Repeated Median Regression ChannelThis script uses the Repeated Median (RM) estimator to construct a linear regression channel and thus offers an alternative to the available codes based on ordinary least squares.
The RM estimator is a robust linear regression algorithm. It was proposed by Siegel in 1982 (1) and has since found many applications in science and engineering for linear trend estimation and data filtering.
The key difference between RM and ordinary least squares methods is that the slope of the RM line is significantly less affected by data points that deviate strongly from the established trend. In statistics, these points are usually called outliers, while in the context of price data, they are associated with gaps, reversals, breaks from the trading range. Thus, robustness to outlier means that the nascent deviation from a predetermined trend will be more clearly seen in the RM regression compared to the least-squares estimate. For the same reason, the RM model is expected to better depict gaps and trend changes (2).
Input Description
Length : Determines the length of the regression line.
Channel Multiplier : Determines the channel width in units of root-mean-square deviation.
Show Channel : If switched off , only the (central) regression line is displayed.
Show Historical Broken Channel : If switched on , the channels that were broken in the past are displayed. Note that a certain historical broken channel is shown only when at least Length / 2 bars have passed since the last historical broken channel.
Print Slope : Displays the value of the current RM slope on the graph.
Method
Calculation of the RM regression line is done as follows (1,3):
For each sample point ( t (i), y (i)) with i = 1.. Length , the algorithm calculates the median of all the slopes of the lines connecting this point to the other Length -1 points.
The regression slope is defined as the median of the set of these median slopes.
The regression intercept is defined as the median of the set { y (i) – m * t (i)}.
Computational Time
The present implementation utilizes a brute-force algorithm for computing the RM-slope that takes O ( Length ^2) time. Therefore, the calculation of the historical broken channels might take a relatively long time (depending on the Length parameter). However, when the Show Historical Broken Channel option is off, only the real-time RM channel is calculated, and this is done quite fast.
References
1. A. F. Siegel (1982), Robust regression using repeated medians, Biometrika, 69 , 242–244.
2. P. L. Davies, R. Fried, and U. Gather (2004), Robust signal extraction for on-line monitoring data, Journal of Statistical Planning and Inference 122 , 65-78.
3. en.wikipedia.org
Pesquisar nos scripts por "gaps"
[BMAX] Averan BB(ENGLISH)
Averan is an indicator based on ADR, which shows the volatility of the market based on high-low prices on the selected timeframe. The difference between Averan and ATR is that Averan does not consider GAPs, so it basically consider the actual size of the candles.
This indicator also includes a standard deviation representation, the same as the top portion of the bollinger bands to present the variance of the volatility.
(PORTUGUÊS)
Averan é um indicador baseado no ADR, que apresenta a volatilidade do mercado baseado em máximas e mínimas do tempo gráfico escolhido. A diferença do Averan para o ATR é que o Averan não considera GAPs, portanto é basicamente calculado pelo real tamanho dos candles.
Este indicador também inclui a representação do desvio padrão, representado da mesma maneira que a banda superior do Bollinger Bands, apresentando portanto a variância da volatilidade.
scaled.orders [highwater]FOR EDUCATIONAL PURPOSES
There are multiple tools that allow you to place "scaled orders" on your exchange, namely Alertatron and Bybit Tools. This script is based on some Alertatron features, but you can use it for any grid like order placing strategy. Even if thats not your thing it's an example of how to use arrays in pinescript.
FROM PRICE - is the price to start your orders.
TO PRICE - is the price your orders will end.
SCALED TYPES :
LINEAR - will distribute orders evenly between from and to price.
EASE IN - will cluster orders closer to from price, then start to widen the gaps as you move closer to to price.
EASE OUT - will have wider gaps near from price, and start to cluster near to price.
EASE IN OUT - will cluster orders near both from price and to price.
COUNT - number of orders in each scaled order.
Awesome Oscillator_VTX
Abbreviations:
AO - Awesome Oscillator
AC - Accelerator Oscillator
TP - TimePeriod (1m,2m,5m,1h....)
TP Steps - 1m,3m,12m,1h,5h,D (This steps i use)
Use-case:
Awesome Oscillator best used to find Divergence/Convergence what results in Weakening of Momentum and Price reversals.
This script calculates and plots AO/AC with minute precision, removing GAPS when projecting Higher Period AO/AC.
So you can accommodate all important information on one chart with best precision.
Made for Intraday Perioads.
Best used for DayTrading, when you need to make quick and efficient decisions.
Calculation = Preferred resolution * Length / Present resolution.
As Additional Function, this Awesome Oscillator has AC built in.
Settings:
Resolution - Most used TP included, plus some exclusive paid plans (1m, 2m, 3m, 5m, 12m, 15m, 1h, 4h, 5h, Daily). Default set to 1h
Use AO - You can switch between EMA and SMA for FastMA/SlowMA calculation. Default set to EMA
FastMA - standard function. Default set to 5
SlowMA - standard function. Default set to 34
Signal Line - Plots MA to show Momentum. Uses EMA/SMA based on "Use AO" selection. Default set to 5
Use AC - You can switch between EMA and SMA for AC calculation. Default set to SMA
Offset - standard function. Default set to 0
Accelerator - AC length. Default set to 5
Source - standard function. Default set to hlc3
Why to use it ?
Yes, i know that variable TP is standard now in TradingView. But there are some limitations, especially for DayTraders.
Problem:
Imagine you are trading/scalping on 1m.. 5m.. 15.. charts and you want to see where are your on Higher TP.
-- You can change to 1h and check it, but you will loose the picture from smaller TP.
-- You can use Standard TP function, but your data will update every 15m, 1h (depends on TP). And in result you have Gaps between bars.
Solution:
This script help to solve this problem, by breaking information down to 1m and building from there.
So whatever Intraday TP you choose to trade, your AO/AC will be updated with minute precision.
Limitations:
Sadly nothing without limitations.
1. For Best performance use only Higher TP dividable By Yours (ex. You use 3m chart, then you can plot 12m, 15m, 1h / You use 5m chart, then you can plot 15m, 1h. 12m will already have 3m of information lost using 5m Chart )
Kicker ScannerThe kicker pattern is deemed to be one of the most reliable reversal patterns and usually signifies a dramatic change in the fundamentals of the company in question.
It is a 2-candle pattern, whereby there is a significant gap between the body of the most recent candle and the previous candle.
A bullish kicker is one in which the most recent candle is bullish, and the previous candle is bearish.
A bearish kicker is one in which the most recent candle is bearish, and the previous candle is bullish.
I notice this works best for stocks, as there are many gaps in a stock chart. Currencies have few gaps, and thus few kickers.
From within the settings, you can set the minimum permitted gap between the two candles, specified in price, accurate to 6 decimal places; 0.000001.
Line breakI decided to help TradingView programmers and wrote code that converts a standard candles / bars to a line break chart. The built-in linebreak() and security() functions for constructing a Linear Break chart are bad, the chart is not built correctly, and does not correspond to the Line Breakout chart built into TradingView. I’m talking about simulating the Linear Break lines using the plotcandle() annotation, because these are the same candles without shadows. When you try to use the market simulator, when the gaps are turned on in the security() function, nothing is added to the chart, and when turned off, a completely different line break chart is drawn. Do not try to write strategies based on the built-in linebreak() function! The developers write in the manual: "Please note that you cannot plot Line Break boxes from Pine script exactly as they look. You can only get a series of numbers similar to OHLC values for Line Break charts and use them in your algorithms." However, it is possible to build a “Linear Breakthrough” chart exactly like the “Linear Breakthrough" chart built into TradingView. Personally, I had enough Pine Script functionality.
For a complete understanding of how such a graph is built, you can refer to Steve Nison's book “BEYOND JAPANESE CANDLES” and see the instructions for creating a “Three-Line Breakthrough” chart (the number of lines for a breakthrough is three):
Rule 1: if today's price is above the base price (closing the first candle), draw a white line from the base price to the new maximum price (before closing).
or Rule 2: if today's price is below the base price, draw a black line from the base price to the new low of prices (before closing).
Rule 3: if today's price is no different from the base, do not draw any line.
Rule 4: if today's price rises above the maximum of the first line, shift to the column to the right and draw a new white line from the previous maximum to the new maximum of prices.
Rule 5: if the price is below the low of the first line, move one column to the right and draw a new black line down from the previous low to the new low of prices.
Rule 6: if the price is kept in the range of the first line, nothing is applied to the chart.
Rule 7: if the market reaches a new maximum, surpassing the maximum of previous lines, move to the column to the right and draw a new white line up to a new maximum.
Rule 8: if today's price is below the low of previous lines (i.e. there is a new low), move to the right column and draw a new black line down to a new low.
Rule 9: if the price is in the range of the first two lines, nothing is applied to the chart.
Rule 10: if there is a series of three white lines, a new white line is drawn when a new maximum is reached (even if it is only one tick higher than the old one). Under the same conditions, for drawing a black reversal line, the price should fall below the minimum of the series of the last three white lines. Such a black line is called a black reversal line. It runs from the base of the highest white line to a new low of price.
Rule 11: if there is a series of three black lines, a new black line is drawn when a new minimum is reached. Under the same conditions, for drawing a white line, called a white reversal line, the price must exceed the maximum of the previous three black lines. This line is drawn from the top of the lowest black line to a new high of the price.
So, the script was not small, but the idea is extremely simple: if you need to break n lines to build a line, then among these n lines (or less, if this is the beginning of the chart), the maximum or minimum of closures and openings will be searched. If the current candles closed above or below these highs or lows, then a new line is added to the chart on the current candles (trend or breakout). According to my observations, this script draws a chart that is completely identical to the Line Breakout chart built into TradingView, but of course with gaps, as there is time in the candles / bar chart. I stuffed all the logic into a wrapper in the form of the get_linebreak() function, which returns a tuple of OHLC values. And these series with the help of the plotcandle() annotation can be converted to the "Linear Breakthrough" chart. I also want to note that with a large number of candles on the chart, outrages about the buffer size uncertainty are heard from the TradingView black box. Because of this, in the annotation study() set the value to the max_bars_back parameter.
In general, use it (for example, to write strategies)!
Reflex & Trendflex█ OVERVIEW
Reflex and Trendflex are zero-lag oscillators that decompose price into independent cycle and trend components using SuperSmoother filtering. These indicators isolate each component separately, providing clearer identification of cyclical reversals (Reflex) versus trending movements (Trendflex).
Based on Dr. John F. Ehlers' "Reflex: A New Zero-Lag Indicator" article (February 2020, TASC), both oscillators use normalized slope deviation analysis to minimize lag while maintaining signal clarity. The SuperSmoother filter removes high-frequency noise, then deviations from linear regression (Reflex) or current value (Trendflex) are measured and normalized by RMS for consistent amplitude across instruments and timeframes.
█ CONCEPTS
SuperSmoother Filter
Both oscillators begin with a two-pole Butterworth low-pass filter that smooths price data without the excessive lag of simple moving averages. The filter uses exponential decay coefficients and cosine modulation based on the cutoff period, providing aggressive smoothing while preserving signal timing.
Reflex: Cycle Component
Reflex isolates cyclical price behavior by measuring deviation from a linear regression line fitted through the SuperSmoother output. For each bar, the filter calculates a linear slope over the lookback period, then sums how much the smoothed price deviates from this trendline. These deviations represent pure cyclical movement - price oscillations around the dominant trend. The result is normalized by RMS (root mean square) to produce consistent amplitude regardless of volatility or timeframe.
Trendflex: Trend Component
Trendflex extracts trending behavior by measuring cumulative deviation from the current SuperSmoother value. Instead of comparing to a regression line, it simply sums the differences between the current smoothed value and all past values in the period. This captures sustained directional movement rather than oscillations. Like Reflex, normalization by RMS ensures comparable readings across different instruments.
RMS Normalization
Both oscillators normalize their raw deviation measurements using an exponentially weighted RMS calculation: `rms = 0.04 * deviation² + 0.96 * rms `. This adaptive normalization ensures the oscillator amplitude remains stable as volatility changes, making threshold levels meaningful across different market conditions.
█ INTERPRETATION
Reflex (Cycle Component)
Oscillates around zero representing cyclical price behavior isolated from trend:
• Above zero : Price is in upward phase of cycle
• Below zero : Price is in downward phase of cycle
• Zero crossings : Potential cycle reversal points
• Extremes : Indicate stretched cyclical condition, often precede mean reversion
Best used for identifying cyclical turning points in ranging or oscillating markets. More sensitive to reversals than Trendflex.
Trendflex (Trend Component)
Oscillates around zero representing trending behavior isolated from cycles:
• Above zero : Sustained upward trend
• Below zero : Sustained downward trend
• Zero crossings : Trend direction changes
• Magnitude : Strength of trend (larger absolute values = stronger trend)
Best used for confirming trend direction and identifying trend exhaustion. Less noisy than Reflex due to focus on directional movement rather than oscillations.
Combined Analysis
Using both oscillators together provides powerful signal confirmation:
• Both positive: Strong uptrend with positive cycle phase (high probability long setup)
• Both negative: Strong downtrend with negative cycle phase (high probability short setup)
• Divergent signals: Conflicting cycle and trend (choppy conditions, reduce position size)
• Reflex reversal with Trendflex agreement: Cyclical turn within established trend (entry/exit timing)
Dynamic Thresholds
Threshold bands identify statistically significant oscillator readings that warrant attention:
• Breach above +threshold : Strong bullish cycle (Reflex) or trend (Trendflex) behavior - potential overbought condition
• Breach below -threshold : Strong bearish cycle or trend behavior - potential oversold condition
• Return inside thresholds : Signal strength normalizing, potential reversal or consolidation ahead
• Threshold compression : During low volatility, thresholds narrow (especially with StdDev mode), making breaches more frequent
• Threshold expansion : During high volatility, thresholds widen, filtering out minor oscillations
Combine threshold breaches with zero-line position for stronger signals:
• Threshold breach + zero-line cross = high-conviction signal
• Threshold breach without zero-line support = monitor for confirmation
Alert Conditions
Six built-in alerts trigger on bar close (no repainting):
• Above +Threshold : Oscillator crossed above positive threshold (strong bullish behavior)
• Below -Threshold : Oscillator crossed below negative threshold (strong bearish behavior)
• Reflex Above Zero : Reflex crossed above zero (bullish cycle phase)
• Reflex Below Zero : Reflex crossed below zero (bearish cycle phase)
• Trendflex Above Zero : Trendflex crossed above zero (bullish trend shift)
• Trendflex Below Zero : Trendflex crossed below zero (bearish trend shift)
█ SETTINGS & PARAMETER TUNING
Oscillator Settings
• Source : Price series to decompose
• Reflex Period (5-50): SuperSmoother period for cycle component. Lower values increase responsiveness to cyclical turns but add noise. Default 20.
• Trendflex Period (5-50): SuperSmoother period for trend component. Lower values respond faster to trend changes. Default 20.
Display Settings
• Reflex/Trendflex Display : Toggle visibility and customize colors for each oscillator independently
• Zero Line : Reference line showing neutral oscillator position
Dynamic Thresholds
Optional significance bands that identify when oscillator readings indicate strong cyclical or trending behavior:
• Threshold Mode : Choose calculation method based on market characteristics
- MAD (Median Absolute Deviation) : Outlier-resistant, best for markets with occasional spikes (default)
- Standard Deviation : Volatility-sensitive, adapts quickly to regime changes
- Percentile Rank : Fixed probability bands (e.g., 90% = only 10% of values exceed threshold)
• Apply To : Select which oscillator (Reflex or Trendflex) to calculate thresholds for
• Period (2-200): Lookback window for threshold calculation. Default 50.
• Multiplier (k) : Scaling factor for MAD/StdDev modes. Higher values = fewer threshold breaches (default 1.5)
• Percentile (%) : For Percentile mode only. Higher percentile = more selective threshold (default 90%)
Parameter Interactions
• Shorter periods make both oscillators more sensitive but noisier
• Reflex typically more volatile than Trendflex at same period settings
• For ranging markets: shorter Reflex period (10-15) captures swings better
• For trending markets: shorter Trendflex period (10-15) follows trend shifts faster
█ LIMITATIONS
Inherent Characteristics
• Near-zero lag, not zero-lag : Despite the name, some lag remains from SuperSmoother filtering
• Normalization artifacts : RMS normalization can produce unusual readings during volatility regime changes
• Period dependency : Oscillator characteristics change significantly with different period settings - no "correct" universal parameter
Market Conditions to Avoid
• Very low volatility : Normalization amplifies noise in quiet markets, producing false signals
• Sudden gaps : SuperSmoother assumes continuous data; large gaps disrupt filter continuity requiring bars to stabilize
• Micro timeframes : Sub-minute charts contain microstructure noise that overwhelms signal quality
Parameter Selection Pitfalls
• Matching periods to dominant cycle : If period doesn't align with actual market cycle period, signals degrade
• Threshold over-tuning : Optimizing threshold parameters for past data often fails forward - use conservative defaults
• Ignoring component differences : Reflex and Trendflex measure different aspects - don't expect identical behavior
█ NOTES
Credits
These indicators are based on Dr. John F. Ehlers' "Reflex: A New Zero-Lag Indicator" published in the February 2020 issue of Technical Analysis of Stocks & Commodities (TASC) magazine. The article introduces a novel approach to isolating cycle and trend components using SuperSmoother filtering combined with normalized deviation analysis.
For those interested in the underlying mathematics and DSP concepts:
• Ehlers, J.F. (February 2020). "Reflex: A New Zero-Lag Indicator" - Technical Analysis of Stocks & Commodities magazine
• Ehlers, J.F. (2001). Rocket Science for Traders: Digital Signal Processing Applications . John Wiley & Sons
• Various TASC articles by John Ehlers on SuperSmoother filters and oscillator design
by ♚@e2e4
GAP DETECTORGAP DETECTOR is an indicator displaying price gaps that have never been completely filled (only gaps >= 5 pips are considered).
Each gap is defined by two lines (the lower and upper bound of the gap), and a label giving information on its price range
#Parameters:
length: the number of candles being considered in the indicator (max is 3000).
width: the width of the gap lines.
[PX] VWAP Gap LevelHello guys,
another day, another method for detecting support and resistance level. This time it's all about the VWAP and daily gaps it might produce.
How does it work?
The indicator detects when a new daily candle begins and the VWAP makes a big move in either direction. Often it produces a gap and this is where the support or resistance level will be plotted. The idea behind it is, that those gaps get filled at some point in time. You can control how big a VWAP movement ("gap") has to be with the "VWAP Movement %" -setting. Also, you can adjust the style of the level.
If you find this indicator useful, please leave a "like" and hit that "follow" button :)
Have fun and happy trading :)))
Volume Profile Free Ultra SLI (100 Levels Value Area VWAP) - RRBVolume Profile Free Ultra SLI by RagingRocketBull 2019
Version 1.0
This indicator calculates Volume Profile for a given range and shows it as a histogram consisting of 100 horizontal bars.
This is basically the MAX SLI version with +50 more Pinescript v4 line objects added as levels.
It can also show Point of Control (POC), Developing POC, Value Area/VWAP StdDev High/Low as dynamically moving levels.
Free accounts can't access Standard TradingView Volume Profile, hence this indicator.
There are several versions: Free Pro, Free MAX SLI, Free Ultra SLI, Free History. This is the Free Ultra SLI version. The Differences are listed below:
- Free Pro: 25 levels, +Developing POC, Value Area/VWAP High/Low Levels, Above/Below Area Dimming
- Free MAX SLI: 50 levels, 2x SLI modes for Buy/Sell or even higher res 150 levels
- Free Ultra SLI: 100 levels, packed to the limit, 2x SLI modes for Buy/Sell or even higher res 300 levels
- Free History: auto highest/lowest, historic poc/va levels for each session
Features:
- High-Res Volume Profile with up to 100 levels (line implementation)
- 2x SLI modes for even higher res: 300 levels with 3x vertical SLI, 100 buy/sell levels with 2x horiz SLI
- Calculate Volume Profile on full history
- POC, Developing POC Levels
- Buy/Sell/Total volume modes
- Side Cover
- Value Area, VAH/VAL dynamic levels
- VWAP High/Low dynamic levels with Source, Length, StdDev as params
- Show/Hide all levels
- Dim Non Value Area Zones
- Custom Range with Highlighting
- 3 Anchor points for Volume Profile
- Flip Levels Horizontally
- Adjustable width, offset and spacing of levels
- Custom Color for POC/VA/VWAP levels, Transparency for buy/sell levels
WARNING:
- Compilation Time: 1 min 20 sec
Usage:
- specify max_level/min_level/spacing (required)
- select range (start_bar, range length), confirm with range highlighting
- select volume type: Buy/Sell/Total
- select mode Value Area/VWAP to show corresponding levels
- flip/select anchor point to position the buy/sell levels
- use Horiz Buy/Sell SLI mode with 100 or Vertical SLI with 300 levels if needed
- use POC/Developing POC/VA/VWAP High/Low as S/R levels. Usually daily values from 1-3 days back are used as levels for the current day.
SLI:
use SLI modes to extend the functionality of the indicator:
- Horiz Buy/Sell 2x SLI lets you view 100 Buy/Sell Levels at the same time
- Vertical Max_Vol 3x SLI lets you increase the resolution to 300 levels
- you need at least 2 instances of the indicator attached to the same chart for SLI to work
1) Enable Horiz SLI:
- attach 2 indicator instances to the chart
- make sure all instances have the same min_level/max_level/range/spacing settings
- select volume type for each instance: you can have a buy/sell or buy/total or sell/total SLI. Make sure your buy volume instance is the last attached to be displayed on top of sell/total instances without overlapping.
- set buy_sell_sli_mode to true for indicator instances with volume_type = buy/sell, for type total this is optional.
- this basically tells the script to calculate % lengths based on total volume instead of individual buy/sell volumes and use ext offset for sell levels
- Sell Offset is calculated relative to Buy Offset to stack/extend sell after buy. Buy Offset = Zero - Buy Length. Sell Offset = Buy Offset - Sell Length = Zero - Buy Length - Sell Length
- there are no master/slave instances in this mode, all indicators are equal, poc/va levels are not affected and can work independently, i.e. one instance can show va levels, another - vwap.
2) Enable Vertical SLI:
- attach the first instance and evaluate the full range to roughly determine where is the highest max_vol/poc level i.e. 0..20000, poc is in the bottom half (third, middle etc) or
- add more instances and split the full vertical range between them, i.e. set min_level/max_level of each corresponding instance to 0..10000, 10000..20000 etc
- make sure all instances have the same range/spacing settings
- an instance with a subrange containing the poc level of the full range is now your master instance (bottom half). All other instances are slaves, their levels will be calculated based on the max_vol/poc of the master instance instead of local values
- set show_max_vol_sli to true for the master instance. for slave instances this is optional and can be used to check if master/slave max_vol values match and slave can read the master's value. This simply plots the max_vol value
- you can also attach all instances and set show_max_vol_sli to true in all of them - the instance with the largest max_vol should become the master
Auto/Manual Ext Max_Vol Modes:
- for auto vertical max_vol SLI mode set max_vol_sli_src in all slave instances to the max_vol of the master indicator: "VolumeProfileFree_MAX_RRB: Max Volume for Vertical SLI Mode". It can be tricky with 2+ instances
- in case auto SLI mode doesn't work - assign max_vol_sli_ext in all slave instances the max_vol value of the master indicator manually and repeat on each change
- manual override max_vol_sli_ext has higher priority than auto max_vol_sli_src when both values are assigned, when they are 0 and close respectively - SLI is disabled
- master/slave max_vol values must match on each bar at all times to maintain proper level scale, otherwise slave's levels will look larger than they should relative to the master's levels.
- Max_vol (red) is the last param in the long list of indicator outputs
- the only true max_vol/poc in this SLI mode is the master's max_vol/poc. All poc/va levels in slaves will be irrelevant and are disabled automatically. Slaves can only show VWAP levels.
- VA Levels of the master instance in this SLI mode are calculated based on the subrange, not the whole range and may be inaccurate. Cross check with the full range.
WARNING!
- auto mode max_vol_sli_src is experimental and may not work as expected
- you can only assign auto mode max_vol_sli_src = max_vol once due to some bug with unhandled exception/buffer overflow in Tradingview. Seems that you can clear the value only by removing the indicator instance
- sometimes you may see a "study in error state" error when attempting to set it back to close. Remove indicator/Reload chart and start from scratch
- volume profile may not finish to redraw and freeze in an ugly shape after an UI parameter change when max_vol_sli_src is assigned a max_vol value. Assign it to close - VP should redraw properly, but it may not clear the assigned max_vol value
- you can't seem to be able to assign a proper auto max_vol value to the 3rd slave instance
- 2x Vertical SLI works and tested in both auto/manual, 3x SLI - only manual seems to work (you can have a mixed mode: 2nd instance - auto, 3rd - manual)
Notes:
- This code uses Pinescript v3 compatibility framework
- This code is 20x-30x faster (main for cycle is removed) especially on lower tfs with long history - only 4-5 sec load/redraw time vs 30-60 sec of the old Pro versions
- Instead of repeatedly calculating the total sum of volumes for the whole range on each bar, vol sums are now increased on each bar and passed to the next in the range making it a per range vs per bar calculation that reduces time dramatically
- 100 levels consist of 50 main plot levels and 50 line objects used as alternate levels, differences are:
- line objects are always shown on top of other objects, such as plot levels, zero line and side cover, it's not possible to cover/move them below.
- all line objects have variable lengths, use actual x,y coords and don't need side cover, while all plot levels have a fixed length of 100 bars, use offset and require cover.
- all key properties of line objects, such as x,y coords, color can be modified, objects can be moved/deleted, while this is not possible for static plot levels.
- large width values cause line objects to expand only up/down from center while their length remains the same and stays within the level's start/end points similar to an area style.
- large width values make plot levels expand in all directions (both h/v), beyond level start/end points, sometimes overlapping zero line, making them an inaccurate % length representation, as opposed to line objects/plot levels with area style.
- large width values translate into different widths on screen for line objects and plot levels.
- you can't compensate for this unwanted horiz width expansion of plot levels because width uses its own units, that don't translate into bars/pixels.
- line objects are visible only when num_levels > 50, plot levels are used otherwise
- Since line objects are lines, plot levels also use style line because other style implementations will break the symmetry/spacing between levels.
- if you don't see a volume profile check range settings: min_level/max_level and spacing, set spacing to 0 (or adjust accordingly based on the symbol's precision, i.e. 0.00001)
- you can view either of Buy/Sell/Total volumes, but you can't display Buy/Sell levels at the same time using a single instance (this would 2x reduce the number of levels). Use 2 indicator instances in horiz buy/sell sli mode for that.
- Volume Profile/Value Area are calculated for a given range and updated on each bar. Each level has a fixed length. Offsets control visible level parts. Side Cover hides the invisible parts.
- Custom Color for POC/VA/VWAP levels - UI Style color/transparency can only change shape's color and doesn't affect textcolor, hence this additional option
- Custom Width - UI Style supports only width <= 4, hence this additional option
- POC is visible in both modes. In VWAP mode Developing POC becomes VWAP, VA High and Low => VWAP High and Low correspondingly to minimize the number of plot outputs
- You can't change buy/sell level colors from input (only transparency) - this requires 2x plot outputs => 2x reduces the number of levels to fit the max 64 limit. That's why 2 additional plots are used to dim the non Value Area zones
- You can change level transparency of line objects. Due to Pinescript limitations, only discrete values are supported.
- Inverse transp correlation creates the necessary illusion of "covered" line objects, although they are shown on top of the cover all the time
- If custom lines_transp is set the illusion will break because transp range can't be skewed easily (i.e. transp 0..100 is always mapped to 100..0 and can't be mapped to 50..0)
- transparency can applied to lines dynamically but nva top zone can't be completely removed because plot/mixed type of levels are still used when num_levels < 50 and require cover
- transparency can't be applied to plot levels dynamically from script this can be done only once from UI, and you can't change plot color for the past length bars
- All buy/sell volume lengths are calculated as % of a fixed base width = 100 bars (100%). You can't set show_last from input to change it
- Range selection/Anchoring is not accurate on charts with time gaps since you can only anchor from a point in the future and measure distance in time periods, not actual bars, and there's no way of knowing the number of future gaps in advance.
- Adjust Width for Log Scale mode now also works on high precision charts with small prices (i.e. 0.00001)
- in Adjust Width for Log Scale mode Level1 width extremes can be capped using max deviation (when level1 = 0, shift = 0 width becomes infinite)
- There's no such thing as buy/sell volume, there's just volume, but for the purposes of the Volume Profile method, assume: bull candle = buy volume, bear candle = sell volume
P.S. I am your grandfather, Luke! Now, join the Dark Side in your father's steps or be destroyed! Once more the Sith will rule the Galaxy, and we shall have peace...
CME Gap Finder - BitcoinOnly for Bitcoin!
This indicator locates weekly gaps created by the CME Futures market for Bitcoin.
As you can see, Bitcoin tends to close the weekly gaps created in the futures market so I thought this could be a very useful tool.
Instead of having to look between multiple charts, this simply overlays the past weeks open and close should a gap appear.
I hope you find this indicator useful!
Cheers!
T2-%Use a superposition of 30 avarages to stress-out trend changes (points in time where all possible frequencies that create the movment change their phase from prositive to negetive or the opposite). The indicator has one paramater that should be adjusted: 'os'.
By defult the 30 avarages that are tested range from 7 to 63 in gaps of 2. increasing the 'os' parameter moves the ranges by multiplications of 65. therefore if you add 5 indicators ontop of eachother, each scaled to left and set the os of each to another value (0,1,2,3,4) you will have a full spectum of avarages ranging from 7 to 325 in gaps of 2.
GapologyThis indicator can be used as a simple measure of price action tradability. It's an alternative to volume that focuses on the gaps between close and open candle prices. The bigger the gaps, the more spread and slippage you'll get when trading.
Hersheys Volume Pressure v2Hersheys Volume Pressure gives you very nice confirmation of trend starts and stops using volume and price.
For up bars...
If you have a large price change with low volume , that's very bullish .
If you have a small price change with low volume , that's bullish .
For down bars...
If you have a large price change with low volume , that's very bearish .
If you have a small price change with low volume , that's bearish .
Look at the chart and you'll see how trends start and end with a PINCH and widen in the middle of the moves.
You can set the moving average period, 14 is the default.
Good trading!
Brian Hershey
v2 change log...
- issue with price gaps - gaps at the open were sometimes showing incorrect colors
- scaling issues - sometimes a change is so large it scales down all nearby data and renders it hard to view. Code was added to clip those huge values.
v3 what's coming next...
- better scaling - sometimes with thinly traded stocks there is too much clipping. For now increase the chart interval to correct.
True Gap Finder with Revisit DetectionTrue Gap Finder with Revisit Detection
This indicator is a powerful tool for intraday traders to identify and track price gaps. Unlike simple gap indicators, this script actively tracks the status of the gap, visualizing the void until it is filled (revisited) by price.
Key Features:
Active Gap Tracking: Finds gap-up and gap-down occurrences (where Low > Previous High or High < Previous Low) and actively tracks them.
Gap Zones (Clouds): Visually shades the empty "gap zone" (the void between the gap candles), making it instantly obvious where price needs to travel to fill the gap. The cloud disappears automatically once the gap is filled.
Dynamic Labels: automatically displays price labels at the origin of the gap, showing the specific price range (High-Low) that constitutes the gap. Labels are positioned intelligently to avoid cluttering current price action.
Alerts: Configurable alerts notify you the moment a gap is filled.
Customization: Full control over colors, clouds, labels, and alert settings to match your chart style.
How it works: The indicator tracks the most recent gap. If a new gap forms, it becomes the active focus. When price moves back to "close" or "fill" this gap area, the lines and clouds automatically stop plotting, giving you a clean chart that focuses only on open business.
Probability Cone█ Overview:
Probability Cone is based on the Expected Move . While Expected Move only shows the historical value band on every bar, probability panel extend the period in the future and plot a cone or curve shape of the probable range. It plots the range from bar 1 all the way to bar 31.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption; it's not the real distribution of return.
The area of probability range is based on an inverse normal cumulative distribution function. The inverse cumulative distribution gives the range of price for given input probability. People can adjust the range by adjusting the standard deviation in the settings. The probability of the entered standard deviation will be shown at the edges of the probability cone.
The shown 68% and 95% probabilities correspond to the full range between the two blue lines of the cone (68%) and the two purple lines of the cone (95%). The probabilities suggest the % of outcomes or data that are expected to lie within this range. It does not suggest the probability of reaching those price levels.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
█ Volatility Models :
Sample SD : traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson : Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass : Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension : Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers : Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient.
EWMA : Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang : Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation : It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
You can learn more about each of the volatility models in out Historical Volatility Estimators indicator.
█ How to use
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended.
The shape of the cone will be skewed and have a directional bias when the length of mean is short. It might be more adaptive to the current price or trend, but more accurate estimation should use a longer period for the mean.
Using a short look back for mean will make the cone having a directional bias.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
Time back settings shift the estimation period back by the input number. It's the origin of when the probability cone start to estimation it's range.
E.g., When time back = 5, the probability cone start its prediction interval estimation from 5 bars ago. So for time back = 5 , it estimates the probability range from 5 bars ago to X number of bars in the future, specified by the Forecast Period (max 1000).
█ Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
The uncertainty in future bars makes the range wider. The overestimate effect of the body is partly neutralized when it's extended to future bars. We encourage people who use this indicator to further investigate the Historical Volatility Estimators , Fast Autocorrelation Estimator , Expected Move and especially the Linear Moments Indicator .
The probability is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between.
SMC Statistical Liquidity Walls [PhenLabs]📊 SMC Statistical Liquidity Walls
Version: PineScript™ v6
📌 Description
The SMC Statistical Liquidity Walls indicator is designed to visualize market volatility and potential reversal zones using advanced statistical modeling. Unlike traditional Bollinger Bands that use simple lines, this script utilizes an “Inverted Sigmoid” opacity function to create a “fog of war” effect. This visualizes the density of liquidity: the further price moves from the equilibrium (mean), the “harder” the liquidity wall becomes.
This tool solves the problem of over-trading in low-probability areas. By automatically mapping “Premium” (Resistance) and “Discount” (Support) zones based on Standard Deviation (SD), traders can instantly see when price is overextended. The result is a clean, intuitive overlay that helps you identify high-probability mean reversion setups without cluttering your chart with manual drawings.
🚀 Points of Innovation
Inverted Sigmoid Logic: A custom mathematical function maps Standard Deviation to opacity, creating a realistic “wall” density effect rather than linear gradients.
Dynamic “Solidity”: The indicator is transparent at the center (Equilibrium) and becomes visually solid at the edges, mimicking physical resistance.
Separated Directional Bias: distinct Red (Premium) and Green (Discount) coding helps SMC traders instantly recognize expensive vs. cheap pricing.
Smart “Safe” Deviation: Includes fallback logic to handle calculation errors if deviation hits zero, ensuring the indicator never crashes during data gaps.
🔧 Core Components
Basis Calculation: Uses a Simple Moving Average (SMA) to determine the market’s equilibrium point.
Standard Deviation Zones: Calculates 1SD, 2SD, and 3SD levels to define the statistical extremes of price action.
Sigmoid Alpha Calculation: Converts the SD distance into a transparency value (0-100) to drive the visual gradient.
🔥 Key Features
Automated Premium/Discount Zones: Red zones indicate overbought (Premium) areas; Green zones indicate oversold (Discount) areas.
Customizable Density: Users can adjust the “Steepness” and “Midpoint” of the sigmoid curve to control how fast the walls become solid.
Integrated Alerts: Built-in alert conditions trigger when price hits the “Solid” wall (2SD or higher), perfect for automated trading or notifications.
Visual Clarity: The center of the chart remains clear (high transparency) to keep focus on price action where it matters most.
🎨 Visualization
Equilibrium Line: A gray line representing the mean price.
Gradient Fills: The space between bands fills with color that increases in opacity as it moves outward.
Premium Wall: Upper zones fade from transparent red to solid red.
Discount Wall: Lower zones fade from transparent green to solid green.
📖 Usage Guidelines
Range Period: Default 20. Controls the lookback period for the SMA and Standard Deviation calculation.
Source: Default Close. The price data used for calculations.
Center Transparency: Default 100 (Clear). Controls how transparent the middle of the chart is.
Edge Transparency: Default 45 (Solid). Controls the opacity of the outermost liquidity wall.
Wall Steepness: Default 2.5. Adjusts how aggressively the gradient transitions from clear to solid.
Wall Start Point: Default 1.5 SD. The deviation level where the gradient shift begins to accelerate.
✅ Best Use Cases
Mean Reversion Trading: Enter trades when price hits the solid 2SD or 3SD wall and shows rejection wicks.
Take Profit Targets: Use the Equilibrium (Gray Line) as a logical first target for reversal trades.
Trend Filtering: Do not initiate new long positions when price is deep inside the Red (Premium) wall.
⚠️ Limitations
Lagging Nature: As a statistical tool based on Moving Averages, the walls react to past price data and may lag during sudden volatility spikes.
Trending Markets: In strong parabolic trends, price can “ride” the bands for extended periods; mean reversion should be used with caution in these conditions.
💡 What Makes This Unique
Physics-Based Visualization: We treat liquidity as a physical barrier that gets denser the deeper you push, rather than just a static line on a chart.
🔬 How It Works
Step 1: The script calculates the mean (SMA) and the Standard Deviation (SD) of the source price.
Step 2: It defines three zones above and below the mean (1SD, 2SD, 3SD).
Step 3: The custom `get_inverted_sigmoid` function calculates an Alpha (transparency) value based on the SD distance.
Step 4: Plot fills are colored dynamically, creating a seamless gradient that hardens at the extremes to visualize the “Liquidity Wall.”
💡 Note
For best results, combine this indicator with Price Action confirmation (such as pin bars or engulfing candles) when price touches the solid walls.
AliceTears GridAliceTears Grid is a customizable Mean Reversion system designed to capitalize on market volatility during specific trading sessions. Unlike standard grid bots that place blind limit orders, this strategy establishes a daily or session-based "Baseline" and looks for price over-extensions to fade the move back to the mean.
This strategy is best suited for ranging markets (sideways accumulation) or specific forex sessions (e.g., Asian Session or NY/London overlap) where price tends to revert to the opening price.
🛠 How It Works
1. The Baseline & Grid Generation At the start of every session (or the daily open), the script records the Open price. It then projects visual grid lines above and below this price based on your Step % input.
Example: If the Open is $100 and Step is 1%, lines are drawn at $101, $102, $99, $98, etc.
2. Entry Logic: Reversal Mode This script features a "Reversal Mode" (enabled by default) to filter out "falling knives."
Standard Grid: Buys immediately when price touches the line.
AliceTears Logic: Waits for the price to breach a grid level and then close back inside towards the mean. This confirms a potential rejection of that level before entering.
3. Exit Logic
Target Profit: The primary target is the previous grid level (Mean Reversion).
Trailing Stop: If the price continues moving in your favor, a trailing stop activates to maximize the run.
Stop Loss: A manual percentage-based stop loss is available to prevent deep drawdowns in trending markets.
⚙️ Key Features
Visual Grid: Automatically draws entry levels on the chart for the current session, helping you visualize where the "math" is waiting for price.
Timezone & Session Control: Includes a custom Timezone Offset tool. You can trade specific hours (e.g., 09:30–16:00) regardless of your chart's UTC setting.
Grid Management: Independent logic for Long and Short grids with pyramiding capabilities.
Safety Filters: Options to force-close trades at the end of the session to avoid overnight gaps.
⚠️ Risk Warning
Please Read Before Using: This is a Counter-Trend / Grid Strategy.
Pros: High win rate in sideways/ranging markets.
Cons: In strong trending markets (parabolic pumps or crashes), this strategy will add to losing positions ("catch a falling knife").
Recommendation: Always use the Stop Loss and Date Filter inputs. Do not run this on highly volatile assets without strict risk management parameters.
Settings Guide
Entry Reversal Mode: Keep checked for safer entries. Uncheck for aggressive limit-order style execution.
Grid Step (%): The distance between lines. For Forex, use lower values (0.1% - 0.5%). For Crypto, use higher values (1.0% - 3.0%).
UTC Offset: Adjust this to align the Session Hours with your target market (e.g., -5 for New York).
This script is open source. Feel free to use it for educational purposes or modify it to fit your trading style.
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility.
In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution.
Note: Normal distribution is just an assumption, it's not the real distribution of return
Settings:
"Estimation Period Selection" is for selecting the period we want to construct the prediction interval.
For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours.
For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead.
There are also other source selections based on high low.
Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar.
Note: It's probably better to use "probability cone" for visual presentation when time > 1
Volatility Models :
Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias
Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close
Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient
Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps.
about 8 x efficient
Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient
EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster.
YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance.
Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator.
Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters.
Standard deviations:
Standard Deviation One shows the estimated range where the closing price will be about 68% of the time.
Standard Deviation two shows the estimated range where the closing price will be about 95% of the time.
Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time.
Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability.
Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel.
People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1.
When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation.
Note: The significance of autocorrelation can be checked on an ACF indicator.
ACF
The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D
Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds.
Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag.
Note: The range here after smooth no long represent the probability
Panel positions can be adjusted in the settings.
X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left.
Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down.
Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands.
Warnings:
People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails.
Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption).
Expected move Occurrence Test
The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
Market Cycle Master The Market Cycle Master (MCM) by © DarkPoolCrypto is a sophisticated trading system designed to bridge the gap between standard retail trend indicators and institutional-grade risk management. Unlike traditional indicators that simply provide entry signals based on a single timeframe, this system employs a "Confluence Engine" that requires multi-timeframe (MTF) alignment before generating a signal.
Crucially, this script integrates a live Risk Management Calculator directly into the chart overlay. This feature allows traders to stop guessing position sizes and instead execute trades based on a fixed percentage of account equity at risk, calculating the exact lot size relative to the dynamic stop-loss level.
Core Concept and Logic
This system operates on three distinct layers of logic to filter out noise and identifying high-probability trend continuations:
1. The Trend Architecture (Layer 1) At its core, the script utilizes an adaptive ATR-based SuperTrend calculation. This allows the system to adjust to market volatility dynamically. When volatility expands, the trend bands widen to prevent premature stop-outs. When volatility contracts, the bands tighten to capture early reversals.
2. Institutional Context / Multi-Timeframe Filter (Layer 2) This is the primary filter of the Pro system. The script monitors a higher timeframe (default: 4-Hour) in the background.
Bullish Context: If the Higher Timeframe (HTF) is in an uptrend, the script will only permit LONG signals on your current chart.
Bearish Context: If the HTF is in a downtrend, the script will only permit SHORT signals.
Grayscale Filters: If the current chart's trend opposes the Higher Timeframe trend (e.g., a 5-minute uptrend during a 4-hour downtrend), the candles will be painted GRAY. This indicates a low-probability "Counter-Trend" environment, and no signals will be generated.
3. Money Flow Filtering (Layer 3) To prevent buying tops or selling bottoms, the system utilizes the Money Flow Index (MFI). Long signals are filtered if volume-weighted momentum is already overbought, and Short signals are filtered if oversold.
The Risk Management HUD
The Heads-Up Display (HUD) is the distinguishing feature of this tool. It transforms the indicator from a visual aid into a trading terminal.
Trend Direction: Displays the current verified trend.
MTF Status: Shows the state of the Higher Timeframe trend.
Volatility: Displays the current ATR value.
Stop Loss: Displays the exact price level of the trend line.
Risk Calculator:
Risk ($): Shows the total dollar amount you will lose if the stop loss is hit (based on your settings).
Units: Calculates exactly how much Crypto, Stock, or FX lots to purchase to match your risk parameters.
Guide: How to Use
Configuration
Trend Architecture: Adjust the "Volatility Factor" (Default: 3.0). Higher values reduce noise but delay entries. Lower values are faster but riskier.
Institutional Context: Select the "Higher Timeframe."
If trading 1m to 15m charts: Set HTF to 4 Hours (240).
If trading 1H to 4H charts: Set HTF to Daily (1D).
Risk Calculator:
Account Size: Enter your total trading capital.
Risk Per Trade: Enter the percentage of your account you are willing to lose on a single trade (e.g., 1.0%).
Trading Strategy
The Signal: Wait for a "Sniper Long" or "Sniper Short" label. This appears only when price action, volatility, and the higher timeframe consensus all align.
The Execution: Look at the HUD under "Units." Open a position for that specific amount.
The Stop Loss: Place your hard Stop Loss at the price shown in the HUD ("Stop Loss" row). This corresponds to the trend line.
The Exit: Close the position if the candle color turns Gray (loss of momentum/consensus) or if an opposing signal appears.
Disclaimer
This script and the information provided herein are for educational and entertainment purposes only. They do not constitute financial advice, investment advice, trading advice, or any other advice. Trading in financial markets involves a high degree of risk and may result in the loss of your entire capital.
The "Risk Calculator" included in this script provides theoretical values based on mathematical formulas relative to the price data provided by TradingView. It does not account for slippage, spread, exchange fees, or liquidity gaps. Always verify calculations manually before executing live trades. Past performance of any trading system is not indicative of future results. The author assumes no responsibility for any losses incurred while using this script.
CME Bitcoin Weekend Gap (Global) @jerikooDescription:
The Problem: You are watching the wrong hours. Many traders assume CME Bitcoin futures follow standard stock market hours or open Monday morning. This is incorrect.
Stock Market: Opens Monday morning.
CME Bitcoin: Opens Sunday Evening (US Time).
If you are in Europe, this means the market actually opens at Midnight (00:00) Monday. If you are waiting for the "Monday Morning Open," you are late.
The Solution: True Gap Detection This indicator highlights the exact downtime of the CME Bitcoin Futures market to help you identify true liquidity gaps.
Why this script is different: Most gap scripts break when you change your chart's time zone (e.g., switching from UTC to New York). This script is Universal.
Hardcoded Exchange Time: It calculates logic based on "America/Chicago" (CME HQ) time, regardless of your local chart settings.
Manual Offset Fix: Some data feeds have a +/- 1 or 2-hour sync difference depending on the broker. This script includes a "Hour Shift" setting to manually align the box perfectly to your specific candles.
How to use:
Add to your chart.
Look for the Dark Green highlighted zone.
This zone represents the Weekend Gap (Friday Close to Sunday Open).
Troubleshooting: If the box starts 1-2 hours too early or too late, go to Settings and change the "Hour Shift" value (e.g., -1, +1) until it snaps perfectly to the Friday close candle.
Technical Details:
CME Close: Friday 16:00 CT
CME Open: Sunday 17:00 CT
Color: Dark Green (50% Transparency)
Step 3: Categories & Tags
Select these options in the right-hand menu of the publishing page.
Category: Trend Analysis OR Bitcoin
Tags: CME Bitcoin BTC Gap Futures Weekend
Step 4: Final Checklist Before Clicking "Publish"
Load the Code: Make sure the "Manual Fix" version of the code (the last one I gave you) is currently open in the Pine Editor.
Add to Chart: You must click "Add to Chart" so the script is visible on your screen before publishing.
Privacy: Select Public (so others can search for it) or Private (if you only want to share the link).
Visibility: Choose Open (so others can see the code) or Protected (if you want to hide the code, though Open is better for simple scripts like this).
Liquidity Void Zone Detector [PhenLabs]📊 Liquidity Void Zone Detector
Version: PineScript™v6
📌 Description
The Liquidity Void Zone Detector is a sophisticated technical indicator designed to identify and visualize areas where price moved with abnormally low volume or rapid momentum, creating "voids" in market liquidity. These zones represent areas where insufficient trading activity occurred during price movement, often acting as magnets for future price action as the market seeks to fill these gaps.
Built on PineScript v6, this indicator employs a dual-detection methodology that analyzes both volume depletion patterns and price movement intensity relative to ATR. The revolutionary 3D visualization system uses three-layer polyline rendering with adaptive transparency and vertical offsets, creating genuine depth perception where low liquidity zones visually recede and high liquidity zones protrude forward. This makes critical market structure immediately apparent without cluttering your chart.
🚀 Points of Innovation
Dual detection algorithm combining volume threshold analysis and ATR-normalized price movement sensitivity for comprehensive void identification
Three-layer 3D visualization system with progressive transparency gradients (85%, 78%, 70%) and calculated vertical offsets for authentic depth perception
Intelligent state machine logic that tracks consecutive void bars and only renders zones meeting minimum qualification requirements
Dynamic strength scoring system (0-100 scale) that combines inverted volume ratios with movement intensity for accurate void characterization
Adaptive ATR-based spacing calculation that automatically adjusts 3D layering depth to match instrument volatility
Efficient memory management system supporting up to 100 simultaneous void visualizations with automatic array-based cleanup
🔧 Core Components
Volume Analysis Engine: Calculates rolling volume averages and compares current bar volume against dynamic thresholds to detect abnormally thin trading conditions
Price Movement Analyzer: Normalizes bar range against ATR to identify rapid price movements that indicate liquidity exhaustion regardless of instrument or timeframe
Void Tracking State Machine: Maintains persistent tracking of void start bars, price boundaries, consecutive bar counts, and cumulative strength across multiple bars
3D Polyline Renderer: Generates three-layer rectangular polylines with precise timestamp-to-bar index conversion and progressive offset calculations
Strength Calculation System: Combines volume component (inverted ratio capped at 100) with movement component (ATR intensity × 30) for comprehensive void scoring
🔥 Key Features
Automatic Void Detection: Continuously scans price action for low volume conditions or rapid movements, triggering void tracking when thresholds are exceeded
Real-Time Visualization: Creates 3D rectangular zones spanning from void initiation to termination, with color-coded depth indicating liquidity type
Adjustable Sensitivity: Configure volume threshold multiplier (0.1-2.0x), price movement sensitivity (0.5-5.0x), and minimum qualifying bars (1-10) for customized detection
Dual Color Coding: Separate visual treatment for low liquidity voids (receding red) and high liquidity zones (protruding green) based on 50-point strength threshold
Optional Compact Labels: Toggle LV (Low Volume) or HV (High Volume) circular labels at void centers for quick identification without visual clutter
Lookback Period Control: Adjust analysis window from 5 to 100 bars to match your trading timeframe and market volatility characteristics
Memory-Efficient Design: Automatically manages polyline and label arrays, deleting oldest elements when user-defined maximum is reached
Data Window Integration: Plots void detection binary, current strength score, and average volume for detailed analysis in TradingView's data window
🎨 Visualization
Three-Layer Depth System: Each void is rendered as three stacked polylines with progressive transparency (85%, 78%, 70%) and calculated vertical offsets creating authentic 3D appearance
Directional Depth Perception: Low liquidity zones recede with back layer most transparent; high liquidity zones protrude with front layer most transparent for instant visual differentiation
Adaptive Offset Spacing: Vertical separation between layers calculated as ATR(14) × 0.001, ensuring consistent 3D effect across different instruments and volatility regimes
Color Customization: Fully configurable base colors for both low liquidity zones (default: red with 80 transparency) and high liquidity zones (default: green with 80 transparency)
Minimal Chart Clutter: Closed polylines with matching line and fill colors create clean rectangular zones without unnecessary borders or visual noise
Background Highlight: Subtle yellow background (96% transparency) marks bars where void conditions are actively detected in real-time
Compact Labeling: Optional tiny circular labels with 60% transparent backgrounds positioned at void center points for quick reference
📖 Usage Guidelines
Detection Settings
Lookback Period: Default: 10 | Range: 5-100 | Number of bars analyzed for volume averaging and void detection. Lower values increase sensitivity to recent changes; higher values smooth detection across longer timeframes. Adjust based on your trading timeframe: short-term traders use 5-15, swing traders use 20-50, position traders use 50-100.
Volume Threshold: Default: 1.0 | Range: 0.1-2.0 (step 0.1) | Multiplier applied to average volume. Bars with volume below (average × threshold) trigger void conditions. Lower values detect only extreme volume depletion; higher values capture more moderate low-volume situations. Start with 1.0 and decrease to 0.5-0.7 for stricter detection.
Price Movement Sensitivity: Default: 1.5 | Range: 0.5-5.0 (step 0.1) | Multiplier for ATR-normalized price movement detection. Values above this threshold indicate rapid price changes suggesting liquidity voids. Increase to 2.0-3.0 for volatile instruments; decrease to 0.8-1.2 for ranging or low-volatility conditions.
Minimum Void Bars: Default: 10 | Range: 1-10 | Minimum consecutive bars exhibiting void conditions required before visualization is created. Filters out brief anomalies and ensures only sustained voids are displayed. Use 1-3 for scalping, 5-10 for intraday trading, 10+ for swing trading to match your time horizon.
Visual Settings
Low Liquidity Color: Default: Red (80% transparent) | Base color for zones where volume depletion or rapid movement indicates thin liquidity. These zones recede visually (back layer most transparent). Choose colors that contrast with your chart theme for optimal visibility.
High Liquidity Color: Default: Green (80% transparent) | Base color for zones with relatively higher liquidity compared to void threshold. These zones protrude visually (front layer most transparent). Ensure clear differentiation from low liquidity color.
Show Void Labels: Default: True | Toggle display of compact LV/HV labels at void centers. Disable for cleaner charts when trading; enable for analysis and review to quickly identify void types across your chart.
Max Visible Voids: Default: 50 | Range: 10-100 | Maximum number of void visualizations kept on chart. Each void uses 3 polylines, so setting of 50 maintains 150 total polylines. Higher values preserve more history but may impact performance on lower-end systems.
✅ Best Use Cases
Gap Fill Trading: Identify unfilled liquidity voids that price frequently returns to, providing high-probability retest and reversal opportunities when price approaches these zones
Breakout Validation: Distinguish genuine breakouts through established liquidity from false breaks into void zones that lack sustainable volume support
Support/Resistance Confluence: Layer void detection over key horizontal levels to validate structural integrity—levels within high liquidity zones are stronger than those in voids
Trend Continuation: Monitor for new void formation in trend direction as potential continuation zones where price may accelerate due to reduced resistance
Range Trading: Identify void zones within consolidation ranges that price tends to traverse quickly, helping to avoid getting caught in rapid moves through thin areas
Entry Timing: Wait for price to reach void boundaries rather than entering mid-void, as voids tend to be traversed quickly with limited profit-taking opportunities
⚠️ Limitations
Historical Pattern Indicator: Identifies past liquidity voids but cannot predict whether price will return to fill them or when filling might occur
No Volume on Forex: Indicator uses tick volume for forex pairs, which approximates but doesn't represent true trading volume, potentially affecting detection accuracy
Lagging Confirmation: Requires minimum consecutive bars (default 10) before void is visualized, meaning detection occurs after void formation begins
Trending Market Behavior: Strong trends driven by fundamental catalysts may create voids that remain unfilled for extended periods or permanently
Timeframe Dependency: Detection sensitivity varies significantly across timeframes; settings optimized for one timeframe may not perform well on others
No Directional Bias: Indicator identifies liquidity characteristics but provides no predictive signal for price direction after void detection
Performance Considerations: Higher max visible void settings combined with small minimum void bars can generate numerous visualizations impacting chart rendering speed
💡 What Makes This Unique
Industry-First 3D Visualization: Unlike flat volume or liquidity indicators, the three-layer rendering with directional depth perception provides instant visual hierarchy of liquidity quality
Dual-Mode Detection: Combines both volume-based and movement-based detection methodologies, capturing voids that single-approach indicators miss
Intelligent Qualification System: State machine logic prevents premature visualization by requiring sustained void conditions, reducing false signals and chart clutter
ATR-Normalized Analysis: All detection thresholds adapt to instrument volatility, ensuring consistent performance across stocks, forex, crypto, and futures without constant recalibration
Transparency-Based Depth: Uses progressive transparency gradients rather than colors or patterns to create depth, maintaining visual clarity while conveying information hierarchy
Comprehensive Strength Metrics: 0-100 void strength calculation considers both the degree of volume depletion and the magnitude of price movement for nuanced zone characterization
🔬 How It Works
Phase 1: Real-Time Detection
On each bar close, the indicator calculates average volume over the lookback period and compares current bar volume against the volume threshold multiplier
Simultaneously measures current bar's high-low range and normalizes it against ATR, comparing the result to price movement sensitivity parameter
If either volume falls below threshold OR movement exceeds sensitivity threshold, the bar is flagged as exhibiting void characteristics
Phase 2: Void Tracking & Qualification
When void conditions first appear, state machine initializes tracking variables: start bar index, initial top/bottom prices, consecutive bar counter, and cumulative strength accumulator
Each subsequent bar with void conditions extends the tracking, updating price boundaries to envelope all bars and accumulating strength scores
When void conditions cease, system checks if consecutive bar count meets minimum threshold; if yes, proceeds to visualization; if no, discards the tracking and resets
Phase 3: 3D Visualization Construction
Calculates average void strength by dividing cumulative strength by number of bars, then determines if void is low liquidity (>50 strength) or high liquidity (≤50 strength)
Generates three polyline layers spanning from start bar to end bar and from top price to bottom price, each with calculated vertical offset based on ATR
Applies progressive transparency (85%, 78%, 70%) with layer ordering creating recession effect for low liquidity zones and protrusion effect for high liquidity zones
Creates optional center label and pushes all visual elements into arrays for memory management
Phase 4: Memory Management & Display
Continuously monitors polyline array size (each void creates 3 polylines); when total exceeds max visible voids × 3, deletes oldest polylines via array.shift()
Similarly manages label array, removing oldest labels when count exceeds maximum to prevent memory accumulation over extended chart history
Plots diagnostic data to TradingView’s data window (void detection binary, current strength, average volume) for detailed analysis without cluttering main chart
💡 Note:
This indicator is designed to enhance your market structure analysis by revealing liquidity characteristics that aren’t visible through standard price and volume displays. For best results, combine void detection with your existing support/resistance analysis, trend identification, and risk management framework. Liquidity voids are descriptive of past market behavior and should inform positioning decisions rather than serve as standalone entry/exit signals. Experiment with detection parameters across different timeframes to find settings that align with your trading style and instrument characteristics.
Nadaraya-Watson: Rational Quadratic Kernel (Opening Gap Shift)What we did to fix it: We didn't throw out the old data (that made it too jumpy early in the day).
Instead, we "tricked" the kernel by shifting all the previous day's prices up or down by the exact gap amount (e.g., if it gapped up 50 points, add 50 to every old price point). This makes the history "line up" with the new day's starting level.
Created so with a fresh session the Nadaraya-Watson Regression Kernel is relevant from the get go - no catch up on opening gaps.
All credit to jdehorty his full description is below.
What is Nadaraya–Watson Regression?
Nadaraya–Watson Regression is a type of Kernel Regression, which is a non-parametric method for estimating the curve of best fit for a dataset. Unlike Linear Regression or Polynomial Regression, Kernel Regression does not assume any underlying distribution of the data. For estimation, it uses a kernel function, which is a weighting function that assigns a weight to each data point based on how close it is to the current point. The computed weights are then used to calculate the weighted average of the data points.
How is this different from using a Moving Average?
A Simple Moving Average is actually a special type of Kernel Regression that uses a Uniform (Retangular) Kernel function. This means that all data points in the specified lookback window are weighted equally. In contrast, the Rational Quadratic Kernel function used in this indicator assigns a higher weight to data points that are closer to the current point. This means that the indicator will react more quickly to changes in the data.
Why use the Rational Quadratic Kernel over the Gaussian Kernel?
The Gaussian Kernel is one of the most commonly used Kernel functions and is used extensively in many Machine Learning algorithms due to its general applicability across a wide variety of datasets. The Rational Quadratic Kernel can be thought of as a Gaussian Kernel on steroids; it is equivalent to adding together many Gaussian Kernels of differing length scales. This allows the user even more freedom to tune the indicator to their specific needs.
The formula for the Rational Quadratic function is:
K(x, x') = (1 + ||x - x'||^2 / (2 * alpha * h^2))^(-alpha)
where x and x' data are points, alpha is a hyperparameter that controls the smoothness (i.e. overall "wiggle") of the curve, and h is the band length of the kernel.
Does this Indicator Repaint?
No, this indicator has been intentionally designed to NOT repaint. This means that once a bar has closed, the indicator will never change the values in its plot. This is useful for backtesting and for trading strategies that require a non-repainting indicator.
Settings:
Bandwidth. This is the number of bars that the indicator will use as a lookback window.
Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel.
Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes.
