STDV Extension Zones from Daily Open - OnlyFlowSTDV Extension Zones from Daily Open
This indicator plots standard deviation extension zones based on the current day’s opening price. At the start of each trading day, it calculates the daily standard deviation using a configurable lookback and projects price zones at ±0.5 and ±1.0 standard deviations above and below the daily open.
Each zone is displayed as a horizontal band with a center line and a customizable thickness, extending forward throughout the session. Zones automatically reset and lock in place when a new day begins, preserving prior sessions for historical context.
The indicator is designed to visually highlight statistically significant price extensions relative to the daily open, helping users quickly identify areas where price may be stretched, balanced, or reacting around volatility-based levels.
الانحراف المعياري
Std Dev Channel [fmb]What it is
A professional regression channel that combines standard deviation divisions, an extreme price envelope, and a trend quality gauge. It is designed for fast read-and-act decisions on any timeframe, with sensible presets and log-space math for instruments that trend exponentially.
Why it’s different
Most channels draw fixed ±1σ and ±2σ around a regression line. This tool adds:
- Fibonacci-spaced σ divisions for precise scaling
- An objective MaxEnvelope of actual extremes with optional 1.272 and 1.618 extensions
- Pearson’s R labelling that classifies the trend as Strong Up, Moderate, Weak, or Strong Down
- A log-space option so channels behave correctly on long trends and high beta charts
How it works
Base line
- Linear regression of the last Length bars, drawn as a ray.
- Optional colour change by regime using Pearson’s R.
Divisions (StdDev or MaxEnvelope)
- StdDev basis: σ of residuals around the regression line.
- MaxEnvelope basis: distances from the base line to the farthest highs and lows in the lookback.
- Divisions can be Fibonacci multiples (0.382, 0.618, 1.000, 1.272 by default) or uniform steps.
Outer rails
- ENV 1.0 touches the farthest highs and lows within the window.
- Optional extensions at 1.272 and 1.618 highlight stretch and breakout zones.
Trend quality (Pearson’s R)
- R is computed on the same series and window.
- Default thresholds: Strong when |R| ≥ 0.70, Weak when |R| < 0.40.
- The label reads: R 0.XXX • Class, plotted near the most recent base value.
Log-space math
- When enabled, the model runs on ln(price) and converts the outputs back to price.
- Safer on multi-year charts and large percentage trends.
Presets
- Swing: Length 125, StdDev basis, Fib divisions, ENV 1.0 and 1.272 on
- Intraday: Length 240, StdDev basis, simple ±1 and ±2 style divisions, ENV off by default
- Position: Length 200, StdDev basis, compact Fib set for higher timeframes
You can turn preset overrides off to make every input respond instantly.
Inputs you will actually use
- Length, Source, Log-space ON or OFF
- Basis: StdDev or MaxEnvelope
- Divisions: Fib list or Step and Max multiple
- Outer rails: show ENV 1.0, show 1.272, show 1.618
- Labels and sizes, extend left or right
- Hide divisions or outer rails automatically when the regime is Weak
Alerts included
- Close crosses above or below ENV 1.0
- Close crosses above or below ENV 1.272 and 1.618 (if enabled)
Practical playbook
Trend following
- In Strong Uptrend: buy pullbacks near 0.382 to 0.618 above the base with stops just beyond the next lower division.
- In Strong Downtrend: sell bounces into 0.382 to 0.618 below the base with stops just beyond the next upper division.
Mean reversion
- When R is Moderate or Weak, fade moves that tag ENV 1.0 back toward the base.
- If price closes through an ENV extension, treat it as potential regime change and stand down on fades.
Breakouts
- A close through ENV 1.0 with R rising toward Strong often precedes trend acceleration.
- Use the next division or the 1.272 rail as the first target and trail on the base.
Tips
- Keep Length stable across symbols you compare. Consistency beats curve fitting.
- Use log-space on multi-year equities and crypto. Use linear for short intraday work.
- If you want a classic look, disable Fib and rails, set Step 1.0 and Max 2.0.
Notes
- The tool draws more lines when Fib divisions are active. If it feels busy, show divisions only and hide labels, or keep ENV 1.0 plus one extension.
- Pearson’s R is descriptive, not predictive. Combine with price structure and volume for entries.
Volume Weighted LR Z ScoreThis indicator calculates the Volume Weighted Linear Regression
Z-Score (VWLRZS). Unlike a standard Z-Score which measures
deviation from a static mean, this oscillator measures the
statistical distance of price from a dynamic Volume-Weighted
Linear Regression Line (Analysis of Residuals).
Key Features:
1. **Volatility Decomposition:** The indicator separates volatility
based on the 'Estimate Bar Statistics' option.
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
standard Regression Residuals using the selected `Source`
for both the regression line (baseline) and the signal.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):**
Uses a hybrid statistical approach:
a) **The Model (Baseline):** Uses an estimator to calculate
the 'within-bar' mean and fits the Linear Regression
through these statistical centers. This creates a
stable, trend-following expectation model.
b) **The Signal (Observation):** Compares the actual `Source`
(e.g., Close) against this regression line.
(Result: A Z-Score that measures deviations from the current
trend slope rather than a flat average).
2. **Visual Decomposition Logic:** Total Standard Deviation (of
Residuals) is the primary metric displayed. Since Standard
Deviations are not linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)),
this indicator calculates the *exact* Total Z-Score and partitions
the area underneath based on the Variance Ratio. This ensures the
displayed total volatility remains mathematically accurate while
showing relative composition.
3. **Normalization (Exponential Regression):** Includes an optional
'Normalize' mode. When enabled, the indicator calculates the
Linear Regression on logarithmic data. Mathematically, this
transforms the baseline into an **Exponential Regression Curve**,
making it ideal for analyzing assets with compounding growth
characteristics (constant percentage trend).
4. **Full Divergence Suite (Class A, B, C):** The indicator's
primary feature is its integrated divergence engine. It
automatically detects and plots all three major divergence
classes between price and the Z-Score:
- Regular (A): Signals potential trend exhaustion and reversals.
- Hidden (B): Signals potential trend continuations during pullbacks.
- Exaggerated (C): Signals weakness at double tops/bottoms.
5. **Divergence Filtering and Visualization:**
- **Price Tolerance Filter:** Divergence detection is enhanced
with a percentage-based price tolerance (`pivPrcTol`) to
filter out insignificant market noise, leading to more
robust signals.
- **Persistent Visualization:** Divergence markers are plotted
for the entire duration of the signal and are visually
anchored to the oscillator level of the confirming pivot.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library
6. **Note on Confirmation (Lag):** Divergence signals rely on a
pivot confirmation method to ensure they do not repaint.
- The **Start** of a divergence is only detected *after* the
confirming pivot is fully formed (a delay based on
`Pivot Right Bars`).
- The **End** of a divergence is detected either instantly
(if the signal is invalidated by price action) or with
a delay (when a new, non-divergent pivot is confirmed).
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Calculation:** The Z-Score line *itself* can be calculated on a
higher timeframe, with standard options to handle gaps
(`Fill Gaps`) and prevent repainting (`Wait for...`).
- **Limitation:** The Divergence detection engine (`pivDiv`)
is designed for the active timeframe. Using it in MTF mode
is not recommended as step-data can lead to inaccurate
pivot detection.
8. **Integrated Alerts:** Includes a comprehensive set of built-in
alerts for the Z-Score crossing the neutral line, the configured
Threshold levels, and the start/end of all divergence types.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Volume Weighted Z ScoreThis indicator calculates the Volume Weighted Z-Score (VWZS), a
statistical oscillator that measures the number of standard deviations
the price is removed from its mean. It combines robust volatility
decomposition with advanced divergence detection.
Key Features:
1. **Volatility Decomposition:** The indicator separates volatility
based on the 'Estimate Bar Statistics' option.
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
a simple (Volume-Weighted) Standard Deviation using the
selected `Source` for both the baseline and the signal.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):**
Uses a hybrid statistical approach:
a) **The Model (Baseline):** Uses an estimator to calculate
the 'within-bar' mean and volatility. This creates a
stable, mathematically idealized expectation value (mu).
b) **The Signal (Observation):** Compares the actual `Source`
(e.g., Close) against this statistical baseline.
(Result: A Z-Score that combines a noise-filtered trend
baseline with a highly reactive price signal).
2. **Visual Decomposition Logic:** Total Standard Deviation is the
primary metric displayed. Since Standard Deviations are not
linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)), this indicator
plots the *exact* Total StdDev and partitions the area underneath
based on the Variance Ratio. This ensures the displayed total
volatility remains mathematically accurate while showing relative
composition.
3. **Normalization (Geometric Average):** Includes an optional
'Normalize' mode. When enabled, the indicator uses a
Geometric Moving Average (GMA) as its baseline and applies a
statistical correction for the log-normal distribution
ensuring symmetry between upside and downside movements.
4. **Full Divergence Suite (Class A, B, C):** The indicator's
primary feature is its integrated divergence engine. It
automatically detects and plots all three major divergence
classes between price and the Z-Score:
- Regular (A): Signals potential trend exhaustion and reversals.
- Hidden (B): Signals potential trend continuations during pullbacks.
- Exaggerated (C): Signals weakness at double tops/bottoms.
5. **Divergence Filtering and Visualization:**
- **Price Tolerance Filter:** Divergence detection is enhanced
with a percentage-based price tolerance (`pivPrcTol`) to
filter out insignificant market noise, leading to more
robust signals.
- **Persistent Visualization:** Divergence markers are plotted
for the entire duration of the signal and are visually
anchored to the oscillator level of the confirming pivot.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library
6. **Note on Confirmation (Lag):** Divergence signals rely on a
pivot confirmation method to ensure they do not repaint.
- The **Start** of a divergence is only detected *after* the
confirming pivot is fully formed (a delay based on
`Pivot Right Bars`).
- The **End** of a divergence is detected either instantly
(if the signal is invalidated by price action) or with
a delay (when a new, non-divergent pivot is confirmed).
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Calculation:** The Z-Score line *itself* can be calculated on a
higher timeframe, with standard options to handle gaps
(`Fill Gaps`) and prevent repainting (`Wait for...`).
- **Limitation:** The Divergence detection engine (`pivDiv`)
is designed for the active timeframe. Using it in MTF mode
is not recommended as step-data can lead to inaccurate
pivot detection.
8. **Integrated Alerts:** Includes a comprehensive set of built-in
alerts for the Z-Score crossing the neutral line, the configured
Threshold levels, and the start/end of all divergence types.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Adaptive Pullbacks ML v2.5Adaptive Pullbacks ML - Context-Aware Trend Trading
Overview
Adaptive Pullbacks ML is a sophisticated trend-following tool that solves the biggest problem in pullback trading: "Is this a dip to buy, or the start of a reversal?"
Unlike standard indicators that use fixed percentages or static moving averages, this script uses a 5-Dimensional k-Nearest Neighbors (k-NN) machine learning engine to learn the specific characteristics of successful pullbacks for the asset you are trading.
The 5-Dimensional ML Engine
The market is dynamic. A pullback depth that works in a low-volatility lunch session might fail during a high-volatility news event. This indicator tracks 5 key dimensions for every pullback:
Depth (ATR Normalized): How deep is the pullback relative to volatility?
Trend Slope: Is the trend steep (parabolic) or flat (grinding)?
ADX: How strong is the directional energy?
VWAP Distance: Is price extended or close to value?
Time of Day: Is this a morning drive or an afternoon fade?
When a new pullback occurs, the k-NN engine finds the 5 most similar historical events across these dimensions and predicts the probability of success.
Core Features
1. Fractal Normalization
The indicator speaks the language of ATR (Average True Range). It doesn't care if you trade the 15-second chart or the Daily chart. A "1.5 ATR Pullback" is a statistically comparable event across all timeframes, allowing for robust, scale-invariant analysis.
2. HTF Stats Bridge (Higher Timeframe Data)
You can trade on lower timeframes (e.g., 1-minute) while using statistics derived from higher timeframes (e.g., 15-minute). This ensures your signals are based on significant market structure, not microstructure noise.
3. Smart Zones
The indicator plots dynamic "Value Zones" based on learning:
Cyan Zone (Avg Depth): The "Sweet Spot". High probability bounce area.
Yellow Zone (Sigma): The "Extension". Price is stretching elastic limits.
Red Zone (Deep): The "Danger/Opportunity". Statistical anomaly.
4. PQS & k-NN Filters
Two layers of filtering protect your capital:
PQS (Probability Qualification Score): Based on raw win-rate of the zone.
k-NN Probability: Based on similarity to past winners.
Settings Guide
Stats Timeframe: The timeframe to learn from (Leave empty for Chart).
Trend/Trigger Settings: Define what constitutes a trend for your strategy.
k-Neighbors: Number of historical twins to compare (Default: 5).
Min PQS / k-NN: Thresholds for filtering weak signals.
Disclaimer: This tool is for educational purposes. Past performance of the k-NN engine does not guarantee future results.
Adaptive ML VWAP v1.0Overview
Adaptive ML VWAP is a next-generation "Smart Indicator" that moves beyond static deviations (Standard Deviation). Instead of assuming market volatility is distributed normally (Bell Curve), this indicator uses a k-Nearest Neighbors (k-NN) machine learning engine to learn the specific volatility behavior of the asset you are trading.
It answers the question: "When price extends away from VWAP, how far does it actually go before reversing?"
The Adaptive ML Engine
This script features a 5-Dimensional ML Engine that tracks every major extension or pullback event. It records:
Deviation Depth (Normalized to ATR)
Trend Slope (Is the trend steep or flat?)
ADX (Trend Strength)
VWAP Deviation (Relative Position)
Time of Day (Session Context)
When a new setup occurs, the k-NN engine instantly searches its memory for the 5 most similar historical events and calculates the probability of success based on what happened last time.
Two Strategy Modes
You can toggle the logic to suit your trading style:
1. Mean Reversion Mode (Default)
"Fade The Move"
Goal: Catch price at an exhaustion point returning to VWAP.
Signal: Triggers when price touches a Smart Band and reverses back toward the center.
k-NN Learning: Learns which conditions favor a snap-back.
Best For: Ranging markets, Lunch hours, Choppy sessions.
2. Trend Following Mode
"Ride The Move"
Goal: Catch breakouts that are launching away from value.
Signal: Triggers when price breaks out of the Inner Band (1.0).
k-NN Learning: Learns which breakouts tend to extend to the Outer Bands.
Best For: Morning Drives, News Events, Strong Trends.
Visual Guide
The indicator uses a Dynamic Gradient system to visualize risk/reward:
Cyan Mist (0.5 - 1.0): The Value Zone. Noise area. Safe for trend entries.
Deep Cyan (1.0 - 2.0): The Trend Zone. Price is moving proactively.
Orange Glow (2.0 - 3.0): The Danger Zone. Price is statistically overextended. Reversals are highly probable here.
"Fractal" Math
Unlike standard indicators that break when you change timeframes, Adaptive ML VWAP uses Fractal Normalization.
A "2.0 Band" on a 15-second chart means the same statistical extreme as a "2.0 Band" on a 4-hour chart.
Auto-Adaptive Lookback: The indicator automatically boosts the ML memory (Lookback) on lower timeframes (seconds/minutes) where more noise requires larger sample sizes, ensuring robust predictions without manual tweaking.
Settings
Auto-Adapting Lookback: (Default: True) automatically increases Lookback to 100+ for seconds charts and 50+ for minute charts.
Lookback (Events): Manual override base value (Default: 100).
Strategy Mode: Toggle between Mean Reversion and Trend Following.
k-Neighbors: The number of similar past events to structurally compare (Default: 5).
Disclaimer: This tool is for educational purposes. Machine learning performance is dependent on market conditions and historical recursion.
DCA + Martingale strategy.DCA + Martingale: smart synergy for volatile markets
Tame market swings with a powerful hybrid strategy that marries the discipline of Dollar‑Cost Averaging (DCA) with the aggressive recovery logic of the Martingale system. This approach turns price dips into opportunities — systematically building positions while keeping risk in check.
How it works:
1. Entry trigger
The strategy activates when the asset price drops by a predefined percentage on the 1‑hour timeframe. This ensures you only engage when a meaningful pullback occurs, avoiding premature entries.
2. DCA grid for controlled averaging
Once the entry condition is met, a grid of buy orders is deployed:
Each subsequent order is placed at progressively lower price levels (e.g., every 2–5% drop).
Order sizes can be fixed or follow a progressive scale (e.g., 1x, 1.5x, 2x the initial amount).
This dilutes your average entry price, improving the breakeven point as the market corrects.
3. Martingale‑style recovery mechanism
After each unsuccessful trade (i.e., price continues falling), the next position size is increased — not necessarily doubled, but scaled according to your risk tolerance. This accelerates recovery potential when the trend reverses.
4. Take‑profit with a fixed percentage target
A simple, predefined profit target (e.g., +3–7%) is set for the entire averaged position. Once hit, all open trades close, locking in gains. This prevents over‑exposure during uncertain reversals.
Key advantages
Psychological edge: removes emotional decision‑making by automating entries and exits.
Cost optimization: lowers average entry during downtrends, improving profit potential.
Controlled aggression: Martingale logic helps recoup losses faster without infinite scaling.
Flexibility: parameters (entry %, grid spacing, position sizing, TP) are fully customizable.
Risk management essentials
Stop‑loss safeguard: a hard stop‑loss (e.g., 10–15% below the lowest grid level) prevents catastrophic drawdowns in prolonged downtrends.
Position sizing: never risk more than 1–3% of capital per grid cycle.
Market context: best suited for assets with mean‑reverting behavior and moderate volatility. Avoid strong, sustained trends.
Capital buffer: ensure sufficient reserves to withstand multiple grid levels without margin calls.
When to use it
During sideways or range‑bound markets with regular pullbacks.
On assets with historical tendency to recover from short‑term dips.
When you expect a bounce but can’t pinpoint the exact bottom.
Bottom line
DCA + Martingale isn’t a «set‑and‑forget» miracle — it’s a disciplined framework for turning volatility into opportunity. Combine it with rigorous risk rules, and you’ll navigate downtrends with precision, turning market noise into structured profit potential.
BNF (Kotegawa) Strategy [CB Algos]STRATEGY: BNF (Kotegawa) Mean Reversion Strategy
DEVELOPED BY: CB Algos
DESCRIPTION:
This indicator replicates the trading style of Takashi Kotegawa (BNF).
It calculates the percentage deviation of the price from the 25-period SMA.
HOW TO USE:
1. Look for 'Lime' bars (Extreme Buy) or 'Teal' bars (Moderate Buy). These indicate the price has dropped significantly below the average.
2. Look for 'Red' bars (Extreme Sell) as profit-taking zones.
3. Use the Info Panel to see the exact current deviation %.
ATR Stop LinesATR Stop Lines
Plots dynamic stop-loss levels on the price chart based on ATR (Average True Range). Optionally adjusts stop distance based on volatility regime.
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🎯 WHAT IT DOES
Green line — Long stop (Close − ATR × multiplier)
Red line — Short stop (Close + ATR × multiplier)
Lines move with price and volatility. When regime-adjust is enabled, stop distance widens in high volatility and tightens in low volatility.
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📐 REGIME-ADJUSTED MULTIPLIERS
When enabled, the multiplier auto-adjusts based on the ATR percentile:
LOW (< 25th pctl) — 1.0× ATR — Tight stops, small moves expected
NORMAL (25–50th pctl) — 1.5× ATR — Standard distance
HIGH (50–75th pctl) — 2.0× ATR — Wider to avoid noise
EXTREME (> 75th pctl) — 2.5× ATR — Widest, or skip the trade
Disable regime-adjust to use a fixed multiplier for all conditions.
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📈 HOW TO USE
Entry: Note stop line level when entering a trade. Set stop-loss at or beyond that level.
Trailing: Move stop to new line level as price advances in your favor.
Sizing: Wider stop = smaller position to maintain constant risk.
Example:
BTC Daily, ATR = \$2,000, Regime = HIGH (2.0×)
Entry: \$50,000 → Long stop: \$46,000 / Short stop: \$54,000
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📊 STATUS LABEL
VOL — Current regime (LOW / NORMAL / HIGH / EXTREME)
ATR — Raw ATR value in price units
Mult — Active multiplier
Stop Dist — Current stop distance in price units
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⚙️ SETTINGS
ATR Settings:
ATR Length (default: 14)
Percentile Lookback (default: 100)
Timeframe:
Use Fixed Timeframe — Lock to specific TF
Fixed Timeframe (default: D)
Stop Settings:
Regime-Adjusted Multiplier — Toggle auto-adjust on/off
Base ATR Multiplier — Used when regime-adjust is off
LOW/NORMAL/HIGH/EXTREME Multipliers — Customize per regime
Display:
Show Long Stop / Show Short Stop
Show Status Label
Long/Short Stop Colors
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🔔 ALERTS
Vol → EXTREME
Vol → LOW
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💡 COMPANION INDICATOR
Use with ATR Volatility Regime (separate pane) for full context:
Pane indicator → percentile visualization, zone backgrounds
This indicator → actionable stop levels on price chart
Both use identical ATR/percentile logic and stay in sync.
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📝 NOTES
Works on any timeframe
Stops are dynamic — recalculate each bar
Not a signal generator — use with your own entry logic
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🏷️ TAGS
ATR, stop-loss, volatility, risk-management, position-sizing, trailing-stop, swing-trading
OT Zones Pro | Intraday Quantitative & Macro LevelsNote to Moderators & Community: First and foremost, I would like to offer my sincere apologies if the previous description of this tool was insufficient or lacked the technical depth required to demonstrate its originality. My intention is solely to provide a robust analytical tool for the community based on specific mathematical models, and never to mislead or cause harm to any trader. We are committed to transparency regarding the methodology used while protecting the proprietary values of the code.
Concept & Methodology OT Zones Pro is not a standard Support & Resistance indicator, nor does it use public domain formulas like Fibonacci, Pivot Points, or standard Moving Averages. Instead, it is a custom-built Quantitative Volatility Model designed to identify high-probability institutional interest areas specifically for Intraday Trading .
The script operates on two distinct proprietary layers:
Dynamic Volatility Bands (The Math): Unlike static levels or common open-source indicators, this engine operates on a strict institutional quantitative perspective . It calculates dynamic thresholds where each asset class triggers a unique calculation logic. This logic is derived from the asset's specific inherent volatility and potential intraday structural pivoting points, strictly based on mathematical modeling rather than standard technical indicators. This allows the script to project "Primary Dynamic Resistances" (PDR) and "Dynamic Supports" (PDS) that adapt to the asset's specific nature during the session.
Hard-Coded Macro Data (The Database): The script contains an internal, encrypted database of annually pre-calculated macro market zones . These are not generated by recent high/low candles but are fixed structural levels injected into the chart based on proprietary annual analysis. The plotting mechanism controls the visibility of these zones by considering a specific expected movement threshold unique to each asset, ensuring that levels are only displayed when they are statistically relevant to the current price action (filtering out noise).
Optimized for Intraday: The logic relies on Session Open data anchors (09:30 EST), making it designated for timeframes between 1 minute and 30 minutes .
Auto-Asset Recognition (Supported Markets): The script automatically detects the ticker and applies the correct mathematical model for:
Nasdaq: QQQ (ETF), NQ/MNQ (Futures), US100, NAS100 (CFDs).
S&P 500: SPY (ETF), ES/MES (Futures), US500 (CFDs).
Dow Jones: DIA (ETF), YM/MYM (Futures), US30 (CFDs).
Russell 2000: IWM (ETF), RTY/M2K (Futures), US2000 (CFDs).
Bitcoin: IBIT (ETF), BTC (Futures CME), Crypto Spot & Crypto futures.
Metals: Gold & Silver (ETF, Futures, CFDs).
Sentiment Analysis Panel: A real-time logic module that analyzes price behavior throughout the trading session. The sentiment classification is derived from the relationship between the current price and the calculated PDR/PDS levels, combined with an additional layer of private, encrypted quantitative logic to determine the market bias (Neutral, Bullish, Bearish, Extreme).
Macro Zone Alerts: Includes a "Trigger on Entry" feature, allowing traders to set server-side alerts specifically when price breaches a defined Macro Zone.
Why is this "Invite-Only"? The source code is protected because it contains:
Proprietary Math: The asset-specific logic and volatility calculations are the result of extensive quantitative research and are not public domain.
Curated Database: The specific price arrays for the Macro Zones are intellectual property derived from pre-calculated annual structures, not generic chart reading.
Risk Disclaimer & Feedback We are fully open to suggestions and constructive feedback from the community to improve this tool. Our goal is to aid analysis, not to generate financial loss. Please remember that this indicator provides technical levels based on probabilities; it does not guarantee future performance. Trading involves significant risk.
Intermarket Divergence (Futures vs Equity)Intermarket Divergence (Futures vs Equity)
This indicator detects intermarket divergence between a traded instrument (futures, CFD, or spot) and a related equity or ETF.
It highlights moments where price and its underlying market drivers disagree, often appearing before reversals or expansions.
🎯 What It Shows
Bullish divergence:
Price makes a lower low while the equity makes a higher low
Bearish divergence:
Price makes a higher high while the equity makes a lower high
Based on swing pivots, not candle noise
Designed for intraday context, not mechanical entries
✅ Recommended Use
XAUUSD (Gold) → GDX (default)
XAGUSD (Silver) → SIL
USOIL / WTI → XLE
(These guidelines are included directly in the indicator settings.)
🧭 How to Use
Apply on 15m–30m
Look for signals near key levels (PDH/PDL, Asia high/low, HTF structure)
Use price action for entries
Divergence is context, not a signal.
⚠️ Notes
Non-repainting
Signals are selective by design
Best during London & New York sessions
[GYTS] Volatility Toolkit Volatility Toolkit
🌸 Part of GoemonYae Trading System (GYTS) 🌸
🌸 --------- INTRODUCTION --------- 🌸
💮 What is Volatility Toolkit?
Volatility Toolkit is a comprehensive volatility analysis indicator featuring academically-grounded range-based estimators. Unlike simplistic measures like ATR, these estimators extract maximum information from OHLC data — resulting in estimates that are 5-14× more statistically efficient than traditional close-to-close methods.
The indicator provides two configurable estimator slots, weighted aggregation, adaptive threshold detection, and regime identification — all with flexible smoothing options via
GYTS FiltersToolkit integration.
💮 Why Use This Indicator?
Standard volatility measures (like simple standard deviation) are highly inefficient, requiring large amounts of data to produce stable estimates. Academic research has shown that range-based estimators extract far more information from the same price data:
• Statistical Efficiency — Yang-Zhang achieves up to 14× the efficiency of close-to-close variance, meaning you can achieve the same estimation accuracy with far fewer bars
• Drift Independence — Rogers-Satchell and Yang-Zhang correctly isolate variance even in strongly trending markets where simpler estimators become biased
• Gap Handling — Yang-Zhang properly accounts for overnight gaps, critical for equity markets
• Regime Detection — Built-in threshold modes identify when volatility enters elevated or suppressed states
↑ Overview showing Yang-Zhang volatility with dynamic threshold bands and regime background colouring
🌸 --------- HOW IT WORKS --------- 🌸
💮 Core Concept
The toolkit groups volatility estimators by their output scale to ensure valid comparisons and aggregations:
• Log-Return Scale (σ) — Close-to-Close, Parkinson, Garman-Klass, Rogers-Satchell, Yang-Zhang. These are comparable and can be aggregated. Annualisable via √(periods_per_year) scaling.
• Price Unit Scale ($) — ATR. Measures volatility in absolute price terms, directly usable for stop-loss placement.
• Percentage Scale (%) — Chaikin Volatility. Measures the rate of change of the trading range — whether volatility is expanding or contracting.
Only estimators with the same scale can be meaningfully compared or aggregated. The indicator enforces this and warns when mixing incompatible scales.
💮 Range-Based Estimator Overview
Range-based estimators utilise High, Low, Open, and Close prices to extract significantly more information about the underlying diffusion process than close-only methods:
• Parkinson (1980) — Uses High-Low range. ~5× more efficient than close-to-close. Assumes zero drift.
• Garman-Klass (1980) — Incorporates Open and Close. ~7.4× more efficient. Assumes zero drift, no gaps.
• Rogers-Satchell (1991) — Drift-independent. Superior in trending markets where Parkinson/GK become biased.
• Yang-Zhang (2000) — Composite estimator handling both drift and overnight gaps. Up to 14× more efficient.
💮 Theoretical Background
• Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53 (1), 61–65. DOI
• Garman, M.B. & Klass, M.J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53 (1), 67–78. DOI
• Rogers, L.C.G. & Satchell, S.E. (1991). Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1 (4), 504–512. DOI
• Yang, D. & Zhang, Q. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73 (3), 477–491. DOI
🌸 --------- KEY FEATURES --------- 🌸
💮 Feature Reference
Estimators (8 options across 3 scale groups):
• Close-to-Close — Classical benchmark using closing prices only. Least efficient but useful as baseline. Log-return scale.
• Parkinson — Range-based (High-Low), ~5× more efficient than close-to-close. Assumes zero drift. Log-return scale.
• Garman-Klass — OHLC-optimised, ~7.4× more efficient. Assumes zero drift, no gaps. Log-return scale.
• Rogers-Satchell — Drift-independent, handles trending markets where Parkinson/GK become biased. Log-return scale.
• Yang-Zhang — Gap-aware composite, most comprehensive (up to 14× efficient). Uses internal rolling variance (unsmoothed). Log-return scale.
• Std Dev — Standard deviation of log returns. Log-return scale.
• ATR — Average True Range in absolute price units. Useful for stop-loss placement. Price unit scale.
• Chaikin — Rate of change of range. Measures volatility expansion/contraction, not level. Percentage scale.
Smoothing Filters (10 options via FiltersToolkit):
• SMA / EMA — Classical moving averages
• Super Smoother (2-Pole / 3-Pole) — Ehlers IIR filter with excellent noise reduction
• Ultimate Smoother (2-Pole / 3-Pole) — Near-zero lag in passband
• BiQuad — Second-order IIR with configurable Q factor
• ADXvma — Adaptive smoothing, flat during ranging periods
• MAMA — MESA Adaptive Moving Average (cycle-adaptive)
• A2RMA — Adaptive Autonomous Recursive MA
Threshold Modes:
• Static — Fixed threshold values you define (e.g., 0.025 annualised)
• Dynamic — Adaptive bands: baseline ± (standard deviation × multiplier)
• Percentile — Threshold at Nth percentile of recent history (e.g., 80th percentile for high)
Visual Features:
• Level-based colour gradient — Line colour shifts with percentile rank (warm = high vol, cool = low vol)
• Fill to zero — Gradient fill intensity proportional to volatility level
• Threshold fills — Intensity-scaled fills when thresholds are breached
• Regime background — Chart background indicates HIGH/NORMAL/LOW volatility state
• Legend table — Displays estimator names, parameters, current values with percentile ranks (P##)
💮 Dual Estimator Slots
Compare two volatility estimators side-by-side. Each slot independently configures:
• Estimator type (8 options across three scale groups)
• Lookback period and smoothing filter
• Colour palette and visual style
This enables direct comparison between estimators (e.g., Yang-Zhang vs Rogers-Satchell) or between different parameterisations of the same estimator.
↑ Yang-Zhang (reddish) and Rogers-Satchell (greenish)
💮 Flexible Smoothing via FiltersToolkit
All estimators (except Yang-Zhang, which uses internal rolling variance) support configurable smoothing through 10 filter types. Using Infinite Impulse Response (IIR) filters instead of SMA avoids the "drop-off artefact" where volatility readings crash when old spikes exit the window.
Example: Same estimator (Parkinson) with different smoothing filters
Add two instances of Volatility Toolkit to your chart:
• Instance 1: Parkinson with SMA smoothing (lookback 14)
• Instance 2: Parkinson with Super Smoother 2-Pole (lookback 14)
Notice how SMA creates sharp drops when volatile bars exit the window, while Super Smoother maintains a gradual transition.
↑ Two Parkinson estimators — SMA (red mono-colour, showing drop-off artefacts) vs Super Smoother (turquoise mono colour, with smooth transitions)
↑ Garman-Klass with BiQuad (orangy) and 2-pole SuperSmoother filters (greenish)
💮 Weighted Aggregation
Combine multiple estimators into a single weighted average. The indicator automatically:
• Validates scale compatibility (only same-scale estimators can be aggregated)
• Normalises weights (so 2:1 means 67%:33%)
• Displays clear warnings when scales differ
Example: Robust volatility estimate
Combine Yang-Zhang (handles gaps) with Rogers-Satchell (handles drift) using equal weights:
• E1: Yang-Zhang (14)
• E2: Rogers-Satchell (14)
• Aggregation: Enabled, weights 1:1
The aggregated line (with "fill to zero" enabled) provides a more robust estimate by averaging two complementary methodologies.
↑ Yang-Zhang + Rogers-Satchell with aggregation line (thicker) showing combined estimate (notice how opening gaps are handled differently)
Example: Trend-weighted aggregation
In strongly trending markets, weight Rogers-Satchell more heavily since it's drift-independent:
• Estimator 1: Garman-Klass (faster, higher weight in ranging)
• Estimator 2: Rogers-Satchell (drift-independent, higher weight in trends)
• Aggregation: weights 1:2 (favours RS during trends)
💮 Adaptive Threshold Detection
Three threshold modes for identifying volatility regime shifts. Threshold breaches are visualised with intensity-scaled fills that grow stronger the further volatility exceeds the threshold.
Example: Dynamic thresholds for regime detection
Configure dynamic thresholds to automatically adapt to market conditions:
• High Threshold Mode: Dynamic (baseline + 2× std dev)
• Low Threshold Mode: Dynamic (baseline - 2× std dev)
• Show threshold fills: Enabled
This creates adaptive bands that widen during volatile periods and narrow during calm periods.
Example: Percentile-based thresholds
Use percentile mode for context-aware regime detection:
• High Threshold Mode: Percentile (96th)
• Low Threshold Mode: Percentile (4th)
• Percentile Lookback: 500
This identifies when volatility enters the top/bottom 4% of its recent distribution.
↑ Different threshold settings, where the dynamic and percentile methods show adaptive bands that widen during volatile periods, with fill intensity varying by breach magnitude. Regime detection (see next) is enabled too.
💮 Regime Background Colouring
Optional background colouring indicates the current volatility regime:
• High Volatility — Warm/alert background colour
• Normal — No background (neutral)
• Low Volatility — Cool/calm background colour
Select which source (Estimator 1, Estimator 2, or Aggregation) drives the regime display.
Example: Regime filtering for trade decisions
Use regime background to filter trading signals from other indicators:
• Regime Source: Aggregation
• Background Transparency: 90 (subtle)
When the background shows HIGH volatility (warm), consider tighter stops. When LOW (cool), watch for breakout setups.
↑ Regime background emphasis for breakout strategies. Note the interesting A2RMA smoothing for this case.
🌸 --------- USAGE GUIDE --------- 🌸
💮 Getting Started
1. Add the indicator to your chart
2. Estimator 1 defaults to Yang-Zhang (14) — the most comprehensive estimator for gapped markets
3. Keep "Annualise Volatility" enabled to express values in standard annualised form
4. Observe the legend table for current values and percentile ranks (P##). Hover over the table cells to see a little more info in the tooltip.
💮 Choosing an Estimator
• Trending equities with gaps — Yang-Zhang. Handles both drift and overnight gaps optimally.
• Crypto (24/7 trading) — Rogers-Satchell. Drift-independent without Yang-Zhang's multi-period lag.
• Ranging markets — Garman-Klass or Parkinson. Simpler, no drift adjustment needed.
• Price-based stops — ATR. Output in price units, directly usable for stop distances.
• Regime detection — Combine any estimator with threshold modes enabled.
💮 Interpreting Output
• Value (P##) — The volatility reading with percentile rank. "0.1523 (P75)" means 0.1523 annualised volatility at the 75th percentile of recent history.
• Colour gradient — Warmer colours = higher percentile (elevated volatility), cooler colours = lower percentile.
• Threshold fills — Intensity indicates how far beyond the threshold the current reading is.
• ⚠️ HIGH / 🔻 LOW — Table indicators when thresholds are breached.
🌸 --------- ALERTS --------- 🌸
💮 Direction Change Alerts
• Estimator 1/2 direction change — Triggers when volatility inflects (rising to falling or vice versa)
💮 Cross Alerts
• E1 crossed E2 — Triggers when the two estimator lines cross
💮 Threshold Alerts
• E1/E2/Aggr High Volatility — Triggers when volatility breaches the high threshold
• E1/E2/Aggr Low Volatility — Triggers when volatility falls below the low threshold
💮 Regime Change Alerts
• E1/E2/Aggr Regime Change — Triggers when the volatility regime transitions (High ↔ Normal ↔ Low)
🌸 --------- LIMITATIONS --------- 🌸
• Drift bias in Parkinson/GK — These estimators overestimate variance in trending conditions. Switch to Rogers-Satchell or Yang-Zhang for trending markets.
• Yang-Zhang minimum lookback — Requires at least 2 bars (enforced internally). Cannot produce instantaneous readings like other estimators.
• Flat candles — Single-tick bars produce near-zero variance readings. Use higher timeframes for illiquid assets.
• Discretisation bias — Estimates degrade when ticks-per-bar is very small. Consider higher timeframes for thinly traded instruments.
• Scale mixing — Different scale groups (log-return, price unit, percentage) cannot be meaningfully compared or aggregated. The indicator warns but does not prevent display.
🌸 --------- CREDITS --------- 🌸
💮 Academic Sources
• Parkinson, M. (1980). The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53 (1), 61–65. DOI
• Garman, M.B. & Klass, M.J. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business, 53 (1), 67–78. DOI
• Rogers, L.C.G. & Satchell, S.E. (1991). Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1 (4), 504–512. DOI
• Yang, D. & Zhang, Q. (2000). Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73 (3), 477–491. DOI
• Wilder, J.W. (1978). New Concepts in Technical Trading Systems . Trend Research.
💮 Libraries Used
• VolatilityToolkit Library — Range-based estimators, smoothing, and aggregation functions
• FiltersToolkit Library — Advanced smoothing filters (Super Smoother, Ultimate Smoother, BiQuad, etc.)
• ColourUtilities Library — Colour palette management and gradient calculations
Standard Deviation Channel with SignalsStandard Deviation Channel with Signals
This Pine Script is a **Standard Deviation Channel (or Linear Regression Channel) indicator** designed for TradingView. It automatically draws a channel around price action based on statistical deviation from a central linear regression trendline.
Here is a breakdown of its key features:
* **Trend Identification:** It calculates a linear regression line (the "mean" price) over a user-defined length (default 128 bars) to show the current trend direction.
* **Volatility Bands:** It plots parallel upper and lower bands at specific standard deviations (e.g., ±1 and ±2 deviations) from the center line. These act as dynamic support and resistance levels.
* **Actionable Signals:** It generates **"BUY"** signals when the price crosses below the lower deviation band (suggesting the asset is oversold) and **"SELL"** signals when the price crosses above the upper deviation band (suggesting it is overbought). This logic is based on a Mean Reversion strategy.
* **Historical & Live Visualization:** Unlike standard versions that only show the channel for the *current* moment, this script plots the historical path of the bands so you can backtest visual signals, while also projecting the live channel forward for real-time analysis.
Liquidation Bubbles [OmegaTools]🔴🟢 Liquidation Bubbles — Advanced Volume & Price Stress Detector
Liquidation Bubbles is a professional-grade analytical tool designed to identify forced positioning events, stop-runs, and liquidation clusters by combining price displacement and volume imbalance into a single, statistically normalized framework.
This indicator is not a repainting signal tool and not a simple volume spike detector. It is a contextual market stress mapper, built to highlight areas where one-sided positioning becomes unstable and the probability of forced order execution (liquidations, stops, margin calls) materially increases.
---
## 🔬 Core Concept
Market liquidations do not occur randomly.
They emerge when price deviates aggressively from its volume-weighted equilibrium while volume itself becomes abnormal.
Liquidation Bubbles detects exactly this condition by:
* Estimating a **dynamic equilibrium price** using an *inverted volume-weighted moving average*
* Measuring **directional price stress** relative to that equilibrium
* Measuring **volume stress** relative to its own adaptive baseline
* Normalizing both into **Z-score–like metrics**
* Highlighting only **statistically extreme, asymmetric events**
The result is a clear visual map of stress points where market participants are most vulnerable.
---
⚙️ Methodology (How It Works)
1️⃣ Advanced Inverted VWMA (Equilibrium Engine)
The script uses a custom Advanced VWMA, where:
* High volume bars receive less weight
* Low volume bars receive more weight
This produces a **robust equilibrium level**, resistant to manipulation and volume bursts.
This equilibrium is used for **both price and volume normalization**, creating a consistent statistical framework.
---
2️⃣ Price Stress (Directional)
Price stress is calculated as:
* The **maximum deviation** between high/low and equilibrium
* Directionally signed (upside vs downside)
* Normalized by its own historical volatility
This allows the script to distinguish:
* Aggressive upside exhaustion
* Aggressive downside capitulation
---
3️⃣ Volume Stress
Volume stress is measured as:
* Deviation from volume equilibrium
* Normalized by historical volume dispersion
This filters out:
* Normal high-volume sessions
* Illiquid noise
And isolates abnormal participation imbalance.
---
4️⃣ Liquidation Logic
A liquidation event is flagged when:
* Both price stress and volume stress exceed adaptive thresholds
* The imbalance is directional and statistically extreme
Optional Combined Score Mode allows aggregation of price & volume stress into a single composite metric for smoother signals.
---
🔵 Bubble System (Signal Hierarchy)
The indicator plots **two tiers of bubbles**:
🟢🔴 Small Bubbles
* Early warning stress points
* Localized stop-runs
* Micro-liquidations
* Often precede reactions or short-term reversals
🟢🔴 Big Bubbles
* Full liquidation clusters
* Forced unwinds
* High probability exhaustion zones
* Frequently align with:
* Intraday extremes
* Range boundaries
* Reversal pivots
* Volatility expansions
Bubble color:
* **Green** → Downside liquidation (sell-side exhaustion)
* **Red** → Upside liquidation (buy-side exhaustion)
Bubble placement is **ATR-adjusted**, ensuring visual clarity without overlapping price.
---
🔄 Cross-Market Volume Analysis
The script allows optional **external volume sourcing**, enabling:
* Futures volume applied to CFDs
* Index volume applied to ETFs
* Spot volume applied to derivatives
This is critical when:
* Your traded instrument has unreliable volume
* You want **institutional-grade confirmation**
---
🧠 How to Use Liquidation Bubbles
This indicator is **not meant to be traded alone**.
Best use cases:
* 🔹 Confluence with support & resistance
* 🔹 Contextual confirmation for reversals
* 🔹 Identifying fake breakouts
* 🔹 Liquidity sweep detection
* 🔹 Risk management (avoid entering into liquidation zones)
Ideal for:
* Futures
* Indices
* Crypto
* High-liquidity FX pairs
* Intraday & swing trading
---
🎯 Who This Tool Is For
Liquidation Bubbles is designed for:
* Advanced discretionary traders
* Order-flow & liquidity-based traders
* Macro & index traders
* Professionals seeking **context**, not signals
If you want **where the market is fragile**, not just where price moved — this tool was built for you.
---
📌 Key Characteristics
✔ Non-repainting
✔ Statistically normalized
✔ Adaptive to volatility
✔ Works on all timeframes
✔ Futures & crypto ready
✔ No lagging indicators
✔ No moving average crosses
---
Liquidation Bubbles does not predict the future.
It shows you where the market is most likely to break.
— OmegaTools
Volume-Weighted Hybrid Channel [Capitalize Labs]Volume-Weighted Hybrid Channel (VWHC) is a channel-only indicator designed to visualise mean and volatility structure using a blended framework. It combines a configurable mean engine (SuperSmoother, EMA, SMA, or RMA) with an anchored VWAP component, then builds a four-level band ladder around a hybrid mean using a hybrid width that blends a range engine (ATR or true range variants) with anchored, volume-weighted standard deviation. The result is a smooth, adaptive channel intended to help us contextualise price location and volatility expansion or contraction relative to the hybrid mean.
The indicator supports Weekly or Session anchoring for the VWAP and sigma components, and includes optional transition smoothing after anchor resets to reduce visual stepping. Band levels are user-defined (with automatic ordering enforcement), and optional gradient fills can be enabled for clearer zone recognition. An optional Band Occupancy Table is included to show how frequently price closes inside each zone, either over a rolling lookback or since the most recent anchor reset. This table is informational only and does not generate signals.
This script is an indicator, not a strategy. It does not place trades, generate alerts, or provide entry or exit instructions. Outputs depend on chart symbol, timeframe, and data quality, including volume availability. The channel is designed to be non-repainting in the sense that it uses confirmed bar data and does not use forward-looking logic; however, like all indicators, the current bar can update until it closes.
Risk Warning
This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument. Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose. Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed.
The analysis presented does not take into account your objectives, financial situation, or risk tolerance. Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional. Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured. You are solely responsible for all trading decisions, including position sizing and risk management. No external links, promotions, or contact details are provided, in line with TradingView House Rules.
Disclaimer
Use of this indicator is at our own discretion and risk. It is a visual analysis tool and should be validated through independent testing and a documented trading plan before being used in live decision-making.
Session OHLC Statistical MappingSession OHLC Statistical Mapping — Mean/Median Price Zones (Percent-Normalized)
Session OHLC Statistical Mapping plots statistically derived price zones around a session’s Open using historical behavior of that same session on a higher “mapping” timeframe (15m / 1H / 4H / Daily / Weekly).
It is designed to answer a simple question:
“Based on historical sessions, how far does price typically displace away from the open — and how far does it typically manipulate against it?”
Instead of using fixed ATR or arbitrary ranges, this tool builds zones from real distribution data collected from previous sessions, using Mean / Median / Both and optional robust estimators (including Power Mean).
What the levels mean
The indicator draws five core levels (or zones when Mean+Median are shown together):
OPEN (O) — The anchor of the session.
+DIS — “Displacement” in the direction of the session’s move (typical expansion away from open).
-DIS — Displacement on the opposite side (symmetrical reference).
-MAN — “Manipulation” zone above open (often where price runs stops/liq before moving).
+MAN — Manipulation zone below open.
DIS vs MAN logic (per historical candle):
If the session candle is bullish:
DIS = High − Open
MAN = Open − Low
If the session candle is bearish:
DIS = Open − Low
MAN = High − Open
That means DIS captures directional expansion and MAN captures the typical counter-move or wick against the open.
Why Percent Mode matters (important for multi-asset trading)
This indicator supports Points and Percent modes.
Points mode is direct and works well when your instrument is stable in price scale.
Percent mode normalizes distances by the historical session open:
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✅ Percent mode is usually the best choice when you trade multiple instruments (Gold, indices, FX, crypto) or when price levels change over time, because it keeps the zones comparable across regimes.
Mean vs Median (and why “Both” is powerful)
Median represents the “typical” session behavior and is more resistant to outliers.
Mean reflects the average including rare but large expansions (fat tails).
If you select Both, the indicator draws a zone between mean and median, which effectively becomes a distribution band.
Practical interpretation:
Median area = common/expected range
Mean extension = “higher probability tail events” / stretched sessions
This is especially useful in volatile markets where occasional big days pull the mean away from the median.
Robust averaging (Power Mean, RMS, EMA)
Markets often have non-normal distributions (skew, fat tails). Standard arithmetic mean can be influenced heavily by rare extreme sessions. This script allows alternative estimators:
Power Mean (p > 1): increases sensitivity to larger values in a controlled way (useful when you want zones that respect occasional expansions without fully jumping to outlier extremes).
RMS: strongly weights larger moves.
EMA: prioritizes recent behavior (good when volatility regime changes quickly).
These options let you match the zones to how the market actually behaves instead of assuming a perfect bell curve.
How to use it in trading
1) Intraday bias around the Open
If price holds above OPEN, you can treat upside zones as the primary magnet (+MAN → +DIS).
If price holds below OPEN, downside zones matter more (+MAN → -DIS).
2) Targets and take-profit mapping
A simple structured approach:
First target: nearest MAN zone
Second target: DIS zone
Extended target: mean/median extremes (if “Both” is enabled)
3) Rejection / reversal zones
MAN zones often behave like “liquidity sweep” regions:
Price runs into a MAN zone, wicks, and returns through OPEN → reversal potential.
Price enters a DIS zone and stalls → partial take profit or tighten stops.
4) Session-to-session context
Because zones are drawn for historical sessions, they can act like:
daily/weekly range expectations
contextual “where price should struggle”
systematic reference levels for day structure
Best markets to use this on
This indicator is built for anything liquid and session-driven, including:
Futures (ES, NQ, YM, CL, GC, etc.)
Great for mapping daily/4H session expansions and where stop-runs occur.
Gold (XAUUSD / GC)
Percent mode helps because gold moves in changing volatility regimes.
Forex (EURUSD, GBPUSD, USDJPY, etc.)
Percent normalization is ideal for FX pairs and long historical comparisons.
Crypto (BTC, ETH)
Works well with EMA or Power Mean when volatility shifts frequently.
Tips for best results
Start with Mapping TF = Daily, Lookback = 60.
Use Percent mode if you compare different assets or time periods.
Use Both (Mean+Median) to see distribution width and avoid overconfidence in a single number.
Use Power Mean (p ≈ 1.4–1.8) if arithmetic mean feels too tight or too distorted by outliers.
Combine with structure: previous highs/lows, session highs/lows, and rejection candles.
What this indicator is not
It does not predict direction by itself.
It’s a statistical mapping tool: it tells you where price typically expands and where it often “manipulates” around the session open.
Your edge comes from combining these zones with confirmation (market structure, orderflow, volume, candlesticks, etc.).
777 mean reversion engineA guy asked his librarian if they had any books on "paranoia." She leaned in and whispered, "They're right behind you." He hasn't been back to the library since.
777 expected Movehell yeaaaaaaaaaaaah, we back at it again yfm, some bs right here, will NOT tap ever!!!!!!
Repent Deviationsalot of levels, use at own risk, ict method, idk wth to type here just use ts and delete it instantly
VWATR Stop-Loss BandsPurpose
The script provides an adaptive stop‑loss framework built from VWATR, it anchors protective levels to price extremes and scales them with both volatility and volume. The objective is to create stop‑loss zones that reflect real market intensity rather than arbitrary fixed distances.
How it works
The script computes true range, multiplies it by volume, and smooths both the volume‑weighted range and raw volume using the selected moving average, their ratio forms VWATR, a volatility measure normalized by traded volume. It then calculates the standard deviation of VWATR to capture volatility‑of‑volatility. Stop‑loss levels are constructed by offsetting the low and high by one VWATR, with additional layers created by adding or subtracting one to five standard deviations. The plots use strong colors for core levels and progressively lighter tones for outer layers, establishing a clear visual hierarchy.
Rationale
This structure gives the trader stop‑loss levels that adapt to changing market conditions, expanding during high‑energy phases and contracting during quiet periods, which reduces premature stop‑outs and aligns risk with actual volatility. The standard deviation layers provide a graded map of volatility stress, allowing the user to assess how far price must travel to breach increasingly extreme thresholds. The result is a stop‑loss system that is both reactive and context‑aware, offering more informed decision‑making than static offsets.
Standard Deviation Vidya Moving Average | QuantLapseStandard Deviation Vidya MA by QuantLapse
Overview
The Standard Deviation Vidya MA indicator by QuantLapse is an dynamic and unique trend-following tool that leverages Variable Index Dynamic Average (VIDYA) along with a statistical measure of standard deviation to assess trend strength, direction and volatility. By utilizing adaptive smoothing and volatility adjustment this indicator provides a more responsive and robust signal framework for traders.
______
Technical Composition, Calculation, Key Components & Features
📌 VIDYA (Variable Index Dynamic Average)
An adaptive moving average that automatically adjusts its sensitivity based on prevailing market volatility.
Employs a volatility-weighted smoothing constant derived from standard deviation ratios, allowing the average to respond faster during high-momentum phases and slow down during consolidation.
Reduces lag during trend expansion while suppressing noise in low-volatility environments.
Provides clearer trend structure and regime awareness compared to fixed-length moving averages.
Serves as a dynamic baseline for volatility envelopes and trend-state classification within the system.
📌 Volatility Adjustment – Standard Deviation
The system constructs a volatility-adaptive envelope around the VIDYA baseline using standard deviation, allowing band width to expand and contract dynamically with changing market conditions.
VIDYA’s smoothing factor is adjusted by comparing short-term and longer-term standard deviation, increasing responsiveness during volatility expansion and dampening noise during compression.
Upper and lower bands are calculated by applying a configurable standard deviation multiplier to the VIDYA value, creating a proportional volatility boundary rather than a fixed offset.
Price movement beyond these bands confirms volatility-supported momentum, while price contained within the bands signals consolidation or transitional phases.
📌 Trend Signal Calculation
A bullish trend state is triggered when price closes above the upper standard deviation band, indicating sustained upward momentum with volatility confirmation.
A bearish trend state is triggered when price closes below the lower band, confirming downside momentum under expanding volatility.
Once established, the trend state persists until an opposing volatility break occurs, reducing whipsaw and improving regime stability.
Trend direction is visually reinforced through dynamic color-coding of the VIDYA line and its envelope, providing immediate directional context at a glance.
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How It Works in Trading
✅ Trend Strength Detection – Evaluates cumulative price movement over a defined window to assess directional conviction.
✅ Noise Reduction – Applies adaptive smoothing techniques to minimize whipsaws during choppy conditions.
✅ Dynamic Thresholding – Utilizes volatility-aware bands to define customizable trend continuation and invalidation levels.
✅ Color-Coded Visualization – Enhances chart readability by clearly distinguishing bullish, bearish, and neutral states.
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Visual Representation
Trend Signals on Moving Average and Background Color:
🟢 Green/Teal Moving Average – Strong Uptrend
🔴 Red/Pink Candles – Strong Downtrend
✅ Long & Short Labels can be turned on or off for trade signal clarity.
📊 Display of entry & exit points based on entry and exit criteria's.
📊 Display of Indicators equity and buy and hold equity to compare performance.
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Features and User Inputs
The Standard Deviation Vidya MA framework incorporates a flexible set of user-defined inputs designed to balance adaptability, clarity, and analytical control.
VIDYA Configuration – Customize the Variable Index Dynamic Average length and price source to control trend responsiveness based on volatility-adjusted smoothing.
Volatility & Deviation Controls – Adjust standard deviation lookback periods and multipliers to fine-tune adaptive upper and lower thresholds used for trend qualification.
Backtesting & Date Filters – Define a start date for historical evaluation and enable range filtering to analyze performance during specific market periods.
Display & Visualization Options – Toggle labels, equity curves, and visual overlays to tailor the chart presentation to personal trading preferences.
Color Customization – Fully configurable buy/sell colors for both trend signals and equity curves, allowing intuitive visual differentiation between bullish and bearish phases.
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Practical Applications
The Standard Deviation VIDYA MA is designed for traders seeking an adaptive trend-following framework that dynamically responds to changing market volatility. By combining VIDYA’s volatility-sensitive smoothing with standard deviation–based thresholds, the indicator offers a robust approach to directional analysis across multiple market conditions.
Key applications include:
Adaptive Trend Identification – Detect sustained bullish and bearish trends using a volatility-adjusted moving average that automatically accelerates or slows based on market activity.
Volatility-Aware Entry & Exit Signals – Utilize standard deviation bands to define dynamic breakout and invalidation zones, helping reduce false signals during low-volatility consolidation phases.
Noise-Filtered Trend Participation – Avoid whipsaws by requiring price expansion beyond adaptive deviation thresholds before confirming trend direction.
Systematic Backtesting & Evaluation – Analyze historical trend performance using built-in equity curves and date filters to assess effectiveness across different market regimes.
Visual Trend Confirmation – Leverage color-coded VIDYA lines, deviation zones, and optional labels to clearly interpret trend state and momentum strength in real time.
This framework bridges volatility analysis with adaptive trend logic, providing a disciplined and data-driven method for trend participation while maintaining clarity and interpretability in live trading environments.
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Conclusion
The Standard Deviation VIDYA MA by QuantLapse represents a modern evolution of adaptive trend analysis, blending volatility-weighted smoothing with statistically driven deviation thresholds. By integrating VIDYA’s responsiveness with standard deviation-based confirmation, the system delivers clearer trend structure, reduced noise, and more reliable directional context across varying market regimes.
This indicator is particularly well-suited for traders who value adaptability, clarity, and rule-based decision-making over static moving average techniques.
🔹 Who should use Standard Deviation VIDYA MA:
📊 Trend-Following Traders – Identify and stay aligned with sustained directional moves while avoiding premature reversals.
⚡ Momentum Traders – Capture volatility-supported expansions when price breaks beyond adaptive deviation bands.
🤖 Systematic & Algorithmic Traders – Ideal as a volatility-aware trend filter for rule-based entries, exits, and portfolio frameworks.
🔹 Disclaimer: Past performance does not guarantee future results. All trading involves risk, and no indicator or methodology can ensure profitability.
🔹 Strategic Advice: Always backtest thoroughly, optimize parameters responsibly, and align settings with your personal risk tolerance, timeframe, and market conditions before deploying the indicator in live trading.
