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.
Indikator
Custom Built IndicatorCustom Built Indicator | MisinkoMaster
Trading is often viewed as a purely mathematical or technical discipline, but the truth is that successful trading requires immense creativity. There are thousands of brilliant traders who have incredible, unique structural concepts in their minds but feel held back because they do not know how to write code. The Custom Built Indicator (CBI) was created to bridge that gap.
This indicator acts as a blank, programmable canvas designed to unlock your inner quantitative designer. It is a fully modular trading framework that allows you to build, test, and personalize your own technical systems without touching a single line of code. By giving you absolute control over the baseline foundation, the volatility wrapper, the smoothing layer, and the conditional trend logic, CBI makes algorithmic design accessible to everyone. Think of it as trading art—a sandbox where you can bring your most detailed visual concepts to life, spark your curiosity, and perhaps even inspire you to take your first steps into learning Pine Script development.
How It Works: The Modular Sandbox
Instead of trapping you inside a single, rigid formula, CBI breaks down technical analysis into five independent, hot-swapping algorithmic layers:
Baseline Settings: This establishes the gravitational core of your asset's price action. You can set this baseline using standard moving averages, advanced low-lag options, mathematical centerpoints like the median or statistical mode, or even a pure historical price offset.
Volatility Settings: This dictates how your system measures market expansion and compression. You can wrap your baseline using standard range tools, pure standard deviation, or robust absolute deviation models to map out precise market extremes.
Smoothing Settings: A unique layer that allows you to smooth out the upper and lower boundary bands independently of the central baseline. Applying secondary smoothing allows you to create highly tailored, fluid bands that conform uniquely to market noise.
Trend Logic Settings: The brain of your strategy. Here, you decide exactly what constitutes a market regime shift. You can define trend conditions based on price breaking the outer channels, crossing the baseline, or even pure momentum acceleration.
Confirmation Filters: To minimize false signals, you can apply secondary algorithmic checks—such as volume verification, rate of change agreement, or candle validation—before any structural trend shift is confirmed.
An Ocean of Creative Possibilities
To understand just how massive this sandbox is, we can calculate the exact number of unique logical setups available. If we completely ignore all numerical values (like lookback periods or band multipliers) and only look at the dropdown menus, the sheer volume of structural combinations is staggering:
Baseline Type: 11 options
Volatility Type: 5 options
Upper Band Smoothing Type: 11 options
Lower Band Smoothing Type: 11 options
Long Signal Logic: 3 options
Short Signal Logic: 3 options
Confirmation Type: 4 options
The Custom Built Indicator provides exactly 239,580 unique, without numerical inputs, meaning everyone will have a completely unique layout that fits them and their style.
When you factor in that the crossover and crossunder source inputs can also be independently assigned to any price data point, the mathematical possibilities soar into the millions. Every single trader can find, name, and perfect a structural footprint that is entirely their own.
Key System Features
On-Chart Canvas Synchronization: The system automatically tracks your custom logical state and dynamically projects it back onto the screen, shifting candle colors and painting custom visual envelopes to represent your unique market regime.
Asymmetric Modeling: Because the upper and lower multipliers and smoothing options are completely separated, you can build asymmetric strategies—such as tight, highly sensitive upper boundaries for fast momentum breakouts combined with wide, volatile lower boundaries to catch major macroscopic market drops.
Forward-Looking Integration: The conditional logic allows you to experiment with advanced structural confirmations, such as requiring two consecutive breakout bars or demanding expanding volume before confirming a trend pivot.
Input Parameters Layout
General & Baseline Settings
Source: The primary price feed running into your system core.
Baseline Type & Lookback: Chooses the foundational trend line, offering options ranging from traditional SMA, EMA, and WMA, to advanced zero-lag options like TEMA, HMA, ALMA, or statistical Mode and Median.
Volatility & Smoothing Settings
Volatility Type & Lookback: Defines the range measurement matrix (Average True Range, Median True Range, Standard Deviation, Mean Absolute Deviation, or Median Absolute Deviation).
Upper & Lower Multipliers: Independently scales the distance of the bands from the baseline.
Additional Smoothing Type & Lookback: Provides an extra filtering pass specifically for the outer bands to eliminate jagged lines and smooth out execution zones.
Trend Logic Settings
Crossover/Crossunder Source: Selects the specific price sources required to breach the upper and lower boundaries.
Long/Short Signal Logic: Sets the core activation condition (breaking bands, crossing the baseline, or tracking positive/negative rate of change).
Confirmation Type: Applies an optional secondary validation layer (Volume, Baseline ROC, or Extra Bar validation).
Embrace the Art of Strategy Design
The ultimate goal of the Custom Built Indicator is to prove that technical analysis doesn't have to be rigid or intimidating. It is a playground for your ideas. Load it onto your chart, test out your most unconventional theories, mix architectures that traditional packages keep separate, and discover what works for your unique visual style. If you find a combination that speaks to you, use that spark to look under the hood—because the journey from clicking options to writing your own custom scripts is much shorter than you think.
Disclaimer: Trading financial markets involves high risk. This technical script is designed as an educational and informational tool to support your rule-based mechanical execution system and does not constitute financial advice.
Final Note: If you find any bugs, errors, contact me either through DMs or in the comments, and I will fix them and update the script. Indikator
Median Gaussian Trend | NAL1. Overview
Median Gaussian Trend | NAL is an adaptive trend-band indicator built from a median price baseline, Gaussian smoothing, and Gaussian-weighted volatility bands.
The indicator is designed to filter raw price movement into a smoother directional structure. Instead of using a simple moving average or standard deviation channel, it first compresses price through a median calculation, then applies Gaussian smoothing to create a cleaner baseline. Around that baseline, it builds adaptive upper and lower bands using Gaussian-weighted deviation.
The result is a robust, smooth trend regime tool that identifies when price breaks outside its filtered volatility structure.
2. Calculation
The indicator starts by calculating a median of the selected source. This helps reduce noise by focusing on the central value of recent price action instead of reacting directly to every candle.
That median value is then passed through a Gaussian filter. The Gaussian filter gives more structured weighting to the lookback window, producing a smoother baseline while still preserving directional movement.
The indicator then calculates a Gaussian-weighted deviation around the smoothed median baseline. This creates a custom volatility measurement that is more aligned with the filtered baseline rather than raw price alone.
The upper and lower bands are then built around the Gaussian-smoothed median. The script allows separate upper and lower multipliers, which lets the band structure be asymmetric if needed.
upper = median_base + sd_range * sd_mul
lower = median_base - sd_range * sd_mulb
A bullish state triggers when price closes above the upper band. A bearish state triggers when price closes below the lower band. When price remains inside the bands, the previous regime is held.
3. Key Features
Median-based price filtering.
Gaussian-smoothed baseline.
Gaussian-weighted volatility deviation.
Adaptive upper and lower trend bands.
Separate upper and lower band multipliers.
State-based candle coloring, band coloring, glow effect, and directional fills.
4. Use
Median Gaussian Trend is designed to identify when price escapes its smoothed median-volatility structure. A close above the upper band reflects bullish expansion, while a close below the lower band reflects bearish expansion.
The median component helps reduce noisy price behavior, while the Gaussian smoothing and deviation engine create a more refined trend envelope. This makes the indicator useful for reading directional structure without relying on a raw moving average channel.
This indicator is best used as a specialized module within a complete strategy framework. Its role is to isolate a filtered volatility-trend layer of price behavior, where the real value comes from how the signal is integrated into a broader process for regime, timing, and execution.
Indikator
Median Point of ControlMedian Point of Control calculates and displays the median OHLC4 price for customizable time periods, along with percentage-based upper and lower bands. It provides a statistical reference framework for identifying potential support, resistance, and mean-reversion zones.
What It Does
This indicator computes the median of all OHLC4 values within a defined period and plots three horizontal levels:
Median Line — The statistical middle price of the period
Upper Band — Median + user-defined percentage
Lower Band — Median - user-defined percentage
The current period's levels update dynamically as new bars form. Completed periods are preserved as historical reference lines.
Why Median Instead of Average?
The median is the "middle" value when all prices are sorted. Unlike the mean (average), the median is resistant to outliers — a single extreme wick or price spike won't distort the level. This makes it more representative of typical price action during the period.
Two Calculation Modes
Timeframe Mode Define periods using standard timeframes: 4H, Daily, Weekly, Monthly, etc. The indicator automatically detects when each period begins and ends.
Bars Mode Define periods by a fixed number of bars (e.g., every 50 bars, every 100 bars). Useful for non-time-based analysis or custom period lengths that don't align with standard timeframes.
Settings
Calculation Mode: Choose between Timeframe or Bars
Timeframe: Period length when using Timeframe mode
Bars: Number of bars per period when using Bars mode
Band Distance (%): Percentage offset for upper/lower bands from median
Line Colors: Customize colors for median, upper, and lower bands
Line Width: Thickness of the plotted lines
Historical Transparency: Opacity of completed period lines
Max Historical Periods: Number of past periods to display
How To Use
Identify the range: The upper and lower bands create a price envelope based on the period's median. When price approaches these levels, watch for reactions.
Mean reversion reference: The median line represents the "fair value" of the period. Price tends to oscillate around this level.
Breakout detection: If price breaks and holds beyond a band, it may signal trend continuation rather than reversion.
Multi-timeframe analysis: Use Daily median on intraday charts to see where price stands relative to the day's statistical center.
Important Notes
This indicator does not predict price direction. It provides statistical reference levels only.
The current period's median updates with each new bar — this is expected behavior, not repainting.
Historical period lines are fixed once their period closes.
For best results, use on liquid instruments (stocks, forex majors, major crypto pairs).
Indikator
Median ATR SD OscillatorMedian ATR SD Oscillator
Median ATR SD Oscillator is a trend-following volatility oscillator that measures the distance between price and two independent reference levels — an ATR band and a standard deviation band — both anchored to a percentile-based median. The asymmetric design uses different volatility measures for long and short signals, creating a natural bias toward staying in bullish trends longer while reacting quickly to breakdowns.
The result is an area oscillator that expands above zero in a confirmed bullish state and contracts below zero in a bearish state, with a white EMA line as a momentum confluence filter.
How It Works
A percentile median is calculated from a configurable price source — giving a robust, noise-resistant central reference level that adapts to recent price behavior.
Two independent volatility bands are then derived from this median:
ATR Band — median plus ATR multiplied by a configurable factor. The short condition uses the ATR band — price must fall below it to confirm a bearish state
SD Band — median plus the standard deviation of close. The long condition uses the standard deviation band — price must break above it to confirm a bullish state
Once a directional state is confirmed, the oscillator measures the distance between price and its reference level. An EMA of this distance acts as a confluence filter — the final signal only confirms when the distance is not only positive or negative but also above or below its own EMA, ensuring momentum is genuinely building in that direction.
Why This Approach Works
Most oscillators use a single volatility measure for both long and short signals. The Median ATR SD Oscillator deliberately uses two different measures — ATR for shorts and SD for longs — because they capture different market dynamics and create a natural asymmetry between entries and exits.
This asymmetric design reflects the structural reality of markets like crypto — price spends more time trending upward than downward. Longs require a statistically significant breakout above the SD band, while shorts only need price to fall back below the ATR band. The result is a system that stays in bullish trends longer while reacting quickly when momentum fades.
Settings
Median Source — Price source for the percentile median calculation (default: hl2)
Median Length — Lookback period for the percentile median (default: 63)
ATR Length — Lookback period for the ATR calculation (default: 4)
ATR Factor — Controls the width of the ATR band (default: 1.0)
SD Length — Lookback period for the standard deviation calculation (default: 29)
Use EMA — Enables the EMA confluence filter (default: true)
EMA Length — Lookback period for the EMA confluence filter (default: 35)
Use Bar Coloring — Colors bars based on the current state (default: true)
Color Background — Enables background coloring of the chart based on the current state (default: false)
Background Transparency — Controls the transparency of the background color (default: 85)
How to trade it
Long — when the oscillator expands above zero and the area turns blue, a bullish state has been confirmed. This is the signal to look for long entries or to hold existing long positions
Short / Cash — when the oscillator contracts below zero and the area turns red, the momentum has faded. This is the signal to exit longs, move to cash, or look for short entries depending on your strategy
EMA line — when the area is above the white EMA line momentum is building, when it crosses below momentum is weakening
Recommended Usage
Best used on the 1D timeframe for clean and reliable signal generation
Should not be used alone for trade entries — combine with an additional confirmation indicator for best results
The asymmetric ATR/SD design makes this oscillator particularly well suited for bullish-biased markets like crypto
Higher ATR Factor values make short exits more sensitive — lower values make them less reactive
Higher SD Length values create more stable long signals — lower values make them more reactive to short-term price movements
All signals are confirmed on bar close. Indikator
Descriptive Statistics [Median, Quartiles, Outliers]This indicator seeks to provide insight to traders by modeling market structure using widely accepted statistical methods applied to price data. It does not predict direction; instead, it describes how current price behaves relative to its historical distribution.
It is built around non-parametric statistics, making it resistant to distortion from extreme price movements.
What it shows?
1. Median (Q2): The central equilibrium level of price distribution.
2. Quartiles (Q1, Q3): Boundaries of the “normal” trading range.
3. Interquartile Range (IQR): Measures the width of the core market structure.
4. Outlier Bands (1.5 × IQR rule): Statistical extremes where price becomes unusual relative to recent behavior.
How it works?
The indicator collects price data either through:
1. Reset Mode: Builds a new distribution each session (Daily, Weekly, Monthly, or chart timeframe).
2. Length Mode: Uses a rolling window of the last N candles.
All values are sorted to construct a real-time price distribution, from which median, quartiles, and outlier thresholds are derived.
How to use it?
1. Price inside Q1–Q3 range → normal market conditions
2. Price near Median → equilibrium / fair value zone
3. Price outside Outlier bands → statistically extreme conditions (potential exhaustion, expansion zones or news driven events)
4. Large expansions between Q1 and Q3 → increased volatility and potential momentum in either direction
Key concept?
This tool does not forecast price. It provides a distribution map of market behavior, helping traders understand structure, deviation, and statistical positioning of price.
⚠️ Note
This indicator is for educational and analytical purposes only and should not be used as standalone trading advice.
Author: TUGUME WILLIAM MUTARA Indikator
Dynamic Median Momentum Oscillator [AlgoAlpha]🟠 OVERVIEW
This script provides a momentum oscillator that uses a median-based approach rather than traditional averages to find the center of price action. By calculating the distance between the current price and a rolling median (HLC3), it identifies how far the market has stretched from its historical equilibrium. The indicator is designed to filter out the noise typical of standard momentum tools, using a standardized range calculation to provide fixed overbought and oversold zones. It helps traders identify trend strength, potential exhaustion, and mean reversion opportunities across different market conditions.
🟠 CONCEPTS
The core of this tool is the Dynamic Median basis, which uses a rolling median of the HLC3 price to establish a "fair value" line. Unlike a simple moving average, the median is less sensitive to extreme price spikes, making the resulting oscillator more robust against outliers. To ensure the oscillator remains readable across different assets, the raw difference between price and median is standardized by the average candle range (EMA of High-Low). This normalization allows for the use of fixed thresholds (e.g., +/- 200, 250, 300) regardless of the asset's price. The median sets the context for the baseline, while the smoothed MCD and its signal line provide the timing for entries and exits.
🟠 FEATURES
Standardization feature to enable fixed overbought/oversold levels across any asset
Multi-component display: Fast (histogram), Slow (lines), and Super Slow (filled zones)
Reversion markers (triangles) indicating price returning from extreme levels
🟠 USAGE
Setup : Add the script to your chart and choose your preferred Display Mode. Use "All" to see the full picture or "Slow" for a cleaner view of trend direction. Ensure "Standardize" is checked if you want to use the built-in overbought/oversold bands effectively.
Read the chart : Look for the Smooth MCD (white line) crossing the Signal (orange line) for momentum shifts. Values above 0 indicate bullish momentum, while values below 0 indicate bearish momentum. Triangles appear at the top or bottom of the oscillator when price reaches extreme levels (300/-300) and begins to revert to the mean.
Settings that matter : The Basis Length determines how much historical data defines the "center" of the market; longer lengths are better for higher timeframes. Smoothing Length controls the reactivity of the main white line—increase this if you find the oscillator is giving too many false signals in choppy markets.
Indikator
Adaptive Momentum RibbonWhat It Does
Adaptive Momentum Ribbon (AMR) is a directional momentum tool that identifies shifts in price equilibrium and marks them directly on the price chart with entry labels and dynamic invalidation levels. It pairs a robust statistical oscillator with a Keltner Channel envelope to give traders both timing signals and volatility context in a single overlay.
How It Works
The indicator is built on three independent components working together:
1. Median Absolute Deviation (MAD) Z-Score
Instead of using a standard deviation z-score (which is heavily influenced by outlier candles and spike wicks), AMR calculates a rolling median of price, then measures how far price has deviated from that median, normalized by the Median Absolute Deviation. MAD is a statistical measure of dispersion that is approximately 50% more resistant to outliers than standard deviation. The raw MAD value is scaled by 1.4826 to make it directly comparable to standard deviation under normal distribution assumptions. The result is a z-score that reflects genuine shifts in price positioning rather than reacting disproportionately to single volatile bars.
2. Butterworth Low-Pass Filter
The raw z-score is then passed through a second-order Butterworth low-pass filter. Unlike cascaded EMAs (which introduce cumulative lag at each stage), the Butterworth filter is designed to have a maximally flat frequency response in the passband. This means it preserves the shape of genuine momentum moves while attenuating high-frequency noise. The filter's two-pole design provides a steeper roll-off than any single-pole smoothing method, delivering cleaner zero-line crossovers with less delay.
3. Keltner Channel Envelope
A standard Keltner Channel (moving average +/- ATR multiple) provides volatility context. Unlike Bollinger Bands, which use standard deviation and tend to expand/contract sharply on individual outlier bars, Keltner Channels use Average True Range, producing smoother, more stable bands that account for gap behavior and true intrabar range.
Signal Logic
A bullish signal (upward label) fires when the filtered z-score crosses above zero, indicating that price has shifted above its rolling median by a statistically meaningful amount after smoothing.
A bearish signal (downward label) fires when the filtered z-score crosses below zero.
Each signal generates an invalidation level (dashed line), placed at the low (for bullish) or high (for bearish) of the signal bar, offset by a long-term volatility measure (100-period EMA of True Range). If price breaches this level, an X marker appears, indicating the signal's premise has been violated.
The invalidation level extends forward in real time until it is either breached or replaced by a new signal in the opposite direction.
How to Use It
Trend Confirmation: Use the signal labels alongside the Keltner Channel. Bullish signals firing near the lower KC band suggest mean-reversion opportunities. Bullish signals near or above the midline suggest trend continuation.
Invalidation as Risk Management: The dashed invalidation line can serve as a reference for stop-loss placement. When the X appears, the statistical basis for the entry no longer holds.
Parameter Guidance:
Momentum Period (default 20): Controls the lookback for the median and MAD calculation. Shorter periods react faster but produce more signals. Longer periods are smoother but slower.
Butterworth Smoothing (default 3): Controls noise filtering. A value of 1 passes the raw z-score through with minimal filtering. Values of 3-5 work well for most timeframes.
KC settings: Standard Keltner Channel parameters. The defaults (20 EMA, 14 ATR, 2x multiplier) are widely used and work across most instruments and timeframes.
Limitations and Honest Caveats
This indicator does not predict future price. It identifies statistical shifts in recent price positioning relative to a rolling median. These shifts may or may not lead to sustained moves.
Like all zero-crossing oscillators, it will generate false signals during choppy, range-bound markets. The Keltner Channel can help filter these visually (signals near the midline in a flat channel are lower conviction).
The invalidation levels are references, not guaranteed stop levels. Slippage and gaps can cause exits beyond these prices.
The MAD z-score assumes sufficient data variation. In extremely low-volatility environments where price barely moves, the MAD can approach zero. A fallback to standard deviation is built in for this edge case, but signals during such periods should be treated with extra caution.
No repainting. All signals fire on the confirmed close of the signal bar and reference the prior bar's high/low for level placement. No future data is accessed.
Summary
AMR combines outlier-robust statistics (MAD z-score), signal-processing-grade smoothing (Butterworth filter), and volatility-adaptive context (Keltner Channel) into a single chart overlay. It is designed for traders who want clean directional signals with built-in invalidation logic, without relying on indicators that overreact to spike wicks or require a separate oscillator pane. Indikator
True Baseline Median SuperTrendTrue Baseline Median SuperTrend (TBM SuperTrend) | MisinkoMaster
True Baseline Median SuperTrend is a volatility-adaptive trend indicator designed to refine traditional SuperTrend logic by introducing a volatility-filtered baseline and median-based smoothing techniques.
Instead of relying on a fixed midpoint calculation, TBM SuperTrend dynamically constructs its baseline from structurally significant price observations, then applies layered median smoothing to reduce noise while preserving trend integrity.
The result is a cleaner, more stable trend-following tool that reacts to meaningful shifts in volatility and directional pressure without excessive whipsaws.
Core Philosophy
Most SuperTrend-style indicators anchor their bands to a simple price midpoint and apply an ATR-based offset. While effective, this approach can be overly sensitive during volatile consolidations.
TBM SuperTrend improves this structure by:
• Building a volatility-qualified baseline
• Filtering insignificant price movements
• Applying median smoothing instead of simple averaging
• Retaining ATR-based adaptive band distance
This creates a trend structure that prioritizes meaningful price expansion over random noise.
Key Features
Volatility-qualified baseline construction
Median-smoothed upper and lower bands
ATR-based adaptive volatility envelope
Dynamic trend state detection
Automatic candle coloring
Clear long and short transition labels
Reduced whipsaw behavior compared to standard SuperTrend
Works across intraday and higher timeframes
Designed for trend continuation and breakout frameworks
How It Works (Conceptual)
The indicator operates in three structural layers:
Volatility Measurement
Market volatility is assessed using an ATR-based structure.
Baseline Construction
Instead of averaging all recent prices, the script filters price samples based on volatility conditions. Only structurally relevant bars contribute to the baseline calculation. This ensures that the baseline reflects meaningful movement rather than passive drift.
Median Smoothing
Both the volatility-adjusted bands and the baseline structure undergo median smoothing. Median smoothing is less sensitive to outliers than standard averaging, which helps stabilize the trend line during erratic price spikes.
After the adaptive bands are constructed, price interaction with those bands determines directional bias:
• Price closing above the upper threshold confirms bullish trend state
• Price closing below the lower threshold confirms bearish trend state
Internal implementation details remain proprietary in the protected version.
Trend Logic Explained
Bullish State
When price maintains strength above the adaptive upper boundary, the indicator confirms a long bias. The trailing structure shifts beneath price, acting as dynamic support.
Bearish State
When price closes below the adaptive lower boundary, the indicator confirms a short bias. The trailing structure shifts above price, acting as dynamic resistance.
State transitions occur only when decisive boundary breaks happen, helping reduce false flips.
Visual Components
Trend Lines
Only the active directional band is displayed, reducing clutter and emphasizing current bias.
Shaded Volatility Zone
A filled region between price and the active band visually highlights trend dominance.
Long / Short Labels
Clear on-chart labels mark confirmed trend transitions.
Candle Coloring
Price candles automatically reflect current trend state for immediate visual recognition.
Inputs Overview
Source
Defines the price series used for baseline construction.
ATR Length
Controls the volatility lookback period.
True Baseline Length
Determines the window used for constructing the volatility-qualified baseline.
Factor
Adjusts the volatility multiplier that expands or contracts the adaptive bands.
Median Period
Controls the median smoothing strength applied to the bands.
Lower values increase responsiveness.
Higher values improve stability and reduce noise.
Why Median Smoothing Matters
Traditional smoothing methods (like EMA or SMA) can be distorted by sharp price spikes. Median-based smoothing reduces the impact of extreme values, making TBM SuperTrend particularly effective in:
• Crypto markets
• High-volatility equities
• News-driven instruments
• Lower timeframe trading
This improves structural consistency during sudden volatility expansions.
Best Use Cases
Trend-following systems
Breakout confirmation
Pullback entries within established trends
Trailing stop framework
Directional bias filtering
Volatility-adaptive strategy design
Parameter Tuning Guidance
Shorter ATR Length
→ Faster adaptation
→ More sensitivity
→ Suitable for intraday trading
Longer ATR Length
→ Smoother volatility structure
→ Better for swing trading
Higher Factor
→ Wider bands
→ Fewer signals
→ Stronger trend confirmation
Lower Factor
→ Tighter bands
→ Earlier entries
→ More reversals
Longer Median Period
→ Smoother band structure
→ Reduced whipsaws
Shorter Median Period
→ Faster reaction
→ More sensitivity to shifts
Practical Strategy Integration
Use TBM SuperTrend as:
• Primary directional filter
• Trailing stop mechanism
• Confirmation layer for breakout systems
• Bias alignment tool across multiple timeframes
It performs best when combined with momentum confirmation or volume expansion tools.
Summary
True Baseline Median SuperTrend enhances traditional SuperTrend logic by introducing volatility-qualified baseline construction and median smoothing for structural stability.
The result is a cleaner, more adaptive trend tool that prioritizes meaningful price movement while minimizing noise. It is well suited for traders seeking a disciplined, volatility-aware trend framework that remains robust across changing market conditions. Indikator
Robust Scaled Dema | OquantOverview
The Robust Scaled DEMA indicator is a tool designed for traders seeking to identify potential trend directions in financial markets. It combines the smoothing capabilities of a Double Exponential Moving Average (DEMA) with a robust scaling mechanism to normalize the data, making it more resilient to outliers and extreme price movements. This scaling helps in generating long and short signals based on predefined thresholds, visualized through color-coded plots and bars. The indicator aims to provide a balanced view of market momentum, reducing the impact of noise while highlighting significant shifts in price behavior.
Key Factors/Components
DEMA (Double Exponential Moving Average): Serves as the core smoothing component, reducing lag compared to simple averages by emphasizing recent price action more effectively.
Robust Scaling Mechanism: Utilizes statistical measures like median and interquartile range to normalize the DEMA values, ensuring the indicator is less sensitive to extreme values or price spikes.
Thresholds: User-defined upper and lower levels that trigger long or short signals when the scaled DEMA crosses them.
Visual Elements: Includes plotted lines for the scaled DEMA and thresholds, plus color-coded candlestick bars for intuitive interpretation.
Alerts: Built-in conditions for notifying users of potential entry points for long or short positions.
How It Works
The indicator starts by applying a DEMA to the chosen price source to create a smoothed representation of the market's direction. This smoothed value is then scaled using a robust statistical approach that accounts for the distribution of recent DEMA values, centering it around a median and adjusting for variability to minimize the influence of outliers. The resulting scaled metric is compared against user-set upper and lower thresholds: crossing above the upper suggests a bullish momentum (long signal), while dipping below the lower indicates bearish conditions (short signal). A state variable tracks these conditions to color the chart accordingly, helping traders visualize regime changes. Optional alerts fire on transitions.
For Who Is Best/Recommended Use Cases
This indicator is ideal for traders who employ trend-following or momentum-based strategies and need tools that perform well in non-normal market conditions, such as during high volatility or in assets prone to spikes. Use cases include identifying entry/exit points in trending environments, confirming breakouts, or integrating into multi-indicator systems for added confirmation. Quantitative traders or those backtesting strategies will appreciate its customizable parameters for optimization.
Settings and Default Settings
Source: The price data input for calculations, such as close, open, high, or low. Default: close.
DEMA Length: Controls the period for the DEMA smoothing; shorter values increase responsiveness but may add noise, longer ones provide more lag but smoother signals. Default: 25.
Robust Scaling Length: Defines the lookback period for the scaling statistics; affects how adaptive the normalization is to recent data distributions. Default: 40.
Upper Threshold: The level above which a long signal is triggered; higher values make signals rarer but potentially more reliable. Default: 0.5.
Lower Threshold: The level below which a short signal is triggered; lower values allow for more aggressive bearish detection. Default: 0.
Conclusion
The Robust Scaled DEMA offers an outlier-resistant alternative to traditional moving average indicators, empowering traders to navigate volatile markets. By blending exponential smoothing with statistical robustness, it provides actionable insights into trend shifts while minimizing false positives from extreme events..
⚠️ Disclaimer: This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate indicators/strategies before applying them in live markets. Use at your own risk. Indikator
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Smooth Theil-SenI wanted to build a Theil-Sen estimator that could run on more than one bar and produce smoother output than the standard implementation. Theil-Sen regression is a non-parametric method that calculates the median slope between all pairs of points in your dataset, which makes it extremely robust to outliers. The problem is that median operations produce discrete jumps, especially when you're working with limited sample sizes. Every time the median shifts from one value to another, you get a step change in your regression line, which creates visual choppiness that can be distracting even though the underlying calculations are sound.
The solution I ended up going with was convolving a Gaussian kernel around the center of the sorted lists to get a more continuous median estimate. Instead of just picking the middle value or averaging the two middle values when you have an even sample size, the Gaussian kernel weights the values near the center more heavily and smoothly tapers off as you move away from the median position. This creates a weighted average that behaves like a median in terms of robustness but produces much smoother transitions as new data points arrive and the sorted list shifts.
There are variance tradeoffs with this approach since you're no longer using the pure median, but they're minimal in practice. The kernel weighting stays concentrated enough around the center that you retain most of the outlier resistance that makes Theil-Sen useful in the first place. What you gain is a regression line that updates smoothly instead of jumping discretely, which makes it easier to spot genuine trend changes versus just the statistical noise of median recalculation. The smoothness is particularly noticeable when you're running the estimator over longer lookback periods where the sorted list is large enough that small kernel adjustments have less impact on the overall center of mass.
The Gaussian kernel itself is a bell curve centered on the median position, with a standard deviation you can tune to control how much smoothing you want. Tighter kernels stay closer to the pure median behavior and give you more discrete steps. Wider kernels spread the weighting further from the center and produce smoother output at the cost of slightly reduced outlier resistance. The default settings strike a balance that keeps the estimator robust while removing most of the visual jitter.
Running Theil-Sen on multiple bars means calculating slopes between all pairs of points across your lookback window, sorting those slopes, and then applying the Gaussian kernel to find the weighted center of that sorted distribution. This is computationally more expensive than simple moving averages or even standard linear regression, but Pine Script handles it well enough for reasonable lookback lengths. The benefit is that you get a trend estimate that doesn't get thrown off by individual spikes or anomalies in your price data, which is valuable when working with noisy instruments or during volatile periods where traditional regression lines can swing wildly.
The implementation maintains sorted arrays for both the slope calculations and the final kernel weighting, which keeps everything organized and makes the Gaussian convolution straightforward. The kernel weights are precalculated based on the distance from the center position, then applied as multipliers to the sorted slope values before summing to get the final smoothed median slope. That slope gets combined with an intercept calculation to produce the regression line values you see plotted on the chart.
What this really demonstrates is that you can take classical statistical methods like Theil-Sen and adapt them with signal processing techniques like kernel convolution to get behavior that's more suited to real-time visualization. The pure mathematical definition of a median is discrete by nature, but financial charts benefit from smooth, continuous lines that make it easier to track changes over time. By introducing the Gaussian kernel weighting, you preserve the core robustness of the median-based approach while gaining the visual smoothness of methods that use weighted averages. Whether that smoothness is worth the minor variance tradeoff depends on your use case, but for most charting applications, the improved readability makes it a good compromise. Indikator
Quantile-Based Adaptive Detection🙏🏻 Dedicated to John Tukey. He invented the boxplot, and I finalized it.
QBAD (Quantile-Based Adaptive Detection) is ‘the’ adaptive (also optionally weighted = ready for timeseries) boxplot with more senseful fences. Instead of hardcoded multipliers for outer fences, I base em on a set of quantile-based asymmetry metrics (you can view it as an ‘algorithmic’ counter part of central & standardized moments). So outer bands are Not hardcoded, not optimized, not cross-validated etc, simply calculated at O(nlogn).
You can use it literally everywhere in any context with any continuous data, in any task that requires statistical control, novelty || outlier detection, without worrying and doubting the sense in arbitrary chosen thresholds. Obviously, given the robust nature of quantiles, it would fit best the cases where data has problems.
The thresholds are:
Basis: the model of the data (median in our case);
Deviations: represent typical spread around basis, together form “value” in general sense;
Extensions: estimate data’s extremums via combination of quantile-based asymmetry metrics without relying on actual blunt min and max, together form “range” / ”frame”. Datapoints outside the frame/range are novelties or outliers;
Limits: based also on quantile asymmetry metrics, estimate the bounds within which values can ‘ever’ emerge given the current data generating process stays the same, together form “field”. Datapoints outside the field are very rare, happen when a significant change/structural break happens in current data-generating process, or when a corrupt datapoint emerges.
…
The first part of the post is for locals xd, the second is for the wanderers/wizards/creators/:
First part:
In terms of markets, mostly u gotta worry about dem instruments that represent crypto & FX assets: it’s either activity hence data sources there are decentralized, or data is fishy.
For a higher algocomplexity cost O(nlong), unlike MBAD that is 0(n), this thing (a control system in fact) works better with ishy data (contaminated with wrong values, incomplete, missing values etc). Read about the “ breakdown point of an estimator ” if you wanna understand it.
Even with good data, in cases when you have multiple instruments that represent the same asset, e.g. CL and BRN futures, and for some reason you wanna skip constructing a proper index of em (while you should), QBAD should be better put on each instrument individually.
Another reason to use this algo-based rather than math-based tool, might be in cases when data quality is all good, but the actual causal processes that generate the data are a bit inconsistent and/or possess ‘increased’ activity in a way. SO in high volatility periods, this tool should provide better.
In terms of built-ins you got 2 weightings: by sequence and by inferred volume delta. The former should be ‘On’ all the time when you work with timeseries, unless for a reason you want to consciously turn it off for a reason. The latter, you gotta keep it ‘On’ unless you apply the tool on another dataset that ain’t got that particular additional dimension.
Ain’t matter the way you gonna use it, moving windows, cumulative windows with or without anchors, that’s your freedom of will, but some stuff stays the same:
Basis and deviations are “value” levels. From process control perspective, if you pls, it makes sense to Not only fade or push based on these levels, but to also do nothing when things are ambiguous and/or don’t require your intervention
Extensions and limits are extreme levels. Here you either push or fade, doing nothing is not an option, these are decisive points in all the meanings
Another important thing, lately I started to see one kind of trend here on tradingview as well and in general in near quant sources, of applying averages, percentiles etc ‘on’ other stationary metrics, so called “indicators”. And I mean not for diagnostic or development reasons, for decision making xd
This is not the evil crime ofc, but hillbilly af, cuz the metrics are stationary it means that you can model em, fit a distribution, like do smth sharper. Worst case you have Bayesian statistics armed with high density intervals and equal tail intervals, and even some others. All this stuff is not hard to do, if u aint’t doing it, it’s on you.
So what I’m saying is it makes sense to apply QBAD on returns ‘of your strategy’, on volume delta, but Not on other metrics that already do calculations over their own moving windows.
...
Second part:
Looks like some finna start to have lil suspicions, that ‘maybe’ after all math entities in reality are more like blueprints, while actual representations are physical/mechanical/algorithmic. Std & centralized moments is a math entity that represents location, scale & asymmetry info, and we can use it no problem, when things are legit and consistent especially. Real world stuff tho sometimes deviates from that ideal, so we need smth more handy and real. Add to the mix the algo counter part of means: quantiles.
Unlike the legacy quantile-based asymmetry metrics from the previous century (check quantile skewness & kurtosis), I don’t use arbitrary sets of quantiles, instead we get a binary pattern that is totally geometric & natural (check the code if interested, I made it very damn explicit). In spirit with math based central & standardized moments, each consequent pair is wider empathizing tail info more and more for each higher order metric.
Unlike the classic box plot, where inner thresholds are quartiles and the rest are based on em, here the basis is median (minimises L1), I base inner thresholds on it, and we continue the pattern by basing the further set of levels on the previous set. So unlike the classic box plot, here we have coherency in construction, symmetry.
Another thing to pay attention to, tho for some reason ain’t many talk about it, it’s not conceptually right to think that “you got data and you apply std moments on it”. No, you apply it to ‘centered around smth’ data. That ‘smth’ should minimize L2 error in case of math, L1 error in case of algo, and L0 error in case of learning/MLish/optimizational/whatever-you-cal-it stuff. So in the case of L0, that’s actually the ‘mode’ of KDE, but that’s for another time. Anyways, in case of L2 it’s mean, so we center data around mean, and apply std moments on residuals. That’s the precise way of framing it. If you understand this, suddenly very interesting details like 0th and 1st central moments start to make sense. In case of quantiles, we center data around the median, and do further processing on residuals, same.
Oth moment (I call it init) is always 1, tho it’s interesting to extrapolate backwards the sequence for higher order moments construction, to understand how we actually end up with this zero.
1st moment (I call it bias) of residuals would be zero if you match centering and residuals analysis methods. But for some reason you didn’t do that (e.g centered data around midhinge or mean and applied QBAD on the centered data), you have to account for that bias.
Realizing stuff > understanding stuff
Learning 2981234 human invented fields < realizing the same unified principles how the Universe works
∞ Indikator
Theil-Sen Line Filter [BackQuant]Theil-Sen Line Filter
A robust, median-slope baseline that tracks price while resisting outliers. Designed for the chart pane as a clean, adaptive reference line with optional candle coloring and slope-flip alerts.
What this is
A trend filter that estimates the underlying slope of price using a Theil-Sen style median of past slopes, then advances a baseline by a controlled fraction of that slope each bar. The result is a smooth line that reacts to real directional change while staying calm through noise, gaps, and single-bar shocks.
Why Theil-Sen
Classical moving averages are sensitive to outliers and shape changes. Ordinary least squares is sensitive to large residuals. The Theil-Sen idea replaces a single fragile estimate with the median of many simple slopes, which is statistically robust and less influenced by a few extreme bars. That makes the baseline steadier in choppy conditions and cleaner around regime turns.
What it plots
Filtered baseline that advances by a fraction of the robust slope each bar.
Optional candle coloring by baseline slope sign for quick trend read.
Alerts when the baseline slope turns up or down.
How it behaves (high level)
Looks back over a fixed window and forms many “current vs past” bar-to-bar slopes.
Takes the median of those slopes to get a robust estimate for the bar.
Optionally caps the magnitude of that per-bar slope so a single volatile bar cannot yank the line.
Moves the baseline forward by a user-controlled fraction of the estimated slope. Lower fractions are smoother. Higher fractions are more responsive.
Inputs and what they do
Price Source — the series the filter tracks. Typical is close; HL2 or HLC3 can be smoother.
Window Length — how many bars to consider for slopes. Larger windows are steadier and slower. Smaller windows are quicker and noisier.
Response — fraction of the estimated slope applied each bar. 1.00 follows the robust slope closely; values below 1.00 dampen moves.
Slope Cap Mode — optional guardrail on each bar’s slope:
None — no cap.
ATR — cap scales with recent true range.
Percent — cap scales with price level.
Points — fixed absolute cap in price points.
ATR Length / Mult, Cap Percent, Cap Points — tune the chosen cap mode’s size.
UI Settings — show or hide the line, paint candles by slope, choose long and short colors.
How to read it
Up-slope baseline and green candles indicate a rising robust trend. Pullbacks that do not flip the slope often resolve in trend direction.
Down-slope baseline and red candles indicate a falling robust trend. Bounces against the slope are lower-probability until proven otherwise.
Flat or frequent flips suggest a range. Increase window length or decrease response if you want fewer whipsaws in sideways markets.
Use cases
Bias filter — only take longs when slope is up, shorts when slope is down. It is a simple way to gate faster setups.
Stop or trail reference — use the line as a trailing guide. If price closes beyond the line and the slope flips, consider reducing exposure.
Regime detector — widen the window on higher timeframes to define major up vs down regimes for asset rotation or risk toggles.
Noise control — enable a cap mode in very volatile symbols to retain the line’s continuity through event bars.
Tuning guidance
Quick swing trading — shorter window, higher response, optionally add a percent cap to keep it stable on large moves.
Position trading — longer window, moderate response. ATR cap tends to scale well across cycles.
Low-liquidity or gappy charts — prefer longer window and a points or ATR cap. That reduces jumpiness around discontinuities.
Alerts included
Theil-Sen Up Slope — baseline’s one-bar change crosses above zero.
Theil-Sen Down Slope — baseline’s one-bar change crosses below zero.
Strengths
Robust to outliers through median-based slope estimation.
Continuously advances with price rather than re-anchoring, which reduces lag at turns.
User-selectable slope caps to tame shock bars without over-smoothing everything.
Minimal visuals with optional candle painting for fast regime recognition.
Notes
This is a filter, not a trading system. It does not account for execution, spreads, or gaps. Pair it with entry logic, risk management, and higher-timeframe context if you plan to use it for decisions.
Indikator
P/E Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Earnings (P/E) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/E ratio drops below the lower Bollinger Band, which is calculated directly from the P/E ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed. Indikator
Interpolated Median Volatility LSMA | OttoThis indicator combines trend-following and volatility analysis by enhancing traditional LSMA with percentile-based linear interpolation applied to both the Least Squares Moving Average (LSMA) and standard deviation. Rather than relying on raw values, it uses the interpolated median (50th percentile) to smooth out noise while preserving sensitivity to significant price shifts. This approach produces a cleaner trend signal that remains responsive to real market changes, adapts to evolving volatility conditions, and improves the accuracy of breakout detection.
Core Concept
The indicator builds on these core components:
LSMA (Least Squares Moving Average): A linear regression-based moving average that fits line using user selected source over user defined period. It offers a smoother and more reactive trend signal compared to standard moving averages.
Standard Deviation shows how much price varies from the mean. In this indicator, it’s used to measure market volatility.
Volatility Bands: Instead of traditional Bollinger-style bands, this script calculates custom upper and lower bands using percentile-based linear interpolation on both the LSMA and standard deviation. This method produces smoother bands that filter out noise while remaining adaptive to meaningful price movements, making them more aligned with real market behavior and helping reduce false signals.
Percentile interpolation estimates a specific percentile (like the median — the 50th percentile) from a set of values — even when that percentile doesn't fall exactly on one data point. Instead of selecting a single nearest value, it calculates a smoothed value between nearby points. In this script, it’s used to find the median of past LSMA and standard deviation values, reducing the impact of outliers and smoothing the trend and volatility signals for more robust results.
Signal Logic: A long signal is identified when close price goes above the upper band, and a short signal when close price goes below the lower band.
⚙️ Inputs
Source: The price source used in calculations
LSMA Length: Period for calculating LSMA
Standard Deviation Length: Period for calculating volatility
Percentile Length: Period used for interpolating percentile values of LSMA and standard deviation
Multiplier: Controls the width of the bands by scaling the interpolated standard deviation
📈 Visual Output
Colored LSMA Line: Changes color based on signal (green for bullish, purple for bearish)
Upper & Lower Bands: Volatility bands calculated using interpolated values (green for bullish, purple for bearish)
Bar Coloring: Price bars are colored to reflect signal state (green for bullish, purple for bearish)
Optional Candlestick Overlay: Enhances visual context by coloring candles to match the signal state (green for bullish, purple for bearish)
How to Use
Add the indicator to your chart and look for signals when close price goes above or below the bands.
Long Signal: close Price goes above the upper band
Short Signal: close Price goes below the lower band
🔔 Alerts:
This script supports alert conditions for long and short signals. You can set alerts based on band crossovers to be notified of potential entries/exits.
⚠️ Disclaimer:
This indicator is intended for educational and informational purposes only. Trading/investing involves risk, and past performance does not guarantee future results. Always test and evaluate strategies before applying them in live markets. Use at your own risk.
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Savitzky Golay Median Filtered RSI [BackQuant]Savitzky Golay Median Filtered RSI
Introducing BackQuant's Savitzky Golay Median Filtered RSI, a cutting-edge indicator that enhances the classic Relative Strength Index (RSI) by applying both a Savitzky-Golay filter and a median filter to provide smoother and more reliable signals. This advanced approach helps reduce noise and captures true momentum trends with greater precision. Let’s break down how the indicator works, the features it offers, and how it can improve your trading strategy.
Core Concept: Relative Strength Index (RSI)
The Relative Strength Index (RSI) is a widely used momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100, with levels above 70 typically indicating overbought conditions and levels below 30 indicating oversold conditions. However, the standard RSI can sometimes generate noisy signals, especially in volatile markets, making it challenging to identify reliable entry and exit points.
To improve upon the traditional RSI, this indicator introduces two powerful filters: the Savitzky-Golay filter and a median filter.
Savitzky-Golay Filter: Smoothing with Precision
The Savitzky-Golay filter is a digital filtering technique used to smooth data while preserving important features, such as peaks and trends. Unlike simple moving averages that can distort important price data, the Savitzky-Golay filter uses polynomial regression to fit the data, providing a more accurate and less lagging result.
In this script, the Savitzky-Golay filter is applied to the RSI values to smooth out short-term fluctuations and provide a more reliable signal. By using a window size of 5 and a polynomial degree of 2, the filter effectively reduces noise without compromising the integrity of the underlying price movements.
Median Filter: Reducing Outliers
After applying the Savitzky-Golay filter, the median filter is applied to the smoothed RSI values. The median filter is particularly effective at removing short-lived outliers, further enhancing the accuracy of the RSI by reducing the impact of sudden and temporary price spikes or drops. This combination of filters creates an ultra-smooth RSI that is better suited for detecting true market trends.
Long and Short Signals
The Savitzky Golay Median Filtered RSI generates long and short signals based on user-defined threshold levels:
Long Signals: A long signal is triggered when the filtered RSI exceeds the Long Threshold (default set at 176). This indicates that momentum is shifting upward, and it may present a good buying opportunity.
Short Signals: A short signal is generated when the filtered RSI falls below the Short Threshold (default set at 162). This suggests that momentum is weakening, potentially signaling a selling opportunity or exit from a long position.
These threshold levels can be adjusted to suit different market conditions and timeframes, allowing traders to fine-tune the sensitivity of the indicator.
Customization and Visualization Options
The Savitzky Golay Median Filtered RSI comes with several customization options, enabling traders to tailor the indicator to their specific needs:
Calculation Source: Select the price source for the RSI calculation (default is OHLC4, but it can be changed to close, open, high, or low prices).
RSI Period: Adjust the lookback period for the RSI calculation (default is 14).
Median Filter Length: Control the length of the median filter applied to the smoothed RSI, affecting how much noise is removed from the signal.
Threshold Levels: Customize the long and short thresholds to define the sensitivity for generating buy and sell signals.
UI Settings: Choose whether to display the RSI and thresholds on the chart, color the bars according to trend direction, and adjust the line width and colors used for long and short signals.
Visual Feedback: Color-Coded Signals and Thresholds
To make the signals easier to interpret, the indicator offers visual feedback by coloring the price bars and the RSI plot according to the current market trend:
Green Bars indicate long signals when momentum is bullish.
Red Bars indicate short signals when momentum is bearish.
Gray Bars indicate neutral or undecided conditions when no clear signal is present.
In addition, the Long and Short Thresholds can be plotted directly on the chart to provide a clear reference for when signals are triggered, allowing traders to visually gauge the strength of the RSI relative to its thresholds.
Alerts for Automation
For traders who prefer automated notifications, the Savitzky Golay Median Filtered RSI includes built-in alert conditions for long and short signals. You can configure these alerts to notify you when a buy or sell condition is met, ensuring you never miss a trading opportunity.
Trading Applications
This indicator is versatile and can be used in a variety of trading strategies:
Trend Following: The combination of Savitzky-Golay and median filtering makes this RSI particularly useful for identifying strong trends without being misled by short-term noise. Traders can use the long and short signals to enter trades in the direction of the prevailing trend.
Reversal Trading: By adjusting the threshold levels, traders can use this indicator to spot potential reversals. When the RSI moves from overbought to oversold levels (or vice versa), it may signal a shift in market direction.
Swing Trading: The smoothed RSI provides a clear signal for short to medium-term price movements, making it an excellent tool for swing traders looking to capitalize on momentum shifts.
Risk Management: The filtered RSI can be used as part of a broader risk management strategy, helping traders avoid false signals and stay in trades only when the momentum is strong.
Final Thoughts
The Savitzky Golay Median Filtered RSI takes the classic RSI to the next level by applying advanced smoothing techniques that reduce noise and improve signal reliability. Whether you’re a trend follower, swing trader, or reversal trader, this indicator provides a more refined approach to momentum analysis, helping you make better-informed trading decisions.
As with all indicators, it is important to backtest thoroughly and incorporate sound risk management strategies when using the Savitzky Golay Median Filtered RSI in your trading system.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
BINANCE:SOLUSD
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series_collectionLibrary "series_collection"
A personal collection of commonly used series types like moving averages that are supported directly by
the pinescript library ('ALMA', 'DEMA', 'EMA', 'HMA', 'RMA', 'SMA', 'SWMA', 'VWMA', 'WMA'), highest and lowest source,
median and pivots. One single function (with overloads) that can be configured easily by the user input and can be
used as a core piece of functionality for many user cases. This library was created to abstract away and re-use this
commonly used functionality in my "Two MA Signal Indicator" script and the "Template Trailing Strategy" script. Both
of them use the "two_ma_logic" for defining entry and exit signals. While this piece of work does not contain any
novel mathematical expressions and just adds a convinient (and configurable) way to do things, I hope that might add
value to other scripts as well and future projects.
cust_series(length, seriesType, source)
cust_series - Calculate the custom series of the given source for the given length and type
Parameters:
length (simple int) : - The length of the custom series
seriesType (simple string) : - The type of the custom series
source (float) : - The source of the values
Returns: - The resulting value of the calculations of the custom series
cust_series(length, seriesType, source)
cust_series - Calculate the custom series of the given source for the given length and type
Parameters:
length (simple float) : - The length of the custom series (ceiled)
seriesType (simple string) : - The type of the custom series
source (float) : - The source of the values
Returns: - The resulting value of the calculations of the custom series Perpustakaan
Standardized Median Proximity [AlgoAlpha]Introducing the Standardized Median Proximity by AlgoAlpha 🚀📊 – a dynamic tool designed to enhance your trading strategy by analyzing price fluctuations relative to the median value. This indicator is built to provide clear visual cues on the price deviation from its median, allowing for a nuanced understanding of market trends and potential reversals.
🔍 Key Features:
1. 📈 Median Tracking: At the core of this indicator is the calculation of the median price over a specified lookback period. By evaluating the current price against this median, the indicator provides a sense of whether the price is trending above or below its recent median value.
medianValue = ta.median(priceSource, lookbackLength)
2. 🌡️ Normalization of Price Deviation: The deviation of the price from the median is normalized using standard deviation, ensuring that the indicator's readings are consistent and comparable across different time frames and instruments.
standardDeviation = ta.stdev(priceDeviation, 45)
normalizedValue = priceDeviation / (standardDeviation + standardDeviation)
3. 📌 Boundary Calculations: The indicator sets upper and lower boundaries based on the normalized values, helping to identify overbought and oversold conditions.
upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
4. 🎨 Visual Appeal and Clarity: With carefully chosen colors, the plots provide an intuitive and clear representation of market states. Rising trends are indicated in a shade of green, while falling trends are shown in red.
5. 🚨 Alert Conditions: Stay ahead of market movements with customizable alerts for trend shifts and impulse signals, enabling timely decisions.
alertcondition(ta.crossover(normalizedValue, 0), "Bullish Trend Shift", "Median Proximity Crossover Zero Line")
🔧 How to Use:
- 🎯 Set your preferred lookback lengths and standard deviation multipliers to tailor the indicator to your trading style.
- 💹 Utilize the boundary plots to understand potential overbought or oversold conditions.
- 📈 Analyze the color-coded column plots for quick insights into the market's direction relative to the median.
- ⏰ Set alerts to notify you of significant trend changes or conditions that match your trading criteria.
Basic Logic Explained:
- The indicator first calculates the median of the selected price source over your chosen lookback period. This median serves as a baseline for measuring price deviation.
- It then standardizes this deviation by dividing it by the standard deviation of the price deviation over a 45-period lookback, creating a normalized value.
- Upper and lower boundaries are computed using the exponential moving average (EMA) and standard deviation of these normalized values, adjusted by your selected multiplier.
- Finally, color-coded plots provide a visual representation of these calculations, offering at-a-glance insights into market conditions.
Remember, while this tool offers valuable insights, it's crucial to use it as part of a comprehensive trading strategy, complemented by other analysis and indicators. Happy trading!
🚀 Indikator
Median Proximity Percentile [AlgoAlpha]📊🚀 Introducing the "Median Proximity Percentile" by AlgoAlpha, a dynamic and sophisticated trading indicator designed to enhance your market analysis! This tool efficiently tracks median price proximity over a specified lookback period and finds it's percentile between 2 dynamic standard deviation bands, offering valuable insights for traders looking to make informed decisions.
🌟 Key Features:
Color-Coded Visuals: Easily interpret market trends with color-coded plots indicating bullish or bearish signals.
Flexibility: Customize the indicator with your preferred price source and lookback lengths to suit your trading strategy.
Advanced Alert System: Stay ahead with customizable alerts for key trend shifts and market conditions.
🔍 Deep Dive into the Code:
Choose your preferred price data source and define lookback lengths for median and EMA calculations. priceSource = input.source(close, "Source") and lookbackLength = input.int(21, minval = 1, title = "Lookback Length")
Calculate median value, price deviation, and normalized value to analyze market position relative to the median. medianValue = ta.median(priceSource, lookbackLength)
Determine upper and lower boundaries based on standard deviation and EMA. upperBoundary = ta.ema(positiveValues, lookbackLength) + ta.stdev(positiveValues, lookbackLength) * stdDevMultiplier
lowerBoundary = ta.ema(negativeValues, lookbackLength) - ta.stdev(negativeValues, lookbackLength) * stdDevMultiplier
Compute the percentile value to track market position within these boundaries. percentileValue = 100 * (normalizedValue - lowerBoundary)/(upperBoundary - lowerBoundary) - 50
Enhance your analysis with Hull Moving Average (HMA) for smoother trend identification. emaValue = ta.hma(percentileValue, emaLookbackLength)
Visualize trends with color-coded plots and characters for easy interpretation. plotColor = percentileValue > 0 ? colorUp : percentileValue < 0 ? colorDown : na
Set up advanced alerts to stay informed about significant market movements. // Alerts
alertcondition(ta.crossover(emaValue, 0), "Bullish Trend Shift", "Median Proximity Percentile Crossover Zero Line")
alertcondition(ta.crossunder(emaValue, 0), "Bearish Trend Shift", "Median Proximity Percentile Crossunder Zero Line")
alertcondition(ta.crossunder(emaValue,emaValue ) and emaValue > 90, "Bearish Reversal", "Median Proximity Percentile Bearish Reversal")
alertcondition(ta.crossunder(emaValue ,emaValue) and emaValue < -90, "Bullish Reversal", "Median Proximity Percentile Bullish Reversal")
🚨 Remember, the "Median Proximity Percentile " is a tool to aid your analysis. It’s essential to combine it with other analysis techniques and market understanding for best results. Happy trading! 📈📉 Indikator
FlexiMA Variance Tracker [presentTrading]🔶 Introduction and How it is Different
The FlexiMA Variance Tracker (FlexiMA-VT) represents a novel approach in technical analysis, distinctively standing out in the realm of financial market indicators. It leverages the concept of a variable Length Moving Average (MA) to create a versatile and dynamic oscillator. Unlike traditional oscillators that rely on a fixed-length MA, the FlexiMA-VT adapts to market conditions by varying the length of the MA, offering a more responsive and nuanced view of market trends. (*The achieved method took reference from SuperTrend Polyfactor Oscillator)
This innovative design allows the FlexiMA-VT to capture a broader spectrum of market movements, making it highly effective in diverse trading environments. Whether in stable or volatile markets, its adaptability ensures consistent relevance, providing traders with deeper insights into potential market swings.
The proposed oscillator accentuates several key aspects through a distinctive mesh of bars, which are derived from the differences between the price and a set of 20 Moving Averages, each altered by varying factors. The intensity of the mesh's colors serves as an indicator, with brighter hues signifying a greater convergence of Moving Average signals.
Starting Length = 5
Starting Length = 40
🔶 Strategy, How it Works: Detailed Explanation
1. Core Concept:
The FlexiMA-VT operates by comparing the price or an average value (indicator source) against a set of moving averages with varying lengths.
These lengths are dynamically adjusted through a starting factor and multiple increment factors, ensuring a comprehensive analysis over different time scales.
2. Normalization and Standard Deviation Calculation:
Once deviations are calculated, they undergo a normalization process, which can be set to 'None', 'Max-Min', or 'Absolute Sum'.
This step is crucial as it standardizes the deviations, allowing for a consistent scale of comparison.
The standard deviation of these normalized deviations is then calculated, offering insights into the market’s volatility and potential trend strength.
🔹Normalization
3. Median Value and Oscillator Creation:
The median of the normalized deviations forms the core of the FlexiMA-VT oscillator.
This median value provides a balanced central point, reflecting the consensus of various MA lengths.
The standard deviation bands plotted around the median enhance the interpretative power of the oscillator, indicating potential overbought or oversold conditions.
4. Multi-Factor Analysis:
The FlexiMA-VT uses multiple increment factors to generate a range of MAs, each factor representing a different scale of trend analysis.
By averaging the results from these different scales, the FlexiMA-VT forms a more comprehensive and reliable oscillator.
🔹Consensus
5. Practical Application:
Traders can use the FlexiMA-VT for various purposes, including identifying trend reversals, gauging market momentum, and determining overbought or oversold conditions.
Its dynamic nature makes it adaptable to different trading strategies, from short-term scalping to long-term position trading.
🔶 Settings
1. Indicator Source (indicatorSource): Determines the base data for calculations, typically a price average (HLC3).
2. Indicator Length (indicatorLength): Sets the base length for Moving Averages, influencing initial calculations.
3. Starting Factor (startingFactor): Initial multiplier for MA length, impacting the starting point of analysis.
4. Increment Factors (incrementFactor_1, incrementFactor_2, incrementFactor_3): Modulate the rate of change in MA lengths, adding variability.
5. Normalization Method (normalizeMethod): Standardizes deviations, with methods like 'Max-Min' and 'Absolute Sum' for comparability.
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