ARDO - Adaptive Regression Deviation Oscillator (v2.4.6)ARDO – Adaptive Regression Deviation Oscillator (v2.4.6)
ARDO (Adaptive Regression Deviation Oscillator) quantifies deviation of price structure from a regression-based equilibrium baseline using adaptive moving-average spreads. It combines percentile-normalized distance, linear-regression slope, and dynamic gradient scaling to reveal trend extension, exhaustion, and regime shifts—offering a structural view of trend integrity and mean-reversion timing beyond traditional momentum oscillators. It is designed to help you answer two questions:
Where are we in the regime? (extended, neutral, or reversal-prone)
Is this a “trade” environment or a “stand aside” environment? (Gate PASS vs Gate BLOCK / drift)
ARDO is best used as a context + timing framework , not a standalone entry/exit system.
What you see in the ARDO pane
1) Spread A (% vs baseline)
Primary “timing” spread (default: stepline). Spread A is colored by a 4-state maColor model:
GREEN : above baseline and strengthening
ORANGE : above baseline but weakening
RED : below baseline and weakening
GRAY : below baseline but improving
2) Spread B (% vs baseline)
Secondary “context” spread (default: columns). Same 4-state color model as above, often used to confirm or filter Spread A behavior.
3) LinReg (slope-gradient)
A LinReg line fit to a selected source (Spread A / Spread B / Spread A+B). ARDO applies a slope-magnitude gradient (opacity/intensity) to visualize regime:
Stronger slope magnitude = stronger directional regime
Fading / low slope magnitude = drift / dead-zone (lower edge, choppy conditions, or end-of-move)
4) Tier zones (Q0–Q2, H2–H4)
ARDO classifies LinReg values into percentile tiers (extremes and mid-tiers). These tiers can be rendered as:
Background regions, or
Zero-line marker circles (“MK …” plots)
Important: Background colors do not export . The “MK Q0 … MK H4” series are emitted so you can reconstruct tier membership in CSV/backtests.
5) Gate PASS / Gate BLOCK
A compact “permission layer” that can require:
Spread A > LinReg
EMA Fast > EMA Slow
Minimum Spread A threshold
Minimum absolute LinReg slope
Use Gate PASS to focus on higher-quality conditions; use Gate BLOCK as a “do nothing / reduce size” warning.
Key settings (what they change)
Tier Mode
Standard: symmetric cut structure (general purpose)
Asymmetric: separate tuning for highs vs lows (often better when upside and downside behavior are not symmetric)
Tier Population
All Bars (LinReg): tiers represent the full LinReg distribution
Pivots Only: tiers are computed from pivot events only (can tighten “extreme” definition and change how frequently zones appear)
Render Mode
Background: easiest to read visually
Zero-line Markers: best for export/backtesting workflows (MK series)
Gating options
Turn on/off each rule independently; adjust thresholds to match symbol volatility and timeframe.
Color overrides
Optional per-state color customization for Spread A, Spread B, and LinReg (4-state).
Alerts included (v2.4.6)
ARDO exposes named alerts you can use for automation or review, including:
Gradient / regime alerts (HIGH vs LOW slope-magnitude regimes; regime shift transitions)
Color-state changes (Spread B → GREEN/ORANGE/RED/GRAY; LinReg state changes)
Tier entry alert s (LinReg entering key tiers such as Q0/Q1/H3/H4)
Structural primitives (Bullish A > B, Bearish A < B, Gate PASS/BLOCK, crosses of 0, etc.)
How to use (practical workflow)
Anchor timeframe (65m or Daily): identify regime (tiers + gradient) and whether you should be aggressive or defensive.
Execution timeframe (5m/1m): time entries using Spread A/B structure and Gate PASS, aligned with the anchor regime.
Avoid forcing trades in drift: fading gradient + mid/low-edge tiers often marks “dead-zone” conditions.
Notes / limitations
ARDO is a context engine: it describes regime and location, not guaranteed direction.
Tier thresholds are distribution-based and will vary by window/timeframe.
Always apply your own risk management; this script is not financial advice.
Regression
Sen Channel LiteSen Channel Lite
Sen Channel Lite calculates a robust, median-based regression channel using the Theil–Sen slope method. This visual tool helps traders identify trend direction and potential breakout zones in real time.
Key Features
Dynamic Trend Line: The median-based regression line adapts to price movement, providing a central reference for trend direction.
Upper and Lower Bands: Automatically updated bands highlight potential breakout or reversal areas.
Breakout Markers: Optional triangles indicate when price crosses above the upper band or below the lower band.
Midline (EMA/SMA): Toggleable trend line for additional context on price direction.
VWAP Anchor: Optional VWAP plot to visualize volume-weighted average price levels.
Customizable Inputs:
Lookback Period for slope calculation
Band Multiplier to adjust sensitivity
Option to use Standard Deviation or ATR for band width
Midline type, length, and color
VWAP visibility and color
Channel cloud transparency
How to Read Signals (Educational Use Only):
Trend Context: The midline provides a reference for general trend direction. Price above the midline suggests bullish bias; below indicates bearish bias.
Breakouts:
Triangle up → price crossed above the upper band; potential strong move upward.
Triangle down → price crossed below the lower band; potential strong move downward.
Channel Interpretation:
Price near the upper band → market may be overextended.
Price near the lower band → market may be oversold.
Price moving within the channel → trend is balanced; use additional analysis for direction.
VWAP Context: Compare price to VWAP for intraday support/resistance insights.
Usage Notes:
Fully visual tool; no trading or financial advice.
All calculations are protected to preserve intellectual property.
Results reflect real-time calculations; no repainting.
Suitable for intraday to daily timeframes.
Important: This script is published as protected/closed-source to safeguard GammaBulldog intellectual property.
[iQ]PRO Quadratic Spectral Regression Channel and Heatmap+✨ PRO Quadratic Spectral Regression Channel and Heatmap+ : Next-Generation Market Analysis
The PRO QSRCH+ indicator is an advanced, proprietary analytical tool designed for the discerning trader, combining sophisticated statistical models with high-frequency momentum detection. This unique fusion provides a multi-dimensional view of market structure, separating the persistent, underlying trend from the volatile, short-term cycle.
📊 Precision Channeling with Weighted Regression
At its core, PRO QSRCH+ utilizes a dynamically weighted regression channel to establish the primary market trajectory and define statistically significant deviation boundaries.
Adaptive Trend Definition: The center line of the channel serves as a highly responsive mean value, calculated over a user-defined lookback length. This weighting prioritizes recent price action, ensuring the trend definition remains relevant to current market conditions.
Volatile Boundaries: The upper and lower bands are precisely calibrated using a standard deviation factor to measure volatility and establish zones of statistical overextension.
Trend Coloring: The channel's appearance changes based on the calculated slope, providing an instantaneous visual confirmation of the macro trend direction (Bullish or Bearish).
Exhaustion Signals: Subtle markers are placed when price touches these boundaries, signaling potential short-term market exhaustion and a high probability of mean reversion.
🔬 High-Resolution Spectral Momentum
Integrated with the regression channel is a specialized Spectral Momentum Heatmap Histogram. This proprietary oscillator is engineered to isolate the cyclical (micro) component of price movement.
Residual Analysis: The indicator first extracts the residual price movement—the high-frequency fluctuations that exist outside the established regression trend—effectively acting as an intelligent high-pass filter.
Cycle Detection: This residual data is then processed through a proprietary spectral filter and smoothing mechanism. This process isolates the dominant market cycle, revealing hidden bursts of momentum and the precise timing of cyclical turns.
Heatmap Visualization: The Spectral Momentum is visualized in a separate pane as a vibrant histogram, dynamically colored and weighted based on its magnitude to provide an intuitive visual gauge of market energy.
🧩 The Multi-Factor State Engine
PRO QSRCH+ uniquely combines these two components into a comprehensive market state engine, visible directly on the price bars and via clear trading signals:
Candle Coloring: Price bars are painted with a four-state system, distinguishing between:
Strong Trend: Macro Trend (Channel Slope) and Micro Cycle (Spectral Momentum) are aligned.
Pullback/Rally: Macro Trend is maintained, but the Micro Cycle is currently counter-trend, signaling temporary consolidation or retracement.
Validated Signals: High-probability BUY/SELL signals are generated only when the fast Spectral Momentum cycle crosses zero in alignment with the macro trend defined by the Regression Slope. This validation filter is key to minimizing false signals and maximizing the probability of sustained directional moves.
PRO QSRCH+ provides a superior framework for market structure analysis, allowing traders to distinguish between low-risk trend continuation and high-risk cyclical exhaustion.
MagFlow X: @Cissora <--MagFlow Trend is a premium trend model created as a quantitative counterpart to widely used commercial indicators. Its structure draws from exchange-oriented analytical concepts to establish a flexible, noise-resistant framework for directional movement. The design prioritizes clarity, reduced lag, and responsiveness across varying market conditions. Developed from original research and external visual models, MagFlow Trend is engineered to reflect a more mathematically disciplined trend engine.
Bollinger Bands Regression Forecast [BigBeluga]🔵 OVERVIEW
The Bollinger Bands Regression Forecast combines volatility envelopes from Bollinger Bands with a linear regression-based projection model .
It visualizes both current and future price zones by extrapolating the Bollinger channel forward in time, giving traders a statistical forecast of probable support and resistance behavior.
🔵 CONCEPTS
Classic Bollinger Bands use a moving average (basis) and standard deviation (deviation) to form dynamic envelopes around price.
This indicator enhances them with linear regression slope detection , allowing it to forecast how the band may expand or contract in the future.
Regression is applied to both the band’s basis and deviation components to predict their trajectory for a user-defined number of Forecast Bars .
The resulting forecast creates a smoothed, funnel-shaped projection that dynamically adapts to volatility.
▲ and ▼ markers highlight potential mean reversion points when price crosses the outer bounds of the bands.
🔵 FEATURES
Forecast Engine : Uses linear regression to project Bollinger Band movement into the future.
Dynamic Channel Width : Adapts standard deviation and slope for realistic volatility modeling.
Auto-Labeled Levels : Displays live upper and lower forecast values for quick reference.
Cross Signals : Marks potential overbought and oversold zones with ▲/▼ signals when price exits the band.
Trend-Adaptive Basis Color : Basis line automatically switches color to represent short-term trend direction.
Customizable Colors and Widths for complete visual control.
🔵 HOW TO USE
Apply the indicator to visualize both current Bollinger structure and its forward projection.
Use ▲/▼ breakout markers to identify short-term reversals or volatility shifts.
When price consistently rides the upper band forecast, the trend is strong and likely continuing.
When regression shows narrowing bands ahead, expect a volatility contraction or consolidation period.
For range traders, outer projected bands can be used as potential mean reversion entry points .
Combine with volume or momentum filters to confirm whether breakouts are genuine or fading.
🔵 CONCLUSION
Bollinger Bands Regression Forecast transforms classic Bollinger analysis into a predictive forecasting model .
By merging volatility dynamics with regression-based extrapolation, it provides traders with a forward-looking visualization of likely price boundaries — revealing not only where volatility is but also where it’s heading next.
Volume Weighted Volatility RegimeThe Volume-Weighted Volatility Regime (VWVR) is a market analysis tool that dissects total volatility to classify the current market 'character' or 'regime'. Using a Linear Regression model, it decomposes volatility into Trend, Residual (mean-reversion), and Within-Bar (noise) components.
Key Features:
Seven-Stage Regime Classification: The indicator's primary output is a regime value from -3 to +3, identifying the market state:
+3 (Strong Bull Trend): High directional, upward volatility.
+2 (Choppy Bull): Moderate upward trend with noise.
+1 (Quiet Bull): Low volatility, slight upward drift.
0 (Neutral): No clear directional bias.
-1 (Quiet Bear): Low volatility, slight downward drift.
-2 (Choppy Bear): Moderate downward trend with noise.
-3 (Strong Bear Trend): High directional, downward volatility.
Advanced Volatility Decomposition: The regime is derived from a three-component volatility model that separates price action into Trend (momentum), Residual (mean-reversion), and Within-Bar (noise) variance. The classification is determined by comparing the 'Trend' ratio against the user-defined 'Trend Threshold' and 'Quiet Threshold'.
Dual-Level Analysis: The indicator analyzes market character on two levels simultaneously:
Inter-Bar Regime (Background Color): Based on the main StdDev Length, showing the overall market character.
Intra-Bar Regime (Column Color): Based on a high-resolution analysis within each single bar ('Intra-Bar Timeframe'), showing the micro-structural character.
Calculation Options:
Statistical Model: The 'Estimate Bar Statistics' option (enabled by default) uses a statistical model ('Estimator') to perform the decomposition. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used instead).
Normalization: An optional 'Normalize Volatility' setting calculates an Exponential Regression Curve (log-space).
Volume Weighting: An option (Volume weighted) applies volume weighting to all volatility calculations.
Multi-Timeframe (MTF) Capability: The entire dual-level analysis can be run on a higher timeframe (using the Timeframe input), with standard options to handle gaps (Fill Gaps) and prevent repainting (Wait for...).
Integrated Alerts: Includes 22 comprehensive alerts that trigger whenever the 'Inter-Bar Regime' or the 'Intra-Bar Regime' crosses one of the key thresholds (e.g., 'Regime crosses above Neutral Line'), or when the 'Intra-Bar Dominance' crosses the 50% mark.
Caution: Real-Time Data Behavior (Intra-Bar Repainting) This indicator uses high-resolution intra-bar data. As a result, the values on the current, unclosed bar (the real-time bar) will update dynamically as new intra-bar data arrives. This behavior is normal and necessary for this type of analysis. Signals should only be considered final after the main chart bar has closed.
DISCLAIMER
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.
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.
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.
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.
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 Intra Bar LR Standard DeviationThis indicator analyzes market character by providing a detailed view of volatility. It applies a Linear Regression model to intra-bar price action, dissecting the total volatility of each bar into three distinct components.
Key Features:
Three-Component Volatility Decomposition: By analyzing a lower timeframe ('Intra-Bar Timeframe'), the indicator separates each bar's volatility into:
Trend Volatility (Green/Red): Volatility explained by the intra-bar linear regression slope (Momentum).
Residual Volatility (Yellow): Volatility from price oscillating around the intra-bar trendline (Mean-Reversion).
Within-Bar Volatility (Blue): Volatility derived from the range of each intra-bar candle (Noise/Choppiness).
Layered Column Visualization: The indicator plots these components as a layered column chart. The size of each colored layer visually represents the dominance of each volatility character.
Dual Display Modes: The indicator offers two modes to visualize this decomposition:
Absolute Mode: Displays the total standard deviation as the column height, showing the absolute magnitude of volatility and the contribution of each component.
Normalized Mode: Displays the components as a 100% stacked column chart (scaled from 0 to 1), focusing purely on the percentage ratio of Trend, Residual, and Noise.
Calculation Options:
Statistical Model: The 'Estimate Bar Statistics' option (enabled by default) uses a statistical model ('Estimator') to perform the decomposition. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used instead).
Normalization: An optional 'Normalize Volatility' setting calculates an Exponential Regression Curve (log-space).
Volume Weighting: An option (Volume weighted) applies volume weighting to all intra-bar calculations.
Multi-Component Pivot Detection: Includes a pivot detector that identifies significant turning points (highs and lows) in both the Total Volatility and the Trend Volatility Ratio. (Note: These pivots are only plotted when 'Plot Mode' is set to 'Absolute').
Note on Confirmation (Lag): Pivot signals are confirmed using a lookback method. A pivot is only plotted after the Pivot Right Bars input has passed, which introduces an inherent lag.
Multi-Timeframe (MTF) Capability:
MTF Analysis: The entire intra-bar analysis can be run on a higher timeframe (using the Timeframe input), with standard options to handle gaps (Fill Gaps) and prevent repainting (Wait for...).
Limitation: The Pivot detection (Calculate Pivots) is disabled if a Higher Timeframe (HTF) is selected.
Integrated Alerts: Includes 9 comprehensive alerts for:
Volatility character changes (e.g., 'Character Change from Noise to Trend').
Dominant character emerging (e.g., 'Bullish Trend Character Emerging').
Total Volatility pivot (High/Low) detection.
Trend Volatility pivot (High/Low) detection.
Caution! Real-Time Data Behavior (Intra-Bar Repainting) This indicator uses high-resolution intra-bar data. As a result, the values on the current, unclosed bar (the real-time bar) will update dynamically as new intra-bar data arrives. This behavior is normal and necessary for this type of analysis. Signals should only be considered final after the main chart bar has closed.
DISCLAIMER
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.
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.
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.
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.
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 LR Standard DeviationThis indicator analyzes market character by decomposing total volatility into three distinct, interpretable components based on a Linear Regression model.
Key Features:
Three-Component Volatility Decomposition: The indicator separates volatility based on the 'Estimate Bar Statistics' option.
Standard Mode (Estimate Bar Statistics = OFF): Calculates volatility based on the selected Source (dies führt hauptsächlich zu 'Trend'- und 'Residual'-Volatilität).
Decomposition Mode (Estimate Bar Statistics = ON): The indicator uses a statistical model ('Estimator') to calculate within-bar volatility. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used instead). This separates volatility into:
Trend Volatility (Green/Red): Volatility explained by the regression's slope (Momentum).
Residual Volatility (Yellow): Volatility from price oscillating around the regression line (Mean-Reversion).
Within-Bar Volatility (Blue): Volatility from the high-low range of each bar (Noise/Choppiness).
Dual Display Modes: The indicator offers two modes to visualize this decomposition:
Absolute Mode: Displays the total standard deviation as a stacked area chart, partitioned by the variance ratio of the three components.
Normalized Mode: Displays the direct variance ratio (proportion) of each component relative to the total (0-1), ideal for identifying the dominant market character.
Calculation Options:
Normalization: An optional 'Normalize Volatility' setting calculates an Exponential Regression Curve (log-space), making the analysis suitable for growth assets.
Volume Weighting: An option (Volume weighted) applies volume weighting to all regression and volatility calculations.
Multi-Component Pivot Detection: Includes a pivot detector that identifies significant turning points (highs and lows) in both the Total Volatility and the Trend Volatility Ratio. (Note: These pivots are only plotted when 'Plot Mode' is set to 'Absolute').
Note on Confirmation (Lag): Pivot signals are confirmed using a lookback method. A pivot is only plotted after the Pivot Right Bars input has passed, which introduces an inherent lag.
Multi-Timeframe (MTF) Capability:
MTF Volatility Lines: The volatility lines can be calculated on a higher timeframe, with standard options to handle gaps (Fill Gaps) and prevent repainting (Wait for...).
Limitation: The Pivot detection (Calculate Pivots) is disabled if a Higher Timeframe (HTF) is selected.
Integrated Alerts: Includes 9 comprehensive alerts for:
Volatility character changes (e.g., 'Character Change from Noise to Trend').
Dominant character emerging (e.g., 'Bullish Trend Character Emerging').
Total Volatility pivot (High/Low) detection.
Trend Volatility pivot (High/Low) detection.
DISCLAIMER
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.
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.
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.
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.
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 Linear Regression BandThe Volume-Weighted Linear Regression Band (VWLRBd) is a volatility channel that uses a Linear Regression line as its dynamic baseline. Its primary feature is the decomposition of total volatility into two distinct components, visualized as layered bands.
Key Features:
Volatility Decomposition: The indicator separates volatility based on the 'Estimate Bar Statistics' option.
Standard Mode (Estimate Bar Statistics = OFF): The indicator functions as a standard (Volume-Weighted) Linear Regression Channel. It plots a single set of bands based on the standard deviation of the residuals (the error between the Source price and the regression line).
Decomposition Mode (Estimate Bar Statistics = ON): The indicator uses a statistical model ('Estimator') to calculate within-bar volatility. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used for the regression). This mode displays two sets of bands:
Inner Bands: Show only the contribution of the 'residual' (trend noise) volatility, calculated proportionally.
Outer Bands: Show the total volatility (the sum of residual and within-bar components).
Regression Baseline (Linear / Exponential): The central line is a (Volume-Weighted) Linear Regression curve. An optional 'Normalize' mode performs all calculations in logarithmic space, transforming the baseline into an Exponential Regression Curve and the bands into constant percentage deviations, suitable for analyzing growth assets.
Volume Weighting: An option (Volume weighted) allows for volume to be incorporated into the calculation of both the regression baseline and the volatility decomposition, giving more influence to high-participation bars.
Multi-Timeframe (MTF) Engine: The indicator includes an MTF conversion block. When a Higher Timeframe (HTF) is selected, advanced options become available: Fill Gaps handles data gaps, and Wait for timeframe to close prevents repainting by ensuring the indicator only updates when the HTF bar closes.
Integrated Alerts: Includes a full set of built-in alerts for the source price crossing over or under the central regression line and the outermost calculated volatility band.
DISCLAIM_
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.
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.
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.
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.
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 Linear Regression ChannelThis indicator plots a dynamic channel around a Linear Regression trendline. It provides a framework for identifying the prevailing trend and assessing price extremes based on volatility.
Key Features:
Linear Regression Baseline: The channel's centerline is a (Volume-Weighted) Linear Regression line. This line represents the 'best fit' for the recent price action, serving as a responsive baseline for the trend.
Volatility Decomposition: The indicator's primary feature is its ability to decompose volatility, controlled by the 'Estimate Bar Statistics' option.
Standard Mode (Estimate Bar Statistics = OFF): Calculates a standard linear regression channel. The bands represent the standard deviation of the residuals (the error) between the Source price and the regression line.
Decomposition Mode (Estimate Bar Statistics = ON): The indicator uses a statistical model ('Estimator') to calculate within-bar volatility. (Assumption: In this mode, the Source input is ignored, and an estimated mean for each bar is used for the regression). This mode displays two sets of bands:
Inner Bands: Show only the contribution of the 'residual' (trend noise) volatility, calculated proportionally.
Outer Bands: Show the total volatility (the sum of residual and within-bar components).
Volume Weighting: An option (Volume weighted) allows for volume to be incorporated into the calculation of both the linear regression and the volatility decomposition, giving more influence to high-participation bars.
Trend Projection: The calculated channel is plotted as a projection, which can be extended forward (Extend Forward) and backward (Extend Backward) in time to provide a visual guide for potential support and resistance.
Integrated Alerts: Includes a full set of built-in alerts for the Source price crossing over or under the calculated upper band, lower band, and the central regression line.
DISCLAIMER
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.
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.
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.
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.
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.
LibWghtLibrary "LibWght"
This is a library of mathematical and statistical functions
designed for quantitative analysis in Pine Script. Its core
principle is the integration of a custom weighting series
(e.g., volume) into a wide array of standard technical
analysis calculations.
Key Capabilities:
1. **Universal Weighting:** All exported functions accept a `weight`
parameter. This allows standard calculations (like moving
averages, RSI, and standard deviation) to be influenced by an
external data series, such as volume or tick count.
2. **Weighted Averages and Indicators:** Includes a comprehensive
collection of weighted functions:
- **Moving Averages:** `wSma`, `wEma`, `wWma`, `wRma` (Wilder's),
`wHma` (Hull), and `wLSma` (Least Squares / Linear Regression).
- **Oscillators & Ranges:** `wRsi`, `wAtr` (Average True Range),
`wTr` (True Range), and `wR` (High-Low Range).
3. **Volatility Decomposition:** Provides functions to decompose
total variance into distinct components for market analysis.
- **Two-Way Decomposition (`wTotVar`):** Separates variance into
**between-bar** (directional) and **within-bar** (noise)
components.
- **Three-Way Decomposition (`wLRTotVar`):** Decomposes variance
relative to a linear regression into **Trend** (explained by
the LR slope), **Residual** (mean-reversion around the
LR line), and **Within-Bar** (noise) components.
- **Local Volatility (`wLRLocTotStdDev`):** Measures the total
"noise" (within-bar + residual) around the trend line.
4. **Weighted Statistics and Regression:** Provides a robust
function for Weighted Linear Regression (`wLinReg`) and a
full suite of related statistical measures:
- **Between-Bar Stats:** `wBtwVar`, `wBtwStdDev`, `wBtwStdErr`.
- **Residual Stats:** `wResVar`, `wResStdDev`, `wResStdErr`.
5. **Fallback Mechanism:** All functions are designed for reliability.
If the total weight over the lookback period is zero (e.g., in
a no-volume period), the algorithms automatically fall back to
their unweighted, uniform-weight equivalents (e.g., `wSma`
becomes a standard `ta.sma`), preventing errors and ensuring
continuous calculation.
---
**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.
wSma(source, weight, length)
Weighted Simple Moving Average (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
the arithmetic mean if Σweight = 0.
wEma(source, weight, length)
Weighted EMA (exponential kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Exponential-kernel weighted mean; falls
back to classic EMA if Σweight = 0.
wWma(source, weight, length)
Weighted WMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic WMA if Σweight = 0.
wRma(source, weight, length)
Weighted RMA (Wilder kernel, α = 1/len).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Wilder-kernel weighted mean; falls back to
classic RMA if Σweight = 0.
wHma(source, weight, length)
Weighted HMA (linear kernel).
Parameters:
source (float) : series float Data to average.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Linear-kernel weighted mean; falls back to
classic HMA if Σweight = 0.
wRsi(source, weight, length)
Weighted Relative Strength Index.
Parameters:
source (float) : series float Price series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted RSI; uniform if Σw = 0.
wAtr(tr, weight, length)
Weighted ATR (Average True Range).
Implemented as WRMA on *true range*.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (simple int) : simple int Look-back length ≥ 1.
Returns: series float Weighted ATR; uniform weights if Σw = 0.
wTr(tr, weight, length)
Weighted True Range over a window.
Parameters:
tr (float) : series float True Range series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of TR; uniform if Σw = 0.
wR(r, weight, length)
Weighted High-Low Range over a window.
Parameters:
r (float) : series float High-Low per bar.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 1.
Returns: series float Weighted mean of range; uniform if Σw = 0.
wBtwVar(source, weight, length, biased)
Weighted Between Variance (biased/unbiased).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
variance series float The calculated between-bar variance (σ²btw), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wBtwStdDev(source, weight, length, biased)
Weighted Between Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σbtw uniform if Σw = 0.
wBtwStdErr(source, weight, length, biased)
Weighted Between Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²btw / N_eff) uniform if Σw = 0.
wTotVar(mu, sigma, weight, length, biased)
Weighted Total Variance (= between-group + within-group).
Useful when each bar represents an aggregate with its own
mean* and pre-estimated σ (e.g., second-level ranges inside a
1-minute bar). Assumes the *weight* series applies to both the
group means and their σ estimates.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns:
varBtw series float The between-bar variance component (σ²btw).
varWtn series float The within-bar variance component (σ²wtn).
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wTotStdDev(mu, sigma, weight, length, biased)
Weighted Total Standard Deviation.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σtot.
wTotStdErr(mu, sigma, weight, length, biased)
Weighted Total Standard Error.
SE = √( total variance / N_eff ) with the same effective sample
size logic as `wster()`.
Parameters:
mu (float) : series float Group means (e.g., HL2 of 1-second bars).
sigma (float) : series float Pre-estimated σ of each group (same basis).
weight (float) : series float Weight series (volume, ticks, …).
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²tot / N_eff).
wLinReg(source, weight, length)
Weighted Linear Regression.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns:
mid series float The estimated value of the regression line at the most recent bar.
slope series float The slope of the regression line.
intercept series float The intercept of the regression line.
wResVar(source, weight, midLine, slope, length, biased)
Weighted Residual Variance.
linear regression – optionally biased (population) or
unbiased (sample).
Parameters:
source (float) : series float Data series.
weight (float) : series float Weighting series (volume, etc.).
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population variance (σ²_P), denominator ≈ N_eff.
false → sample variance (σ²_S), denominator ≈ N_eff - 2.
(Adjusts for 2 degrees of freedom lost to the regression).
Returns:
variance series float The calculated residual variance (σ²res), either biased or unbiased.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wResStdDev(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Deviation.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float σres; uniform if Σw = 0.
wResStdErr(source, weight, midLine, slope, length, biased)
Weighted Residual Standard Error.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
midLine (float) : series float Regression value at the last bar.
slope (float) : series float Slope per bar.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population (biased); false → sample.
Returns: series float √(σ²res / N_eff); uniform if Σw = 0.
wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Variance **around the
window’s weighted mean μ**.
σ²_tot = E_w ⟶ *within-group variance*
+ Var_w ⟶ *residual variance*
+ Var_w ⟶ *trend variance*
where each bar i in the look-back window contributes
m_i = *mean* (e.g. 1-sec HL2)
σ_i = *sigma* (pre-estimated intrabar σ)
w_i = *weight* (volume, ticks, …)
ŷ_i = b₀ + b₁·x (value of the weighted LR line)
r_i = m_i − ŷ_i (orthogonal residual)
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns:
varRes series float The residual variance component (σ²res).
varWtn series float The within-bar variance component (σ²wtn).
varTrd series float The trend variance component (σ²trd), explained by the linear regression.
sumW series float The sum of weights over the lookback period (Σw).
sumW2 series float The sum of squared weights over the lookback period (Σw²).
wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Deviation.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²tot).
wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Total Standard Error.
SE = √( σ²_tot / N_eff ) with N_eff = Σw² / Σw² (like in wster()).
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²res, σ²wtn, σ²trd) / N_eff).
wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Deviation.
Measures the total "noise" (within-bar + residual) around the trend.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √(σ²wtn + σ²res).
wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
Weighted Linear-Regression Local Total Standard Error.
Parameters:
mu (float) : series float Per-bar mean m_i.
sigma (float) : series float Pre-estimated σ_i of each bar.
weight (float) : series float Weight series w_i (≥ 0).
midLine (float) : series float Regression value at the latest bar (ŷₙ₋₁).
slope (float) : series float Slope b₁ of the regression line.
length (int) : series int Look-back length ≥ 2.
biased (bool) : series bool true → population; false → sample.
Returns: series float √((σ²wtn + σ²res) / N_eff).
wLSma(source, weight, length)
Weighted Least Square Moving Average.
Parameters:
source (float) : series float Data series.
weight (float) : series float Weight series.
length (int) : series int Look-back length ≥ 2.
Returns: series float Least square weighted mean. Falls back
to unweighted regression if Σw = 0.
Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound.
The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts.
There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass.
The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter.
Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly.
The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart.
What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise.
Smart Money Dynamics Blocks - Pearson MatrixSmart Money Dynamics Blocks — Pearson Matrix
A structural fusion of Prime Number Theory, Pearson Correlation, and Cumulative Delta Geometry.
1. Mathematical Foundation
This indicator is built on the intersection of Prime Number Theory and the Pearson correlation coefficient, creating a structural framework that quantifies how price and time evolve together.
Prime numbers — unique, indivisible, and irregular — are used here as nonlinear time intervals. Each prime length (2, 3, 5, 7, 11…97) represents a regression horizon where correlation is measured between price and time. The result is a multi-scale correlation lattice — a geometric matrix that captures hidden directional strength and temporal bias beyond traditional moving averages.
2. The Pearson Matrix Logic
For every prime interval p, the indicator calculates the linear correlation:
r_p = corr(price, bar_index, p)
Each r_p reflects how closely price and time move together across a prime-defined window. All r_p values are then averaged to create avgR, a single adaptive coefficient summarizing overall structural coherence.
- When avgR > 0.8 → strong positive correlation (labeled R+).
- When avgR < -0.8 → strong negative correlation (labeled R−).
This approach gives a mathematically grounded definition of trend — one that isn’t based on pattern recognition, but on measurable correlation strength.
3. Sequential Prime Slope and Median Pivot
Using the ordered sequence of 25 prime intervals, the model computes sequential slopes between adjacent primes. These slopes represent the rate of change of structure between two prime scales. A robust median aggregator smooths the slopes, producing a clean, stable directional vector.
The system anchors this slope to the 41-bar pivot — the median of the first 25 primes — serving as the geometric midpoint of the prime lattice. The resulting yellow line on the chart is not an ordinary regression line; it’s a dynamic prime-slope function, adapting continuously with correlation feedback.
4. Regression-Style Parallel Bands
Around this prime-slope line, the indicator constructs parallel bands using standard deviation envelopes — conceptually similar to a regression channel but recalculated through the prime–Pearson matrix.
These bands adjust dynamically to:
- Volatility, via standard deviation of residuals.
- Correlation strength, via avgR sign weighting.
Together, they visualize statistical deviation geometry, making it easier to observe symmetry, expansion, and contraction phases of price structure.
5. Volume and Cumulative Delta Peaks
Below the geometric layer, the indicator incorporates a custom lower-timeframe volume feed — by default using 15-second data (custom_tf_input_volume = “15S”). This allows precise delta computation between up-volume and down-volume even on higher timeframe charts.
From this feed, the indicator accumulates delta over a configurable period (default: 100 bars). When cumulative delta reaches a local maximum or minimum, peak and trough markers appear, showing the precise bar where buying or selling pressure statistically peaked.
This combination of geometry and order flow reveals the intersection of market structure and energy — where liquidity pressure expresses itself through mathematical form.
6. Chart Interpretation
The primary chart view represents the live execution of the indicator. It displays the relationship between structural correlation and volume behavior in real time.
Orange “R+” and blue “R−” labels indicate regions of strong positive or negative Pearson correlation across the prime matrix. The yellow median prime-slope line serves as the structural backbone of the indicator, while green and red parallel bands act as dynamic regression boundaries derived from the underlying correlation strength. Peaks and troughs in cumulative delta — displayed as numerical annotations — mark statistically significant shifts in buying and selling pressure.
The secondary visualization (Prime Regression Concept) expands on this by illustrating how regression behavior evolves across prime intervals. Each colored regression fan corresponds to a prime number window (2, 3, 5, 7, …, 97), demonstrating how multiple regression lines would appear if drawn independently. The indicator integrates these into one unified geometric model — eliminating the need to plot tens of regression lines manually. It’s a conceptual tool to help visualize the internal logic: the synthesis of many small-scale regressions into a single coherent structure.
7. Interpretive Insight
This model is not a prediction tool; it’s an instrument of mathematical observation. By translating price dynamics into a prime-structured correlation space, it reveals how coherence unfolds through time — not as a forecast, but as a measurable evolution of structure.
It unifies three analytical domains:
- Prime distribution — defines a nonlinear temporal architecture.
- Pearson correlation — quantifies statistical cohesion.
- Cumulative delta — expresses behavioral imbalance in order flow.
The synthesis creates a geometric analysis of liquidity and time — where structure meets energy, and where the invisible rhythm of market flow becomes measurable.
8. Contribution & Feedback
Share your observations in the comments:
- The time gap and alternation between R+ and R− clusters.
- How different timeframes change delta sensitivity or reveal compression/expansion.
- Prime intervals/clusters that tend to sit near turning points or liquidity shifts.
- How avgR behaves across assets or regimes (trending, ranging, high-vol).
- Notable interactions with the parallel bands (touches, breaks, mean-revert).
Your field notes help others read the model more effectively and compare contexts.
Summary
- Primes define the structure.
- Pearson quantifies coherence.
- Slope median stabilizes geometry.
- Regression bands visualize deviation.
- Cumulative delta locates imbalance.
Together, they construct a framework where mathematics meets market behavior.
Z-Score Regression Bands [BOSWaves]Z-Score Regression Bands – Adaptive Trend and Volatility Insight
Overview
The Z-Score Regression Bands is a trend and volatility analysis framework designed to give traders a clear, structured view of price behavior. It combines Least Squares Moving Average (LSMA) regression, a statistical method to detect underlying trends, with Z-Score standardization, which measures how far price deviates from its recent average.
Traditional moving average bands, like Bollinger Bands, often lag behind trends or generate false signals in noisy markets. Z-Score Regression Bands addresses these limitations by:
Tracking trends accurately using LSMA regression
Normalizing deviations with Z-Scores to identify statistically significant price extremes
Visualizing multiple bands for normal, strong, and extreme moves
Highlighting trend shifts using diamond markers based on Z-Score crossings
This multi-layered approach allows traders to understand trend strength, detect overextensions, and identify periods of low or high volatility — all from a single, clear chart overlay. It is designed for traders of all levels and can be applied across scalping, day trading, swing trading, and longer-term strategies.
Theoretical Foundation
The Z-Score Regression Bands are grounded in statistical and trend analysis principles. Here’s the idea in plain terms:
Least Squares Moving Average (LSMA) – Unlike standard moving averages, LSMA fits a straight line to recent price data using regression. This “best-fit” line shows the underlying trend more precisely and reduces lag, helping traders see trend changes earlier.
Z-Score Standardization – A Z-Score expresses how far the LSMA is from its recent mean in standard deviation units. This shows whether price is unusually high or low, which can indicate potential reversals, pullbacks, or acceleration of a trend.
Multi-Band Structure – The three bands represent: Band #1: Normal range of price fluctuations; Band #2: Significant deviation from the trend; Band #3: Extreme price levels that are statistically rare. The distance between bands dynamically adapts to market volatility, allowing traders to visualize expansions (higher volatility) and contractions (lower volatility).
Trend Signals – When Z-Score crosses zero, diamonds appear on the chart. These markers signal potential trend initiation, continuation, or reversal, offering a simple alert for shifts in market momentum.
How It Works
The indicator calculates and plots several layers of information:
LSMA Regression (Trend Detection)
Computes a line that best fits recent price points.
The LSMA line smooths out minor fluctuations while reflecting the general direction of the market.
Z-Score Calculation (Deviation Measurement)
Standardizes the LSMA relative to its recent average.
Positive Z-Score → LSMA above average, negative → LSMA below average.
Helps identify overbought or oversold conditions relative to the trend.
Multi-Band Construction (Volatility Envelope)
Upper and lower bands are placed at configurable multiples of standard deviation.
Band #1 captures typical price movement, Band #2 signals stronger deviation, Band #3 highlights extreme moves.
Bands expand and contract with volatility, giving an intuitive visual guide to market conditions.
Trend Signals (Diamonds)
Appear when Z-Score crosses zero.
Indicates moments when momentum may shift, helping traders time entries or exits.
Visual Interpretation
Band width = volatility: wide bands indicate strong movement; narrow bands indicate calm periods.
LSMA shows underlying trend direction, while bands show how far price has strayed from that trend.
Interpretation
The Z-Score Regression Bands provide a multi-dimensional view of market behavior:
Trend Analysis – LSMA line slope shows general market direction.
Momentum & Volatility – Z-Score indicates whether the trend is accelerating or losing strength; band width indicates volatility levels.
Price Extremes – Price touching Band #2 or #3 may suggest overextension and potential reversals.
Trend Shifts – Diamonds signal statistically significant changes in momentum.
Cycle Awareness – Standard deviation bands help distinguish normal market fluctuations from extreme events.
By combining these insights, traders can avoid false signals and react to meaningful structural shifts in the market.
Strategy Integration
Trend Following
Enter trades when diamonds indicate momentum aligns with LSMA direction.
Use Band #1 and #2 for stop placement and partial exits.
Breakout Trading
Watch for narrow bands (low volatility) followed by price pushing outside Band #1 or #2.
Confirm with Z-Score movement in the breakout direction.
Mean Reversion/Pullback
If price reaches Band #2 or #3 without continuation, expect a pullback toward LSMA.
Exhaustion & Reversals
Flattening Z-Score near zero while price remains at extreme bands signals trend weakening.
Tighten stops or scale out before a potential reversal.
Multi-Timeframe Confirmation
High timeframe LSMA confirms the main trend.
Lower timeframe bands provide refined entry and exit points.
Technical Implementation
LSMA Regression : Best-fit line minimizes lag and captures trend slope.
Z-Score Standardization : Normalizes deviation to allow consistent interpretation across markets.
Multi-Band Envelope : Three layers for normal, strong, and extreme deviations.
Trend Signals : Automatic diamonds for Z-Score zero-crossings.
Band Fill Options : Optional shading to visualize volatility expansions and contractions.
Optimal Application
Asset Classes:
Forex : Capture breakouts, overextensions, and trend shifts.
Crypto : High-volatility adaptation with adjustable band multipliers.
Stocks/ETFs : Identify trending sectors, reversals, and pullbacks.
Indices/Futures : Track cycles and structural trends.
Timeframes:
Scalping (1–5 min) : Focus on Band #1 and trend signals for fast entries.
Intraday (15m–1h) : Use Bands #1–2 for continuation and breakout trades.
Swing (4h–Daily) : Bands #2–3 capture trend momentum and exhaustion.
Position (Daily–Weekly) : LSMA trend dominates; Bands #3 highlight regime extremes.
Performance Characteristics
Strong Performance:
Trending markets with moderate-to-high volatility
Assets with steady liquidity and identifiable cycles
Weak Performance:
Flat or highly choppy markets
Very short timeframes (<1 min) dominated by noise
Integration Tips
Combine with support/resistance, volume, or order flow analysis for confirmation.
Use bands for stops, targets, or scaling positions.
Apply multi-timeframe analysis: higher timeframe LSMA confirms main trend, lower timeframe bands refine entries.
Disclaimer
The Z-Score Regression Bands is a trading analysis tool, not a guaranteed profit system. Its effectiveness depends on market conditions, parameter selection, and disciplined risk management. Use it as part of a broader trading strategy, not in isolation.
Linear Regression Trend Navigator [QuantAlgo]🟢 Overview
The Linear Regression Trend Navigator is a trend-following indicator that combines statistical regression analysis with adaptive volatility bands to identify and track dominant market trends. It employs linear regression mathematics to establish the underlying trend direction, while dynamically adjusting trend boundaries based on standard deviation calculations to filter market noise and maintain trend continuity. The result is a straightforward visual system where green indicates bullish conditions favoring buy/long positions, and red signals bearish conditions supporting sell/short trades.
🟢 How It Works
The indicator operates through a three-phase computational process that transforms raw price data into adaptive trend signals. In the first phase, it calculates a linear regression line over the specified period, establishing the mathematical best-fit line through recent price action to determine the underlying directional bias. This regression line serves as the foundation for trend analysis by smoothing out short-term price variations while preserving the essential directional characteristics.
The second phase constructs dynamic volatility boundaries by calculating the standard deviation of price movements over the defined period and applying a user-adjustable multiplier. These upper and lower bounds create a volatility-adjusted channel around the regression line, with wider bands during volatile periods and tighter bands during stable conditions. This adaptive boundary system operates entirely behind the scenes, ensuring the trend signal remains relevant across different market volatility regimes without cluttering the visual display.
In the final phase, the system generates a simple trend line that dynamically positions itself within the volatility boundaries. When price action pushes the regression line above the upper bound, the trend line adjusts to the upper boundary level. Conversely, when the regression line falls below the lower bound, the trend line moves to the lower boundary. The result is a single colored line that transitions between green (rising trend line = buy/long) and red (declining trend line = sell/short).
🟢 How to Use
Green Trend Line: Upward momentum indicating favorable conditions for long positions, buy signals, and bullish strategies
Red Trend Line: Downward momentum signaling optimal timing for short positions, sell signals, and bearish approaches
Rising Green Line: Accelerating bullish momentum with steepening angles indicating strengthening upward pressure and potential for trend continuation
Declining Red Line: Intensifying bearish momentum with increasing negative slopes suggesting persistent downward pressure and shorting opportunities
Flattening Trend Lines: Gradual reduction in slope regardless of color may indicate approaching consolidation or momentum exhaustion requiring position review
🟢 Pro Tips for Trading and Investing
→ Entry/Exit Timing: Trade exclusively on band color transitions rather than price patterns, as each color change represents a statistically-confirmed shift that has passed through volatility filtering, providing higher probability setups than traditional technical analysis.
→ Parameter Optimization for Asset Classes: Customize the linear regression period based on your trading style. For example, use 5-10 bars for day trading to capture short-term statistical shifts, 14-20 for swing trading to balance responsiveness with stability, and 25-50 for position trading to filter out medium-term noise.
→ Volatility Calibration Strategy: Adjust the standard deviation multiplier according to market volatility. For instance, increase to 2.0+ during high-volatility periods like earnings or news events to reduce false signals, decrease to 1.0-1.5 during stable market conditions to maintain sensitivity to genuine trends.
→ Cross-Timeframe Statistical Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes for directional bias and lower timeframes for entry timing.
→ Alert-Based Systematic Trading: Use built-in alerts to eliminate discretionary decision-making and ensure you capture every statistically-significant trend change, particularly effective for traders who cannot monitor charts continuously.
→ Risk Allocation Based on Signal Strength: Increase position sizes during periods of strong directional movement while reducing exposure during frequent band color changes that indicate statistical uncertainty or ranging conditions.
Polynomial Regression HeatmapPolynomial Regression Heatmap – Advanced Trend & Volatility Visualizer
Overview
The Polynomial Regression Heatmap is a sophisticated trading tool designed for traders who require a clear and precise understanding of market trends and volatility. By applying a second-degree polynomial regression to price data, the indicator generates a smooth trend curve, augmented with adaptive volatility bands and a dynamic heatmap. This framework allows users to instantly recognize trend direction, potential reversals, and areas of market strength or weakness, translating complex price action into a visually intuitive map.
Unlike static trend indicators, the Polynomial Regression Heatmap adapts to changing market conditions. Its visual design—including color-coded candles, regression bands, optional polynomial channels, and breakout markers—ensures that price behavior is easy to interpret. This makes it suitable for scalping, swing trading, and longer-term strategies across multiple asset classes.
How It Works
The core of the indicator relies on fitting a second-degree polynomial to a defined lookback period of price data. This regression curve captures the non-linear nature of market movements, revealing the true trajectory of price beyond the distortions of noise or short-term volatility.
Adaptive upper and lower bands are constructed using ATR-based scaling, surrounding the regression line to reflect periods of high and low volatility. When price moves toward or beyond these bands, it signals areas of potential overextension or support/resistance.
The heatmap colors each candle based on its relative position within the bands. Green shades indicate proximity to the upper band, red shades indicate proximity to the lower band, and neutral tones represent mid-range positioning. This continuous gradient visualization provides immediate feedback on trend strength, market balance, and potential turning points.
Optional polynomial channels can be overlaid around the regression curve. These three-line channels are based on regression residuals and a fixed width multiplier, offering additional reference points for analyzing price deviations, trend continuation, and reversion zones.
Signals and Breakouts
The Polynomial Regression Heatmap includes statistical pivot-based signals to highlight actionable price movements:
Buy Signals – A triangular marker appears below the candle when a pivot low occurs below the lower regression band.
Sell Signals – A triangular marker appears above the candle when a pivot high occurs above the upper regression band.
These markers identify significant deviations from the regression curve while accounting for volatility, providing high-quality visual cues for potential entry points.
The indicator ensures clarity by spacing markers vertically using ATR-based calculations, preventing overlap during periods of high volatility. Users can rely on these signals in combination with heatmap intensity and regression slope for contextual confirmation.
Interpretation
Trend Analysis :
The slope of the polynomial regression line represents trend direction. A rising curve indicates bullish bias, a falling curve indicates bearish bias, and a flat curve indicates consolidation.
Steeper slopes suggest stronger momentum, while gradual slopes indicate more moderate trend conditions.
Volatility Assessment :
Band width provides an instant visual measure of market volatility. Narrow bands correspond to low volatility and potential consolidation, whereas wide bands indicate higher volatility and significant price swings.
Heatmap Coloring :
Candle colors visually represent price position within the bands. This allows traders to quickly identify zones of bullish or bearish pressure without performing complex calculations.
Channel Analysis (Optional) :
The polynomial channel defines zones for evaluating potential overextensions or retracements. Price interacting with these lines may suggest areas where mean-reversion or trend continuation is likely.
Breakout Signals :
Buy and Sell markers highlight pivot points relative to the regression and volatility bands. These are statistical signals, not arbitrary triggers, and should be interpreted in context with trend slope, band width, and heatmap intensity.
Strategy Integration
The Polynomial Regression Heatmap supports multiple trading approaches:
Trend Following – Enter trades in the direction of the regression slope while using the heatmap for momentum confirmation.
Pullback Entries – Use breakouts or deviations from the regression bands as low-risk entry points during trend continuation.
Mean Reversion – Price reaching outer channel boundaries can indicate potential reversal or retracement opportunities.
Multi-Timeframe Alignment – Overlay on higher and lower timeframes to filter noise and improve entry timing.
Stop-loss levels can be set just beyond the opposing regression band, while take-profit targets can be informed by the distance between the bands or the curvature of the polynomial line.
Advanced Techniques
For traders seeking greater precision:
Combine the Polynomial Regression Heatmap with volume, momentum, or volatility indicators to validate signals.
Observe the width and slope of the regression bands over time to anticipate expanding or contracting volatility.
Track sequences of breakout signals in conjunction with heatmap intensity for systematic trade management.
Adjusting regression length allows customization for different assets or timeframes, balancing responsiveness and smoothing. The combination of polynomial curve, adaptive bands, heatmap, and optional channels provides a comprehensive statistical framework for informed decision-making.
Inputs and Customization
Regression Length – Determines the number of bars used for polynomial fitting. Shorter lengths increase responsiveness; longer lengths improve smoothing.
Show Bands – Toggle visibility of the ATR-based regression bands.
Show Channel – Enable or disable the polynomial channel overlay.
Color Settings – Customize bullish, bearish, neutral, and accent colors for clarity and visual preference.
All other internal parameters are fixed to ensure consistent statistical behavior and minimize potential misconfiguration.
Why Use Polynomial Regression Heatmap
The Polynomial Regression Heatmap transforms complex price action into a clear, actionable visual framework. By combining non-linear trend mapping, adaptive volatility bands, heatmap visualization, and breakout signals, it provides a multi-dimensional perspective that is both quantitative and intuitive.
This indicator allows traders to focus on execution, interpret market structure at a glance, and evaluate trend strength, overextensions, and potential reversals in real time. Its design is compatible with scalping, swing trading, and long-term strategies, providing a robust tool for disciplined, data-driven trading.
BTC Power Law Valuation BandsBTC Power Law Rainbow
A long-term valuation framework for Bitcoin based on Power Law growth — designed to help identify macro accumulation and distribution zones, aligned with long-term investor behavior.
🔍 What Is a Power Law?
A Power Law is a mathematical relationship where one quantity varies as a power of another. In this model:
Price ≈ a × (Time)^b
It captures the non-linear, exponentially slowing growth of Bitcoin over time. Rather than using linear or cyclical models, this approach aligns with how complex systems, such as networks or monetary adoption curves, often grow — rapidly at first, and then more slowly, but persistently.
🧠 Why Power Law for BTC?
Bitcoin:
Has finite supply and increasing adoption.
Operates as a monetary network , where Metcalfe’s Law and power laws naturally emerge.
Exhibits exponential growth over logarithmic time when viewed on a log-log chart .
This makes it uniquely well-suited for power law modeling.
🌈 How to Use the Valuation Bands
The central white line represents the modeled fair value according to the power law.
Colored bands represent deviations from the model in logarithmic space, acting as macro zones:
🔵 Lower Bands: Deep value / Accumulation zones.
🟡 Mid Bands: Fair value.
🔴 Upper Bands: Euphoria / Risk of macro tops.
📐 Smart Money Concepts (SMC) Alignment
Accumulation: Occurs when price consolidates near lower bands — often aligning with institutional positioning.
Markup: As price re-enters or ascends the bands, we often see breakout behavior and trend expansion.
Distribution: When price extends above upper bands, potential for exit liquidity creation and distribution events.
Reversion: Historically, price mean-reverts toward the model — rarely staying outside the bands for long.
This makes the model useful for:
Cycle timing
Long-term DCA strategy zones
Identifying value dislocations
Filtering short-term noise
⚠️ Disclaimer
This tool is for educational and informational purposes only . It is not financial advice. The power law model is a non-predictive, mathematical framework and does not guarantee future price movements .
Always use additional tools, risk management, and your own judgment before making trading or investment decisions.
Forecasting Quadratic Regression [UPDATED V6] Forecasting Quadratic Regression applies a second-degree polynomial regression model to price data, offering a non-linear alternative to traditional linear regression. By fitting a quadratic curve of the form:
y=a+bx+cx2
the indicator captures both directional trend and curvature, allowing traders to detect momentum shifts earlier than with straight-line models.
🔹 Core Features
Fits a quadratic regression curve to user-defined lookback periods
Extends the fitted curve forward to generate forecast projections
Calculates slope curvature to highlight trend acceleration or deceleration
Adapts dynamically as new bars are added
🔹 Trading Applications
Identify potential reversal zones when the curve inflects (2nd derivative sign change)
Forecast near-term mean reversion targets or extended trend continuations
Filter trades by measuring momentum curvature rather than linear slope
Visualize higher-order structure in price beyond standard regression lines
⚠️ Note: This model is statistical and assumes past curvature informs short-term future price paths. It should be combined with confirmation signals (volume, oscillators, support/resistance) to reduce false inflection points.
Bitcoin Expectile Model [LuxAlgo]The Bitcoin Expectile Model is a novel approach to forecasting Bitcoin, inspired by the popular Bitcoin Quantile Model by PlanC. By fitting multiple Expectile regressions to the price, we highlight zones of corrections or accumulations throughout the Bitcoin price evolution.
While we strongly recommend using this model with the Bitcoin All Time History Index INDEX:BTCUSD on the 3 days or weekly timeframe using a logarithmic scale, this model can be applied to any asset using the daily timeframe or superior.
Please note that here on TradingView, this model was solely designed to be used on the Bitcoin 1W chart, however, it can be experimented on other assets or timeframes if of interest.
🔶 USAGE
The Bitcoin Expectile Model can be applied similarly to models used for Bitcoin, highlighting lower areas of possible accumulation (support) and higher areas that allow for the anticipation of potential corrections (resistance).
By default, this model fits 7 individual Expectiles Log-Log Regressions to the price, each with their respective expectile ( tau ) values (here multiplied by 100 for the user's convenience). Higher tau values will return a fit closer to the higher highs made by the price of the asset, while lower ones will return fits closer to the lower prices observed over time.
Each zone is color-coded and has a specific interpretation. The green zone is a buy zone for long-term investing, purple is an anomaly zone for market bottoms that over-extend, while red is considered the distribution zone.
The fits can be extrapolated, helping to chart a course for the possible evolution of Bitcoin prices. Users can select the end of the forecast as a date using the "Forecast End" setting.
While the model is made for Bitcoin using a log scale, other assets showing a tendency to have a trend evolving in a single direction can be used. See the chart above on QQQ weekly using a linear scale as an example.
The Start Date can also allow fitting the model more locally, rather than over a large range of prices. This can be useful to identify potential shorter-term support/resistance areas.
🔶 DETAILS
🔹 On Quantile and Expectile Regressions
Quantile and Expectile regressions are similar; both return extremities that can be used to locate and predict prices where tops/bottoms could be more likely to occur.
The main difference lies in what we are trying to minimize, which, for Quantile regression, is commonly known as Quantile loss (or pinball loss), and for Expectile regression, simply Expectile loss.
You may refer to external material to go more in-depth about these loss functions; however, while they are similar and involve weighting specific prices more than others relative to our parameter tau, Quantile regression involves minimizing a weighted mean absolute error, while Expectile regression minimizes a weighted squared error.
The squared error here allows us to compute Expectile regression more easily compared to Quantile regression, using Iteratively reweighted least squares. For Quantile regression, a more elaborate method is needed.
In terms of comparison, Quantile regression is more robust, and easier to interpret, with quantiles being related to specific probabilities involving the underlying cumulative distribution function of the dataset; on the other expectiles are harder to interpret.
🔹 Trimming & Alterations
It is common to observe certain models ignoring very early Bitcoin price ranges. By default, we start our fit at the date 2010-07-16 to align with existing models.
By default, the model uses the number of time units (days, weeks...etc) elapsed since the beginning of history + 1 (to avoid NaN with log) as independent variable, however the Bitcoin All Time History Index INDEX:BTCUSD do not include the genesis block, as such users can correct for this by enabling the "Correct for Genesis block" setting, which will add the amount of missed bars from the Genesis block to the start oh the chart history.
🔶 SETTINGS
Start Date: Starting interval of the dataset used for the fit.
Correct for genesis block: When enabled, offset the X axis by the number of bars between the Bitcoin genesis block time and the chart starting time.
🔹 Expectiles
Toggle: Enable fit for the specified expectile. Disabling one fit will make the script faster to compute.
Expectile: Expectile (tau) value multiplied by 100 used for the fit. Higher values will produce fits that are located near price tops.
🔹 Forecast
Forecast End: Time at which the forecast stops.
🔹 Model Fit
Iterations Number: Number of iterations performed during the reweighted least squares process, with lower values leading to less accurate fits, while higher values will take more time to compute.
Squeeze Momentum Regression Clouds [SciQua]╭──────────────────────────────────────────────╮
☁️ Squeeze Momentum Regression Clouds
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🔍 Overview
The Squeeze Momentum Regression Clouds (SMRC) indicator is a powerful visual tool for identifying price compression , trend strength , and slope momentum using multiple layers of linear regression Clouds. Designed to extend the classic squeeze framework, this indicator captures the behavior of price through dynamic slope detection, percentile-based spread analytics, and an optional UI for trend inspection — across up to four customizable regression Clouds .
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⚙️ Core Features
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Up to 4 Regression Clouds – Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine – Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap – Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring – Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles – Optional candle coloring reflects the real-time composite score.
UI Table – A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling – Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options – Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
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🧪 How to Use the Indicator
1. Understand Trend Bias with Cloud Colors
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Each Cloud changes color based on its current slope:
Green indicates a rising trend.
Red indicates a falling trend.
Gray indicates a flat slope — often seen during chop or transitions.
Cloud 1 typically reflects short-term structure, while Cloud 4 represents long-term directional bias. Watch for multi-Cloud alignment — when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
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2. Use Compression Heat to Anticipate Breakouts
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The space between each Cloud’s top and bottom regression lines is measured, normalized, and analyzed over time. When this spread tightens relative to its history, the script highlights the band with a yellow compression glow .
This visual cue helps identify squeeze zones before volatility expands. If you see compression paired with a changing slope color (e.g., gray to green), this may indicate an impending breakout.
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3. Leverage the Optional Table UI
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The indicator includes a dynamic, floating table that displays real-time metrics per Cloud. These include:
Slope direction and value , with historical Min/Max reference.
Top and Bottom percentile ranks , showing how price sits within the Cloud range.
Current spread width , compared to its historical norms.
Composite score , which blends trend, slope, and compression for that Cloud.
You can customize the table’s position, theme, transparency, and whether to show a combined summary score in the header.
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4. Analyze Candle Color for Composite Signals
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When enabled, the indicator colors candles based on a weighted composite score. This score factors in:
The signed slope of each Cloud (up, down, or flat)
The percentile pressure from the top and bottom bands
The degree of spread compression
Expect green candles in bullish trend phases, red candles during bearish regimes, and gray candles in mixed or low-conviction zones.
Candle coloring provides a visual shorthand for market conditions , useful for intraday scanning or historical backtesting.
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🧰 Configuration Guidance
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To tailor the indicator to your strategy:
Use Cloud lengths like 21, 34, 55, and 89 for a balanced multi-timeframe view.
Adjust the slope threshold (default 0.05) to control how sensitive the trend coloring is.
Set the spread floor (e.g., 0.15) to tune when compression is detected and visualized.
Choose your weighting style : Inverse Length (favor faster bands), Equal, or Max Pooling (most aggressive).
Set composite weights to emphasize trend slope, percentile bias, or compression—depending on your market edge.
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✅ Best Practices
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Use aligned Cloud colors across all bands to confirm trend conviction.
Combine slope direction with compression glow for early breakout entry setups.
In choppy markets, watch for Clouds 1 and 2 turning flat while Clouds 3 and 4 remain directional — a sign of potential trend exhaustion or consolidation.
Keep the table enabled during backtesting to manually evaluate how each Cloud behaved during price turns and consolidations.
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📌 License & Usage Terms
╰───────────────────────╯
This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
✅ You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
❌ You are not allowed to:
Resell or redistribute the script without permission
Use it inside any paid product or service
Republish without giving clear attribution to the original author
For commercial licensing , private customization, or collaborations, please contact Joshua Danford directly.
Smart Trend Signals [QuantAlgo]🟢 Overview
The Smart Trend Signals indicator is created to address a fundamental challenge in technical analysis: generating timely trend signals while adapting to varying market volatility conditions. The indicator distinguishes itself by employing volatility-adjusted calculations that automatically modify signal sensitivity based on current market conditions, rather than using fixed parameters that perform inconsistently across different market environments. By processing Long and Short signals through separate dynamic calculation engines, each optimized for its respective directional bias, the indicator reduces the common issue of delayed or conflicting signals that plague many traditional trend-following tools. Additionally, the integration of linear regression-based trend confirmation adds another layer of signal validation, helping to filter market noise while maintaining responsiveness to genuine price movements. This adaptive approach makes the indicator practical for both traders and investors across different asset classes and timeframes, from short-term forex/crypto scalping to long-term equity position analysis.
🟢 How It Works
The indicator uses a straightforward calculation process that combines volatility measurement with momentum detection to generate directional signals. The system first calculates Average True Range (ATR) over a user-defined period to measure current market volatility. This ATR value is then multiplied by the Smart Trend Multiplier setting to create dynamic reference levels that expand during volatile periods and contract during calmer market conditions.
For signal generation, the indicator maintains separate calculation paths for Long/Buy and Short/Sell opportunities. Long signals are generated when price moves above a dynamically calculated level below the current price, confirmed by an exponential moving average crossover in the same direction. Short signals work in reverse, triggering when price moves below a calculated level above the current price, also requiring EMA confirmation. This dual-path approach allows each signal type to operate with parameters suited to its directional bias.
🟢 How to Use
Long Signals (Green Labels): Appear as "Long" labels below price bars when the indicator detects upward price momentum above the calculated reference level, confirmed by EMA crossover. These signals identify moments when price action demonstrates bullish characteristics based on the volatility-adjusted calculations.
Short Signals (Red Labels): Display as "Short" labels above price bars when downward price momentum below the reference level is detected and confirmed by EMA crossover. These signals highlight instances where price action exhibits bearish characteristics according to the indicator's mathematical framework.
Customizable Bar Coloring: This feature colors individual price bars to match the current signal direction. When enabled, each bar reflects the indicator's current directional bias, creating a continuous visual representation of trend periods across the chart timeline.
Built-in Alert System: Provides automatic notifications for new signals with detailed exchange and ticker information. The alert system monitors the indicator's calculations continuously and triggers notifications when new long or short signals are generated, allowing traders/investors to track multiple instruments simultaneously.
🟢 Pro Tips for Trading and Investing
→ Parameter Adjustment: Higher Smart Trend Multiplier settings generate fewer signals that may be more selective, while lower settings produce more frequent signals that may include more false positives. Test different settings to find what works for your trading style and market conditions.
→ Timeframe Analysis: Using higher timeframes for general trend direction and lower timeframes for entry timing is a common approach.
→ Risk Management: No indicator eliminates the need for proper risk management. Use appropriate position sizing and stop-loss strategies regardless of signal quality or frequency.
→ Market Conditions: The indicator may perform differently in trending versus ranging markets. Frequent signal changes might indicate choppy conditions. Backtest and paper trade before risking real capital.
Adaptive Market Profile – Auto Detect & Dynamic Activity ZonesAdaptive Market Profile is an advanced indicator that automatically detects and displays the most relevant trend channel and market profile for any asset and timeframe. Unlike standard regression channel tools, this script uses a fully adaptive approach to identify the optimal period, providing you with the channel that best fits the current market dynamics. The calculation is based on maximizing the statistical significance of the trend using Pearson’s R coefficient, ensuring that the most relevant trend is always selected.
Within the selected channel, the indicator generates a dynamic market profile, breaking the price range into configurable zones and displaying the most active areas based on volume or the number of touches. This allows you to instantly identify high-activity price levels and potential support/resistance zones. The “most active lines” are plotted in real-time and always stay parallel to the channel, dynamically adapting to market structure.
Key features:
- Automatic detection of the optimal regression period: The script scans a wide range of lengths and selects the channel that statistically represents the strongest trend.
- Dynamic market profile: Visualizes the distribution of volume or price touches inside the trend channel, with customizable section count.
- Most active zones: Highlights the most traded or touched price levels as dynamic, parallel lines for precise support/resistance reading.
- Manual override: Optionally, users can select their own channel period for full control.
- Supports both linear and logarithmic charts: Simple toggle to match your chart scaling.
Use cases:
- Trend following and channel trading strategies.
- Quick identification of dynamic support/resistance and liquidity zones.
- Objective selection of the most statistically significant trend channel, without manual guesswork.
- Suitable for all assets and timeframes (crypto, stocks, forex, futures).
Originality:
This script goes beyond basic regression channels by integrating dynamic profile analysis and fully adaptive period detection, offering a comprehensive tool for modern technical analysts. The combination of trend detection, market profile, and activity zone mapping is unique and not available in TradingView built-ins.
Instructions:
Add Adaptive Market Profile to your chart. By default, the script automatically detects the optimal channel period and displays the corresponding regression channel with dynamic profile and activity zones. If you prefer manual control, disable “Auto trend channel period” and set your preferred period. Adjust profile settings as needed for your asset and timeframe.
For questions, suggestions, or further customization, contact Julien Eche (@Julien_Eche) directly on TradingView.
52SIGNAL RECIPE CCI Linreg Bands═══ 52SIGNAL RECIPE CCI Linreg Bands ═══
◆ Overview
52SIGNAL RECIPE CCI Linreg Bands is an advanced technical indicator that combines the CCI (Commodity Channel Index) with Linear Regression Bands. This indicator visualizes the volatility of the CCI using linear regression bands, helping to clearly identify overbought/oversold areas and more accurately capture potential trend reversal points.
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◆ Key Features
• CCI-Based Overbought/Oversold Analysis: Uses the traditional CCI indicator to identify overbought/oversold conditions in the market
• Integrated Linear Regression Bands: Applies linear regression analysis to the CCI to visually represent the direction and strength of trends
• Dual Overbought/Oversold Levels: Sets overbought/oversold levels for both CCI and Linear Regression Bands to increase the accuracy of signals
• Advanced Visualization: Intuitive chart analysis is possible with color changes according to trend direction and clear band display
• Multiple Alert Settings: Alert functions for various conditions ensure you don't miss important trading moments
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◆ Technical Foundation
■ CCI (Commodity Channel Index)
• Basic Settings: 20-period CCI with Weighted Moving Average (WMA) applied
• Calculation Method: Measures the deviation from the average price normalized to a specific range
• Overbought/Oversold Levels: Default values set to +150 (overbought) and -150 (oversold)
■ Linear Regression Bands
• Period: Default value of 100 days
• Deviation: Default value of 4.5 standard deviations
• Center Line: The center line of the linear regression analysis for the CCI values
• Band Width: Displays the range of volatility around the center line based on the calculated standard deviation
• Overbought/Oversold Levels: Default values set to +250 (overbought) and -250 (oversold)
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◆ Practical Applications
■ Identifying Trading Signals
• Buy Signal:
▶ When the CCI falls below the oversold level (-150)
▶ When the lower band of the Linear Regression Bands falls below the oversold level (-250)
▶ When both conditions are met simultaneously (extreme oversold state) - a strong buy signal
• Sell Signal:
▶ When the CCI rises above the overbought level (+150)
▶ When the upper band of the Linear Regression Bands rises above the overbought level (+250)
▶ When both conditions are met simultaneously (extreme overbought state) - a strong sell signal
■ Trend Analysis
• Uptrend: When the linear regression center line is rising and the CCI is moving above the zero line
• Downtrend: When the linear regression center line is falling and the CCI is moving below the zero line
• Trend Strength: The wider the gap between the bands, the greater the volatility; the narrower, the more stable the trend
■ Divergence Confirmation
• Bearish Divergence: Price forms a new high, but the CCI is lower than the previous high (potential bearish signal)
• Bullish Divergence: Price forms a new low, but the CCI is higher than the previous low (potential bullish signal)
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◆ Advanced Setting Options
■ CCI Setting Adjustments
• CCI Source: Selectable options include Close (default), Open, High, Low, HL2, HLC3, OHLC4, etc.
• CCI Length: Adjust to lower values for short-term volatility, higher values for long-term trends
■ Linear Regression Setting Adjustments
• Period: Use lower values (20-50) for short-term analysis, higher values (100-200) for long-term analysis
• Deviation: Higher values create wider bands (more signals), lower values create narrower bands (more accurate signals)
■ Overbought/Oversold Level Adjustments
• CCI Levels: Adjust to more extreme values (±200) in highly volatile markets
• Linear Regression Band Levels: Adjustable to ±300 or ±200 depending on market conditions
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◆ Synergy with Other Indicators
• Bollinger Bands: Use alongside Bollinger Bands on the price chart to compare price volatility with CCI volatility
• MACD: Use with MACD for momentum and trend confirmation
• Fibonacci Retracement: Check CCI Linreg Bands signals with key support/resistance levels
• Moving Averages: Combine moving average crossovers with CCI Linreg Bands signals to improve reliability
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◆ Conclusion
52SIGNAL RECIPE CCI Linreg Bands provides a powerful and accurate technical analysis tool by combining traditional CCI with linear regression analysis. The dual overbought/oversold system increases the accuracy of trading signals and clearly visualizes trend direction and strength to help traders make decisions. You can achieve optimal results by adjusting various settings to match your trading style and market conditions.
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※ Disclaimer: Past performance does not guarantee future results. Always use appropriate risk management strategies.
═══ 52SIGNAL RECIPE CCI 선형회귀 밴드 ═══
◆ 개요
52SIGNAL RECIPE CCI 선형회귀 밴드는 CCI(Commodity Channel Index)와 선형회귀 밴드를 결합한 고급 기술적 지표입니다. 이 지표는 선형회귀 밴드를 사용하여 CCI의 변동성을 시각화하여 과매수/과매도 영역을 명확하게 식별하고 잠재적인 추세 반전 지점을 더 정확하게 포착하는 데 도움을 줍니다.
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◆ 주요 특징
• CCI 기반 과매수/과매도 분석: 전통적인 CCI 지표를 사용하여 시장의 과매수/과매도 상태를 식별
• 통합된 선형회귀 밴드: CCI에 선형회귀 분석을 적용하여 추세의 방향과 강도를 시각적으로 표현
• 이중 과매수/과매도 레벨: CCI와 선형회귀 밴드 모두에 과매수/과매도 레벨을 설정하여 신호의 정확도 향상
• 고급 시각화: 추세 방향에 따른 색상 변화와 명확한 밴드 표시로 직관적인 차트 분석 가능
• 다중 알림 설정: 다양한 조건에 대한 알림 기능으로 중요한 트레이딩 시점을 놓치지 않도록 보장
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◆ 기술적 기반
■ CCI (Commodity Channel Index)
• 기본 설정: 20기간 CCI에 가중이동평균(WMA) 적용
• 계산 방법: 평균 가격에 대한 편차를 측정하여 정규화한 값으로 표현
• 과매수/과매도 레벨: 기본값으로 +150(과매수)과 -150(과매도) 설정
■ 선형회귀 밴드
• 기간: 기본값 100일
• 편차: 기본값 4.5 표준편차
• 중심선: CCI 값에 대한 선형회귀 분석의 중심선
• 밴드 폭: 계산된 표준편차에 기반하여 중심선 주변의 변동성 범위 표시
• 과매수/과매도 레벨: 기본값으로 +250(과매수)와 -250(과매도) 설정
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◆ 실용적 응용
■ 트레이딩 신호 식별
• 매수 신호:
▶ CCI가 과매도 레벨(-150) 아래로 떨어질 때
▶ 선형회귀 밴드의 하단이 과매도 레벨(-250) 아래로 떨어질 때
▶ 두 조건이 동시에 충족될 때(극단적 과매도 상태) - 강한 매수 신호
• 매도 신호:
▶ CCI가 과매수 레벨(+150) 위로 상승할 때
▶ 선형회귀 밴드의 상단이 과매수 레벨(+250) 위로 상승할 때
▶ 두 조건이 동시에 충족될 때(극단적 과매수 상태) - 강한 매도 신호
■ 추세 분석
• 상승 추세: 선형회귀 중심선이 상승하고 CCI가 0선 위로 움직일 때
• 하락 추세: 선형회귀 중심선이 하락하고 CCI가 0선 아래로 움직일 때
• 추세 강도: 밴드 사이의 간격이 넓을수록 변동성이 크고, 좁을수록 추세가 안정적
■ 다이버전스 확인
• 약세 다이버전스: 가격이 신고점을 형성하지만 CCI가 이전 고점보다 낮을 때(잠재적 약세 신호)
• 강세 다이버전스: 가격이 신저점을 형성하지만 CCI가 이전 저점보다 높을 때(잠재적 강세 신호)
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◆ 고급 설정 옵션
■ CCI 설정 조정
• CCI 소스: 선택 가능한 옵션에는 종가(기본값), 시가, 고가, 저가, HL2, HLC3, OHLC4 등이 포함
• CCI 길이: 단기 변동성을 위해 낮은 값으로, 장기 추세를 위해 높은 값으로 조정
■ 선형회귀 설정 조정
• 기간: 단기 분석을 위해 낮은 값(20-50), 장기 분석을 위해 높은 값(100-200) 사용
• 편차: 높은 값은 더 넓은 밴드(더 많은 신호), 낮은 값은 더 좁은 밴드(더 정확한 신호) 생성
■ 과매수/과매도 레벨 조정
• CCI 레벨: 변동성이 큰 시장에서는 더 극단적인 값(±200)으로 조정
• 선형회귀 밴드 레벨: 시장 상황에 따라 ±300 또는 ±200으로 조정 가능
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◆ 다른 지표와의 시너지
• 볼린저 밴드: 가격 차트의 볼린저 밴드와 함께 사용하여 가격 변동성과 CCI 변동성 비교
• MACD: 모멘텀과 추세 확인을 위해 MACD와 함께 사용
• 피보나치 되돌림: CCI 선형회귀 밴드 신호를 주요 지지/저항 레벨과 함께 확인
• 이동평균선: 이동평균 교차와 CCI 선형회귀 밴드 신호를 결합하여 신뢰성 향상
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◆ 결론
52SIGNAL RECIPE CCI 선형회귀 밴드는 전통적인 CCI와 선형회귀 분석을 결합하여 강력하고 정확한 기술적 분석 도구를 제공합니다. 이중 과매수/과매도 시스템은 트레이딩 신호의 정확도를 높이고 추세 방향과 강도를 명확하게 시각화하여 트레이더의 의사 결정을 돕습니다. 다양한 설정을 트레이딩 스타일과 시장 상황에 맞게 조정하여 최적의 결과를 얻을 수 있습니다.
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※ 면책 조항: 과거 성과가 미래 결과를 보장하지 않습니다. 항상 적절한 리스크 관리 전략을 사용하세요.
