在脚本中搜索"algo"
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.
Volume Weighted Intra Bar KurtosisThis indicator analyzes market sentiment by providing a detailed
view of Excess Kurtosis ("Fat Tails"). It uses data from a lower,
intra-bar timeframe to separate the total kurtosis of a single bar
into distinct, interpretable components.
Key Features:
1. **Intra-Bar Kurtosis Decomposition:** For each bar on the chart,
the indicator analyzes the underlying price action on a smaller
timeframe ('Intra-Bar Timeframe'). Unlike Variance, the Fourth
Central Moment (Kurtosis) decomposes into three parts:
- **Between-Bar Kurtosis (Gold):** Peakedness of the price
path *between* the intra-bar candles. High values indicate
that the macro movement happened in jumps or gaps rather
than a smooth progression.
- **Within-Bar Kurtosis (Blue):** Fat tails derived from the
microstructure (extreme wicks) *inside* the 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 Kurtosis coefficients are not
linearly additive, this indicator calculates the *exact* Total
Kurtosis and partitions the columns based on the additive
Fourth Moment Decomposition (`M4Tot = M4Btw + M4Wtn + M4Int`).
3. **Dual Display Modes:** The indicator offers two modes to
visualize this information:
- **Absolute Mode:** Plots the *total* kurtosis as a
stacked column chart, showing the *absolute magnitude* of
tail risk and the contribution of each component.
- **Relative Mode:** Plots the components as a 100% stacked
column chart (scaled from 0 to 1), focusing purely on the
*energy ratio* of the components.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
calculates an **Exponential Regression Curve** (log-space),
making the analysis suitable for comparing assets with
different scales (e.g., BTC vs EURUSD).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all mean and moment calculations.
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 alerts for:
- Kurtosis magnitude (High Positive / High Negative).
- Kurtosis character changes/emerging/fading.
- 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.
Smart SafeZone Stops [MarkitTick]💡 This script represents a sophisticated evolution of volatility-based trailing stop methodologies. It is designed to assist traders in managing trend-following positions by dynamically adjusting stop-loss levels based on market noise, directional momentum, and volume flows. Unlike static trailing stops that move by a fixed percentage or simple ATR multiples, this tool calculates the "safe zone" by analyzing how far price has penetrated against the trend over a specific lookback period, offering a granular approach to risk management that adapts to changing market conditions.
✨ Originality and Utility
The primary utility of this indicator lies in its ability to filter out market noise while remaining tight enough to protect profits during strong trends. While the classic SafeZone concept (popularized by Dr. Alexander Elder) is effective, this script introduces several modern enhancements that increase its robustness:
● Dynamic ADX Integration Standard SafeZone stops use a fixed multiplier. This script integrates the Average Directional Index (ADX) to gauge trend strength. When the trend is strong, the stop tightens (Aggressive Multiplier) to lock in profits rapidly. When the trend is weak or choppy, the stop widens (Conservative Multiplier) to prevent premature shakeouts. ● Volume-Weighted Noise Price movement on low volume is often considered "noise," while high-volume movement signifies conviction. This script optionally weights the noise calculation by Relative Volume. A downward spike on low volume will affect the stop level less than a downward spike on high volume.
● 3-Day Smoothing Mechanism To prevent the stop line from becoming too jagged or reacting to single-bar anomalies, the script applies a 3-day smoothing algorithm. It utilizes the "worst-case" scenario of the last three calculated stop levels, ensuring the stop only moves when the trend structure genuinely shifts.
🔬 Methodology and Concepts
The underlying logic operates on a "Ratchet" mechanism, meaning the stop line can only move in the direction of the trade (up for longs, down for shorts) and never retraces until a trend reversal occurs.
● Directional Noise Calculation The script separates market noise into two components: Downside Penetration (for Longs): The distance the price dips below the previous bar's low. Upside Penetration (for Shorts): The distance the price spikes above the previous bar's high. The average of these penetrations is calculated over the Noise Lookback Period .
● The SafeZone Formula The raw stop level is derived as follows: Long Stop = Previous Low - (Average Downside Noise × Multiplier) Short Stop = Previous High + (Average Upside Noise × Multiplier)
● Adaptive Multiplier Logic If Dynamic ADX is enabled: If ADX > Strong Threshold: Use Aggressive Multiplier (e.g., 1.5x). If ADX < Weak Threshold: Use Conservative Multiplier (e.g., 3.5x). Otherwise: Use the Base Safety Coefficient.
● Exhaustion Detection The script calculates the distance between the current Close price and the Active Stop. If this distance exceeds a specific multiple of the ATR (Average True Range), it flags a "Mean Reversion" or "Exhaustion" warning, suggesting price has extended too far from equilibrium.
🎨 Visual Guide
The indicator plots distinct visual elements to guide decision-making without cluttering the chart excessively.
● Trailing Stop Lines Green Line (Solid): Represents the SafeZone Long Stop. This line appears below price during an uptrend. As long as price closes above this line, the bullish bias is intact. Red Line (Solid): Represents the SafeZone Short Stop. This line appears above price during a downtrend. A close above this line signals a potential short exit or reversal.
● Trend Signals Green Triangle (Below Bar): Marks the "Bull Start." This occurs when the price crosses above the Trend Filter EMA and the trend logic flips to bullish. Red Triangle (Above Bar): Marks the "Bear Start." Indicates the start of a downtrend sequence.
● Exhaustion Warnings Yellow Labels (⚠️): These appear when price has extended significantly away from the stop line (based on the ATR Exhaustion Multiplier). This is not an immediate sell signal but a warning that the trend may be overextended and a pullback is probable.
● MTF Consensus Cloud Background Color: If enabled, the chart background changes color to reflect the Higher Timeframe (HTF) trend. Green Background: Current trend matches HTF Uptrend. Red Background: Current trend matches HTF Downtrend. Gray Background: Trends are mismatched (Consolidation/Conflict).
● Quantitative Dashboard A table located in the top-right corner displays real-time statistics: Trend: Current state (BULLISH/BEARISH). Age: Number of bars since the trend began. Stop Price: Exact price level of the trailing stop. Risk %: The percentage distance from the current Close to the Stop. If this exceeds 3%, the text turns red to highlight elevated risk. Active Mult: The current multiplier being used (Dynamic or Fixed). ADX State: Shows if the trend is Strong, Weak, or Normal.
📖 How to Use
1. Entry Timing Wait for a Trend Switch signal (Triangle). For a long entry (Green Triangle), ensure the price is above the Trend Baseline (EMA). Ideally, look for confluence with the MTF Cloud (Green Background).
2. Position Management Once in a trade, use the Trailing Stop Line as your hard exit or invalidation point. Do not manually move the stop away from price; the script automatically "ratchets" the stop tighter as the trend progresses.
3. Taking Profits Use the "Exhaustion Warnings" (⚠️) as opportunities to scale out of positions. When price moves parabolically away from the stop line, the probability of a snap-back increases.
4. Managing Chop If the dashboard shows "ADX State: WEAK," expect the stop line to remain wider. This allows the asset "room to breathe" without stopping you out on random volatility.
⚙️ Inputs and Settings
The script is highly customizable to fit different asset classes (Crypto, Forex, Stocks).
● Trend Definitions Trend Filter (EMA Length): Determines the baseline trend bias (Default: 22). Price must be above this EMA to initiate a long calculation.
● Noise Calculation Noise Lookback Period: The number of bars used to calculate average penetration (Default: 10). Base Safety Coefficient: The standard multiplier applied to the noise average (Default: 2.5). Higher values = wider stops. Use Volume Weighting: Enables the volume-adjustment logic. Use 3-Day Smoothing: Recommended keeping this TRUE to avoid stop-hunts.
● Dynamic Multiplier (ADX) Enable Dynamic ADX: Toggles the adaptive multiplier. Strong/Weak Thresholds: The ADX levels that trigger aggressive or conservative multipliers.
● Multi-Timeframe Consensus Higher Timeframe: Select the TF for the cloud background (e.g., Daily or Weekly).
● Exhaustion Warning ATR Multiplier: Defines how far price must be from the stop to trigger a warning (Default: 3.0).
🔍 Deconstruction of the Underlying Scientific and Academic Framework
The "Smart SafeZone" indicator is grounded in the statistical analysis of market noise versus signal.
● Theory of Noise Penetration Conventional stops often use Standard Deviation (Bollinger Bands) or Average True Range (Keltner Channels/Chandelier Stops). While effective, these measures assume volatility is symmetrical. This script adopts the view that directional volatility matters more. In an uptrend, upside volatility is "good" signal, while downside volatility is "noise." By explicitly calculating the average downside penetration (Low - Low), the script isolates the specific counter-trend force acting on the asset. ● Volume-Weighted Price Analysis (VWPA) The inclusion of volume weighting draws upon Dow Theory principles, which state that volume must confirm the trend. Math: Penetration × (Volume / AverageVolume) This formula asserts that a price drop on low volume is statistically less significant than a drop on high volume. By dampening the impact of low-volume moves, the stop becomes more resistant to liquidity vacuums and algorithmic stop-hunts.
● Trend Efficiency (ADX) The integration of J. Welles Wilder’s ADX (Average Directional Index) adds a dimension of Trend Efficiency. High ADX values indicate a highly efficient trend with little retracement. Mathematically, this justifies a lower standard deviation (or noise multiplier) for the stop, as the probability of a deep retracement without a trend change is lower in high-momentum environments.
⚠️ Disclaimer
All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.
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 KurtosisThis indicator analyzes market sentiment by decomposing total
Excess Kurtosis ("Fat Tails") into distinct, interpretable components:
"Between-Bar" (Trend Path) and "Within-Bar" (Microstructure).
Key Features:
1. **Moment-Based Kurtosis Decomposition:** The indicator
separates kurtosis based on the 'Estimate Bar Statistics' option.
It leverages the additive property of the Fourth Central Moment
(Cumulants) to ensure mathematical rigor:
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
simple kurtosis of the selected `Source`.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):** The
indicator uses a statistical model ('Estimator') to
calculate *within-bar* kurtosis.
This separates the tail risk into:
- **Between-Bar Kurtosis (Gold):** Peakedness of the price
path itself. High values indicate the trend moves in jumps
or gaps rather than a smooth progression.
- **Within-Bar Kurtosis (Blue):** Fat tails within the
microstructure. High values imply significant outliers
inside the bars (e.g., extreme wicks).
- **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 Kurtosis coefficients are not
linearly additive, this indicator calculates the *exact* Total
Kurtosis and partitions the area/ratios based on the additive
Fourth Moment Decomposition (`M4Tot = M4Btw + M4Wtn + M4Int`). This
ensures the displayed total kurtosis remains mathematically accurate.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *Total Kurtosis* as the main
line, with the background filled by the stacked components.
Shows the *magnitude* of the tail risk.
- **Relative Mode:** Displays the **Contribution Ratios**
of each component (-1.0 to 1.0). This isolates the
*structure/quality* of the risk (e.g., "Is the risk
driven by the trend jumps or by the candle instability?").
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
kurtosis of *returns* rather than absolute prices.
(Essential for correct statistical properties).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all 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 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).
- Character changes (Inter-Bar vs. Intra-Bar dominance).
- 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 Intra Bar SkewnessThis indicator analyzes market sentiment by providing a detailed
view of skewness (asymmetry). It uses data from a lower, intra-bar
timeframe to separate the total skewness of a single bar into
distinct, interpretable components.
Key Features:
1. **Intra-Bar Skewness Decomposition:** For each bar on the chart,
the indicator analyzes the underlying price action on a smaller
timeframe ('Intra-Bar Timeframe'). Unlike Variance, the Third
Central Moment (Skewness) decomposes into three parts:
- **Between-Bar Skewness (Gold):** Asymmetry of the price
path *between* the intra-bar candles. Indicates if the macro
movements within the bar accelerated in one direction.
- **Within-Bar Skewness (Blue):** Asymmetry of the
microstructure (wicks vs. tails) *inside* the intra-bar candles.
- **Interaction Skewness (Grey):** The component arising from
the comovement of local means and local variances (e.g.,
does volatility increase when price drops?).
2. **Visual Decomposition Logic:** Total Skewness is the
primary metric displayed. Since Skewness coefficients are not
linearly additive, this indicator calculates the *exact* Total
Skewness and partitions the columns based on the additive
Third Moment Decomposition (`M3Tot = M3Btw + M3Wtn + M3Int`).
3. **Dual Display Modes:** The indicator offers two modes to
visualize this information:
- **Absolute Mode:** Plots the *total* skewness as a
stacked column chart, showing the *absolute magnitude* of
asymmetry and the contribution of each component.
- **Relative Mode:** Plots the components as a 100% stacked
column chart (scaled from 0 to 1), focusing purely on the
*energy ratio* of the components.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
calculates an **Exponential Regression Curve** (log-space),
making the analysis suitable for comparing assets with
different scales (e.g., BTC vs EURUSD).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all mean and moment calculations.
5. **Skewness Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (highs and lows) 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 alerts for:
- Skewness magnitude (High Positive / High Negative).
- Skewness character changes/emerging/fading.
- 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 SkewnessThis indicator analyzes market sentiment by decomposing total
skewness (asymmetry) into two distinct, interpretable components:
"Between-Bar" (Inter-Bar) and "Within-Bar" (Intra-Bar) skewness.
Key Features:
1. **Moment-Based Skewness decomposition:** The indicator
separates skewness based on the 'Estimate Bar Statistics' option.
It leverages the additive property of the Third Central Moment
to ensure mathematical rigor:
- **Standard Mode (`Estimate Bar Statistics` = OFF):** Calculates
simple skewness of the selected `Source`.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):** The
indicator uses a statistical model ('Estimator') to
calculate *within-bar* skewness.
This separates the asymmetry into:
- **Between-Bar Skewness (Gold):** Asymmetry of the price
path itself. A positive value indicates that the trend
moves more aggressively upwards than downwards.
- **Within-Bar Skewness (Blue):** Asymmetry of the
microstructure (wicks vs. tails). A positive value implies
strong buying pressure within the bars (long tails).
2. **Visual Decomposition Logic:** Total Skewness is the
primary metric displayed. Since Skewness coefficients are not
linearly additive, this indicator calculates the *exact* Total
Skewness and partitions the area/ratios based on the additive
Third Moment Decomposition (`M3Tot = M3Btw + M3Wtn`). This
ensures the displayed total skewness remains mathematically accurate.
3. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *Total Skewness* as the main
line, with the background filled by the stacked components.
Shows the *magnitude* and direction of the tail risk.
- **Relative Mode:** Displays the **Contribution Ratios**
of each component (-1.0 to 1.0). This isolates the
*structure/quality* of the asymmetry (e.g., "Is the skewness
driven by the trend or by the candle shapes?").
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
transforms inputs into logarithmic space, analyzing the
skewness of *returns* rather than absolute prices.
(Essential for correct statistical properties).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all 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 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).
- Character changes (Inter-Bar vs. Intra-Bar dominance).
- 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 Intra Bar CorrelationThis indicator analyzes market character by providing a detailed
view of correlation. It uses data from a lower, intra-bar timeframe
to separate the total correlation of a single bar into two distinct
components.
Key Features:
1. **Intra-Bar Correlation Decomposition:** For each bar on the chart,
the indicator analyzes the underlying price action on a smaller
timeframe ('Intra-Bar Timeframe') and quantifies two types of correlation:
- **Between-Bar Correlation (Directional):** Calculated from price
movements *between* the intra-bar candles. This component
represents the **macro-movement** correlation within the main bar.
- **Within-Bar Correlation (Non-Directional):** Calculated from
price fluctuations *inside* each intra-bar candle. This
component represents the **microstructure/noise** correlation.
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 information:
- **Absolute Mode:** Plots the *total* correlation as a
stacked column chart, showing the *absolute magnitude* of
correlation and the contribution of each component.
- **Relative Mode:** Plots the components as a 100% stacked
column chart (scaled from 0 to 1), focusing purely on the
*energy ratio* of 'between-bar' (macro) and 'within-bar' (micro)
correlation.
4. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
calculates an **Exponential Regression Curve** (log-space),
making the analysis suitable for comparing assets with
different scales (e.g., BTC vs EURUSD).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all mean and covariance 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 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 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 CorrelationThis indicator analyzes the structural relationship between two
assets by decomposing the Total Correlation into three distinct,
interpretable components using a Weighted Linear Regression model
and a Hybrid Copula Estimator.
Key Features:
1. **Hybrid Copula Estimator:** Unlike standard correlation, which
often fails on High/Low range data, this indicator fuses two
metrics to ensure mathematical rigor:
- **Magnitude:** Derived from Rogers-Satchell Volatility (robust to trend).
- **Direction:** Derived from Log-Returns.
This allows for precise correlation estimates even on intra-bar data.
2. **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`.
- **Decomposition Mode (`Estimate Bar Statistics` = ON):** The
indicator uses a statistical model ('Estimator') to
calculate *within-bar* correlation.
This separates the relationship into:
- **Trend Correlation (Green/Red):** Correlation of the regression
slopes. Indicates if assets are trending in the same direction.
- **Residual Correlation (Yellow):** Correlation of the noise
around the trend (Cointegration). Indicates if assets
mean-revert together, even if trends differ.
- **Within-Bar Correlation (Blue):** Correlation of the
microstructure (intra-bar volatility).
3. **Visual Decomposition Logic:** Total Correlation is the
primary metric displayed. Since Correlation Coefficients are not
linearly additive, this indicator calculates the *exact* Total
Correlation and partitions the area/ratios based on the additive
Covariance Decomposition. This ensures the displayed total
correlation remains mathematically accurate.
4. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *Total Correlation* as the main
line, with the background filled by the stacked components
(Trend, Residual, Within). Shows the *magnitude* of the relationship.
- **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0)
of each component using L1-Normalization. This isolates the
*structure/quality* of the relationship (e.g., "Is the
correlation driven by Trend or just by Noise?").
5. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
calculates an **Exponential Regression Curve** (log-space),
creating a constant percentage variance environment. Essential
for comparing assets with different scales (e.g., BTC vs EURUSD).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all regression and covariance calculations.
6. **Correlation Cycle Analysis:**
- **Pivot Detection:** Includes a built-in pivot detector
that identifies significant turning points (highs and lows) in
the *Total Correlation* line.
- **Flexible Pivot Algorithms:** Supports various underlying
mathematical models for pivot detection provided by the
core library.
7. **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.
8. **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.
9. **Integrated Alerts:** Includes comprehensive alerts for:
- Correlation magnitude (High Positive / High Inverse).
- Correlation character changes/emerging/fading.
- Total Correlation 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 CorrelationThis indicator analyzes the structural relationship between two
assets by decomposing the Total Correlation into two distinct,
interpretable components: "Between-Bar" (Inter-Bar) and
"Within-Bar" (Intra-Bar) correlation.
Key Features:
1. **Hybrid Copula Estimator:** Unlike standard correlation, which
often fails on High/Low range data, this indicator fuses two
metrics to ensure mathematical rigor:
- **Magnitude:** Derived from Rogers-Satchell Volatility.
- **Direction:** Derived from Log-Returns.
This allows for precise correlation estimates even on intra-bar data.
2. **Two-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` (Close-to-Close).
- **Decomposition Mode (`Estimate Bar Statistics` = ON):** The
indicator uses a statistical model ('Estimator') to
calculate *within-bar* correlation.
This separates the relationship into:
- **Between-Bar Correlation (Green/Red):** Correlation of the
price paths (means). Indicates if the macro movements of the
assets are aligned (Inter-Bar correlation).
- **Within-Bar Correlation (Blue):** Correlation of the
microstructure (Intra-Bar volatility/noise).
3. **Visual Decomposition Logic:** Total Correlation is the
primary metric displayed. Since Correlation Coefficients are not
linearly additive, this indicator calculates the *exact* Total
Correlation and partitions the area/ratios based on the additive
Covariance Decomposition (`CovTot = CovBtw + CovWtn`). This
ensures the displayed total correlation remains mathematically accurate.
4. **Dual Display Modes:** The indicator offers two modes to
visualize this decomposition:
- **Absolute Mode:** Displays the *Total Correlation* as the main
line, with the background filled by the stacked components
(Between vs. Within). Shows the *magnitude* of the relationship.
- **Relative Mode:** Displays the **Energy Ratios** (-1.0 to 1.0)
of each component using L1-Normalization. This isolates the
*structure/quality* of the relationship (e.g., "Is the correlation
driven by price movement or just by volatility coupling?").
5. **Calculation Options:**
- **Normalization:** An optional 'Normalize' setting
calculates an **Exponential Regression Curve** (log-space),
creating a constant percentage variance environment. Essential
for comparing assets with different scales (e.g., BTC vs EURUSD).
- **Volume Weighting:** An option (`Volume weighted`) applies
volume weighting to all mean and covariance calculations.
6. **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.
7. **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.
8. **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.
9. **Integrated Alerts:** Includes comprehensive alerts for:
- Correlation magnitude (High Positive / High Inverse).
- Character changes (Inter-Bar vs. Intra-Bar dominance).
- Total Correlation 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.
MACD Enhanced [DCAUT]█ MACD Enhanced
📊 ORIGINALITY & INNOVATION
The MACD Enhanced represents a significant improvement over traditional MACD implementations. While Gerald Appel's original MACD from the 1970s was limited to exponential moving averages (EMA), this enhanced version expands algorithmic options by supporting 21 different moving average calculations for both the main MACD line and signal line independently.
This improvement addresses an important limitation of traditional MACD: the inability to adapt the indicator's mathematical foundation to different market conditions. By allowing traders to select from algorithms ranging from simple moving averages (SMA) for stability to advanced adaptive filters like Kalman Filter for noise reduction, this implementation changes MACD from a fixed-algorithm tool into a flexible instrument that can be adjusted for specific market environments and trading strategies.
The enhanced histogram visualization system uses a four-color gradient that helps communicate momentum strength and direction more clearly than traditional single-color histograms.
📐 MATHEMATICAL FOUNDATION
The core calculation maintains the proven MACD formula: Fast MA(source, fastLength) - Slow MA(source, slowLength), but extends it with algorithmic flexibility. The signal line applies the selected smoothing algorithm to the MACD line over the specified signal period, while the histogram represents the difference between MACD and signal lines.
Available Algorithms:
The implementation supports a comprehensive spectrum of technical analysis algorithms:
Basic Averages: SMA (arithmetic mean), EMA (exponential weighting), RMA (Wilder's smoothing), WMA (linear weighting)
Advanced Averages: HMA (Hull's low-lag), VWMA (volume-weighted), ALMA (Arnaud Legoux adaptive)
Mathematical Filters: LSMA (least squares regression), DEMA (double exponential), TEMA (triple exponential), ZLEMA (zero-lag exponential)
Adaptive Systems: T3 (Tillson T3), FRAMA (fractal adaptive), KAMA (Kaufman adaptive), MCGINLEY_DYNAMIC (reactive to volatility)
Signal Processing: ULTIMATE_SMOOTHER (low-pass filter), LAGUERRE_FILTER (four-pole IIR), SUPER_SMOOTHER (two-pole Butterworth), KALMAN_FILTER (state-space estimation)
Specialized: TMA (triangular moving average), LAGUERRE_BINOMIAL_FILTER (binomial smoothing)
Each algorithm responds differently to price action, allowing traders to match the indicator's behavior to market characteristics: trending markets benefit from responsive algorithms like EMA or HMA, while ranging markets require stable algorithms like SMA or RMA.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Histogram Interpretation:
Positive Values: Indicate bullish momentum when MACD line exceeds signal line, suggesting upward price pressure and potential buying opportunities
Negative Values: Reflect bearish momentum when MACD line falls below signal line, indicating downward pressure and potential selling opportunities
Zero Line Crosses: MACD crossing above zero suggests transition to bullish bias, while crossing below indicates bearish bias shift
Momentum Changes: Rising histogram (regardless of positive/negative) signals accelerating momentum in the current direction, while declining histogram warns of momentum deceleration
Advanced Signal Recognition:
Divergences: Price making new highs/lows while MACD fails to confirm often precedes trend reversals
Convergence Patterns: MACD line approaching signal line suggests impending crossover and potential trade setup
Histogram Peaks: Extreme histogram values often mark momentum exhaustion points and potential reversal zones
🎯 STRATEGIC APPLICATIONS
Comprehensive Trend Confirmation Strategies:
Primary Trend Validation Protocol:
Identify primary trend direction using higher timeframe (4H or Daily) MACD position relative to zero line
Confirm trend strength by analyzing histogram progression: consistent expansion indicates strong momentum, contraction suggests weakening
Use secondary confirmation from MACD line angle: steep angles (>45°) indicate strong trends, shallow angles suggest consolidation
Validate with price structure: trending markets show consistent higher highs/higher lows (uptrend) or lower highs/lower lows (downtrend)
Entry Timing Techniques:
Pullback Entries in Uptrends: Wait for MACD histogram to decline toward zero line without crossing, then enter on histogram expansion with MACD line still above zero
Breakout Confirmations: Use MACD line crossing above zero as confirmation of upward breakouts from consolidation patterns
Continuation Signals: Look for MACD line re-acceleration (steepening angle) after brief consolidation periods as trend continuation signals
Advanced Divergence Trading Systems:
Regular Divergence Recognition:
Bullish Regular Divergence: Price creates lower lows while MACD line forms higher lows. This pattern is traditionally considered a potential upward reversal signal, but should be combined with other confirmation signals
Bearish Regular Divergence: Price makes higher highs while MACD shows lower highs. This pattern is traditionally considered a potential downward reversal signal, but trading decisions should incorporate proper risk management
Hidden Divergence Strategies:
Bullish Hidden Divergence: Price shows higher lows while MACD displays lower lows, indicating trend continuation potential. Use for adding to existing long positions during pullbacks
Bearish Hidden Divergence: Price creates lower highs while MACD forms higher highs, suggesting downtrend continuation. Optimal for adding to short positions during bear market rallies
Multi-Timeframe Coordination Framework:
Three-Timeframe Analysis Structure:
Primary Timeframe (Daily): Determine overall market bias and major trend direction. Only trade in alignment with daily MACD direction
Secondary Timeframe (4H): Identify intermediate trend changes and major entry opportunities. Use for position sizing decisions
Execution Timeframe (1H): Precise entry and exit timing. Look for MACD line crossovers that align with higher timeframe bias
Timeframe Synchronization Rules:
Daily MACD above zero + 4H MACD rising = Strong uptrend context for long positions
Daily MACD below zero + 4H MACD declining = Strong downtrend context for short positions
Conflicting signals between timeframes = Wait for alignment or use smaller position sizes
1H MACD signals only valid when aligned with both higher timeframes
Algorithm Considerations by Market Type:
Trending Markets: Responsive algorithms like EMA, HMA may be considered, but effectiveness should be tested for specific market conditions
Volatile Markets: Noise-reducing algorithms like KALMAN_FILTER, SUPER_SMOOTHER may help reduce false signals, though results vary by market
Range-Bound Markets: Stability-focused algorithms like SMA, RMA may provide smoother signals, but individual testing is required
Short Timeframes: Low-lag algorithms like ZLEMA, T3 theoretically respond faster but may also increase noise
Important Note: All algorithm choices and parameter settings should be thoroughly backtested and validated based on specific trading strategies, market conditions, and individual risk tolerance. Different market environments and trading styles may require different configuration approaches.
📋 DETAILED PARAMETER CONFIGURATION
Comprehensive Source Selection Strategy:
Price Source Analysis and Optimization:
Close Price (Default): Most commonly used, reflects final market sentiment of each period. Best for end-of-day analysis, swing trading, daily/weekly timeframes. Advantages: widely accepted standard, good for backtesting comparisons. Disadvantages: ignores intraday price action, may miss important highs/lows
HL2 (High+Low)/2: Midpoint of the trading range, reduces impact of opening gaps and closing spikes. Best for volatile markets, gap-prone assets, forex markets. Calculation impact: smoother MACD signals, reduced noise from price spikes. Optimal when asset shows frequent gaps, high volatility during specific sessions
HLC3 (High+Low+Close)/3: Weighted average emphasizing the close while including range information. Best for balanced analysis, most asset classes, medium-term trading. Mathematical effect: 33% weight to high/low, 33% to close, provides compromise between close and HL2. Use when standard close is too noisy but HL2 is too smooth
OHLC4 (Open+High+Low+Close)/4: True average of all price points, most comprehensive view. Best for complete price representation, algorithmic trading, statistical analysis. Considerations: includes opening sentiment, smoothest of all options but potentially less responsive. Optimal for markets with significant opening moves, comprehensive trend analysis
Parameter Configuration Principles:
Important Note: Different moving average algorithms have distinct mathematical characteristics and response patterns. The same parameter settings may produce vastly different results when using different algorithms. When switching algorithms, parameter settings should be re-evaluated and tested for appropriateness.
Length Parameter Considerations:
Fast Length (Default 12): Shorter periods provide faster response but may increase noise and false signals, longer periods offer more stable signals but slower response, different algorithms respond differently to the same parameters and may require adjustment
Slow Length (Default 26): Should maintain a reasonable proportional relationship with fast length, different timeframes may require different parameter configurations, algorithm characteristics influence optimal length settings
Signal Length (Default 9): Shorter lengths produce more frequent crossovers but may increase false signals, longer lengths provide better signal confirmation but slower response, should be adjusted based on trading style and chosen algorithm characteristics
Comprehensive Algorithm Selection Framework:
MACD Line Algorithm Decision Matrix:
EMA (Standard Choice): Mathematical properties: exponential weighting, recent price emphasis. Best for general use, traditional MACD behavior, backtesting compatibility. Performance characteristics: good balance of speed and smoothness, widely understood behavior
SMA (Stability Focus): Equal weighting of all periods, maximum smoothness. Best for ranging markets, noise reduction, conservative trading. Trade-offs: slower signal generation, reduced sensitivity to recent price changes
HMA (Speed Optimized): Hull Moving Average, designed for reduced lag. Best for trending markets, quick reversals, active trading. Technical advantage: square root period weighting, faster trend detection. Caution: can be more sensitive to noise
KAMA (Adaptive): Kaufman Adaptive MA, adjusts smoothing based on market efficiency. Best for varying market conditions, algorithmic trading. Mechanism: fast smoothing in trends, slow smoothing in sideways markets. Complexity: requires understanding of efficiency ratio
Signal Line Algorithm Optimization Strategies:
Matching Strategy: Use same algorithm for both MACD and signal lines. Benefits: consistent mathematical properties, predictable behavior. Best when backtesting historical strategies, maintaining traditional MACD characteristics
Contrast Strategy: Use different algorithms for optimization. Common combinations: MACD=EMA, Signal=SMA for smoother crossovers, MACD=HMA, Signal=RMA for balanced speed/stability, Advanced: MACD=KAMA, Signal=T3 for adaptive behavior with smooth signals
Market Regime Adaptation: Trending markets: both fast algorithms (EMA/HMA), Volatile markets: MACD=KALMAN_FILTER, Signal=SUPER_SMOOTHER, Range-bound: both slow algorithms (SMA/RMA)
Parameter Sensitivity Considerations:
Impact of Parameter Changes:
Length Parameter Sensitivity: Small parameter adjustments can significantly affect signal timing, while larger adjustments may fundamentally change indicator behavior characteristics
Algorithm Sensitivity: Different algorithms produce different signal characteristics. Thoroughly test the impact on your trading strategy before switching algorithms
Combined Effects: Changing multiple parameters simultaneously can create unexpected effects. Recommendation: adjust parameters one at a time and thoroughly test each change
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Response Characteristics by Algorithm:
Fastest Response: ZLEMA, HMA, T3 - minimal lag but higher noise
Balanced Performance: EMA, DEMA, TEMA - good trade-off between speed and stability
Highest Stability: SMA, RMA, TMA - reduced noise but increased lag
Adaptive Behavior: KAMA, FRAMA, MCGINLEY_DYNAMIC - automatically adjust to market conditions
Noise Filtering Capabilities:
Advanced algorithms like KALMAN_FILTER and SUPER_SMOOTHER help reduce false signals compared to traditional EMA-based MACD. Noise-reducing algorithms can provide more stable signals in volatile market conditions, though results will vary based on market conditions and parameter settings.
Market Condition Adaptability:
Unlike fixed-algorithm MACD, this enhanced version allows real-time optimization. Trending markets benefit from responsive algorithms (EMA, HMA), while ranging markets perform better with stable algorithms (SMA, RMA). The ability to switch algorithms without changing indicators provides greater flexibility.
Comparative Performance vs Traditional MACD:
Algorithm Flexibility: 21 algorithms vs 1 fixed EMA
Signal Quality: Reduced false signals through noise filtering algorithms
Market Adaptability: Optimizable for any market condition vs fixed behavior
Customization Options: Independent algorithm selection for MACD and signal lines vs forced matching
Professional Features: Advanced color coding, multiple alert conditions, comprehensive parameter control
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always combine with proper risk management and thorough strategy testing.
PSAR Laboratory [DAFE]PSAR Laboratory : The Ultimate Adaptive Trailing Stop & Reversal Engine
23 Advanced Algorithms. Adaptive Acceleration. Smart Flip Logic. Parabolic SAR Reimagined.
█ PHILOSOPHY: WELCOME TO THE LABORATORY
The standard Parabolic SAR, created by the legendary J. Welles Wilder Jr., is a tool of beautiful simplicity. But in today's complex, algorithm-driven markets, its simplicity is its fatal flaw. Its fixed acceleration and rigid flip logic cause it to fail precisely when you need it most: it whipsaws in choppy conditions and gives back too much profit in strong trends.
The PSAR Laboratory was not created to be just another PSAR. It was engineered to be the definitive evolution of Wilder's original concept. This is not an indicator; it is a powerful, interactive research environment. It is a sandbox where you, the trader, can move beyond the static "one-size-fits-all" approach and forge a PSAR that is perfectly adapted to your specific market, timeframe, and trading style.
We have deconstructed the very DNA of the Parabolic SAR and rebuilt it from the ground up, infusing it with modern quantitative techniques. The result is an institutional-grade suite of 23 distinct, mathematically diverse algorithms that dynamically control every aspect of the PSAR's behavior.
█ WHAT MAKES THIS A "LABORATORY"? THE CORE INNOVATIONS
This tool stands in a class of its own. It is a collection of what could be 23 separate indicators, all seamlessly integrated into one powerful engine.
The 23 Algorithm Engine: This is the heart of the Laboratory. Instead of one rigid formula, you have a library of 23 unique mathematical engines at your command. These algorithms are not simple tweaks; they are complete re-imaginings of how the PSAR should behave, based on concepts from information theory, digital signal processing, fractal geometry, and institutional analysis.
Truly Adaptive Acceleration (AF): The standard PSAR's "gas pedal" (the AF) is dumb; it accelerates at a fixed rate. Our algorithms make it intelligent. The AF can now speed up in clean, trending environments to lock in profits, and automatically slow down in choppy, chaotic conditions to avoid whipsaws.
Advanced Flip Confirmation Logic: Say goodbye to noise-driven flips. You are no longer at the mercy of a single wick touching the SAR. The Laboratory provides multiple layers of flip confirmation, including requiring a bar close beyond the SAR, a volume spike to validate the reversal, or even a multi-bar confirmation .
Comprehensive Noise Filtering Core: In a revolutionary step, you can apply one of over 30 advanced signal processing filters directly to the SAR output itself. From ultra-low-lag filters like the Hull MA and DAFE Spectral Laguerre to adaptive filters like KAMA and FRAMA , you can surgically remove noise while preserving the responsiveness of the core signal.
Integrated Performance Engine: How do you know which of the 23 algorithms is best for your market? You test it. The built-in Performance Dashboard is a comprehensive backtesting and analytics engine that tracks every trade, providing real-time data on Win Rate, Profit Factor, Max Drawdown, and more. It allows you to scientifically validate your chosen configuration.
█ A GUIDED TOUR OF THE ALGORITHMS: 23 PATHS TO AN EDGE
b]These 23 algorithms are not simple settings; they are distinct mathematical philosophies for how a Parabolic SAR should adapt to the market. They are grouped into three primary categories: those that adapt the Acceleration Factor (AF) , those that enhance the Extreme Point (EP) detection, and those that redefine the Flip Logic .
CATEGORY A: ACCELERATION FACTOR (AF) ADAPTATION
These algorithms dynamically change the "gas pedal" of the PSAR.
1. Volatility-Scaled AF
Core Concept: Treats volatility as market friction. The PSAR should be more forgiving in high-volatility environments.
How It Works: It calculates a Volatility Ratio by comparing the short-term ATR to the long-term ATR. If current volatility is high (ratio > 1), it reduces the AF Step. If volatility is low (ratio < 1), it increases the AF Step to trail tighter.
Ideal Use Case: The best all-rounder. Excellent for any market, especially those with clear shifts between high and low volatility regimes (like indices and crypto).
2. Efficiency Ratio (ER) AF
Core Concept: The PSAR should accelerate aggressively in clean, efficient trends and slow down dramatically in choppy, inefficient markets.
How It Works: It uses Kaufman's Efficiency Ratio (ER), which measures the net directional movement versus the total price movement. A high ER (near 1.0) signifies a pure trend, triggering a high AF multiplier. A low ER (near 0.0) signifies chop, triggering a low AF multiplier.
Ideal Use Case: Markets that alternate between strong trends and sideways chop. It is exceptionally good at surviving ranging periods.
3. Shannon Entropy AF
Core Concept: Uses Information Theory to measure market disorder. The PSAR should be conservative in chaos and aggressive in order.
How It Works: It calculates the Shannon Entropy of recent price changes. High entropy means the market is unpredictable ("chaotic"), causing the AF to slow down. Low entropy means the market is organized and trending, causing the AF to speed up.
Ideal Use Case: Advanced traders looking for a mathematically pure way to distinguish between a tradable trend and random noise.
4. Fractal Dimension (FD) AF
Core Concept: Measures the "jaggedness" or complexity of the price path. A smooth path is a trend; a jagged, space-filling path is chop.
How It Works: It calculates the Fractal Dimension of the price series. An FD near 1.0 is a smooth line (high AF). An FD near 1.5 is a random walk (low AF).
Ideal Use Case: Visually identifying the moment a smooth trend begins to break down into chaotic, unpredictable movement.
5. ADX-Gated AF
Core Concept: Uses the classic ADX indicator to confirm the presence of a trend before allowing the PSAR to accelerate.
How It Works: If the ADX value is above a "Strong" threshold (e.g., 25), the AF accelerates normally. If the ADX is below a "Weak" threshold (e.g., 15), the AF is "frozen" and will not increase, preventing the SAR from tightening up in a non-trending market.
Ideal Use Case: For classic trend-following purists who trust the ADX as their primary regime filter.
6. Kalman AF Estimator
Core Concept: A sophisticated signal processing algorithm that predicts the "true" optimal AF by filtering out price "noise."
How It Works: It treats the PSAR's AF as a state to be estimated. It makes a prediction, then corrects it based on how far the actual price deviates. It's like a GPS constantly refining its position. The "Process Noise" input controls how fast it thinks the AF can change, while "Measurement Noise" controls how much it trusts the price data.
Ideal Use Case: Smooth, high-inertia markets like commodities or major forex pairs. It creates an incredibly smooth and responsive AF.
7. Volume-Momentum AF
Core Concept: A trend's acceleration is only valid if confirmed by both volume and price momentum.
How It Works: The AF will only increase if a new Extreme Point is made on above-average volume AND the Rate of Change (ROC) of the price is aligned with the trend's direction.
Ideal Use Case: Any market with reliable volume data (stocks, futures, crypto). It's excellent for filtering out low-conviction moves.
8. Garman-Klass (GK) AF
Core Concept: Uses a more advanced, statistically efficient measure of volatility (Garman-Klass, which uses OHLC data) to adapt the AF.
How It Works: It modulates the AF based on whether the current GK volatility is higher or lower than its historical average. Unlike the standard Volatility-Scaled algo, it tends to slow down more in high volatility and speed up less in low volatility, making it more conservative.
Ideal Use Case: Traders who want a volatility-adaptive model that is more focused on risk reduction during volatile periods.
9. RSI-Modulated AF
Core Concept: The RSI can identify points of potential trend exhaustion or strong momentum.
How It Works: If a trend is bullish but the RSI enters the "Overbought" zone, the AF slows down, anticipating a pullback. Conversely, if the RSI is in the strong momentum mid-range (40-60), the AF is boosted to trail more aggressively.
Ideal Use Case: Mean-reversion traders or those who want to automatically loosen their trail stop near potential exhaustion points.
10. Bollinger Squeeze AF
Core Concept: A Bollinger Band Squeeze signals a period of volatility compression, often preceding an explosive breakout.
How It Works: When the algorithm detects that the Bollinger Band Width is in a "Squeeze" (below a certain historical percentile), it boosts the AF in anticipation of a fast move, allowing the PSAR to catch the breakout quickly.
Ideal Use Case: Breakout traders. This algorithm primes the PSAR to be maximally responsive right at the moment a breakout is most likely.
11. Keltner Adaptive AF
Core Concept: Keltner Channels provide a robust measure of a trend's "normal" volatility channel.
How It Works: When price is trading strongly outside the Keltner Channel, it's considered a powerful trend, and the AF is boosted. When price falls back inside the channel, it's considered a consolidation or pullback, and the AF is slowed down.
Ideal Use Case: Trend followers who use channel breakouts as their primary confirmation.
12. Choppiness-Gated AF
Core Concept: Uses the Choppiness Index to quantify whether the market is trending or consolidating.
How It Works: If the Choppiness Index is below the "Trend" threshold (e.g., 38.2), the AF is boosted. If it's above the "Range" threshold (e.g., 61.8), the AF is significantly reduced.
Ideal Use Case: A more responsive alternative to the ADX-Gated algorithm for distinguishing between trending and ranging markets.
13. VIDYA-Style AF
Core Concept: Uses a Chande Momentum Oscillator (CMO) to create a variable-speed acceleration factor.
How It Works: The absolute value of the CMO is used to create a dynamic smoothing constant. Strong momentum (high absolute CMO) results in a faster, more responsive AF. Weak momentum results in a slower, smoother AF.
Ideal Use Case: Momentum traders who want their trailing stop's speed directly tied to the momentum of the price itself.
14. Hilbert Cycle AF
Core Concept: Uses Ehlers' Hilbert Transform to extract the dominant cycle period of the market and synchronizes the PSAR with it.
How It Works: It dynamically adjusts the AF based on the detected cycle period (shorter cycles = faster AF) and can also modulate it based on the current phase within that cycle (e.g., accelerate faster near cycle tops/bottoms).
Ideal Use Case: Markets with clear cyclical behavior, like commodities and some forex pairs.
CATEGORY B: EXTREME POINT (EP) ENHANCEMENT
These algorithms make the detection of new highs/lows more intelligent.
15. Volume-Weighted EP
Core Concept: A new high or low is more significant if it occurs on high volume.
How It Works: It can be configured to only accept a new EP if the volume on that bar is above average. It can also "weight" the EP by volume, pushing it further out on high-volume bars.
Ideal Use Case: Filtering out weak, low-conviction price probes in markets with reliable volume.
16. Wavelet Filtered EP
Core Concept: Uses wavelet decomposition (a signal processing technique) to separate the underlying trend from high-frequency noise.
How It Works: It calculates a smoothed, wavelet-filtered version of the price. A new EP is only registered if the actual high/low significantly exceeds this smoothed baseline, effectively ignoring minor noise spikes.
Ideal Use Case: Noisy markets where small, insignificant wicks can cause the AF to accelerate prematurely.
17. ATR-Validated EP
Core Concept: A new EP should represent a meaningful move, not just a one-tick poke.
How It Works: It requires a new high/low to exceed the previous EP by a minimum amount, defined as a multiple of the current ATR. This ensures only volatility-significant advances are counted.
Ideal Use Case: A simple, robust way to filter out "noise" EPs and slow down the AF's acceleration in choppy conditions.
18. Statistical EP Filter
Core Concept: A new EP is only valid if the price change that created it is statistically significant.
How It Works: It calculates the Z-Score of the bar's price change relative to recent history. A new EP is only accepted if its Z-Score exceeds a certain threshold (e.g., 1.5 sigma), meaning it was an unusually strong move.
Ideal Use Case: For quantitative traders who want to ensure their trailing stop only tightens in response to statistically meaningful price action.
CATEGORY C: FLIP LOGIC & CONFIRMATION
These algorithms change the very rules of when and why the PSAR reverses.
19. Dual-PSAR Gate
Core Concept: Uses two PSARs—one fast and one slow—to confirm a reversal.
How It Works: A flip signal for the main PSAR is only considered valid if both the fast (sensitive) PSAR and the slow (structural) PSAR have flipped. This acts as a powerful trend filter.
Ideal Use Case: An excellent method for reducing whipsaws. It forces the PSAR to wait for both short-term and longer-term momentum to align before signaling a reversal.
20. MTF Coherence PSAR
Core Concept: Do not flip against the higher timeframe macro trend.
How It Works: It pulls PSAR data from two higher timeframes. A flip is only allowed if the new direction does not contradict the trend on at least one (or both) of those higher timeframes. It also boosts the AF when all timeframes are aligned.
Ideal Use Case: The ultimate tool for multi-timeframe traders who want to ensure their entries and exits are in sync with the bigger picture.
21. Momentum-Gated Flip
Core Concept: A reversal is only valid if it is supported by a significant surge of momentum.
How It Works: A price cross of the SAR is not enough. The script also requires the Rate of Change (ROC) to exceed a certain threshold for a set number of bars, confirming that there is real force behind the reversal.
Ideal Use Case: Filtering out weak, drifting reversals and only taking signals that are initiated with explosive power.
22. Close-Only PSAR
Core Concept: Wicks are noise; the bar's close is the final decision.
How It Works: This algorithm modifies the flip logic to ignore wicks. A flip only occurs if one or more bars close beyond the SAR line.
Ideal Use Case: One of the most effective and simple ways to reduce false signals from volatile wicks. A fantastic default choice for any trader.
23. Ultimate PSAR Consensus
Core Concept: The highest conviction signal comes from the agreement of multiple, diverse mathematical models.
How It Works: This is the capstone algorithm. It runs a "vote" between a selection of the top-performing algorithms (e.g., Volatility-Scaled, Efficiency Ratio, Dual-PSAR). A flip is only signaled if a majority consensus is reached. It can even weight the votes based on each algorithm's recent performance.
Ideal Use Case: For traders who want the absolute highest level of confirmation and are willing to accept fewer, but more robust, signals.
█ PART II: THE NOISE FILTERING CORE - The Shield
This is a revolutionary feature that allows you to apply a second layer of signal processing directly to the SAR line itself, surgically removing noise before the flip logic is even considered.
FILTER CATEGORIES
Basic Filters (SMA, EMA, WMA, RMA): The classic moving averages. They provide basic smoothing but introduce significant lag. Best used for educational purposes.
Low-Lag Filters (DEMA, TEMA, Hull MA, ZLEMA): A family of filters designed to reduce the lag inherent in basic moving averages. The Hull MA is a standout, offering a superb balance of smoothness and responsiveness.
Adaptive Filters (KAMA, VIDYA, FRAMA): These are "smart" filters. They automatically adjust their smoothing level based on market conditions. They will be very smooth in choppy markets and become highly responsive in trending markets.
Advanced DSP & DAFE Filters: This is the pinnacle of signal processing.
Ehlers Filters (SuperSmoother, 2-Pole, 3-Pole): Based on the work of John Ehlers, these use digital signal processing techniques to remove high-frequency noise with minimal lag.
Gaussian & ALMA: These use a bell-curve weighting, giving the most importance to recent data in a smooth, non-linear fashion.
DAFE Spectral Laguerre: A proprietary, non-linear filter that uses a feedback loop and adapts its "gamma" based on volatility, providing exceptional tracking in all market conditions.
How to Choose a Filter
Start with "None": First, find an algorithm you like with no filtering to understand its raw behavior.
Introduce Low Lag: If you are getting too many whipsaws from noise, apply a short-length Hull MA (e.g., 5-8). This is often the best solution.
Go Adaptive: If your market has very distinct trend/chop regimes, try an Adaptive KAMA .
Maximum Purity: For the smoothest possible output with excellent responsiveness, use the DAFE Spectral Laguerre or Ehlers SuperSmoother .
█ THE VISUAL EXPERIENCE: DATA AS ART
The PSAR Laboratory is not just functional; it is beautiful. The visualization engine is designed to provide you with an intuitive, at-a-glance understanding of the market's state.
Algorithm-Specific Theming: Each of the 23 algorithms comes with its own unique, professionally designed color palette. This not only provides visual variety but allows you to instantly recognize which engine is active.
Dynamic Glow Effects: For many algorithms, the PSAR dots will emit a soft "glow." The brightness and color of this glow are not random; they are tied to a key metric of the active algorithm (e.g., trend strength, volatility, consensus), providing a subtle, visual cue about the health of the trend.
Adaptive Volatility Bands: Certain algorithms will display dynamic bands around the PSAR. These are not standard deviation bands; their width is controlled by the specific logic of the active algorithm, showing you a visual representation of the market's expected range or energy level.
Secondary Reference Lines: For algorithms like the Dual-PSAR or MTF Coherence, a secondary line will be plotted on the chart, giving you a clear visual of the underlying data (e.g., the slow PSAR, the HTF trend) that is driving the decision-making process.
█ THE MASTER DASHBOARD: YOUR MISSION CONTROL
The comprehensive dashboard is your unified command center for analysis and performance tracking.
Engine Status: See the currently selected Algorithm, the active Noise Filter, the Trend direction, and a real-time progress bar of the current Acceleration Factor (AF).
Algorithm-Specific Metrics: This is the most powerful section. It displays the key real-time data from the currently active algorithm. If you're using "Shannon Entropy," you'll see the Entropy score. If you're using "ADX-Gated," you'll see the ADX value. This gives you a direct, quantitative look under the hood.
Performance Readout: When enabled, this section provides a full breakdown of your backtesting results, including Win Rate, Profit Factor, Net P&L, Max Drawdown, and your current trade status.
█ DEVELOPMENT PHILOSOPHY
The PSAR Laboratory was born from a deep respect for Wilder's original work and a relentless desire to push it into the 21st century. We believe that in modern markets, static tools are obsolete. The future of trading lies in adaptation. This indicator is for the serious trader, the tinkerer, the scientist—the individual who is not content with a black box, but who seeks to understand, test, and refine their edge with surgical precision. It is a tool for forging, not just following.
The PSAR Laboratory is designed to be the ultimate tool for that evolution, allowing you to discover and codify the rules that truly fit you.
█ DISCLAIMER AND BEST PRACTICES
THIS IS A TOOL, NOT A STRATEGY: This indicator provides a sophisticated trailing stop and reversal signal. It must be integrated into a complete trading plan that includes risk management, position sizing, and your own contextual analysis.
TEST, DON'T GUESS: The power of this tool is its adaptability. Use the Performance Dashboard to rigorously test different algorithms and settings on your chosen asset and timeframe. Find what works, and build your strategy around that data.
START SIMPLE: Begin with the "Volatility-Scaled AF" algorithm, as it is a powerful and intuitive all-rounder. Once you are comfortable, begin experimenting with other engines.
RISK MANAGEMENT IS PARAMOUNT: All trading involves substantial risk. The backtesting results are hypothetical and do not account for slippage or psychological factors. Never risk more capital than you are prepared to lose.
"I don't think traders can follow rules for very long unless they reflect their own trading style. Eventually, a breaking point is reached and the trader has to quit or change, or find a new set of rules he can follow. This seems to be part of the process of evolution and growth of a trader."
— Ed Seykota, Market Wizard
Taking you to school. - Dskyz, Trade with Volume. Trade with Density. Trade with DAFE
Multi Cycles Predictive System ML - GBM IntegratedMulti-Cycle Predictive System: The Gradient Boosting Machine (GBM) Revolution
Introduction: The Death of Static Analysis
The financial markets are not static; they are a living, breathing, and chaotic system. Yet, for decades, traders have relied on static indicators—using the same RSI settings, the same MACD parameters, and the same Moving Averages regardless of whether the market is trending, chopping, or crashing.
The Multi-Cycle Predictive System (MCPS) represents a paradigm shift. It is not just an indicator; it is an Adaptive Machine Learning Engine running directly on your chart.
By integrating a fully functional Gradient Boosting Machine (GBM), this script does not guess—it learns. It monitors 13 distinct algorithmic models, calculates their real-time accuracy against future price action, and dynamically reallocates influence to the "winning" models using gradient descent.
This is Survival of the Fittest applied to technical analysis.
1. The Core Engine: Gradient Boosting & Adaptive Learning
At the heart of the MCPS is a custom-coded Gradient Boosting Machine. While most "ML" scripts on TradingView simply average a few indicators, this system replicates the architecture of advanced data science models.
How the GBM Works:
Ensemble Prediction: The system aggregates signals from 13 different mathematical models.
Residual Calculation: It compares the ensemble's previous predictions against the actual price movement (Price Return) to calculate the error (Residual).
Gradient Descent: It calculates the gradient of the loss function. We utilize a Huber Loss Gradient, which is robust against outliers (market spikes), ensuring the model doesn't overreact to volatility.
Weight Optimization: Using a configurable learning rate, the system updates the weights of each sub-algorithm. Models that predicted correctly gain weight; models that failed lose influence.
Softmax Normalization: Finally, weights are passed through a Softmax function (with Temperature control) to convert them into probabilities that sum to 1.0.
The "Winner-Takes-All" Philosophy
A common failure in ensemble systems is "Signal Dilution"—where good signals are drowned out by bad ones.
The MCPS solves this with Aggressive Weight Concentration:
Top 3 Logic: The script identifies the top 3 performing algorithms based on historical accuracy.
The 90% Rule: It forces the system to allocate up to 90% of the total decision weight to these top 3 performers.
Result: If Ehlers and Schaff are reading the market correctly, but MACD is failing, MACD is effectively silenced. The system listens only to the winners.
2. The 13 Algorithmic Pillars
The MCPS draws from a diverse library of Digital Signal Processing (DSP), Statistical, and Momentum algorithms. It does not rely on simple moving averages.
Ehlers Bandpass Filter: Isolates the dominant cycle in price data, removing trend and noise.
Zero-Lag EMA (ZLEMA): Reduces lag to near-zero to track momentum shifts instantly.
Coppock Curve: A classic long-term momentum indicator, modified here for adaptive responsiveness.
Detrended Price Oscillator (DPO): Eliminates the trend to identify short-term cycles.
Schaff Trend Cycle (STC): A double-smoothed stochastic of the MACD, excellent for identifying cycle turns.
Fisher Transform: Converts price into a Gaussian normal distribution to pinpoint turning points.
MESA Adaptive: Uses Maximum Entropy Spectral Analysis to detect the current dominant cycle period.
Goertzel Algorithm: A DSP technique used to identify the magnitude of specific frequency components in the price wave.
Hilbert Transform: Extracts the instantaneous amplitude and phase of the price action.
Autocorrelation: Measures the similarity between the price series and a lagged version of itself to detect periodicity.
Singular Spectrum Analysis (SSA): Decomposes the time series into trend, seasonal, and noise components (Simplified).
Wavelet Transform: Analyzes data at different scales (frequencies) simultaneously.
Empirical Mode Decomposition (EMD): Splits data into Intrinsic Mode Functions (IMFs) to isolate pure cycles.
3. The Dashboard: Total Transparency
Black-box algorithms are dangerous. You need to know why a signal is being generated. The MCPS features two detailed dashboards (tables) located at the bottom of your screen.
The Weight & Accuracy Table (Bottom Right)
This is your "Under the Hood" view. It displays:
Algorithm: The name of the model.
Accuracy: The rolling historical accuracy of that specific model over the lookback period (e.g., 58.2%).
Weight: The current influence that model has on the final signal. Watch this change in real-time. You will see the system "giving up" on bad models and "betting heavy" on good ones.
Prob/Sig: The raw probability and directional signal (Up/Down).
The GBM Stats Table (Bottom Left)
Tracks the health of the Machine Learning engine:
Iterations: How many learning cycles have occurred.
Entropy: A measure of market confusion. High entropy means weights are spread out (models disagree). Low entropy means the models are aligned.
Top 3 Weight: Shows how concentrated the decision power is. If this is >80%, the system is highly confident in specific models.
Confidence & Agreement: Statistical measures of the signal strength.
4. How to Trade with MCPS
This system outputs a single, composite Cycle Line (oscillating between -1 and 1) and a background Regime Color.
Strategy A: The Zero-Cross (Trend Reversal)
Bullish: When the Cycle Line crosses above 0. This indicates that the weighted average of the top-performing algorithms has shifted to a net-positive expectation.
Bearish: When the Cycle Line crosses below 0.
Strategy B: Probability Extremes (Mean Reversion)
Strong Buy: When the Cycle Line drops below -0.5 (Oversold) and turns up. This indicates a high-probability cycle bottom.
Strong Sell: When the Cycle Line rises above +0.5 (Overbought) and turns down.
Strategy C: Regime Filtering
The background color changes based on the aggregate consensus:
Green/Lime: Bullish Regime. Look primarily for Long entries. Ignore weak sell signals.
Red/Orange: Bearish Regime. Look primarily for Short entries.
Gray: Neutral/Choppy. Reduce position size or wait.
5. Configuration & GBM Settings
The script is highly customizable for advanced users who want to tune the Machine Learning hyperparameters.
Prediction Horizon: How many days into the future are we trying to predict? (Default: 3).
Accuracy Lookback: How far back does the model check to calculate "Accuracy"?
GBM Learning Rate: Controls how fast the model adapts.
High (0.2+): Adapts instantly to new market conditions but may be "jumpy."
Low (0.05): Very stable, long-term adaptation.
Temperature: Controls the "Softmax" function. Higher temperatures allow for softer, more distributed weights. Lower temperatures force a "Winner Takes All" outcome.
Max Top 3 Weight: The cap on how much power the top 3 models can hold (Default: 90%).
6. Technical Nuances (For the Geeks)
Huber Gradient: We use Huber loss rather than MSE (Mean Squared Error) for the gradient descent. This is crucial for financial time series because price spikes (outliers) can destroy the learning process of standard ML models. Huber loss transitions from quadratic to linear error, making the model robust.
Regularization: L2 Regularization is applied to prevent overfitting, ensuring the model doesn't just memorize past noise.
Memory Decay: The model has a "fading memory." Recent accuracy is weighted more heavily than accuracy from 200 bars ago, allowing the system to detect Regime Shifts (e.g., transitioning from a trending market to a ranging market).
Disclaimer:
This tool is a sophisticated analytical instrument, not a crystal ball. Machine Learning attempts to optimize probabilities based on historical patterns, but no algorithm can predict black swan events or fundamental news shocks. Always use proper risk management.
The "Warmup Period" is required. The script needs to process 50 bars of history before the GBM engine initializes and produces signals.
Author's Note:
I built the MCPS because I was tired of indicators that stopped working when the market "personality" changed. By integrating GBM, this script adapts to the market's personality in real-time. If the market is cycling, Ehlers and Goertzel take over. If the market is trending, Coppock and ZLEMA take the lead. You don't have to choose—the math chooses for you.
Please leave a boost and a comment if you find this helpful!
Multi Cycles Slope-Fit System MLMulti Cycles Predictive System : A Slope-Adaptive Ensemble
Executive Summary:
The MCPS-Slope (Multi Cycles Slope-Fit System) represents a paradigm shift from static technical analysis to adaptive, probabilistic market modeling. Unlike traditional indicators that rely on a single algorithm with fixed settings, this system deploys a "Mixture of Experts" (MoE) ensemble comprising 13 distinct cycle and trend algorithms.
Using a Gradient-Based Memory (GBM) learning engine, the system dynamically solves the "Cycle Mode" problem by real-time weighting. It aggressively curve-fits the Slope of component cycles to the Slope of the price action, rewarding algorithms that successfully predict direction while suppressing those that fail.
This is a non-repainting, adaptive oscillator designed to identify market regimes, pinpoint high-probability reversals via OB/OS logic, and visualize the aggregate consensus of advanced signal processing mathematics.
1. The Core Philosophy: Why "Slope" Matters:
In technical analysis, most traders focus on Levels (Price is above X) or Values (RSI is at 70). However, the primary driver of price action is Momentum, which is mathematically defined as the Rate of Change, or the Slope.
This script introduces a novel approach: Slope Fitting.
Instead of asking "Is the cycle high or low?", this system asks: "Is the trajectory (Slope) of this cycle matching the trajectory of the price?"
The Dual-Functionality of the Normalized Oscillator
The final output is a normalized oscillator bounded between -1.0 and +1.0. This structure serves two critical functions simultaneously:
Directional Bias (The Slope):
When the Combined Cycle line is rising (Positive Slope), the aggregate consensus of the 13 algorithms suggests bullish momentum. When falling (Negative Slope), it suggests bearish momentum. The script measures how well these slopes correlate with price action over a rolling lookback window to assign confidence weights.
Overbought / Oversold (OB/OS) Identification:
Because the output is mathematically clipped and normalized:
Approaching +1.0 (Overbought): Indicates that the top-weighted algorithms have reached their theoretical maximum amplitude. This is a statistical extreme, often preceding a mean reversion or trend exhaustion.
Approaching -1.0 (Oversold): Indicates the aggregate cycle has reached maximum bearish extension, signaling a potential accumulation zone.
Zero Line (0.0): The equilibrium point. A cross of the Zero Line is the most traditional signal of a trend shift.
2. The "Mixture of Experts" (MoE) Architecture:
Markets are dynamic. Sometimes they trend (Trend Following works), sometimes they chop (Mean Reversion works), and sometimes they cycle cleanly (Signal Processing works). No single indicator works in all regimes.
This system solves that problem by running 13 Algorithms simultaneously and voting on the outcome.
The 13 "Experts" Inside the Code:
All algorithms have been engineered to be Non-Repainting.
Ehlers Bandpass Filter: Extracts cycle components within a specific frequency bandwidth.
Schaff Trend Cycle: A double-smoothed stochastic of the MACD, excellent for cycle turning points.
Fisher Transform: Normalizes prices into a Gaussian distribution to pinpoint turning points.
Zero-Lag EMA (ZLEMA): Reduces lag to track price changes faster than standard MAs.
Coppock Curve: A momentum indicator originally designed for long-term market bottoms.
Detrended Price Oscillator (DPO): Removes trend to isolate short-term cycles.
MESA Adaptive (Sine Wave): Uses Phase accumulation to detect cycle turns.
Goertzel Algorithm: Uses Digital Signal Processing (DSP) to detect the magnitude of specific frequencies.
Hilbert Transform: Measures the instantaneous position of the cycle.
Autocorrelation: measures the correlation of the current price series with a lagged version of itself.
SSA (Simplified): Singular Spectrum Analysis approximation (Lag-compensated, non-repainting).
Wavelet (Simplified): Decomposes price into approximation and detail coefficients.
EMD (Simplified): Empirical Mode Decomposition approximation using envelope theory.
3. The Adaptive "GBM" Learning Engine
This is the "Machine Learning" component of the script. It does not use pre-trained weights; it learns live on your chart.
How it works:
Fitting Window: On every bar, the system looks back 20 days (configurable).
Slope Correlation: It calculates the correlation between the Slope of each of the 13 algorithms and the Slope of the Price.
Directional Bonus: It checks if the algorithm is pointing in the same direction as the price.
Weight Optimization:
Algorithms that match the price direction and correlation receive a higher "Fit Score."
Algorithms that diverge from price action are penalized.
A "Softmax" style temperature function and memory decay allow the weights to shift smoothly but aggressively.
The Result: If the market enters a clean sine-wave cycle, the Ehlers and Goertzel weights will spike. If the market explodes into a linear trend, ZLEMA and Schaff will take over, suppressing the cycle indicators that would otherwise call for a premature top.
4. How to Read the Interface:
The visual interface is designed for maximum information density without clutter.
The Dashboard (Bottom Left - GBM Stats)
Combined Fit: A percentage score (0-100%). High values (>70%) mean the system is "Locked In" and tracking price accurately. Low values suggest market chaos/noise.
Entropy: A measure of disorder. High entropy means the algorithms disagree (Neutral/Chop). Low entropy means the algorithms are unanimous (Strong Trend).
Top 1 / Top 3 Weight: Shows how concentrated the decision is. If Top 1 Weight is 50%, one algorithm is dominating the decision.
The Matrix (Bottom Right - Weight Table)
This table lifts the hood on the engine.
Fit Score: How well this specific algo is performing right now.
Corr/Dir: Raw correlation and Direction Match stats.
Weight: The actual percentage influence this algorithm has on the final line.
Cycle: The current value of that specific algorithm.
Regime: Identifies if the consensus is Bullish, Bearish, or Neutral.
The Chart Overlay
The Line: The Gradient-Colored line is the Weighted Ensemble Prediction.
Green: Bullish Slope.
Red: Bearish Slope.
Triangles: Zero-Cross signals (Bullish/Bearish).
"STRONG" Labels: Appears when the cycle sustains a value above +0.5 or below -0.5, indicating strong momentum.
Background Color: Changes subtly to reflect the aggregate Regime (Strong Up, Bullish, Neutral, Bearish, Strong Down).
5. Trading Strategies:
A. The Slope Reversal (OB/OS Fade)
Concept: Catching tops and bottoms using the -1/+1 normalization.
Signal: Wait for the Combined Cycle to reach extreme values (>0.8 or <-0.8).
Trigger: The entry is taken not when it hits the level, but when the Slope flips.
Short: Cycle hits +0.9, color turns from Green to Red (Slope becomes negative).
Long: Cycle hits -0.9, color turns from Red to Green (Slope becomes positive).
B. The Zero-Line Trend Join
Concept: Joining an established trend after a correction.
Signal: Price is trending, but the Cycle pulls back to the Zero line.
Trigger: A "Triangle" signal appears as the cycle crosses Zero in the direction of the higher timeframe trend.
C. Divergence Analysis
Concept: Using the "Fit Score" to identify weak moves.
Signal: Price makes a Higher High, but the Combined Cycle makes a Lower High.
Confirmation: Check the GBM Stats table. If "Combined Fit" is dropping while price is rising, the trend is decoupling from the cycle logic. This is a high-probability reversal warning.
6. Technical Configuration:
Fitting Window (Default: 20): The number of bars the ML engine looks back to judge algorithm performance. Lower (10-15) for scalping/quick adaptation. Higher (30-50) for swing trading and stability.
GBM Learning Rate (Default: 0.25): Controls how fast weights change.
High (>0.3): The system reacts instantly to new behaviors but may be "jumpy."
Low (<0.15): The system is very smooth but may lag in regime changes.
Max Single Weight (Default: 0.55): Prevents one single algorithm from completely hijacking the system, ensuring an ensemble effect remains.
Slope Lookback: The period over which the slope (velocity) is calculated.
7. Disclaimer & Notes:
Repainting: This indicator utilizes closed bar data for calculations and employs non-repainting approximations of SSA, EMD, and Wavelets. It does not repaint historical signals.
Calculations: The "ML" label refers to the adaptive weighting algorithm (Gradient-based optimization), not a neural network black box.
Risk: No indicator guarantees future performance. The "Fit Score" is a backward-looking metric of recent performance; market regimes can shift instantly. Always use proper risk management.
Author's Note
The MCPS-Slope was built to solve the frustration of "indicator shopping." Instead of switching between an RSI, a MACD, and a Stochastic depending on the day, this system mathematically determines which one is working best right now and presents you with a single, synthesized data stream.
If you find this tool useful, please leave a Boost and a Comment below!
ORB Fusion🎯 CORE INNOVATION: INSTITUTIONAL ORB FRAMEWORK WITH FAILED BREAKOUT INTELLIGENCE
ORB Fusion represents a complete institutional-grade Opening Range Breakout system combining classic Market Profile concepts (Initial Balance, day type classification) with modern algorithmic breakout detection, failed breakout reversal logic, and comprehensive statistical tracking. Rather than simply drawing lines at opening range extremes, this system implements the full trading methodology used by professional floor traders and market makers—including the critical concept that failed breakouts are often higher-probability setups than successful breakouts .
The Opening Range Hypothesis:
The first 30-60 minutes of trading establishes the day's value area —the price range where the majority of participants agree on fair value. This range is formed during peak information flow (overnight news digestion, gap reactions, early institutional positioning). Breakouts from this range signal directional conviction; failures to hold breakouts signal trapped participants and create exploitable reversals.
Why Opening Range Matters:
1. Information Aggregation : Opening range reflects overnight news, pre-market sentiment, and early institutional orders. It's the market's initial "consensus" on value.
2. Liquidity Concentration : Stop losses cluster just outside opening range. Breakouts trigger these stops, creating momentum. Failed breakouts trap traders, forcing reversals.
3. Statistical Persistence : Markets exhibit range expansion tendency —when price accepts above/below opening range with volume, it often extends 1.0-2.0x the opening range size before mean reversion.
4. Institutional Behavior : Large players (market makers, institutions) use opening range as reference for the day's trading plan. They fade extremes in rotation days and follow breakouts in trend days.
Historical Context:
Opening Range Breakout methodology originated in commodity futures pits (1970s-80s) where floor traders noticed consistent patterns: the first 30-60 minutes established a "fair value zone," and directional moves occurred when this zone was violated with conviction. J. Peter Steidlmayer formalized this observation in Market Profile theory, introducing the "Initial Balance" concept—the first hour (two 30-minute periods) defining market structure.
📊 OPENING RANGE CONSTRUCTION
Four ORB Timeframe Options:
1. 5-Minute ORB (0930-0935 ET):
Captures immediate market direction during "opening drive"—the explosive first few minutes when overnight orders hit the tape.
Use Case:
• Scalping strategies
• High-frequency breakout trading
• Extremely liquid instruments (ES, NQ, SPY)
Characteristics:
• Very tight range (often 0.2-0.5% of price)
• Early breakouts common (7 of 10 days break within first hour)
• Higher false breakout rate (50-60%)
• Requires sub-minute chart monitoring
Psychology: Captures panic buyers/sellers reacting to overnight news. Range is small because sample size is minimal—only 5 minutes of price discovery. Early breakouts often fail because they're driven by retail FOMO rather than institutional conviction.
2. 15-Minute ORB (0930-0945 ET):
Balances responsiveness with statistical validity. Captures opening drive plus initial reaction to that drive.
Use Case:
• Day trading strategies
• Balanced scalping/swing hybrid
• Most liquid instruments
Characteristics:
• Moderate range (0.4-0.8% of price typically)
• Breakout rate ~60% of days
• False breakout rate ~40-45%
• Good balance of opportunity and reliability
Psychology: Includes opening panic AND the first retest/consolidation. Sophisticated traders (institutions, algos) start expressing directional bias. This is the "Goldilocks" timeframe—not too reactive, not too slow.
3. 30-Minute ORB (0930-1000 ET):
Classic ORB timeframe. Default for most professional implementations.
Use Case:
• Standard intraday trading
• Position sizing for full-day trades
• All liquid instruments (equities, indices, futures)
Characteristics:
• Substantial range (0.6-1.2% of price)
• Breakout rate ~55% of days
• False breakout rate ~35-40%
• Statistical sweet spot for extensions
Psychology: Full opening auction + first institutional repositioning complete. By 10:00 AM ET, headlines are digested, early stops are hit, and "real" directional players reveal themselves. This is when institutional programs typically finish their opening positioning.
Statistical Advantage: 30-minute ORB shows highest correlation with daily range. When price breaks and holds outside 30m ORB, probability of reaching 1.0x extension (doubling the opening range) exceeds 60% historically.
4. 60-Minute ORB (0930-1030 ET) - Initial Balance:
Steidlmayer's "Initial Balance"—the foundation of Market Profile theory.
Use Case:
• Swing trading entries
• Day type classification
• Low-frequency institutional setups
Characteristics:
• Wide range (0.8-1.5% of price)
• Breakout rate ~45% of days
• False breakout rate ~25-30% (lowest)
• Best for trend day identification
Psychology: Full first hour captures A-period (0930-1000) and B-period (1000-1030). By 10:30 AM ET, all early positioning is complete. Market has "voted" on value. Subsequent price action confirms (trend day) or rejects (rotation day) this value assessment.
Initial Balance Theory:
IB represents the market's accepted value area . When price extends significantly beyond IB (>1.5x IB range), it signals a Trend Day —strong directional conviction. When price remains within 1.0x IB, it signals a Rotation Day —mean reversion environment. This classification completely changes trading strategy.
🔬 LTF PRECISION TECHNOLOGY
The Chart Timeframe Problem:
Traditional ORB indicators calculate range using the chart's current timeframe. This creates critical inaccuracies:
Example:
• You're on a 5-minute chart
• ORB period is 30 minutes (0930-1000 ET)
• Indicator sees only 6 bars (30min ÷ 5min/bar = 6 bars)
• If any 5-minute bar has extreme wick, entire ORB is distorted
The Problem Amplifies:
• On 15-minute chart with 30-minute ORB: Only 2 bars sampled
• On 30-minute chart with 30-minute ORB: Only 1 bar sampled
• Opening spike or single large wick defines entire range (invalid)
Solution: Lower Timeframe (LTF) Precision:
ORB Fusion uses `request.security_lower_tf()` to sample 1-minute bars regardless of chart timeframe:
```
For 30-minute ORB on 15-minute chart:
- Traditional method: Uses 2 bars (15min × 2 = 30min)
- LTF Precision: Requests thirty 1-minute bars, calculates true high/low
```
Why This Matters:
Scenario: ES futures, 15-minute chart, 30-minute ORB
• Traditional ORB: High = 5850.00, Low = 5842.00 (range = 8 points)
• LTF Precision ORB: High = 5848.50, Low = 5843.25 (range = 5.25 points)
Difference: 2.75 points distortion from single 15-minute wick hitting 5850.00 at 9:31 AM then immediately reversing. LTF precision filters this out by seeing it was a fleeting wick, not a sustained high.
Impact on Extensions:
With inflated range (8 points vs 5.25 points):
• 1.5x extension projects +12 points instead of +7.875 points
• Difference: 4.125 points (nearly $200 per ES contract)
• Breakout signals trigger late; extension targets unreachable
Implementation:
```pinescript
getLtfHighLow() =>
float ha = request.security_lower_tf(syminfo.tickerid, "1", high)
float la = request.security_lower_tf(syminfo.tickerid, "1", low)
```
Function returns arrays of 1-minute high/low values, then finds true maximum and minimum across all samples.
When LTF Precision Activates:
Only when chart timeframe exceeds ORB session window:
• 5-minute chart + 30-minute ORB: LTF used (chart TF > session bars needed)
• 1-minute chart + 30-minute ORB: LTF not needed (direct sampling sufficient)
Recommendation: Always enable LTF Precision unless you're on 1-minute charts. The computational overhead is negligible, and accuracy improvement is substantial.
⚖️ INITIAL BALANCE (IB) FRAMEWORK
Steidlmayer's Market Profile Innovation:
J. Peter Steidlmayer developed Market Profile in the 1980s for the Chicago Board of Trade. His key insight: market structure is best understood through time-at-price (value area) rather than just price-over-time (traditional charts).
Initial Balance Definition:
IB is the price range established during the first hour of trading, subdivided into:
• A-Period : First 30 minutes (0930-1000 ET for US equities)
• B-Period : Second 30 minutes (1000-1030 ET)
A-Period vs B-Period Comparison:
The relationship between A and B periods forecasts the day:
B-Period Expansion (Bullish):
• B-period high > A-period high
• B-period low ≥ A-period low
• Interpretation: Buyers stepping in after opening assessed
• Implication: Bullish continuation likely
• Strategy: Buy pullbacks to A-period high (now support)
B-Period Expansion (Bearish):
• B-period low < A-period low
• B-period high ≤ A-period high
• Interpretation: Sellers stepping in after opening assessed
• Implication: Bearish continuation likely
• Strategy: Sell rallies to A-period low (now resistance)
B-Period Contraction:
• B-period stays within A-period range
• Interpretation: Market indecisive, digesting A-period information
• Implication: Rotation day likely, stay range-bound
• Strategy: Fade extremes, sell high/buy low within IB
IB Extensions:
Professional traders use IB as a ruler to project price targets:
Extension Levels:
• 0.5x IB : Initial probe outside value (minor target)
• 1.0x IB : Full extension (major target for normal days)
• 1.5x IB : Trend day threshold (classifies as trending)
• 2.0x IB : Strong trend day (rare, ~10-15% of days)
Calculation:
```
IB Range = IB High - IB Low
Bull Extension 1.0x = IB High + (IB Range × 1.0)
Bear Extension 1.0x = IB Low - (IB Range × 1.0)
```
Example:
ES futures:
• IB High: 5850.00
• IB Low: 5842.00
• IB Range: 8.00 points
Extensions:
• 1.0x Bull Target: 5850 + 8 = 5858.00
• 1.5x Bull Target: 5850 + 12 = 5862.00
• 2.0x Bull Target: 5850 + 16 = 5866.00
If price reaches 5862.00 (1.5x), day is classified as Trend Day —strategy shifts from mean reversion to trend following.
📈 DAY TYPE CLASSIFICATION SYSTEM
Four Day Types (Market Profile Framework):
1. TREND DAY:
Definition: Price extends ≥1.5x IB range in one direction and stays there.
Characteristics:
• Opens and never returns to IB
• Persistent directional movement
• Volume increases as day progresses (conviction building)
• News-driven or strong institutional flow
Frequency: ~20-25% of trading days
Trading Strategy:
• DO: Follow the trend, trail stops, let winners run
• DON'T: Fade extremes, take early profits
• Key: Add to position on pullbacks to previous extension level
• Risk: Getting chopped in false trend (see Failed Breakout section)
Example: FOMC decision, payroll report, earnings surprise—anything creating one-sided conviction.
2. NORMAL DAY:
Definition: Price extends 0.5-1.5x IB, tests both sides, returns to IB.
Characteristics:
• Two-sided trading
• Extensions occur but don't persist
• Volume balanced throughout day
• Most common day type
Frequency: ~45-50% of trading days
Trading Strategy:
• DO: Take profits at extension levels, expect reversals
• DON'T: Hold for massive moves
• Key: Treat each extension as a profit-taking opportunity
• Risk: Holding too long when momentum shifts
Example: Typical day with no major catalysts—market balancing supply and demand.
3. ROTATION DAY:
Definition: Price stays within IB all day, rotating between high and low.
Characteristics:
• Never accepts outside IB
• Multiple tests of IB high/low
• Decreasing volume (no conviction)
• Classic range-bound action
Frequency: ~25-30% of trading days
Trading Strategy:
• DO: Fade extremes (sell IB high, buy IB low)
• DON'T: Chase breakouts
• Key: Enter at extremes with tight stops just outside IB
• Risk: Breakout finally occurs after multiple failures
Example: [/b> Pre-holiday trading, summer doldrums, consolidation after big move.
4. DEVELOPING:
Definition: Day type not yet determined (early in session).
Usage: Classification before 12:00 PM ET when IB extension pattern unclear.
ORB Fusion's Classification Algorithm:
```pinescript
if close > ibHigh:
ibExtension = (close - ibHigh) / ibRange
direction = "BULLISH"
else if close < ibLow:
ibExtension = (ibLow - close) / ibRange
direction = "BEARISH"
if ibExtension >= 1.5:
dayType = "TREND DAY"
else if ibExtension >= 0.5:
dayType = "NORMAL DAY"
else if close within IB:
dayType = "ROTATION DAY"
```
Why Classification Matters:
Same setup (bullish ORB breakout) has opposite implications:
• Trend Day : Hold for 2.0x extension, trail stops aggressively
• Normal Day : Take profits at 1.0x extension, watch for reversal
• Rotation Day : Fade the breakout immediately (likely false)
Knowing day type prevents catastrophic errors like fading a trend day or holding through rotation.
🚀 BREAKOUT DETECTION & CONFIRMATION
Three Confirmation Methods:
1. Close Beyond Level (Recommended):
Logic: Candle must close above ORB high (bull) or below ORB low (bear).
Why:
• Filters out wicks (temporary liquidity grabs)
• Ensures sustained acceptance above/below range
• Reduces false breakout rate by ~20-30%
Example:
• ORB High: 5850.00
• Bar high touches 5850.50 (wick above)
• Bar closes at 5848.00 (inside range)
• Result: NO breakout signal
vs.
• Bar high touches 5850.50
• Bar closes at 5851.00 (outside range)
• Result: BREAKOUT signal confirmed
Trade-off: Slightly delayed entry (wait for close) but much higher reliability.
2. Wick Beyond Level:
Logic: [/b> Any touch of ORB high/low triggers breakout.
Why:
• Earliest possible entry
• Captures aggressive momentum moves
Risk:
• High false breakout rate (60-70%)
• Stop runs trigger signals
• Requires very tight stops (difficult to manage)
Use Case: Scalping with 1-2 point profit targets where any penetration = trade.
3. Body Beyond Level:
Logic: [/b> Candle body (close vs open) must be entirely outside range.
Why:
• Strictest confirmation
• Ensures directional conviction (not just momentum)
• Lowest false breakout rate
Example: Trade-off: [/b> Very conservative—misses some valid breakouts but rarely triggers on false ones.
Volume Confirmation Layer:
All confirmation methods can require volume validation:
Volume Multiplier Logic: Rationale: [/b> True breakouts are driven by institutional activity (large size). Volume spike confirms real conviction vs. stop-run manipulation.
Statistical Impact: [/b>
• Breakouts with volume confirmation: ~65% success rate
• Breakouts without volume: ~45% success rate
• Difference: 20 percentage points edge
Implementation Note: [/b>
Volume confirmation adds complexity—you'll miss breakouts that work but lack volume. However, when targeting 1.5x+ extensions (ambitious goals), volume confirmation becomes critical because those moves require sustained institutional participation.
Recommended Settings by Strategy: [/b>
Scalping (1-2 point targets): [/b>
• Method: Close
• Volume: OFF
• Rationale: Quick in/out doesn't need perfection
Intraday Swing (5-10 point targets): [/b>
• Method: Close
• Volume: ON (1.5x multiplier)
• Rationale: Balance reliability and opportunity
Position Trading (full-day holds): [/b>
• Method: Body
• Volume: ON (2.0x multiplier)
• Rationale: Must be certain—large stops require high win rate
🔥 FAILED BREAKOUT SYSTEM
The Core Insight: [/b>
Failed breakouts are often more profitable [/b> than successful breakouts because they create trapped traders with predictable behavior.
Failed Breakout Definition: [/b>
A breakout that:
1. Initially penetrates ORB level with confirmation
2. Attracts participants (volume spike, momentum)
3. Fails to extend (stalls or immediately reverses)
4. Returns inside ORB range within N bars
Psychology of Failure: [/b>
When breakout fails:
• Breakout buyers are trapped [/b>: Bought at ORB high, now underwater
• Early longs reduce: Take profit, fearful of reversal
• Shorts smell blood: See failed breakout as reversal signal
• Result: Cascade of selling as trapped bulls exit + new shorts enter
Mirror image for failed bearish breakouts (trapped shorts cover + new longs enter).
Failure Detection Parameters: [/b>
1. Failure Confirmation Bars (default: 3): [/b>
How many bars after breakout to confirm failure?
Logic: Settings: [/b>
• 2 bars: Aggressive failure detection (more signals, more false failures)
• 3 bars Balanced (default)
• 5-10 bars: Conservative (wait for clear reversal)
Why This Matters:
Too few bars: You call "failed breakout" when price is just consolidating before next leg.
Too many bars: You miss the reversal entry (price already back in range).
2. Failure Buffer (default: 0.1 ATR): [/b>
How far inside ORB must price return to confirm failure?
Formula: Why Buffer Matters: clear rejection [/b> (not just hovering at level).
Settings: [/b>
• 0.0 ATR: No buffer, immediate failure signal
• 0.1 ATR: Small buffer (default) - filters noise
• [b>0.2-0.3 ATR: Large buffer - only dramatic failures count
Example: Reversal Entry System: [/b>
When failure confirmed, system generates complete reversal trade:
For Failed Bull Breakout (Short Reversal): [/b>
Entry: [/b> Current close when failure confirmed
Stop Loss: [/b> Extreme high since breakout + 0.10 ATR padding
Target 1: [/b> ORB High - (ORB Range × 0.5)
Target 2: Target 3: [/b> ORB High - (ORB Range × 1.5)
Example:
• ORB High: 5850, ORB Low: 5842, Range: 8 points
• Breakout to 5853, fails, reverses to 5848 (entry)
• Stop: 5853 + 1 = 5854 (6 point risk)
• T1: 5850 - 4 = 5846 (-2 points, 1:3 R:R)
• T2: 5850 - 8 = 5842 (-6 points, 1:1 R:R)
• T3: 5850 - 12 = 5838 (-10 points, 1.67:1 R:R)
[b>Why These Targets? [/b>
• T1 (0.5x ORB below high): Trapped bulls start panic
• T2 (1.0x ORB = ORB Mid): Major retracement, momentum fully reversed
• T3 (1.5x ORB): Reversal extended, now targeting opposite side
Historical Performance: [/b>
Failed breakout reversals in ORB Fusion's tracking system show:
• Win Rate: 65-75% (significantly higher than initial breakouts)
• Average Winner: 1.2x ORB range
• Average Loser: 0.5x ORB range (protected by stop at extreme)
• Expectancy: Strongly positive even with <70% win rate
Why Failed Breakouts Outperform: [/b>
1. Information Advantage: You now know what price did (failed to extend). Initial breakout trades are speculative; reversal trades are reactive to confirmed failure.
2. Trapped Participant Pressure: Every trapped bull becomes a seller. This creates sustained pressure.
3. Stop Loss Clarity: Extreme high is obvious stop (just beyond recent high). Breakout trades have ambiguous stops (ORB mid? Recent low? Too wide or too tight).
4. Mean Reversion Edge: Failed breakouts return to value (ORB mid). Initial breakouts try to escape value (harder to sustain).
Critical Insight: [/b>
"The best trade is often the one that trapped everyone else."
Failed breakouts create asymmetric opportunity because you're trading against [/b> trapped participants rather than with [/b> them. When you see a failed breakout signal, you're seeing real-time evidence that the market rejected directional conviction—that's exploitable.
📐 FIBONACCI EXTENSION SYSTEM
Six Extension Levels: [/b>
Extensions project how far price will travel after ORB breakout. Based on Fibonacci ratios + empirical market behavior.
1. 1.272x (27.2% Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 0.272)
Psychology: [/b> Initial probe beyond ORB. Early momentum + trapped shorts (on bull side) covering.
Probability of Reach: [/b> ~75-80% after confirmed breakout
Trading: [/b>
• First resistance/support after breakout
• Partial profit target (take 30-50% off)
• Watch for rejection here (could signal failure in progress)
Why 1.272? [/b> Related to harmonic patterns (1.272 is √1.618). Empirically, markets often stall at 25-30% extension before deciding whether to continue or fail.
2. 1.5x (50% Extension):
Formula: [/b> ORB High/Low + (ORB Range × 0.5)
Psychology: [/b> Breakout gaining conviction. Requires sustained buying/selling (not just momentum spike).
Probability of Reach: [/b> ~60-65% after confirmed breakout
Trading: [/b>
• Major partial profit (take 50-70% off)
• Move stops to breakeven
• Trail remaining position
Why 1.5x? [/b> Classic halfway point to 2.0x. Markets often consolidate here before final push. If day type is "Normal," this is likely the high/low for the day.
3. 1.618x (Golden Ratio Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 0.618)
Psychology: [/b> Strong directional day. Institutional conviction + retail FOMO.
Probability of Reach: [/b> ~45-50% after confirmed breakout
Trading: [/b>
• Final partial profit (close 80-90%)
• Trail remainder with wide stop (allow breathing room)
Why 1.618? [/b> Fibonacci golden ratio. Appears consistently in market geometry. When price reaches 1.618x extension, move is "mature" and reversal risk increases.
4. 2.0x (100% Extension): [/b>
Formula: ORB High/Low + (ORB Range × 1.0)
Psychology: [/b> Trend day confirmed. Opening range completely duplicated.
Probability of Reach: [/b> ~30-35% after confirmed breakout
Trading: Why 2.0x? [/b> Psychological level—range doubled. Also corresponds to typical daily ATR in many instruments (opening range ~ 0.5 ATR, daily range ~ 1.0 ATR).
5. 2.618x (Super Extension):
Formula: [/b> ORB High/Low + (ORB Range × 1.618)
Psychology: [/b> Parabolic move. News-driven or squeeze.
Probability of Reach: [/b> ~10-15% after confirmed breakout
[b>Trading: Why 2.618? [/b> Fibonacci ratio (1.618²). Rare to reach—when it does, move is extreme. Often precedes multi-day consolidation or reversal.
6. 3.0x (Extreme Extension): [/b>
Formula: [/b> ORB High/Low + (ORB Range × 2.0)
Psychology: [/b> Market melt-up/crash. Only in extreme events.
[b>Probability of Reach: [/b> <5% after confirmed breakout
Trading: [/b>
• Close immediately if reached
• These are outlier events (black swans, flash crashes, squeeze-outs)
• Holding for more is greed—take windfall profit
Why 3.0x? [/b> Triple opening range. So rare it's statistical noise. When it happens, it's headline news.
Visual Example:
ES futures, ORB 5842-5850 (8 point range), Bullish breakout:
• ORB High : 5850.00 (entry zone)
• 1.272x : 5850 + 2.18 = 5852.18 (first resistance)
• 1.5x : 5850 + 4.00 = 5854.00 (major target)
• 1.618x : 5850 + 4.94 = 5854.94 (strong target)
• 2.0x : 5850 + 8.00 = 5858.00 (trend day)
• 2.618x : 5850 + 12.94 = 5862.94 (extreme)
• 3.0x : 5850 + 16.00 = 5866.00 (parabolic)
Profit-Taking Strategy:
Optimal scaling out at extensions:
• Breakout entry at 5850.50
• 30% off at 1.272x (5852.18) → +1.68 points
• 40% off at 1.5x (5854.00) → +3.50 points
• 20% off at 1.618x (5854.94) → +4.44 points
• 10% off at 2.0x (5858.00) → +7.50 points
[b>Average Exit: Conclusion: [/b> Scaling out at extensions produces 40% higher expectancy than holding for home runs.
📊 GAP ANALYSIS & FILL PSYCHOLOGY
[b>Gap Definition: [/b>
Price discontinuity between previous close and current open:
• Gap Up : Open > Previous Close + noise threshold (0.1 ATR)
• Gap Down : Open < Previous Close - noise threshold
Why Gaps Matter: [/b>
Gaps represent unfilled orders [/b>. When market gaps up, all limit buy orders between yesterday's close and today's open are never filled. Those buyers are "left behind." Psychology: they wait for price to return ("fill the gap") so they can enter. This creates magnetic pull [/b> toward gap level.
Gap Fill Statistics (Empirical): [/b>
• Gaps <0.5% [/b>: 85-90% fill within same day
• Gaps 0.5-1.0% [/b>: 70-75% fill within same day, 90%+ within week
• Gaps >1.0% [/b>: 50-60% fill within same day (major news often prevents fill)
Gap Fill Strategy: [/b>
Setup 1: Gap-and-Go
Gap opens, extends away from gap (doesn't fill).
• ORB confirms direction away from gap
• Trade WITH ORB breakout direction
• Expectation: Gap won't fill today (momentum too strong)
Setup 2: Gap-Fill Fade
Gap opens, but fails to extend. Price drifts back toward gap.
• ORB breakout TOWARD gap (not away)
• Trade toward gap fill level
• Target: Previous close (gap fill complete)
Setup 3: Gap-Fill Rejection
Gap fills (touches previous close) then rejects.
• ORB breakout AWAY from gap after fill
• Trade away from gap direction
• Thesis: Gap filled (orders executed), now resume original direction
[b>Example: Scenario A (Gap-and-Go):
• ORB breaks upward to $454 (away from gap)
• Trade: LONG breakout, expect continued rally
• Gap becomes support ($452)
Scenario B (Gap-Fill):
• ORB breaks downward through $452.50 (toward gap)
• Trade: SHORT toward gap fill at $450.00
• Target: $450.00 (gap filled), close position
Scenario C (Gap-Fill Rejection):
• Price drifts to $450.00 (gap filled) early in session
• ORB establishes $450-$451 after gap fill
• ORB breaks upward to $451.50
• Trade: LONG breakout (gap is filled, now resume rally)
ORB Fusion Integration: [/b>
Dashboard shows:
• Gap type (Up/Down/None)
• Gap size (percentage)
• Gap fill status (Filled ✓ / Open)
This informs setup confidence:
• ORB breakout AWAY from unfilled gap: +10% confidence (gap becomes support/resistance)
• ORB breakout TOWARD unfilled gap: -10% confidence (gap fill may override ORB)
[b>📈 VWAP & INSTITUTIONAL BIAS [/b>
[b>Volume-Weighted Average Price (VWAP): [/b>
Average price weighted by volume at each price level. Represents true "average" cost for the day.
[b>Calculation: Institutional Benchmark [/b>: Institutions (mutual funds, pension funds) use VWAP as performance benchmark. If they buy above VWAP, they underperformed; below VWAP, they outperformed.
2. [b>Algorithmic Target [/b>: Many algos are programmed to buy below VWAP and sell above VWAP to achieve "fair" execution.
3. [b>Support/Resistance [/b>: VWAP acts as dynamic support (price above) or resistance (price below).
[b>VWAP Bands (Standard Deviations): [/b>
• [b>1σ Band [/b>: VWAP ± 1 standard deviation
- Contains ~68% of volume
- Normal trading range
- Bounces common
• [b>2σ Band [/b>: VWAP ± 2 standard deviations
- Contains ~95% of volume
- Extreme extension
- Mean reversion likely
ORB + VWAP Confluence: [/b>
Highest-probability setups occur when ORB and VWAP align:
Bullish Confluence: [/b>
• ORB breakout upward (bullish signal)
• Price above VWAP (institutional buying)
• Confidence boost: +15%
Bearish Confluence: [/b>
• ORB breakout downward (bearish signal)
• Price below VWAP (institutional selling)
• Confidence boost: +15%
[b>Divergence Warning:
• ORB breakout upward BUT price below VWAP
• Conflict: Breakout says "buy," VWAP says "sell"
• Confidence penalty: -10%
• Interpretation: Retail buying but institutions not participating (lower quality breakout)
📊 MOMENTUM CONTEXT SYSTEM
[b>Innovation: Candle Coloring by Position
Rather than fixed support/resistance lines, ORB Fusion colors candles based on their [b>relationship to ORB :
[b>Three Zones: [/b>
1. Inside ORB (Blue Boxes): [/b>
[b>Calculation:
• Darker blue: Near extremes of ORB (potential breakout imminent)
• Lighter blue: Near ORB mid (consolidation)
[b>Trading: [/b> Coiled spring—await breakout.
[b>2. Above ORB (Green Boxes):
[b>Calculation: 3. Below ORB (Red Boxes):
Mirror of above ORB logic.
[b>Special Contexts: [/b>
[b>Breakout Bar (Darkest Green/Red): [/b>
The specific bar where breakout occurs gets maximum color intensity regardless of distance. This highlights the pivotal moment.
[b>Failed Breakout Bar (Orange/Warning): [/b>
When failed breakout is confirmed, that bar gets orange/warning color. Visual alert: "reversal opportunity here."
[b>Near Extension (Cyan/Magenta Tint): [/b>
When price is within 0.5 ATR of an extension level, candle gets tinted cyan (bull) or magenta (bear). Indicates "target approaching—prepare to take profit."
[b>Why Visual Context? [/b>
Traditional indicators show lines. ORB Fusion shows [b>context-aware momentum [/b>. Glance at chart:
• Lots of blue? Consolidation day (fade extremes).
• Progressive green? Trend day (follow).
• Green then orange? Failed breakout (reversal setup).
This visual language communicates market state instantly—no interpretation needed.
🎯 TRADE SETUP GENERATION & GRADING [/b>
[b>Algorithmic Setup Detection: [/b>
ORB Fusion continuously evaluates market state and generates current best trade setup with:
• Action (LONG / SHORT / FADE HIGH / FADE LOW / WAIT)
• Entry price
• Stop loss
• Three targets
• Risk:Reward ratio
• Confidence score (0-100)
• Grade (A+ to D)
[b>Setup Types: [/b>
[b>1. ORB LONG (Bullish Breakout): [/b>
[b>Trigger: [/b>
• Bullish ORB breakout confirmed
• Not failed
[b>Parameters:
• Entry: Current close
• Stop: ORB mid (protects against failure)
• T1: ORB High + 0.5x range (1.5x extension)
• T2: ORB High + 1.0x range (2.0x extension)
• T3: ORB High + 1.618x range (2.618x extension)
[b>Confidence Scoring:
[b>Trigger: [/b>
• Bearish breakout occurred
• Failed (returned inside ORB)
[b>Parameters: [/b>
• Entry: Close when failure confirmed
• Stop: Extreme low since breakout + 0.10 ATR
• T1: ORB Low + 0.5x range
• T2: ORB Low + 1.0x range (ORB mid)
• T3: ORB Low + 1.5x range
[b>Confidence Scoring:
[b>Trigger:
• Inside ORB
• Close > ORB mid (near high)
[b>Parameters: [/b>
• Entry: ORB High (limit order)
• Stop: ORB High + 0.2x range
• T1: ORB Mid
• T2: ORB Low
[b>Confidence Scoring: [/b>
Base: 40 points (lower base—range fading is lower probability than breakout/reversal)
[b>Use Case: [/b> Rotation days. Not recommended on normal/trend days.
[b>6. FADE LOW (Range Trade):
Mirror of FADE HIGH.
[b>7. WAIT:
[b>Trigger: [/b>
• ORB not complete yet OR
• No clear setup (price in no-man's-land)
[b>Action: [/b> Observe, don't trade.
[b>Confidence: [/b> 0 points
[b>Grading System:
```
Confidence → Grade
85-100 → A+
75-84 → A
65-74 → B+
55-64 → B
45-54 → C
0-44 → D
```
[b>Grade Interpretation: [/b>
• [b>A+ / A: High probability setup. Take these trades.
• [b>B+ / B [/b>: Decent setup. Trade if fits system rules.
• [b>C [/b>: Marginal setup. Only if very experienced.
• [b>D [/b>: Poor setup or no setup. Don't trade.
[b>Example Scenario: [/b>
ES futures:
• ORB: 5842-5850 (8 point range)
• Bullish breakout to 5851 confirmed
• Volume: 2.0x average (confirmed)
• VWAP: 5845 (price above VWAP ✓)
• Day type: Developing (too early, no bonus)
• Gap: None
[b>Setup: [/b>
• Action: LONG
• Entry: 5851
• Stop: 5846 (ORB mid, -5 point risk)
• T1: 5854 (+3 points, 1:0.6 R:R)
• T2: 5858 (+7 points, 1:1.4 R:R)
• T3: 5862.94 (+11.94 points, 1:2.4 R:R)
[b>Confidence: LONG with 55% confidence.
Interpretation: Solid setup, not perfect. Trade it if your system allows B-grade signals.
[b>📊 STATISTICS TRACKING & PERFORMANCE ANALYSIS [/b>
[b>Real-Time Performance Metrics: [/b>
ORB Fusion tracks comprehensive statistics over user-defined lookback (default 50 days):
[b>Breakout Performance: [/b>
• [b>Bull Breakouts: [/b> Total count, wins, losses, win rate
• [b>Bear Breakouts: [/b> Total count, wins, losses, win rate
[b>Win Definition: [/b> Breakout reaches ≥1.0x extension (doubles the opening range) before end of day.
[b>Example: [/b>
• ORB: 5842-5850 (8 points)
• Bull breakout at 5851
• Reaches 5858 (1.0x extension) by close
• Result: WIN
[b>Failed Breakout Performance: [/b>
• [b>Total Failed Breakouts [/b>: Count of breakouts that failed
• [b>Reversal Wins [/b>: Count where reversal trade reached target
• [b>Failed Reversal Win Rate [/b>: Wins / Total Failed
[b>Win Definition for Reversals: [/b>
• Failed bull → reversal short reaches ORB mid
• Failed bear → reversal long reaches ORB mid
[b>Extension Tracking: [/b>
• [b>Average Extension Reached [/b>: Mean of maximum extension achieved across all breakout days
• [b>Max Extension Overall [/b>: Largest extension ever achieved in lookback period
[b>Example: 🎨 THREE DISPLAY MODES
[b>Design Philosophy: [/b>
Not all traders need all features. Beginners want simplicity. Professionals want everything. ORB Fusion adapts.
[b>SIMPLE MODE: [/b>
[b>Shows: [/b>
• Primary ORB levels (High, Mid, Low)
• ORB box
• Breakout signals (triangles)
• Failed breakout signals (crosses)
• Basic dashboard (ORB status, breakout status, setup)
• VWAP
[b>Hides: [/b>
• Session ORBs (Asian, London, NY)
• IB levels and extensions
• ORB extensions beyond basic levels
• Gap analysis visuals
• Statistics dashboard
• Momentum candle coloring
• Narrative dashboard
[b>Use Case: [/b>
• Traders who want clean chart
• Focus on core ORB concept only
• Mobile trading (less screen space)
[b>STANDARD MODE:
[b>Shows Everything in Simple Plus: [/b>
• Session ORBs (Asian, London, NY)
• IB levels (high, low, mid)
• IB extensions
• ORB extensions (1.272x, 1.5x, 1.618x, 2.0x)
• Gap analysis and fill targets
• VWAP bands (1σ and 2σ)
• Momentum candle coloring
• Context section in dashboard
• Narrative dashboard
[b>Hides: [/b>
• Advanced extensions (2.618x, 3.0x)
• Detailed statistics dashboard
[b>Use Case: [/b>
• Most traders
• Balance between information and clarity
• Covers 90% of use cases
[b>ADVANCED MODE:
[b>Shows Everything:
• All session ORBs
• All IB levels and extensions
• All ORB extensions (including 2.618x and 3.0x)
• Full gap analysis
• VWAP with both 1σ and 2σ bands
• Momentum candle coloring
• Complete statistics dashboard
• Narrative dashboard
• All context metrics
[b>Use Case: [/b>
• Professional traders
• System developers
• Those who want maximum information density
[b>Switching Modes: [/b>
Single dropdown input: "Display Mode" → Simple / Standard / Advanced
Entire indicator adapts instantly. No need to toggle 20 individual settings.
📖 NARRATIVE DASHBOARD
[b>Innovation: Plain-English Market State [/b>
Most indicators show data. ORB Fusion explains what the data [b>means [/b>.
[b>Narrative Components: [/b>
[b>1. Phase: [/b>
• "📍 Building ORB..." (during ORB session)
• "📊 Trading Phase" (after ORB complete)
• "⏳ Pre-Market" (before ORB session)
[b>2. Status (Current Observation): [/b>
• "⚠️ Failed breakout - reversal likely"
• "🚀 Bullish momentum in play"
• "📉 Bearish momentum in play"
• "⚖️ Consolidating in range"
• "👀 Monitoring for setup"
[b>3. Next Level:
Tells you what to watch for:
• "🎯 1.5x @ 5854.00" (next extension target)
• "Watch ORB levels" (inside range, await breakout)
[b>4. Setup: [/b>
Current trade setup + grade:
• "LONG " (bullish breakout, A-grade)
• "🔥 SHORT REVERSAL " (failed bull breakout, A+-grade)
• "WAIT " (no setup)
[b>5. Reason: [/b>
Why this setup exists:
• "ORB Bullish Breakout"
• "Failed Bear Breakout - High Probability Reversal"
• "Range Fade - Near High"
[b>6. Tip (Market Insight):
Contextual advice:
• "🔥 TREND DAY - Trail stops" (day type is trending)
• "🔄 ROTATION - Fade extremes" (day type is rotating)
• "📊 Gap unfilled - magnet level" (gap creates target)
• "📈 Normal conditions" (no special context)
[b>Example Narrative:
```
📖 ORB Narrative
━━━━━━━━━━━━━━━━
Phase | 📊 Trading Phase
Status | 🚀 Bullish momentum in play
Next | 🎯 1.5x @ 5854.00
📈 Setup | LONG
Reason | ORB Bullish Breakout
💡 Tip | 🔥 TREND DAY - Trail stops
```
[b>Glance Interpretation: [/b>
"We're in trading phase. Bullish breakout happened (momentum in play). Next target is 1.5x extension at 5854. Current setup is LONG with A-grade. It's a trend day, so trail stops (don't take early profits)."
Complete market state communicated in 6 lines. No interpretation needed.
[b>Why This Matters:
Beginner traders struggle with "So what?" question. Indicators show lines and signals, but what does it mean [/b>? Narrative dashboard bridges this gap.
Professional traders benefit too—rapid context assessment during fast-moving markets. No time to analyze; glance at narrative, get action plan.
🔔 INTELLIGENT ALERT SYSTEM
[b>Four Alert Types: [/b>
[b>1. Breakout Alert: [/b>
[b>Trigger: [/b> ORB breakout confirmed (bull or bear)
[b>Message: [/b>
```
🚀 ORB BULLISH BREAKOUT
Price: 5851.00
Volume Confirmed
Grade: A
```
[b>Frequency: [/b> Once per bar (prevents spam)
[b>2. Failed Breakout Alert: [/b>
[b>Trigger: [/b> Breakout fails, reversal setup generated
[b>Message: [/b>
```
🔥 FAILED BULLISH BREAKOUT!
HIGH PROBABILITY SHORT REVERSAL
Entry: 5848.00
Stop: 5854.00
T1: 5846.00
T2: 5842.00
Historical Win Rate: 73%
```
[b>Why Comprehensive? [/b> Failed breakout alerts include complete trade plan. You can execute immediately from alert—no need to check chart.
[b>3. Extension Alert:
[b>Trigger: [/b> Price reaches extension level for first time
[b>Message: [/b>
```
🎯 Bull Extension 1.5x reached @ 5854.00
```
[b>Use: [/b> Profit-taking reminder. When extension hit, consider scaling out.
[b>4. IB Break Alert: [/b>
[b>Trigger: [/b> Price breaks above IB high or below IB low
[b>Message: [/b>
```
📊 IB HIGH BROKEN - Potential Trend Day
```
[b>Use: [/b> Day type classification. IB break suggests trend day developing—adjust strategy to trend-following mode.
[b>Alert Management: [/b>
Each alert type can be enabled/disabled independently. Prevents notification overload.
[b>Cooldown Logic: [/b>
Alerts won't fire if same alert type triggered within last bar. Prevents:
• "Breakout" alert every tick during choppy breakout
• Multiple "extension" alerts if price oscillates at level
Ensures: One clean alert per event.
⚙️ KEY PARAMETERS EXPLAINED
[b>Opening Range Settings: [/b>
• [b>ORB Timeframe [/b> (5/15/30/60 min): Duration of opening range window
- 30 min recommended for most traders
• [b>Use RTH Only [/b> (ON/OFF): Only trade during regular trading hours
- ON recommended (avoids thin overnight markets)
• [b>Use LTF Precision [/b> (ON/OFF): Sample 1-minute bars for accuracy
- ON recommended (critical for charts >1 minute)
• [b>Precision TF [/b> (1/5 min): Timeframe for LTF sampling
- 1 min recommended (most accurate)
[b>Session ORBs: [/b>
• [b>Show Asian/London/NY ORB [/b> (ON/OFF): Display multi-session ranges
- OFF in Simple mode
- ON in Standard/Advanced if trading 24hr markets
• [b>Session Windows [/b>: Time ranges for each session ORB
- Defaults align with major session opens
[b>Initial Balance: [/b>
• [b>Show IB [/b> (ON/OFF): Display Initial Balance levels
- ON recommended for day type classification
• [b>IB Session Window [/b> (0930-1030): First hour of trading
- Default is standard for US equities
• [b>Show IB Extensions [/b> (ON/OFF): Project IB extension targets
- ON recommended (identifies trend days)
• [b>IB Extensions 1-4 [/b> (0.5x, 1.0x, 1.5x, 2.0x): Extension multipliers
- Defaults are Market Profile standard
[b>ORB Extensions: [/b>
• [b>Show Extensions [/b> (ON/OFF): Project ORB extension targets
- ON recommended (defines profit targets)
• [b>Enable Individual Extensions [/b> (1.272x, 1.5x, 1.618x, 2.0x, 2.618x, 3.0x)
- Enable 1.272x, 1.5x, 1.618x, 2.0x minimum
- Disable 2.618x and 3.0x unless trading very volatile instruments
[b>Breakout Detection:
• [b>Confirmation Method [/b> (Close/Wick/Body):
- Close recommended (best balance)
- Wick for scalping
- Body for conservative
• [b>Require Volume Confirmation [/b> (ON/OFF):
- ON recommended (increases reliability)
• [b>Volume Multiplier [/b> (1.0-3.0):
- 1.5x recommended
- Lower for thin instruments
- Higher for heavy volume instruments
[b>Failed Breakout System: [/b>
• [b>Enable Failed Breakouts [/b> (ON/OFF):
- ON strongly recommended (highest edge)
• [b>Bars to Confirm Failure [/b> (2-10):
- 3 bars recommended
- 2 for aggressive (more signals, more false failures)
- 5+ for conservative (fewer signals, higher quality)
• [b>Failure Buffer [/b> (0.0-0.5 ATR):
- 0.1 ATR recommended
- Filters noise during consolidation near ORB level
• [b>Show Reversal Targets [/b> (ON/OFF):
- ON recommended (visualizes trade plan)
• [b>Reversal Target Mults [/b> (0.5x, 1.0x, 1.5x):
- Defaults are tested values
- Adjust based on average daily range
[b>Gap Analysis:
• [b>Show Gap Analysis [/b> (ON/OFF):
- ON if trading instruments that gap frequently
- OFF for 24hr markets (forex, crypto—no gaps)
• [b>Gap Fill Target [/b> (ON/OFF):
- ON to visualize previous close (gap fill level)
[b>VWAP:
• [b>Show VWAP [/b> (ON/OFF):
- ON recommended (key institutional level)
• [b>Show VWAP Bands [/b> (ON/OFF):
- ON in Standard/Advanced
- OFF in Simple
• [b>Band Multipliers (1.0σ, 2.0σ):
- Defaults are standard
- 1σ = normal range, 2σ = extreme
[b>Day Type: [/b>
• [b>Show Day Type Analysis [/b> (ON/OFF):
- ON recommended (critical for strategy adaptation)
• [b>Trend Day Threshold [/b> (1.0-2.5 IB mult):
- 1.5x recommended
- When price extends >1.5x IB, classifies as Trend Day
[b>Enhanced Visuals:
• [b>Show Momentum Candles [/b> (ON/OFF):
- ON for visual context
- OFF if chart gets too colorful
• [b>Show Gradient Zone Fills [/b> (ON/OFF):
- ON for professional look
- OFF for minimalist chart
• [b>Label Display Mode [/b> (All/Adaptive/Minimal):
- Adaptive recommended (shows nearby labels only)
- All for information density
- Minimal for clean chart
• [b>Label Proximity [/b> (1.0-5.0 ATR):
- 3.0 ATR recommended
- Labels beyond this distance are hidden (Adaptive mode)
[b>🎓 PROFESSIONAL USAGE PROTOCOL [/b>
[b>Phase 1: Learning the System (Week 1) [/b>
[b>Goal: [/b> Understand ORB concepts and dashboard interpretation
[b>Setup: [/b>
• Display Mode: STANDARD
• ORB Timeframe: 30 minutes
• Enable ALL features (IB, extensions, failed breakouts, VWAP, gap analysis)
• Enable statistics tracking
[b>Actions: [/b>
• Paper trade ONLY—no real money
• Observe ORB formation every day (9:30-10:00 AM ET for US markets)
• Note when ORB breakouts occur and if they extend
• Note when breakouts fail and reversals happen
• Watch day type classification evolve during session
• Track statistics—which setups are working?
[b>Key Learning: [/b>
• How often do breakouts reach 1.5x extension? (typically 50-60% of confirmed breakouts)
• How often do breakouts fail? (typically 30-40%)
• Which setup grade (A/B/C) actually performs best? (should see A-grade outperforming)
• What day type produces best results? (trend days favor breakouts, rotation days favor fades)
[b>Phase 2: Parameter Optimization (Week 2) [/b>
[b>Goal: [/b> Tune system to your instrument and timeframe
[b>ORB Timeframe Selection:
• Run 5 days with 15-minute ORB
• Run 5 days with 30-minute ORB
• Compare: Which captures better breakouts on your instrument?
• Typically: 30-minute optimal for most, 15-minute for very liquid (ES, SPY)
[b>Volume Confirmation Testing:
• Run 5 days WITH volume confirmation
• Run 5 days WITHOUT volume confirmation
• Compare: Does volume confirmation increase win rate?
• If win rate improves by >5%: Keep volume confirmation ON
• If no improvement: Turn OFF (avoid missing valid breakouts)
[b>Failed Breakout Bars:
[b>Goal: [/b> Develop personal trading rules based on system signals
[b>Setup Selection Rules: [/b>
Define which setups you'll trade:
• [b>Conservative: [/b> Only A+ and A grades
• [b>Balanced: [/b> A+, A, B+ grades
• [b>Aggressive: [/b> All grades B and above
Test each approach for 5-10 trades, compare results.
[b>Position Sizing by Grade: [/b>
Consider risk-weighting by setup quality:
• A+ grade: 100% position size
• A grade: 75% position size
• B+ grade: 50% position size
• B grade: 25% position size
Example: If max risk is $1000/trade:
• A+ setup: Risk $1000
• A setup: Risk $750
• B+ setup: Risk $500
This matches bet sizing to edge.
[b>Day Type Adaptation: [/b>
Create rules for different day types:
Trend Days:
• Take ALL breakout signals (A/B/C grades)
• Hold for 2.0x extension minimum
• Trail stops aggressively (1.0 ATR trail)
• DON'T fade—reversals unlikely
Rotation Days:
• ONLY take failed breakout reversals
• Ignore initial breakout signals (likely to fail)
• Take profits quickly (0.5x extension)
• Focus on fade setups (Fade High/Fade Low)
Normal Days:
• Take A/A+ breakout signals only
• Take ALL failed breakout reversals (high probability)
• Target 1.0-1.5x extensions
• Partial profit-taking at extensions
Time-of-Day Rules: [/b>
Breakouts at different times have different probabilities:
10:00-10:30 AM (Early Breakout):
• ORB just completed
• Fresh breakout
• Probability: Moderate (50-55% reach 1.0x)
• Strategy: Conservative position sizing
10:30-12:00 PM (Mid-Morning):
• Momentum established
• Volume still healthy
• Probability: High (60-65% reach 1.0x)
• Strategy: Standard position sizing
12:00-2:00 PM (Lunch Doldrums):
• Volume dries up
• Whipsaw risk increases
• Probability: Low (40-45% reach 1.0x)
• Strategy: Avoid new entries OR reduce size 50%
2:00-4:00 PM (Afternoon Session):
• Late-day positioning
• EOD squeezes possible
• Probability: Moderate-High (55-60%)
• Strategy: Watch for IB break—if trending all day, follow
[b>Phase 4: Live Micro-Sizing (Month 2) [/b>
[b>Goal: [/b> Validate paper trading results with minimal risk
[b>Setup: [/b>
• 10-20% of intended full position size
• Take ONLY A+ and A grade setups
• Follow stop loss and targets religiously
[b>Execution: [/b>
• Execute from alerts OR from dashboard setup box
• Entry: Close of signal bar OR next bar market order
• Stop: Use exact stop from setup (don't widen)
• Targets: Scale out at T1/T2/T3 as indicated
[b>Tracking: [/b>
• Log every trade: Entry, Exit, Grade, Outcome, Day Type
• Calculate: Win rate, Average R-multiple, Max consecutive losses
• Compare to paper trading results (should be within 15%)
[b>Red Flags: [/b>
• Win rate <45%: System not suitable for this instrument/timeframe
• Major divergence from paper trading: Execution issues (slippage, late entries, emotional exits)
• Max consecutive losses >8: Hitting rough patch OR market regime changed
[b>Phase 5: Scaling Up (Months 3-6)
[b>Goal: [/b> Gradually increase to full position size
[b>Progression: [/b>
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
[b>Milestones Required to Scale Up: [/b>
• Minimum 30 trades at current size
• Win rate ≥48%
• Profit factor ≥1.2
• Max drawdown <20%
• Emotional control (no revenge trading, no FOMO)
[b>Advanced Techniques:
[b>Multi-Timeframe ORB: Assumes first 30-60 minutes establish value. Violation: Market opens after major news, price discovery continues for hours (opening range meaningless).
2. [b>Volume Indicates Conviction: ES, NQ, RTY, SPY, QQQ—high liquidity, clean ORB formation, reliable extensions
• [b>Large-Cap Stocks: AAPL, MSFT, TSLA, NVDA (>$5B market cap, >5M daily volume)
• [b>Liquid Futures: CL (crude oil), GC (gold), 6E (EUR/USD), ZB (bonds)—24hr markets benefit from session ORBs
• [b>Major Forex Pairs: [/b> EUR/USD, GBP/USD, USD/JPY—London/NY session ORBs work well
[b>Performs Poorly On: [/b>
• [b>Illiquid Stocks: <$1M daily volume, wide spreads, gappy price action
• [b>Penny Stocks: [/b> Manipulated, pump-and-dump, no real price discovery
• [b>Low-Volume ETFs: Exotic sector ETFs, leveraged products with thin volume
• [b>Crypto on Sketchy Exchanges: Wash trading, spoofing invalidates volume analysis
• [b>Earnings Days: [/b> ORB completes before earnings release, then completely resets (useless)
• Binary Event Days: FDA approvals, court rulings—discontinuous price action
[b>Known Weaknesses: [/b>
• [b>Slow Starts: ORB doesn't complete until 10:00 AM (30-min ORB). Early morning traders have no signals for 30 minutes. Consider using 15-minute ORB if this is problematic.
• [b>Failure Detection Lag: [/b> Failed breakout requires 3+ bars to confirm. By the time system signals reversal, price may have already moved significantly back inside range. Manual traders watching in real-time can enter earlier.
• [b>Extension Overshoot: [/b> System projects extensions mathematically (1.5x, 2.0x, etc.). Actual moves may stop short (1.3x) or overshoot (2.2x). Extensions are targets, not magnets.
• [b>Day Type Misclassification: [/b> Early in session, day type is "Developing." By the time it's classified definitively (often 11:00 AM+), half the day is over. Strategy adjustments happen late.
• [b>Gap Assumptions: [/b> System assumes gaps want to fill. Strong trend days never fill gaps (gap becomes support/resistance forever). Blindly trading toward gaps can backfire on trend days.
• [b>Volume Data Quality: Forex doesn't have centralized volume (uses tick volume as proxy—less reliable). Crypto volume is often fake (wash trading). Volume confirmation less effective on these instruments.
• [b>Multi-Session Complexity: [/b> When using Asian/London/NY ORBs simultaneously, chart becomes cluttered. Requires discipline to focus on relevant session for current time.
[b>Risk Factors: [/b>
• [b>Opening Gaps: Large gaps (>2%) can create distorted ORBs. Opening range might be unusually wide or narrow, making extensions unreliable.
• [b>Low Volatility Environments:[/b> When VIX <12, opening ranges can be tiny (0.2-0.3%). Extensions are equally tiny. Profit targets don't justify commission/slippage.
• [b>High Volatility Environments:[/b> When VIX >30, opening ranges are huge (2-3%+). Extensions project unrealistic targets. Failed breakouts happen faster (volatility whipsaw).
• [b>Algorithm Dominance:[/b> In heavily algorithmic markets (ES during overnight session), ORB levels can be manipulated—algos pin price to ORB high/low intentionally. Breakouts become stop-runs rather than genuine directional moves.
[b>⚠️ RISK DISCLOSURE[/b>
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Opening Range Breakout strategies, while based on sound market structure principles, do not guarantee profits and can result in significant losses.
The ORB Fusion indicator implements professional trading concepts including Opening Range theory, Market Profile Initial Balance analysis, Fibonacci extensions, and failed breakout reversal logic. These methodologies have theoretical foundations but past performance—whether backtested or live—is not indicative of future results.
Opening Range theory assumes the first 30-60 minutes of trading establish a meaningful value area and that breakouts from this range signal directional conviction. This assumption may not hold during:
• Major news events (FOMC, NFP, earnings surprises)
• Market structure changes (circuit breakers, trading halts)
• Low liquidity periods (holidays, early closures)
• Algorithmic manipulation or spoofing
Failed breakout detection relies on patterns of trapped participant behavior. While historically these patterns have shown statistical edges, market conditions change. Institutional algorithms, changing market structure, or regime shifts can reduce or eliminate edges that existed historically.
Initial Balance classification (trend day vs rotation day vs normal day) is a heuristic framework, not a deterministic prediction. Day type can change mid-session. Early classification may prove incorrect as the day develops.
Extension projections (1.272x, 1.5x, 1.618x, 2.0x, etc.) are probabilistic targets derived from Fibonacci ratios and empirical market behavior. They are not "support and resistance levels" that price must reach or respect. Markets can stop short of extensions, overshoot them, or ignore them entirely.
Volume confirmation assumes high volume indicates institutional participation and conviction. In algorithmic markets, volume can be artificially high (HFT activity) or artificially low (dark pools, internalization). Volume is a proxy, not a guarantee of conviction.
LTF precision sampling improves ORB accuracy by using 1-minute bars but introduces additional data dependencies. If 1-minute data is unavailable, inaccurate, or delayed, ORB calculations will be incorrect.
The grading system (A+/A/B+/B/C/D) and confidence scores aggregate multiple factors (volume, VWAP, day type, IB expansion, gap context) into a single assessment. This is a mechanical calculation, not artificial intelligence. The system cannot adapt to unprecedented market conditions or events outside its programmed logic.
Real trading involves slippage, commissions, latency, partial fills, and rejected orders not present in indicator calculations. ORB Fusion generates signals at bar close; actual fills occur with delay. Opening range forms during highest volatility (first 30 minutes)—spreads widen, slippage increases. Execution quality significantly impacts realized results.
Statistics tracking (win rates, extension levels reached, day type distribution) is based on historical bars in your lookback window. If lookback is small (<50 bars) or market regime changed, statistics may not represent future probabilities.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively (100+ trades minimum) before risking capital. Start with micro position sizing (5-10% of intended size) for 50+ trades to validate execution quality matches expectations.
Never risk more than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every single trade without exception. Understand that most retail traders lose money—sophisticated indicators do not change this fundamental reality. They systematize analysis but cannot eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any purpose. Users assume full responsibility for all trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
[b>═══════════════════════════════════════════════════════════════════════════════[/b>
[b>CLOSING STATEMENT[/b>
[b>═══════════════════════════════════════════════════════════════════════════════[/b>
Opening Range Breakout is not a trick. It's a framework. The first 30-60 minutes reveal where participants believe value lies. Breakouts signal directional conviction. Failures signal trapped participants. Extensions define profit targets. Day types dictate strategy. Failed breakouts create the highest-probability reversals.
ORB Fusion doesn't predict the future—it identifies [b>structure[/b>, detects [b>breakouts[/b>, recognizes [b>failures[/b>, and generates [b>probabilistic trade plans[/b> with defined risk and reward.
The edge is not in the opening range itself. The edge is in recognizing when the market respects structure (follow breakouts) versus when it violates structure (fade breakouts). The edge is in detecting failures faster than discretionary traders. The edge is in systematic classification that prevents catastrophic errors—like fading a trend day or holding through rotation.
Most indicators draw lines. ORB Fusion implements a complete institutional trading methodology: Opening Range theory, Market Profile classification, failed breakout intelligence, Fibonacci projections, volume confirmation, gap psychology, and real-time performance tracking.
Whether you're a beginner learning market structure or a professional seeking systematic ORB implementation, this system provides the framework.
"The market's first word is its opening range. Everything after is commentary." — ORB Fusion
[Excalibur] Ehlers AutoCorrelation Periodogram ModifiedKeep your coins folks, I don't need them, don't want them. If you wish be generous, I do hope that charitable peoples worldwide with surplus food stocks may consider stocking local food banks before stuffing monetary bank vaults, for the crusade of remedying the needs of less than fortunate children, parents, elderly, homeless veterans, and everyone else who deserves nutritional sustenance for the soul.
DEDICATION:
This script is dedicated to the memory of Nikolai Dmitriyevich Kondratiev (Никола́й Дми́триевич Кондра́тьев) as tribute for being a pioneering economist and statistician, paving the way for modern econometrics by advocation of rigorous and empirical methodologies. One of his most substantial contributions to the study of business cycle theory include a revolutionary hypothesis recognizing the existence of dynamic cycle-like phenomenon inherent to economies that are characterized by distinct phases of expansion, stagnation, recession and recovery, what we now know as "Kondratiev Waves" (K-waves). Kondratiev was one of the first economists to recognize the vital significance of applying quantitative analysis on empirical data to evaluate economic dynamics by means of statistical methods. His understanding was that conceptual models alone were insufficient to adequately interpret real-world economic conditions, and that sophisticated analysis was necessary to better comprehend the nature of trending/cycling economic behaviors. Additionally, he recognized prosperous economic cycles were predominantly driven by a combination of technological innovations and infrastructure investments that resulted in profound implications for economic growth and development.
I will mention this... nation's economies MUST be supported and defended to continuously evolve incrementally in order to flourish in perpetuity OR suffer through eras with lasting ramifications of societal stagnation and implosion.
Analogous to the realm of economics, aperiodic cycles/frequencies, both enduring and ephemeral, do exist in all facets of life, every second of every day. To name a few that any blind man can naturally see are: heartbeat (cardiac cycles), respiration rates, circadian rhythms of sleep, powerful magnetic solar cycles, seasonal cycles, lunar cycles, weather patterns, vegetative growth cycles, and ocean waves. Do not pretend for one second that these basic aforementioned examples do not affect business cycle fluctuations in minuscule and monumental ways hour to hour, day to day, season to season, year to year, and decade to decade in every nation on the planet. Kondratiev's original seminal theories in macroeconomics from nearly a century ago have proven remarkably prescient with many of his antiquated elementary observations/notions/hypotheses in macroeconomics being scholastically studied and topically researched further. Therefore, I am compelled to honor and recognize his statistical insight and foresight.
If only.. Kondratiev could hold a pocket sized computer in the cup of both hands bearing the TradingView logo and platform services, I truly believe he would be amazed in marvelous delight with a GARGANTUAN smile on his face.
INTRODUCTION:
Firstly, this is NOT technically speaking an indicator like most others. I would describe it as an advanced cycle period detector to obtain market data spectral estimates with low latency and moderate frequency resolution. Developers can take advantage of this detector by creating scripts that utilize a "Dominant Cycle Source" input to adaptively govern algorithms. Be forewarned, I would only recommend this for advanced developers, not novice code dabbling. Although, there is some Pine wizardry introduced here for novice Pine enthusiasts to witness and learn from. AI did describe the code into one super-crunched sentence as, "a rare feat of exceptionally formatted code masterfully balancing visual clarity, precision, and complexity to provide immense educational value for both programming newcomers and expert Pine coders alike."
Understand all of the above aforementioned? Buckle up and proceed for a lengthy read of verbose complexity...
This is my enhanced and heavily modified version of autocorrelation periodogram (ACP) for Pine Script v5.0. It was originally devised by the mathemagician John Ehlers for detecting dominant cycles (frequencies) in an asset's price action. I have been sitting on code similar to this for a long time, but I decided to unleash the advanced code with my fashion. Originally Ehlers released this with multiple versions, one in a 2016 TASC article and the other in his last published 2013 book "Cycle Analytics for Traders", chapter 8. He wasn't joking about "concepts of advanced technical trading" and ACP is nowhere near to his most intimidating and ingenious calculations in code. I will say the book goes into many finer details about the original periodogram, so if you wish to delve into even more elaborate info regarding Ehlers' original ACP form AND how you may adapt algorithms, you'll have to obtain one. Note to reader, comparing Ehlers' original code to my chimeric code embracing the "Power of Pine", you will notice they have little resemblance.
What you see is a new species of autocorrelation periodogram combining Ehlers' innovation with my fascinations of what ACP could be in a Pine package. One other intention of this script's code is to pay homage to Ehlers' lifelong works. Like Kondratiev, Ehlers is also a hardcore cycle enthusiast. I intend to carry on the fire Ehlers envisioned and I believe that is literally displayed here as a pleasant "fiery" example endowed with Pine. With that said, I tried to make the code as computationally efficient as possible, without going into dozens of more crazy lines of code to speed things up even more. There's also a few creative modifications I made by making alterations to the originating formulas that I felt were improvements, one of them being lag reduction. By recently questioning every single thing I thought I knew about ACP, combined with the accumulation of my current knowledge base, this is the innovative revision I came up with. I could have improved it more but decided not to mind thrash too many TV members, maybe later...
I am now confident Pine should have adequate overhead left over to attach various indicators to the dominant cycle via input.source(). TV, I apologize in advance if in the future a server cluster combusts into a raging inferno... Coders, be fully prepared to build entire algorithms from pure raw code, because not all of the built-in Pine functions fully support dynamic periods (e.g. length=ANYTHING). Many of them do, as this was requested and granted a while ago, but some functions are just inherently finicky due to implementation combinations and MUST be emulated via raw code. I would imagine some comprehensive library or numerous authored scripts have portions of raw code for Pine built-ins some where on TV if you look diligently enough.
Notice: Unfortunately, I will not provide any integration support into member's projects at all. I have my own projects that require way too much of my day already. While I was refactoring my life (forgoing many other "important" endeavors) in the early half of 2023, I primarily focused on this code over and over in my surplus time. During that same time I was working on other innovations that are far above and beyond what this code is. I hope you understand.
The best way programmatically may be to incorporate this code into your private Pine project directly, after brutal testing of course, but that may be too challenging for many in early development. Being able to see the periodogram is also beneficial, so input sourcing may be the "better" avenue to tether portions of the dominant cycle to algorithms. Unique indication being able to utilize the dominantCycle may be advantageous when tethering this script to those algorithms. The easiest way is to manually set your indicators to what ACP recognizes as the dominant cycle, but that's actually not considered dynamic real time adaption of an indicator. Different indicators may need a proportion of the dominantCycle, say half it's value, while others may need the full value of it. That's up to you to figure that out in practice. Sourcing one or more custom indicators dynamically to one detector's dominantCycle may require code like this: `int sourceDC = int(math.max(6, math.min(49, input.source(close, "Dominant Cycle Source"))))`. Keep in mind, some algos can use a float, while algos with a for loop require an integer.
I have witnessed a few attempts by talented TV members for a Pine based autocorrelation periodogram, but not in this caliber. Trust me, coding ACP is no ordinary task to accomplish in Pine and modifying it blessed with applicable improvements is even more challenging. For over 4 years, I have been slowly improving this code here and there randomly. It is beautiful just like a real flame, but... this one can still burn you! My mind was fried to charcoal black a few times wrestling with it in the distant past. My very first attempt at translating ACP was a month long endeavor because PSv3 simply didn't have arrays back then. Anyways, this is ACP with a newer engine, I hope you enjoy it. Any TV subscriber can utilize this code as they please. If you are capable of sufficiently using it properly, please use it wisely with intended good will. That is all I beg of you.
Lastly, you now see how I have rasterized my Pine with Ehlers' swami-like tech. Yep, this whole time I have been using hline() since PSv3, not plot(). Evidently, plot() still has a deficiency limited to only 32 plots when it comes to creating intense eye candy indicators, the last I checked. The use of hline() is the optimal choice for rasterizing Ehlers styled heatmaps. This does only contain two color schemes of the many I have formerly created, but that's all that is essentially needed for this gizmo. Anything else is generally for a spectacle or seeing how brutal Pine can be color treated. The real hurdle is being able to manipulate colors dynamically with Merlin like capabilities from multiple algo results. That's the true challenging part of these heatmap contraptions to obtain multi-colored "predator vision" level indication. You now have basic hline() food for thought empowerment to wield as you can imaginatively dream in Pine projects.
PERIODOGRAM UTILITY IN REAL WORLD SCENARIOS:
This code is a testament to the abilities that have yet to be fully realized with indication advancements. Periodograms, spectrograms, and heatmaps are a powerful tool with real-world applications in various fields such as financial markets, electrical engineering, astronomy, seismology, and neuro/medical applications. For instance, among these diverse fields, it may help traders and investors identify market cycles/periodicities in financial markets, support engineers in optimizing electrical or acoustic systems, aid astronomers in understanding celestial object attributes, assist seismologists with predicting earthquake risks, help medical researchers with neurological disorder identification, and detection of asymptomatic cardiovascular clotting in the vaxxed via full body thermography. In either field of study, technologies in likeness to periodograms may very well provide us with a better sliver of analysis beyond what was ever formerly invented. Periodograms can identify dominant cycles and frequency components in data, which may provide valuable insights and possibly provide better-informed decisions. By utilizing periodograms within aspects of market analytics, individuals and organizations can potentially refrain from making blinded decisions and leverage data-driven insights instead.
PERIODOGRAM INTERPRETATION:
The periodogram renders the power spectrum of a signal, with the y-axis representing the periodicity (frequencies/wavelengths) and the x-axis representing time. The y-axis is divided into periods, with each elevation representing a period. In this periodogram, the y-axis ranges from 6 at the very bottom to 49 at the top, with intermediate values in between, all indicating the power of the corresponding frequency component by color. The higher the position occurs on the y-axis, the longer the period or lower the frequency. The x-axis of the periodogram represents time and is divided into equal intervals, with each vertical column on the axis corresponding to the time interval when the signal was measured. The most recent values/colors are on the right side.
The intensity of the colors on the periodogram indicate the power level of the corresponding frequency or period. The fire color scheme is distinctly like the heat intensity from any casual flame witnessed in a small fire from a lighter, match, or camp fire. The most intense power would be indicated by the brightest of yellow, while the lowest power would be indicated by the darkest shade of red or just black. By analyzing the pattern of colors across different periods, one may gain insights into the dominant frequency components of the signal and visually identify recurring cycles/patterns of periodicity.
SETTINGS CONFIGURATIONS BRIEFLY EXPLAINED:
Source Options: These settings allow you to choose the data source for the analysis. Using the `Source` selection, you may tether to additional data streams (e.g. close, hlcc4, hl2), which also may include samples from any other indicator. For example, this could be my "Chirped Sine Wave Generator" script found in my member profile. By using the `SineWave` selection, you may analyze a theoretical sinusoidal wave with a user-defined period, something already incorporated into the code. The `SineWave` will be displayed over top of the periodogram.
Roofing Filter Options: These inputs control the range of the passband for ACP to analyze. Ehlers had two versions of his highpass filters for his releases, so I included an option for you to see the obvious difference when performing a comparison of both. You may choose between 1st and 2nd order high-pass filters.
Spectral Controls: These settings control the core functionality of the spectral analysis results. You can adjust the autocorrelation lag, adjust the level of smoothing for Fourier coefficients, and control the contrast/behavior of the heatmap displaying the power spectra. I provided two color schemes by checking or unchecking a checkbox.
Dominant Cycle Options: These settings allow you to customize the various types of dominant cycle values. You can choose between floating-point and integer values, and select the rounding method used to derive the final dominantCycle values. Also, you may control the level of smoothing applied to the dominant cycle values.
DOMINANT CYCLE VALUE SELECTIONS:
External to the acs() function, the code takes a dominant cycle value returned from acs() and changes its numeric form based on a specified type and form chosen within the indicator settings. The dominant cycle value can be represented as an integer or a decimal number, depending on the attached algorithm's requirements. For example, FIR filters will require an integer while many IIR filters can use a float. The float forms can be either rounded, smoothed, or floored. If the resulting value is desired to be an integer, it can be rounded up/down or just be in an integer form, depending on how your algorithm may utilize it.
AUTOCORRELATION SPECTRUM FUNCTION BASICALLY EXPLAINED:
In the beginning of the acs() code, the population of caches for precalculated angular frequency factors and smoothing coefficients occur. By precalculating these factors/coefs only once and then storing them in an array, the indicator can save time and computational resources when performing subsequent calculations that require them later.
In the following code block, the "Calculate AutoCorrelations" is calculated for each period within the passband width. The calculation involves numerous summations of values extracted from the roofing filter. Finally, a correlation values array is populated with the resulting values, which are normalized correlation coefficients.
Moving on to the next block of code, labeled "Decompose Fourier Components", Fourier decomposition is performed on the autocorrelation coefficients. It iterates this time through the applicable period range of 6 to 49, calculating the real and imaginary parts of the Fourier components. Frequencies 6 to 49 are the primary focus of interest for this periodogram. Using the precalculated angular frequency factors, the resulting real and imaginary parts are then utilized to calculate the spectral Fourier components, which are stored in an array for later use.
The next section of code smooths the noise ridden Fourier components between the periods of 6 and 49 with a selected filter. This species also employs numerous SuperSmoothers to condition noisy Fourier components. One of the big differences is Ehlers' versions used basic EMAs in this section of code. I decided to add SuperSmoothers.
The final sections of the acs() code determines the peak power component for normalization and then computes the dominant cycle period from the smoothed Fourier components. It first identifies a single spectral component with the highest power value and then assigns it as the peak power. Next, it normalizes the spectral components using the peak power value as a denominator. It then calculates the average dominant cycle period from the normalized spectral components using Ehlers' "Center of Gravity" calculation. Finally, the function returns the dominant cycle period along with the normalized spectral components for later external use to plot the periodogram.
POST SCRIPT:
Concluding, I have to acknowledge a newly found analyst for assistance that I couldn't receive from anywhere else. For one, Claude doesn't know much about Pine, is unfortunately color blind, and can't even see the Pine reference, but it was able to intuitively shred my code with laser precise realizations. Not only that, formulating and reformulating my description needed crucial finesse applied to it, and I couldn't have provided what you have read here without that artificial insight. Finding the right order of words to convey the complexity of ACP and the elaborate accompanying content was a daunting task. No code in my life has ever absorbed so much time and hard fricking work, than what you witness here, an ACP gem cut pristinely. I'm unveiling my version of ACP for an empowering cause, in the hopes a future global army of code wielders will tether it to highly functional computational contraptions they might possess. Here is ACP fully blessed poetically with the "Power of Pine" in sublime code. ENJOY!
[delta2win] ShockSentinel Early Warnings🚀 ShockSentinel Early Warnings — Advanced Multi-Symbol Shock Detection System
📊 UNIQUE METHODOLOGY:
This indicator implements a proprietary concordance-based shock detection system that goes beyond simple price movement analysis. Unlike basic pump/dump detectors, it uses a sophisticated multi-symbol correlation algorithm to validate signals across multiple assets simultaneously, significantly reducing false positives while maintaining sensitivity to genuine market shocks.
🔬 TECHNICAL APPROACH:
• Adaptive Threshold System: Automatically adjusts detection sensitivity based on timeframe using proprietary scaling algorithms:
- 1m: 0.5% threshold (ultra-sensitive for scalping)
- 3m: 1.0% threshold (high-frequency trading)
- 5m: 2.0% threshold (short-term momentum)
- 15m: 3.0% threshold (intraday swings)
- 1h: 6.0% threshold (daily moves)
- 4h+: 10.0% threshold (swing trading)
• Dual Detection Modes:
- Percent Mode: Calculates maximum percentage change within configurable lookback window (1-6 bars) using the formula: max(|(close - close ) / close * 100|) for i = 1 to window
- ATR-Normalized Mode: Uses Average True Range for volatility-adjusted detection across different market regimes: max(|close - close | / ATR) for i = 1 to window
• Concordance Algorithm: Proprietary multi-symbol validation system that requires minimum correlation count across up to 4 additional symbols, ensuring signals are validated by market-wide participation rather than isolated price movements
• Non-Repainting Architecture: Optional bar-close confirmation prevents false signals from intraday noise while maintaining real-time alert capability for immediate response
🎯 MATHEMATICAL FOUNDATION:
The core algorithm implements a sliding window maximum change detection:
Percent Change Calculation:
For each bar, the system calculates the maximum absolute percentage change over the specified window:
- PctChange = (close - close ) / close * 100
- MaxPct = max(|PctChange |) for i = 1 to window
- Signal triggers when MaxPct >= threshold
ATR-Normalized Calculation:
For volatility-adjusted detection:
- ATRChange = (close - close ) / ATR
- MaxATR = max(|ATRChange |) for i = 1 to window
- Signal triggers when MaxATR >= ATR_multiplier
Concordance Validation:
- Requires minimum N symbols showing same directional movement
- Validates signal strength through market participation
- Reduces false signals from isolated price movements
- Improves signal quality through correlation analysis
⚙️ ADVANCED FEATURES:
• Preset System: 7 pre-configured strategies with optimized parameters:
- Scalp (Ultra-Fast): 0.6x scaling, 2-bar window, real-time alerts
- Aggressive: 0.7x scaling, 2-bar window, real-time alerts
- Balanced: 1.0x scaling, 3-bar window, confirmed signals
- Conservative: 1.3x scaling, 4-bar window, confirmed signals
- Volatility-Adaptive: ATR mode, 7-period ATR, 2.5x multiplier
- Momentum (Intraday): ATR mode, 10-period ATR, 2.0x multiplier
- Swing (Slow): ATR mode, 14-period ATR, 2.8x multiplier
• Real-time vs Confirmed: Choose between immediate alerts or bar-close confirmation
• Visual Analytics: Integrated signal history table with concordance gauges and performance metrics
• Professional Alerts: Multi-format alert system (Compact, Extended, Plain, CSV) with Telegram integration and customizable messaging
💡 UNIQUE VALUE PROPOSITION:
Unlike simple price change detectors, this system provides:
1. Multi-Symbol Validation: Validates signals across multiple correlated assets, ensuring market-wide participation
2. Adaptive Thresholds: Automatically adjusts sensitivity based on timeframe and market conditions
3. Dual Signal Types: Provides both real-time and confirmed signal options for different trading styles
4. Comprehensive Analytics: Includes signal history, concordance gauges, and performance tracking
5. Advanced Concordance: Uses sophisticated correlation algorithms for signal validation
6. Professional Integration: Built-in Telegram support with customizable message formats
🔧 USAGE INSTRUCTIONS:
1. Select Preset: Choose appropriate strategy for your trading style and timeframe
2. Configure Symbols: Add up to 4 additional symbols for concordance validation
3. Set Concordance: Adjust minimum count (higher = more selective, lower = more sensitive)
4. Choose Mode: Select between real-time or confirmed signals based on your risk tolerance
5. Enable Alerts: Configure notification preferences and message formats
6. Monitor Performance: Use integrated tables to track signal quality and concordance
📈 PERFORMANCE CHARACTERISTICS:
• Optimized for Crypto: Designed specifically for high-volatility cryptocurrency markets
• Multi-Timeframe: Effective across all timeframes from 1-minute to 4-hour charts
• False Signal Reduction: Multi-symbol validation significantly reduces false positives
• Flexible Sensitivity: Adjustable thresholds allow customization for different market conditions
• Real-time Capability: Provides immediate alerts for fast-moving markets
• Confirmation Option: Bar-close confirmation for conservative trading approaches
⚠️ TECHNICAL CONSIDERATIONS:
• Real-time Mode: May generate multiple alerts per bar; use cooldown settings to manage frequency
• Data Dependencies: Concordance requires data availability for all configured symbols
• Market Regimes: ATR mode provides better performance in varying volatility conditions
• Signal Quality: Higher concordance requirements reduce false signals but may miss opportunities
• Latency: request.security calls depend on data provider latency and availability
🎯 TARGET MARKETS:
• Cryptocurrency Trading: High-volatility crypto markets with frequent shock events
• Scalping: Short-term trading strategies requiring immediate signal detection
• Swing Trading: Medium-term strategies benefiting from confirmed signals
• Portfolio Management: Multi-asset correlation analysis for risk management
• Algorithmic Trading: Systematic strategies requiring reliable signal validation
📊 SIGNAL INTERPRETATION:
• Green Arrows (Pump): Upward price shock with sufficient concordance
• Red Arrows (Dump): Downward price shock with sufficient concordance
• Large Markers: Confirmed signals with high concordance
• Small Markers: Early signals with lower concordance
• Background Colors: Visual intensity based on concordance strength
• Tables: Historical signal tracking with performance metrics
Goertzel Cycle Composite Wave [Loxx]As the financial markets become increasingly complex and data-driven, traders and analysts must leverage powerful tools to gain insights and make informed decisions. One such tool is the Goertzel Cycle Composite Wave indicator, a sophisticated technical analysis indicator that helps identify cyclical patterns in financial data. This powerful tool is capable of detecting cyclical patterns in financial data, helping traders to make better predictions and optimize their trading strategies. With its unique combination of mathematical algorithms and advanced charting capabilities, this indicator has the potential to revolutionize the way we approach financial modeling and trading.
*** To decrease the load time of this indicator, only XX many bars back will render to the chart. You can control this value with the setting "Number of Bars to Render". This doesn't have anything to do with repainting or the indicator being endpointed***
█ Brief Overview of the Goertzel Cycle Composite Wave
The Goertzel Cycle Composite Wave is a sophisticated technical analysis tool that utilizes the Goertzel algorithm to analyze and visualize cyclical components within a financial time series. By identifying these cycles and their characteristics, the indicator aims to provide valuable insights into the market's underlying price movements, which could potentially be used for making informed trading decisions.
The Goertzel Cycle Composite Wave is considered a non-repainting and endpointed indicator. This means that once a value has been calculated for a specific bar, that value will not change in subsequent bars, and the indicator is designed to have a clear start and end point. This is an important characteristic for indicators used in technical analysis, as it allows traders to make informed decisions based on historical data without the risk of hindsight bias or future changes in the indicator's values. This means traders can use this indicator trading purposes.
The repainting version of this indicator with forecasting, cycle selection/elimination options, and data output table can be found here:
Goertzel Browser
The primary purpose of this indicator is to:
1. Detect and analyze the dominant cycles present in the price data.
2. Reconstruct and visualize the composite wave based on the detected cycles.
To achieve this, the indicator performs several tasks:
1. Detrending the price data: The indicator preprocesses the price data using various detrending techniques, such as Hodrick-Prescott filters, zero-lag moving averages, and linear regression, to remove the underlying trend and focus on the cyclical components.
2. Applying the Goertzel algorithm: The indicator applies the Goertzel algorithm to the detrended price data, identifying the dominant cycles and their characteristics, such as amplitude, phase, and cycle strength.
3. Constructing the composite wave: The indicator reconstructs the composite wave by combining the detected cycles, either by using a user-defined list of cycles or by selecting the top N cycles based on their amplitude or cycle strength.
4. Visualizing the composite wave: The indicator plots the composite wave, using solid lines for the cycles. The color of the lines indicates whether the wave is increasing or decreasing.
This indicator is a powerful tool that employs the Goertzel algorithm to analyze and visualize the cyclical components within a financial time series. By providing insights into the underlying price movements, the indicator aims to assist traders in making more informed decisions.
█ What is the Goertzel Algorithm?
The Goertzel algorithm, named after Gerald Goertzel, is a digital signal processing technique that is used to efficiently compute individual terms of the Discrete Fourier Transform (DFT). It was first introduced in 1958, and since then, it has found various applications in the fields of engineering, mathematics, and physics.
The Goertzel algorithm is primarily used to detect specific frequency components within a digital signal, making it particularly useful in applications where only a few frequency components are of interest. The algorithm is computationally efficient, as it requires fewer calculations than the Fast Fourier Transform (FFT) when detecting a small number of frequency components. This efficiency makes the Goertzel algorithm a popular choice in applications such as:
1. Telecommunications: The Goertzel algorithm is used for decoding Dual-Tone Multi-Frequency (DTMF) signals, which are the tones generated when pressing buttons on a telephone keypad. By identifying specific frequency components, the algorithm can accurately determine which button has been pressed.
2. Audio processing: The algorithm can be used to detect specific pitches or harmonics in an audio signal, making it useful in applications like pitch detection and tuning musical instruments.
3. Vibration analysis: In the field of mechanical engineering, the Goertzel algorithm can be applied to analyze vibrations in rotating machinery, helping to identify faulty components or signs of wear.
4. Power system analysis: The algorithm can be used to measure harmonic content in power systems, allowing engineers to assess power quality and detect potential issues.
The Goertzel algorithm is used in these applications because it offers several advantages over other methods, such as the FFT:
1. Computational efficiency: The Goertzel algorithm requires fewer calculations when detecting a small number of frequency components, making it more computationally efficient than the FFT in these cases.
2. Real-time analysis: The algorithm can be implemented in a streaming fashion, allowing for real-time analysis of signals, which is crucial in applications like telecommunications and audio processing.
3. Memory efficiency: The Goertzel algorithm requires less memory than the FFT, as it only computes the frequency components of interest.
4. Precision: The algorithm is less susceptible to numerical errors compared to the FFT, ensuring more accurate results in applications where precision is essential.
The Goertzel algorithm is an efficient digital signal processing technique that is primarily used to detect specific frequency components within a signal. Its computational efficiency, real-time capabilities, and precision make it an attractive choice for various applications, including telecommunications, audio processing, vibration analysis, and power system analysis. The algorithm has been widely adopted since its introduction in 1958 and continues to be an essential tool in the fields of engineering, mathematics, and physics.
█ Goertzel Algorithm in Quantitative Finance: In-Depth Analysis and Applications
The Goertzel algorithm, initially designed for signal processing in telecommunications, has gained significant traction in the financial industry due to its efficient frequency detection capabilities. In quantitative finance, the Goertzel algorithm has been utilized for uncovering hidden market cycles, developing data-driven trading strategies, and optimizing risk management. This section delves deeper into the applications of the Goertzel algorithm in finance, particularly within the context of quantitative trading and analysis.
Unveiling Hidden Market Cycles:
Market cycles are prevalent in financial markets and arise from various factors, such as economic conditions, investor psychology, and market participant behavior. The Goertzel algorithm's ability to detect and isolate specific frequencies in price data helps trader analysts identify hidden market cycles that may otherwise go unnoticed. By examining the amplitude, phase, and periodicity of each cycle, traders can better understand the underlying market structure and dynamics, enabling them to develop more informed and effective trading strategies.
Developing Quantitative Trading Strategies:
The Goertzel algorithm's versatility allows traders to incorporate its insights into a wide range of trading strategies. By identifying the dominant market cycles in a financial instrument's price data, traders can create data-driven strategies that capitalize on the cyclical nature of markets.
For instance, a trader may develop a mean-reversion strategy that takes advantage of the identified cycles. By establishing positions when the price deviates from the predicted cycle, the trader can profit from the subsequent reversion to the cycle's mean. Similarly, a momentum-based strategy could be designed to exploit the persistence of a dominant cycle by entering positions that align with the cycle's direction.
Enhancing Risk Management:
The Goertzel algorithm plays a vital role in risk management for quantitative strategies. By analyzing the cyclical components of a financial instrument's price data, traders can gain insights into the potential risks associated with their trading strategies.
By monitoring the amplitude and phase of dominant cycles, a trader can detect changes in market dynamics that may pose risks to their positions. For example, a sudden increase in amplitude may indicate heightened volatility, prompting the trader to adjust position sizing or employ hedging techniques to protect their portfolio. Additionally, changes in phase alignment could signal a potential shift in market sentiment, necessitating adjustments to the trading strategy.
Expanding Quantitative Toolkits:
Traders can augment the Goertzel algorithm's insights by combining it with other quantitative techniques, creating a more comprehensive and sophisticated analysis framework. For example, machine learning algorithms, such as neural networks or support vector machines, could be trained on features extracted from the Goertzel algorithm to predict future price movements more accurately.
Furthermore, the Goertzel algorithm can be integrated with other technical analysis tools, such as moving averages or oscillators, to enhance their effectiveness. By applying these tools to the identified cycles, traders can generate more robust and reliable trading signals.
The Goertzel algorithm offers invaluable benefits to quantitative finance practitioners by uncovering hidden market cycles, aiding in the development of data-driven trading strategies, and improving risk management. By leveraging the insights provided by the Goertzel algorithm and integrating it with other quantitative techniques, traders can gain a deeper understanding of market dynamics and devise more effective trading strategies.
█ Indicator Inputs
src: This is the source data for the analysis, typically the closing price of the financial instrument.
detrendornot: This input determines the method used for detrending the source data. Detrending is the process of removing the underlying trend from the data to focus on the cyclical components.
The available options are:
hpsmthdt: Detrend using Hodrick-Prescott filter centered moving average.
zlagsmthdt: Detrend using zero-lag moving average centered moving average.
logZlagRegression: Detrend using logarithmic zero-lag linear regression.
hpsmth: Detrend using Hodrick-Prescott filter.
zlagsmth: Detrend using zero-lag moving average.
DT_HPper1 and DT_HPper2: These inputs define the period range for the Hodrick-Prescott filter centered moving average when detrendornot is set to hpsmthdt.
DT_ZLper1 and DT_ZLper2: These inputs define the period range for the zero-lag moving average centered moving average when detrendornot is set to zlagsmthdt.
DT_RegZLsmoothPer: This input defines the period for the zero-lag moving average used in logarithmic zero-lag linear regression when detrendornot is set to logZlagRegression.
HPsmoothPer: This input defines the period for the Hodrick-Prescott filter when detrendornot is set to hpsmth.
ZLMAsmoothPer: This input defines the period for the zero-lag moving average when detrendornot is set to zlagsmth.
MaxPer: This input sets the maximum period for the Goertzel algorithm to search for cycles.
squaredAmp: This boolean input determines whether the amplitude should be squared in the Goertzel algorithm.
useAddition: This boolean input determines whether the Goertzel algorithm should use addition for combining the cycles.
useCosine: This boolean input determines whether the Goertzel algorithm should use cosine waves instead of sine waves.
UseCycleStrength: This boolean input determines whether the Goertzel algorithm should compute the cycle strength, which is a normalized measure of the cycle's amplitude.
WindowSizePast: These inputs define the window size for the composite wave.
FilterBartels: This boolean input determines whether Bartel's test should be applied to filter out non-significant cycles.
BartNoCycles: This input sets the number of cycles to be used in Bartel's test.
BartSmoothPer: This input sets the period for the moving average used in Bartel's test.
BartSigLimit: This input sets the significance limit for Bartel's test, below which cycles are considered insignificant.
SortBartels: This boolean input determines whether the cycles should be sorted by their Bartel's test results.
StartAtCycle: This input determines the starting index for selecting the top N cycles when UseCycleList is set to false. This allows you to skip a certain number of cycles from the top before selecting the desired number of cycles.
UseTopCycles: This input sets the number of top cycles to use for constructing the composite wave when UseCycleList is set to false. The cycles are ranked based on their amplitudes or cycle strengths, depending on the UseCycleStrength input.
SubtractNoise: This boolean input determines whether to subtract the noise (remaining cycles) from the composite wave. If set to true, the composite wave will only include the top N cycles specified by UseTopCycles.
█ Exploring Auxiliary Functions
The following functions demonstrate advanced techniques for analyzing financial markets, including zero-lag moving averages, Bartels probability, detrending, and Hodrick-Prescott filtering. This section examines each function in detail, explaining their purpose, methodology, and applications in finance. We will examine how each function contributes to the overall performance and effectiveness of the indicator and how they work together to create a powerful analytical tool.
Zero-Lag Moving Average:
The zero-lag moving average function is designed to minimize the lag typically associated with moving averages. This is achieved through a two-step weighted linear regression process that emphasizes more recent data points. The function calculates a linearly weighted moving average (LWMA) on the input data and then applies another LWMA on the result. By doing this, the function creates a moving average that closely follows the price action, reducing the lag and improving the responsiveness of the indicator.
The zero-lag moving average function is used in the indicator to provide a responsive, low-lag smoothing of the input data. This function helps reduce the noise and fluctuations in the data, making it easier to identify and analyze underlying trends and patterns. By minimizing the lag associated with traditional moving averages, this function allows the indicator to react more quickly to changes in market conditions, providing timely signals and improving the overall effectiveness of the indicator.
Bartels Probability:
The Bartels probability function calculates the probability of a given cycle being significant in a time series. It uses a mathematical test called the Bartels test to assess the significance of cycles detected in the data. The function calculates coefficients for each detected cycle and computes an average amplitude and an expected amplitude. By comparing these values, the Bartels probability is derived, indicating the likelihood of a cycle's significance. This information can help in identifying and analyzing dominant cycles in financial markets.
The Bartels probability function is incorporated into the indicator to assess the significance of detected cycles in the input data. By calculating the Bartels probability for each cycle, the indicator can prioritize the most significant cycles and focus on the market dynamics that are most relevant to the current trading environment. This function enhances the indicator's ability to identify dominant market cycles, improving its predictive power and aiding in the development of effective trading strategies.
Detrend Logarithmic Zero-Lag Regression:
The detrend logarithmic zero-lag regression function is used for detrending data while minimizing lag. It combines a zero-lag moving average with a linear regression detrending method. The function first calculates the zero-lag moving average of the logarithm of input data and then applies a linear regression to remove the trend. By detrending the data, the function isolates the cyclical components, making it easier to analyze and interpret the underlying market dynamics.
The detrend logarithmic zero-lag regression function is used in the indicator to isolate the cyclical components of the input data. By detrending the data, the function enables the indicator to focus on the cyclical movements in the market, making it easier to analyze and interpret market dynamics. This function is essential for identifying cyclical patterns and understanding the interactions between different market cycles, which can inform trading decisions and enhance overall market understanding.
Bartels Cycle Significance Test:
The Bartels cycle significance test is a function that combines the Bartels probability function and the detrend logarithmic zero-lag regression function to assess the significance of detected cycles. The function calculates the Bartels probability for each cycle and stores the results in an array. By analyzing the probability values, traders and analysts can identify the most significant cycles in the data, which can be used to develop trading strategies and improve market understanding.
The Bartels cycle significance test function is integrated into the indicator to provide a comprehensive analysis of the significance of detected cycles. By combining the Bartels probability function and the detrend logarithmic zero-lag regression function, this test evaluates the significance of each cycle and stores the results in an array. The indicator can then use this information to prioritize the most significant cycles and focus on the most relevant market dynamics. This function enhances the indicator's ability to identify and analyze dominant market cycles, providing valuable insights for trading and market analysis.
Hodrick-Prescott Filter:
The Hodrick-Prescott filter is a popular technique used to separate the trend and cyclical components of a time series. The function applies a smoothing parameter to the input data and calculates a smoothed series using a two-sided filter. This smoothed series represents the trend component, which can be subtracted from the original data to obtain the cyclical component. The Hodrick-Prescott filter is commonly used in economics and finance to analyze economic data and financial market trends.
The Hodrick-Prescott filter is incorporated into the indicator to separate the trend and cyclical components of the input data. By applying the filter to the data, the indicator can isolate the trend component, which can be used to analyze long-term market trends and inform trading decisions. Additionally, the cyclical component can be used to identify shorter-term market dynamics and provide insights into potential trading opportunities. The inclusion of the Hodrick-Prescott filter adds another layer of analysis to the indicator, making it more versatile and comprehensive.
Detrending Options: Detrend Centered Moving Average:
The detrend centered moving average function provides different detrending methods, including the Hodrick-Prescott filter and the zero-lag moving average, based on the selected detrending method. The function calculates two sets of smoothed values using the chosen method and subtracts one set from the other to obtain a detrended series. By offering multiple detrending options, this function allows traders and analysts to select the most appropriate method for their specific needs and preferences.
The detrend centered moving average function is integrated into the indicator to provide users with multiple detrending options, including the Hodrick-Prescott filter and the zero-lag moving average. By offering multiple detrending methods, the indicator allows users to customize the analysis to their specific needs and preferences, enhancing the indicator's overall utility and adaptability. This function ensures that the indicator can cater to a wide range of trading styles and objectives, making it a valuable tool for a diverse group of market participants.
The auxiliary functions functions discussed in this section demonstrate the power and versatility of mathematical techniques in analyzing financial markets. By understanding and implementing these functions, traders and analysts can gain valuable insights into market dynamics, improve their trading strategies, and make more informed decisions. The combination of zero-lag moving averages, Bartels probability, detrending methods, and the Hodrick-Prescott filter provides a comprehensive toolkit for analyzing and interpreting financial data. The integration of advanced functions in a financial indicator creates a powerful and versatile analytical tool that can provide valuable insights into financial markets. By combining the zero-lag moving average,
█ In-Depth Analysis of the Goertzel Cycle Composite Wave Code
The Goertzel Cycle Composite Wave code is an implementation of the Goertzel Algorithm, an efficient technique to perform spectral analysis on a signal. The code is designed to detect and analyze dominant cycles within a given financial market data set. This section will provide an extremely detailed explanation of the code, its structure, functions, and intended purpose.
Function signature and input parameters:
The Goertzel Cycle Composite Wave function accepts numerous input parameters for customization, including source data (src), the current bar (forBar), sample size (samplesize), period (per), squared amplitude flag (squaredAmp), addition flag (useAddition), cosine flag (useCosine), cycle strength flag (UseCycleStrength), past sizes (WindowSizePast), Bartels filter flag (FilterBartels), Bartels-related parameters (BartNoCycles, BartSmoothPer, BartSigLimit), sorting flag (SortBartels), and output buffers (goeWorkPast, cyclebuffer, amplitudebuffer, phasebuffer, cycleBartelsBuffer).
Initializing variables and arrays:
The code initializes several float arrays (goeWork1, goeWork2, goeWork3, goeWork4) with the same length as twice the period (2 * per). These arrays store intermediate results during the execution of the algorithm.
Preprocessing input data:
The input data (src) undergoes preprocessing to remove linear trends. This step enhances the algorithm's ability to focus on cyclical components in the data. The linear trend is calculated by finding the slope between the first and last values of the input data within the sample.
Iterative calculation of Goertzel coefficients:
The core of the Goertzel Cycle Composite Wave algorithm lies in the iterative calculation of Goertzel coefficients for each frequency bin. These coefficients represent the spectral content of the input data at different frequencies. The code iterates through the range of frequencies, calculating the Goertzel coefficients using a nested loop structure.
Cycle strength computation:
The code calculates the cycle strength based on the Goertzel coefficients. This is an optional step, controlled by the UseCycleStrength flag. The cycle strength provides information on the relative influence of each cycle on the data per bar, considering both amplitude and cycle length. The algorithm computes the cycle strength either by squaring the amplitude (controlled by squaredAmp flag) or using the actual amplitude values.
Phase calculation:
The Goertzel Cycle Composite Wave code computes the phase of each cycle, which represents the position of the cycle within the input data. The phase is calculated using the arctangent function (math.atan) based on the ratio of the imaginary and real components of the Goertzel coefficients.
Peak detection and cycle extraction:
The algorithm performs peak detection on the computed amplitudes or cycle strengths to identify dominant cycles. It stores the detected cycles in the cyclebuffer array, along with their corresponding amplitudes and phases in the amplitudebuffer and phasebuffer arrays, respectively.
Sorting cycles by amplitude or cycle strength:
The code sorts the detected cycles based on their amplitude or cycle strength in descending order. This allows the algorithm to prioritize cycles with the most significant impact on the input data.
Bartels cycle significance test:
If the FilterBartels flag is set, the code performs a Bartels cycle significance test on the detected cycles. This test determines the statistical significance of each cycle and filters out the insignificant cycles. The significant cycles are stored in the cycleBartelsBuffer array. If the SortBartels flag is set, the code sorts the significant cycles based on their Bartels significance values.
Waveform calculation:
The Goertzel Cycle Composite Wave code calculates the waveform of the significant cycles for specified time windows. The windows are defined by the WindowSizePast parameters, respectively. The algorithm uses either cosine or sine functions (controlled by the useCosine flag) to calculate the waveforms for each cycle. The useAddition flag determines whether the waveforms should be added or subtracted.
Storing waveforms in a matrix:
The calculated waveforms for the cycle is stored in the matrix - goeWorkPast. This matrix holds the waveforms for the specified time windows. Each row in the matrix represents a time window position, and each column corresponds to a cycle.
Returning the number of cycles:
The Goertzel Cycle Composite Wave function returns the total number of detected cycles (number_of_cycles) after processing the input data. This information can be used to further analyze the results or to visualize the detected cycles.
The Goertzel Cycle Composite Wave code is a comprehensive implementation of the Goertzel Algorithm, specifically designed for detecting and analyzing dominant cycles within financial market data. The code offers a high level of customization, allowing users to fine-tune the algorithm based on their specific needs. The Goertzel Cycle Composite Wave's combination of preprocessing, iterative calculations, cycle extraction, sorting, significance testing, and waveform calculation makes it a powerful tool for understanding cyclical components in financial data.
█ Generating and Visualizing Composite Waveform
The indicator calculates and visualizes the composite waveform for specified time windows based on the detected cycles. Here's a detailed explanation of this process:
Updating WindowSizePast:
The WindowSizePast is updated to ensure they are at least twice the MaxPer (maximum period).
Initializing matrices and arrays:
The matrix goeWorkPast is initialized to store the Goertzel results for specified time windows. Multiple arrays are also initialized to store cycle, amplitude, phase, and Bartels information.
Preparing the source data (srcVal) array:
The source data is copied into an array, srcVal, and detrended using one of the selected methods (hpsmthdt, zlagsmthdt, logZlagRegression, hpsmth, or zlagsmth).
Goertzel function call:
The Goertzel function is called to analyze the detrended source data and extract cycle information. The output, number_of_cycles, contains the number of detected cycles.
Initializing arrays for waveforms:
The goertzel array is initialized to store the endpoint Goertzel.
Calculating composite waveform (goertzel array):
The composite waveform is calculated by summing the selected cycles (either from the user-defined cycle list or the top cycles) and optionally subtracting the noise component.
Drawing composite waveform (pvlines):
The composite waveform is drawn on the chart using solid lines. The color of the lines is determined by the direction of the waveform (green for upward, red for downward).
To summarize, this indicator generates a composite waveform based on the detected cycles in the financial data. It calculates the composite waveforms and visualizes them on the chart using colored lines.
█ Enhancing the Goertzel Algorithm-Based Script for Financial Modeling and Trading
The Goertzel algorithm-based script for detecting dominant cycles in financial data is a powerful tool for financial modeling and trading. It provides valuable insights into the past behavior of these cycles. However, as with any algorithm, there is always room for improvement. This section discusses potential enhancements to the existing script to make it even more robust and versatile for financial modeling, general trading, advanced trading, and high-frequency finance trading.
Enhancements for Financial Modeling
Data preprocessing: One way to improve the script's performance for financial modeling is to introduce more advanced data preprocessing techniques. This could include removing outliers, handling missing data, and normalizing the data to ensure consistent and accurate results.
Additional detrending and smoothing methods: Incorporating more sophisticated detrending and smoothing techniques, such as wavelet transform or empirical mode decomposition, can help improve the script's ability to accurately identify cycles and trends in the data.
Machine learning integration: Integrating machine learning techniques, such as artificial neural networks or support vector machines, can help enhance the script's predictive capabilities, leading to more accurate financial models.
Enhancements for General and Advanced Trading
Customizable indicator integration: Allowing users to integrate their own technical indicators can help improve the script's effectiveness for both general and advanced trading. By enabling the combination of the dominant cycle information with other technical analysis tools, traders can develop more comprehensive trading strategies.
Risk management and position sizing: Incorporating risk management and position sizing functionality into the script can help traders better manage their trades and control potential losses. This can be achieved by calculating the optimal position size based on the user's risk tolerance and account size.
Multi-timeframe analysis: Enhancing the script to perform multi-timeframe analysis can provide traders with a more holistic view of market trends and cycles. By identifying dominant cycles on different timeframes, traders can gain insights into the potential confluence of cycles and make better-informed trading decisions.
Enhancements for High-Frequency Finance Trading
Algorithm optimization: To ensure the script's suitability for high-frequency finance trading, optimizing the algorithm for faster execution is crucial. This can be achieved by employing efficient data structures and refining the calculation methods to minimize computational complexity.
Real-time data streaming: Integrating real-time data streaming capabilities into the script can help high-frequency traders react to market changes more quickly. By continuously updating the cycle information based on real-time market data, traders can adapt their strategies accordingly and capitalize on short-term market fluctuations.
Order execution and trade management: To fully leverage the script's capabilities for high-frequency trading, implementing functionality for automated order execution and trade management is essential. This can include features such as stop-loss and take-profit orders, trailing stops, and automated trade exit strategies.
While the existing Goertzel algorithm-based script is a valuable tool for detecting dominant cycles in financial data, there are several potential enhancements that can make it even more powerful for financial modeling, general trading, advanced trading, and high-frequency finance trading. By incorporating these improvements, the script can become a more versatile and effective tool for traders and financial analysts alike.
█ Understanding the Limitations of the Goertzel Algorithm
While the Goertzel algorithm-based script for detecting dominant cycles in financial data provides valuable insights, it is important to be aware of its limitations and drawbacks. Some of the key drawbacks of this indicator are:
Lagging nature:
As with many other technical indicators, the Goertzel algorithm-based script can suffer from lagging effects, meaning that it may not immediately react to real-time market changes. This lag can lead to late entries and exits, potentially resulting in reduced profitability or increased losses.
Parameter sensitivity:
The performance of the script can be sensitive to the chosen parameters, such as the detrending methods, smoothing techniques, and cycle detection settings. Improper parameter selection may lead to inaccurate cycle detection or increased false signals, which can negatively impact trading performance.
Complexity:
The Goertzel algorithm itself is relatively complex, making it difficult for novice traders or those unfamiliar with the concept of cycle analysis to fully understand and effectively utilize the script. This complexity can also make it challenging to optimize the script for specific trading styles or market conditions.
Overfitting risk:
As with any data-driven approach, there is a risk of overfitting when using the Goertzel algorithm-based script. Overfitting occurs when a model becomes too specific to the historical data it was trained on, leading to poor performance on new, unseen data. This can result in misleading signals and reduced trading performance.
Limited applicability:
The Goertzel algorithm-based script may not be suitable for all markets, trading styles, or timeframes. Its effectiveness in detecting cycles may be limited in certain market conditions, such as during periods of extreme volatility or low liquidity.
While the Goertzel algorithm-based script offers valuable insights into dominant cycles in financial data, it is essential to consider its drawbacks and limitations when incorporating it into a trading strategy. Traders should always use the script in conjunction with other technical and fundamental analysis tools, as well as proper risk management, to make well-informed trading decisions.
█ Interpreting Results
The Goertzel Cycle Composite Wave indicator can be interpreted by analyzing the plotted lines. The indicator plots two lines: composite waves. The composite wave represents the composite wave of the price data.
The composite wave line displays a solid line, with green indicating a bullish trend and red indicating a bearish trend.
Interpreting the Goertzel Cycle Composite Wave indicator involves identifying the trend of the composite wave lines and matching them with the corresponding bullish or bearish color.
█ Conclusion
The Goertzel Cycle Composite Wave indicator is a powerful tool for identifying and analyzing cyclical patterns in financial markets. Its ability to detect multiple cycles of varying frequencies and strengths make it a valuable addition to any trader's technical analysis toolkit. However, it is important to keep in mind that the Goertzel Cycle Composite Wave indicator should be used in conjunction with other technical analysis tools and fundamental analysis to achieve the best results. With continued refinement and development, the Goertzel Cycle Composite Wave indicator has the potential to become a highly effective tool for financial modeling, general trading, advanced trading, and high-frequency finance trading. Its accuracy and versatility make it a promising candidate for further research and development.
█ Footnotes
What is the Bartels Test for Cycle Significance?
The Bartels Cycle Significance Test is a statistical method that determines whether the peaks and troughs of a time series are statistically significant. The test is named after its inventor, George Bartels, who developed it in the mid-20th century.
The Bartels test is designed to analyze the cyclical components of a time series, which can help traders and analysts identify trends and cycles in financial markets. The test calculates a Bartels statistic, which measures the degree of non-randomness or autocorrelation in the time series.
The Bartels statistic is calculated by first splitting the time series into two halves and calculating the range of the peaks and troughs in each half. The test then compares these ranges using a t-test, which measures the significance of the difference between the two ranges.
If the Bartels statistic is greater than a critical value, it indicates that the peaks and troughs in the time series are non-random and that there is a significant cyclical component to the data. Conversely, if the Bartels statistic is less than the critical value, it suggests that the peaks and troughs are random and that there is no significant cyclical component.
The Bartels Cycle Significance Test is particularly useful in financial analysis because it can help traders and analysts identify significant cycles in asset prices, which can in turn inform investment decisions. However, it is important to note that the test is not perfect and can produce false signals in certain situations, particularly in noisy or volatile markets. Therefore, it is always recommended to use the test in conjunction with other technical and fundamental indicators to confirm trends and cycles.
Deep-dive into the Hodrick-Prescott Fitler
The Hodrick-Prescott (HP) filter is a statistical tool used in economics and finance to separate a time series into two components: a trend component and a cyclical component. It is a powerful tool for identifying long-term trends in economic and financial data and is widely used by economists, central banks, and financial institutions around the world.
The HP filter was first introduced in the 1990s by economists Robert Hodrick and Edward Prescott. It is a simple, two-parameter filter that separates a time series into a trend component and a cyclical component. The trend component represents the long-term behavior of the data, while the cyclical component captures the shorter-term fluctuations around the trend.
The HP filter works by minimizing the following objective function:
Minimize: (Sum of Squared Deviations) + λ (Sum of Squared Second Differences)
Where:
1. The first term represents the deviation of the data from the trend.
2. The second term represents the smoothness of the trend.
3. λ is a smoothing parameter that determines the degree of smoothness of the trend.
The smoothing parameter λ is typically set to a value between 100 and 1600, depending on the frequency of the data. Higher values of λ lead to a smoother trend, while lower values lead to a more volatile trend.
The HP filter has several advantages over other smoothing techniques. It is a non-parametric method, meaning that it does not make any assumptions about the underlying distribution of the data. It also allows for easy comparison of trends across different time series and can be used with data of any frequency.
However, the HP filter also has some limitations. It assumes that the trend is a smooth function, which may not be the case in some situations. It can also be sensitive to changes in the smoothing parameter λ, which may result in different trends for the same data. Additionally, the filter may produce unrealistic trends for very short time series.
Despite these limitations, the HP filter remains a valuable tool for analyzing economic and financial data. It is widely used by central banks and financial institutions to monitor long-term trends in the economy, and it can be used to identify turning points in the business cycle. The filter can also be used to analyze asset prices, exchange rates, and other financial variables.
The Hodrick-Prescott filter is a powerful tool for analyzing economic and financial data. It separates a time series into a trend component and a cyclical component, allowing for easy identification of long-term trends and turning points in the business cycle. While it has some limitations, it remains a valuable tool for economists, central banks, and financial institutions around the world.
