Range Deviations PRO | Trade SymmetryRange Deviations PRO — Extended Session Levels
An enhanced version of the original Range Deviations by @joshuuu, retaining the full core logic while adding a key upgrade:
🔹 All session ranges, midlines, and deviation levels now extend into the next trading session, giving seamless multi-session context.
Supports Asia, CBDR, Flout, ONS, and Custom Sessions — with options for half/full standard deviations, equilibrium, and range boxes exactly as in the original.
Extending these levels helps identify:
• Liquidity sweeps
• Trap moves / false breaks
• Daily high/low projections
• Premium–discount behavior across sessions
Ideal for traders using ICT concepts who want clearer continuation of session structure into the next day.
Credit: Original logic by @joshuuu — enhancements by TradeSymmetry.
Disclaimer: Educational use only. Not financial advice.
Deviation
[iQ]PRO Volume Frequency Profile+++🌌 PRO Volume Frequency Profile+++: The Fusion of Precision and Market Flow
The PRO Volume Frequency Profile+++ ( PRO VFP+++) is a next-generation analytical instrument designed for the discerning professional trader. It masterfully synthesizes multiple advanced concepts—Dynamic Linear Regression, High-Fidelity Frequency Analysis, and a Volumetric Distribution Profile—into a single, unified view of market structure. This powerful fusion provides unparalleled context for identifying high-probability turning points and key areas of interest.
🔬 Core Innovation: The Symbiotic Market Model
At its heart, the PRO VFP+++ is built on a proprietary methodology that transcends traditional price action by analyzing the frequency and distribution of traded volume relative to the dominant price trend.
Adaptive Regression Channel: The indicator establishes a highly dynamic Linear Regression channel, which acts as the core gravity well of the current trend. This channel is then protected by multi-tier Standard Deviation (SD) Bands with highly optimized, non-standard multipliers, defining the boundaries of expected price movement.
High-Resolution Frequency Bands: An integrated, proprietary Frequency Analysis component detects the underlying rhythmic oscillation in the market. This mechanism generates Frequency Bands that fluctuate around the core regression line, providing an exceptionally sensitive, leading, and dynamic channel for short-term mean-reversion and continuation signals.
Volumetric Profile Insight: A sophisticated Volume Frequency Profile is meticulously constructed over the look back period defined by the Linear Regression. This profile maps the distribution of trading activity, with an advanced implementation that provides a directional bias (Buy/Sell color gradient) within the volume nodes themselves, offering a deeper understanding of market participation.
✨ The Edge: Strategic Node Detection
The indicator's most compelling feature is its Intelligent Node Detection System. This system is specifically engineered to filter out market noise and highlight critical confluence zones:
Confluence Nodes: Automatically identifies and marks prices where the statistically significant Volume Nodes from the profile interact with the calculated Linear Regression lines and Standard Deviation Bands. These intersection points are areas where technical structure and realized market flow align, signaling price magnets or potential reversal zones.
Customizable Sensitivity: The system is governed by a Node Sensitivity parameter, allowing the user to fine-tune the filter for market conditions, ensuring only the most robust interactions are flagged.
🚨 Real-Time Opportunity & Security
To ensure maximum efficiency, the PRO VFP+++ features comprehensive, real-time Alerts based on all three core components:
Significant Node Cross: Alerts when price intersects a high-confluence interaction node.
Regression Line Touch: Alerts when price tests the core regression line, indicating a re-test of the dominant trend.
SD Band Touch: Alerts upon contact with the statistical boundaries, signaling potential overextension or trend strength.
This is a professional-grade, proprietary instrument. The source code is intentionally closed and protected to preserve the unique advantage of its underlying algorithms, which are the result of extensive research and optimization. Access is restricted and may be limited to invited, paying members only.
Unlock the next level of market structure analysis with the PRO VFP+++.
Tactical Deviation🎯 TACTICAL DEVIATION - Volume-Backed VWAP Deviation Analysis
What Makes This Different?
Unlike basic VWAP indicators, Tactical Deviation combines:
• Multi-timeframe VWAP deviation bands (Daily/Weekly/Monthly)
• Volume spike intelligence - signals only appear with volume confirmation
• Pivot reversal detection at deviation extremes
• Optional multi-VWAP confluence system
• Smart defaults for quality over quantity
This unique combination filters weak setups and identifies high-probability entries at extreme price deviations from fair value.
📊 DEFAULT SETTINGS (Ready to Use)
✅ Daily VWAP with ±2σ deviation bands
✅ Volume spike detection (1.5x average required)
✅ 2σ minimum deviation for signals
❌ Weekly/Monthly VWAPs (enable for multi-timeframe)
❌ Pivot reversal requirement (enable for stronger signals)
❌ Fill zones (optional visual enhancement)
Why: Daily VWAP is most relevant for intraday trading. 2σ bands catch meaningful moves. Volume spikes ensure conviction. Clean chart focuses on what matters.
🚀 HOW TO USE
BASIC USAGE:
• Green triangles (below bars) = Long signals at oversold deviations
• Red triangles (above bars) = Short signals at overbought deviations
SIGNAL QUALITY:
• Normal size, bright colors = Volume spike (best quality)
• Small size, lighter colors = Volume momentum
• Tiny size = No volume confirmation
DEVIATION ZONES:
• ±2σ = Extreme deviation (signals appear here)
• ±1σ to ±2σ = Extended but not extreme
• Within ±1σ = Normal range
TRADING APPROACHES:
Mean Reversion:
→ Enter when price reaches ±2σ with volume spike
→ Target: Return to VWAP or opposite band
→ Stop: Beyond extreme deviation
Trend Continuation:
→ Use bands to identify pullbacks
→ Enter pullback to VWAP in trending market
→ Volume confirms continuation
Reversal Trading:
→ Enable "Require Pivot Reversal" for stronger signals
→ Signals only when deviation + pivot reversal occur
→ Higher probability, fewer signals
⚙️ EXPLORE SETTINGS FOR FULL USE
VWAP SETTINGS:
• Show Weekly/Monthly VWAP = Multi-timeframe context
• Show ±1σ Bands = Normal deviation range
• Show ±3σ Bands = Extreme extremes (rare but powerful)
SIGNAL SETTINGS:
• Min Deviation: 1σ (more signals) | 2σ (default) | 3σ (fewer, extreme only)
• Require Pivot Reversal: OFF (default) | ON (stronger but fewer)
• Volume Spike Threshold: 1.5x (default) | 2.0x+ (major spikes) | 1.2x (more signals)
CONFLUENCE SETTINGS:
• Require Multi-VWAP Confluence: OFF (default) | ON (2+ VWAPs must agree)
• Min VWAPs: 2 (Daily + Weekly/Monthly) | 3 (all must agree)
VISUAL SETTINGS:
• Show Fill Zones = Shaded areas between bands
• Fill Opacity = Transparency adjustment
• Line Widths = Customize thickness
💡 PRO TIPS
1. Start with defaults, then enable features as you learn
2. Volume spike requirement filters weak moves - keep it enabled
3. Enable Weekly/Monthly VWAPs for higher timeframe context
4. Enable confluence for swing trading setups
5. Pivot reversals: ON for reversals, OFF for continuations
6. Check top-right info table for current deviation levels
🎨 VISUAL GUIDE
• Cyan Line = Daily VWAP (fair value)
• Cyan Bands = Daily deviation zones
• Orange Line = Weekly VWAP (if enabled)
• Purple Line = Monthly VWAP (if enabled)
• Green Triangle = Long signal (oversold)
• Red Triangle = Short signal (overbought)
⚠️ IMPORTANT
Educational purposes only. Always use proper risk management. Signals are based on statistical deviation, not guarantees. Volume confirmation improves quality but doesn't guarantee outcomes. Combine with your own analysis.
The unique combination of VWAP deviation analysis, volume profile confirmation, pivot identification, and multi-timeframe confluence in a single clean interface makes Tactical Deviation different from basic VWAP indicators.
Happy Trading! 📈
LibBrStLibrary "LibBrSt"
This is a library for quantitative analysis, designed to estimate
the statistical properties of price movements *within* a single
OHLC bar, without requiring access to tick data. It provides a
suite of estimators based on various statistical and econometric
models, allowing for analysis of intra-bar volatility and
price distribution.
Key Capabilities:
1. **Price Distribution Models (`PriceEst`):** Provides a selection
of estimators that model intra-bar price action as a probability
distribution over the range. This allows for the
calculation of the intra-bar mean (`priceMean`) and standard
deviation (`priceStdDev`) in absolute price units. Models include:
- **Symmetric Models:** `uniform`, `triangular`, `arcsine`,
`betaSym`, and `t4Sym` (Student-t with fat tails).
- **Skewed Models:** `betaSkew` and `t4Skew`, which adjust
their shape based on the Open/Close position.
- **Model Assumptions:** The skewed models rely on specific
internal constants. `betaSkew` uses a fixed concentration
parameter (`BETA_SKEW_CONCENTRATION = 4.0`), and `t4Sym`/`t4Skew`
use a heuristic scaling factor (`T4_SHAPE_FACTOR`)
to map the distribution.
2. **Econometric Log-Return Estimators (`LogEst`):** Includes a set of
econometric estimators for calculating the volatility (`logStdDev`)
and drift (`logMean`) of logarithmic returns within a single bar.
These are unit-less measures. Models include:
- **Parkinson (1980):** A High-Low range estimator.
- **Garman-Klass (1980):** An OHLC-based estimator.
- **Rogers-Satchell (1991):** An OHLC estimator that accounts
for non-zero drift.
3. **Distribution Analysis (PDF/CDF):** Provides functions to work
with the Probability Density Function (`pricePdf`) and
Cumulative Distribution Function (`priceCdf`) of the
chosen price model.
- **Note on `priceCdf`:** This function uses analytical (exact)
calculations for the `uniform`, `triangular`, and `arcsine`
models. For all other models (e.g., `betaSkew`, `t4Skew`),
it uses **numerical integration (Simpson's rule)** as
an approximation of the cumulative probability.
4. **Mathematical Functions:** The library's Beta distribution
models (`betaSym`, `betaSkew`) are supported by an internal
implementation of the natural log-gamma function, which is
based on the Lanczos approximation.
---
**DISCLAIMER**
This library is provided "AS IS" and for informational and
educational purposes only. It does not constitute financial,
investment, or trading advice.
The author assumes no liability for any errors, inaccuracies,
or omissions in the code. Using this library to build
trading indicators or strategies is entirely at your own risk.
As a developer using this library, you are solely responsible
for the rigorous testing, validation, and performance of any
scripts you create based on these functions. The author shall
not be held liable for any financial losses incurred directly
or indirectly from the use of this library or any scripts
derived from it.
priceStdDev(estimator, offset)
Estimates **σ̂** (standard deviation) *in price units* for the current
bar, according to the chosen `PriceEst` distribution assumption.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float σ̂ ≥ 0 ; `na` if undefined (e.g. zero range).
priceMean(estimator, offset)
Estimates **μ̂** (mean price) for the chosen `PriceEst` within the
current bar.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float μ̂ in price units.
pricePdf(estimator, price, offset)
Probability-density under the chosen `PriceEst` model.
**Returns 0** when `p` is outside the current bar’s .
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
price (float) : series float Price level to evaluate.
offset (int) : series int To offset the calculated bar
Returns: series float Density value.
priceCdf(estimator, upper, lower, steps, offset)
Cumulative probability **between** `upper` and `lower` under
the chosen `PriceEst` model. Outside-bar regions contribute zero.
Uses a fast, analytical calculation for Uniform, Triangular, and
Arcsine distributions, and defaults to numerical integration
(Simpson's rule) for more complex models.
Parameters:
estimator (series PriceEst) : series PriceEst Distribution assumption (see enum).
upper (float) : series float Upper Integration Boundary.
lower (float) : series float Lower Integration Boundary.
steps (int) : series int # of sub-intervals for numerical integration (if used).
offset (int) : series int To offset the calculated bar.
Returns: series float Probability mass ∈ .
logStdDev(estimator, offset)
Estimates **σ̂** (standard deviation) of *log-returns* for the current bar.
Parameters:
estimator (series LogEst) : series LogEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float σ̂ (unit-less); `na` if undefined.
logMean(estimator, offset)
Estimates μ̂ (mean log-return / drift) for the chosen `LogEst`.
The returned value is consistent with the assumptions of the
selected volatility estimator.
Parameters:
estimator (series LogEst) : series LogEst Distribution assumption (see enum).
offset (int) : series int To offset the calculated bar
Returns: series float μ̂ (unit-less log-return).
Matrix bands by JaeheeMatrix Bands — multi-sigma EMA bands for price dispersion context (no signals)
📌 What it is
Matrix Bands draws an EMA-based central line with multiple standard-deviation envelopes at ±1σ, ±1.618σ, ±2σ, ±2.618σ, ±3σ.
Thin core lines show the precise band levels, while subtle outer “glow” lines improve readability without obscuring candles.
📌 How it works (concept)
Basis: EMA of the selected source (default: close)
Dispersion: Rolling sample standard deviation over the same length
Bands: Basis ± k·σ for k ∈ {1, 1.618, 2, 2.618, 3}
This is not a strategy and does not generate trade signals.
It provides price dispersion context only.
📌 Why these levels together (justification of the combination)
Using multiple σ layers reveals graduated risk zones in one view:
±1σ: routine fluctuation
±1.618σ & ±2σ: extended but still common excursions
±2.618σ & ±3σ: statistically rare extremes, where mean-reversion risk or trend acceleration risk increases
Combining these specific multipliers allows traders to judge positioning vs. volatility instantly, without switching between separate indicators or re-configuring a single band.
📌 How it differs from classic Bollinger Bands
Unlike classic Bollinger Bands, which typically use an SMA basis and only ±2σ envelopes,
Matrix Bands uses an EMA basis for faster trend responsiveness and plots five sigma levels (±1, ±1.618, ±2, ±2.618, ±3).
This design allows traders to visualize market dispersion across multiple statistical thresholds simultaneously, making it more versatile for both trend-following and mean-reversion contexts.
📌 How to read it (context, not signals)
Mean-reversion context: Moves beyond ±2σ may indicate stretched conditions; wait for your own confirmation signals before acting
Trend context: In strong trends, price can “ride” the outer bands; sustained closes near +2σ~+3σ (uptrend) or −2σ~−3σ (downtrend) suggest persistent momentum
Regime observation: Band width expands in high volatility and contracts in quiet regimes; adjust stops and sizing accordingly
📌 Inputs
BB Length: lookback period for EMA and σ (default: 20)
Source: price source for calculations
📌 Design notes
Thin inner lines = exact levels
Soft outer lines = readability “glow” only; no effect on calculations
Overlay display keeps the chart uncluttered
📌 Limitations & good practice
No entry/exit logic; use with your own strategy rules
Volatility interpretation varies by timeframe
Past patterns do not guarantee future outcomes; risk management is essential
📌 Defaults & scope
Works on any symbol with OHLCV
No alerts, no strategy results, no performance claims
Angle Market Structure [BigBeluga]🔵 OVERVIEW
Angle Market Structure is a smart pivot-based tool that dynamically adapts to price action by accelerating breakout and breakdown detection. It draws market structure levels based on pivot highs/lows and gradually adjusts those levels closer to price using an angle threshold. Upon breakout, the indicator projects deviation zones with labeled levels (+1, +2, +3 or −1, −2, −3) to track price extension beyond structure.
🔵 CONCEPTS
Adaptive Market Structure: Uses pivots to define structure levels, which dynamically angle closer to price over time to capture breakouts sooner.
Breakout Acceleration: Pivot high levels decrease and pivot low levels increase each bar using a user-defined angle (based on ATR), improving reactivity.
Deviation Zones: Once a breakout or breakdown occurs, 3 deviation levels are projected to show how far price extends beyond the breakout point.
Count Labels: Each successful structure break is numbered sequentially, giving traders insight into momentum and trend persistence.
Visual Clarity: The script uses colored pivot points, trend lines, and extension labels for easy structural interpretation.
🔵 FEATURES
Calculates pivot highs and lows using a customizable length.
Applies an angle modifier (ATR-based) to gradually pull levels closer to price.
Plots breakout and breakdown lines in distinct colors with automatic extension.
Shows deviation zones (+1, +2, +3 or −1, −2, −3) after breakout with customizable size.
Color-coded labels for trend break count (bullish or bearish).
Dynamic label sizing and theme-aware colors.
Smart label positioning to avoid chart clutter.
Built-in limit for deviation zones to maintain clarity and performance.
🔵 HOW TO USE
Use pivot-based market structure to identify breakout and breakdown zones.
Watch for crossover (up) or crossunder (down) events as trend continuation or reversal signals.
Observe +1/+2/+3 or -1/-2/-3 levels for overextension opportunities or trailing stop ideas.
Use breakout count as a proxy for trend strength—multiple counts suggest momentum.
Combine with volume or order flow tools for higher confidence entries at breakout points.
Adjust the angle setting to fine-tune sensitivity based on market volatility.
🔵 CONCLUSION
Angle Market Structure enhances traditional pivot-based analysis by introducing breakout acceleration and structured deviation tracking. It’s a powerful tool for traders seeking a cleaner, faster read on market structure and momentum strength—especially during impulsive price moves or structural transitions.
Price Deviation Table by ZonkeyXLProvides a 30 column table showing price deviation per bar close, highlighting larger deviations in red (downside) or green (upside).
Deviations that get highlighted in red/green are calculated to be 2x the amount of price movement in the previous candle, but can be customised to check any deviation size you want in the options panel.
Can be used on any timeframe but you need to specify the number of bars per table column to make it accurate to what you want.
Examples:
If used on the 1 second time frame you could specify bars to 1 and then each column value will check the price as at close on the most recent second for deviations against the close of price on the second prior, showing comparisons up to 30 seconds.
If on the 1 minute time-frame you could specify bars to 2 and then each column value would show deviations from most recent price close to 2 minutes ago, making all 30 columns show deviations for up to an hour.
At the end of the column are 3 orange coloured columns. The first one compares price to 10 bars ago. The second compares current price to 20 bars ago. The 3rd compares current price to 30 bars ago.
In our example on the 1 second above, this would mean deviation is calculated by comparing most recent close to 10 seconds ago, then to 20 seconds ago, and then to 30 seconds ago. The final 3 columns do not highlight red or green, so you can differentiate them properly from the main deviation columns at all times.
Note that the table is rolling - so once it is populated for the first time, only the final column will update while the prior values will shift one column to the left.
Dynamic Gap Probability ToolDynamic Gap Probability Tool measures the percentage gap between price and a chosen moving average, then analyzes your chart history to estimate the likelihood of the next candle moving up or down. It dynamically adjusts its sample size to ensure statistical robustness while focusing on the exact deviation level.
Originality and Value:
• Combines gap-based analysis with dynamic sample aggregation to balance precision and reliability.
• Automatically extends the sample when exact matches are scarce, avoiding misleading signals on rare extreme moves.
• Provides real “next-candle” probabilities based on historical occurrences rather than fixed thresholds or untested heuristics.
• Adds value by giving traders an evidence-based edge: you see how similar past deviations actually played out.
How It Works:
1. Calculate gap = (close – moving average) / moving average * 100.
2. Round the absolute gap to nearest percent (X%).
3. Count historical bars where gap ≥ X% above or ≤ –X% below.
4. If exact X% count is below the minimum occurrences threshold, include gaps at X+1%, X+2%, etc., until threshold is reached.
5. Compute “next-candle” green vs. red probabilities from the aggregated sample.
6. Display current gap, sample size, green probability, and red probability in a table.
Inputs:
• Moving Average Type (SMA, EMA, WMA, VWMA, HMA, SMMA, TMA)
• Moving Average Period (default 200)
• Minimum Occurrences Threshold (default 50)
• Table position and styling options
Examples:
• If price is 3% above the 200-period SMA and 120 occurrences ≥3% are found, with 84 green next candles (70%) and 36 red (30%), the script displays “3% | 120 | 70% green | 30% red.”
• If price is 8% below the SMA but only 20 exact matches exist, the script will include 9% and 10% gaps until it reaches 50 samples, then calculate probabilities from that broader set.
Why It’s Useful:
• Mean-reversion traders see green-probability signals at extreme overbought or oversold levels.
• Trend-followers identify continuation likelihood when red probability is high.
• Risk managers gauge reliability by inspecting sample size before acting on any signal.
Limitations:
• Historical probabilities do not guarantee future performance.
• Results depend on timeframe and symbol, backtest with your data before trading.
• Use realistic slippage and commission when overlaying on strategy scripts.
Volatility Zones (STDEV %)This indicator displays the relative volatility of an asset as a percentage, based on the standard deviation of price over a custom length.
🔍 Key features:
• Uses standard deviation (%) to reflect recent price volatility
• Classifies volatility into three zones:
Low volatility (≤2%) — highlighted in blue
Medium volatility (2–4%) — highlighted in orange
High volatility (>4%) — highlighted in red
• Supports visual background shading and colored line output
• Works on any timeframe and asset
📊 This tool is useful for identifying low-risk entry zones, periods of expansion or contraction in price behavior, and dynamic market regime changes.
You can adjust the STDEV length to suit your strategy or timeframe. Best used in combination with your entry logic or trend filters.
Z Score Overlay [BigBeluga]🔵 OVERVIEW
A clean and effective Z-score overlay that visually tracks how far price deviates from its moving average. By standardizing price movements, this tool helps traders understand when price is statistically extended or compressed—up to ±4 standard deviations. The built-in scale and real-time bin markers offer immediate context on where price stands in relation to its recent mean.
🔵 CONCEPTS
Z Score Calculation:
Z = (Close − SMA) ÷ Standard Deviation
This formula shows how many standard deviations the current price is from its mean.
Statistical Extremes:
• Z > +2 or Z < −2 suggests statistically significant deviation.
• Z near 0 implies price is close to its average.
Standardization of Price Behavior: Makes it easier to compare volatility and overextension across timeframes and assets.
🔵 FEATURES
Colored Z Line: Gradient coloring based on how far price deviates—
• Red = oversold (−4),
• Green = overbought (+4),
• Yellow = neutral (~0).
Deviation Scale Bar: A vertical scale from −4 to +4 standard deviations plotted to the right of price.
Active Z Score Bin: Highlights the current Z-score bin with a “◀” arrow
Context Labels: Clear numeric labels for each Z-level from −4 to +4 along the side.
Live Value Display: Shows exact Z-score on the active level.
Non-intrusive Overlay: Can be applied directly to price chart without changing scaling behavior.
🔵 HOW TO USE
Identify overbought/oversold areas based on +2 / −2 thresholds.
Spot potential mean reversion trades when Z returns from extreme levels.
Confirm strong trends when price remains consistently outside ±2.
Use in multi-timeframe setups to compare strength across contexts.
🔵 CONCLUSION
Z Score Overlay transforms raw price action into a normalized statistical view, allowing traders to easily assess deviation strength and mean-reversion potential. The intuitive scale and color-coded display make it ideal for traders seeking objective, volatility-aware entries and exits.
Head Hunter HHHead Hunter HH - Advanced Market Structure & Volume Analysis Indicator
This indicator combines volume analysis, price action, and VWAP to identify high-probability trading opportunities across multiple timeframes.
Key Features:
• Smart Volume Analysis: Detects institutional volume patterns using dynamic thresholds
• VWAP-Based Market Structure: Multiple standard deviation bands for precision entry/exit
• Daily Level Integration: Previous day's high, low, close, and current day's open
• Advanced Signal Classification: Regular, Super Strong, and Scalp signals
Signal Types:
1. Regular Signals (White/Purple Triangles)
• Volume-confirmed reversals
• Institutional price levels
• Technical momentum alignment
2. Super Strong Signals (Green/Red Diamonds)
• High-volume breakouts
• Strong momentum confirmation
• Multiple timeframe alignment
3. Scalp Signals (Green/Magenta Circles)
• Quick reversal opportunities
• VWAP deviation analysis
• Volume surge confirmation
Visual Components:
• VWAP with Standard Deviation Bands
• 50 MA (optional)
• Daily Reference Levels
• Color-coded signals based on strength
• Bar color changes on confirmed signals
Best Practices:
• Most effective on higher timeframes (1H+)
• Use with major pairs/instruments
• Combine signals with support/resistance
• Monitor volume confirmation
• Wait for candle close confirmation
This indicator helps identify institutional order flow and high-probability reversal zones by analyzing volume patterns, price action, and market structure, providing traders with multiple confirmation layers before entry.
Note: Results may vary based on market conditions and timeframe selection. Always use proper risk management.
Directional Deviation Index (DDI)Directional Deviation Index (DDI) is a streamlined, adaptive indicator for analyzing market cycles, detecting trend direction, and gauging momentum. By measuring how far price deviates from a smoothed average, the DDI adapts dynamically to both bullish and bearish conditions.
Key Features:
Unified Smoothing: Choose SMA or EMA for consistent, predictable signals.
Log Scale: Focus on percentage-based moves—ideal for volatile or higher-priced assets.
Adaptive Trend Levels: Auto-adjust uptrend/downtrend thresholds based on market volatility.
Momentum Visualization: Transparent color fills (green for uptrends, red for downtrends) that intensify with stronger deviations.
Customizable Sensitivity: Fine-tune uptrend and downtrend settings to suit any trading style.
Simple Alerts & Status Line: Get notified on key crossovers and track real-time price without chart clutter.
Comparison to Similar Indicators:
Bollinger Bands: Both use deviations from a moving average, but the DDI emphasizes directional momentum and adaptive threshold levels rather than fixed bands.
RSI/Stochastics: While these oscillators focus on overbought or oversold conditions, the DDI tracks how far price strays from its average, giving a clearer picture of trend strength.
MACD: MACD is built on EMA crossovers, whereas the DDI highlights deviations from a mean and adapts more directly to volatility changes.
Use the DDI to identify trend strength, spot potential reversals, and monitor evolving market conditions across stocks, crypto, forex, and beyond. It’s a versatile yet concise tool for traders seeking faster, more confident decisions.
MA Deviation// -----------------------------------------------------------------------------
// MA Deviation Marking & Alert (MA Divergence)
// -----------------------------------------------------------------------------
// Short Title: MA Deviation Radar
// Author: zhipeng luo
// Version: 1.0
// Date: 2025-04-11
// -----------------------------------------------------------------------------
// Overview:
// This indicator identifies and highlights price bars where the closing price
// deviates significantly from its Simple Moving Average (SMA) by a user-defined
// percentage. It visually marks these bars on the chart and provides
// configurable alert conditions for threshold breaches.
//
// How it Works:
// 1. Calculates the Simple Moving Average (SMA) based on the 'MA Period' input.
// 2. Computes the percentage deviation of the closing price from the SMA value.
// Formula: `((Close - SMA) / SMA) * 100`
// 3. Compares the calculated deviation percentage against the positive and
// negative 'Threshold (%)' input values.
// 4. Marks the background of the price bars when a threshold is exceeded:
// - Red Background: Price deviation is greater than the positive threshold.
// - Green Background: Price deviation is less than the negative threshold.
// 5. Includes an optional, non-visible plot of the MA line itself.
// 6. Offers three distinct alert conditions for automation and notifications.
//
// Features:
// - Customizable Simple Moving Average period.
// - Adjustable deviation threshold percentage.
// - Clear visual signals using background colors on the main chart.
// - Built-in Alert Conditions:
// - MA Positive Deviation Alert (Triggers when price > MA + Threshold %)
// - MA Negative Deviation Alert (Triggers when price < MA - Threshold %)
// - MA Deviation Alert - Any (Triggers on either positive or negative breach)
//
// How to Use:
// - Identify Potential Extremes: Useful for spotting potential overbought (large
// positive deviation) or oversold (large negative deviation) conditions
// which might precede price corrections or mean reversion.
// - Gauge Trend Extension: Extreme deviations can sometimes indicate that a
// trend is overextended and might be due for a pause or reversal.
// - Parameter Tuning: Adjust the 'MA Period' and '(Threshold %)' settings to
// suit the specific asset, timeframe, and volatility characteristics you
// are analyzing. Lower thresholds yield more signals; higher thresholds
// focus on more significant deviations.
// - Alerts: Set up alerts via the TradingView alert menu using the provided
// conditions ("MA Positive Deviation Alert", "MA Negative Deviation Alert",
// "MA Deviation Alert - Any") to get notified of potential setups.
//
// Parameters:
// - MA Period (Default: 200): The lookback period for the SMA calculation.
// - (Threshold %) (Default: 7.0): The percentage deviation (positive and
// negative) from the MA required to trigger a background signal and alert.
//
// Alerts & Important Note:
// Three alert conditions corresponding to the signals are available:
// 1. "MA Positive Deviation Alert"
// 2. "MA Negative Deviation Alert"
// 3. "MA Deviation Alert - Any"
//
// ***Please Note:*** The value shown after "( {{plot_0}}%)" or
// "( {{plot_0}}%)" in the default alert message refers to the
// **Moving Average value** (`plot_0`), not the actual deviation percentage.
// The alert *triggers correctly* based on the deviation percentage crossing
// the threshold, but the number displayed by the `{{plot_0}}` placeholder
// in the message is the MA's value at that time due to the script's
// internal plot order.
//
// Disclaimer: This indicator is provided for informational and analytical
// purposes only. It does not constitute financial advice or a recommendation
// to buy or sell any asset. Always conduct your own research and use proper
// risk management. Trading involves significant risk.
// -----------------------------------------------------------------------------
ZVOL — Z-Score Volume Heatmapⓩ ZVOL transforms raw volume into a statistically calibrated heatmap using Z-score thresholds. Unlike classic volume indicators that rely on fixed MA comparisons, ZVOL calculates how many standard deviations each volume bar deviates from its mean. This makes the reading adaptive across timeframes and assets, in order to distinguish meaningful crowd behavior from random volatility.
📊 The core display is a five-zone histogram, each encoded by color and statistical depth. Optional background shading mirrors these zones across the entire pane, revealing subtle compression or structural rhythm shifts across time. By grounding the volume reading in volatility-adjusted context, ZVOL inhibits impulsive trading tactics by compelling the structure, not the sentiment, to dictate the signal.
🥵 Heatmap Coloration:
🌚 Suppressed volume — congestion, coiling phases
🩱 Stable flow — early trend or resting volume
🏀 High activity — emerging pressure
💔 Extreme — possible climax or institutional print
🎗️ A dynamic Fibonacci-based 21:34-period EMA ribbon overlays the histogram. The fill area inverts color on crossover, providing a real-time read on tempo, expansion, or divergence between price structure and crowd effort.
💡 LTF Usage Suggestions:
• Confirm breakout legs when orange or red zones align with range exits
• Fade overextended moves when red bars appear into resistance
• Watch for rising EMAs and orange volume to front-run impulsive moves
• Combine with volatility suppression (e.g. ATR) to catch compression → expansion transitions
🥂 Ideal Pairings:
• OBVX Conviction Bias — to confirm directional intent behind volume shifts
• SUPeR TReND 2.718 — for directional filters
• ATR Turbulence Ribbon — to detect compression phases
👥 The OBVX Conviction Bias adds a second dimension to ZVOL by revealing whether crowd effort is aligning with price direction or diverging beneath the surface. While ZVOL identifies statistical anomalies in raw volume, OBVX tracks directional commitment using cumulative volume and moving average cross logic. Use them together to spot fake-outs, anticipate structure-confirmed breakouts, or time pullbacks with volume-based conviction.
🔬 ZVOL isn’t just a volume filter — it’s a structural lens. It reveals when crowd effort is meaningful, when it's fading, and when something is about to shift. Designed for structure-aware traders who care about context, not noise.
LinearRegressionLibrary "LinearRegression"
Calculates a variety of linear regression and deviation types, with optional emphasis weighting. Additionally, multiple of slope and Pearson’s R calculations.
calcSlope(_src, _len, _condition)
Calculates the slope of a linear regression over the specified length.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The slope of the linear regression.
calcReg(_src, _len, _condition)
Calculates a basic linear regression, returning y1, y2, slope, and average.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) An array of 4 values: .
calcRegStandard(_src, _len, _emphasis, _condition)
Calculates an Standard linear regression with optional emphasis.
Parameters:
_src (float) : (series float) The source data series.
_len (int) : (int) The length of the lookback period.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegRidge(_src, _len, lambda, _emphasis, _condition)
Calculates a ridge regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The ridge regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLasso(_src, _len, lambda, _emphasis, _condition)
Calculates a Lasso regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda (float) : (float) The Lasso regularization parameter.
_emphasis (float) : (float) The emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcElasticNetLinReg(_src, _len, lambda1, lambda2, _emphasis, _condition)
Calculates an Elastic Net regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
lambda1 (float) : (float) L1 regularization parameter (Lasso).
lambda2 (float) : (float) L2 regularization parameter (Ridge).
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegHuber(_src, _len, delta, iterations, _emphasis, _condition)
Calculates a Huber regression using Iteratively Reweighted Least Squares (IRLS).
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
delta (float) : (float) Huber threshold parameter.
iterations (int) : (int) Number of IRLS iterations.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegLAD(_src, _len, iterations, _emphasis, _condition)
Calculates a Least Absolute Deviations (LAD) regression via IRLS.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
iterations (int) : (int) Number of IRLS iterations for LAD.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRegBayesian(_src, _len, priorMean, priorSpan, sigma, _emphasis, _condition)
Calculates a Bayesian linear regression with optional emphasis.
Parameters:
_src (float) : (float) The source data series.
_len (int) : (int) The length of the lookback period.
priorMean (float) : (float) The prior mean for the slope.
priorSpan (float) : (float) The prior variance (or span) for the slope.
sigma (float) : (float) The assumed standard deviation of residuals.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: (float ) .
calcRFromLinReg(_src, _len, _slope, _average, _y1, _condition)
Calculates the Pearson correlation coefficient (R) based on linear regression parameters.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_average (float) : (float) The average value of the source data series.
_y1 (float) : (float) The starting point (y-intercept of the oldest bar) for the linear regression.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The Pearson correlation coefficient (R) adjusted for the direction of the slope.
calcRFromSource(_src, _len, _condition)
Calculates the correlation coefficient (R) using a specified length and source data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast for efficiency.
Returns: (float) The correlation coefficient (R).
calcSlopeLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is flattest (closest to zero).
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length to consider (minimum of 2).
_minLen (int) : (int) The minimum length to start from (cannot exceed the max length).
_step (int) : (int) The increment step for lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is flattest.
calcSlopeLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is highest.
calcSlopeLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length at which the slope is lowest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the slope is lowest.
calcSlopeLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length at which the absolute slope value is highest.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length at which the absolute slope value is highest.
calcRLengthZero(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest absolute R value.
calcRLengthHighest(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest R value.
calcRLengthLowest(_src, _len, _minLen, _step, _condition)
Identifies the length with the lowest R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the lowest R value.
calcRLengthAbsolute(_src, _len, _minLen, _step, _condition)
Identifies the length with the highest absolute R value.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The maximum lookback length (minimum of 2).
_minLen (int) : (int) The minimum length to start from.
_step (int) : (int) The step for incrementing lengths.
_condition (bool) : (bool) Flag to enable calculation. Set to true to calculate on every bar; otherwise, set to barstate.islast.
Returns: (int) The length with the highest absolute R value.
calcDevReverse(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the regressive linear deviation in reverse order, with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevForward(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the progressive linear deviation in forward order (oldest to most recent bar), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is oldest and _src is most recent.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the most recent bar, adjusted by slope).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevBalanced(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the balanced linear deviation with optional emphasis on recent or older data.
Parameters:
_src (float) : (float) Source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept of the linear regression (value at the oldest bar).
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMean(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the mean absolute deviation from a forward-applied linear trend (oldest to most recent), with optional emphasis.
Parameters:
_src (float) : (float) The source data array, where _src is the most recent and _src is the oldest.
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevMedian(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates the median absolute deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data array (index 0 = oldest, index _len - 1 = most recent).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns:
calcDevPercent(_y1, _inputDev, _condition)
Calculates the percent deviation from a given value and a specified percentage.
Parameters:
_y1 (float) : (float) The base value from which to calculate deviation.
_inputDev (float) : (float) The deviation percentage.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevFitted(_len, _slope, _y1, _emphasis, _condition)
Calculates the weighted fitted deviation based on high and low series data, showing max deviation, with optional emphasis.
Parameters:
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcDevATR(_src, _len, _slope, _y1, _inputDev, _emphasis, _condition)
Calculates an ATR-style deviation with optional emphasis on recent data.
Parameters:
_src (float) : (float) The source data (typically close).
_len (int) : (int) The length of the lookback period.
_slope (float) : (float) The slope of the linear regression.
_y1 (float) : (float) The Y-intercept (oldest bar) of the linear regression.
_inputDev (float) : (float) The input deviation multiplier.
_emphasis (float) : (float) Emphasis factor: 0 for equal weight; >0 emphasizes recent bars; <0 emphasizes older bars.
_condition (bool) : (bool) Flag to enable calculation (true = calculate).
Returns: A 2-element tuple: .
calcPricePositionPercent(_top, _bot, _src)
Calculates the percent position of a price within a linear regression channel. Top=100%, Bottom=0%.
Parameters:
_top (float) : (float) The top (positive) deviation, corresponding to 100%.
_bot (float) : (float) The bottom (negative) deviation, corresponding to 0%.
_src (float) : (float) The source price.
Returns: (float) The percent position within the channel.
plotLinReg(_len, _y1, _y2, _slope, _devTop, _devBot, _scaleTypeLog, _lineWidth, _extendLines, _channelStyle, _colorFill, _colUpLine, _colDnLine, _colUpFill, _colDnFill)
Plots the linear regression line and its deviations, with configurable styles and fill.
Parameters:
_len (int) : (int) The lookback period for the linear regression.
_y1 (float) : (float) The starting y-value of the regression line.
_y2 (float) : (float) The ending y-value of the regression line.
_slope (float) : (float) The slope of the regression line (used to determine line color).
_devTop (float) : (float) The top deviation to add to the line.
_devBot (float) : (float) The bottom deviation to subtract from the line.
_scaleTypeLog (bool) : (bool) Use a log scale if true; otherwise, linear scale.
_lineWidth (int) : (int) The width of the plotted lines.
_extendLines (string) : (string) How lines should extend (none, left, right, both).
_channelStyle (string) : (string) The style of the channel lines (solid, dashed, dotted).
_colorFill (bool) : (bool) Whether to fill the space between the top and bottom deviation lines.
_colUpLine (color) : (color) Line color when slope is positive.
_colDnLine (color) : (color) Line color when slope is negative.
_colUpFill (color) : (color) Fill color when slope is positive.
_colDnFill (color) : (color) Fill color when slope is negative.
AE - ATR Exhaustion ChannelAE - ATR Exhaustion Channel
📈 Overview
Identify Exhaustion Zones & Trend Breakouts with ATR Precision!
The AE - ATR Exhaustion Channel is a powerful volatility-based trading tool that combines an averaged SMA with ATR bands to dynamically highlight potential trend exhaustion zones. It provides real-time breakout detection by marking when price moves beyond key volatility bands, helping traders spot overextensions and reversals with ease.
🔑 Key Features
✔️ ATR-SMA Hybrid Channel: Uses an averaged SMA as the core trend filter while incorporating adaptive ATR-based bands for precise volatility tracking.
✔️ Dynamic Exhaustion Markers: Marks red crosses when price exceeds the upper band and green crosses when price drops below the lower band.
✔️ Customizable ATR Sensitivity: Adjust the ATR multiplier and length settings to fine-tune band sensitivity based on market conditions.
✔️ Clear Channel Visualization: A gray SMA midpoint and a blue-filled ATR band zone make it easy to track market structure.
📚 How It Works
1️⃣ Averaged SMA Calculation: The script calculates an averaged SMA over a user-defined range (min/max period). This smooths out short-term fluctuations while preserving trend direction.
2️⃣ ATR Band Construction: The ATR value (adjusted by a multiplier) is added to/subtracted from the SMA to form dynamic upper and lower volatility bands.
3️⃣ Exhaustion Detection:
If high > upper ATR band, a red cross is plotted (potential overextension).
If low < lower ATR band, a green cross is plotted (potential reversal zone).
4️⃣ Filled ATR Channel: The area between the upper and lower bands is shaded blue, providing a visual trading range.
🎨 Customization & Settings
⚙️ ATR Length – Adjusts the ATR calculation period (default: 14).
⚙️ ATR Multiplier – Scales the ATR bands for tighter or wider volatility tracking (default: 0.8, adjustable in 0.1 steps).
⚙️ SMA Range (Min/Max Length) – Defines the period range for calculating the averaged SMA (default: 5-20).
⚙️ Rolling Lookback Length – Controls how far back the high/low comparison is calculated (default: 50 bars).
🚀 Practical Usage
📌 Spotting Exhaustion Zones – Look for red/green markers appearing outside the ATR bands, signaling potential trend exhaustion and possible reversal opportunities.
📌 Breakout Confirmation – Price consistently breaching the upper band with momentum could indicate continuation, while repeated touches without strong closes may hint at reversal zones.
📌 Trend Reversal Signals – Watch for green markers below the lower band in uptrends (buy signals) and red markers above the upper band in downtrends (sell signals).
🔔 Alerts & Notifications
📢 Set Alerts for Exhaustion Signals!
Traders can configure alerts to trigger when price breaches the ATR bands, allowing for instant notifications when volatility-based exhaustion is detected.
📊 Example Scenarios
✔ Trend Exhaustion in Overextended Moves – A series of red crosses near resistance may indicate a short opportunity.
✔ Trend Exhaustion in Overextended Moves – A series of red crosses near resistance may indicate an opportunity to open a short trade.
✔ Volatility Compression Breakouts – If price consolidates within the ATR bands and suddenly breaks out, it could signify a momentum shift.
✔ Reversal Catching in Trending Markets – Spot potential trend reversals by looking for green markers below the ATR bands in bullish markets.
🌟 Why Choose AE - ATR Exhaustion Channel?
Trade with Confidence. Spot Volatility. Catch Breakouts.
The AE - ATR Exhaustion Channel is an essential tool for traders looking to identify trend exhaustion, detect breakouts, and manage volatility effectively. Whether you're trading stocks, crypto, or forex, this ATR-SMA hybrid system provides clear visual cues to help you stay ahead of market moves.
✅ Customizable to Fit Any Market
✅ Combines Volatility & Trend Analysis
✅ Easy-to-Use with Instant Breakout Detection
Dynamic Deviation Levels [BigBeluga]Dynamic Deviation Levels is an innovative indicator designed to analyze price deviations relative to a smoothed midline. It provides traders with visual cues for overbought/oversold zones, price momentum, levels through labeled deviations and gradient candle coloring.
🔵Key Features:
Smoothed Midline:
A central line calculated as a smoothed median of the price source, serving as the baseline for price deviation analysis.
Dynamic Deviation Levels:
- Three deviation levels are plotted above and below the midline, with labels (1, 2, 3, -1, -2, -3) marking significant price movements.
- Helps traders identify overbought and oversold market conditions.
Heat-Colored Candles:
- Candle colors shift in intensity based on the deviation level, with four gradient shades for both upward and downward movements.
- Quickly highlights market extremes or stable zones.
Interactive Color Scale:
- A gradient scale at the bottom right of the chart visually represents deviation values.
- A triangle marker indicates the current price deviation in real time.
Optional Deviation Levels Display:
- Traders can enable all dynamic levels on the chart to visualize support and resistance areas dynamically.
🔵Usage and Benefits:
Identify Overbought/Oversold Zones: Use labeled deviation levels and heat-colored candles to spot stretched market conditions.
Track Trend Reversals and Momentum: Monitor price interactions with deviation levels for potential trend continuation or reversal signals.
Real-Time Deviation Insights: Leverage the color scale and triangle marker for live deviation tracking and actionable insights.
Map Dynamic Support and Resistance: Enable dynamic levels to highlight key areas where price reactions are likely to occur.
Dynamic Deviation Levels is an indispensable tool for traders aiming to combine price dynamics, momentum analysis, and visual clarity in their trading strategies.
E9 Bollinger RangeThe E9 Bollinger Range is a technical trading tool that leverages Bollinger Bands to track volatility and price deviations, along with additional trend filtering via EMAs.
The script visually enhances price action with a combination of trend-filtering EMAs, bar colouring for trend direction, signals to indicate potential buy and sell points based on price extension and engulfing patterns.
Here’s a breakdown of its key components:
Bollinger Bands: The strategy plots multiple Bollinger Band deviations to create different price levels. The furthest deviation bands act as warning signs for traders when price extends significantly, signaling potential overbought or oversold conditions.
Bar Colouring: Visual bar colouring is applied to clearly indicate trend direction: green bars for an uptrend and red bars for a downtrend.
EMA Filtering: Two EMAs (50 and 200) are used to help filter out false signals, giving traders a better sense of the underlying trend.
This combination of signals, visual elements, and trend filtering provides traders with a systematic approach to identifying price deviations and taking advantage of market corrections.
Brief History of Bollinger Bands
Bollinger Bands were developed by John Bollinger in the early 1980s as a tool to measure price volatility in financial markets. The bands consist of a moving average (typically 20 periods) with upper and lower bands placed two standard deviations away. These bands expand and contract based on market volatility, offering traders a visual representation of price extremes and potential reversal zones.
John Bollinger’s work revolutionized technical analysis by incorporating volatility into trend detection. His bands remain widely used across markets, including stocks, commodities, and cryptocurrencies. With the ability to highlight overbought and oversold conditions, Bollinger Bands have become a staple in many trading strategies.
Multi-Step FlexiSuperTrend - Indicator [presentTrading]This version of the indicator is built upon the foundation of a strategy version published earlier. However, this indicator version focuses on providing visual insights and alerts for traders, rather than executing trades. This one is mostly for @thorcmt.
█ Introduction and How it is Different
The **Multi-Step FlexiSuperTrend Indicator** is a versatile tool designed to provide traders with a highly customizable and flexible approach to trend analysis. Unlike traditional supertrend indicators, which focus on a single factor or threshold, the **FlexiSuperTrend** allows users to define multiple levels of take-profit targets and incorporate different trend normalization methods.
It comes with several advanced customization features, including multi-step take profits, deviation plotting, and trend normalization, making it suitable for both novice and expert traders.
BTCUSD 6hr Performance
█ Strategy, How It Works: Detailed Explanation
The **Multi-Step FlexiSuperTrend** works by calculating a supertrend based on multiple factors and incorporating oscillations from trend deviations. Here’s a breakdown of how it functions:
🔶 SuperTrend Calculation
At the heart of the indicator is the SuperTrend formula, which dynamically adjusts based on price movements.
🔶 Normalization of Deviations
To enhance accuracy, the **FlexiSuperTrend** calculates multiple deviations from the trend and normalizes them.
🔶 Multi-Step Take Profit Levels
The indicator allows setting up to three take profit levels, which are displayed via price level alerts. lows traders to exit part of their position at various profit intervals.
For more detail, please check the strategy version - Multi-Step-FlexiSuperTrend-Strategy:
and 'FlexiSuperTrend-Strategy'
█ Trade Direction
The **Multi-Step FlexiSuperTrend Indicator** supports both long and short trade directions.
This flexibility allows traders to adapt to trending, volatile, or sideways markets.
█ Usage
To use the **FlexiSuperTrend Indicator**, traders can set up their preferences for the following key features:
- **Trading Direction**: Choose whether to focus on long, short, or both signals.
- **Indicator Source**: The price source to calculate the trend (e.g., close, hl2).
- **Indicator Length**: The number of periods to calculate the ATR and trend (the larger the value, the smoother the trend).
- **Starting and Increment Factor**: These adjust how reactive the trend is to price movements. The starting factor dictates how far the initial trend band is from the price, and the increment factor adjusts subsequent trend deviations.
The indicator then displays buy and sell signals on the chart, along with alerts for each take-profit level.
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█ Default Settings
The default settings of the **Multi-Step FlexiSuperTrend** are carefully designed to provide an optimal balance between sensitivity and accuracy. Let’s examine these default parameters and their effect on performance:
🔶 Indicator Length (Default: 10)
The **Indicator Length** determines the lookback period for the ATR calculation. A smaller value makes the indicator more reactive to price changes, but may generate more false signals. A longer length smooths the trend and reduces noise but may delay signals.
Effect on performance: Shorter lengths perform better in volatile markets, while longer lengths excel in trending markets.
🔶 Starting Factor (Default: 0.618)
This factor adjusts the starting distance of the SuperTrend from the current price. The smaller the starting factor, the closer the trend is to the price, making it more sensitive. Conversely, a larger factor allows more distance, reducing sensitivity but filtering out false signals.
Effect on performance: A smaller factor provides quicker signals but can lead to frequent false positives. A larger factor generates fewer but more reliable signals.
🔶 Increment Factor (Default: 0.382)
The **Increment Factor** controls how the trend bands adjust as the price moves. It increases the distance of the bands from the price with each iteration.
Effect on performance: A higher increment factor can result in wider stop-loss or trend reversal bands, allowing for longer trends to develop without frequent exits. A lower factor keeps the bands closer to the price and is more suited for shorter-term trades.
🔶 Take Profit Levels (Default: 2%, 8%, 18%)
The default take-profit levels are set at 2%, 8%, and 18%. These values represent the thresholds at which the trader can partially exit their positions. These multi-step levels are highly customizable depending on the trader’s risk tolerance and strategy.
Effect on performance: Lower take-profit levels (e.g., 2%) capture small, quick profits in volatile markets, while higher levels (8%-18%) allow for a more gradual exit in strong trends.
🔶 Normalization Method (Default: None)
The default normalization method is **None**, meaning the deviations are not normalized. However, enabling normalization (e.g., **Max-Min**) can improve the clarity of the indicator’s signals in volatile or choppy markets by smoothing out the noise.
Effect on performance: Using a normalization method can reduce the effect of extreme deviations, making signals more stable and less prone to false positives.
itradesize /\ Previous Liquidity x ICTI’d like to introduce a clean and simple RTH gap and liquidity levels indicator with additional Asian and London ranges, along with standard deviation levels and many customizable options.
Previous D/W/M highs and lows are areas where liquidity tends to accumulate. This is because many traders place stop-loss orders around these levels, creating a concentration of buy stops above the previous day's high and sell stops below the previous day's low. High-frequency trading algorithms and institutional traders often target these areas to capture liquidity.
What the indicator could show in summary?
- Regular trading hours gap with deviations
- Asia with deviations (lines or boxes)
- London with deviations (lines or boxes)
- Weekdays on chart
- 3 AM candle marker
- Previous D/W/M levels
- Important opening times (08:00, 09:30, 10:00, 14:00, 00:00, 18:00)
- Daily separators
By marking out the previous day's highs and lows, traders can create a framework for their trading day. This helps in identifying potential setups and understanding where significant price action might occur. It also aids in filtering out noise and focusing on the most relevant price levels.
These levels can also act as potential reversal points. When the market reaches a previous high or low, it might reverse direction, especially if it has raided the liquidity resting there. This concept is part of a strategy where traders look for the market to raid these levels and then reverse, providing trading opportunities
The indicator shows previous liquidity levels on a daily, weekly, and monthly basis. It also displays opening times at 8:30, 9:30-10:00, 14:00-00:00, and 18:00. Opening times are crucial in trading because they help define specific periods when market activity is expected to be higher, which can lead to better trading opportunities. The script has been made mostly for indices.
You can create various entry and exit strategies based on the indicator. Please remember, that adequate knowledge of ICT is necessary for this to be beneficial.
You might wonder why only these times are shown. This is because these are the times when the futures market is active or should be active. It's important to note that opening times can vary between different asset classes.
18:00 A new daily candle open
00:00 Midnight open
02:00 New 4-hour candle open
08:30 High-impact news
09:30 NY Equities open
10:00 New 4-hour candle open
The concept of "Asian Killzone Standard Deviations" involves using the Asian trading session's price range to project potential price movements during subsequent trading sessions, such as the London or New York sessions. This is done by calculating standard deviations from the Asian range, which can help traders identify potential support and resistance levels.
You can create a complete model by exclusively focusing on the Asian time zone. Deviations within this zone may have varying impacts on future price movements, and the Interbank Price Delivery Agreement (IPDA) often reflects Asia's high, close, and low prices.
A similar approach can be taken with the London time zone. The standard deviation levels within each zone could potentially serve as support or indicate reversals, including liquidity hunts. It's important to backtest these ideas to gain reliable insights into when and where to apply them.
* Asian Range: This is the price range established during the Asian trading session. It serves as a reference point for calculating standard deviations.
* London Range: The same applies to the London range as well. Combine standard deviation projections with other technical analysis tools, such as order blocks or fair value gaps, to enhance accuracy.
* Standard Deviations: These are statistical measures that indicate the amount of variation or dispersion from the average. In trading, they are used to project potential price levels beyond the current range.
You can also use regular trading hours gap as a standalone model. The 4 STDV and 2.5 STDV levels are important for determining the high or low of the current price action.
The RTH gap is created when there is a difference between the closing price of a market at the end of one trading day and the opening price at the start of the next trading day. This gap can be upward (gap higher), downward (gap lower), or unchanged. It is significant because it often indicates market sentiment and can create inefficiencies that traders look to exploit.
Alternatively, you can combine these elements to create a complete strategy for different scenarios.
VWAP Bands [TradingFinder] 26 Brokers Data (Forex + Crypto)🔵 Introduction
Indicators are tools that help analysts predict the price trend of a stock through mathematical calculations on price or trading volume. It is evident that trading volume significantly impacts the price trend of a stock symbol.
The Volume-Weighted Average Price (VWAP) indicator combines the influence of trading volume and price, providing technical analysts with a practical tool.
This technical indicator determines the volume-weighted average price of a symbol over a specified time period. Consequently, this indicator can be used to identify trends and entry or exit points.
🟣 Calculating the VWAP Indicator
Adding the VWAP indicator to a chart will automatically perform all calculations for you. However, if you wish to understand how this indicator is calculated, the following explains the steps involved.
Consider a 5-minute chart. In the first candle of this chart (which represents price information in the first 5 minutes), sum the high, low, and close prices, and divide by 3. Multiply the resulting number by the volume for the period and call it a variable (e.g., X).
Then, divide the resulting output by the total volume for that period to calculate your VWAP. To maintain the VWAP sequence throughout the trading day, it is necessary to add the X values obtained from each period to the previous period and divide by the total volume up to that time. It is worth noting that the calculation method is the same for intervals shorter than a day.
The mathematical formula for this VWAP indicator : VWAP = ∑ (Pi×Vi) / ∑ Vi
🔵 How to Use
Traders might consider the VWAP indicator as a tool for predicting trends. For example, they might buy a stock when the price is above the VWAP level and sell it when the price is below the VWAP.
In other words, when the price is above the VWAP, the price is rising, and when it is below the VWAP, the price is falling. Major traders and investment funds also use the VWAP ratio to help enter or exit stocks with the least possible market impact.
It is important to note that one should not rely solely on the VWAP indicator when analyzing symbols. This is because if prices rise quickly, the VWAP indicator may not adequately describe the conditions. This indicator is generally used for daily or shorter time frames because using longer intervals can distort the average.
Since this indicator uses past data in its calculations, it can be considered a lagging indicator. As a result, the more data there is, the greater the delay.
🟣 Difference Between VWAP and Simple Moving Average
On a chart, the VWAP and the simple moving average may look similar, but these two indicators have different calculations. The VWAP calculates the total price considering volume, while the simple moving average does not consider volume.
In simpler terms, the VWAP indicator measures each day's price change relative to the trading volume that occurred that day. In contrast, the simple moving average implicitly assumes that all trading days have the same volume.
🟣 Reasons Why Traders Like the VWAP Indicator
The VWAP Considers Volume: Since VWAP takes volume into account, it can be more reliable than a simple arithmetic average of prices. Theoretically, one person can buy 200,000 shares of a symbol in one transaction at a single price.
However, during the same time frame, 100 other people might place 200 different orders at various prices that do not total 100,000 shares. In this case, if you only consider the average price, you might be mistaken because trading volume is ignored.
The Indicator Can Help Day Traders: While reviewing your trades, you might notice that the shares you bought at market price are trading below the VWAP indicator.
In this case, there's no need to worry because with the help of VWAP, you always get a price below the average. By knowing the volume-weighted average price of a stock, you can easily make an informed decision about paying more or less than other traders for the stock.
VWAP Can Signal Market Trend Changes: Buying low and selling high can be an excellent strategy for individuals. However, you are looking to buy when prices start to rise and sell your shares when prices start to fall.
Since the VWAP indicator simulates a balanced price in the market, when the price crosses above the VWAP line, one can assume that traders are willing to pay more to acquire shares, and as a result, the market will grow. Conversely, when the price crosses below the line, this can be considered a sign of a downward movement.
🔵 Setting
Period : Indicator calculation time frame.
Source : The Price used for calculations.
Market Ultra Data : If you turn on this feature, 26 large brokers will be included in the calculation of the trading volume.
The advantage of this capability is to have more reliable volume data. You should be careful to specify the market you are in, FOREX brokers and Crypto brokers are different.
Multiplier : Coefficient of band lines.
RSI DeviationAn oscillator which de-trends the Relative Strength Index. Rather, it takes a moving average of RSI and plots it's standard deviation from the MA, similar to a Bollinger %B oscillator. This seams to highlight short term peaks and troughs, Indicating oversold and overbought conditions respectively. It is intended to be used with a Dollar Cost Averaging strategy, but may also be useful for Swing Trading, or Scalping on lower timeframes.
When the line on the oscillator line crosses back into the channel, it signals a trade opportunity.
~ Crossing into the band from the bottom, indicates the end of an oversold condition, signaling a potential reversal. This would be a BUY signal.
~ Crossing into the band from the top, indicates the end of an overbought condition, signaling a potential reversal. This would be a SELL signal.
For ease of use, I've made the oscillator highlight the main chart when Overbought/Oversold conditions are occurring, and place fractals upon reversion to the Band. These repaint as they are calculated at close. The earliest trade would occur upon open of the following day.
I have set the default St. Deviation to be 2, but in my testing I have found 1.5 to be quite reliable. By decreasing the St. Deviation you will increase trade frequency, to a point, at the expense of efficiency.
Cheers
DJSnoWMan06
London Killzone + Deviations[MK]For traders that use the London Killzone session high/low to project possible take profit targets.
The indicator will determine the current day London killzone high and low range and draw a range box to the right of the last candle on the chart. Drawing to the right of the chart keeps the workspace cleaner.
The high/low range is then used to project Standard Deviation levels above and below the London range.
Levels projected are +/- 1, 2, 2.5, 3, 4.
Users of the script should conduct proper backtesting using a large data range before applying to live accounts.
