Volume Weighted Intra Bar LR KurtosisThis indicator analyzes market character by decomposing total
Excess Kurtosis ("Fat Tails") of a SINGLE BAR into four distinct,
interpretable components based on a Linear Regression model.
Key Features:
1. **Intra-Bar LR Kurtosis Decomposition:** For each bar on the chart,
the indicator analyzes the underlying price action on a smaller
timeframe ('Intra-Bar Timeframe'). It fits a Linear Regression
line through the intra-bar data to decompose the 4th Moment:
- **Trend Kurtosis (Gold):** Peakedness of the regression line
itself. High values indicate the price path within the bar
moves in sudden jumps, steps, or gaps (discontinuous path).
- **Residual Kurtosis (Red):** Excess Kurtosis of the noise
around the regression line. Captures "Hidden Tail Risk" or
extreme outliers within the bar relative to the trend.
- **Within-Bar Kurtosis (Blue):** Fat tails derived from the
microstructure of individual intra-bar candles.
- **Interaction Variance (Dark Grey):** The comovement of variance
and mean deviations (volatility clustering relative to trend).
- **Interaction Skewness (Darker Grey):** The comovement of skewness
and mean deviations (asymmetry relative to trend).
2. **Visual Decomposition Logic:** Total Excess Kurtosis is the
primary metric displayed. Since statistical moments are additive,
this indicator calculates the *exact* Total Kurtosis and partitions
the columns based on the Law of Total Moments.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Plots the *total* kurtosis as a
stacked column chart. Stacking logic groups components to
ensure visual clarity of the magnitude.
- **Relative Mode:** Plots the direct *contribution ratio*
(proportion) of each component relative to the total sum,
ideal for identifying the dominant driver (Trend vs. Noise).
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
kurtosis of *returns* rather than absolute prices.
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all regression and moment calculations,
emphasizing high-participation moves.
5. **Kurtosis Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (peaks/valleys) in
the *total* kurtosis line. (Note: This is only visible
in 'Absolute Mode').
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
6. **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.
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Analysis Lines:** 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.
8. **Integrated Alerts:** Includes comprehensive alerts for:
- Kurtosis magnitude (High Positive / High Negative).
- Character changes (Trend Jumps vs. Noise Outliers).
- Total Kurtosis 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**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Linear
Volume Weighted LR KurtosisThis indicator analyzes market character by decomposing total
Excess Kurtosis ("Fat Tails") into four distinct, interpretable
components based on a Linear Regression model.
Key Features:
1. **Four-Component Kurtosis Decomposition:** The indicator
separates market tail risk based on the 'Estimate Bar Statistics' option.
It leverages the Law of Total Moments to provide an additive
breakdown of the 4th Statistical Moment:
- **Trend Kurtosis (Gold):** Peakedness of the regression line
itself. High values indicate the trend moves in sudden jumps,
steps, or gaps (discontinuous path).
- **Residual Kurtosis (Red):** Excess Kurtosis of the noise
around the regression line. This captures the "Hidden Tail Risk"
(extreme outliers relative to the trend).
- **Within-Bar Kurtosis (Blue):** Fat tails derived from the
microstructure of individual bars (requires 'Estimate Bar Statistics').
- **Interaction Variance (Dark Grey):** The comovement of variance
and mean deviations (volatility clustering relative to trend).
- **Interaction Skewness (Darker Grey):** The comovement of skewness
and mean deviations (asymmetry relative to trend).
2. **Visual Decomposition Logic:** Total Excess Kurtosis is the
primary metric displayed. Since statistical moments are additive,
this indicator calculates the *exact* Total Kurtosis and partitions
the area to visualize the contribution (weight) of each
structural source to the overall tail risk.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *total* kurtosis as a
stacked area chart, allowing to see the magnitude of tail risk.
Stacking logic groups components to ensure visual clarity.
- **Relative Mode:** Displays the direct *contribution ratio*
(proportion) of each component relative to the total sum,
ideal for identifying the dominant driver of the risk.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
kurtosis of *returns* rather than absolute prices.
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all regression and moment calculations,
emphasizing high-participation moves.
5. **Kurtosis Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (peaks/valleys) in
the *total* kurtosis line. This helps identify extremes in
market fragility or structural changes.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
6. **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.
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Kurtosis Lines:** The kurtosis 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.
8. **Integrated Alerts:** Includes comprehensive alerts for:
- Kurtosis magnitude (High Positive / High Negative).
- Kurtosis character changes/emerging/fading.
- Total Kurtosis pivot (High/Low) detection.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Volume Weighted Intra Bar LR SkewnessThis indicator analyzes market character by decomposing total
skewness (asymmetry) of a SINGLE BAR into four distinct,
interpretable components based on a Linear Regression model.
Key Features:
1. **Intra-Bar LR Skewness Decomposition:** For each bar on the chart,
the indicator analyzes the underlying price action on a smaller
timeframe ('Intra-Bar Timeframe'). It fits a Linear Regression
line through the intra-bar data to decompose the 3rd Moment:
- **Trend Skewness (Green/Red):** Asymmetry originating from
the slope of the intra-bar regression line. Indicates if the
price path within the bar is geometrically trend-driven.
- **Residual Skewness (Yellow):** Asymmetry of the noise
around the regression line. Captures "Tail Risk" or sudden
shocks within the bar that deviate from the main path.
- **Within-Bar Skewness (Blue):** Asymmetry derived from the
microstructure of individual intra-bar candles.
- **Interaction Skewness (Dark Grey):** Asymmetry caused by
the correlation between price levels and volatility within
the bar (e.g., volatility expanding as price drops).
2. **Visual Decomposition Logic:** Total Skewness is the
primary metric displayed. Since statistical moments are additive,
this indicator calculates the *exact* Total Skewness and partitions
the columns based on the Law of Total Moments.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Plots the *total* skewness as a
stacked column chart. Stacking logic groups components with
the same sign to ensure visual clarity.
- **Relative Mode:** Plots the direct *contribution ratio*
(proportion) of each component relative to the total sum,
ideal for identifying the dominant driver (Trend vs. Noise).
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
skewness of *returns* rather than absolute prices.
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all regression and moment calculations,
emphasizing high-participation moves.
5. **Skewness Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (peaks/valleys) in
the *total* skewness line. (Note: This is only visible
in 'Absolute Mode').
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
6. **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.
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Analysis Lines:** 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.
8. **Integrated Alerts:** Includes comprehensive alerts for:
- Skewness magnitude (High Positive / High Negative).
- Character changes (Trend vs. Noise dominance).
- Total Skewness 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**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Volume Weighted LR SkewnessThis indicator analyzes market character by decomposing total
skewness (asymmetry) into four distinct, interpretable components
based on a Linear Regression model.
Key Features:
1. **Four-Component Skewness Decomposition:** The indicator
separates market asymmetry based on the 'Estimate Bar Statistics' option.
It leverages the Law of Total Moments to provide an additive
breakdown of the 3rd Statistical Moment:
- **Trend Skewness (Green/Red):** Asymmetry originating from
the slope of the regression line itself. Indicates if the
trend path is geometrically skewed.
- **Residual Skewness (Yellow):** Asymmetry of the noise
around the regression line. Captures "Tail Risk" (e.g.,
sudden spikes against the trend).
- **Within-Bar Skewness (Blue):** Asymmetry derived from the
microstructure of individual bars (requires 'Estimate Bar Statistics').
- **Interaction Skewness (Dark Grey):** Asymmetry caused by the
correlation between price levels and volatility (e.g.,
volatility expanding as price moves in one direction).
*Dominance of this component indicates an unstable, emotional market.*
2. **Visual Decomposition Logic:** Total Skewness is the
primary metric displayed. Since statistical moments are additive,
this indicator calculates the *exact* Total Skewness and partitions
the area to visualize the contribution (weight) of each
structural source to the overall market bias.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *total* skewness as a
stacked area chart, allowing to see the magnitude of tail risk.
Stacking logic groups components with the same sign to ensure
visual clarity.
- **Relative Mode:** Displays the direct *contribution ratio*
(proportion) of each component relative to the total sum,
ideal for identifying the dominant driver of asymmetry.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
skewness of *returns* rather than absolute prices.
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all regression and moment calculations,
emphasizing high-participation moves.
5. **Skewness Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (peaks/valleys) in
the *total* skewness line. This helps identify extremes in
market sentiment or structural bias.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
6. **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.
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Skewness Lines:** The skewness 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.
8. **Integrated Alerts:** Includes comprehensive alerts for:
- Skewness magnitude (High Positive / High Negative).
- Skewness character changes/emerging/fading.
- Total Skewness pivot (High/Low) detection.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Volume Weighted Intra Bar LR CorrelationThis indicator analyzes market character by providing a detailed
view of correlation. It applies a Linear Regression model to
intra-bar price action, dissecting the total correlation of
each bar into three distinct components.
Key Features:
1. **Three-Component Correlation Decomposition:** The indicator
separates correlation based on the 'Estimate Bar Statistics' option.
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
correlation based on the selected `Source` (this results
mainly in 'Trend' and 'Residual' correlation).
- **Decomposition Mode (`Estimate Bar Statistics` = ON):** The
indicator uses a statistical model ('Estimator') to
calculate *within-bar* correlation.
(Assumption: In this mode, the `Source` input is
**ignored**, and an estimated mean for each bar is used
instead).
This separates correlation into:
- **Trend Correlation (Green/Red):** Correlation explained by the
regression's slope (Directional Alignment).
- **Residual Correlation (Yellow):** Correlation from price
oscillating around the regression line (Mean-Reversion/Cointegration).
- **Within-Bar Correlation (Blue):** Correlation from the
high-low range of each bar (Microstructure/Noise).
2. **Visual Decomposition Logic:** Total Correlation is the
primary metric displayed. Since Correlation Coefficients are not
linearly additive, this indicator plots the *exact* Total
Correlation and partitions the area underneath based on the
Covariance Ratio. This ensures the displayed total correlation
remains mathematically accurate while showing relative composition.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *total* correlation as a
stacked area chart, partitioned by the ratio of
the three components.
- **Relative Mode:** Displays the direct *energy ratio*
(proportion) of each component relative to the total (0-1),
ideal for identifying the dominant market character.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' 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 correlation calculations.
5. **Correlation Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (highs and lows) in
the *total* correlation line. (Note: This is only visible
in 'Absolute Mode').
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
6. **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.
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Correlation Lines:** The correlation 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.
8. **Integrated Alerts:** Includes comprehensive alerts for:
- Correlation magnitude (High Positive / High Inverse).
- Correlation character changes/emerging/fading.
- Total Correlation 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**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Volume Weighted LR Z ScoreThis indicator calculates the Volume Weighted Linear Regression
Z-Score (VWLRZS). Unlike a standard Z-Score which measures
deviation from a static mean, this oscillator measures the
statistical distance of price from a dynamic Volume-Weighted
Linear Regression Line (Analysis of Residuals).
Key Features:
1. **Volatility Decomposition:** The indicator separates volatility
based on the 'Estimate Bar Statistics' option.
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
standard Regression Residuals using the selected `Source`
for both the regression line (baseline) and the signal.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):**
Uses a hybrid statistical approach:
a) **The Model (Baseline):** Uses an estimator to calculate
the 'within-bar' mean and fits the Linear Regression
through these statistical centers. This creates a
stable, trend-following expectation model.
b) **The Signal (Observation):** Compares the actual `Source`
(e.g., Close) against this regression line.
(Result: A Z-Score that measures deviations from the current
trend slope rather than a flat average).
2. **Visual Decomposition Logic:** Total Standard Deviation (of
Residuals) is the primary metric displayed. Since Standard
Deviations are not linearly additive (sqrt(a+b) != sqrt(a)+sqrt(b)),
this indicator calculates the *exact* Total Z-Score and partitions
the area underneath based on the Variance Ratio. This ensures the
displayed total volatility remains mathematically accurate while
showing relative composition.
3. **Normalization (Exponential Regression):** Includes an optional
'Normalize' mode. When enabled, the indicator calculates the
Linear Regression on logarithmic data. Mathematically, this
transforms the baseline into an **Exponential Regression Curve**,
making it ideal for analyzing assets with compounding growth
characteristics (constant percentage trend).
4. **Full Divergence Suite (Class A, B, C):** The indicator's
primary feature is its integrated divergence engine. It
automatically detects and plots all three major divergence
classes between price and the Z-Score:
- Regular (A): Signals potential trend exhaustion and reversals.
- Hidden (B): Signals potential trend continuations during pullbacks.
- Exaggerated (C): Signals weakness at double tops/bottoms.
5. **Divergence Filtering and Visualization:**
- **Price Tolerance Filter:** Divergence detection is enhanced
with a percentage-based price tolerance (`pivPrcTol`) to
filter out insignificant market noise, leading to more
robust signals.
- **Persistent Visualization:** Divergence markers are plotted
for the entire duration of the signal and are visually
anchored to the oscillator level of the confirming pivot.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library
6. **Note on Confirmation (Lag):** Divergence signals rely on a
pivot confirmation method to ensure they do not repaint.
- The **Start** of a divergence is only detected *after* the
confirming pivot is fully formed (a delay based on
`Pivot Right Bars`).
- The **End** of a divergence is detected either instantly
(if the signal is invalidated by price action) or with
a delay (when a new, non-divergent pivot is confirmed).
7. **Multi-Timeframe (MTF) Capability:**
- **MTF Calculation:** The Z-Score line *itself* can be calculated on a
higher timeframe, with standard options to handle gaps
(`Fill Gaps`) and prevent repainting (`Wait for...`).
- **Limitation:** The Divergence detection engine (`pivDiv`)
is designed for the active timeframe. Using it in MTF mode
is not recommended as step-data can lead to inaccurate
pivot detection.
8. **Integrated Alerts:** Includes a comprehensive set of built-in
alerts for the Z-Score crossing the neutral line, the configured
Threshold levels, and the start/end of all divergence types.
---
**DISCLAIMER**
1. **For Informational/Educational Use Only:** This indicator is
provided for informational and educational purposes only. It does
not constitute financial, investment, or trading advice, nor is
it a recommendation to buy or sell any asset.
2. **Use at Your Own Risk:** All trading decisions you make based on
the information or signals generated by this indicator are made
solely at your own risk.
3. **No Guarantee of Performance:** Past performance is not an
indicator of future results. The author makes no guarantee
regarding the accuracy of the signals or future profitability.
4. **No Liability:** The author shall not be held liable for any
financial losses or damages incurred directly or indirectly from
the use of this indicator.
5. **Signals Are Not Recommendations:** The alerts and visual signals
(e.g., crossovers) generated by this tool are not direct
recommendations to buy or sell. They are technical observations
for your own analysis and consideration.
Regression ChannelAn enhanced version of TradingView's Linear Regression Channel that displays multiple upper and lower deviation channels with support for both linear and exponential regression models.
Getting Started & Usage
This indicator overlays a regression channel with up to 4 customizable standard deviation levels above and below the regression line. By default, it uses linear regression, but you can switch to an exponential regression model for curved price trends.
For detailed explanations of the statistical concepts and additional usage examples, please visit the documentation .
Quantum Regression Oscillator [ICN]The Problem: The Lag of Standard Oscillators
Most traders rely on the Relative Strength Index (RSI) or MACD to gauge momentum. While these are legendary tools, they suffer from a critical flaw: Lag. They calculate what has happened, often giving signals after the move is already halfway done.
The Quantum Regression Oscillator (QRO) was built to solve this. It is not a simple average; it is a predictive engine.
The "Quantum" Math (How It Works)
Instead of using standard smoothing (like SMA or EMA) which drags data backward, the QRO uses Linear Regression Analysis on the RSI data itself.
Linear Regression Core : The script calculates the "Line of Best Fit" for momentum in real-time. This allows the oscillator to react to price changes faster than price itself in some instances, effectively "predicting" the next tick of momentum.
Dynamic Volatility Bands : Unlike fixed bands (e.g., 70/30 on RSI), the QRO uses standard deviation bands that expand and contract with market volatility. This means "Overbought" is not a fixed numberโit adapts to the market's energy.
Visual Guide : Reading the Oscillator
1. The Quantum Line (The Main Curve)
What it is : The smooth, fast-moving line oscillating between 0 and 100.
How to read it:
Crossing Midline (50) : The baseline for trend. Above 50 is Bullish Momentum; Below 50 is Bearish Momentum.
Slope : Because it uses regression, the angle of the line is a signal itself. A sharp turn often precedes price action.
2. The Dynamic Bands (The Shaded Zones)
What they are: The Blue (Lower) and Red (Upper) zones.
How to read it:
Oversold (Blue Zone) : When the line enters the Blue zone, price is statistically overextended to the downside. This is a "Sniper Buy" zone.
Overbought (Red Zone) : When the line enters the Red zone, price is statistically overextended to the upside. This is a "Sniper Sell" zone.
3. Divergence Detection
The QRO is excellent at spotting divergences. If Price makes a Higher High but the QRO makes a Lower High (while in the Red Zone), a reversal is mathematically probable.
Integration with the ICN Suite
While this oscillator is powerful as a standalone tool, it is the "Engine" behind the Institutional Confluence Nexus .
Standalone : Use it to spot divergences and momentum shifts with zero lag.
With ICN : The main chart indicator reads data from this oscillator to generate "Sniper" and "Pullback" signals automatically.
Settings & Customization
QRO Length: The lookback period for the base RSI calculation.
Regression Length: The sensitivity of the linear regression curve (Lower = Faster/More Noise, Higher = Smoother/More Lag).
Smoothing: Additional filtering to remove market noise.
For Developers (Open Source)
I believe in the power of open-source education. Developers can view the source code to learn:
How to implement ta.linreg (Linear Regression) on top of other indicators.
How to create dynamic bands using ta.stdev (Standard Deviation).
How to create smooth color gradients using plot transparency.
Disclaimer:
This tool is a mathematical aid for technical analysis. It does not predict the future. Always use proper risk management.
LogTrend Retest EngineLogTrend Retest Engine (LTRE)
LogTrend Retest Engine (LTRE) is an advanced trend-continuation overlay designed to identify high-probability breakout retests using logarithmic regression , volatility-adjusted deviation bands , and market regime filtering .
Unlike traditional channels or moving averages, LTRE models price behavior in log space , allowing it to adapt naturally to exponential market moves common in crypto, indices, and long-term trends.
๐น How It Works
Logarithmic Regression Core
Performs linear regression on log-transformed price and time
Produces a structurally accurate trend midline that scales with price growth
Volatility-Adjusted Deviation Bands
Dynamic upper and lower zones based on statistical deviation
ATR weighting expands or contracts bands as volatility changes
Adaptive Lookback (Optional)
Automatically adjusts regression length using volatility pressure
Faster response in high-volatility environments, smoother in consolidation
๐น Market Regime Detection
LTRE actively filters conditions using:
Rยฒ trend strength (trend quality, not just slope)
Volatility compression vs expansion
User-defined minimum trend strength threshold
Signals are disabled during ranging or low-quality conditions .
๐น Breakout โ Retest Signal Logic
LTRE does not chase breakouts.
Signals trigger only when:
1. Price breaks cleanly outside the deviation band
2. Market regime is confirmed as trending
3. Price performs a controlled retest within a user-defined tolerance
BUY
Break above upper band โ retest โ trend confirmed
SELL
Break below lower band โ retest โ trend confirmed
This structure is designed to reduce false breakouts and late entries.
๐น Visual & Projection Tools
Clean midline and deviation bands
Optional filled zones
Optional future trend projection for forward structure planning
On-chart statistics for trend strength and volatility compression
๐น Best Use Cases
Trend continuation & pullback strategies
Crypto, Forex, Indices, and equities
Works best on 15m and higher timeframes
โ ๏ธ Disclaimer
LTRE is a decision-support tool , not a complete trading system. Always use proper risk management and confirm signals with additional structure, volume, or higher-timeframe context.
Built for traders who wait for structure โ not noise.
ARDO (v2.4.7) Moving Averages v1.1ARDO Moving Averages v1.1 (Overlay)
Companion overlay that recreates ARDO driver states (Spreads A/B, LinReg state + slope/gradient, tiers/MK tiers, gate pass/block) and maps those states onto up to 5 moving average overlays + one optional MA-to-MA fill.
ARDO v2.4.6 (original indicator)
What this overlay does
Computes ARDO โdriver statesโ internally (no external source required): Spread A, Spread B, LinReg (4-state), LinReg slope/accel โ gradient opacity, quartile/tier regimes, MK tiers, and Gate pass/block.
Paints MA overlays using selectable โColor Modesโ (Spread A, Spread B, ARDO LinReg, MK Tier, Quartile Background, Gate Pass, Bull/Bear A vs B, or Fixed).
Optional Fill between two overlay MAs using a selected color mode (intended for regime/bull-bear shading between MA lines).
Core concepts (quick read)
Baseline / MA A / MA B define Spread A and Spread B (% distance vs baseline).
LinReg is a regression of a selected source (Spread A, Spread B, or Spread(A+B)).
LinReg State (4 colors) is derived from slope sign and acceleration (trend speeding up vs slowing down): Green / Orange / Red / Gray.
Gradient Opacity scales line opacity based on slope magnitude (strong vs weak).
Tier / Quartile maps current regime into bins (Q0โH4) using rolling percentiles (or manual thresholds).
MK Tier is an alternate tier engine (Standard / Asymmetric / Mirror BG).
Gate is a boolean pass/block that can combine spread and trend requirements (optional).
How to set it up (recommended workflow)
Pick ARDO Core MAs (Baseline, MA A, MA B) and your main LinReg Source.
Tune LinReg Length + Gradient Scale to match your timeframe (shorter = faster flips, longer = smoother).
Decide Tier mode (Standard vs Asymmetric) and whether tiers use All Bars or Pivots Only .
Set up Gate (or leave off): use it as a โpermission layerโ for entries.
Configure your overlay MAs (1โ5) and assign each a Color Mode aligned to its job:
MA1 = fast impulse (often Spread A)
MA2 = trend state (often ARDO LinReg)
MA3 = slower confirmation (often Spread B)
MA4 = gate/permission readout (Gate Pass)
MA5 = regime (MK Tier)
Enable Fill only if you want regime shading between two MAs (keep it simple: one fill only).
Inputs explained (by group)
1) Sources & Moving Averages (ARDO Core)
Price Source : price used for MA calculations (default close).
Baseline MA Type/Length : reference MA for spreads.
MA A Type/Length : โAโ spread driver (usually faster).
MA B Type/Length : โBโ spread driver (often slower fast MA).
EMA Fast / EMA Slow : used only if the EMA gate toggle is enabled.
2) Linear Regression & Gradient
LinReg Length : lookback used by regression.
LinReg Source : Spread A, Spread B, or Spread(A+B).
Slope Lookback : bars used to compute slope as (linreg - linreg ) / n.
Adaptive Opacity Scale : derives slope โcapโ from a rolling percentile (reduces volatility-regime distortion).
Fixed Scale Cap : used if adaptive scaling is off.
Min/Max Opacity : clamps gradient range.
3) Tiers & Population
Tier Mode : Standard vs Asymmetric (changes percentile boundary logic).
Tier Population : All Bars vs Pivots Only.
Manual Thresholds : if enabled, uses user cutoffs instead of computed percentiles.
Auto-Percentile Window : rolling window size for percentiles.
4) Region Rendering (BG / regime palette)
BG colors for Q0/Q1/Q2/Q3/Q4/H4 : the palette used for โQuartile Backgroundโ color mode and MK โMirror BGโ.
Pivot Sensitivity : relevant only for Pivots Only population.
5) Gate (Pass/Block)
Gate: SpreadA > LinReg (toggle)
Gate: EMA Fast > EMA Slow (toggle)
Min Spread A (%)
Min |LinReg Slope|
Gate PASS/BLOCK colors : also used by Gate Pass color mode.
6) Overlay Moving Averages (MA1โMA5)
MA Len / Type : SMA, EMA, WMA, Wilder, Triangular, HMA, Adaptive.
Color Mode :
Fixed
ARDO Spread A
ARDO Spread B
ARDO LinReg (4-state + gradient opacity)
MK Tier
Quartile Background (Q0โH4 palette)
Gate Pass
Bull/Bear (A vs B)
Base Color : used for Fixed (and as fallback).
Line Width
Style (if present): line / stepline / markers depending on the MA slot.
Bull/Bear (A vs B) definition
Bull when MA A > MA B
Bear when MA A < MA B
Alerts (built-in alertconditions in v1.1)
Spread A State
State changed (any change)
Turned Green / Orange / Red / Gray
LinReg State
State changed (any change)
Turned Green / Orange / Red / Gray
LinReg Gradient
Gradient High (slope strength high)
Gradient Low (slope strength low)
Gate
Gate Pass ON
Gate Pass OFF
Bull/Bear Flip
Bullish flip (A crosses above B)
Bearish flip (A crosses below B)
Tier / Quartile
Entered Q0
Entered Q1
Entered H3
Entered H4
Simple Alignment
LinReg Green AND SpreadA Green (basic โmomentum alignedโ condition)
How to use Gate (and how to loosen/tighten it)
Use Gate as a filter , not as the entire strategy: itโs best as โpermission to tradeโ plus your own trigger.
If Gate is too strict :
Disable EMA Fast > EMA Slow gate (trend filter) OR disable SpreadA > LinReg gate (structure filter).
Lower Min Spread A threshold.
Lower Min |LinReg Slope| threshold.
Increase LinReg Length slightly to reduce noisy flips (sometimes helps pass stability).
If Gate is too loose :
Enable both gate components (SpreadA>LinReg AND EMA Fast>Slow).
Raise Min Spread A and/or Min |LinReg Slope|.
Shorten LinReg Length to react faster (but can increase chop).
Practical โreadโ using the default overlay roles
MA1 (fast, Spread A mode) : impulse / early acceleration cues.
MA2 (trend, LinReg mode) : regime + momentum state; opacity tells you strength.
MA3 (confirmation, Spread B) : slower confirmation; helps avoid โone-candle impulse trapsโ.
MA4 (Gate Pass) : permission layer; reduces counter-trend entries.
MA5 (MK Tier) : regime band; helps distinguish โdeep OS/OB contextโ vs mid-zone noise.
Notes
This is an overlay; itโs designed to complement the original ARDO oscillator pane.
ARDO - Adaptive Regression Deviation Oscillator (v2.4.6)ARDO โ 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 shiftsโoffering 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 โtradeโ environment or a โstand asideโ 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 โtimingโ 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 โcontextโ 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 (Q0โQ2, H2โH4)
ARDO classifies LinReg values into percentile tiers (extremes and mid-tiers). These tiers can be rendered as:
Background regions, or
Zero-line marker circles (โMK โฆโ plots)
Important: Background colors do not export . The โMK Q0 โฆ MK H4โ series are emitted so you can reconstruct tier membership in CSV/backtests.
5) Gate PASS / Gate BLOCK
A compact โpermission layerโ 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 โdo nothing / reduce sizeโ 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 โextremeโ 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 โ 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 โdead-zoneโ 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.
Bollinger Bands Regression Forecast [BigBeluga]๐ต 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.
๐ต 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 bandโs 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.
โฒ and โผ markers highlight potential mean reversion points when price crosses the outer bounds of the bands.
๐ต 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 โฒ/โผ 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.
๐ต HOW TO USE
Apply the indicator to visualize both current Bollinger structure and its forward projection.
Use โฒ/โผ 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.
๐ต 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 โ revealing not only where volatility is but also where itโs 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 fรผhrt hauptsรคchlich zu 'Trend'- und 'Residual'-Volatilitรคt).
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)
โโWeighted Simple Moving Average (linear kernel).
โโParameters:
โโโโ source (float) : series float Data to average.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 1.
โโReturns: series float Linear-kernel weighted mean; falls back to
the arithmetic mean if ฮฃweight = 0.
wEma(source, weight, length)
โโWeighted EMA (exponential kernel).
โโParameters:
โโโโ source (float) : series float Data to average.
โโโโ weight (float) : series float Weight series.
โโโโ length (simple int) : simple int Look-back length โฅ 1.
โโReturns: series float Exponential-kernel weighted mean; falls
back to classic EMA if ฮฃweight = 0.
wWma(source, weight, length)
โโWeighted WMA (linear kernel).
โโParameters:
โโโโ source (float) : series float Data to average.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 1.
โโReturns: series float Linear-kernel weighted mean; falls back to
classic WMA if ฮฃweight = 0.
wRma(source, weight, length)
โโWeighted RMA (Wilder kernel, ฮฑ = 1/len).
โโParameters:
โโโโ source (float) : series float Data to average.
โโโโ weight (float) : series float Weight series.
โโโโ length (simple int) : simple int Look-back length โฅ 1.
โโReturns: series float Wilder-kernel weighted mean; falls back to
classic RMA if ฮฃweight = 0.
wHma(source, weight, length)
โโWeighted HMA (linear kernel).
โโParameters:
โโโโ source (float) : series float Data to average.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 1.
โโReturns: series float Linear-kernel weighted mean; falls back to
classic HMA if ฮฃweight = 0.
wRsi(source, weight, length)
โโWeighted Relative Strength Index.
โโParameters:
โโโโ source (float) : series float Price series.
โโโโ weight (float) : series float Weight series.
โโโโ length (simple int) : simple int Look-back length โฅ 1.
โโReturns: series float Weighted RSI; uniform if ฮฃw = 0.
wAtr(tr, weight, length)
โโWeighted ATR (Average True Range).
Implemented as WRMA on *true range*.
โโParameters:
โโโโ tr (float) : series float True Range series.
โโโโ weight (float) : series float Weight series.
โโโโ length (simple int) : simple int Look-back length โฅ 1.
โโReturns: series float Weighted ATR; uniform weights if ฮฃw = 0.
wTr(tr, weight, length)
โโWeighted True Range over a window.
โโParameters:
โโโโ tr (float) : series float True Range series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 1.
โโReturns: series float Weighted mean of TR; uniform if ฮฃw = 0.
wR(r, weight, length)
โโWeighted High-Low Range over a window.
โโParameters:
โโโโ r (float) : series float High-Low per bar.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 1.
โโReturns: series float Weighted mean of range; uniform if ฮฃw = 0.
wBtwVar(source, weight, length, biased)
โโWeighted Between Variance (biased/unbiased).
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns:
variance series float The calculated between-bar variance (ฯยฒbtw), either biased or unbiased.
sumW series float The sum of weights over the lookback period (ฮฃw).
sumW2 series float The sum of squared weights over the lookback period (ฮฃwยฒ).
wBtwStdDev(source, weight, length, biased)
โโWeighted Between Standard Deviation.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float ฯbtw uniform if ฮฃw = 0.
wBtwStdErr(source, weight, length, biased)
โโWeighted Between Standard Error.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float โ(ฯยฒbtw / N_eff) uniform if ฮฃw = 0.
wTotVar(mu, sigma, weight, length, biased)
โโWeighted Total Variance (= between-group + within-group).
Useful when each bar represents an aggregate with its own
mean* and pre-estimated ฯ (e.g., second-level ranges inside a
1-minute bar). Assumes the *weight* series applies to both the
group means and their ฯ estimates.
โโParameters:
โโโโ mu (float) : series float Group means (e.g., HL2 of 1-second bars).
โโโโ sigma (float) : series float Pre-estimated ฯ of each group (same basis).
โโโโ weight (float) : series float Weight series (volume, ticks, โฆ).
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns:
varBtw series float The between-bar variance component (ฯยฒbtw).
varWtn series float The within-bar variance component (ฯยฒwtn).
sumW series float The sum of weights over the lookback period (ฮฃw).
sumW2 series float The sum of squared weights over the lookback period (ฮฃwยฒ).
wTotStdDev(mu, sigma, weight, length, biased)
โโWeighted Total Standard Deviation.
โโParameters:
โโโโ mu (float) : series float Group means (e.g., HL2 of 1-second bars).
โโโโ sigma (float) : series float Pre-estimated ฯ of each group (same basis).
โโโโ weight (float) : series float Weight series (volume, ticks, โฆ).
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float ฯtot.
wTotStdErr(mu, sigma, weight, length, biased)
โโWeighted Total Standard Error.
SE = โ( total variance / N_eff ) with the same effective sample
size logic as `wster()`.
โโParameters:
โโโโ mu (float) : series float Group means (e.g., HL2 of 1-second bars).
โโโโ sigma (float) : series float Pre-estimated ฯ of each group (same basis).
โโโโ weight (float) : series float Weight series (volume, ticks, โฆ).
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float โ(ฯยฒtot / N_eff).
wLinReg(source, weight, length)
โโWeighted Linear Regression.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 2.
โโReturns:
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)
โโWeighted Residual Variance.
linear regression โ optionally biased (population) or
unbiased (sample).
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weighting series (volume, etc.).
โโโโ midLine (float) : series float Regression value at the last bar.
โโโโ slope (float) : series float Slope per bar.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population variance (ฯยฒ_P), denominator โ N_eff.
false โ sample variance (ฯยฒ_S), denominator โ N_eff - 2.
(Adjusts for 2 degrees of freedom lost to the regression).
โโReturns:
variance series float The calculated residual variance (ฯยฒres), either biased or unbiased.
sumW series float The sum of weights over the lookback period (ฮฃw).
sumW2 series float The sum of squared weights over the lookback period (ฮฃwยฒ).
wResStdDev(source, weight, midLine, slope, length, biased)
โโWeighted Residual Standard Deviation.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ midLine (float) : series float Regression value at the last bar.
โโโโ slope (float) : series float Slope per bar.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float ฯres; uniform if ฮฃw = 0.
wResStdErr(source, weight, midLine, slope, length, biased)
โโWeighted Residual Standard Error.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ midLine (float) : series float Regression value at the last bar.
โโโโ slope (float) : series float Slope per bar.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population (biased); false โ sample.
โโReturns: series float โ(ฯยฒres / N_eff); uniform if ฮฃw = 0.
wLRTotVar(mu, sigma, weight, midLine, slope, length, biased)
โโWeighted Linear-Regression Total Variance **around the
windowโs weighted mean ฮผ**.
ฯยฒ_tot = E_w โถ *within-group variance*
+ Var_w โถ *residual variance*
+ Var_w โถ *trend variance*
where each bar i in the look-back window contributes
m_i = *mean* (e.g. 1-sec HL2)
ฯ_i = *sigma* (pre-estimated intrabar ฯ)
w_i = *weight* (volume, ticks, โฆ)
ลท_i = bโ + bโยทx (value of the weighted LR line)
r_i = m_i โ ลท_i (orthogonal residual)
โโParameters:
โโโโ mu (float) : series float Per-bar mean m_i.
โโโโ sigma (float) : series float Pre-estimated ฯ_i of each bar.
โโโโ weight (float) : series float Weight series w_i (โฅ 0).
โโโโ midLine (float) : series float Regression value at the latest bar (ลทโโโ).
โโโโ slope (float) : series float Slope bโ of the regression line.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population; false โ sample.
โโReturns:
varRes series float The residual variance component (ฯยฒres).
varWtn series float The within-bar variance component (ฯยฒwtn).
varTrd series float The trend variance component (ฯยฒtrd), explained by the linear regression.
sumW series float The sum of weights over the lookback period (ฮฃw).
sumW2 series float The sum of squared weights over the lookback period (ฮฃwยฒ).
wLRTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
โโWeighted Linear-Regression Total Standard Deviation.
โโParameters:
โโโโ mu (float) : series float Per-bar mean m_i.
โโโโ sigma (float) : series float Pre-estimated ฯ_i of each bar.
โโโโ weight (float) : series float Weight series w_i (โฅ 0).
โโโโ midLine (float) : series float Regression value at the latest bar (ลทโโโ).
โโโโ slope (float) : series float Slope bโ of the regression line.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population; false โ sample.
โโReturns: series float โ(ฯยฒtot).
wLRTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
โโWeighted Linear-Regression Total Standard Error.
SE = โ( ฯยฒ_tot / N_eff ) with N_eff = ฮฃwยฒ / ฮฃwยฒ (like in wster()).
โโParameters:
โโโโ mu (float) : series float Per-bar mean m_i.
โโโโ sigma (float) : series float Pre-estimated ฯ_i of each bar.
โโโโ weight (float) : series float Weight series w_i (โฅ 0).
โโโโ midLine (float) : series float Regression value at the latest bar (ลทโโโ).
โโโโ slope (float) : series float Slope bโ of the regression line.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population; false โ sample.
โโReturns: series float โ((ฯยฒres, ฯยฒwtn, ฯยฒtrd) / N_eff).
wLRLocTotStdDev(mu, sigma, weight, midLine, slope, length, biased)
โโWeighted Linear-Regression Local Total Standard Deviation.
Measures the total "noise" (within-bar + residual) around the trend.
โโParameters:
โโโโ mu (float) : series float Per-bar mean m_i.
โโโโ sigma (float) : series float Pre-estimated ฯ_i of each bar.
โโโโ weight (float) : series float Weight series w_i (โฅ 0).
โโโโ midLine (float) : series float Regression value at the latest bar (ลทโโโ).
โโโโ slope (float) : series float Slope bโ of the regression line.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population; false โ sample.
โโReturns: series float โ(ฯยฒwtn + ฯยฒres).
wLRLocTotStdErr(mu, sigma, weight, midLine, slope, length, biased)
โโWeighted Linear-Regression Local Total Standard Error.
โโParameters:
โโโโ mu (float) : series float Per-bar mean m_i.
โโโโ sigma (float) : series float Pre-estimated ฯ_i of each bar.
โโโโ weight (float) : series float Weight series w_i (โฅ 0).
โโโโ midLine (float) : series float Regression value at the latest bar (ลทโโโ).
โโโโ slope (float) : series float Slope bโ of the regression line.
โโโโ length (int) : series int Look-back length โฅ 2.
โโโโ biased (bool) : series bool true โ population; false โ sample.
โโReturns: series float โ((ฯยฒwtn + ฯยฒres) / N_eff).
wLSma(source, weight, length)
โโWeighted Least Square Moving Average.
โโParameters:
โโโโ source (float) : series float Data series.
โโโโ weight (float) : series float Weight series.
โโโโ length (int) : series int Look-back length โฅ 2.
โโReturns: series float Least square weighted mean. Falls back
to unweighted regression if ฮฃw = 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.
Linear Regression Trend Navigator [QuantAlgo]๐ข 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.
๐ข 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).
๐ข 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
๐ข Pro Tips for Trading and Investing
โ 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.
โ 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.
โ 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.
โ Cross-Timeframe Statistical Validation: Apply the indicator across multiple timeframes simultaneously, using higher timeframes for directional bias and lower timeframes for entry timing.
โ 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.
โ 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.
Squeeze Momentum Regression Clouds [SciQua]โญโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฎ
โ โ โ โ โ โ โโ โโ โ โ โ โ โโ โโ โ โ๏ธ Squeeze Momentum Regression Clouds
โฐโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโฏ
๐ 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 โ across up to four customizable regression Clouds .
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โ โ โ โ โโ โโ โ โ๏ธ Core Features
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Up to 4 Regression Clouds โ Each Cloud is created from a top and bottom linear regression line over a configurable lookback window.
Slope Detection Engine โ Identifies whether each band is rising, falling, or flat based on slope-to-ATR thresholds.
Spread Compression Heatmap โ Highlights compressed zones using yellow intensity, derived from historical spread analysis.
Composite Trend Scoring โ Aggregates directional signals from each Cloud using your chosen weighting model.
Color-Coded Candles โ Optional candle coloring reflects the real-time composite score.
UI Table โ A toggleable info table shows slopes, compression levels, percentile ranks, and direction scores for each Cloud.
Gradient Cloud Styling โ Apply gradient coloring from Cloud 1 to Cloud 4 for visual slope intensity.
Weight Aggregation Options โ Use equal weighting, inverse-length weighting, or max pooling across Clouds to determine composite trend strength.
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โ โ โ โ โ โ โ โ โ โโ โโ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ โ ๐งช How to Use the Indicator
โ โ โ โ โ โ โ โ โ โโ โโ โ โ โ โ โ โ 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 โ 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 โ when all Clouds are green or red, the trend is strong. Divergence among Clouds often signals a potential shift.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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โ โ โ โ โ โ โ โ โ โ โ โโ โโ โโ โโ โ โ โ โ 2. Use Compression Heat to Anticipate Breakouts
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The space between each Cloudโs 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.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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โ โ โ โ โ โ โ โ โ โ โโ โโ โ โ โ 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 tableโs position, theme, transparency, and whether to show a combined summary score in the header.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
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โ โ โ โ โ โ โ โโ โโ โ โ โ โ โ โ โ โ โ 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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โ โ โ โ โโ โโโ โ โ โ ๐งฐ 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 compressionโdepending on your market edge.
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โ โ โ โโ โโโ โ โ โ โ
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 โ 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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โ โ โ โโ โ โ โโ โโ โ ๐ License & Usage Terms
โฐโโโโโโโโโโโโโโโโโโโโโโโโฏ
This script is provided under the Creative Commons Attribution-NonCommercial 4.0 International License .
โ
You are allowed to:
Use this script for personal or educational purposes
Study, learn, and adapt it for your own non-commercial strategies
โ 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]๐ข 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.
๐ข 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.
๐ข 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.
๐ข Pro Tips for Trading and Investing
โ 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.
โ Timeframe Analysis: Using higher timeframes for general trend direction and lower timeframes for entry timing is a common approach.
โ 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.
โ 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.
52SIGNAL RECIPE CCI Linreg Bandsโโโ 52SIGNAL RECIPE CCI Linreg Bands โโโ
โ 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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โ Key Features
โข CCI-Based Overbought/Oversold Analysis: Uses the traditional CCI indicator to identify overbought/oversold conditions in the market
โข Integrated Linear Regression Bands: Applies linear regression analysis to the CCI to visually represent the direction and strength of trends
โข Dual Overbought/Oversold Levels: Sets overbought/oversold levels for both CCI and Linear Regression Bands to increase the accuracy of signals
โข Advanced Visualization: Intuitive chart analysis is possible with color changes according to trend direction and clear band display
โข Multiple Alert Settings: Alert functions for various conditions ensure you don't miss important trading moments
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โ Technical Foundation
โ CCI (Commodity Channel Index)
โข Basic Settings: 20-period CCI with Weighted Moving Average (WMA) applied
โข Calculation Method: Measures the deviation from the average price normalized to a specific range
โข Overbought/Oversold Levels: Default values set to +150 (overbought) and -150 (oversold)
โ Linear Regression Bands
โข Period: Default value of 100 days
โข Deviation: Default value of 4.5 standard deviations
โข Center Line: The center line of the linear regression analysis for the CCI values
โข Band Width: Displays the range of volatility around the center line based on the calculated standard deviation
โข Overbought/Oversold Levels: Default values set to +250 (overbought) and -250 (oversold)
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โ Practical Applications
โ Identifying Trading Signals
โข Buy Signal:
โถ When the CCI falls below the oversold level (-150)
โถ When the lower band of the Linear Regression Bands falls below the oversold level (-250)
โถ When both conditions are met simultaneously (extreme oversold state) - a strong buy signal
โข Sell Signal:
โถ When the CCI rises above the overbought level (+150)
โถ When the upper band of the Linear Regression Bands rises above the overbought level (+250)
โถ When both conditions are met simultaneously (extreme overbought state) - a strong sell signal
โ Trend Analysis
โข Uptrend: When the linear regression center line is rising and the CCI is moving above the zero line
โข Downtrend: When the linear regression center line is falling and the CCI is moving below the zero line
โข Trend Strength: The wider the gap between the bands, the greater the volatility; the narrower, the more stable the trend
โ Divergence Confirmation
โข Bearish Divergence: Price forms a new high, but the CCI is lower than the previous high (potential bearish signal)
โข Bullish Divergence: Price forms a new low, but the CCI is higher than the previous low (potential bullish signal)
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โ Advanced Setting Options
โ CCI Setting Adjustments
โข CCI Source: Selectable options include Close (default), Open, High, Low, HL2, HLC3, OHLC4, etc.
โข CCI Length: Adjust to lower values for short-term volatility, higher values for long-term trends
โ Linear Regression Setting Adjustments
โข Period: Use lower values (20-50) for short-term analysis, higher values (100-200) for long-term analysis
โข Deviation: Higher values create wider bands (more signals), lower values create narrower bands (more accurate signals)
โ Overbought/Oversold Level Adjustments
โข CCI Levels: Adjust to more extreme values (ยฑ200) in highly volatile markets
โข Linear Regression Band Levels: Adjustable to ยฑ300 or ยฑ200 depending on market conditions
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โ Synergy with Other Indicators
โข Bollinger Bands: Use alongside Bollinger Bands on the price chart to compare price volatility with CCI volatility
โข MACD: Use with MACD for momentum and trend confirmation
โข Fibonacci Retracement: Check CCI Linreg Bands signals with key support/resistance levels
โข Moving Averages: Combine moving average crossovers with CCI Linreg Bands signals to improve reliability
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โ 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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โป Disclaimer: Past performance does not guarantee future results. Always use appropriate risk management strategies.
โโโ 52SIGNAL RECIPE CCI ์ ํํ๊ท ๋ฐด๋ โโโ
โ ๊ฐ์
52SIGNAL RECIPE CCI ์ ํํ๊ท ๋ฐด๋๋ CCI(Commodity Channel Index)์ ์ ํํ๊ท ๋ฐด๋๋ฅผ ๊ฒฐํฉํ ๊ณ ๊ธ ๊ธฐ์ ์ ์งํ์
๋๋ค. ์ด ์งํ๋ ์ ํํ๊ท ๋ฐด๋๋ฅผ ์ฌ์ฉํ์ฌ CCI์ ๋ณ๋์ฑ์ ์๊ฐํํ์ฌ ๊ณผ๋งค์/๊ณผ๋งค๋ ์์ญ์ ๋ช
ํํ๊ฒ ์๋ณํ๊ณ ์ ์ฌ์ ์ธ ์ถ์ธ ๋ฐ์ ์ง์ ์ ๋ ์ ํํ๊ฒ ํฌ์ฐฉํ๋ ๋ฐ ๋์์ ์ค๋๋ค.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ์ฃผ์ ํน์ง
โข CCI ๊ธฐ๋ฐ ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ถ์: ์ ํต์ ์ธ CCI ์งํ๋ฅผ ์ฌ์ฉํ์ฌ ์์ฅ์ ๊ณผ๋งค์/๊ณผ๋งค๋ ์ํ๋ฅผ ์๋ณ
โข ํตํฉ๋ ์ ํํ๊ท ๋ฐด๋: CCI์ ์ ํํ๊ท ๋ถ์์ ์ ์ฉํ์ฌ ์ถ์ธ์ ๋ฐฉํฅ๊ณผ ๊ฐ๋๋ฅผ ์๊ฐ์ ์ผ๋ก ํํ
โข ์ด์ค ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ ๋ฒจ: CCI์ ์ ํํ๊ท ๋ฐด๋ ๋ชจ๋์ ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ ๋ฒจ์ ์ค์ ํ์ฌ ์ ํธ์ ์ ํ๋ ํฅ์
โข ๊ณ ๊ธ ์๊ฐํ: ์ถ์ธ ๋ฐฉํฅ์ ๋ฐ๋ฅธ ์์ ๋ณํ์ ๋ช
ํํ ๋ฐด๋ ํ์๋ก ์ง๊ด์ ์ธ ์ฐจํธ ๋ถ์ ๊ฐ๋ฅ
โข ๋ค์ค ์๋ฆผ ์ค์ : ๋ค์ํ ์กฐ๊ฑด์ ๋ํ ์๋ฆผ ๊ธฐ๋ฅ์ผ๋ก ์ค์ํ ํธ๋ ์ด๋ฉ ์์ ์ ๋์น์ง ์๋๋ก ๋ณด์ฅ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๊ธฐ์ ์ ๊ธฐ๋ฐ
โ CCI (Commodity Channel Index)
โข ๊ธฐ๋ณธ ์ค์ : 20๊ธฐ๊ฐ CCI์ ๊ฐ์ค์ด๋ํ๊ท (WMA) ์ ์ฉ
โข ๊ณ์ฐ ๋ฐฉ๋ฒ: ํ๊ท ๊ฐ๊ฒฉ์ ๋ํ ํธ์ฐจ๋ฅผ ์ธก์ ํ์ฌ ์ ๊ทํํ ๊ฐ์ผ๋ก ํํ
โข ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ ๋ฒจ: ๊ธฐ๋ณธ๊ฐ์ผ๋ก +150(๊ณผ๋งค์)๊ณผ -150(๊ณผ๋งค๋) ์ค์
โ ์ ํํ๊ท ๋ฐด๋
โข ๊ธฐ๊ฐ: ๊ธฐ๋ณธ๊ฐ 100์ผ
โข ํธ์ฐจ: ๊ธฐ๋ณธ๊ฐ 4.5 ํ์คํธ์ฐจ
โข ์ค์ฌ์ : CCI ๊ฐ์ ๋ํ ์ ํํ๊ท ๋ถ์์ ์ค์ฌ์
โข ๋ฐด๋ ํญ: ๊ณ์ฐ๋ ํ์คํธ์ฐจ์ ๊ธฐ๋ฐํ์ฌ ์ค์ฌ์ ์ฃผ๋ณ์ ๋ณ๋์ฑ ๋ฒ์ ํ์
โข ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ ๋ฒจ: ๊ธฐ๋ณธ๊ฐ์ผ๋ก +250(๊ณผ๋งค์)์ -250(๊ณผ๋งค๋) ์ค์
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ์ค์ฉ์ ์์ฉ
โ ํธ๋ ์ด๋ฉ ์ ํธ ์๋ณ
โข ๋งค์ ์ ํธ:
โถ CCI๊ฐ ๊ณผ๋งค๋ ๋ ๋ฒจ(-150) ์๋๋ก ๋จ์ด์ง ๋
โถ ์ ํํ๊ท ๋ฐด๋์ ํ๋จ์ด ๊ณผ๋งค๋ ๋ ๋ฒจ(-250) ์๋๋ก ๋จ์ด์ง ๋
โถ ๋ ์กฐ๊ฑด์ด ๋์์ ์ถฉ์กฑ๋ ๋(๊ทน๋จ์ ๊ณผ๋งค๋ ์ํ) - ๊ฐํ ๋งค์ ์ ํธ
โข ๋งค๋ ์ ํธ:
โถ CCI๊ฐ ๊ณผ๋งค์ ๋ ๋ฒจ(+150) ์๋ก ์์นํ ๋
โถ ์ ํํ๊ท ๋ฐด๋์ ์๋จ์ด ๊ณผ๋งค์ ๋ ๋ฒจ(+250) ์๋ก ์์นํ ๋
โถ ๋ ์กฐ๊ฑด์ด ๋์์ ์ถฉ์กฑ๋ ๋(๊ทน๋จ์ ๊ณผ๋งค์ ์ํ) - ๊ฐํ ๋งค๋ ์ ํธ
โ ์ถ์ธ ๋ถ์
โข ์์น ์ถ์ธ: ์ ํํ๊ท ์ค์ฌ์ ์ด ์์นํ๊ณ CCI๊ฐ 0์ ์๋ก ์์ง์ผ ๋
โข ํ๋ฝ ์ถ์ธ: ์ ํํ๊ท ์ค์ฌ์ ์ด ํ๋ฝํ๊ณ CCI๊ฐ 0์ ์๋๋ก ์์ง์ผ ๋
โข ์ถ์ธ ๊ฐ๋: ๋ฐด๋ ์ฌ์ด์ ๊ฐ๊ฒฉ์ด ๋์์๋ก ๋ณ๋์ฑ์ด ํฌ๊ณ , ์ข์์๋ก ์ถ์ธ๊ฐ ์์ ์
โ ๋ค์ด๋ฒ์ ์ค ํ์ธ
โข ์ฝ์ธ ๋ค์ด๋ฒ์ ์ค: ๊ฐ๊ฒฉ์ด ์ ๊ณ ์ ์ ํ์ฑํ์ง๋ง CCI๊ฐ ์ด์ ๊ณ ์ ๋ณด๋ค ๋ฎ์ ๋(์ ์ฌ์ ์ฝ์ธ ์ ํธ)
โข ๊ฐ์ธ ๋ค์ด๋ฒ์ ์ค: ๊ฐ๊ฒฉ์ด ์ ์ ์ ์ ํ์ฑํ์ง๋ง CCI๊ฐ ์ด์ ์ ์ ๋ณด๋ค ๋์ ๋(์ ์ฌ์ ๊ฐ์ธ ์ ํธ)
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๊ณ ๊ธ ์ค์ ์ต์
โ CCI ์ค์ ์กฐ์
โข CCI ์์ค: ์ ํ ๊ฐ๋ฅํ ์ต์
์๋ ์ข
๊ฐ(๊ธฐ๋ณธ๊ฐ), ์๊ฐ, ๊ณ ๊ฐ, ์ ๊ฐ, HL2, HLC3, OHLC4 ๋ฑ์ด ํฌํจ
โข CCI ๊ธธ์ด: ๋จ๊ธฐ ๋ณ๋์ฑ์ ์ํด ๋ฎ์ ๊ฐ์ผ๋ก, ์ฅ๊ธฐ ์ถ์ธ๋ฅผ ์ํด ๋์ ๊ฐ์ผ๋ก ์กฐ์
โ ์ ํํ๊ท ์ค์ ์กฐ์
โข ๊ธฐ๊ฐ: ๋จ๊ธฐ ๋ถ์์ ์ํด ๋ฎ์ ๊ฐ(20-50), ์ฅ๊ธฐ ๋ถ์์ ์ํด ๋์ ๊ฐ(100-200) ์ฌ์ฉ
โข ํธ์ฐจ: ๋์ ๊ฐ์ ๋ ๋์ ๋ฐด๋(๋ ๋ง์ ์ ํธ), ๋ฎ์ ๊ฐ์ ๋ ์ข์ ๋ฐด๋(๋ ์ ํํ ์ ํธ) ์์ฑ
โ ๊ณผ๋งค์/๊ณผ๋งค๋ ๋ ๋ฒจ ์กฐ์
โข CCI ๋ ๋ฒจ: ๋ณ๋์ฑ์ด ํฐ ์์ฅ์์๋ ๋ ๊ทน๋จ์ ์ธ ๊ฐ(ยฑ200)์ผ๋ก ์กฐ์
โข ์ ํํ๊ท ๋ฐด๋ ๋ ๋ฒจ: ์์ฅ ์ํฉ์ ๋ฐ๋ผ ยฑ300 ๋๋ ยฑ200์ผ๋ก ์กฐ์ ๊ฐ๋ฅ
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๋ค๋ฅธ ์งํ์์ ์๋์ง
โข ๋ณผ๋ฆฐ์ ๋ฐด๋: ๊ฐ๊ฒฉ ์ฐจํธ์ ๋ณผ๋ฆฐ์ ๋ฐด๋์ ํจ๊ป ์ฌ์ฉํ์ฌ ๊ฐ๊ฒฉ ๋ณ๋์ฑ๊ณผ CCI ๋ณ๋์ฑ ๋น๊ต
โข MACD: ๋ชจ๋ฉํ
๊ณผ ์ถ์ธ ํ์ธ์ ์ํด MACD์ ํจ๊ป ์ฌ์ฉ
โข ํผ๋ณด๋์น ๋๋๋ฆผ: CCI ์ ํํ๊ท ๋ฐด๋ ์ ํธ๋ฅผ ์ฃผ์ ์ง์ง/์ ํญ ๋ ๋ฒจ๊ณผ ํจ๊ป ํ์ธ
โข ์ด๋ํ๊ท ์ : ์ด๋ํ๊ท ๊ต์ฐจ์ CCI ์ ํํ๊ท ๋ฐด๋ ์ ํธ๋ฅผ ๊ฒฐํฉํ์ฌ ์ ๋ขฐ์ฑ ํฅ์
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โ ๊ฒฐ๋ก
52SIGNAL RECIPE CCI ์ ํํ๊ท ๋ฐด๋๋ ์ ํต์ ์ธ CCI์ ์ ํํ๊ท ๋ถ์์ ๊ฒฐํฉํ์ฌ ๊ฐ๋ ฅํ๊ณ ์ ํํ ๊ธฐ์ ์ ๋ถ์ ๋๊ตฌ๋ฅผ ์ ๊ณตํฉ๋๋ค. ์ด์ค ๊ณผ๋งค์/๊ณผ๋งค๋ ์์คํ
์ ํธ๋ ์ด๋ฉ ์ ํธ์ ์ ํ๋๋ฅผ ๋์ด๊ณ ์ถ์ธ ๋ฐฉํฅ๊ณผ ๊ฐ๋๋ฅผ ๋ช
ํํ๊ฒ ์๊ฐํํ์ฌ ํธ๋ ์ด๋์ ์์ฌ ๊ฒฐ์ ์ ๋์ต๋๋ค. ๋ค์ํ ์ค์ ์ ํธ๋ ์ด๋ฉ ์คํ์ผ๊ณผ ์์ฅ ์ํฉ์ ๋ง๊ฒ ์กฐ์ ํ์ฌ ์ต์ ์ ๊ฒฐ๊ณผ๋ฅผ ์ป์ ์ ์์ต๋๋ค.
โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
โป ๋ฉด์ฑ
์กฐํญ: ๊ณผ๊ฑฐ ์ฑ๊ณผ๊ฐ ๋ฏธ๋ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฅํ์ง ์์ต๋๋ค. ํญ์ ์ ์ ํ ๋ฆฌ์คํฌ ๊ด๋ฆฌ ์ ๋ต์ ์ฌ์ฉํ์ธ์.
