Regime Detector [StrixEDGE]📊 WHAT IT DOES
StrixEDGE Regime Detector automatically classifies the market into four distinct states — Strong Trend, Weak Trend, Ranging, or Volatile Chop — using a proprietary four-metric analysis system. Subtle background colors make the current regime instantly visible without cluttering your chart.
🔬 WHY IT'S DIFFERENT
Most regime indicators rely solely on ADX. This indicator combines four independent dimensions: ADX for trend strength, RSI range-shift analysis for bull/bear regime identification, KAMA slope for adaptive trend direction, and ATR volatility ratio for market character assessment. The four-layer approach catches regime changes that single-metric tools miss entirely.
⚙️ HOW IT WORKS
The indicator evaluates four metrics simultaneously:
• ADX measures raw trend strength (>25 = trending)
• RSI tracks whether momentum is operating in bull mode (40-80) or bear mode (20-60)
• KAMA's normalized slope detects whether price is directional or flat
• ATR ratio reveals if volatility is above or below its historical average
These combine into a decision matrix: all four must agree for a "Strong Trend" classification. Partial agreement produces "Weak Trend." Low ADX + flat KAMA = "Ranging." High volatility without trend = "Volatile Chop."
📈 HOW TO USE
• Green background = Strong Uptrend → trade with trend, trail stops
• Red background = Strong Downtrend → look for shorts or stay flat
• Blue background = Ranging → use mean-reversion setups, avoid trend strategies
• Amber background = Volatile Chop → reduce size or sit out
• Diamond markers appear when regime shifts — these are key decision points
🎛️ INPUTS & DEFAULTS
ADX Period: 14 | RSI Period: 14 | KAMA Length: 21 | ATR Period: 14
ATR Lookback: 50 | Flat Threshold: 0.05 | Sensitivity: Normal
All inputs adjustable. Conservative mode raises thresholds for fewer signals. Aggressive lowers them.
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🔧 CUSTOMIZATION
All parameters are fully adjustable through the indicator settings panel. Inputs are grouped logically:
• ⚙️ Core Parameters — main calculation settings
• 📊 Table Settings — table size (Tiny to Huge), position (4 corners), visibility toggle
• 🎨 Visual Settings — colors, show/hide elements
• 🔔 Alert Settings — threshold values for notifications
📊 DATA TABLE
A built-in data table displays all key metrics in real-time. Adjust the table size from Tiny to Huge to match your chart layout. Position it in any corner. Toggle visibility on/off.
🔔 ALERTS
Pre-built alert conditions for all major signals. Set up alerts via PulseWire's alert dialog — select this indicator and choose from the available conditions.
⏱️ RECOMMENDED TIMEFRAMES
Works on all timeframes. Recommended: 1H, 4H, Daily for best signal quality. Lower timeframes produce more signals but with higher noise. Weekly/Monthly for position trading context.
✅ COMPLIANCE
• No repainting — all signals based on confirmed bar close data
• No future data references
• Open-source code — verify the logic yourself
⚠️ DISCLAIMER
This indicator is a technical analysis tool, not financial advice. It does not predict future price movements. Past patterns and signals do not guarantee future results. Trading involves substantial risk of loss. Always use proper risk management, including stop losses and appropriate position sizing. Never risk more than you can afford to lose. Indicator
Indicator
AlphaVault - Regime FilterTwo long-term trend filters and one rule: price above both is a bull regime, below both is
a bear regime, anything in between is neutral. It plots the filters, tints the background
by state, marks each change, and shows how long the current state has held.
NO REPAINTING. Both filters are read from the previous COMPLETED daily and weekly bar
(lookahead_off plus a one-bar offset), so the regime shown for a closed bar never changes
afterwards. This is worth verifying yourself on any indicator you did not write: scroll
back, note the state on an old bar, reload the chart, and check it is the same.
What it is for: deciding whether to be doing anything at all. Most trend systems lose the
majority of their money trying to trade against a long-term downtrend, and a neutral state
is genuinely common — roughly a fifth of all bars on BTC since 2018. Treating "no clear
regime" as a valid answer rather than a gap to be filled is most of the value here.
What it is not: an entry signal. It tells you which direction is permitted, not when to
act, and it will keep you out of some large moves that begin before the filters confirm.
It is deliberately slow. On BTC it changes state a handful of times a year.
Defaults are a 200-period SMA on daily closes and a 20-period EMA on weekly closes. Both
lengths are configurable, though the defaults are the ones worth arguing about — a filter
you re-tune until it looks good on the chart in front of you has stopped being a filter.
Alerts fire on a turn to bull, a turn to bear, or any change. Regime changes are rare by
construction; an alert that fires constantly is one you learn to ignore.
Open source. Read it, change it, take it apart. Indicator
Wavelet Trend FilterWhat it is
A denoised price line whose smoothing is derived from measured noise, not a period you pick — plus the tool's signature number, the noise fraction (what share of the recent move is noise vs structure). Most published "wavelet denoising improves prediction" results denoise the whole series including future bars, which is lookahead-contaminated and impossible live. This tool refuses that: it runs a Haar wavelet transform over a trailing power-of-two window and keeps only the endpoint as today's value, fixed at close and never revised. Strictly causal, non-repainting, no forecast.
How it's built
The engine is a five-step Donoho-Johnstone wavelet shrinkage, run every bar over a trailing window (default 128 bars): a Haar DWT (shortest support, fewest boundary coefficients — the right choice for short causal series); noise scale σ = MAD(finest detail coefficients)/0.6745 (robust to outliers); the universal threshold λ = σ·√(2 ln n) with an optional multiplier; soft or hard shrinkage of the detail coefficients; and an inverse transform, keeping the endpoint. Optional symmetric reflection padding reduces edge distortion. Because the threshold self-scales from measured noise, a quiet tape is smoothed less and a noisy tape more — automatically, on any timeframe.
Derived reads: noise fraction = 1 − var(denoised)/var(raw) over the window (share the transform removed), ranked into a percentile; trend slope in noise-σ units; and a signal-to-noise strength = slope (σ) × structure share (1 − noise fraction), the single "is this trend real?" number.
How you read it — and the honest actionable signal
The noise fraction gates everything. A bold chop/structure ribbon along the bottom of the chart shows the whole regime spectrum (green = structure/low noise/trend trustworthy, red = chop/high noise/stand aside), and a ±1σ noise envelope around the denoised line makes the removed noise visible — so it's obviously not just a moving average.
The actionable markers are structure-gated: a green ▲ / red ▼ prints when the denoised slope turns up/down and the tape has structure (low noise); in chop the tool withholds and marks the turn with a small caution dot instead. A hysteresis on the structure state keeps the signals from re-firing as noise oscillates. This is not a blind crossover generator — it goes quiet exactly when the tape is too noisy to trust, which is when ordinary signals whipsaw. The panel's "Read" row states the current call in plain English ("up-trend · structure ✓", "chop — stand aside").
Exports & alerts
EXP_Denoised · EXP_NoiseSigma · EXP_NoiseFrac (0–1) · EXP_Slope (σ) · EXP_SNR (trend strength) · EXP_Stance (+1 up·structure / −1 down·structure / 0). Alerts: noise fraction entering top / bottom quartile (chop / structure) · denoised slope turning up / down · confirmed up-trend / down-trend with structure present. Indicator
Pymander's EZ Trend Alignment**Pymander’s EZ Trend Alignment** is a clean and easy-to-read trend-following indicator designed to help traders quickly identify bullish and bearish market direction.
The tool combines the Commodity Channel Index with an ATR-based trailing trend line. In simple terms, the CCI measures whether momentum favors buyers or sellers, while the ATR calculation adjusts the trend line to current market volatility.
When bullish momentum is present, the line trails beneath price and displays green. When bearish momentum takes control, the line moves above price and displays red. The trailing logic helps traders follow established trends while recognizing possible shifts in direction.
The indicator includes two coloring styles:
* **Original Coloring:** Changes color based directly on bullish or bearish CCI momentum.
* **Trend Coloring:** Changes color based on whether the trailing line is rising or falling.
Pymander’s EZ Trend Alignment may be used to confirm market direction, filter long and short setups, identify possible trend changes, follow directional moves, and avoid trading against established momentum.
Its strength is simplicity. Rather than cluttering the chart with unnecessary signals, it provides a clear visual guide to help traders determine whether price, momentum, and volatility are aligned.
As with any indicator, use it alongside proper risk management, market structure, and a tested trading plan. No indicator can guarantee profitable results.
Best of luck in the markets, traders. Stay patient, remain disciplined, and protect your capital.
— **Pymander**
Indicator
EMA Ribbon Trend Filter [StrixEDGE]EMA Ribbon Trend Filter is a multi-layered trend analysis system built around a triple EMA ribbon (8/21/55) enhanced with squeeze detection, a composite momentum score, multi-timeframe confluence, and dynamic slope-adaptive coloring.
This is not another moving average overlay. It is a full trend diagnostics dashboard designed to answer five questions at a glance: what is the trend, how strong is it, how long has it been running, is the ribbon compressing toward a breakout, and do higher timeframes agree.
🔹 CORE CONCEPT
Three exponential moving averages — fast (8), mid (21), and slow (55) — form a visual ribbon on the chart. When the EMAs stack in order (fast > mid > slow), the trend is bullish. When they invert, the trend is bearish. Anything else is a transitional state labeled neutral.
The ribbon is more than directional. The distance between the fastest and slowest EMA (the "spread") measures trend strength as a percentage, and the rate at which the ribbon expands or contracts reveals momentum shifts before price confirms them.
🔹 WHAT MAKES THIS DIFFERENT
Most EMA ribbons stop at direction and color. This indicator adds four analytical layers that standard ribbons lack:
▸ Ribbon Squeeze Detection
The indicator continuously measures ribbon width against its own moving average. When the three EMAs converge below a configurable threshold (default: 30% of average width), the ribbon turns yellow and the dashboard flags an active squeeze. Compression precedes expansion — a squeeze ending often marks the start of a directional move. Dedicated markers appear on the chart when a squeeze releases into a bullish or bearish trend.
▸ Composite Momentum Score (0–100)
A single number synthesizing four components, each weighted equally at 25 points:
— EMA alignment: full bullish or bearish stack scores 25, mixed scores 0
— Slope agreement: all three EMAs rising or all falling scores 25, partial agreement scores 12
— Spread strength: scaled between 0–25 based on where the current spread falls relative to the user-defined weak and strong thresholds
— Price position: price above the ribbon in a bullish trend (or below in bearish) scores 25, inside the ribbon scores 10, on the wrong side scores 0
The score is color-coded: cyan (80+), teal (60–79), orange (40–59), red (below 40).
▸ Dynamic Slope-Adaptive Colors
When enabled, each EMA line independently changes color based on its own slope — rising EMAs render green, falling EMAs render red. This provides an early visual warning when individual EMAs begin to flatten or turn, even while the overall stack remains intact. A bullish stack where the slow EMA has turned red is a qualitatively different signal than one where all three are green.
▸ Multi-Timeframe Dashboard
A built-in table displays trend state, spread, and grade across five timeframes simultaneously: the current chart timeframe plus 15-minute, 1-hour, 4-hour, and daily. The active chart timeframe is marked with ► in the MTF rows if it matches one of the fixed timeframes. A confluence row at the bottom counts how many timeframes are bullish versus bearish and outputs a directional bias: Strong Bull (4–5 aligned), Bull Lean (3), Neutral (mixed), Bear Lean (3 bearish), or Strong Bear (4–5 bearish).
🔹 CROSSOVER SIGNAL HIERARCHY
Not all EMA crossovers carry equal weight. The indicator differentiates three tiers with distinct marker sizes and shapes:
▸ Fast × Mid (tiny triangles) — Early signal. The 8 EMA crossing the 21 EMA indicates short-term momentum shift. Frequent, noisy, best used as an alert rather than a trigger.
▸ Fast × Slow (standard triangles) — Confirmation signal. The 8 EMA crossing the 55 EMA has more significance and filters out minor pullbacks.
▸ Mid × Slow (diamonds) — Trend shift signal. The 21 EMA crossing the 55 EMA typically marks a genuine change in trend direction. This is the least frequent and most reliable crossover in the set.
All crossover markers can be toggled off independently.
🔹 DASHBOARD COMPONENTS
The top-right dashboard (position and size adjustable) contains:
Row 1 — TREND: Current trend state (▲ Bullish / ▼ Bearish / ◆ Neutral) with bar count showing how long the trend has been active.
Row 2 — ZONE: Where price sits relative to the ribbon (Above / Inside / Below) alongside the momentum score out of 100.
Row 3 — SQUEEZE: Active squeeze status with a visual strength bar (██████░░░░) showing current spread intensity relative to its recent range.
Rows 4–9 — MTF OVERVIEW: Trend, spread, and grade for the current chart, 15m, 1H, 4H, and 1D timeframes.
Row 10 — BIAS: Multi-timeframe confluence verdict with bull/bear count.
🔹 SETTINGS
EMA Settings
▸ Fast / Mid / Slow EMA Period — Default 8/21/55. Periods must be in ascending order.
▸ Source — Close, Open, High, Low, HL2, HLC3, or OHLC4.
Overlay Settings
▸ Background Transparency — Controls the intensity of the trend-colored chart background (0–99).
▸ Crossover Signals — Toggle the three-tier crossover markers.
▸ Ribbon Fill — Toggle the colored fill between EMA lines. Fill turns yellow during active squeeze.
▸ Dynamic EMA Colors — Toggle slope-based EMA coloring (green = rising, red = falling).
▸ Bar Coloring — Optional candle coloring by trend state and price position. Off by default.
▸ Squeeze Detection — Toggle squeeze markers and dashboard squeeze status.
Dashboard
▸ Table Size — Tiny, Small, Normal, or Large.
▸ Table Position — 8 positions (corners, centers, sides).
Thresholds
▸ Weak/Strong spread thresholds (%) — Define what constitutes a weak, moderate, or strong trend spread. These should be adjusted per asset class (e.g., lower for forex, higher for crypto).
▸ Squeeze Lookback — Number of bars used to calculate the average ribbon width for squeeze detection.
▸ Squeeze Ratio — The compression threshold. A ribbon narrower than this ratio × average width triggers a squeeze flag.
🔹 ALERTS
Nine configurable alert conditions:
▸ Bullish / Bearish EMA Crossover (any tier)
▸ Mid × Slow Bullish / Bearish Shift
▸ Bullish / Bearish Stack Formed
▸ Squeeze Detected (compression begins)
▸ Squeeze Released (expansion starts)
▸ High Momentum (score crosses above 80)
🔹 SUGGESTED USE
This indicator works as a trend filter, not a standalone entry signal. Use it to:
▸ Confirm directional bias across timeframes before entering trades
▸ Identify compression phases where breakouts are likely
▸ Gauge trend quality and exhaustion via the momentum score and bar duration
▸ Filter crossover signals — a Fast × Mid cross during an active squeeze with MTF confluence is a higher-probability setup than the same cross in isolation
Pairs well with oscillators (RSI, Stochastic), volume-based indicators, or support/resistance tools for entry timing. Indicator
Median Cascade Trend [BackQuant]Median Cascade Trend
Overview
Median Cascade Trend is a noise-resistant trend-following indicator that combines multiple causal median filters , an Ehlers-style two-pole Super Smoother , and a configurable chop-gating system .
Its purpose is to identify meaningful directional structure while rejecting the types of price movement that frequently cause conventional moving averages to whipsaw:
Single-bar spikes
Short-lived price shocks
Irregular wicks
Low-efficiency sideways movement
Weak slopes that do not represent genuine directional progress
The indicator processes price in three distinct stages:
A median-filter cascade removes impulsive and short-duration noise.
A two-pole low-pass filter smooths the remaining structural signal.
A chop gate decides whether a change in direction is strong enough to update the active trend state.
This creates a trend line that is intentionally more selective than a standard moving average. The line itself can continue evolving, but trend-state changes are only accepted when the underlying movement passes the chosen quality tests.
The result is a system that separates:
The estimated trend line
The raw direction of that line
The confirmed trend regime
That distinction is important. The indicator does not assume that every small turn in a smoothed line represents a valid trend reversal.
Core idea
Most trend filters are built around linear averaging. They combine historical prices using fixed or exponentially decaying weights.
Examples include:
SMA
EMA
WMA
HMA
DEMA
These filters are effective for general smoothing, but they have a weakness: a single extreme observation can influence the output immediately.
If one bar produces a large wick or temporary price shock, a moving average will usually be pulled toward that value because it includes the magnitude of every observation.
A median filter behaves differently.
Instead of averaging the values inside a window, it sorts them and selects the middle observation. This makes it highly resistant to isolated extremes.
Median Cascade Trend uses this property repeatedly before applying traditional low-pass smoothing.
The central idea is:
Remove impulsive noise first.
Smooth the cleaned signal second.
Confirm direction only when the movement is efficient or sufficiently steep.
This ordering is deliberate. Smoothing a noisy series and filtering noise before smoothing are not the same thing.
Where median filtering comes from
Median filters are widely used in digital signal processing, image processing, and engineering.
One of their best-known applications is the removal of impulse noise , sometimes called salt-and-pepper noise in image processing.
Impulse noise consists of isolated extreme observations that are not representative of the surrounding signal.
In market data, comparable events include:
Temporary liquidity gaps
Single-bar stop runs
Erroneous prints
Sharp wick reversals
News spikes that immediately retrace
A linear smoother such as an SMA or EMA cannot distinguish between an important sustained move and an isolated outlier. Both are included according to their numerical magnitude.
A median filter is nonlinear. It selects the central-ranked value rather than calculating an arithmetic mean.
For example, consider five observations:
100
101
102
103
140
The average is:
109.2
The median is:
102
The isolated value at 140 significantly distorts the average but has almost no effect on the median.
This is why median filtering is especially useful when the objective is to preserve structural turns while rejecting isolated noise.
What makes a median filter nonlinear
A normal moving average can be represented as a weighted sum of its inputs.
A median filter cannot.
Its output depends on the ordering of observations, not their arithmetic combination. This makes it a nonlinear filter.
That has several important consequences:
Outliers have limited influence.
Edges and structural shifts can be preserved more cleanly.
The response is not equivalent to ordinary low-pass averaging.
The filter may hold a value until enough observations confirm a change.
In trading terms, the median filter often behaves more like a structural selector than a conventional average.
What “causal” means
The median stages in this indicator are causal.
A causal filter uses:
The current bar
Past bars
It does not use future observations.
This matters because many visually smooth filtering methods can only produce their best result by centering the calculation around the current point, which requires future data.
This script does not do that.
Every value is calculated from information available at that bar, making the filter suitable for live use.
Why use a cascade of median filters
A single median filter can remove isolated spikes, but noise does not always occur as one extreme bar.
It can appear as:
Two-bar shocks
Short bursts of alternating movement
Small clusters of irregular candles
Residual distortion left after the first filtering pass
A cascade applies multiple median stages sequentially:
Stage 1 processes the original source.
Stage 2 processes the output of Stage 1.
Stage 3 processes the output of Stage 2.
Each stage removes a different layer of short-duration instability.
The process is similar to progressively cleaning a signal:
The first stage removes obvious impulse noise.
The second stage removes remaining short-term irregularity.
The third stage produces a more stable structural series before low-pass smoothing.
Median Stage 1
The first median stage is intended to reject isolated one-bar disturbances.
With a short setting such as 3:
The filter remains responsive.
Single-bar spikes are strongly suppressed.
Most genuine multi-bar movement remains visible.
This is the most tactical stage.
Median Stage 2
The second stage receives an already-cleaned input.
Its role is to remove:
Short-duration residual noise
Small alternating movements
Irregularity that survived the first stage
A slightly longer setting such as 5 creates stronger structural consistency.
Median Stage 3
The third stage provides the final nonlinear cleaning pass.
With a longer setting such as 7:
The output becomes more stable.
Short-lived reversals are reduced.
Only more persistent changes are passed into the low-pass stage.
This is the most conservative median stage.
Cascade Stages input
The user can select how many median stages are active:
1 stage: fastest and most responsive
2 stages: balanced noise rejection
3 stages: strongest impulse-noise suppression
The unused stages are still calculated internally, but the selected stage determines which output is sent into the Super Smoother.
Why cascade instead of one very long median
Applying several shorter filters is not always equivalent to applying one very long median filter.
A long median window can:
Delay structural changes heavily
Flatten shorter trend legs
Become insensitive to local turns
A cascade allows the filtering process to be distributed across stages.
This provides greater control over:
How aggressively isolated noise is removed
How much short-duration movement survives
How smooth the final structural input becomes
It also lets users build a progression such as 3, 5, and 7 rather than applying one blunt window.
The low-pass stage
After the median cascade, the signal is passed into a two-pole Super Smoother .
The median cascade removes nonlinear impulse noise, but its output can still contain:
Small step changes
Residual high-frequency movement
Sharp transitions caused by median replacement
The Super Smoother converts that cleaned but potentially stepped series into a smoother trend line.
This creates a hybrid filtering system:
Nonlinear median filtering for outlier rejection
Recursive low-pass filtering for smooth trend extraction
These components solve different problems.
Where the Super Smoother comes from
The Super Smoother is associated with John F. Ehlers’ application of digital signal processing techniques to financial markets.
It is based on the behavior of a two-pole low-pass filter and is designed to suppress high-frequency components more effectively than many conventional moving averages.
Traditional moving averages are simple smoothers, but they are not necessarily optimized as frequency-selective filters.
The Super Smoother uses recursively calculated coefficients derived from:
Exponential decay
Trigonometric terms
A selected cutoff period
The objective is to reduce high-frequency market noise while retaining lower-frequency directional structure.
What a low-pass filter does
A low-pass filter allows slow-moving components of a signal to pass while reducing fast-moving components.
In market terms:
Slow components represent broader trend structure.
Fast components represent short-term noise, rapid oscillation, and microstructure disturbance.
The Low-Pass Length determines the separation.
Lower values:
Allow more short-term movement through.
React faster.
Produce more turns.
Higher values:
Reject more high-frequency movement.
Produce a smoother trend line.
Respond later to structural changes.
Why use a two-pole filter
The number of poles broadly describes the order and steepness of a filter.
A two-pole filter generally provides:
Stronger attenuation of high-frequency noise than a one-pole filter.
A smoother output.
A more defined frequency response.
It also uses prior filter outputs recursively, allowing substantial smoothing without requiring an extremely long direct averaging window.
The full signal-processing chain
The calculation can be understood as:
Raw price source
Median Stage 1
Optional Median Stage 2
Optional Median Stage 3
Two-pole Super Smoother
Direction measurement
Chop-quality validation
Persistent trend state
Each layer has a distinct purpose.
Median cascade
Rejects isolated and short-lived noise.
Super Smoother
Reduces the remaining high-frequency variation.
Direction logic
Determines whether the final line is rising or falling.
Chop gate
Determines whether that directional change is trustworthy enough to update the confirmed trend.
Trend direction calculation
The raw direction is determined by comparing the current trend line with its value several bars ago:
Bullish if trendLine > trendLine
Bearish if trendLine < trendLine
Neutral if equal
The Direction Lookback controls how much movement is required before the line is classified as rising or falling.
A one-bar comparison is extremely responsive but can detect tiny slope changes.
A longer comparison:
Measures movement across a wider interval.
Reduces sensitivity to one-bar flattening.
Produces more stable raw direction.
Raw trend versus confirmed trend
One of the most important design choices is that the script separates:
Raw trend
Confirmed trend
The raw trend reflects the current direction of the filtered line.
The confirmed trend is persistent and changes only when:
The raw trend points in a new direction.
The chop gate is open.
If the line turns but the gate is closed:
The active trend state remains unchanged.
The attempted flip can optionally be displayed as blocked.
This prevents every small line turn from immediately becoming a regime reversal.
Why a chop filter is needed
Even an advanced smoother can turn repeatedly during sideways markets.
Smoothing reduces noise, but it does not determine whether movement is:
Directional
Efficient
Large enough relative to volatility
A line can move upward and downward in a range while making almost no net progress.
The chop gate addresses this by evaluating two separate properties:
Directional efficiency
ATR-normalized slope
Efficiency Ratio
The first gate component measures how efficiently the trend line has moved over a selected window.
The calculation compares:
Net movement
Total movement
Net movement is:
|trendLine - trendLine |
Total movement is:
Sum of |bar-to-bar changes in trendLine|
The Efficiency Ratio is:
Efficiency = Net Movement / Total Movement
The result ranges approximately from 0 to 1.
Efficiency near 1
The line has moved mostly in one direction.
Example:
It advanced 10 units.
Its total path was approximately 11 units.
This indicates a clean directional move.
Efficiency near 0
The line has moved back and forth without achieving much net progress.
Example:
It travelled 20 units in total.
But ended only 1 unit from where it started.
This indicates chop.
Where the Efficiency Ratio comes from
The Efficiency Ratio is commonly associated with Perry Kaufman’s work on adaptive market analysis and the Kaufman Adaptive Moving Average.
Its purpose is to distinguish directional movement from noisy movement.
It does not measure the size of a move alone.
It measures the quality of the path.
This makes it highly suitable as a trend gate.
A market can be volatile but inefficient. It can move aggressively in both directions and still make little progress.
The Efficiency Ratio helps identify that distinction.
Minimum Efficiency
The Minimum Efficiency input determines the directional quality required for the efficiency condition to pass.
Lower values:
Allow more irregular movement.
Open the gate more frequently.
Produce earlier but noisier trend changes.
Higher values:
Require cleaner directional travel.
Block more sideways flips.
Produce fewer but more selective regime changes.
ATR-Normalized Slope
The second gate component measures how large the trend-line movement is relative to current market volatility.
It calculates:
|trendLine - trendLine | / ATR
This converts the line’s movement into ATR units.
Without normalization, a slope of 10 points could be:
Large for one instrument
Negligible for another
Large in a quiet regime
Small during extreme volatility
ATR normalization makes the slope more comparable across assets and regimes.
What normalized slope measures
The Efficiency Ratio asks:
Was the movement directionally clean?
ATR-normalized slope asks:
Was the movement large enough to matter?
These are different questions.
A move can be highly efficient but extremely small.
For example:
A perfectly smooth drift of only 0.02 ATR may not justify a new trend regime.
A move can also be large but inefficient:
A violent range may travel several ATRs while repeatedly reversing.
Using both filters gives a more complete view of trend quality.
Minimum ATR-Normalized Slope
The Minimum ATR-Normalized Slope defines the movement threshold.
Lower values:
Accept weaker slopes.
React sooner.
Allow more low-energy flips.
Higher values:
Require stronger displacement.
Reduce weak trend transitions.
Delay signals until movement becomes more meaningful.
Gate modes
The indicator provides four chop-gate modes.
Efficiency Ratio
Only directional efficiency is required.
Best suited for users who care primarily about whether movement is clean, regardless of its exact magnitude.
ATR-Normalized Slope
Only slope strength is required.
Best suited for users who want movement to exceed a volatility-adjusted threshold, even if the path is not perfectly efficient.
Both
Both conditions must pass:
Efficiency must be high enough.
Slope must be strong enough.
This is the strictest mode and generally provides the strongest chop rejection.
Either
Only one condition must pass.
This is more permissive:
A very clean slow trend may pass through efficiency.
A powerful but less orderly move may pass through slope.
It provides a balance between responsiveness and filtering.
Gate behavior
When the gate is open:
A new raw direction can update the trend state.
Bullish and bearish flips can be confirmed.
When the gate is closed:
The previous confirmed trend persists.
Attempted changes are blocked.
The display can dim, turn neutral, or hide.
This means the indicator behaves like a regime-holding system during chop.
It does not constantly reset to neutral. It retains the last confirmed direction until sufficient evidence supports a new one.
Blocked flips
A blocked flip occurs when:
The filtered line attempts to change direction.
The attempted direction differs from the confirmed trend.
The chop gate is closed.
The raw direction has just changed.
These can optionally be displayed as small X markers.
Blocked flips are useful because they show:
Where a basic slope indicator would have reversed.
Where the chop filter rejected that reversal.
How much signal noise the gate is removing.
They are not trade signals. They are diagnostic information.
Trend persistence
The trend variable is persistent.
Once bullish:
It remains bullish until a valid bearish change passes the gate.
Once bearish:
It remains bearish until a valid bullish change passes the gate.
This persistence is central to the design.
The indicator is not merely coloring every local slope. It is maintaining a confirmed market regime.
Display modes when the gate is closed
The indicator offers three ways to display gated conditions.
Dim
The current trend color remains visible but becomes transparent.
This communicates:
The last confirmed trend is still active.
Current conditions are not strong enough to confirm a new direction.
This is useful when you want regime continuity without overstating conviction.
Neutral
The trend line and candles switch to the selected gate color.
This communicates:
The directional regime is being withheld.
Current conditions are considered non-trending or uncertain.
Hide
The trend line disappears while the gate is closed.
This is the strictest visual mode.
It communicates:
No actionable trend state should be displayed during the gated condition.
Trend line coloring
The base color is determined by the confirmed trend:
Bullish trend uses the long color.
Bearish trend uses the short color.
Uninitialized state uses gray.
The gate display can then modify the final presentation.
This allows the chart to show:
Direction
Confirmation status
Chop-filter activity
without needing a separate panel.
Gradient fill
The indicator fills the area between price and the trend line.
When price is above the trend line:
A bullish gradient is displayed.
When price is below the trend line:
A bearish gradient is displayed.
The fill is stronger near the trend line and fades toward price.
This emphasizes the trend line as the structural reference.
The fill color follows the gate display state:
Full trend color when active
Dimmed during a closed gate in Dim mode
Neutral when configured
Hidden when the trend line is hidden
Trend glow
A soft glow is created around the filtered line using a small ATR-scaled distance.
The glow width is:
ATR(14) × 0.06
Using ATR ensures the glow remains visually proportional across assets and price scales.
The glow does not affect the calculation. It improves readability and reinforces the line as the central structural output.
Candle coloring
Candles can be colored using the confirmed trend state.
When the gate is open:
Bull trend produces bullish candles.
Bear trend produces bearish candles.
When the gate is closed:
Dim mode fades the existing trend color.
Neutral mode uses the gate color.
Hide mode removes the candle override.
This gives an immediate full-chart view of both trend and gate status.
Signal logic
Signals only occur when the confirmed trend changes.
Bullish flip
A bullish signal requires:
The active trend becomes bullish.
The previous trend was bearish.
The gate accepted the change.
Bearish flip
A bearish signal requires:
The active trend becomes bearish.
The previous trend was bullish.
The gate accepted the change.
This is more selective than simply detecting a change in line slope.
How to interpret the indicator
Bullish confirmed regime
A bullish regime means:
The median-filtered and low-pass-smoothed trend line is rising.
The move satisfied the selected chop-filter conditions when the regime changed.
The last accepted direction remains bullish.
Bearish confirmed regime
A bearish regime means:
The final trend line is falling.
The bearish change passed the selected quality gate.
The last accepted direction remains bearish.
Gate closed in an existing trend
This means:
The market is no longer moving with sufficient efficiency or slope.
A new reversal cannot currently be confirmed.
The previous regime remains stored.
This often occurs during:
Consolidation
Pullbacks
Low-volatility drift
Transition phases
Repeated blocked flips
Repeated blocked changes suggest:
The filtered line is oscillating.
Directional quality is weak.
A normal trend-following system would likely be whipsawing.
This is exactly the environment the gate is designed to avoid.
How to use it in practice
1. Directional regime filter
Use the confirmed trend to filter other setups:
Favor long trades during bullish regimes.
Favor short trades during bearish regimes.
Reduce activity when the gate is closed.
2. Trend-following entries
Bullish and bearish flips can be used as directional entry triggers, especially when confirmed by:
Price structure
Breakouts
Volume expansion
Higher-timeframe alignment
3. Pullback framework
During an established trend:
Price returning toward the trend line may represent a pullback.
If the gate remains closed but the trend does not flip, the move may be consolidation rather than reversal.
If the opposite direction eventually passes the gate, the regime has changed more meaningfully.
4. Chop avoidance
The most direct use is avoiding repeated entries during low-quality conditions.
When the display is dim, neutral, or hidden:
Reduce confidence in new trend signals.
Wait for efficiency or slope to recover.
5. Diagnostic comparison
Enable blocked flips to see how often the raw trend attempts to reverse without confirmation.
This helps tune:
Efficiency threshold
Slope threshold
Gate mode
Input guide
Source
Selects the price series used by the median cascade.
Close is the standard choice.
Alternatives such as hl2 or hlc3 may produce a slightly smoother structural input.
Cascade Stages
Controls how many median filters are used.
1: responsive
2: balanced
3: strongest noise rejection
Median Stage Lengths
Control the window used at each stage.
Shorter values:
Preserve responsiveness.
Remove only very short-duration noise.
Longer values:
Produce stronger structural filtering.
Delay shorter turns.
Low-Pass Length
Controls the two-pole Super Smoother.
Lower:
Faster
More reactive
Higher:
Smoother
More conservative
Direction Lookback
Controls how far back the current line is compared when determining raw direction.
Lower:
Faster slope changes
Higher:
More stable directional measurement
Efficiency Length
Controls the window used to measure net movement versus total path movement.
Shorter:
More responsive efficiency reading
More sensitive to recent chop
Longer:
More stable directional-quality assessment
Minimum Efficiency
Controls how clean the directional path must be.
Higher values make the gate stricter.
Slope Length
Controls the interval across which trend-line movement is measured.
Slope ATR Length
Controls the ATR baseline used to normalize slope.
Minimum ATR-Normalized Slope
Controls the minimum volatility-adjusted displacement required.
Higher values require stronger movement.
Tuning examples
Faster trend configuration
1 or 2 median stages
Short median windows
Lower Super Smoother length
Direction Lookback of 1 or 2
Either gate mode
Lower efficiency and slope thresholds
Balanced configuration
2 or 3 median stages
Progressive windows such as 3, 5, 7
Moderate Super Smoother length
Both gate mode
Moderate thresholds
Conservative configuration
3 stages
Longer median windows
Higher Super Smoother length
Longer Direction Lookback
Both gate mode
Higher minimum efficiency and slope
How this differs from a normal moving average
A standard moving average:
Uses linear averaging.
Responds directly to outlier magnitude.
Changes direction whenever its slope changes.
Median Cascade Trend:
Uses nonlinear outlier-resistant preprocessing.
Applies a proper recursive low-pass stage.
Separates raw direction from confirmed regime.
Blocks changes during inefficient or weak movement.
How this differs from a normal median filter
A basic median filter only removes impulse noise.
This indicator extends the idea by adding:
Multiple median stages
Low-pass smoothing
Direction measurement
Efficiency gating
Volatility-normalized slope gating
Persistent trend states
Signals and alerts
It is not simply a median-smoothed line. It is a complete trend-regime system built on median preprocessing.
How this differs from a Supertrend
A Supertrend uses ATR bands around price and changes state when price crosses the trailing boundary.
Median Cascade Trend does not use a volatility band to define direction.
Instead:
Direction comes from the slope of the filtered trend line.
ATR is used only to normalize slope and scale visuals.
Trend changes are controlled by movement quality rather than price crossing a stop band.
Strengths
Strong rejection of isolated price spikes
Preserves broader structural movement
Combines nonlinear and linear filtering
Separates raw turns from confirmed trend changes
Configurable chop rejection
Volatility-normalized slope testing
Persistent directional regimes
Blocked-flip diagnostics
Fully causal calculation
Limitations
Strong filtering can delay genuine reversals.
Long median windows may suppress shorter trend legs.
Strict gate settings can hold the previous regime too long.
Loose gate settings reduce the benefit of chop filtering.
Median filters can produce stepped transitions before low-pass smoothing.
The indicator remains reactive rather than predictive.
Best use cases
Median Cascade Trend is particularly suited for:
Trend filtering in noisy markets
Reducing false reversals caused by wicks
Swing-trading regime identification
Filtering entries from faster systems
Dynamic trend-structure visualization
Avoiding sideways-market whipsaws
It can be applied across:
Equities
Indices
Futures
Forex
Cryptocurrency
Commodities
Alerts
The indicator provides alerts for:
Confirmed bullish trend changes
Confirmed bearish trend changes
Trend changes blocked by the chop filter
The blocked-flip alert is especially useful for monitoring when the line attempts to reverse but market quality remains insufficient.
Summary
Median Cascade Trend is a multi-stage trend extraction system designed to reject impulsive market noise before determining direction. It first applies up to three causal median filters, progressively removing isolated spikes and short-duration disturbances. The cleaned series is then passed through a two-pole Super Smoother to extract a stable low-frequency trend line.
Rather than accepting every change in slope, the indicator evaluates the quality of the movement using directional efficiency and ATR-normalized slope. Depending on the selected gate mode, trend changes can require clean directional travel, sufficient volatility-adjusted displacement, or both.
This produces a persistent trend state that changes only when the filtered line turns and the surrounding movement is strong enough to justify the reversal. The final result is a robust trend-following framework that combines outlier rejection, low-pass smoothing, directional-quality measurement, and chop suppression into a single overlay.
Indicator
Gaussian RSI | NAL1. Overview
Gaussian RSI | NAL is a smoothed momentum-regime indicator built around an RSI engine filtered through a Gaussian weighting model. Instead of plotting raw RSI, the indicator applies Gaussian smoothing to reduce noise and create a cleaner momentum line.
The signal is then refined with an optional Gaussian confluence filter. This adds a second smoothing layer that acts as a directional confirmation structure, helping separate stronger momentum regimes from weaker internal fluctuations.
2. Calculation
The indicator starts by calculating RSI from the selected source. This creates the base momentum reading used by the rest of the model.
The RSI is then passed through a Gaussian filter. The Gaussian filter weights the lookback window using a bell-curve style distribution, creating a smoother momentum line while still preserving directional movement.
A second Gaussian filter can also be applied as a confluence line. This creates a slower reference layer for the Gaussian RSI, allowing the indicator to judge whether the current RSI structure is aligned with its own smoothed trend.
The bullish condition requires the Gaussian RSI to move above the upper threshold. When confluence is enabled, the Gaussian RSI must also be above the Gaussian confluence line.
The bearish condition requires the Gaussian RSI to move below the lower threshold. When confluence is enabled, the Gaussian RSI must also be below the Gaussian confluence line.
The final state holds its previous direction when neither condition is active. This creates a cleaner regime output instead of constantly flipping to neutral between threshold zones.
3. Key Features
Gaussian-smoothed RSI momentum engine.
Optional Gaussian confluence filter.
Upper and lower threshold-based regime detection.
State-based candle coloring and RSI coloring.
Glow-style RSI plot, regime fills, confluence line, and transition labels.
Designed to reduce raw RSI noise while preserving momentum structure.
4. Use
Gaussian RSI is designed to identify when momentum begins shifting into a stronger bullish or bearish regime. A move above the upper threshold reflects bullish momentum pressure, while a move below the lower threshold reflects bearish momentum pressure.
The confluence filter adds an additional layer of structure by requiring the Gaussian RSI to align with its own smoother reference line. This can help separate cleaner momentum expansions from weaker internal movement.
This indicator is best used as a specialized momentum module within a complete strategy framework. Its role is to isolate a refined RSI-based momentum layer, where the full value comes from how the signal is integrated into a broader process for regime, timing, and execution.
Indicator
AI SuperTrend [PickMyTrade]THE PROBLEM WITH A FIXED MULTIPLIER
Every standard SuperTrend applies the same ATR multiplier across all market conditions — the same constant during a strong trending breakout, a narrow choppy range, and a volatility spike. A value calibrated for one regime is miscalibrated for the others. Most traders compensate by manually switching timeframes or parameters. This script automates that decision.
The question it asks: what if the ATR multiplier were selected from historical bars that most resembled the current market regime — matched by Hurst state and volatility rank — rather than set by the user as a fixed constant?
────────────────────────────────────────
THREE AI LAYERS
Layer 1 — Hurst Exponent (Regime Gate)
The Hurst Exponent is computed via Rescaled Range (R/S) Analysis. It measures the degree of long-range dependence (memory) in the price series:
H ≥ 0.55 → Persistent (trending) — SuperTrend active, signals enabled
H ≈ 0.50 → Random walk — candles turn gray, no signal generated
H ≤ 0.45 → Anti-persistent (mean-reverting) — signals suppressed
The Hurst gate is the first filter. Signals only fire when market structure is historically associated with persistence — not randomness or mean-reversion.
Layer 2 — Garman-Klass Volatility Rank
Garman-Klass (1980) estimates realized volatility from OHLC prices rather than close-to-close returns, capturing intrabar price range and making it more sensitive to volatility changes. The current reading is percentile-ranked against recent history (0–100%) and used as the second feature dimension for the KNN search.
Layer 3 — KNN Multiplier Optimizer
K-Nearest Neighbors searches a rolling memory bank of feature pairs from previous bars. For each current bar it finds the K most similar historical bars by Euclidean distance in that 2D feature space. From those neighbors it retrieves the ATR multipliers that were in effect — weighted by the profitability of the bar that followed. The result is the AI Multiplier: a context-aware value drawn from the most similar past conditions, not a fixed constant.
────────────────────────────────────────
WHAT YOU SEE ON THE CHART
Candle color — the defining visual. Orange = bull trend regime, blue = bear trend regime, gray = random walk or mean-reverting. The regime state is readable on every bar without checking the table.
Signals — ● (circle) marks trend flips with Hurst ≥ 0.65, the high-conviction threshold. ▲▼ (triangle) marks standard threshold crossings. No signal fires in gray (random or mean-reverting) regimes.
SL / TP lines — dashed lines drawn automatically at each signal bar, sized from current ATR × the AI Multiplier active at that bar.
Info table (top right) — live display of Hurst value, Regime label, Direction, AI Multiplier, Vol Rank, and KNN memory bar count. Shows WARMUP until KNN has stored enough bars to begin optimizing.
────────────────────────────────────────
HOW TO USE
A signal requires all three conditions to be true simultaneously:
KNN is warmed up (table shows ● LIVE)
Hurst confirms a trending regime (H ≥ Trend Threshold input)
Price is on the correct side of the EMA filter
Circle signals (●) indicate Hurst has exceeded 0.65 — stronger persistence than the standard threshold. Triangle signals (▲▼) are at the user-defined threshold. Gray candles indicate the market is not in a trending regime; reducing exposure or standing aside is appropriate during those periods.
The regime background shading (faint orange or blue fill) shows when the SuperTrend is in an active directional state.
────────────────────────────────────────
INPUTS
AI Engine — Hurst Lookback, KNN Neighbors (K), KNN Memory (bars), Trend Threshold, Mean-Revert Threshold
SuperTrend — ATR Length, Base Multiplier, Volatility Window, EMA Period
Visual — Bull/Bear colors, SL/TP lines toggle, SL ATR Multiplier, Risk:Reward ratio, Regime Background
Display — Zen Mode (hides labels and table), Show Info Table
ALERTS
Three alert conditions: Long Signal, Short Signal, Any Signal.
────────────────────────────────────────
NOTES
KNN requires warmup equal to the KNN Memory setting before the AI Multiplier activates. During warmup the base multiplier scaled by Hurst and volatility rank is used instead.
No repainting. All signals are confirmed on bar close. KNN stores lagged values only.
Garman-Klass citation: Garman, M. & Klass, M. (1980). On the Estimation of Security Price Volatilities from Historical Data. Journal of Business , 53(1), 67–78.
Indicator
Dynamic Volatility Filter [QuantAlgo]🟢 Overview
Dynamic Volatility Filter is a trend-following indicator built on an adaptive volatility threshold rather than fixed bands or moving average crossovers. It quantifies the realized volatility of recent price movement to establish a dynamic noise floor that price must overcome before the line responds, anchoring a filtered trend line that only shifts when a directional move exceeds the prevailing volatility regime, helping traders separate statistically significant trend change from noise-driven fluctuation across every timeframe and market.
🟢 How It Works
The foundation of the indicator is a rolling volatility estimate derived from the Average True Range over a configurable lookback window, scaled by a noise multiplier to produce the threshold used in all line logic:
threshold = ta.atr(lookback) * noise_mult
This threshold functions as a deviation barrier the line will not cross until price movement breaches it. On each bar the filter measures the displacement between price and the current line position, and only when that displacement exceeds the volatility threshold does the line update:
float diff = src - dvf_line
if math.abs(diff) > threshold
dvf_line := dvf_line + diff * snap_speed
The line remains stationary through movement that falls within the volatility envelope and only commits once displacement clears the threshold. Rather than converging directly onto price, the line advances by a fraction of the residual distance governed by the catch-up coefficient, producing a damped response instead of an instantaneous one. Lower catch-up values introduce deliberate lag that requires a move to persist before the line follows, while higher values tighten the track to price.
Direction state is derived from the line's own first difference, comparing its current position against the prior bar:
if dvf_line > dvf_line
trend_dir := 1
else if dvf_line < dvf_line
trend_dir := -1
Because the line holds flat whenever displacement stays inside the threshold, those periods register no direction change. With Show Neutral enabled the state resets to neutral during these pauses, and with it disabled the line retains its last directional reading until the next threshold breach.
🟢 Signal Interpretation
▶ Bullish Trend (Green): When the filter line registers positive displacement against its prior position, the indicator enters bullish mode with green coloring applied across the line, gradient fill, and volatility bands. This state persists through pullbacks contained within the threshold, since direction only updates when the line moves. The transition into green marks a potential long/buy opportunity, with pullbacks toward the line during an established bullish reading offering potential continuation entries.
▶ Bearish Trend (Red): When the filter line registers negative displacement against its prior position, the indicator enters bearish mode with red coloring across all visual elements. The reading holds bearish until price clears the volatility threshold in the opposite direction. The transition into red marks a potential short/sell opportunity, with rallies back toward the line during an established bearish reading offering potential continuation entries on the downside.
▶ Neutral (Gray): When Show Neutral is enabled, the line and fills turn gray during flat stretches where price stays inside the threshold and the line holds still. This state signals an absence of confirmed direction and is best treated as a stand-aside condition, where waiting for the line to commit back to green or red avoids entering during indecisive, range-bound conditions.
🟢 Features
▶ Preconfigured Presets: Three parameter sets cover different trading approaches. "Default" targets swing trading on 4-hour and daily charts with a balanced threshold that filters moderate noise while staying responsive to genuine regime shifts. "Fast Response" lowers the volatility barrier and shortens the lookback for intraday charts where the line needs to adapt to shorter-duration moves. "Smooth Trend" raises the threshold and slows the catch-up for position trading on daily and weekly timeframes, where the cost of a false flip exceeds the cost of a delayed one. Selecting a preset overrides the individual noise, period, and catch-up inputs.
▶ Built-in Alerts: Three alert conditions cover all directional states. "Bullish Trend Signal" fires on the bar where the trend confirms bullish. "Bearish Trend Signal" fires on the bar where it confirms bearish. "Any Trend Change" combines both into a single condition for traders who want a unified notification regardless of direction.
▶ Visual Customization: Six color presets (Classic, Aqua, Cosmic, Cyber, Neon, and Custom) apply coordinated bullish and bearish color schemes across the line, gradient fill, volatility bands, and optional bar and background coloring. The volatility bands plot one threshold above and below the line to frame the deviation envelope price must breach, and can be hidden for a clean line-only view. Bar coloring tints price candles with the active trend color at a configurable transparency level, and background coloring extends the directional tint across the full chart pane. Both are disabled by default and controlled independently.
*Recommendation: Layer the Dynamic Volatility Filter with complementary analysis rather than treating it as a standalone decision tool. Combine direction changes with volume context, since expanding volume on a flip bar suggests the move has broader participation behind it, and read transitions against key structural levels, as a flip occurring near major support or resistance carries more weight than one in open space. Pairing this script with volume, open interest, CVD, market structure, and mean reversion indicators from our QuantAlgo toolkit can further validate directional bias before entry. Indicator
Anchored VWAP Reversion ChannelAnchored VWAP Reversion Channel — Regime-Gated Fade Framework
## What this script does
This is an **analytical study** that frames mean-reversion ("fade") setups around an **anchored, volume-weighted regression channel**, then **gates** those setups by a statistical market-state test and **scores** them against their own forward outcomes. It does not place orders and it is not a signal service — its purpose is to let you see, on your own instrument and timeframe, whether fading a stretched move actually has an edge, instead of assuming it does.
It plots one channel (a centre line plus inner/outer residual-σ bands), marks fade setups at the outer band, draws supporting context (volume-profile POC / value area, untested prior-session POCs, momentum divergences, liquidity sweeps, and multi-timeframe trend lines), and reports a compact validation panel.
## Why these components are combined (mashup rationale)
Fading an extreme is really three separate questions, and no single classic indicator answers all three. Stacking look-alike indicators just echoes one input, so this tool deliberately combines **three non-redundant lenses and makes them check each other**:
1. **WHERE is price stretched?** — A **volume-weighted polynomial regression** anchored at the most recent swing pivot, with **residual-σ bands**. Because the curve tilts with the active leg, an outer-band tag stays meaningful even inside a trend, where a flat cumulative VWAP would not. A **volume profile** anchored to the *same* window supplies POC and value area, and prior-session POCs that have never since been traded through become **reversion targets**.
2. **Is a reversion actually firing here?** — Three orthogonal **tells** evaluated only at the band: a **close-back rejection**, a **band-confluent momentum divergence**, and an **equal-high/low liquidity sweep** (stop-run). Crucially, all three are derived from the same stretch, so their agreement is shrunk by a **design-effect correction** (effective-sample-size): three correlated echoes are not allowed to masquerade as three independent confirmations.
3. **Is the market in a reverting state at all?** — A **regime gate** combining a **variance-ratio test** and a **reversion-trust correlation** only lets a fade through when recent increments are offsetting (mean-reverting) rather than compounding (trending).
The pieces are not bolted together side by side: they share **one geometry** (the anchored channel) and **one volatility unit** (residual σ / ATR), and each can veto the others. A band tag with no tell does nothing; a tell with no reverting regime does nothing. The design goal is to **suppress** low-quality fades — into a trend, mid-range, or backed by a single echoed tell — more than to generate them.
## The honesty layer (what makes this more than a drawing)
Every fade that fires is logged and, a fixed horizon later, **resolved**: its forward return is measured in ATR units and tabulated **with the regime gate ON versus OFF**, reporting follow-through %, whipsaw %, a Wilson 95% confidence interval, and the **mean return per fade**. A per-fade series also exports to the Data Window so you can study the full return distribution offline. The gate has to **beat its own ungated baseline** to justify itself — the framework is built to be tested, not trusted blindly.
## How to use it
1. Set the **Price source** (group 01). It works on any symbol and any market; volume-based parts need a real volume feed.
2. A fade **arms** when price tags the outer band **and** at least one tell prints, then **passes** only if the regime gate reports a reverting state. Solid triangles are gated fades; the target is the centre line or the nearest untested POC.
3. Read the panel top-down: does **Gate ON** beat **Gate OFF** on both follow-through and mean R, with non-overlapping intervals and a reasonable sample size? If not, the edge is not present on this symbol/timeframe — change them rather than forcing the trade.
4. The signal lives on **higher intraday timeframes**; one-minute data is mostly noise.
## Defaults
Shipped tuned for **NSE:NIFTY** index futures on intraday timeframes (sources, pivot lengths, value-area %, and the Tuesday-style weekly session context reflect that instrument). Every value is exposed as an input — change the **Price source** and the relevant lengths to run the framework on any other instrument or market.
## What is original
The original work is the **coordination**, not any single formula: an anchored polynomial-regression channel used as a reversion frame, three decorrelated band tells fused by a design-effect shrink, a statistical regime gate, and a built-in A/B + forward-return validation harness — combined so each lens can veto the others and the whole thing reports its own hit rate. It is not a re-skin of one indicator.
## Concept credits (techniques are standard; this implementation is original)
Anchored VWAP (standard); volume-weighted least-squares / polynomial regression (standard); residual-σ channel (standard); Volume Profile, Value Area and POC — Market Profile, Steidlmayer / CBOT; Variance-Ratio test — Lo & MacKinlay (1988); design effect / effective sample size — Kish (1965); proportion confidence interval — Wilson (1927); ATR trailing stop / Supertrend (classic, used for the multi-timeframe context lines); RSI — Wilder; Stochastic — Lane.
## Disclaimer
For research and education only. This is an analytical study, **not** financial advice, **not** a recommendation, and **not** a guarantee of future results. All statistics shown are **in-sample** on loaded history, close-to-close, without costs or slippage — a study aid, not a backtest. Mean reversion fails in trends and through regime breaks. Do your own research and manage your own risk.
Indicator
NLMS Adaptive Trend Filter [BackQuant]NLMS Adaptive Trend Filter
Overview
The NLMS Adaptive Trend Filter is a machine learning inspired trend-following indicator built around one of the most important adaptive filtering algorithms in signal processing: the Normalized Least Mean Squares (NLMS) filter .
Unlike traditional moving averages that use fixed weighting schemes, the NLMS filter continuously learns from incoming market data and updates its internal coefficients in real time. Rather than assuming that price behavior remains constant, the filter attempts to adapt its structure as market conditions evolve.
This approach originates from the field of digital signal processing, where adaptive filters have been used for decades in applications such as:
• Telecommunications
• Radar systems
• Echo cancellation
• Noise reduction
• Speech processing
• Control systems
• Financial signal extraction
The goal of this indicator is to bring one of these adaptive filtering concepts into market analysis by creating a trend model that continually adjusts itself based on prediction error rather than relying on static averaging methods.
Historical Background
The roots of the NLMS filter can be traced back to the work of Bernard Widrow and Ted Hoff in the late 1950s and early 1960s.
While working at Stanford University, they developed what became known as the:
Least Mean Squares (LMS) Algorithm
The LMS algorithm was revolutionary because it provided a computationally simple method for training adaptive systems using gradient descent.
Rather than solving a complex optimization problem all at once, the LMS algorithm updates its weights incrementally after each observation.
The basic concept was:
1. Make a prediction.
2. Measure the prediction error.
3. Adjust the model slightly.
4. Repeat indefinitely.
This idea eventually became one of the foundational concepts behind modern machine learning and online optimization.
Many modern neural networks still rely on the same underlying principle:
Error → Gradient → Weight Update
The LMS algorithm later evolved into several variants, one of the most important being:
Normalized Least Mean Squares (NLMS)
NLMS improves stability by scaling weight updates according to the energy of the input signal.
This prevents learning rates from becoming too aggressive during high-volatility periods and too weak during low-volatility periods.
As a result, NLMS became one of the most widely used adaptive filtering algorithms in engineering.
What Makes NLMS Different From Moving Averages?
Traditional moving averages use predetermined weights.
For example:
Simple Moving Average (SMA)
Every observation receives equal weight.
Example:
20-period SMA
Each bar contributes:
1 / 20 = 5%
regardless of market conditions.
Exponential Moving Average (EMA)
Recent observations receive more weight.
The weighting structure is fixed and never changes.
Weighted Moving Average (WMA)
Uses linearly decreasing weights.
Again, the weighting scheme is fixed.
The problem is that markets do not operate under fixed conditions.
Volatility changes.
Trend persistence changes.
Noise levels change.
Market structure changes.
Yet traditional moving averages continue using the exact same weighting model.
NLMS takes a different approach.
Instead of assigning permanent weights, it learns them dynamically.
The filter constantly asks
"What weighting structure would have predicted the current market best?"
It then updates itself accordingly.
The Core Idea Behind Adaptive Filters
Imagine trying to forecast today's price using the previous 20 bars.
A normal moving average assumes a fixed weighting pattern.
An adaptive filter attempts to learn the optimal weighting pattern.
At every bar:
• A prediction is generated.
• Actual price is observed.
• Prediction error is measured.
• Weights are adjusted.
The process repeats indefinitely.
Over time, the filter learns which historical observations are most useful and which are less important.
Understanding Filter Taps
One of the most important concepts in adaptive filtering is the idea of:
Taps
A tap is simply a historical observation used as an input.
If the indicator uses:
20 taps
it means:
Price
Price
Price
...
Price
are all being used to generate the prediction.
Each tap receives a learned weight.
Instead of:
Current Estimate =Average of past 20 bars
the filter becomes:
Current Estimate =
(w1 × Price ) +
(w2 × Price ) +
(w3 × Price )
...
(w20 × Price )
The weights are continuously adjusted through learning.
How Prediction Works
The indicator attempts to estimate current price using previous observations.
Mathematically:
Prediction = Σ(weight × historical price)
This prediction becomes the filter output.
If the prediction is accurate:
Weights change very little.
If the prediction is poor:
Weights adjust more aggressively.
This allows the model to gradually adapt to changing market conditions.
Prediction Error
The engine measures:
Error = Actual Price − Predicted Price
This error drives all learning.
Large error means:
The model is wrong.
Small error means:
The model is performing well.
The objective is to minimize prediction error over time.
The LMS Learning Rule
The original LMS update rule is:
New Weight =Old Weight + Learning Rate × Error × Input
This is effectively a form of gradient descent.
The filter moves its weights in the direction that reduces future prediction error.
This is conceptually identical to many machine learning optimization methods.
Why Normalization Matters
The original LMS algorithm has a weakness.
When input values become very large:
Weight updates can become unstable.
This is particularly problematic in financial markets where volatility constantly changes.
NLMS solves this problem by normalizing updates according to signal energy.
Instead of:
Weight Update ∝ Error
it becomes:
Weight Update ∝ Error / Signal Power
This creates adaptive scaling.
When volatility expands:
Updates automatically shrink.
When volatility contracts:
Updates automatically expand.
This improves stability significantly.
How the Indicator Uses NLMS
The script implements an online one-step predictor.
For every new bar:
1. Previous M bars are gathered.
2. Current price is predicted.
3. Prediction error is calculated.
4. Weight vector is updated.
5. New estimate becomes available.
This process occurs continuously as new data arrives.
Because no future data is used, the filter remains fully causal and suitable for live trading.
Weight Initialization
Initially all weights are equal:
1 / M
This effectively starts the model as a simple moving average.
Over time the filter learns a custom weighting structure based on market behavior.
The initial equal-weight state acts as a neutral prior.
Step Size (μ)
The learning rate controls how aggressively the filter adapts.
Lower values:
• More stable
• Smoother output
• Slower adaptation
Higher values:
• Faster adaptation
• More responsiveness
• Greater noise sensitivity
Think of μ as controlling the intelligence speed of the model.
Small values make it conservative.
Large values make it reactive.
Regularization (ε)
Regularization prevents division by very small values.
Without it:
Periods of extremely low signal power could create unstable updates.
Regularization improves numerical stability and robustness.
It acts as a safety mechanism for the learning process.
Output Smoothing
After the NLMS estimate is generated, an optional EMA can be applied.
This smoothing is not part of the NLMS algorithm itself.
It exists purely for visual clarity.
The raw adaptive filter already contains the learning logic.
The smoothing stage simply reduces small fluctuations.
Setting smoothing to 1 effectively disables it.
Trend Detection
Trend direction is derived from the slope of the adaptive filter.
Bullish:
NLMS Output > Previous Output
Bearish:
NLMS Output < Previous Output
This creates a directional state machine.
Unlike crossover systems, trend changes occur whenever the adaptive estimate changes slope.
Bullish Flips
A bullish signal occurs when:
Trend changes from bearish to bullish.
This means the adaptive filter has transitioned from declining to rising.
Bearish Flips
A bearish signal occurs when:
Trend changes from bullish to bearish.
This means the adaptive filter has transitioned from rising to falling.
Visual Components
The indicator includes several visualization layers.
Adaptive Filter Line
The main output of the NLMS model.
This represents the learned trend estimate.
Gradient Fill
The space between price and filter is colorized.
Price Above Filter:
Bullish shading.
Price Below Filter:
Bearish shading.
This provides immediate visual context regarding trend alignment.
Edge Glow
An ATR-based glow surrounds price.
This helps emphasize directional conditions while improving chart readability.
Trend Candles
Candles can optionally inherit trend coloration.
Green:
Adaptive trend rising.
Red:
Adaptive trend falling.
This allows traders to visualize the model's directional state directly on price.
How It Differs From Traditional Trend Filters
Most trend indicators answer:
"What is the average price?"
NLMS attempts to answer:
"What weighting structure best predicts current price?"
This distinction is extremely important.
The indicator is not simply smoothing price.
It is continuously learning how price behaves.
Traditional indicators use fixed mathematics.
NLMS uses adaptive mathematics.
Strengths
• Self-adjusting weighting structure.
• Adapts to changing market conditions.
• Based on established signal-processing theory.
• Stable due to normalization.
• Less reliant on arbitrary moving-average formulas.
• Learns continuously.
• Fully causal and non-lookahead.
Limitations
• Not a predictive model in the forecasting sense.
• Can still lag during major regime shifts.
• Excessively large learning rates may introduce noise.
• Small tap counts can become unstable.
• Large tap counts can become sluggish.
Like all adaptive systems, there is a tradeoff between responsiveness and stability.
Best Use Cases
The NLMS Adaptive Trend Filter is particularly effective for:
• Trend identification.
• Regime classification.
• Dynamic support/resistance visualization.
• Adaptive trend following.
• Noise reduction.
• Signal confirmation.
Summary
The NLMS Adaptive Trend Filter applies one of the most important adaptive algorithms in modern signal processing to financial markets. Rather than relying on fixed moving-average weights, it continuously learns from prediction error and updates its internal model in real time. Built upon the pioneering work of Widrow and Hoff, the indicator combines adaptive filtering, normalized gradient descent, and online learning principles into a practical trend-following tool that evolves alongside changing market conditions. The result is a trend model that is fundamentally different from traditional moving averages, not because it smooths price differently, but because it learns how to smooth price as new information arrives.
Indicator
Multi Trend FilterOverview
Multi Trend Filter shows the market's underlying trend with a single, clean line. It uses the Daily timeframe as the base, then layers 4H and 30m confirmation on top, coloring the trend in three intuitive states: green / yellow / red. No matter which chart timeframe you view it on (15m, 1h, 4h, etc.), it stays consistent on the Daily basis, and a two-pass smoothing keeps the line smooth on any chart.
WHAT THE COLORS MEAN
Green (UPTREND): Uptrend confirmed.
Yellow (TRANSITION): Trend is shifting — awaiting confirmation.
Red (DOWNTREND): Downtrend confirmed.
HOW IT WORKS — 3-STAGE MULTI-TIMEFRAME CONFIRMATION
Set the broad direction with higher timeframes first, then confirm the entry with the lower timeframe last.
Daily (Base / Reference line) — the line drawn on the chart; the anchor of the larger trend.
4H (Primary confirmation) — when both the close and the 4H flow align above/below the Daily line, the base direction is set.
30m (Final confirmation) — in that base direction, once the candle body closes across the 30m line, the color is finalized:
Close closes ABOVE the 30m line → Green
Close closes BELOW the 30m line → Red
Yellow (Transition) is the period before both stages confirm — the 4H hasn't committed yet, or it has but the close hasn't cleared the 30m line. Once green/red is confirmed, the color ignores minor noise while the base holds (latch), so it won't flicker.
HOW TO READ IT
Green: uptrend intact. Look for pullback entries.
Red: downtrend intact. Look for bounce exits / stay aside.
Yellow: direction unclear; safer to wait until it confirms green/red.
Yellow to Green = bullish shift. Yellow to Red = bearish shift.
KEY SETTINGS
Trend Line — First / Second Smoothing Length: line smoothness and responsiveness.
Display Smoothing — Smooth Line, Smooth Strength: keeps the line smooth on any timeframe (higher = smoother, slightly more lag).
Confirmation Lines — Show 30m Line (Signal) / Show 4H Line (Base): reveal the lines used for confirmation.
Style — up/down/transition colors, line width, fill and transparency, trend label.
TIPS
Use it as a trend direction and shift filter, not a standalone trade signal.
Reliability increases when the trend color agrees with your own setup (support/resistance, volume, etc.).
If the line looks choppy, raise Smooth Strength.
DISCLAIMER
This indicator is a reference tool to help judge trend direction. It does not guarantee trading profits. All trading decisions and responsibility rest solely with the user. Indicator
Hilbert Bandwidth [LB]🔬 Concept
The Hilbert Bandwidth Index, derived from John Ehlers' analytic signal approach, measures the instantaneous stability of the dominant market cycle by computing the bandwidth — the absolute deviation between the raw instantaneous period and its smoothed counterpart. A narrow bandwidth indicates a clean, well-defined cycle suitable for trading.
📐 Mathematical Foundation
The price median is transformed via a 7‑coefficient FIR Hilbert Transform to extract the analytic signal's real and imaginary components :
real = 0.0962·P + 0.5769·P - 0.5769·P - 0.0962·P
imag = 0.0962·P + 0.5769·P - 0.5769·P - 0.0962·P
The instantaneous phase φ is obtained via the two‑argument arctangent of imag and real . After exponential smoothing, the phase difference Δφ between consecutive bars yields the instantaneous period :
T = 2π / |Δφ|
Finally, the bandwidth is defined as :
B = |T - EMA(T, L) |
where L is the period smoothing length. The result is expressed in bars.
🎯 What Problem Does It Solve ?
Traditional cycle indicators assume a persistent dominant cycle, producing unreliable signals during chaotic or transitional markets — the Hilbert Bandwidth quantifies cycle cleanliness in real time, allowing traders to filter out low‑quality cyclic signals and act only when the market exhibits a stable, tradeable rhythm.
📊 How To Interpret
Bandwidth below threshold (background colored) → the dominant cycle is narrow, well‑defined, and stable ; trend‑following and cycle‑based strategies have higher probability of success.
Bandwidth above threshold → the cycle is broad and unstable ; the market is either noisy or in transition ; avoid cycle‑dependent entries.
Bandwidth rapidly contracting → the market is shifting from chaos to order ; anticipate a breakout or the emergence of a clean trend.
⚙️ Parameters
Phase Smoothing – exponential smoothing length applied to the instantaneous phase (default 50) ; higher values stabilize the phase estimate but introduce lag.
Period Smoothing – EMA length applied to the instantaneous period (default 10) ; controls the responsiveness of the bandwidth calculation.
Narrow Band Threshold – the bandwidth value in bars below which the cycle is considered "clean" (default 3.0) ; the background is highlighted when bandwidth falls below this level.
📚 Reference
Ehlers J.F., "Rocket Science for Traders : Digital Signal Processing Applications", Chapter 7 – The Hilbert Transform, John Wiley & Sons, 2001.
Ehlers J.F., "Cycle Analytics for Traders", Chapter 9 – Bandwidth Measurement, John Wiley & Sons, 2014. Indicator
Echelon Trend Filter [JOAT]Echelon Trend Filter
Introduction
Echelon Trend Filter is a recursive digital trend filter that creates a clean trend spine, optional step-state transitions, EMA context, and pivot zones.
This open-source indicator is designed as a context tool, not a standalone trading system. It focuses on explaining the current market state with restrained visuals and confirmed-bar logic where signals are used.
Core Concepts
1. Recursive Filter
The source is smoothed recursively to reduce minor bar noise.
2. Step-State Transitions
Optional step mode holds the trend state until a meaningful transition occurs.
3. EMA Context
A long EMA gives broader context for the filtered state.
4. Pivot Context Zones
Recent pivots create reference zones around prior turning areas.
state = step > step ? 1 : step < step ? -1 : state
Features
Digital trend spine
Optional step-state behavior
EMA cloud context
Pivot context zones
Confirmed transition alerts
Input Parameters
Filter length and source
Step mode toggle
Institutional EMA length
Pivot length and zone width
Cloud, candle, and panel toggles
How to Use This Script
Use the spine as trend context. Long and short transition labels mark confirmed state changes, while the EMA cloud helps judge broader alignment.
Limitations
The script uses historical OHLCV data and cannot know future prices.
Signals and states can be late during fast reversals because confirmed-bar logic is used to reduce repainting.
Model outputs should be interpreted with market context, risk controls, and independent analysis.
No visual state should be treated as a certain trade outcome.
Originality Statement
Echelon is original in combining recursive smoothing, held state transitions, EMA context, and pivot zones into a minimal trend overlay.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any financial instrument. All calculations are derived from historical market data and may produce inaccurate readings in some market conditions. No indicator can predict future market behavior. Use proper risk management and independent judgment.
-Made with passion by jackofalltrades
Indicator
Ichimoku Regime ClassifierIchimoku Regime Classifier is an open-source market regime filter that labels conditions as TREND UP, TREND DOWN, or VOLATILE.
This script is designed to solve a practical problem: many entries fail because traders apply the same setup in all environments.
Its purpose is to classify the current market context first, so strategy rules can be adapted to regime.
Methodology
The classifier uses Daily Ichimoku structure plus ADX trend-strength confirmation:
Bullish regime:
Price above the Kumo
ADX above threshold
Tenkan above Kijun
Chikou-style confirmation (current price vs past price)
Bearish regime:
Price below the Kumo
ADX above threshold
Tenkan below Kijun
Opposite Chikou-style confirmation
Volatile regime:
If neither bullish nor bearish set is fully confirmed
Why this combination
Ichimoku provides structural trend context, while ADX filters weak directional phases.
The combination aims to reduce false directional bias during choppy periods and keep regime logic explicit.
How to use
TREND UP: prioritize long-biased setups
TREND DOWN: prioritize short-biased setups
VOLATILE: reduce risk, be selective, or wait for structure
This indicator is intentionally minimal on-chart (single regime label) to keep output readable and unambiguous.
Limitations
Regime transitions can lag, especially after sharp reversals.
This tool is a context filter, not a standalone entry/exit system.
Open-source notice
Published as open source for transparency, review, and customization.
Educational content only. Not financial advice. Indicator
Fractional CUSUM Regime Filter [Jamallo]🔹 Intro
The Fractional CUSUM Regime Filter is a quantitative trend and volatility channel indicator designed to identify structural market regimes with high stability. Unlike traditional filters that suffer from lag or whipsaw signals, this indicator applies a dual-layered preprocessor combining Lopez de Prado’s Fixed-Window Fractional Differentiation (FFD) with a classic Cumulative Sum (CUSUM) statistical trigger.
🔹 Break down
Fractional Differentiation (FFD) : Stationarizes pricing input while retaining long-term historical memory (controlled by the "d" parameter).
Classic CUSUM Filter : Accumulates deviations from a rolling baseline relative to current volatility. When cumulative deviation exceeds the threshold multiplier (h), a new regime change is triggered, and the baseline steps dynamically to the new price level.
Vol-Adjusted Percentile Bands : Linear interpolation percentiles of price deviation relative to the stepped baseline. The distances are "locked" and only update on CUSUM regime triggers to prevent wobbly bands.
🔹 Visual Guide: Indicator Anatomy
Here we have the structure of the indicator, including the CUSUM baseline, the 68% inner percentile band, and the 95% outer percentile band.
How to use: Mean Reversion (Pullbacks to the Mean)
The channel boundaries represent statistical extremes. When the price is pushed outside the bands, it is mathematically overextended and highly likely to revert back to the CUSUM baseline.
Long Setup (Pullbacks in Bullish Trend): During a green CUSUM uptrend, watch for price to pull back to the lower bands ("micro pullback") and enter as it heads back up to the baseline mean.
Short Setup (Pullbacks in Bearish Trend): During a red CUSUM downtrend, watch for price to rally into the upper bands ("micro pullback") and enter as it reverts down to the baseline mean.
🔹 How to use: Trend Following & Risk Management
The indicator is designed to capture sustained macro trends while providing clear risk parameters.
Entering on Breakthroughs : Enter when the BUY/SELL signal flags appear (indicating a new CUSUM regime shift).
Stop Loss Placement : Place stop loss orders just below the opposite outer band or below the stepped baseline.
Letting Winners Run : Ride the trend as long as the CUSUM baseline maintains its colored regime state (Green for Long, Red for Short).
🔹 Settings Parameters
d (0.01 - 0.99) : Differentiation order. Lower values retain more historical memory; higher values approach first-difference.
CUSUM Drift & Threshold : Controls baseline sensitivity to regime changes.
Percentile Lookback & Targets : Set the statistical width of the inner and outer boundaries.
Indicator
Elaris Smart Scalping IndicatorElaris Smart Scalping Indicator is a non-repainting trend and momentum scalping tool designed to help traders identify higher-quality buy and sell conditions using a structured confluence model.
The indicator combines EMA trend direction, RSI momentum, MACD histogram confirmation, volume strength, ATR volatility filtering, optional higher-timeframe bias, and session filtering into a clean signal-scoring system. Signals are confirmed only after candle close, helping reduce intrabar noise and repainting behavior.
It also includes visual TP/SL guide levels, trend background shading, buy/sell labels, alert conditions, and a compact dashboard showing trend state, HTF bias, RSI, ATR percentage, volume filter status, and signal score.
This tool is designed for scalping and short-term trading analysis across crypto, forex, indices, and other liquid markets. It is not financial advice and should be used with proper risk management and additional market context.
Key Features
Non-repainting confirmed buy/sell signals
EMA-based trend engine with adjustable strictness
RSI and MACD momentum confirmation
Optional higher-timeframe trend filter
Volume and ATR volatility quality filters
Optional session filter
Signal score system from 0–100
Visual entry, stop loss, TP1, and TP2 guide levels
Clean dashboard for live market state
Built-in PulseWire alert conditions Indicator
Aureon Pressure Lens [JOAT]Aureon Pressure Lens
Introduction
Aureon Pressure Lens is an open-source pressure oscillator designed to classify directional participation, conviction, and transition states in a separate pane. It blends price impulse, EMA structure, momentum, range location, candle body pressure, and relative volume into one bounded score.
The problem it solves is signal quality. A single oscillator can fire during weak, low-participation moves. Aureon Pressure Lens requires pressure, signal-line behavior, relative volume, and component consensus to align before confirmed buy or sell labels appear.
Core Concepts
1. Multi-Component Pressure Blend
The oscillator uses several independent inputs: impulse from prior price, fast/slow structural slope, normalized momentum, range position, and candle body direction.
2. Tanh Normalization
Each component is normalized into a stable bounded range so one volatile input does not dominate the entire reading.
pressureScore = f_tanh(pressureBlend * 1.60) * 100.0
signalLine = ta.ema(pressureScore, signalLength)
3. Consensus Filter
The confidence reading measures how closely the components agree. A signal must satisfy the minimum conviction threshold before it can print.
4. Relative Volume Participation
The script measures current volume against a moving average and uses that reading as a participation gate. The default is permissive enough for broad use while still filtering extremely quiet conditions.
Features
Separate-pane pressure score: Bounded -100 to +100 directional pressure reading
Signal line: Smoothed reference for pressure resets and crossovers
Gradient pressure color: Score color transitions between bearish, neutral, and bullish states
Pressure cloud: Optional fill between pressure and signal line
Confirmed BUY/SELL labels: Closed-bar events filtered by consensus and RVOL
Top-right dashboard: State, bias, pressure, signal, RVOL/conviction, and action
Alerts: Bullish and bearish confirmed pressure resets
Input Parameters
Calculation:
Core Lookback: Main analysis window for impulse and range context
Fast Lens / Slow Lens: EMA structure lengths
Signal Lens: Smoothing length for the signal line
Pressure Sensitivity: Normalization intensity
Min Relative Volume: Participation gate for labels
Min Conviction: Minimum component agreement required for labels
How to Use This Indicator
Step 1: Read the pressure score relative to zero.
Step 2: Use the cloud and signal line to identify pressure resets.
Step 3: Check dashboard conviction and RVOL before acting on labels.
Step 4: Combine with an overlay structure or regime tool for full chart context.
Indicator Limitations
The oscillator measures current pressure, not future price direction
Relative volume can behave differently on symbols with limited volume data
Choppy markets can create repeated signal-line crosses
Confirmed labels appear only after the bar closes
Originality Statement
Aureon Pressure Lens is original because it combines impulse, structure, momentum, range position, candle body pressure, relative volume, and component consensus into a single closed-bar pressure engine with a dedicated dashboard. It does not copy third-party source code.
Disclaimer
This open-source indicator is for educational and informational purposes only. It is not financial advice. Markets can change quickly, and no pressure reading guarantees a future move. Use risk controls and independent analysis.
-Made with passion by jackofalltrades
Indicator
Covenant Regime Atlas [JOAT]Covenant Regime Atlas
Introduction
Covenant Regime Atlas is an open-source Pine Script v6 market-regime indicator built to classify directional state through trend, expansion, persistence, and retest quality. Its purpose is not to predict the next trade by itself, but to create a durable bias layer that tells the trader whether the market is developing a bullish regime, a bearish regime, or a maturing directional environment worth respecting.
The problem this script solves is context instability. Many traders can spot a moving-average crossover or a burst in ATR, but that alone does not answer whether the regime is actually mature, whether momentum has real separation, or whether recent retests are behaving consistently with the dominant trend. Covenant Regime Atlas addresses this by blending multiple regime components into one overlay and dashboard.
The script uses a dual-mid framework derived from EMA and HMA references, ATR-scaled cloud and envelope bands, persistence measurement, heat normalization, slope impulse, and retest memory. This lets it move beyond a simple bullish-versus-bearish cross and instead describe whether the regime is developing, mature, expanding efficiently, or internally cooling.
The result is an indicator for traders who want a cleaner read of bias before interpreting any trigger tool. It is especially useful as a regime filter for execution indicators and strategies that should behave differently in mature directional flow versus unstable transition periods.
Core Concepts
1. Directional Mid Versus Structural Mid
The script creates a fast directional midpoint and a slower structural midpoint using blended EMA and HMA references. The spread between those two curves forms the backbone of regime direction.
float directionalMid = math.avg(emaFast, hmaFast)
float structuralMid = math.avg(emaSlow, hmaSlow)
bool trendBull = directionalMid > structuralMid
This gives the regime engine more shape than a single moving average crossover. The directional mid measures active flow. The structural mid measures slower context.
2. Regime Strength Through Separation And Heat
Regime strength is calculated from ATR-normalized spread plus the distance of normalized heat from its midpoint. In other words, the regime is strongest when the fast and slow structures are well separated and price is also positioned decisively within its recent range.
This helps avoid overvaluing tiny directional crosses that occur with little actual separation or energy.
3. Persistence And Maturity
Every regime needs time to prove itself. The script counts how long the current directional condition has been intact and compares that against a user-defined persistence floor. Once the threshold is met, the regime is treated as mature rather than merely developing.
This matters because a fresh directional flip is different from a directional condition that has held for many bars and survived multiple retest opportunities.
4. Retest Memory
After a mature regime forms, the indicator watches for controlled retests of the directional midpoint. Bull retests occur when price revisits the midline from above and closes back above it. Bear retests use the opposite condition. The last retest is stored as a dotted line and extended forward until it becomes irrelevant.
This gives the trader a simple memory of where the market most recently confirmed trend participation.
5. Pulse, Expansion, And Efficiency
The script also measures volatility expansion, slope impulse, heat drift, trend separation percentage, and directional travel efficiency. These metrics allow the dashboard to distinguish between a mature regime that is expanding forcefully and one that is mature but internally cooling or grinding.
Features
Bull and bear regime classification: Uses fast-versus-slow blended midpoints to define directional control
Maturity logic: Distinguishes developing regimes from mature ones using persistence counting
ATR-scaled cloud and envelope: Frames the current directional corridor directly on the chart
Retest memory engine: Stores the latest mature-regime retest level for forward reference
Initiation band: Preserves the regime start envelope so traders can judge distance from the original launch zone
Pulse ribbon: Adds a compact visual band around price to reflect internal heat conditions
Regime backdrop shading: Tints the chart according to the active directional state
Detailed dashboard: Displays strength, heat, persistence, expansion, slope pulse, retest distance, maturity, efficiency, and more
Confirmed-bar alerts: Includes mature bias, retest, expansion, continuation, efficient trend, and heat-reset conditions
Data-window outputs: Exposes regime internals for systematic reading or comparison
Visual Elements
Directional cloud: The gap between the fast and slow regime mids shows whether the market is operating with clean separation
Envelope bands: ATR-based boundaries help frame the active directional corridor around price
Initiation band: The regime launch area stays visible so users can measure how far the trend has traveled from origin
Retest line memory: The latest confirmed retest is preserved as a direct chart reference
Backdrop and pulse ribbon: Context shading and the pulse band make regime character readable without overloading the chart
Best Practices
Treat mature regimes differently from developing ones because the same trigger can behave very differently in each state
Watch heat drift when a regime remains mature but starts losing internal energy
Use retest memory to frame participation zones rather than chasing every extension away from the midline
Give more weight to regimes that show both persistence and expansion instead of one without the other
Use the atlas as a context engine first and an alert source second
Input Parameters
Trend Engine:
Fast Length: Sets the faster directional reference
Slow Length: Sets the slower structural reference
Heat Window: Defines the range-normalization window for heat calculations
ATR Length: Controls volatility normalization
Cloud Width Factor: Sets the width of the directional cloud and envelope
Retest Engine:
Show Retest Memory: Toggles retest storage and line rendering
Retest Cooldown Bars: Prevents retests from firing too frequently
Persistence Floor: Sets how many bars are required before a regime is considered mature
Show Initiation Band: Displays the preserved start range of the current regime
Maturity Window: Controls maturity scaling and travel-efficiency measurements
Display:
Show Dashboard toggle
Show Regime Backdrop toggle
Show Pulse Ribbon toggle
Independent bull, bear, neutral, and panel colors
How to Use This Indicator
Step 1: Read Regime Tag And Strength
Begin with the dashboard’s regime tag. It tells you whether the market is bullish or bearish and whether that state is still developing or already mature. Pair that with the strength reading to avoid confusing a weak directional bias with a strong one.
Step 2: Check Persistence And Expansion
Persistence tells you how long the regime has survived. Expansion tells you whether volatility is supporting the move. A mature regime with positive expansion usually deserves more respect than a new regime with weak expansion.
Step 3: Use Retest Memory As A Structural Anchor
When the retest line is present, it marks the last meaningful participation check inside the trend. That line can help frame whether the current move is still building from a healthy base or drifting too far away from supportive structure.
Step 4: Watch Heat Drift And Efficiency
Heat drift helps show whether the regime is internally warming or cooling. Efficiency tells you whether directional travel has been orderly. These readings are helpful when deciding whether the trend still looks clean or is becoming unstable.
Step 5: Use It As The Bias Layer For Other Tools
Covenant Regime Atlas is best used as a bias filter. It helps define whether you should be thinking continuation, pullback participation, or caution. Pair it with your own trigger logic rather than using the regime alone as a full trading plan.
Indicator Limitations
A developing regime can fail before reaching maturity, especially in choppy markets
Retest memory is useful for context, but the stored retest level is not guaranteed to hold on future tests
Efficiency and heat drift are descriptive metrics, not predictive guarantees of continuation
The indicator can still classify a directional state during periods where execution conditions are poor for actual trading
Originality Statement
Covenant Regime Atlas is original in the way it blends trend separation, maturity, retest memory, expansion, and efficiency into a unified regime overlay. It is not just a moving-average cloud with added cosmetics:
It separates directional identity from maturity, allowing the user to distinguish developing and established regimes
It stores retest memory as a living structural feature instead of relying only on static crossover logic
It combines heat, slope, expansion, and efficiency into one dashboard so regime quality can be judged from multiple dimensions
It preserves the initiation band of the current regime, which gives context that typical trend overlays do not maintain
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice or a recommendation to buy or sell any financial instrument. Regime readings describe current market structure and internal state based on historical prices. They do not guarantee future movement or profitable trading decisions. Always use independent judgment and proper risk management.
-Made with passion by jackofalltrades
Indicator
Volatility Percentile [EXCAVO]ATR Percentile Rank with Four-State Volatility Classification and Trend Detection
The Volatility Percentile measures where the current Average True Range stands
relative to its own history using a percentile rank. Rather than comparing ATR to a
fixed threshold, the indicator continuously evaluates whether current volatility is
low, normal, elevated, or extreme relative to the recent distribution - adapting
automatically to each instrument and timeframe.
This is not a basic ATR display. The percentile rank provides a normalized, context-aware
measure of volatility that remains comparable across assets, timeframes, and market cycles.
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▸ HOW TO USE
Step 1 → Add the indicator to a new pane. The histogram appears immediately,
colored by the current volatility state (blue, gray, orange, or red).
Step 2 → Read the state from the histogram color and dashboard. Blue = Low,
Gray = Normal, Orange = Elevated, Red = Extreme.
Step 3 → Note the percentile value. Above 80% indicates the current ATR is
in the top 20% of its historical range - unusually wide price swings.
Below 25% indicates unusually quiet conditions.
Step 4 → Check the Vol Trend row in the dashboard. RISING means the ATR is
accelerating above its 20-bar average. FALLING means it is contracting.
Step 5 → Set up state-change alerts to receive notifications when volatility
transitions between states on bar close.
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▸ HOW IT CALCULATES
◆ ATR Percentile Rank
The indicator computes ATR(length) on each bar, then applies ta.percentrank() over
the lookback period. The percentile rank answers: "Out of the last N bars, what
percentage had an ATR lower than today's?" A result of 80 means 80% of recent bars
had a smaller ATR - current volatility is in the top 20% historically. The rank
always falls between 0 and 100, making it directly comparable across instruments.
◆ Smoothing
The raw percentile rank is passed through a simple moving average of configurable
length (default 3 bars). This suppresses single-bar spikes that would otherwise
cause false state transitions. The smoothed value is used for both the histogram
display and state classification. Setting smoothing to 1 disables it entirely.
◆ Four-State Classification
The smoothed percentile is compared against three configurable thresholds to assign
one of four states: Low (below 25th percentile), Normal (25th to 60th), Elevated
(60th to 80th), or Extreme (above 80th). State boundaries are user-adjustable,
allowing calibration for instruments that spend more time at elevated volatility
levels (such as crypto) versus lower-volatility markets.
◆ Volatility Trend
A secondary calculation compares the current ATR to its 20-bar simple moving average.
ATR above 105% of the SMA is classified as RISING. ATR below 95% of the SMA is
classified as FALLING. Between those bands the trend is STABLE. This early-warning
layer detects momentum in volatility before a state boundary is crossed.
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▸ WHAT MAKES IT DIFFERENT
◆ Percentile Rank vs Fixed Threshold
A fixed ATR threshold (e.g., "ATR > 500 = high volatility") fails when applied across
different instruments or timeframes. The percentile approach self-calibrates: the same
indicator works on BTCUSD hourly and EURUSD daily without parameter changes, because
it measures volatility relative to the instrument's own recent history.
◆ Four States Instead of Two
Most volatility filters use a binary high/low split. Separating Normal from Elevated
and Extreme provides more granular awareness: Elevated volatility may still be
tradable, while Extreme conditions warrant a different approach to position sizing.
The four-state model also makes transitions visible earlier - Elevated appears before
Extreme is reached.
◆ Volatility Trend Detection
The Vol Trend layer detects whether ATR is currently accelerating or decelerating,
independently of which state it is in. It is possible to be in Normal state with
RISING trend (volatility building) or in Elevated state with FALLING trend
(volatility contracting back toward normal). This combination is visible only in the
dashboard and is not represented by the histogram color alone.
◆ ATR / Price Ratio
The dashboard shows ATR expressed as a percentage of the current close price. This
normalized measure is directly comparable across instruments and useful for estimating
stop distance relative to price without manual calculation.
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▸ DASHBOARD
Real-time panel showing current volatility conditions:
State - active volatility state (LOW / NORMAL / ELEVATED / EXTREME), colored by type
Percentile - current smoothed ATR percentile rank (0-100%)
ATR - raw ATR value in price units for the current bar
ATR / Price - ATR expressed as a percentage of close price
Vol Trend - volatility momentum (RISING / FALLING / STABLE) vs 20-bar ATR average
Lookback - active lookback period used for percentile calculation
Legend table (bottom left) explains histogram colors and boundary lines. Both panels
toggle in Dashboard settings.
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▸ SETTINGS
Engine
ATR Length - 14 bars (period for Average True Range calculation)
Lookback Period - 200 bars (history window for percentile rank)
Smoothing - 3 bars (applied to raw percentile; 1 = no smoothing)
State Thresholds
Low / Normal - 25 (percentile boundary between Low and Normal states)
Normal / Elevated - 60 (percentile boundary between Normal and Elevated states)
Elevated / Extreme - 80 (percentile boundary between Elevated and Extreme states)
Visualization
Low Vol Color - default blue
Extreme Vol Color - default red
Elevated Vol Color - default orange
Show State Ribbon - OFF (colored markers at pane bottom)
Background Highlight - ON (subtle tint matching active state)
Alerts
JSON Alerts - OFF (enable for bot integration)
Dashboard
Dashboard Position - Top Right
Show Dashboard - ON
Show Legend - ON
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▸ ALERTS
Extreme Volatility - state transitioned to Extreme on bar close
Elevated Volatility - state transitioned to Elevated on bar close
Normal Volatility - state transitioned to Normal on bar close
Low Volatility - state transitioned to Low on bar close
Volatility State Change - any state transition detected on bar close
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Best regards,
EXCAVO
Disclaimer
Trading involves significant risk. This indicator is a technical analysis tool
and does not constitute financial advice, investment recommendations, or a
guarantee of future results. Past indicator behavior does not guarantee future
performance. Always use proper risk management and your own judgment.
Indicator
Helix Trend Ensemble [JOAT]Helix Trend Ensemble
Introduction
Helix Trend Ensemble is an open-source trend overlay built around a three-member weighted ensemble. Instead of relying on one moving average or one crossover, Helix evaluates multiple configurable members, normalizes slope behavior, and produces a consensus trend state only when enough internal agreement is present.
The problem Helix solves is false certainty. Single-line trend tools are easy to read but easy to break. Multi-line tools often create clutter without resolving disagreement. Helix is designed to preserve a clean chart while still exposing the quality of alignment between fast, intermediate, and structural trend engines.
Core Concepts
1. Multi-Member Trend Architecture
Three independent members can each use different MA types, smoothing methods, lengths, and weights. This allows the ensemble to mix responsiveness with structural stability.
2. Weighted Consensus
The final state is not a simple majority vote. Each member contributes according to its configured weight, and the ensemble requires sufficient agreement before it promotes a directional state.
3. Slope Normalization
Raw slope values are normalized so the dashboard can express trend energy in a stable way across different length combinations.
4. Filter Layer
ATR and ADX filters help suppress weak trend states and reduce low-quality directional transitions.
5. Confirmed Regime Transitions
Directional state changes are only recognized on confirmed bars, which keeps the ensemble consistent with real-time use.
Features
Three fully configurable members: Each member supports multiple MA and smoothing combinations
Weighted consensus engine: Final state depends on internal agreement quality, not one crossover
Normalized slope score: Slope behavior is translated into a stable strength readout
Ribbon and cloud system: Trend geometry is expressed through layered fills instead of cluttered markers
Optional candle coloring: Price bars can reflect the ensemble state without altering logic
Top-right dashboard: Regime, consensus, strength, slope, agreement, filters, and last flip are summarized continuously
How to Use This Indicator
Step 1: Read regime and consensus together
A bullish or bearish state is more meaningful when consensus is high and filters are passing.
Step 2: Watch slope and strength
An aligned ensemble with weakening slope often signals late-trend conditions rather than fresh expansion.
Step 3: Use Helix as a bias filter
Helix works well as a directional framework for execution models that need a clean trend gate.
Indicator Limitations
Longer member lengths will intentionally delay reversals
High responsiveness settings can increase whipsaws
Consensus does not eliminate all false trends; it only improves structural filtering
The script is a trend-classification tool, not a full strategy
Originality Statement
Helix Trend Ensemble is original in the way it combines configurable member diversity, weighted consensus, slope normalization, and clean institutional visualization into one open-source trend framework.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Trend-state tools can fail during rapid reversals, compressed markets, or structurally irregular conditions. Use proper risk control at all times.
Indicator
Delta Pressure Ledger [JOAT]Delta Pressure Ledger
Introduction
Delta Pressure Ledger is an open-source lower-pane pressure model built entirely from chart-derived proxies. It combines anchored VWAP context, candle pressure, volume impulse, crowding stretch, volatility pressure, and settlement skew into a normalized composite ledger that classifies whether pressure is balanced, directional, crowded, or stressed.
The problem this script solves is hidden market pressure. Many traders rely on unavailable data feeds or vendor-only metrics to estimate crowding or liquidation risk. Delta Pressure Ledger uses only chart-accessible inputs and standardizes them through z-score normalization so pressure states can still be read in a consistent way across instruments.
Core Concepts
1. Chart-Derived Pressure Proxy
The script estimates directional pressure from candle settlement, intrabar range occupation, and volume impulse rather than external order flow feeds.
2. Anchored VWAP Context
Pressure is interpreted relative to anchored value, allowing the user to distinguish directional expansion from overstretched crowding.
3. Z-Score Normalization
All sub-engines are normalized over a configurable lookback, which makes the composite reading more portable across symbols and timeframes.
4. Crowding and Stress Logic
The script tracks when price and derived sentiment become stretched enough to imply elevated liquidation or unwind risk.
5. Composite Verdict
Pressure, crowding, volatility, and skew are merged into one verdict state so the user can quickly determine whether the market is orderly, imbalanced, or stressed.
Features
Anchored VWAP context: Session, weekly, or monthly value anchor
Pressure engine: Candle and volume-derived directional pressure model
Crowding engine: Stretch and behavioral excess detection
Volatility and skew layers: Pressure quality and instability are separated from raw direction
Normalized composite score: All sub-engines standardized into one comparable ledger
Risk meter: Liquidation-style stress estimate derived from crowding and instability
Confirmed-bar transitions: State changes and alerts are held to confirmed bars
Top-right dashboard: Regime, pressure, crowding, volatility, risk, composite score, and last confirmed flip
How to Use This Indicator
Step 1: Read the composite verdict
The verdict gives the fastest summary of whether the market is balanced, directionally pressured, or entering a crowded stress state.
Step 2: Separate pressure from crowding
A bullish pressure reading with low crowding is different from a bullish pressure reading with extreme crowding and high risk.
Step 3: Respect risk transitions
When the risk meter moves into elevated territory, directional continuation setups deserve more caution.
Indicator Limitations
This script uses chart-derived proxies rather than exchange-level liquidation or true open-interest feeds
Normalized readings can still behave differently across asset classes with unusual volume structure
Stress conditions can remain elevated for extended periods during strong trends
The script classifies pressure and risk context; it does not execute trades by itself
Originality Statement
Delta Pressure Ledger is original in the way it builds a portable, chart-derived pressure and crowding framework without depending on unavailable external feeds, while still organizing the result into a normalized composite and risk ledger.
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice. Derived pressure and crowding models can be wrong, especially during atypical market events. Use proper risk management and independent judgment.
Indicator
