Standard Error of the Estimate -Jon Andersen- V2Original implementation idea of bands by:
Traders issue: Stocks & Commodities V. 14:9 (375-379):
Standard Error Bands by Jon Andersen
Standard Error Bands are quite different than Bollinger's.
First, they are bands constructed around a linear regression curve.
Second, the bands are based on two standard errors above and below this regression line.
The error bands measure the standard error of the estimate around the linear regression line.
Therefore, as a price series follows the course of the regression line the bands will narrow , showing little error in the estimate. As the market gets noisy and random, the error will be greater resulting in wider bands .
Thanks to the work of @glaz & @XeL_arjona
In this version you can change the type of moving averages and the source of the bands.
Add a few studies of @dgtrd
1- ADX Colored Directional Movement Line
Directional Movement (DMI) (created by J. Welles Wilder ) consists of the Average Directional Index ( ADX ), to define whether or not there is a trend present, and Plus Directional Indicator (+D I) and Minus Directional Indicator (-D I) serve the purpose of determining trend direction
ADX Colored Directional Movement Line is custom interpretation of Directional Movement (DMI) with aim to present all 3 DMI indicator components with SINGLE line and ability to be added on top of the price chart (main chart)
How to interpret :
* triangle shapes:
▲- bullish : diplus >= diminus
▼- bearish : diplus < diminus
* colors:
green - bullish trend : adx >= strongTrend and di+ > di-
red - bearish trend : adx >= strongTrend and di+ < di-
gray - no trend : weekTrend < adx < strongTrend
yellow - week trend : adx < weekTrend
* color density:
darker : adx growing
lighter : adx falling
2- Volatility Colored Price/MA Line
Custom interpretation of the idea “Prices high above the moving average (MA) or low below it are likely to be remedied in the future by a reverse price movement”. Further details can be found under study “Price Distance to its MA by DGT”
How to interpret :
-▲ – Bullish , Price Action above Moving Average
-▼ – Bearish , Price Action below Moving Average
-Gray/Black - Low Volatility
-Green/Red – Price Action in Threshold Bands
-Dark Green/Red – Price Action Exceeds Threshold Bands
3- Volume Weighted Bar s
Volume Weighted Bars, a study of Kıvanç Özbilgiç, aims to present whether volume supports price movements. Volume Weighted Bars are calculated based on volume moving average.
How to interpret :
-Volume high above the volume moving average be displayed with red/green colors
-Average volume values will remain as they are and
-Volume low below the volume moving average will be indicated with darker colors
4- Fear & Greed index value, using technical anlysis approach calculated based on :
⮩1 - Price Momentum : Price Distance to its Moving Average
⮩2 - Strenght : Rate of Return, price movement over a period of time
⮩3 - Money Flow : Chaikin Money Flow, quantify changes in buying and selling pressure. CMF calculations is based on Accumulation/Distribution
⮩4 - Market Volatility : CBOE Volatility Index ( VIX ), the Volatility Index, or VIX , is a real-time market index that represents the market's expectation. It provides a measure of market risk and investors' sentiments
⮩5 -Safe Haven Demand: in this study GOLD demand is assumed
스크립트에서 "Volatility"에 대해 찾기
Williams Vix Fix BB + RVI + LinReg & Squeeze (Keltner) BBW + %BLegend:
Entery signal: When line color turns to lime (lighter green) after a blue dot appears
Exit signal: When line color turns to red (darker red) after a red dot appears
Note: it is more affective as an entry signal (Bottom is stronger)
- When line touches or crosses red band it is Top signal (Williams Vix Fix)
- When line touches or crosses blue band it is Bottom signal (Williams Vix Fix)
- Red dot at the top of indicator is a Top signal (Relative Volatility Index)
- Blue dot at the top of indicator is a Bottom signal (Relative Volatility Index)
- Gray dot at the bottom of indicator is a Keltner Squeeze signal (filtered by either BBW or %B)
- Silver dot at the bottom of indicator is a weaker Keltner Squeeze signal (Doesn't meet either BBW or %B filter)
- Purple is a 'Half Squeeze' only 1 Bollinger Band crossed the Keltner Channel
This is an attempt to make use of the main features of all 6 of these Volatility tools:
- Williams Vix Fix + Bollinger Bands
- Relative Volatility Index (RVI)
- Linear Regression (detects Vix Fix starts to rise or fall to a certain degree in order to help validate bottom/top)
Note : There is also added precision on Linear Regression entry by dividing WVF by square roots of basis.
- The crossing of Keltner Channel by the Bollinger Bands (Squeeze)
Conditions to Help Filter Keltner Squeeze:
- When the Bollinger Bands Width (BBW) value is lower than the lowest value within a period plus a margin of error (percentage)
- When the %B value goes up or down by the impulse value (threshold value in setting) detailed in LazyBears indicator. (www.tradingview.com)
If it meets one of these 2 filters and there is a Keltner Channel Squeeze than gray color or else if the squeeze doesn’t meet one of the 2 filters than silver color (weaker Squeeze).
The goal is to find the best tool to find bottoms and top relative to volatility and filter squeeze.
Note: You can also change the threshold for RVI top and bottom.
And this work builds on my last indicators:
- Williams Vix Fix + BB & RVI (Top/Bottom) & Squeeze ()
- Williams Vix Fix BB + RVI & Squeeze (Keltner) filtered BBW + %B ()
If you have ideas on this work or have ideas on potential combinations please message me, I always want to learn or get perspective on how it can be improved.
Sharing is how we get better (Parameter tuning, ideas, discussion)
I don’t reinvent the wheel, just trying to make the wheel better.
BankNifty Multi-TimeFrames Price Panel [MaestroTrader]█ OVERVIEW
Price Panel provides Nifty /BankNifty Index comprehensive Price Insights on different time intervals. It helps to determine the trend of Index using top Index Heavy Weights along with Dow, India VIX & Index Spot Prices. It helps to determine the price behavior of the underlying Index/stock to make informed decisions while trading.
█ FEATURES
a) Displays Price in Multi Time Frames for Multi time frame analysis
b) Displays Weighted Securities price for Weighted INDEX price analysis.
c) Displays INDIA VIX and DOW for Combined INDIX VOLATALITY Analysis
█ MUTLI TIME FRAME ANALYSIS
How to use Multiple time frame analysis?
Multiple time frame analysis follows a top-down approach when trading and allows traders to gauge the longer-term trend while spotting ideal entries on a smaller time frame. Traders can then conduct technical analysis using multiple time frames to confirm or reject their trading bias.
Multiple time frame analysis, is the process of viewing the same symbols under different time frames. Usually, the larger time frame is used to establish a longer-term trend, while a shorter time frame is used to spot ideal entries into the market.
Let’s Say 75 & 15 TF’s Trend is up, then shorter time 5M is used to spot ideal entries on long side.
█ WEIGHTED INDEXS PRICE ANALYSIS
How to use Weighted Index Price Movement in Multi timeframes?
The index future trading price is based on the trading prices of the individual securities (stocks) that comprise the index basket. In other words, the stocks with higher weights will have more impact on the movement of the index. Price Panel provides the insights of these heavy weight stock price movement in different time frames, that can help you confirm or reject your trading bias.
HDFC Bank (28% Weight) will have more impact on the BankNifty Movement. By looking the top 4 bank's price movement in different timeframes, you can derive the BankNifty price trend.
█ VOLATALITY ANALYSIS
India VIX is a short form for India Volatility Index. It is the volatility index that measures the market’s expectation of volatility over the near term.
A lower VIX level usually implies that the market is confident about the movement and is expecting lower volatility and a stable range.
A higher VIX level usually signals high volatility and lower trader confidence about the current range of the market. A major directional move can be expected in the market and a quick broadening of range can be expected.
█ SETTINGS
• Time Frame Settings: Configure Time Frames 5 Min, 15 Min, 75 Min
• Table Settings: Configure Table Styles- Position- Font Color
• Symbol Settings: Configure Securities. Toggle (on/Off) Securities display.
• Index Settings: Display Bank Nifty or Nifty Heavy Weights.
█ PANEL DISPLAY VARIATIONS
BANK NIFTY VIEW
NIFTY VIEW
WITHOUT STOCKS - ONLY INDEX, VIX, DOW
█ THANKS
Thanks to Pine Team for this new great feature tables & Thanks to PineCoders for the `f_strRightOf` function.
█ DISCLIAMER
Indicator is built for educational purposes. Test it before use.
Hope - These features help you get quick insights of the price movement to take informed trades.
You are free to use the code, please share the credit for reuse.
Happy Trading !!
Bollinger Band Calculation ToolIntroducing the Bollinger Band Calculation Tool
What are Bollinger Bands ?
According to Investopedia ....
"In the 1980s, John Bollinger, a long-time technician of the markets, developed the technique of using a moving average with two trading bands above and below it.
Unlike a percentage calculation from a normal moving average, Bollinger Bands® simply add and subtract a standard deviation calculation.
Standard deviation is a mathematical formula that measures volatility, showing how the stock price can vary from its true value.
By measuring price volatility, Bollinger Bands® adjust themselves to market conditions.
This is what makes them so handy for traders; they can find almost all of the price data needed between the two bands."
Classic interpretations of Bollinger bands from Fidelity Investments....
"When the bands tighten during a period of low volatility, it raises the likelihood of a sharp price move in either direction.
This may begin a trending move. Watch out for a false move in opposite direction which reverses before the proper trend begins.
When the bands separate by an unusual large amount, volatility increases and any existing trend may be ending.
Prices have a tendency to bounce within the bands' envelope, touching one band then moving to the other band.
You can use these swings to help identify potential profit targets.
For example, if a price bounces off the lower band and then crosses above the moving average, the upper band then becomes the profit target.
Price can exceed or hug a band envelope for prolonged periods during strong trends.
On divergence with a momentum oscillator, you may want to do additional research to determine if taking additional profits is appropriate for you.
A strong trend continuation can be expected when the price moves out of the bands.
However, if prices move immediately back inside the band, then the suggested strength is negated."
This indicator contains a standard set of Bollinger Bands with the addition of a Test Closing Price calculation function.
It displays a standard set of Bollinger Bands by default.
How do I use the Test Closing Price function ?
Enter a test price in the Test Closing Price box in the settings, and then click the "Use Test Price" button.
The indicator will then replace the current Bollinger upper, lower and basis-lines with plots showing the resultant lines if price were to close at the Test Closing Price.
An information panel will appear which displays the test closing price and the resulting Bollinger-upper, Bollinger-lower and basis-line prices.
Can display up to 10 decimal places and has adjustable label offset.
It will also plot lines outlining the resultant closed candle body for clarity.
To return to "Standard Bollingers" just click off the "Use Test Price" button.
Knowing exactly what the Bollinger bands and Basis will do if a particular closing price is met can be useful in a variety of ways to traders who use Bollinger Bands® in their trading.
It is possible to work out exactly what closing price is required to get above or below a Bollinger band which is normally difficult as Bollingers react to the change in price.
Users can also experiment with different Test Closing Prices [/i to see exactly what effect this would have on the Basis moving average and on the Bollinger bands themselves.
MOVE/VXTLT CorrelationMany know of the VIX for equity trading. Yet, many are unaware that there is the same kind of volatility measure for trading bonds, called the MOVE Index.
"The Merrill Lynch Option Volatility Estimate (MOVE) Index is a yield curve weighted index of the normalized implied volatility on 1-month Treasury options which are weighted on the 2, 5, 10, and 30 year contracts."
With this script one can see the the correlation and divergences between bonds and its volatility measure to make educated decisions in trading or hedging.
The idea of this script comes from NicTheMajestic.
Turtle N NormalizedSimple script that calculates the normalized value of N. Rules taken from an online PDF containing the original Turtle system:
"The Turtles used a volatility-based constant percentage risk position sizing algorithm. The Turtles used a concept that Richard Dennis and Bill Eckhardt called N to represent the underlying volatility of a particular market.
N is simply the 20-day exponential moving average of the True Range, which is now more commonly known as the ATR. Conceptually, N represents the average range in price movement that a particular market makes in a single day, accounting for opening gaps. N was measured in the same points as the underlying contract.
The Turtles built positions in pieces which we called Units. Units were sized so that 1 N represented 1% of the account equity. Thus, a unit for a given market or commodity can be calculated using the following formula:
Unit = 1% of Account/(N x Dollars per Point)"
To normalize the Unit formula, this script instead takes the value of (close/N). Dollars per point = 1 for stocks and crypto, but will change depending on the contract specifications for individual futures.
"Since the Turtles used the Unit as the base measure for position size, and since those units were volatility risk adjusted, the Unit was a measure of both the risk of a position, and of the entire portfolio of positions."
When the value of N is high, volatility is low and you should be more risk-on.
When the value of N is low, volatility is high and you should be more risk-off.
Bermaui Deviation PercentHow it works
Red & Under 90 = Bearish Volatility
Blue & Under 90 = Bullish Volatility
Red & Under 10 = Strong Bearish Volatility
Blue & Under 10 = Strong Bullish Volatility
White & Over 90 = No Volatility (Indicating trendless chop)
I tried uploading this months ago but was banned or something from doing so.
originally created by Muhammad Elbermawi
www.mql5.com
Multi-Exchange Volume (30 Tickers) by kurtsmock + BV + rVolauthor: kurtsmock
Fully Customizable ticker set. Up to 30 Tickers. Bitcoin set as default.
-- IMPORTANT NOTE: --
30 Exchanges are a lot. It can take a while to load. You can fully customize this indicator to your liking. Here's how:
1. Load indicator
2. Open Settings
3. Uncheck the switch box for exchanges you want unincluded
4. At the bottom of the settings menu click "Defaults" and hit "Save as Default"
5. To turn them all back on, hit "Reset Settings" in that same "Defaults" menu and click "Save as Default" again.
Also, you don't have to use this with Bitcoin. This works with any asset, just change the ticker in the settings.
There's a lot going on with this indicator so the following is descriptions and instructions to help you better understand what's going on here. Thanks!
Goal:
- To provide a mechanism for assets on multiple exchanges to have their volume evaluated together
Edge:
- Having better and more complete volume information
Notes:
- The Default Exchanges for this indicator are highest volume bitcoin exchanges, but may contain "fake volume"
- Indicator is set for Bitcoin by default. However, you can change the tickers to reflect any asset you want
////// rVol //////
Goal:
- To understand how much volume is being executed relative to the same candle on previous days/periods
Edge:
- Higher rVol implies higher volatility and market interest.
- High rVol = higher than average volume . Markets move on volume so higher than average volume indicates increased market activity/volatility
- rVol is an indirect measure of active or anticipated volatility
Definitions:
- rVol: The volume of a period compared to the Average Volume of that same period in past sessions
- Important to note it does NOT add up the last 10 (default) candles, but rather the last 10 candles at session intervals.
- Example:
-- On a Tuesday, 1h chart it will add up the last ten Tuesday, 9:00 am candles, not including the current, active candle.
-- It then averages those lookback candles.
-- It then plots the percentage relationship between the most recent candle and the average of the lookback candles
-- Avg Vol of Lookback candles = 5000,
-- Volume of most recent candle = 4000: Output = rVol = 80:
-- Volume of most recent candle was 80% of the average volume in the 9 am time period of the last ten Tuesdays in the 9 am, 1h period
Notes:
- rVol does not add current candle volume into lookback sum. So, you set lookback to be: (not including the current day)
- rVol is on a switch. So, if you want to see rVol instead of volume, hit the switch in the settings
- If you want to see both, load 2 instances of the indicator.
////// Better-er Volume //////
Goal:
To Identify:
- When a candle closes at the highest volume * range relative to the lookback period and close > open
- When a candle closes at the highest volume * range relative to the lookback period and close < open
- When a candle closes at the highest volume / price relative to the lookback period
Edge:
- Identifies beginnings of price expansion, climax of price expansion, breakouts, pivots, and take profit points on the volume chart
Notes:
- Based generally on Barry Taylor's "Better Volume" indicator and ideas from Pascal Willain's book "Value in Time."
- Better-er Volume rules are applied to both Total Volume or rVol.
-- When rVol is displayed Better-er Volume is applied to rVol
-- When Total Volume is displayed Better-er Volume is applied to Total Volume
// Plot Key: //
Green Triangle Up = Often marks the beginning and/or end of price expansion to the upside
Red Triangle Up = Often marks the beginning and/or end of price expansion to the downside
Yellow Square = High Volume but Tight Range. Implies a Battle of Bulls and Bears. High Liquidity area. Provided Liquidity is not enough to move price. Thick Limit Order Book.
Purple Triangle Up or Down = Implies high market participation. Typically at the end of expansion when very significant s/r is hit
category: volume Volatility
tags: Volume rVol relativevolume Bitcoin cryptocurrency bettervolume
Many More Volume Indicators Coming Out Soon!
Squeeze PRO Arrows [Makit0]SQUEEZE PRO INDICATOR v0.5Beta
Script based in:
original John Carter's ideas (SQUEEZE & SQUEEZE PRO)
LazyBear's script (Squeeze Momentum Indicator)
USE IT IN CONJUNCTION WITH THE SQUEEZE PRO INDICATOR
This system is based in the volatility reversion to the mean: volatility contraction leads to volatility expansion and the other way on
The arrows signal is a warning of volatility compression, more often than not this leads to a expansion of volatility and a move in the action price usually bigger than the expected move
Be aware of the trend direction don't take the arrows direction as certanty, use instead the momentum histogram in the Squeeze PRO Indicator to see the slope direction
By default the arrows are setted at 5 dots, they fire in the sixth dot after 5 dots of the same color. Try differents values to get more or less signals
here are 3 levels of compression:
Level 1: ORANGE, the lesser compresion level
Level 2: RED, the normal level marked by the original squeeze indicator
Level 3: YELLOW, the max compression level
The more the compression the bigger the after move
Simple and Exponential Moving Averages
There are 2 groups of Moving Averages within the indicator, the 8 & 21 EMAs and the 50, 100 & 200 SMAs
They are disabled by default, turn it on at your peace
Please check the John Carter's book (Mastering the Trade) and attend his webinars for more insight about the squeeze & squeeze pro systems
I'm starting at trading and learning every day, I attended one of his webinars about the Squeeze Pro, and with help of the LazyBear's Squeeze Momentum Indicator code up the Squeeze PRO.
Please be aware, I'm not an expert trader, only a developer with an idea: learn to pull out money from the market in a consistent way.
This is a Beta version, please feel free to comment and give feedback, anything you consider iteresting, the more you elaborate the better :D
Thanks you all!!!
HV ID/ND4 BreakoutThis indicator is based on Linda Raschke's ID/ND4 Historical Volatility Breakout strategy. It finds days where the high and low are within the previous day high and lows (Inside days), that have also, the narrowest trading range within the last 4 days (it basically checks if the current day has the narrowest range comparing it with the previous 3 days) when the short term historical volatility (6 period default) is relatively low compared to the longer term historical volatility (100 period default) (The condition is that the 6/100 Historical volatility is below 50% of its annual range).
More information about how to trade this strategy is described in the book but basically, you would want to place a resting buy and sell stops at the high and low of the day highlighted and enter if you get filled the next day.
QMA/SMA DifferenceIntroduction
The quadratic moving average (QMA) or quadratic weighted moving average (QWMA) is a type of moving average who is closer to the price when price is up trending. This moving average is defined as the square root of the moving average of the squared price. The QMA-SMA difference use this moving average to provide a new volatility indicator who aim to be reactive and filter noisy volatility in order to only provide essential information.
QMA - SMA
This indicator is defined as the difference between a quadratic moving average and a simple moving average of same period. Since the QMA emphasize up movements and tend to be away from down movements she is always greater than the simple moving average, so a simple difference between those moving average provide our volatility indicator. Below is a comparison with a standard deviation and the indicator of both period 100.
Since its a difference between two moving average it can be interesting to use a simple moving as source for the standard deviation to provide another comparison
The standard deviation is smoother but still contain more information as well as having less reactivity.
Conclusion
I have a presented a new volatility indicator based on the quadratic moving average and compared it with a classic standard deviation. It is possible to change the power order of the QMA in order to provide different results, in order to do so you must also change the root, this is done in pine with : pow(sma(pow(close,w),length),1/w) where w is the power order, notice that an high power order can provide non attributed values.
BKSqueezeThis is a price volatility compression and expansion indicator that uses the ratio of the Bollinger Band and Keltner Ratio.
Red segments indicate extreme price volatility compression that can be ideal entry points for stock/futures/forex and/or options positions.
Aqua segments indicate price volatility is expanding.
Blue segments indicate price volatility is compressing - can be used as an exit point or partial scale out point.
Note that the indicator doesn't indicate direction. One suggestion is to use the DMI indicator for this purpose - really depends on how early you enter the trade.
Suggest using a time period of 15 bars for volatile stocks, such as TSLA for example, otherwise a period of 20 bars suits most stocks/futures/forex symbols.
High-Probability Scalper (Market Open)Market open is where volatility is real, spreads are tight, and momentum shows itself early. This scalping strategy is built specifically to operate during that window, filtering out low-quality signals that usually appear later in the session.
Instead of trading all day, the logic is restricted to the first 90 minutes after market open, where continuation moves and fast pullbacks are more reliable.
What This Strategy Does
This script looks for short-term momentum alignment using:
Fast vs slow EMA structure
RSI confirmation to avoid chasing extremes
ATR-based risk control
Session-based filtering to trade only when volume matters
It’s designed for intraday scalping, not swing trading.
Core Trading Logic
1. Market Open Filter
Trades are allowed only between 09:30 – 11:00 exchange time.
This avoids low-liquidity chop and focuses on the period where most breakouts and reversals form.
2. Trend Confirmation
Bullish bias: 9 EMA crosses above 21 EMA
Bearish bias: 9 EMA crosses below 21 EMA
This keeps trades aligned with short-term direction instead of random entries.
3. Momentum Check (RSI)
RSI is used as a quality filter, not as an overbought/oversold signal.
Long trades only when RSI is strong but not extended
Short trades only when RSI shows weakness without exhaustion
This removes late entries and reduces whipsaws.
Entries & Exits
Entries
Executed only on confirmed candles
No intrabar repainting
One position at a time
Risk Management
Stop-loss based on ATR
Take-profit calculated using a fixed risk–reward ratio
Same structure for both long and short trades
This keeps risk consistent across different symbols and volatility levels.
Why This Strategy Works Better at Market Open
Volume is highest
False breakouts are fewer
EMA crosses have follow-through
RSI behaves more cleanly
By not trading all day, the strategy avoids most of the noise that kills scalpers.
Best Use Cases
Index futures
High-liquidity stocks
Major crypto pairs during active sessions
1m to 5m timeframes
What This Strategy Is NOT
Not a martingale
Not grid-based
Not designed for ranging markets
Not a “set and forget” system
It’s a controlled scalping template meant for disciplined execution.
How to Use It Properly
Test on multiple symbols
Adjust ATR length for volatility
Tune RSI ranges per market
Always forward-test before live alerts
Final Note
This strategy focuses on structure, timing, and risk, not indicator stacking.
If you trade the open, this gives you a clear framework instead of emotional entries.
If you want:
Alerts
Session customization
News filters
Partial exits
You can extend this logic without breaking the core system.
GC1 Orderflow Engine - sudoTLDR
This indicator measures relative buying and selling pressure by comparing GC1! futures returns against XAU price returns, normalized by their own volatility and weighted by GC1! volume. The result is a pressure histogram and line that show whether futures orderflow is leading, lagging, or diverging from spot gold in real time.
What this indicator does
The Orderflow Engine is designed to answer one core question:
Is GC1! futures orderflow applying net pressure in the same direction as XAU, or pushing against it?
It does this by isolating relative strength and weakness between futures and spot, rather than looking at price direction alone.
How the pressure calculation works
1. GC1! futures returns and XAU returns are calculated bar by bar
2. Each return is normalized by its own recent volatility
3. The normalized XAU return is subtracted from the normalized GC1! return
This creates a relative pressure value:
Positive pressure - GC1! futures are outperforming XAU
Negative pressure - GC1! futures are underperforming XAU
Near zero - futures and spot are moving in balance
To emphasize meaningful activity:
GC1! volume is converted into a normalized score
Higher-than-normal futures volume increases the weight of the pressure
Low-volume pressure is naturally dampened
The final output is clamped to keep the scale stable across different market conditions.
Visual output
Histogram
Green bars - positive futures pressure
Red bars - negative futures pressure
Gray bars - neutral or minimal pressure
Pressure line
A smoother view of the same pressure data
Useful for spotting momentum shifts and divergence
Zero line
Represents balance between futures and spot
Crosses often mark changes in orderflow control
Optional annotations
Regime shift markers based on futures participation
Optional percent-change labels for studying pressure acceleration
How to use it
-Confirm whether price moves are supported by futures orderflow
-Spot early divergence between GC1! and XAU
-Identify absorption , distribution , or initiative behavior
-Filter entries by trading only when pressure aligns with your bias
-This tool is best used as confirmation and context, not as a standalone signal generator.
Design philosophy
-Self-normalizing across sessions and volatility regimes
-No fixed thresholds that break over time
-Focused on relative behavior, not prediction
-Built to pair naturally with the Participation Regime indicator
ATR Regime Filter (ATR14 vs SMA20)ATR volatility + ATR SMA
Green ATR above Red SMA + green background
→ Volatility expanding
→ Trend mode only
Green ATR below Red SMA + blue background
→ Volatility compressing
→ Mean reversion allowed
Crossovers / flickering
→ Transition
→ Size down or stay flat
[Kpt-Ahab] Assistant: Risk & DCA PlannerScript Description – Assistant: Risk & DCA Planner
The Risk & DCA Planner is a technical assistant for position and risk management.
It automatically calculates, based on volatility (ATR%), swing structure, and your settings:
Stop-Loss (SL) and corresponding Take-Profit targets (TPs) in R-multiples
DCA (Dollar-Cost-Averaging) levels — both price and amount
A market suitability check (based on volatility & volume)
Plus a clear table and summary label displayed on the chart
The script helps you plan risk, scaling, and profit targets consistently and quantitatively.
Core Logic
Risk Profile
Three modes: Low, Normal, High.
These define how reactive the script behaves internally:
Low → conservative, longer lookbacks, tighter analysis
Normal → balanced
High → aggressive, faster reaction, wider stops
Stop-Loss (SL)
Automatically calculated from ATR% and recent swing structure, limited by minimum and maximum thresholds.
The SL percentage defines the R-unit, which all TPs and DCA levels are based on.
Take-Profits (TPs)
Up to six targets, each a multiple of the defined risk (e.g., 1R, 2R, 3R).
Prices are automatically adjusted depending on long or short direction.
DCA Strategy
Optional. Adds scaling levels evenly between Entry and SL or in multiples of the ATR.
Each DCA allocation grows geometrically until the maximum position size is reached.
Suitability Check
Evaluates whether the market is within an appropriate ATR% range and has sufficient volume.
The table displays “OK” or “Caution” depending on volatility and historical consistency.
Visualization
Lines for SL, TPs, and DCA levels
A table with all parameters, prices, and risk data
A chart label summarizing key info (profile, direction, SL%, TPs, DCA, etc.)
EMA Oscillator [Alpha Extract]A precision mean reversion analysis tool that combines advanced Z-score methodology with dual threshold systems to identify extreme price deviations from trend equilibrium. Utilizing sophisticated statistical normalization and adaptive percentage-based thresholds, this indicator provides high-probability reversal signals based on standard deviation analysis and dynamic range calculations with institutional-grade accuracy for systematic counter-trend trading opportunities.
🔶 Advanced Statistical Normalization
Calculates normalized distance between price and exponential moving average using rolling standard deviation methodology for consistent interpretation across timeframes. The system applies Z-score transformation to quantify price displacement significance, ensuring statistical validity regardless of market volatility conditions.
// Core EMA and Oscillator Calculation
ema_values = ta.ema(close, ema_period)
oscillator_values = close - ema_values
rolling_std = ta.stdev(oscillator_values, ema_period)
z_score = oscillator_values / rolling_std
🔶 Dual Threshold System
Implements both statistical significance thresholds (±1σ, ±2σ, ±3σ) and percentage-based dynamic thresholds calculated from recent oscillator range extremes. This hybrid approach ensures consistent probability-based signals while adapting to varying market volatility regimes and maintaining signal relevance during structural market changes.
// Statistical Thresholds
mild_threshold = 1.0 // ±1σ (68% confidence)
moderate_threshold = 2.0 // ±2σ (95% confidence)
extreme_threshold = 3.0 // ±3σ (99.7% confidence)
// Percentage-Based Dynamic Thresholds
osc_high = ta.highest(math.abs(z_score), lookback_period)
mild_pct_thresh = osc_high * (mild_pct / 100.0)
moderate_pct_thresh = osc_high * (moderate_pct / 100.0)
extreme_pct_thresh = osc_high * (extreme_pct / 100.0)
🔶 Signal Generation Framework
Triggers buy/sell alerts when Z-score crosses extreme threshold boundaries, indicating statistically significant price deviations with high mean reversion probability. The system generates continuation signals at moderate levels and reversal signals at extreme boundaries with comprehensive alert integration.
// Extreme Signal Detection
sell_signal = ta.crossover(z_score, selected_extreme)
buy_signal = ta.crossunder(z_score, -selected_extreme)
// Dynamic Color Coding
signal_color = z_score >= selected_extreme ? #ff0303 : // Extremely Overbought
z_score >= selected_moderate ? #ff6a6a : // Overbought
z_score >= selected_mild ? #b86456 : // Mildly Overbought
z_score > -selected_mild ? #a1a1a1 : // Neutral
z_score > -selected_moderate ? #01b844 : // Mildly Oversold
z_score > -selected_extreme ? #00ff66 : // Oversold
#00ff66 // Extremely Oversold
🔶 Visual Structure Analysis
Provides a six-tier color gradient system with dynamic background zones indicating mild, moderate, and extreme conditions. The histogram visualization displays Z-score intensity with threshold reference lines and zero-line equilibrium context for precise mean reversion timing.
snapshot
4H
1D
🔶 Adaptive Threshold Selection
Features intelligent threshold switching between statistical significance levels and percentage-based dynamic ranges. The percentage system automatically adjusts to current volatility conditions using configurable lookback periods, while statistical thresholds maintain consistent probability-based signal generation across market cycles.
🔶 Performance Optimization
Utilizes efficient rolling calculations with configurable EMA periods and threshold parameters for optimal performance across all timeframes. The system includes comprehensive alert functionality with customizable notification preferences and visual signal overlay options.
🔶 Market Oscillator Interpretation
Z-score > +3σ indicates statistically significant overbought conditions with high reversal probability, while Z-score < -3σ signals extreme oversold levels suitable for counter-trend entries. Moderate thresholds (±2σ) capture 95% of normal price distributions, making breaches statistically significant for systematic trading approaches.
snapshot
🔶 Intelligent Signal Management
Automatic signal filtering prevents false alerts through extreme threshold crossover requirements, while maintaining sensitivity to genuine statistical deviations. The dual threshold system provides both conservative statistical approaches and adaptive market condition responses for varying trading styles.
Why Choose EMA Oscillator ?
This indicator provides traders with statistically-grounded mean reversion analysis through sophisticated Z-score normalization methodology. By combining traditional statistical significance thresholds with adaptive percentage-based extremes, it maintains effectiveness across varying market conditions while delivering high-probability reversal signals based on quantifiable price displacement from trend equilibrium, enabling systematic counter-trend trading approaches with defined statistical confidence levels and comprehensive risk management parameters.
Market Regime Matrix [Alpha Extract]A sophisticated market regime classification system that combines multiple technical analysis components into an intelligent scoring framework to identify and track dominant market conditions. Utilizing advanced ADX-based trend detection, EMA directional analysis, volatility assessment, and crash protection protocols, the Market Regime Matrix delivers institutional-grade regime classification with BULL, BEAR, and CHOP states. The system features intelligent scoring with smoothing algorithms, duration filters for stability, and structure-based conviction adjustments to provide traders with clear, actionable market context.
🔶 Multi-Component Regime Engine Integrates five core analytical components: ADX trend strength detection, EMA-200 directional bias, ROC momentum analysis, Bollinger Band volatility measurement, and zig-zag structure verification. Each component contributes to a sophisticated scoring system that evaluates market conditions across multiple dimensions, ensuring comprehensive regime assessment with institutional precision.
// Gate Keeper: ADX determines market type
is_trending = adx_value > adx_trend_threshold
is_ranging = adx_value <= adx_trend_threshold
is_maximum_chop = adx_value <= adx_chop_threshold
// BULL CONDITIONS with Structure Veto
if price_above_ema and di_bullish
if use_structure_filter and isBullStructure
raw_bullScore := 5.0 // MAXIMUM CONVICTION: Strong signals + Bull structure
else if use_structure_filter and not isBullStructure
raw_bullScore := 3.0 // REDUCED: Strong signals but broken structure
🔶 Intelligent Scoring System Employs a dynamic 0-5 scale scoring mechanism for each regime type (BULL/BEAR/CHOP) with adaptive conviction levels. The system automatically adjusts scores based on signal alignment, market structure confirmation, and volatility conditions. Features decision margin requirements to prevent false regime changes and includes maximum conviction thresholds for high-probability setups.
🔶 Advanced Structure Filter Implements zig-zag based market structure analysis using configurable deviation thresholds to identify significant pivot points. The system tracks Higher Highs/Higher Lows (HH/HL) for bullish structure and Lower Lows/Lower Highs (LL/LH) for bearish structure, applying structure veto logic that reduces conviction when price action contradicts the underlying trend framework.
// Define Market Structure (Bull = HH/HL, Bear = LL/LH)
isBullStructure = not na(last_significant_high) and not na(prev_significant_high) and
not na(last_significant_low) and not na(prev_significant_low) and
last_significant_high > prev_significant_high and last_significant_low > prev_significant_low
isBearStructure = not na(last_significant_high) and not na(prev_significant_high) and
not na(last_significant_low) and not na(prev_significant_low) and
last_significant_low < prev_significant_low and last_significant_high < prev_significant_high
🔶 Superior Engine Components Features dual-layer regime stabilization through score smoothing and duration filtering. The score smoothing component reduces noise by averaging raw scores over configurable periods, while the duration filter requires minimum regime persistence before confirming changes. This eliminates whipsaws and ensures regime transitions represent genuine market shifts rather than temporary fluctuations.
🔶 Crash Detection & Active Penalties Incorporates sophisticated crash detection using Rate of Change (ROC) analysis with severity classification. When crash conditions are detected, the system applies active penalties (-5.0) to BULL and CHOP scores while boosting BEAR conviction based on crash severity. This ensures immediate regime response to major market dislocations and drawdown events.
// === CRASH OVERRIDE (Active Penalties) ===
is_crash = roc_value < crash_threshold
if is_crash
// Calculate crash severity
crash_severity = math.abs(roc_value / crash_threshold)
crash_bonus = 4.0 + (crash_severity - 1.0) * 2.0
// ACTIVE PENALTIES: Force bear dominance
raw_bearScore := math.max(raw_bearScore, crash_bonus)
raw_bullScore := -5.0 // ACTIVE PENALTY
raw_chopScore := -5.0 // ACTIVE PENALTY
❓How It Works
🔶 ADX-Based Market Classification The Market Regime Matrix uses ADX (Average Directional Index) as the primary gatekeeper to distinguish between trending and ranging market conditions. When ADX exceeds the trend threshold, the system activates BULL/BEAR regime logic using DI+/DI- crossovers and EMA positioning. When ADX falls below the ranging threshold, CHOP regime logic takes precedence, with maximum conviction assigned during ultra-low ADX periods.
🔶 Dynamic Conviction Scaling Each regime receives conviction ratings from UNCERTAIN to MAXIMUM based on signal alignment and score magnitude. MAXIMUM conviction (5.0 score) requires perfect signal alignment plus favorable market structure. The system progressively reduces conviction when signals conflict or structure breaks, ensuring traders understand the reliability of each regime classification.
🔶 Regime Transition Management Implements decision margin requirements where new regimes must exceed existing regimes by configurable thresholds before transitions occur. Combined with duration filtering, this prevents premature regime changes and maintains stability during consolidation periods. The system tracks both raw regime signals and final regime output for complete transparency.
🔶 Visual Regime Mapping Provides comprehensive visual feedback through colored candle overlays, background regime highlighting, and real-time information tables. The system displays regime history, conviction levels, structure status, and key metrics in an organized dashboard format. Regime changes trigger immediate visual alerts with detailed transition information.
🔶 Performance Optimization Features efficient array management for zig-zag calculations, smart variable updating to prevent recomputation, and configurable debug modes for strategy development. The system maintains optimal performance across all timeframes while providing institutional-grade analytical depth.
Why Choose Market Regime Matrix ?
The Market Regime Matrix represents the evolution of market regime analysis, combining traditional technical indicators with modern algorithmic decision-making frameworks. By integrating multiple analytical dimensions with intelligent scoring, structure verification, and crash protection, it provides traders with institutional-quality market context that adapts to changing conditions. The sophisticated filtering system eliminates noise while preserving responsiveness, making it an essential tool for traders seeking to align their strategies with dominant market regimes and avoid adverse market environments.
Volatility Squeeze – Blue Zone (classic) Volatility Squeeze – Blue Zone
Highlights periods when volatility contracts by showing a blue band between the Bollinger Bands (BB) whenever they fall inside the Keltner Channel (KC).
Blue zone = squeeze: BB upper & lower are inside KC – market coiling.
Automatic breakout alert: optional alert fires on the first bar after the squeeze releases.
Fully adjustable: BB/KC length, BB σ, KC ATR multiplier, zone colour & opacity, border on/off.
Clean overlay: zone hugs price bar-by-bar and disappears only when the squeeze ends, so past squeezes remain visible for context.
Use it to spot low-volatility setups, then watch for momentum or volume confirmations when the squeeze breaks.
Liquidity Trap Zones [PhenLabs]📊 Liquidity Trap Zones
Version: PineScript™ v6
📌 Description
The goal of the Liquidity Trap Zones indicator is to try and help traders identify areas where market liquidity appears abundant but is actually thin or artificial, helping traders avoid potential fake outs and false breakouts. This advanced indicator analyzes the relationship between price wicks and volume to detect “mirage” zones where large price movements occur on low volume, indicating potential liquidity traps.
By highlighting these deceptive zones on your charts, the indicator helps traders recognize where institutional players might be creating artificial liquidity to trap retail traders. This enables more informed decision-making and better risk management when approaching key price levels.
🚀 Points of Innovation
Mirage Score Algorithm: Proprietary calculation that normalizes wick size relative to volume and average bar size
Dynamic Zone Creation: Automatically generates gradient-filled zones at trap locations with ATR-based sizing
Intelligent Zone Management: Maintains clean charts by limiting displayed zones and auto-updating existing ones
Scale-Invariant Design: Works across all assets and timeframes with intelligent normalization
Real-Time Detection: Identifies trap zones as they form, not after the fact
Volume-Adjusted Analysis: Incorporates tick volume when available for more accurate detection
🔧 Core Components
Mirage Score Calculator: Analyzes the ratio of price wicks to volume, normalized by average bar size
ATR-Based Filter: Ensures only significant price movements are considered for trap zone creation
EMA Smoothing: Reduces noise in the mirage score for clearer signals
Gradient Zone Renderer: Creates visually distinct zones with multiple opacity levels for better visibility
🔥 Key Features
Real-Time Trap Detection: Identifies liquidity mirages as they develop during live trading
Dynamic Zone Sizing: Adjusts zone height based on current market volatility (ATR)
Smart Zone Management: Automatically maintains a clean chart by limiting the number of displayed zones
Customizable Sensitivity: Fine-tune detection parameters for different market conditions
Visual Clarity: Gradient-filled zones with distinct borders for easy identification
Status Line Display: Shows current mirage score and threshold for quick reference
🎨 Visualization
Gradient Trap Zones: Purple gradient boxes with darker centers indicating trap strength
Mirage Score Line: Orange line in status area showing current liquidity quality
Threshold Reference: Gray line showing your configured detection threshold
Extended Zone Display: Zones automatically extend forward as new bars form
📖 Usage Guidelines
Detection Settings
Smoothing Length (EMA) - Default: 10 - Range: 1-50 - Description: Controls responsiveness of mirage score. Lower values make detection more sensitive to recent price action
Mirage Threshold - Default: 5.0 - Range: 0.1-20.0 - Description: Score above this level triggers trap zone creation. Higher values reduce false positives but may miss subtle traps
Filter Settings
ATR Length for Range Filter - Default: 14 - Range: 1-50 - Description: Period for volatility calculation. Standard 14 works well for most timeframes
ATR Multiplier - Default: 1.0 - Range: 0.0-5.0 - Description: Minimum bar range as multiple of ATR. Higher values filter out smaller moves
Display Settings
Zone Height Multiplier - Default: 0.5 - Range: 0.1-2.0 - Description: Controls trap zone height relative to ATR. Adjust for visual preference
Max Trap Zones - Default: 5 - Range: 1-20 - Description: Maximum zones displayed before oldest are removed. Balance clarity vs. history
✅ Best Use Cases
Identifying potential fakeout levels before entering trades
Confirming support/resistance quality by checking for liquidity traps
Avoiding stop-loss placement in trap zones where sweeps are likely
Timing entries after trap zones are cleared
Scalping opportunities when price approaches known trap zones
⚠️ Limitations
Requires volume data - less effective on instruments without reliable volume
May generate false signals during news events or genuine volume spikes
Not a standalone system - combine with price action and other indicators
Zone creation is based on historical data - future price behavior not guaranteed
💡 What Makes This Unique
First indicator to specifically target liquidity mirages using wick-to-volume analysis
Proprietary normalization ensures consistent performance across all markets
Visual gradient design makes trap zones immediately recognizable
Combines multiple volatility and volume metrics for robust detection
🔬 How It Works
1. Wick Analysis: Calculates upper and lower wicks for each bar. Normalizes by average bar size to ensure scale independence
2. Mirage Score Calculation: Divides total wick size by volume to identify thin liquidity. Applies EMA smoothing to reduce noise. Scales result for optimal visibility
3. Zone Creation: Triggers when smoothed score crosses threshold. Creates gradient boxes centered on trap bar. Sizes zones based on current ATR for market-appropriate scaling
💡 Note: Liquidity Trap Zones works best when combined with traditional support/resistance analysis and volume profile indicators. The zones highlight areas of deceptive liquidity but should not be the sole factor in trading decisions. Always use proper risk management and confirm signals with price action.
Rolling Log Returns [BackQuant]Rolling Log Returns
The Rolling Log Returns indicator is a versatile tool designed to help traders, quants, and data-driven analysts evaluate the dynamics of price changes using logarithmic return analysis. Widely adopted in quantitative finance, log returns offer several mathematical and statistical advantages over simple returns, making them ideal for backtesting, portfolio optimization, volatility modeling, and risk management.
What Are Log Returns?
In quantitative finance, logarithmic returns are defined as:
ln(Pₜ / Pₜ₋₁)
or for rolling periods:
ln(Pₜ / Pₜ₋ₙ)
where P represents price and n is the rolling lookback window.
Log returns are preferred because:
They are time additive : returns over multiple periods can be summed.
They allow for easier statistical modeling , especially when assuming normally distributed returns.
They behave symmetrically for gains and losses, unlike arithmetic returns.
They normalize percentage changes, making cross-asset or cross-timeframe comparisons more consistent.
Indicator Overview
The Rolling Log Returns indicator computes log returns either on a standard (1-period) basis or using a rolling lookback period , allowing users to adapt it to short-term trading or long-term trend analysis.
It also supports a comparison series , enabling traders to compare the return structure of the main charted asset to another instrument (e.g., SPY, BTC, etc.).
Core Features
✅ Return Modes :
Normal Log Returns : Measures ln(price / price ), ideal for day-to-day return analysis.
Rolling Log Returns : Measures ln(price / price ), highlighting price drift over longer horizons.
✅ Comparison Support :
Compare log returns of the primary instrument to another symbol (like an index or ETF).
Useful for relative performance and market regime analysis .
✅ Moving Averages of Returns :
Smooth noisy return series with customizable MA types: SMA, EMA, WMA, RMA, and Linear Regression.
Applicable to both primary and comparison series.
✅ Conditional Coloring :
Returns > 0 are colored green ; returns < 0 are red .
Comparison series gets its own unique color scheme.
✅ Extreme Return Detection :
Highlight unusually large price moves using upper/lower thresholds.
Visually flags abnormal volatility events such as earnings surprises or macroeconomic shocks.
Quantitative Use Cases
🔍 Return Distribution Analysis :
Gain insight into the statistical properties of asset returns (e.g., skewness, kurtosis, tail behavior).
📉 Risk Management :
Use historical return outliers to define drawdown expectations, stress tests, or VaR simulations.
🔁 Strategy Backtesting :
Apply rolling log returns to momentum or mean-reversion models where compounding and consistent scaling matter.
📊 Market Regime Detection :
Identify periods of consistent overperformance/underperformance relative to a benchmark asset.
📈 Signal Engineering :
Incorporate return deltas, moving average crossover of returns, or threshold-based triggers into machine learning pipelines or rule-based systems.
Recommended Settings
Use Normal mode for high-frequency trading signals.
Use Rolling mode for swing or trend-following strategies.
Compare vs. a broad market index (e.g., SPY or QQQ ) to extract relative strength insights.
Set upper and lower thresholds around ±5% for spotting major volatility days.
Conclusion
The Rolling Log Returns indicator transforms raw price action into a statistically sound return series—equipping traders with a professional-grade lens into market behavior. Whether you're conducting exploratory data analysis, building factor models, or visually scanning for outliers, this indicator integrates seamlessly into a modern quant's toolbox.
ATRWhat the Indicator Shows:
A compact table with four cells is displayed in the bottom-left corner of the chart:
| ATR | % | Level | Lvl+ATR |
Explanation of the Columns:
ATR — The averaged daily range (volatility) calculated with filtering of abnormal bars (extremely large or small daily candles are ignored).
% — The percentage of the daily ATR that the price has already covered today (the difference between the daily Open and Close relative to ATR).
Level — A custom user-defined level set through the indicator settings.
Lvl+ATR — The sum of the daily ATR and the user-defined level. This can be used, for example, as a target or stop-loss reference.
Color Highlighting of the "%" Cell:
The background color of the "%" ATR cell changes depending on the value:
✅ If the value is less than 10% — the cell is green (market is calm, small movement).
➖ If the value is between 10% and 50% — no highlighting (average movement, no signal).
🟡 If the value is between 50% and 70% — the cell is yellow (movement is increasing, be alert).
🔴 If the value is above 70% — the cell is red (the market is actively moving, high volatility).
Key Features:
✔ All ATR calculations and percentage progress are performed strictly based on daily data, regardless of the chart's current timeframe.
✔ The indicator is ideal for intraday traders who want to monitor daily volatility levels.
✔ The table always displays up-to-date information for quick decision-making.
✔ Filtering of abnormal bars makes ATR more stable and objective.
What is Adaptive ATR in this Indicator:
Instead of the classic ATR, which simply averages the true range, this indicator uses a custom algorithm:
✅ It analyzes daily bars over the past 100 days.
✅ Calculates the range High - Low for each bar.
✅ If the bar's range deviates too much from the average (more than 1.8 times higher or lower), the bar is considered abnormal and ignored.
✅ Only "normal" bars are included in the calculation.
✅ The average range of these normal bars is the adaptive ATR.
Detailed Algorithm of the getAdaptiveATR() Function:
The function takes the number of bars to include in the calculation (for example, 5):
The average of the last 5 normal bars is calculated.
pinescript
Копировать
Редактировать
adaptiveATR = getAdaptiveATR(5)
Step-by-Step Process:
An empty array ranges is created to store the ranges.
Daily bars with indices from 1 to 100 are iterated over.
For each bar:
🔹 The daily High and Low with the required offset are loaded via request.security().
🔹 The range High - Low is calculated.
🔹 The temporary average range of the current array is calculated.
🔹 The bar is checked for abnormality (too large or too small).
🔹 If the bar is normal or it's the first bar — its range is added to the array.
Once the array accumulates the required number of bars (count), their average is calculated — this is the adaptive ATR.
If it's not possible to accumulate the required number of bars — na is returned.
Что показывает индикатор:
На графике внизу слева отображается компактная таблица из четырех ячеек:
ATR % Уровень Ур+ATR
Пояснения к столбцам:
ATR — усреднённый дневной диапазон (волатильность), рассчитанный с фильтрацией аномальных баров (слишком большие или маленькие дневные свечи игнорируются).
% — процент дневного ATR, который уже "прошла" цена на текущий день (разница между открытием и закрытием относительно ATR).
Уровень — пользовательский уровень, который задаётся вручную через настройки индикатора.
Ур+ATR — сумма уровня и дневного ATR. Может использоваться, например, как ориентир для целей или стопов.
Цветовая подсветка ячейки "%":
Цвет фона ячейки с процентом ATR меняется в зависимости от значения:
✅ Если значение меньше 10% — ячейка зелёная (рынок пока спокоен, маленькое движение).
➖ Если значение от 10% до 50% — фон не подсвечивается (среднее движение, нет сигнала).
🟡 Если значение от 50% до 70% — ячейка жёлтая (движение усиливается, повышенное внимание).
🔴 Если значение выше 70% — ячейка красная (рынок активно движется, высокая волатильность).
Особенности работы:
✔ Все расчёты ATR и процентного прохождения производятся исключительно по дневным данным, независимо от текущего таймфрейма графика.
✔ Индикатор подходит для трейдеров, которые торгуют внутри дня, но хотят ориентироваться на дневные уровни волатильности.
✔ В таблице всегда отображается актуальная информация для принятия быстрых торговых решений.
✔ Фильтрация аномальных баров делает ATR более устойчивым и объективным.
Что такое адаптивный ATR в этом индикаторе
Вместо классического ATR, который просто усредняет истинный диапазон, здесь используется собственный алгоритм:
✅ Он берет дневные бары за последние 100 дней.
✅ Для каждого из них рассчитывает диапазон High - Low.
✅ Если диапазон бара слишком сильно отличается от среднего (более чем в 1.8 раза больше или меньше), бар считается аномальным и игнорируется.
✅ Только нормальные бары попадают в расчёт.
✅ В итоге считается среднее из диапазонов этих нормальных баров — это и есть адаптивный ATR.
Подробный алгоритм функции getAdaptiveATR()
Функция принимает количество баров для расчёта (например, 5):
Считается 5 последних нормальных баров
pinescript
Копировать
Редактировать
adaptiveATR = getAdaptiveATR(5)
Пошагово:
Создаётся пустой массив ranges для хранения диапазонов.
Перебираются дневные бары с индексами от 1 до 100.
Для каждого бара:
🔹 Через request.security() подгружаются дневные High и Low с нужным смещением.
🔹 Считается диапазон High - Low.
🔹 Считается временное среднее диапазона по текущему массиву.
🔹 Проверяется, не является ли бар аномальным (слишком большой или маленький).
🔹 Если бар нормальный или это самый первый бар — его диапазон добавляется в массив.
Как только массив набирает заданное количество баров (count), берётся их среднее значение — это и есть адаптивный ATR.
Если не удалось набрать нужное количество баров — возвращается na.
ZenLab ATR FNSThis indicator was created specifically for Zen Labs which includes a custom ATR (Average True Range) table that displays the ATR value for a selected period of candles.
ATR is a volatility indicator that measures the average range between high and low prices over a given number of periods. It helps traders assess how much an asset typically moves, providing valuable information for setting stop losses, take profits, or identifying market conditions. It adapts to changing market conditions, making it useful across different timeframes and asset classes.
How the ATR Indicator Works:
The ATR is based on the concept of True Range (TR), which is the greatest of the following three values:
- Current High minus Current Low
- Absolute value of Current High minus Previous Close
- Absolute value of Current Low minus Previous Close
Averaging the True Range:
Once the True Range is calculated for each period, the ATR is computed by averaging these True Ranges over a set number of periods and is displayed in the table.
Interpreting the ATR:
- A higher ATR value indicates higher volatility—prices are moving more significantly.
- A lower ATR value indicates lower volatility—prices are more stable and less active.
Enjoy!
- Rebel Empire
