Adpative Dual Cloud | NAL1. Overview
Adaptive Dual Cloud | NAL is a dual-baseline trend cloud built from two separate smoothing structures: a Kijun-style midpoint baseline and an ALMA baseline. Instead of relying on a single moving average or one volatility model, the indicator builds an adaptive cloud around both baselines and only confirms direction when price escapes the full combined structure.
The purpose of the indicator is to create a stricter trend envelope. The Kijun side captures broader structural balance, while the ALMA side adds a smoother adaptive layer. The final upper and lower cloud boundaries are selected from both systems, forcing price to clear the stronger side of the cloud before a bullish or bearish state is confirmed.
2. Calculation
The indicator starts by creating two independent baselines. The first baseline is a Kijun-style midpoint calculated from the highest and lowest values over the selected lookback. This represents a structural equilibrium zone.
kijun_sen = math.avg(ta.lowest(cloudLen1), ta.highest(cloudLen1))
The second baseline uses ALMA, giving the cloud a smoother weighted-average component with adjustable sigma and offset. This adds a more refined smoothing layer beside the Kijun structure.
alma_base = ta.alma(srcSeries, cloudLen2, cloud2Off, cloud2Sig)
The indicator then calculates volatility using a selectable deviation engine. The volatility source can be price, the residual between price and the cloud average, or the cloud structure itself. This allows the band width to be built from different layers of market behavior.
VolSrc = switch devSrc
"Price" => srcSeries
"Residuals" => srcSeries - math.avg(alma_base, kijun_sen)
"Cloud" => math.avg(alma_base, kijun_sen)
The volatility engine supports multiple deviation types, including standard deviation, mean absolute deviation, median absolute deviation, exponential deviation, ATR, linear regression deviation, Hull deviation, FRAMA deviation, Kauffman adaptive deviation, Gaussian deviation, and quantile deviation.
After volatility is calculated, adaptive upper and lower bands are created around both baselines.
upper_kijun = kijun_sen + vol * multi_u
lower_kijun = kijun_sen - vol * multi_l
upper_alma = alma_base + vol * multi_u
lower_alma = alma_base - vol * multi_l
The final cloud uses the highest upper boundary and the lowest lower boundary. This makes the signal more selective because price must break beyond the combined cloud, not just one individual baseline.
upper = math.max(upper_kijun, upper_alma)
lower = math.min(lower_kijun, lower_alma)
A bullish state triggers when price closes above the final upper cloud. A bearish state triggers when price closes below the final lower cloud. When price remains inside the cloud, the previous state is held.
3. Key Features
Dual-baseline cloud using Kijun structure and ALMA smoothing.
Adaptive upper and lower bands built from selectable volatility models.
Multiple deviation engines for different volatility interpretations.
Selectable volatility source: price, residuals, or cloud structure.
Final cloud requires price to clear the combined upper or lower boundary.
State-based candle coloring, cloud coloring, glow effect, and directional fills.
4. Use
Adaptive Dual Cloud is designed to identify when price escapes a combined structural and smoothed volatility envelope. A close above the upper cloud reflects bullish expansion beyond both baseline systems, while a close below the lower cloud reflects bearish expansion below the combined structure.
The indicator is intentionally stricter than a single-baseline channel. By combining a Kijun-style midpoint with an ALMA baseline, it creates a cloud that filters more of the internal noise before confirming a directional regime.
This indicator is best used as a specialized module within a complete strategy framework. Its role is to isolate a cloud-based volatility and structure layer, where price must prove strength or weakness against more than one adaptive baseline. The full value comes from how this regime signal is integrated into a broader process for timing, structure, and execution.
Indicator
Ichimoku Trend Oscillator [Gabremoku]Ichimoku Trend Oscillator is a custom trend-following oscillator that transforms core Ichimoku components into a normalized trend force model, designed to make bullish and bearish pressure easier to read in a single lower-panel view.
Instead of plotting the full traditional Ichimoku Cloud on price, this script converts the relationship between Tenkan, Kijun, Kumo structure, and price location vs cloud into a smoothed oscillator with:
a central zero line
trend force scoring
histogram confirmation
momentum shift detection
optional Tenkan force overlay on the main chart
The Ichimoku framework is commonly used to evaluate trend, momentum, and support/resistance at a glance, especially through the relationship between price and cloud position, plus the Tenkan/Kijun structure.
What it shows
📉 Trend Force Line — the main oscillator line represents a normalized force score derived from:
Tenkan vs Kijun spread
price location relative to the cloud
bullish or bearish cloud structure
📊 Histogram — visualizes the difference between the force line and its signal line, helping show momentum acceleration or deceleration.
🌈 Soft Gradient Fill — the oscillator fill changes intensity depending on how far trend force is from zero.
📍 Tenkan Force Overlay — optionally plots the Tenkan line on the main chart with dynamic color logic tied to trend condition.
🚦 Signals — the script can display:
LONG on bullish zero-line cross
SHORT on bearish zero-line cross
SHIFT markers when momentum changes sharply without a fresh zero cross
🪧 Dashboard — displays state, force value, trend bias, Tenkan/Kijun relation, price vs Kumo position, and momentum-shift status.
Core logic
The script compresses several Ichimoku readings into one oscillator.
It combines:
Tenkan/Kijun directional spread
price above / inside / below cloud
cloud directional structure
ATR-based normalization
and smoothing for cleaner force transitions
This matters because in standard Ichimoku interpretation:
price above the cloud is usually read as bullish context
price below the cloud is usually read as bearish context
Tenkan above Kijun supports bullish alignment, while the opposite supports bearish alignment
By translating those conditions into a normalized oscillator, the script gives traders a faster way to read trend quality and directional pressure without plotting the full classic system every time.
State model
The oscillator classifies the market into states such as:
Bullish Expansion
Bullish Pressure
Bearish Expansion
Bearish Pressure
Neutral
This is useful because it separates simple directional bias from stronger “expansion” conditions where multiple Ichimoku elements are aligned.
For example:
force above zero suggests bullish pressure
force below zero suggests bearish pressure
stronger positive or negative readings reflect stronger directional alignment
SHIFT signals are used to highlight sudden changes in momentum before a full regime flip
How to use
A practical workflow is:
Use the zero line as the primary directional divider.
Use the force line to judge strength.
Use the histogram to evaluate acceleration or fading momentum.
Watch for SHIFT markers when force changes quickly.
Use the Tenkan overlay on the main chart as an additional structure guide.
In general:
cross above zero = bullish transition
cross below zero = bearish transition
force staying far from zero = stronger trend persistence
shift signals = possible early momentum change without full bias reversal yet
Features
✅ Ichimoku-based lower-panel oscillator
✅ Trend force model built from Tenkan, Kijun, Kumo, and price/cloud relationship
✅ ATR-normalized force engine
✅ Smoothed force line
✅ Signal line and histogram
✅ Soft gradient fill around zero
✅ LONG / SHORT zero-cross signals
✅ Bullish / bearish momentum shift markers
✅ Optional Tenkan force overlay on the main chart
✅ Live dashboard with state and bias information
Notes
This indicator is designed to turn Ichimoku structure into a more compact trend momentum oscillator, not to replace full discretionary Ichimoku analysis. It works best as a directional filter or timing aid when combined with price structure, support/resistance, or a broader trend framework.
Author: Gabremoku
Pine Script v6 Indicator
Ichimoku Adaptive [Gabremoku]Ichimoku Adaptive is a full Ichimoku Cloud framework that combines the traditional Classic settings with an optional Adaptive mode that automatically scales the core Ichimoku periods according to market volatility.
The script keeps the original logic of the Ichimoku system — Tenkan, Kijun, Senkou Span A, Senkou Span B, Chikou, and Kumo structure — while adding a volatility-based adjustment engine that can make the framework more reactive or more stable depending on current conditions. The classical Ichimoku model is designed to show trend direction, momentum, and dynamic support/resistance in a single view.
What it shows
☁️ Full Ichimoku structure — Tenkan-sen, Kijun-sen, Senkou Span A, Senkou Span B, optional Chikou Span, and forward Kumo projection.
🧠 Classic or Adaptive mode — use standard Ichimoku parameters or let the indicator adjust its internal lengths according to ATR-based volatility behavior. Adaptive indicators are commonly designed to become more responsive or more conservative depending on changing volatility conditions.
🌈 Dynamic Kumo gradient — the cloud color and fill intensity vary based on bullish/bearish structure and cloud thickness.
🏷️ TK Cross signals — optional LONG and SHORT labels appear when Tenkan crosses above or below Kijun. Tenkan/Kijun crosses are widely used as Ichimoku signals and are often considered stronger when aligned with price position relative to the cloud.
🪧 Dashboard — displays mode, trend state, price vs Kumo location, Tenkan/Kijun relationship, active parameters, and ATR percentage.
📍 Last value labels — optional labels for Tenkan, Kijun, Span A, and Span B.
Core logic
This script is built around the classic Ichimoku reading process:
price vs cloud
Tenkan vs Kijun
future cloud structure
optional Chikou confirmation
In standard Ichimoku interpretation:
price above the cloud suggests bullish structure
price below the cloud suggests bearish structure
price inside the cloud suggests transition or indecision
This indicator keeps that framework intact, then extends it with an adaptive period engine.
Adaptive mode
In Classic mode, the script uses the traditional fixed settings:
Tenkan
Kijun
Senkou B
displacement
In Adaptive mode, those core lengths are scaled according to an ATR-based volatility ratio.
The idea is straightforward:
when volatility changes, fixed settings may become less efficient
adaptive scaling can make the Ichimoku framework more flexible across different environments
higher or changing volatility can justify a different sensitivity profile than calm market conditions
This does not replace classic Ichimoku logic — it simply changes the speed of the framework while keeping the same structural interpretation.
How to read it
A practical reading sequence is:
Check whether price is above, below, or inside the cloud.
Check whether Tenkan is above or below Kijun.
Observe whether the cloud is bullish or bearish.
Use TK crosses as triggers only after checking context.
In Adaptive mode, monitor how the active parameters shift as volatility changes.
In general:
Above Kumo + Tenkan > Kijun supports bullish trend logic.
Below Kumo + Tenkan < Kijun supports bearish trend logic.
Inside Kumo usually suggests weaker trend clarity or transition.
Features
✅ Full Ichimoku Cloud framework
✅ Classic fixed-parameter mode
✅ Adaptive ATR-based mode
✅ Configurable Tenkan / Kijun / Senkou B / displacement
✅ Dynamic Kumo gradient visualization
✅ Optional Tenkan, Kijun, Senkou A, Senkou B, and Chikou display
✅ TK cross signal labels
✅ Dashboard with live state information
✅ Last-value labels for key lines
✅ Built-in alert conditions for bullish and bearish TK crosses
Notes
This indicator is best used as a context and structure tool, not just as a crossover script. Ichimoku signals are generally stronger when multiple elements align, especially price position relative to the cloud, Tenkan/Kijun direction, and overall Kumo structure.
Author: Gabremoku
Pine Script v6 Indicator
Indicator
Indicator
Indicator
Kalman Hull Kijun [BackQuant]Kalman Hull Kijun
A trend baseline that merges three ideas into one clean overlay, Kalman filtering for noise control, Hull-style responsiveness, and a Kijun-like Donchian midline for structure and bias.
Context and lineage
This indicator sits in the same family as two related scripts:
Kalman Price Filter
This is the foundational building block. It introduces the Kalman filter concept, a state-estimation algorithm designed to infer an underlying “true” signal from noisy measurements, originally used in aerospace guidance and later adopted across robotics, economics, and markets.
Kalman Hull Supertrend
This is the original script made, which people loved. So it inspired me to create this one.
Kalman Hull Kijun uses the same core philosophy as the Supertrend variant, but instead of building a Supertrend band system, it produces a single structural baseline that behaves like a Kijun-style reference line.
What this indicator is trying to solve
Most trend baselines sit on a bad trade-off curve:
If you smooth hard, the line reacts late and misses turns.
If you react fast, the line whipsaws and tracks noise.
Kalman Hull Kijun is designed to land closer to the middle:
Cleaner than typical fast moving averages in chop.
More responsive than slow averages in directional phases.
More “structure aware” than pure averages because the baseline is range-derived (Kijun-like) after filtering.
Core idea in plain language
The plotted line is a Kijun-like baseline, but it is not built from raw candles directly.
High level flow:
Start with a chosen price stream (source input).
Reduce measurement noise using Kalman-style state estimation.
Add Hull-style responsiveness so the filtered stream stays usable for trend work.
Build a Kijun-like baseline by taking a Donchian midpoint of that filtered stream over the base period.
So the output is a single baseline that is intended to be:
Less jittery than a simple fast MA.
Less laggy than a slow MA.
More “range anchored” than standard smoothing lines.
How to read it
1) Trend and bias (the primary use)
Price above the baseline, bullish bias.
Price below the baseline, bearish bias.
Clean flips across the baseline are regime changes, especially when followed by a hold or retest.
2) Retests and dynamic structure
Treat the baseline like dynamic S/R rather than a signal generator:
In uptrends, pullbacks that respect the baseline can act as continuation context.
In downtrends, reclaim failures around the baseline can act as continuation context.
Repeated back-and-forth around the line usually means compression or chop, not clean trend.
3) Extension vs compression (using the fill)
The fill is meant to communicate “distance” and “pressure” visually:
Large separation between price and baseline suggests expansion.
Price compressing into the baseline suggests rebalancing and decision points.
Inputs and what they change
Kijun Base Period
Controls the structural memory of the baseline.
Higher values track broader swings and reduce flips.
Lower values track tighter swings and react faster.
Kalman Price Source
Defines what data the filter is estimating.
Close is usually the cleanest default.
HL2 often “feels” smoother as an average price.
High/Low sources can become more reactive and less stable depending on the market.
Measurement Noise
Think of this as the main smoothness knob:
Higher values generally produce a calmer filtered stream.
Lower values generally produce a faster, more reactive stream.
Process Noise
Think of this as adaptability:
Higher values adapt faster to changing conditions but can get twitchy.
Lower values adapt slower but stay stable.
Plotting and UI (what you see on chart)
1) Adaptive line coloring
Baseline turns bullish color when price is above it.
Baseline turns bearish color when price is below it.
This makes the state readable without extra panels.
2) Gradient “energy” fill
Bull fill appears between price and baseline when above.
Bear fill appears between price and baseline when below.
The goal is clarity on separation and control, not decoration.
3) Rim effect
A subtle band around price that only appears on the active side.
Helps highlight directional control without hiding candles.
4) Candle painting (optional)
Candles can be colored to match the current bias.
Useful for scanning many charts quickly.
Disable if you prefer raw candles.
Alerts
Long state alert when price is above the baseline.
Short state alert when price is below the baseline.
Best used as a bias or regime notification, not a standalone entry trigger.
Where it fits in a workflow
This is a context layer, it pairs well with:
Market structure tools, BOS/MSB, OBs, FVGs.
Momentum triggers that need a regime filter.
Mean reversion tools that need “do not fade trends” context.
Limitations
No baseline eliminates chop whipsaws, tuning only manages the trade-off.
Settings should not be copy pasted across assets without checking behavior.
This does not forecast, it estimates and smooths state, then expresses it as a structural baseline.
Disclaimer
Educational and informational only, not financial advice.
Not a complete trading system.
If you use it in any trading workflow, do proper backtesting, forward testing, and risk management before any live execution.
Indicator
Kijun Sen Standard Deviation | QuantLapse SystemsOverview
The Kijun Sen Standard Deviation indicator by QuantLapse Systems is a volatility-aware trend-following framework that combines the structural equilibrium of the Kijun Sen (基準線) with statistically adaptive standard deviation bands.
By anchoring trend detection to market structure and confirming direction through volatility expansion, the indicator delivers a cleaner, more reliable regime classification across varying market conditions.
Rather than reacting to short-term noise, the system focuses on identifying statistically justified trend phases , making it well-suited for disciplined, rule-based trading.
Technical Composition, Calculation, Key Components & Features
📌 Kijun Sen (基準線) – Structural Trend Baseline
Calculated as the midpoint between the highest high and lowest low over a user-defined period.
Represents market equilibrium and structural balance rather than short-term momentum.
Naturally adapts to expanding and contracting price ranges.
Provides a stable baseline for regime detection and volatility validation.
Acts as the anchor for deviation bands and persistent trend-state logic.
Unlike fast or reactive moving averages, the Kijun Sen emphasizes price structure and equilibrium , making it especially effective for higher-quality trend confirmation.
📌 Volatility Adjustment – Standard Deviation Bands
Standard deviation is calculated over a configurable lookback to measure current price dispersion.
Upper and lower envelopes are formed by applying a deviation multiplier to the Kijun Sen.
Band width expands during volatility surges and contracts during consolidation.
Creates proportional, volatility-aware thresholds instead of static offsets.
Visually represents market energy through expanding and compressing channels.
These adaptive bands ensure that trend signals only occur when volatility supports directional movement.
📌 Trend Signal & Regime Calculation
Bullish Trend is confirmed when price closes above the upper deviation band.
Bearish Trend is confirmed when price closes below the lower deviation band.
Once established, the trend state persists until an opposing volatility break occurs.
This persistence reduces whipsaws and improves regime stability.
Trend state is reinforced with color-coded lines, envelopes, and background shading.
This volatility-confirmed persistence model is visible in the chart, where trends remain intact through minor pullbacks and only flip on decisive expansion.
How It Works in Trading
✅ Volatility-Confirmed Trend Detection – Requires expansion beyond deviation bands.
✅ Noise Suppression – Filters low-energy price movement within volatility envelopes.
✅ Regime Persistence – Maintains trend state until statistical invalidation.
✅ Immediate Visual Context – Direction, strength, and transitions are clear at a glance.
Visual Representation
Trend signals are displayed directly on price using both line and background context:
🟢 Green / Teal Kijun & Envelope → Confirmed bullish regime.
🔴 Red / Pink Kijun & Envelope → Confirmed bearish regime.
Semi-transparent band fill visualizes volatility expansion and compression.
Buy and Sell labels appear only on confirmed regime transitions.
The lower panel includes:
Strategy equity curve based on trend exposure.
Buy & Hold equity for performance comparison.
Background regime shading synchronized with trend state.
Features and User Inputs
The Kijun Sen Standard Deviation framework offers a focused yet powerful set of configurable inputs:
Kijun Sen Length – Controls structural trend sensitivity.
Standard Deviation Controls – Adjust lookback length and multiplier for regime strictness.
Backtesting & Date Filters – Define evaluation periods and starting conditions.
Display Options – Toggle labels, equity curves, and background shading.
Color Customization – Fully configurable buy/sell colors for trends and equity curves.
These controls allow users to balance responsiveness, stability, and clarity without overfitting.
Practical Applications
The Kijun Sen Standard Deviation indicator is designed for traders who prioritize structure, volatility confirmation, and regime awareness.
Primary Trend Filtering – Identify and stay aligned with dominant market direction.
Volatility-Aware Trend Following – Participate only when price expansion confirms intent.
Risk-Managed Exposure – Avoid chop during compression and transitional phases.
Systematic Strategy Development – Use as a regime engine or higher-timeframe filter.
Performance Evaluation – Compare trend-following equity against buy-and-hold benchmarks.
This framework bridges classical Ichimoku structure with modern statistical validation.
Conclusion
The Kijun Sen Standard Deviation indicator by QuantLapse Systems represents a refined evolution of Ichimoku-based trend analysis.
By integrating the structural equilibrium of the Kijun Sen with adaptive standard deviation confirmation, the system delivers clearer regime classification, reduced noise, and more reliable trend participation.
Rather than attempting to predict price, it focuses on confirming when trends are statistically justified .
Who should use Kijun Sen Standard Deviation:
📊 Trend-Following Traders – Stay aligned with dominant market structure.
⚡ Momentum & Swing Traders – Enter only on volatility-backed expansions.
🤖 Systematic & Algorithmic Traders – Ideal as a regime filter or trend-state engine.
Past performance is not indicative of future results.
Disclaimer: All trading involves risk, and no indicator can guarantee profitability.
Strategic Advice: Always backtest thoroughly, optimize parameters responsibly, and align settings with your timeframe, asset class, and risk tolerance before live deployment. Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
Indicator
