Top/Middle/Bottom + Left/Center/Right)
Theme-adaptive colors (automatically adjusts to dark/light mode)
Multiple display layout options
Future Projection System
Visual future growth projections
Timeframe-aware calculations (Daily/Weekly/Monthly charts)
1-30 year projection options
Shows projected portfolio value and total investment amounts
Investment Insights
Performance vs benchmark comparison
ROI from initial investment tracking
Monthly average return analysis
Investment milestone alerts (25%, 50%, 100% gains)
Contribution tracking and next milestone indicators
---
📊 Step-by-Step Setup Guide
1. Investment Settings 💰
Initial Investment: Enter your starting lump sum (e.g., $60,000)
Monthly Contribution: Set your regular DCA amount (e.g., $500/month)
Return Calculation: Choose "Auto (Stock History)" for real data or "Manual" for fixed %
Historical Period: Select 3-20 years for auto calculations (default: 10 years)
Start Year: When you began investing (e.g., 2020)
Current Portfolio Value: Your actual portfolio worth today (e.g., $150,000)
2. Display Settings 📊
Table Sizes: Choose from Tiny, Small, Normal, Large, or Huge
Table Positions: 9 options - Top/Middle/Bottom + Left/Center/Right
Visibility Toggles: Show/hide Main Table and Stats Table independently
3. Future Projection 🔮
Enable Projections: Toggle on to see future growth visualization
Projection Years: Set 1-30 years ahead for analysis
Live Example - NASDAQ:META Analysis:
Settings shown: $60K initial + $500/month + Auto calculation + 10-year history + 2020 start + $150K current value
---
🔬 Pine Script Code Examples
Core DCA Calculations:
// Calculate total invested over time
months_elapsed = (year - start_year) * 12 + month - 1
total_invested = initial_investment + (monthly_contribution * months_elapsed)
// Compound growth formula for initial investment
theoretical_initial_growth = initial_investment * math.pow(1 + annual_return, years_elapsed)
// Future Value of Annuity for monthly contributions
monthly_rate = annual_return / 12
fv_contributions = monthly_contribution * ((math.pow(1 + monthly_rate, months_elapsed) - 1) / monthly_rate)
// Total expected value
theoretical_total = theoretical_initial_growth + fv_contributions
Volatility Detection Logic:
// Detect extreme years for volatility adjustment
extreme_years = 0
for i = 1 to historical_years
yearly_return = ((price_current / price_i_years_ago) - 1) * 100
if yearly_return > 100 or yearly_return < -50
extreme_years += 1
// Use median approach for high volatility stocks
high_volatility = (extreme_years / historical_years) > 0.2
calculated_return = high_volatility ? median_of_returns : standard_cagr
Performance Metrics:
// Calculate key performance indicators
absolute_gain = actual_value - total_invested
total_return_pct = (absolute_gain / total_invested) * 100
roi_initial = ((actual_value - initial_investment) / initial_investment) * 100
cagr = (math.pow(actual_value / initial_investment, 1 / years_elapsed) - 1) * 100
---
📊 Real-World Examples
See the indicator in action across different investment types:
Stable Index Investments:
AMEX:SPY (SPDR S&P 500) - Shows steady compound growth with standard CAGR calculations
Classic DCA success story: $60K initial + $500/month starting 2020. The indicator shows SPY's historical 10%+ returns, demonstrating how consistent broad market investing builds wealth over time. Notice the smooth theoretical growth line vs actual performance tracking.
MIL:VUAA (Vanguard S&P 500 UCITS) - Shows both data limitation and solution approaches
Data limitation example: VUAA shows "Manual (Auto Failed)" and "No Data" when default 10-year historical setting exceeds available data. The indicator gracefully falls back to manual percentage input while maintaining all DCA calculations and projections.
MIL:VUAA (Vanguard S&P 500 UCITS) - European ETF with successful 5-year auto calculation
Solution demonstration: By adjusting historical period to 5 years (matching available data), VUAA auto calculation works perfectly. Shows how users can optimize settings for newer assets. European market exposure with EUR denomination, demonstrating DCA effectiveness across different markets and currencies.
NYSE:BRK.B (Berkshire Hathaway) - Quality value investment with Warren Buffett's proven track record
Value investing approach: Berkshire Hathaway's legendary performance through DCA lens. The indicator demonstrates how quality companies compound wealth over decades. Lower volatility than tech stocks = standard CAGR calculations used.
High-Volatility Growth Stocks:
NASDAQ:NVDA (NVIDIA Corporation) - Demonstrates volatility-adjusted calculations for extreme price swings
High-volatility example: NVIDIA's explosive AI boom creates extreme years that trigger volatility detection. The indicator automatically switches to "Median (High Vol): 50%" calculations for conservative projections, protecting against unrealistic future estimates based on outlier performance periods.
NASDAQ:TSLA (Tesla) - Shows how 10-year analysis can stabilize volatile tech stocks
Stable long-term growth: Despite Tesla's reputation for volatility, the 10-year historical analysis (34.8% CAGR) shows consistent enough performance that volatility detection doesn't trigger. Demonstrates how longer timeframes can smooth out extreme periods for more reliable projections.
NASDAQ:META (Meta Platforms) - Shows stable tech stock analysis using standard CAGR calculations
Tech stock with stable growth: Despite being a tech stock and experiencing the 2022 crash, META's 10-year history shows consistent enough performance (23.98% CAGR) that volatility detection doesn't trigger. The indicator uses standard CAGR calculations, demonstrating how not all tech stocks require conservative median adjustments.
Notice how the indicator automatically detects high-volatility periods and switches to median-based calculations for more conservative projections, while stable investments use standard CAGR methods.
---
📈 Performance Metrics Explained
Current Portfolio Value: Your actual investment worth today
Expected Value: What you should have based on historical returns (Auto) or your target return (Manual)
Total Invested: Your actual money invested (initial + all monthly contributions)
Total Gains/Loss: Absolute dollar difference between current value and total invested
Total Return %: Percentage gain/loss on your total invested amount
ROI from Initial Investment: How your starting lump sum has performed
CAGR: Compound Annual Growth Rate of your initial investment (Note: This shows initial investment performance, not full DCA strategy)
vs Benchmark: How you're performing compared to the expected returns
---
⚠️ Important Notes & Limitations
Data Requirements: Auto mode requires sufficient historical data (minimum 3 years recommended)
CAGR Limitation: CAGR calculation is based on initial investment growth only, not the complete DCA strategy
Projection Accuracy: Future projections are theoretical and based on historical returns - actual results may vary
Timeframe Support: Works ONLY on Daily (1D), Weekly (1W), and Monthly (1M) charts - no other timeframes supported
Update Frequency: Update "Current Portfolio Value" regularly for accurate tracking
---
📚 Educational Use & Disclaimer
This analysis tool can be applied to various stock and ETF charts for educational study of DCA mathematical concepts and historical performance patterns.
Study Examples: Can be used with symbols like AMEX:SPY , NASDAQ:QQQ , AMEX:VTI , NASDAQ:AAPL , NASDAQ:MSFT , NASDAQ:GOOGL , NASDAQ:AMZN , NASDAQ:TSLA , NASDAQ:NVDA for learning purposes.
EDUCATIONAL DISCLAIMER: This indicator is a study tool for analyzing Dollar-Cost Averaging strategies. It does not provide investment advice, trading signals, or guarantees. All calculations are theoretical examples for educational purposes only. Past performance does not predict future results. Users should conduct their own research and consult qualified financial professionals before making any investment decisions.
---
© 2025 TradeVizion. All rights reserved.
QG-Particle OscillatorThis is an advanced oscillator based on auxiliary particle filter. It separates signal from noise and uses smoothing algorithm similar to JMA.
The main oscillator line is a smoothed and detrended version of the price series similar to detrended oscillator line. The purple/aqua lines are a prediction based on an additional adaptive smoothing technique and current volatility.
The prediction is smoothed twice and is supposed to represent the true signal without any noise, thus the prediction should always be less than the raw detrend line. However, certain volatile conditions will cause the prediction to cross above/below the detrend line. When this happens the likelihood of a reversal or pullback is extremely high.
There are 3 dots on the zero line- Red, Green and Yellow. The yellow dots warn of an eminent pullback 2 bars before it actually occurs. This is a non-repainting indicator.
One can also use this indicator to trade CCI signals, similar to zero line rejection in existing trend.
The indicator has 2 settings- Period and Phase. The phase represents cycle phase and Period represents oscillator period.
Credits: This indicator has been originally published for Ninjatrader and this is conversion into pinescript.
3 Bar Reversal3 Bar Reversal
This pattern is described in John Carter's "Mastering the Trade"
The 3 Bar Reversal indicator is a simple but effective price action tool designed to highlight potential short-term reversals in market direction. It monitors consecutive bar behavior and identifies turning points based on a three-bar pattern. This tool can assist traders in spotting trend exhaustion or early signs of a reversal, particularly in scalping or short-term trading strategies.
How It Works
This indicator analyzes the relationship between consecutive bar closes:
It counts how many bars have passed since the price closed higher than the previous close (barssince(close >= close )) — referred to as an "up streak".
It also counts how many bars have passed since the price closed lower than the previous close (barssince(close <= close )) — known as a "down streak".
A reversal condition is met when:
There have been exactly 3 bars in a row moving in one direction (up or down), and
The 4th bar closes in the opposite direction.
When this condition is detected, the script performs two actions:
Plots a triangle on the chart to signal the potential reversal:
A green triangle below the bar for a possible long (buy) opportunity.
A red triangle above the bar for a possible short (sell) opportunity.
Triggers an alert condition so users can set notifications for when a reversal is detected.
Interpretation
Long Signal: The market has printed 3 consecutive lower closes, followed by a higher close — suggesting bullish momentum may be emerging.
Short Signal: The market has printed 3 consecutive higher closes, followed by a lower close — indicating possible bearish momentum.
These patterns are common in market retracements and can act as confirmation signals when used with other indicators such as RSI, MACD, support/resistance, or volume analysis.
Usage Examples
Scalping: Use the reversal signal to quickly enter short-term trades after a short-term exhaustion move.
Swing Trading: Combine this with trend indicators (e.g., moving averages) to time pullbacks within larger trends.
Confirmation Tool: Use this indicator alongside candlestick patterns or support/resistance zones to validate entry or exit points.
Alert Setup: Enable alerts based on the built-in alertcondition to receive instant notifications for potential trade setups.
Limitations
The 3-bar reversal logic does not guarantee a trend change; it signals potential reversals, which may need confirmation.
Best used in conjunction with broader context such as trend direction, market structure, or other technical indicators.
MirPapa:ICT:HTF: FVG OB Threeple# MirPapa:ICT:HTF: FVG OB (Fair Value Gap Order Block)
**Version:** Pine Script® v6
**Author:** © goodia
**License:** MPL-2.0 (Mozilla Public License 2.0)
---
## Overview
“FVG OB” (Fair Value Gap Order Block) identifies higher-timeframe candle ranges where a gap (imbalance) exists between two non-consecutive candles, signaling potential institutional order blocks. This module draws bullish or bearish FVG OB boxes on your lower-timeframe chart, extends them until price interacts a specified number of times, and then finalizes (recolors) the box.
---
## Inputs
- **Enable FVG OB Boxes** (`bool`)
Toggle drawing of HTF FVG OB boxes on the chart.
- **Enable FVG OB Midlines** (`bool`)
Toggle drawing of a midpoint line inside each FVG OB box.
- **FVG OB Close Count** (`int` 1–10)
Number of HTF closes beyond the FVG range required to finalize (recolor) the box.
- **FVG OB Bull Color** (`color`)
Fill & border color for bullish FVG OB boxes.
- **FVG OB Bear Color** (`color`)
Fill & border color for bearish FVG OB boxes.
- **FVG OB Box Transparency** (`int` 1–100)
Opacity level for FVG OB box fills (higher = more transparent).
---
## How It Works
1. **HTF Data Retrieval**
- The script uses `request.security()` (via `GetHTFrevised()`) to fetch HTF OHLC and historical values:
- `_htfHigh3` (high three bars ago) and `_htfLow1` (low one bar ago) for bullish FVG OB.
- `_htfLow3` (low three bars ago) and `_htfHigh1` (high one bar ago) for bearish FVG OB.
- It also tracks the HTF `bar_index` on the lower timeframe to align drawing.
2. **FVG OB Detection**
- **Bullish FVG OB**: Occurs when the HTF low of the previous bar (`low `) is strictly above the HTF high of three bars ago (`high `), creating a gap.
- **Bearish FVG OB**: Occurs when the HTF high of the previous bar (`high `) is strictly below the HTF low of three bars ago (`low `), creating a gap.
3. **Box Creation**
- On each new HTF bar (`ta.change(time(HTF)) != 0`), if a bullish or bearish FVG OB condition is met, the script calls `CreateBoxData()` with:
- **Bullish**: `bottom = HTF low `, `top = HTF high `, `_isBull = true`.
- **Bearish**: `bottom = HTF low `, `top = HTF high `, `_isBull = false`.
- Midline toggled by input.
- A `BoxData` struct is created and stored in either the Bull or Bear array.
4. **Box Extension & Finalization**
- On **every LTF bar**, `ProcessBoxDatas(...)` iterates over all active FVG OB boxes:
1. **Extend Right Edge**: `box.set_right(bar_index)` ensures the box follows the latest bar.
2. **Record Volume Delta**: Tracks buy/sell volume inside the box.
3. **Touch Stage Update**: `modBoxUpdateStage()` increments `_stage` when price touches its “basePoint” (for FVG OB, the basePrice is one side of the gap).
4. **Finalize**: `setBoxFinalize()` checks if the configured number of closes beyond the FVG gap (`FVG OB Close Count`) has occurred. If so:
- `_isActive := false`
- Border and background colors are changed to the “Box Close Color” (input).
- Finalized boxes remain on screen semi-transparent, indicating that the FVG OB zone has been tested.
5. **Midline (Optional)**
- If “Enable FVG OB Midlines” is checked, `ProcessBoxDatas()` also extends a horizontal midpoint line inside the box with `line.set_x2(bar_index)`.
---
## Usage Instructions
1. **Installation**
- Copy the FVG OB section of the Pine Script into TradingView’s Pine Editor (ensure the library import is included).
- Click “Add to Chart.”
2. **Configure Inputs**
- Choose a Higher Time Frame via the dropdown (e.g., “4시간” maps to a 4H timeframe).
- Toggle “Enable FVG OB Boxes” and “Enable FVG OB Midlines.”
- Select colors for bullish and bearish boxes and set transparency.
- Adjust “FVG OB Close Count” to control how many closes beyond the gap finalize the box.
3. **Interpretation**
- **Active FVG OB Boxes** extend to the right until price closes beyond the gap range the specified number of times.
- When finalized, each box changes to the “Box Close Color,” signaling that institutional orders in that gap have likely been filled.
Enjoy precise visualization of higher-timeframe Fair Value Gap Order Blocks on your lower-timeframe chart!
Uptrick: Z-Trend BandsOverview
Uptrick: Z-Trend Bands is a Pine Script overlay crafted to capture high-probability mean-reversion opportunities. It dynamically plots upper and lower statistical bands around an EMA baseline by converting price deviations into z-scores. Once price moves outside these bands and then reenters, the indicator verifies that momentum is genuinely reversing via an EMA-smoothed RSI slope. Signal memory ensures only one entry per momentum swing, and traders receive clear, real-time feedback through customizable bar-coloring modes, a semi-transparent fill highlighting the statistical zone, concise “Up”/“Down” labels, and a live five-metric scoring table.
Introduction
Markets often oscillate between trending and reverting, and simple thresholds or static envelopes frequently misfire when volatility shifts. Standard deviation quantifies how “wide” recent price moves have been, and a z-score transforms each deviation into a measure of how rare it is relative to its own history. By anchoring these bands to an exponential moving average, the script maintains a fluid statistical envelope that adapts instantly to both calm and turbulent regimes. Meanwhile, the Relative Strength Index (RSI) tracks momentum; smoothing RSI with an EMA and observing its slope filters out erratic spikes, ensuring that only genuine momentum flips—upward for longs and downward for shorts—qualify.
Purpose
This indicator is purpose-built for short-term mean-reversion traders operating on lower–timeframe charts. It reveals when price has strayed into the outer 5 percent of its recent range, signaling an increased likelihood of a bounce back toward fair value. Rather than firing on price alone, it demands that momentum follow suit: the smoothed RSI slope must flip in the opposite direction before any trade marker appears. This dual-filter approach dramatically reduces noise-driven, false setups. Traders then see immediate visual confirmation—bar colors that reflect the latest signal and age over time, clear entry labels, and an always-visible table of metric scores—so they can gauge both the validity and freshness of each signal at a glance.
Originality and Uniqueness
Uptrick: Z-Trend Bands stands apart from typical envelope or oscillator tools in four key ways. First, it employs fully normalized z-score bands, meaning ±2 always captures roughly the top and bottom 5 percent of moves, regardless of volatility regime. Second, it insists on two simultaneous conditions—price reentry into the bands and a confirming RSI slope flip—dramatically reducing whipsaw signals. Third, it uses slope-phase memory to lock out duplicate signals until momentum truly reverses again, enforcing disciplined entries. Finally, it offers four distinct bar-coloring schemes (solid reversal, fading reversal, exceeding bands, and classic heatmap) plus a dynamic scoring table, rather than a single, opaque alert, giving traders deep insight into every layer of analysis.
Why Each Component Was Picked
The EMA baseline was chosen for its blend of responsiveness—weighting recent price heavily—and smoothness, which filters market noise. Z-score deviation bands standardize price extremes relative to their own history, adapting automatically to shifting volatility so that “extreme” always means statistically rare. The RSI, smoothed with an EMA before slope calculation, captures true momentum shifts without the false spikes that raw RSI often produces. Slope-phase memory flags prevent repeated alerts within a single swing, curbing over-trading in choppy conditions. Bar-coloring modes provide flexible visual contexts—whether you prefer to track the latest reversal, see signal age, highlight every breakout, or view a continuous gradient—and the scoring table breaks down all five core checks for complete transparency.
Features
This indicator offers a suite of configurable visual and logical tools designed to make reversal signals both robust and transparent:
Dynamic z-score bands that expand or contract in real time to reflect current volatility regimes, ensuring the outer ±zThreshold levels always represent statistically rare extremes.
A smooth EMA baseline that weights recent price more heavily, serving as a fair-value anchor around which deviations are measured.
EMA-smoothed RSI slope confirmation, which filters out erratic momentum spikes by first smoothing raw RSI and then requiring its bar-to-bar slope to flip before any signal is allowed.
Slope-phase memory logic that locks out duplicate buy or sell markers until the RSI slope crosses back through zero, preventing over-trading during choppy swings.
Four distinct bar-coloring modes—Reversal Solid, Reversal Fade, Exceeding Bands, Classic Heat—plus a “None” option, so traders can choose whether to highlight the latest signal, show signal age, emphasize breakout bars, or view a continuous heat gradient within the bands.
A semi-transparent fill between the EMA and the upper/lower bands that visually frames the statistical zone and makes extremes immediately obvious.
Concise “Up” and “Down” labels that plot exactly when price re-enters a band with confirming momentum, keeping chart clutter to a minimum.
A real-time, five-metric scoring table (z-score, RSI slope, price vs. EMA, trend state, re-entry) that updates every two bars, displaying individual +1/–1/0 scores and an averaged Buy/Sell/Neutral verdict for complete transparency.
Calculations
Compute the fair-value EMA over fairLen bars.
Subtract that EMA from current price each bar to derive the raw deviation.
Over zLen bars, calculate the rolling mean and standard deviation of those deviations.
Convert each deviation into a z-score by subtracting the mean and dividing by the standard deviation.
Plot the upper and lower bands at ±zThreshold × standard deviation around the EMA.
Calculate raw RSI over rsiLen bars, then smooth it with an EMA of length rsiEmaLen.
Derive the RSI slope by taking the difference between the current and previous smoothed RSI.
Detect a potential reentry when price exits one of the bands on the prior bar and re-enters on the current bar.
Require that reentry coincide with an RSI slope flip (positive for a lower-band reentry, negative for an upper-band reentry).
On first valid reentry per momentum swing, fire a buy or sell signal and set a memory flag; reset that flag only when the RSI slope crosses back through zero.
For each bar, assign scores of +1, –1, or 0 for the z-score direction, RSI slope, price vs. EMA, trend-state, and reentry status.
Average those five scores; if the result exceeds +0.1, label “Buy,” if below –0.1, label “Sell,” otherwise “Neutral.”
Update bar colors, the semi-transparent fill, reversal labels, and the scoring table every two bars to reflect the latest calculations.
How It Actually Works
On each new candle, the EMA baseline and band widths update to reflect current volatility. The RSI is smoothed and its slope recalculated. The script then looks back one bar to see if price exited either band and forward to see if it reentered. If that reentry coincides with an appropriate RSI slope flip—and no signal has yet been generated in that swing—a concise label appears. Bar colors refresh according to your selected mode, and the scoring table updates to show which of the five conditions passed or failed, along with the overall verdict. This process repeats seamlessly at each bar, giving traders a continuous feed of disciplined, statistically filtered reversal cues.
Inputs
All parameters are fully user-configurable, allowing you to tailor sensitivity, lookbacks, and visuals to your trading style:
EMA length (fairLen): number of bars for the fair-value EMA; higher values smooth more but lag further behind price.
Z-Score lookback (zLen): window for calculating the mean and standard deviation of price deviations; longer lookbacks reduce noise but respond more slowly to new volatility.
Z-Score threshold (zThreshold): number of standard deviations defining the upper and lower bands; common default is 2.0 for roughly the outer 5 percent of moves.
Source (src): choice of price series (close, hl2, etc.) used for EMA, deviation, and RSI calculations.
RSI length (rsiLen): period for raw RSI calculation; shorter values react faster to momentum changes but can be choppier.
RSI EMA length (rsiEmaLen): period for smoothing raw RSI before taking its slope; higher values filter more noise.
Bar coloring mode (colorMode): select from None, Reversal Solid, Reversal Fade, Exceeding Bands, or Classic Heat to control how bars are shaded in relation to signals and band positions.
Show signals (showSignals): toggle on-chart “Up” and “Down” labels for reversal entries.
Show scoring table (enableTable): toggle the display of the five-metric breakdown table.
Table position (tablePos): choose which corner (Top Left, Top Right, Bottom Left, Bottom Right) hosts the scoring table.
Conclusion
By merging a normalized z-score framework, momentum slope confirmation, disciplined signal memory, flexible visuals, and transparent scoring into one Pine Script overlay, Uptrick: Z-Trend Bands offers a powerful yet intuitive tool for intraday mean-reversion trading. Its adaptability to real-time volatility and multi-layered filter logic deliver clear, high-confidence reversal cues without the clutter or confusion of simpler indicators.
Disclaimer
This indicator is provided solely for educational and informational purposes. It does not constitute financial advice. Trading involves substantial risk and may not be suitable for all investors. Past performance is not indicative of future results. Always conduct your own testing and apply careful risk management before trading live.
FvgTypes█ OVERVIEW
This library serves as a foundational module for Pine Script™ projects focused on Fair Value Gaps (FVGs). Its primary purpose is to define and centralize custom data structures (User-Defined Types - UDTs) and enumerations that are utilized across various components of an FVG analysis system. By providing standardized types for FVG characteristics and drawing configurations, it promotes code consistency, readability, and easier maintenance within a larger FVG indicator or strategy.
█ CONCEPTS
The library introduces several key data structures (User-Defined Types - UDTs) and an enumeration to organize Fair Value Gap (FVG) related data logically. These types are central to the functioning of FVG analysis tools built upon this library.
Timeframe Categorization (`tfType` Enum)
To manage and differentiate FVGs based on their timeframe of origin, the `tfType` enumeration is defined. It includes:
`LTF`: Low Timeframe (typically the current chart).
`MTF`: Medium Timeframe.
`HTF`: High Timeframe.
This allows for distinct logic and visual settings to be applied depending on the FVG's source timeframe.
FVG Data Encapsulation (`fvgObject` UDT)
The `fvgObject` is a comprehensive UDT designed to encapsulate all pertinent information and state for an individual Fair Value Gap throughout its lifecycle. Instead of listing every field, its conceptual structure can be understood as holding:
Core Definition: The FVG's fundamental price levels (top, bottom) and its formation time (`startTime`).
Classification Attributes: Characteristics such as its direction (`isBullish`) and whether it qualifies as a Large Volume FVG (`isLV`), along with its originating timeframe category (`tfType`).
Lifecycle State: Current status indicators including full mitigation (`isMitigated`, `mitigationTime`), partial fill levels (`currentTop`, `currentBottom`), midline interaction (`isMidlineTouched`), and overall visibility (`isVisible`).
Drawing Identifiers: References (`boxId`, `midLineId`, `mitLineLabelId`, etc.) to the actual graphical objects drawn on the chart to represent the FVG and its components.
Optimization Cache: Previous-bar state values (`prevIsMitigated`, `prevCurrentTop`, etc.) crucial for optimizing drawing updates by avoiding redundant operations.
This comprehensive structure facilitates easy access to all FVG-related information through a single object, reducing code complexity and improving manageability.
Drawing Configuration (`drawSettings` UDT)
The `drawSettings` UDT centralizes all user-configurable parameters that dictate the visual appearance of FVGs across different timeframes. It's typically populated from script inputs and conceptually groups settings for:
General Behavior: Global FVG classification toggles (e.g., `shouldClassifyLV`) and general display rules (e.g., `shouldHideMitigated`).
FVG Type Specific Colors: Colors for standard and Large Volume FVGs, both active and mitigated (e.g., `lvBullColor`, `mitigatedBearBoxColor`).
Timeframe-Specific Visuals (LTF, MTF, HTF): Detailed parameters for each timeframe category, covering FVG boxes (visibility, colors, extension, borders, labels), midlines (visibility, style, color), and mitigation lines (visibility, style, color, labels, persistence after mitigation).
Contextual Information: The current bar's time (`currentTime`) for accurate positioning of time-dependent drawing elements and timeframe display strings (`tfString`, `mtfTfString`, `htfTfString`).
This centralized approach allows for extensive customization of FVG visuals and simplifies the management of drawing parameters within the main script. Such centralization also enhances the maintainability of the visual aspects of the FVG system.
█ NOTES
User-Defined Types (UDTs): This library extensively uses UDTs (`fvgObject`, `drawSettings`) to group related data. This improves code organization and makes it easier to pass complex data between functions and libraries.
Mutability and Reference Behavior of UDTs: When UDT instances are passed to functions or methods in other libraries (like `fvgObjectLib`), those functions might modify the fields of the passed object if they are not explicitly designed to return new instances. This is because UDTs are passed by reference and are mutable in Pine Script™. Users should be aware of this standard behavior to prevent unintended side effects.
Optimization Fields: The `prev_*` fields in `fvgObject` are crucial for performance optimization in the drawing logic. They help avoid unnecessary redrawing of FVG elements if their state or relevant settings haven't changed.
No Direct Drawing Logic: `FvgTypes` itself does not contain any drawing logic. It solely defines the data structures. The actual drawing and manipulation of these objects are handled by other libraries (e.g., `fvgObjectLib`).
Centralized Definitions: By defining these types in a separate library, any changes to the structure of FVG data or settings can be made in one place, ensuring consistency across all dependent scripts and libraries.
█ EXPORTED TYPES
fvgObject
fvgObject Represents a Fair Value Gap (FVG) object.
Fields:
top (series float) : The top price level of the FVG.
bottom (series float) : The bottom price level of the FVG.
startTime (series int) : The start time (timestamp) of the bar where the FVG formed.
isBullish (series bool) : Indicates if the FVG is bullish (true) or bearish (false).
isLV (series bool) : Indicates if the FVG is a Large Volume FVG.
tfType (series tfType) : The timeframe type (LTF, MTF, HTF) to which this FVG belongs.
isMitigated (series bool) : Indicates if the FVG has been fully mitigated.
mitigationTime (series int) : The time (timestamp) when the FVG was mitigated.
isVisible (series bool) : The current visibility status of the FVG, typically managed by drawing logic based on filters.
isMidlineTouched (series bool) : Indicates if the price has touched the FVG's midline (50% level).
currentTop (series float) : The current top level of the FVG after partial fills.
currentBottom (series float) : The current bottom level of the FVG after partial fills.
boxId (series box) : The drawing ID for the main FVG box.
mitigatedBoxId (series box) : The drawing ID for the box representing the partially filled (mitigated) area.
midLineId (series line) : The drawing ID for the FVG's midline.
mitLineId (series line) : The drawing ID for the FVG's mitigation line.
boxLabelId (series label) : The drawing ID for the FVG box label.
mitLineLabelId (series label) : The drawing ID for the mitigation line label.
testedBoxId (series box) : The drawing ID for the box of a fully mitigated (tested) FVG, if kept visible.
keptMitLineId (series line) : The drawing ID for a mitigation line that is kept after full mitigation.
prevIsMitigated (series bool) : Stores the isMitigated state from the previous bar for optimization.
prevCurrentTop (series float) : Stores the currentTop value from the previous bar for optimization.
prevCurrentBottom (series float) : Stores the currentBottom value from the previous bar for optimization.
prevIsVisible (series bool) : Stores the visibility status from the previous bar for optimization (derived from isVisibleNow passed to updateDrawings).
prevIsMidlineTouched (series bool) : Stores the isMidlineTouched status from the previous bar for optimization.
drawSettings
drawSettings A structure containing settings for drawing FVGs.
Fields:
shouldClassifyLV (series bool) : Whether to classify FVGs as Large Volume (LV) based on ATR.
shouldHideMitigated (series bool) : Whether to hide FVG boxes once they are fully mitigated.
currentTime (series int) : The current bar's time, used for extending drawings.
lvBullColor (series color) : Color for Large Volume Bullish FVGs.
mitigatedLvBullColor (series color) : Color for mitigated Large Volume Bullish FVGs.
lvBearColor (series color) : Color for Large Volume Bearish FVGs.
mitigatedLvBearColor (series color) : Color for mitigated Large Volume Bearish FVGs.
shouldShowBoxes (series bool) : Whether to show FVG boxes for the LTF.
bullBoxColor (series color) : Color for LTF Bullish FVG boxes.
mitigatedBullBoxColor (series color) : Color for mitigated LTF Bullish FVG boxes.
bearBoxColor (series color) : Color for LTF Bearish FVG boxes.
mitigatedBearBoxColor (series color) : Color for mitigated LTF Bearish FVG boxes.
boxLengthBars (series int) : Length of LTF FVG boxes in bars (if not extended).
shouldExtendBoxes (series bool) : Whether to extend LTF FVG boxes to the right.
shouldShowCurrentTfBoxLabels (series bool) : Whether to show labels on LTF FVG boxes.
shouldShowBoxBorder (series bool) : Whether to show a border for LTF FVG boxes.
boxBorderWidth (series int) : Border width for LTF FVG boxes.
boxBorderStyle (series string) : Border style for LTF FVG boxes (e.g., line.style_solid).
boxBorderColor (series color) : Border color for LTF FVG boxes.
shouldShowMidpoint (series bool) : Whether to show the midline (50% level) for LTF FVGs.
midLineWidthInput (series int) : Width of the LTF FVG midline.
midpointLineStyleInput (series string) : Style of the LTF FVG midline.
midpointColorInput (series color) : Color of the LTF FVG midline.
shouldShowMitigationLine (series bool) : Whether to show the mitigation line for LTF FVGs.
(Line always extends if shown)
mitLineWidthInput (series int) : Width of the LTF FVG mitigation line.
mitigationLineStyleInput (series string) : Style of the LTF FVG mitigation line.
mitigationLineColorInput (series color) : Color of the LTF FVG mitigation line.
shouldShowCurrentTfMitLineLabels (series bool) : Whether to show labels on LTF FVG mitigation lines.
currentTfMitLineLabelOffsetX (series float) : The horizontal offset value for the LTF mitigation line's label.
shouldKeepMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated LTF FVGs.
mitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated LTF FVGs.
tfString (series string) : Display string for the LTF (e.g., "Current TF").
shouldShowMtfBoxes (series bool) : Whether to show FVG boxes for the MTF.
mtfBullBoxColor (series color) : Color for MTF Bullish FVG boxes.
mtfMitigatedBullBoxColor (series color) : Color for mitigated MTF Bullish FVG boxes.
mtfBearBoxColor (series color) : Color for MTF Bearish FVG boxes.
mtfMitigatedBearBoxColor (series color) : Color for mitigated MTF Bearish FVG boxes.
mtfBoxLengthBars (series int) : Length of MTF FVG boxes in bars (if not extended).
shouldExtendMtfBoxes (series bool) : Whether to extend MTF FVG boxes to the right.
shouldShowMtfBoxLabels (series bool) : Whether to show labels on MTF FVG boxes.
shouldShowMtfBoxBorder (series bool) : Whether to show a border for MTF FVG boxes.
mtfBoxBorderWidth (series int) : Border width for MTF FVG boxes.
mtfBoxBorderStyle (series string) : Border style for MTF FVG boxes.
mtfBoxBorderColor (series color) : Border color for MTF FVG boxes.
shouldShowMtfMidpoint (series bool) : Whether to show the midline for MTF FVGs.
mtfMidLineWidthInput (series int) : Width of the MTF FVG midline.
mtfMidpointLineStyleInput (series string) : Style of the MTF FVG midline.
mtfMidpointColorInput (series color) : Color of the MTF FVG midline.
shouldShowMtfMitigationLine (series bool) : Whether to show the mitigation line for MTF FVGs.
(Line always extends if shown)
mtfMitLineWidthInput (series int) : Width of the MTF FVG mitigation line.
mtfMitigationLineStyleInput (series string) : Style of the MTF FVG mitigation line.
mtfMitigationLineColorInput (series color) : Color of the MTF FVG mitigation line.
shouldShowMtfMitLineLabels (series bool) : Whether to show labels on MTF FVG mitigation lines.
mtfMitLineLabelOffsetX (series float) : The horizontal offset value for the MTF mitigation line's label.
shouldKeepMtfMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated MTF FVGs.
mtfMitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated MTF FVGs.
mtfTfString (series string) : Display string for the MTF (e.g., "MTF").
shouldShowHtfBoxes (series bool) : Whether to show FVG boxes for the HTF.
htfBullBoxColor (series color) : Color for HTF Bullish FVG boxes.
htfMitigatedBullBoxColor (series color) : Color for mitigated HTF Bullish FVG boxes.
htfBearBoxColor (series color) : Color for HTF Bearish FVG boxes.
htfMitigatedBearBoxColor (series color) : Color for mitigated HTF Bearish FVG boxes.
htfBoxLengthBars (series int) : Length of HTF FVG boxes in bars (if not extended).
shouldExtendHtfBoxes (series bool) : Whether to extend HTF FVG boxes to the right.
shouldShowHtfBoxLabels (series bool) : Whether to show labels on HTF FVG boxes.
shouldShowHtfBoxBorder (series bool) : Whether to show a border for HTF FVG boxes.
htfBoxBorderWidth (series int) : Border width for HTF FVG boxes.
htfBoxBorderStyle (series string) : Border style for HTF FVG boxes.
htfBoxBorderColor (series color) : Border color for HTF FVG boxes.
shouldShowHtfMidpoint (series bool) : Whether to show the midline for HTF FVGs.
htfMidLineWidthInput (series int) : Width of the HTF FVG midline.
htfMidpointLineStyleInput (series string) : Style of the HTF FVG midline.
htfMidpointColorInput (series color) : Color of the HTF FVG midline.
shouldShowHtfMitigationLine (series bool) : Whether to show the mitigation line for HTF FVGs.
(Line always extends if shown)
htfMitLineWidthInput (series int) : Width of the HTF FVG mitigation line.
htfMitigationLineStyleInput (series string) : Style of the HTF FVG mitigation line.
htfMitigationLineColorInput (series color) : Color of the HTF FVG mitigation line.
shouldShowHtfMitLineLabels (series bool) : Whether to show labels on HTF FVG mitigation lines.
htfMitLineLabelOffsetX (series float) : The horizontal offset value for the HTF mitigation line's label.
shouldKeepHtfMitigatedLines (series bool) : Whether to keep showing mitigation lines of fully mitigated HTF FVGs.
htfMitigatedMitLineColor (series color) : Color for kept mitigation lines of mitigated HTF FVGs.
htfTfString (series string) : Display string for the HTF (e.g., "HTF").
MVRV Ratio [Alpha Extract]The MVRV Ratio Indicator provides valuable insights into Bitcoin market cycles by tracking the relationship between market value and realized value. This powerful on-chain metric helps traders identify potential market tops and bottoms, offering clear buy and sell signals based on historical patterns of Bitcoin valuation.
🔶 CALCULATION The indicator processes MVRV ratio data through several analytical methods:
Raw MVRV Data: Collects MVRV data directly from INTOTHEBLOCK for Bitcoin
Optional Smoothing: Applies simple moving average (SMA) to reduce noise
Status Classification: Categorizes market conditions into four distinct states
Signal Generation: Produces trading signals based on MVRV thresholds
Price Estimation: Calculates estimated realized price (Current price / MVRV ratio)
Historical Context: Compares current values to historical extremes
Formula:
MVRV Ratio = Market Value / Realized Value
Smoothed MVRV = SMA(MVRV Ratio, Smoothing Length)
Estimated Realized Price = Current Price / MVRV Ratio
Distance to Top = ((3.5 / MVRV Ratio) - 1) * 100
Distance to Bottom = ((MVRV Ratio / 0.8) - 1) * 100
🔶 DETAILS Visual Features:
MVRV Plot: Color-coded line showing current MVRV value (red for overvalued, orange for moderately overvalued, blue for fair value, teal for undervalued)
Reference Levels: Horizontal lines indicating key MVRV thresholds (3.5, 2.5, 1.0, 0.8)
Zone Highlighting: Background color changes to highlight extreme market conditions (red for potentially overvalued, blue for potentially undervalued)
Information Table: Comprehensive dashboard showing current MVRV value, market status, trading signal, price information, and historical context
Interpretation:
MVRV ≥ 3.5: Potential market top, strong sell signal
MVRV ≥ 2.5: Overvalued market, consider selling
MVRV 1.5-2.5: Neutral market conditions
MVRV 1.0-1.5: Fair value, consider buying
MVRV < 1.0: Potential market bottom, strong buy signal
🔶 EXAMPLES
Market Top Identification: When MVRV ratio exceeds 3.5, the indicator signals potential market tops, highlighting periods where Bitcoin may be significantly overvalued.
Example: During bull market peaks, MVRV exceeding 3.5 has historically preceded major corrections, helping traders time their exits.
Bottom Detection: MVRV values below 1.0, especially approaching 0.8, have historically marked excellent buying opportunities.
Example: During bear market bottoms, MVRV falling below 1.0 has identified the most profitable entry points for long-term Bitcoin accumulation.
Tracking Market Cycles: The indicator provides a clear visualization of Bitcoin's market cycles from undervalued to overvalued states.
Example: Following the progression of MVRV from below 1.0 through fair value and eventually to overvalued territory helps traders position themselves appropriately throughout Bitcoin's market cycle.
Realized Price Support: The estimated realized price often acts as a significant
support/resistance level during market transitions.
Example: During corrections, price often finds support near the realized price level calculated by the indicator, providing potential entry points.
🔶 SETTINGS
Customization Options:
Smoothing: Toggle smoothing option and adjust smoothing length (1-50)
Table Display: Show/hide the information table
Table Position: Choose between top right, top left, bottom right, or bottom left positions
Visual Elements: All plots, lines, and background highlights can be customized for color and style
The MVRV Ratio Indicator provides traders with a powerful on-chain metric to identify potential market tops and bottoms in Bitcoin. By tracking the relationship between market value and realized value, this indicator helps identify periods of overvaluation and undervaluation, offering clear buy and sell signals based on historical patterns. The comprehensive information table delivers valuable context about current market conditions, helping traders make more informed decisions about market positioning throughout Bitcoin's cyclical patterns.
Support and Resistance Logistic Regression | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Logistic Regression Support / Resistance indicator! This tool leverages advanced statistical modeling "Logistic Regressions" to identify and project key price levels where the market is likely to find support or resistance. For more information about the process, please check the "HOW DOES IT WORK ?" section.
Logistic Regression Support / Resistance Features :
Intelligent S/R Identification : The indicator uses a logistic regression model to intelligently identify and plot significant support and resistance levels.
Predictive Probability : Each identified level comes with a calculated probability, indicating how likely it is to act as a true support or resistance based on historical data.
Retest & Break Labels : The indicator clearly marks on your chart when a detected support or resistance level is retested (price touches and respects the level) or broken (price decisively crosses through the level).
Alerts : Real-time alerts for support retests, resistance retests, support breaks, and resistance breaks.
Customizable : You can change support & resistance line style, width and colors.
🚩 UNIQUENESS
What makes this indicator truly unique is its application of logistic regression to the concept of support and resistance. Instead of merely identifying historical highs and lows, our indicator uses a statistical model to predict the future efficacy of these levels. It analyzes underlying market conditions (like RSI and body size at pivot formation) to assign a probability to each potential S/R zone. This predictive insight, combined with dynamic, real-time labeling of retests and breaks, provides a more robust and adaptive understanding of market structure than traditional, purely historical methods.
📌HOW DOES IT WORK ?
The Logistic Regression Support / Resistance indicator operates in several key steps:
First, it identifies significant pivot highs and lows on the chart based on a user-defined "Pivot Length." These pivots are potential areas of support or resistance.
For each detected pivot, the indicator extracts relevant market data at that specific point, including the RSI (Relative Strength Index) and the Body Size (the absolute difference between the open and close price of the candle). These serve as input features for the model.
The core of the indicator lies in its logistic regression model. This model is continuously trained on past pivot data and their subsequent behavior (i.e., whether they were "respected" as support/resistance multiple times). It learns the relationship between the extracted features (RSI, Body Size) and the likelihood of a pivot becoming a significant S/R level.
When a new pivot is identified, the model uses its learned insights to calculate a prediction value—a probability (from 0 to 1) that this specific pivot will act as a strong support or resistance.
If the calculated probability exceeds a user-defined "Probability Threshold," the pivot is designated a "Regression Pivot" and drawn on the chart as a support or resistance line. The indicator then actively tracks how price interacts with these levels, displaying "R" labels for retests when the price bounces off the level and "B" labels for breaks when the price closes beyond it.
⚙️ SETTINGS
1. General Configuration
Pivot Length: This setting defines the number of bars used to determine a significant high or low for pivot detection.
Target Respects: This input specifies how many times a level must be "respected" by price action for it to be considered a strong support or resistance level by the underlying model.
Probability Threshold: This is the minimum probability output from the logistic regression model for a detected pivot to be considered a valid support or resistance level and be plotted on the chart.
2. Style
Show Prediction Labels: Enable or disable labels that display the calculated probability of a newly identified regression S/R level.
Show Retests: Toggle the visibility of "R" labels on the chart, which mark instances where price has retested a support or resistance level.
Show Breaks: Toggle the visibility of "B" labels on the chart, which mark instances where price has broken through a support or resistance level.
Dr Avinash Talele momentum indicaterTrend and Volatility Metrics
EMA10, EMA20, EMA50:
Show the percentage distance of the current price from the 10, 20, and 50-period Exponential Moving Averages.
Positive values indicate the price is above the moving average (bullish momentum).
Negative values indicate the price is below the moving average (bearish or corrective phase).
Use: Helps traders spot if a stock is extended or pulling back to support.
RVol (Relative Volume):
Compares current volume to the 20-day average.
Positive values mean higher-than-average trading activity (potential institutional interest).
Negative values mean lower activity (less conviction).
Use: High RVol often precedes strong moves.
ADR (Average Daily Range):
Shows the average daily price movement as a percentage.
Use: Higher ADR = more volatility = more trading opportunities.
50D Avg. Vol & 50D Avg. Vol ₹:
The 50-day average volume (in millions) and value traded (in crores).
Use: Confirms liquidity and suitability for larger trades.
ROC (Rate of Change) Section
1W, 1M, 3M, 6M, 12M:
Show the percentage price change over the last 1 week, 1 month, 3 months, 6 months, and 12 months.
Positive values (green) = uptrend, Negative values (red) = downtrend.
Use: Quickly see if the stock is gaining or losing momentum over different timeframes.
Momentum Section
1M, 3M, 6M:
Show the percentage gain from the lowest price in the last 1, 3, and 6 months.
Use: Measures how much the stock has bounced from recent lows, helping find strong rebounds or new leaders.
52-Week High/Low Section
From 52WH / From 52WL:
Show how far the current price is from its 52-week high and low, as a percentage.
Closer to 52WH = strong uptrend; Closer to 52WL = possible value or turnaround setup.
Use: Helps traders identify stocks breaking out to new highs or rebounding off lows.
U/D Ratio
U/D Ratio:
The ratio of up-volume to down-volume over the last 50 days.
Above 1 = more buying volume (bullish), Below 1 = more selling volume (bearish).
Use: Confirms accumulation or distribution.
How This Table Helps Analysts and Traders
Instant Trend Assessment:
With EMA distances and ROC, analysts can instantly see if the stock is trending, consolidating, or reversing.
Momentum Confirmation:
ROC and Momentum sections highlight stocks with strong recent moves, ideal for momentum and breakout traders.
Liquidity and Volatility Check:
Volume and ADR ensure the stock is tradable and has enough price movement to justify a trade.
Relative Positioning:
52-week high/low stats show whether the stock is near breakout levels or potential reversal zones.
Volume Confirmation:
RVol and U/D ratio help confirm if moves are backed by real buying/selling interest.
Actionable Insights:
By combining these metrics, traders can filter for stocks with strong trends, robust momentum, and institutional backing—ideal for swing, position, or even intraday trading.
