Zarattini Intra-day Threshold Bands (ZITB)This indicator implements the intraday threshold band methodology described in the research paper by Carlo Zarattini et al.
Overview:
Plots intraday threshold bands based on daily open/close levels.
Supports visualization of BaseUp/BaseDown levels and Threshold Upper/Lower bands.
Optional shading between threshold bands for easier interpretation.
Usage Notes / Limitations:
Originally studied on SPY (US equities), this implementation is adapted for NSE intraday market timing, specifically the NIFTY50 index.
Internally, 2-minute candles are used if the chart timeframe is less than 2 minutes.
Values may be inaccurate if the chart timeframe is more than 1 day.
Lookback days are auto-capped to avoid exceeding TradingView’s 5000-bar limit.
The indicator automatically aligns intraday bars across multiple days to compute average deltas.
For better returns, it is recommended to use this indicator in conjunction with VWAP and a volatility-based position sizing mechanism.
Can be used as a reference for Open Range Breakout (ORB) strategies.
Customizations:
Toggle plotting of base levels and thresholds.
Toggle shading between thresholds.
Line colors and styles can be adjusted in the Style tab.
Intended for educational and research purposes only.
This indicator implements the approach described in the research paper by Zarattini et al.
Note: This implementation is designed for the NSE NIFTY50 index. While Zarattini’s original study was conducted on SPY, this version adapts the methodology for the Indian market.
Methodology Explanation
This indicator is primarily designed for Open Range Breakout (ORB) strategies.
Base Levels
BaseUp = Maximum of today’s open and previous day’s close
BaseDown = Minimum of today’s open and previous day’s close
Delta Calculation
For the past 14 trading days (lookbackDays), the delta for each intraday candle is calculated as the ab
solute difference from the close of the first candle of that day.
Average Delta
For a given intraday time/candle today, deltaAvg is computed as the average of the deltas at the same time across the previous 14 days.
Threshold Bands
ThresholdUp = BaseUp + deltaAvg
ThresholdDown = BaseDown − deltaAvg
Signals
Spot price moving above ThresholdUp → Long signal
Spot price moving below ThresholdDown → Short signal
Tip: For better returns, combine this indicator with VWAP and a volatility-based position sizing mechanism.
חפש סקריפטים עבור "bands"
PulseRPO Zero-Lag BandsPulseRPO is a momentum and volatility timing suite built on a zero-lag Relative Price Oscillator. It pairs an RPO (fast vs slow MA spread, in %) with adaptive volatility envelopes that tighten or widen as conditions change, so you can spot true momentum bursts, exhaustion and “quiet-before-the-move” squeezes—without the usual MA lag.
What it shows
Zero-Lag RPO: Choose EMA, SMA, WMA, RMA, HMA or ZLEMA for the base, then apply ZLEMA/DEMA/TEMA/HMA zero-lag smoothing to cut delay.
Adaptive Bands: StdDev, ATR, Range or Hybrid volatility; bands auto-tighten in high vol and widen in quiet regimes.
Dynamic OB/OS: Levels scale with current regime so extremes mean something even as volatility shifts.
Signal & Histogram: Classic signal cross plus histogram for quick read of acceleration vs deceleration.
Squeeze Paint: Subtle background highlight when band width compresses below its average.
Divergences & Triggers: Optional bullish/bearish divergence tags, plus band-cross and signal-cross alerts out of the box.
How to use it (general guide)
Momentum entries: Look for RPO crossing up its signal from below or snapping out of a squeeze; extra weight if it also re-enters from below the lower band.
Trend continuation: RPO riding outside the upper (or lower) band with rising histogram = power move; trail risk on pullbacks to the signal line.
Exhaustion / fades: Taps beyond dynamic OB/OS or band re-entries can mark mean-revert windows—confirm with price/volume.
Risk filter: During squeeze, size down and prepare for expansion; after expansion, respect extremes.
Tweak the MA type, band method and zero-lag strength to match your timeframe. PulseRPO is designed to be a self-contained read: regime → setup → trigger → alert.
VWAP with Prev. Session BandsVWAP with Prev. Session Bands is an advanced indicator based on TradingView’s original VWAP. It adds configurable standard deviation or percentage-based bands, both for the current and previous session. You can anchor the VWAP to various timeframes or events (like Sessions, Weeks, Months, Earnings, etc.) and selectively show up to three bands.
The unique feature of this script is the ability to display the VWAP and bands from the previous session, helping traders visualize mean reversion levels or historical volatility ranges.
Built on top of the official TradingView VWAP implementation, this version provides enhanced flexibility and visual clarity for intraday and swing traders alike.
Ethereum Logarithmic Regression Bands (Fine-Tuned)This indicator, "Ethereum Logarithmic Regression Bands (Fine-Tuned)," is my attempt to create a tool for estimating long-term trends in Ethereum (ETH/USD) price action using logarithmic regression bands. Please note that I am not an expert in financial modeling or coding—I developed this as a personal project to serve as a rough estimation rather than a precise or professional trading tool. The data was fitted to non-bubble periods of Ethereum's history to provide a general trendline, but it’s far from perfect.
I’m sharing this because I couldn’t find a similar indicator available, and I thought it might be useful for others who are also exploring ETH’s long-term behavior. The bands start from Ethereum’s launch price and are adjustable via input parameters, but they are based on my best effort to align with historical data. With some decent coding experience, I’m sure someone could refine this further—perhaps by optimizing the coefficients or incorporating more advanced fitting techniques. Feel free to tweak the code, suggest improvements, or use it as a starting point for your own projects!
How to Use:
** THIS CHART IS SPECIFICALLY CODED FOR ETH/USD (KRAKEN) ON THE WEEKLY TIMEFRAME IN LOG VIEW**
The main band (blue) represents the logarithmic regression line.
The upper (red) and lower (green) bands provide a range around the main trend, adjustable with multipliers.
Adjust the "Launch Price," "Base Coefficient," "Growth Coefficient," and other inputs to experiment with different fits.
Disclaimer:
This is not financial advice. Use at your own risk, and always conduct your own research before making trading decisions.
Bollinger Bands + RSI StrategyThe Bollinger Bands + RSI strategy combines volatility and momentum indicators to spot trading opportunities in intraday settings. Here’s a concise summary:
Components:
Bollinger Bands: Measures market volatility. The lower band signals potential buying opportunities when the price is considered oversold.
Relative Strength Index (RSI): Evaluates momentum to identify overbought or oversold conditions. An RSI below 30 indicates oversold, suggesting a buy, and above 70 indicates overbought, suggesting a sell.
Strategy Execution:
Buy Signal : Triggered when the price falls below the lower Bollinger Band while the RSI is also below 30.
Sell Signal : Activated when the price exceeds the upper Bollinger Band with an RSI above 70.
Exit Strategy : Exiting a buy position is considered when the RSI crosses back above 50, capturing potential rebounds.
Advantages:
Combines price levels with momentum for more reliable signals.
Clearly defined entry and exit points help minimize emotional trading.
Considerations:
Can produce false signals in very volatile or strongly trending markets.
Best used in markets without a strong prevailing trend.
This strategy aids traders in making decisions based on technical indicators, enhancing their ability to profit from short-term price movements.
Bollinger Bands Weighted Alert System (BBWAS)The idea of this indicator is very similar to my previous published script called BBAS (Bollinger Bands Alert System).
Just with little additions. In this case, we're using a Weighted Moving Average (ta.wma) instead of Simple Moving Average to calculate the basis line.
A breakout in trading refers to a situation where the price of a security or asset moves beyond a defined level of support or resistance, which is typically indicated by technical analysis tools like Bollinger Bands. Bollinger Bands consist of three lines: the upper band, the lower band, and the middle band (or basis). The upper and lower bands are set at a specified number of standard deviations away from the middle band, and they help to define the range within which the price of an asset is expected to fluctuate.
When the price of the asset moves beyond the upper or lower band, it is said to have "broken out" of the range. If the price closes below the lower band, it is considered a bearish breakout, and if it closes above the upper band, it is considered a bullish breakout.
Once a breakout occurs, traders may look for a confirmation signal before entering a trade. In this case, crossing the middle line (or basis) after a breakout may signal a potential trend reversal and a good opportunity to enter a long or short trade, depending on the direction of the breakout.
Dear traders, while we strive to provide you with the best trading tools and resources, we want to remind you to exercise caution and diligence in your investing decisions.
It is important to always do your own research and analysis before making any trades. Remember, the responsibility for your investments ultimately lies with you.
Happy trading!
DEMA Supertrend Bands [Misu]█ Indicator based on DEMA (Double Exponential Moving Average) & Supertrend to show Bands .
DEMA attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
Supertrend aims to detect price trends, it's also used to set protective stops.
█ Usages:
Combining Dema to calculate Supertrend results in nice lower and upper bands.
This can be used to identify potential supports and resistances and set protective stops.
█ Parameters:
Length DEMA: Double Ema lenght used to calculate DEMA. Dema is used by Supertrend indicator.
Length Atr: Atr lenght used to calculate Atr. Atr is used by Supertrend indicator.
Band Mult: Used to calculate Supertrend Bands width.
█ Other Applications:
The mid band can be used to filter bad signals in the manner of a more classical Moving Average.
Bollinger Bands color candlesThis Pine Script indicator applies Bollinger Bands to the price chart and visually highlights candles based on their proximity to the upper and lower bands. The script plots colored candles as follows:
Bullish Close Above Upper Band: Candles are colored green when the closing price is above the upper Bollinger Band, indicating strong bullish momentum.
Bearish Close Below Lower Band: Candles are colored red when the closing price is below the lower Bollinger Band, signaling strong bearish momentum.
Neutral Candles: Candles that close within the bands remain their default color.
This visual aid helps traders quickly identify potential breakout or breakdown points based on Bollinger Band dynamics.
BOLLY BandsThis is a strategy using Bollinger Bands. The strategy is predicated around having low volatility in price action and then looking to capture a move when price starts to trend outside of the Bollinger bands. This strategy has only been backtested for 1 month but it has promising results so I will be sharing it looking for feedback. I run this strategy on the ERUSD 1 min chart.
Percentile Rank of Bollinger BandsThis simple indicator provides you three useful information with Bollinger Bands:
How wide the current width (standard deviation) of the Bollinger Band is.
Compared to the widths in the past, is the current width relatively small or big? Value is expressed in percentile format.
What the "relative position of current price" to the current Bollinger Band is.
This indicator can be useful to identify whether the Bollinger Band has substantially "expanded" or "squeezed."
First, divide the current standard deviation by the current price, we get the current width. The current width is displayed by the columns at the bottom. When the current width becomes wider, the column becomes taller, and the color is dark green. On the contrary, if the width becomes narrower, the column becomes shorter and the color is light green.
Next, compare the current width with the previous N widths, we get the percentile rank for the current width. The percentile rank is shown by the thicker line graph. When the percentile rank grows, it is green; whereas when the rank declines, the color is red.
Lastly, calculate (close - lower)/(upper - lower) and we get an idea of the relative height of the current price, compared to the upper and lower band. This is displayed by the thinner line graph. When the relative position becomes higher, the color is in aqua. It is in blue when the relative position becomes lower. Note that since closing prices can go above the upper band or go below the lower band, the values may be greater than 100 or less than 0.
EMA Bollinger Bands with customized std dev and moving averageTo use EMA with band you need to set input parameter named as "TypeOfMa" to 1.
If you set TypeOfMa = 1 then it will use EMA average for Bollinger bands.
If you set TypeOfMa = 0 then it will use MA average for Bollinger bands.
Z-Score Bands + SignalsZ-Score Statistical Market Analyzer
A multi-dimensional market structure indicator based on standardized deviation & regime logic
English Description
Concept
This indicator builds a statistical model of price behaviour by converting every candle’s movement into a Z-score — how many standard deviations each close is away from its moving average.
It visualizes the normal distribution structure of returns and provides adaptive entry signals for both Mean Reversion and Breakout regimes.
Rather than predicting price direction, it measures statistical displacement from equilibrium and dynamically adjusts the decision logic according to the market’s volatility regime.
⚙️ Main Components
Z-Score Bands (±1σ, ±2σ, ±3σ)
– The core structure visualizes volatility boundaries based on rolling mean and standard deviation.
– Price outside ±2σ often indicates statistical extremes.
Dual Signal Systems
Mean Reversion (MRL / MRS): when price (or return z-score) crosses back inside ±2σ bands.
Breakout (BOL / BOS): when price continues to expand beyond ±2σ.
Volatility Regime Classification
The indicator detects whether the market is currently in a low-vol or high-vol regime using percentile statistics of σ.
Low vol → Mean Reversion preferred
High vol → Breakout preferred
🧠 Adaptive Switches
A. Freeze MA/σ - Use previous-bar stats to avoid repainting and lag.
B. Confirm on Close - Only generate signals once the base-timeframe bar closes (eliminates look-ahead bias).
C. Return-based Signal - Use log-return Z-score instead of price deviation — normalizes volatility across assets.
D. Outlier Filter - Exclude bars with abnormal single-bar returns (e.g., >20%). Reduces false spikes.
E. Regime Gating - Automatically switch between Mean Reversion and Breakout logic depending on volatility percentile.
Each module can be toggled individually to test different statistical behaviours or tailor to a specific market condition.
📊 Interpretation
When the histogram of returns approximates a normal distribution, mean-reversion logic is often more effective.
When price persistently drifts beyond ±2σ or ±3σ, the distribution becomes leptokurtic (fat-tailed) — a breakout structure dominates.
Hence, this tool can help you:
Identify whether an asset behaves more “Gaussian” or “fat-tailed”;
Select the correct trading regime (MR or BO);
Quantitatively measure market tension and volatility clusters.
🧩 Recommended Use
Works on any timeframe and any asset.
Best used on liquid instruments (e.g., XAU/USD, indices, major FX pairs).
Combine with volume, sentiment or structural filters to confirm signals.
For strategy automation, pair with the companion script:
🧠 “Z-Score Strategy • Multi-Source Confirm (MRL/MRS/BOL/BOS)”.
⚠️ Disclaimer
This script is designed for educational and research purposes.
Statistical deviation ≠ directional prediction — use with sound risk management.
Past distribution patterns may shift under new volatility regimes.
==================================================================================
中文说明（简体）
概念简介
该指标基于价格的统计分布原理，将每根 K 线的波动转化为标准化的 Z-Score（标准差偏离值），用于刻画市场处于均衡或偏离状态。
它同时支持 均值回归（Mean Reversion） 与 突破延展（Breakout） 两种逻辑，并可根据市场波动结构自动切换策略模式。
⚙️ 主要功能模块
Z-Score 通道（±1σ / ±2σ / ±3σ）
用滚动均值与标准差动态绘制的统计波动带，价格超出 ±2σ 区域通常意味着极端偏离。
双信号系统
MRL / MRS（均值回归多空）：价格重新回到 ±2σ 以内时触发。
BOL / BOS（突破延展多空）：价格持续运行在 ±2σ 之外时触发。
波动率分层
自动识别市场处于高波动还是低波动区间：
低波动期 → 适合均值回归逻辑；
高波动期 → 适合突破趋势逻辑。
🧠 A–E 模块说明
A. 固定统计参数：使用上一根 K 线的均值和标准差，防止重绘。
B. 收盘确认信号：仅在当前时间框架收盘后生成信号，避免前视偏差。
C. 收益率信号模式：采用对数收益率的 Z-Score，更具普适性。
D. 异常波过滤：忽略单根极端波动（如 >20%）的噪声信号。
E. 波动率调节逻辑：根据市场处于高/低波动区间，自动切换 MRL/MRS 或 BOL/BOS。
📊 应用解读
如果收益率分布接近正态分布 → 市场倾向震荡，MRL/MRS 效果较佳；
若价格频繁偏离 ±2σ 或 ±3σ → 市场呈现“肥尾”分布，趋势延展占主导。
因此，该指标的核心目标是：
识别当前市场的统计结构类型；
根据波动特征自动切换交易逻辑；
提供结构化、可量化的市场状态刻画。
💡 使用建议
适用于所有时间框架与金融品种。
建议结合成交量或结构性指标过滤。
若用于策略回测，可搭配同名 “Z-Score Strategy • Multi-Source Confirm” 策略脚本。
⚠️ 免责声明
本指标仅用于研究与教学，不构成任何投资建议。
统计偏离 ≠ 趋势预测，实际市场行为可能在不同波动结构下改变。
Floating Bands of the Argentine Peso (Sebastian.Waisgold)
The BCRA ( Central Bank of the Argentine Republic ) announced that as of Monday, April 15, 2025, the Argentine Peso (USDARS) will float within a system of divergent exchange rate bands.
The upper band was set at ARS 1400 per USD on 15/04/2025, with a +1% monthly adjustment distributed daily, rising by a fraction each day.
The lower band was set at ARS 1000 per USD on 15/04/2025, with a –1% monthly adjustment distributed daily, falling by a fraction each day.
This indicator is crucial for anyone trading USDARS, since the BCRA will only intervene in these situations:
- Selling : if the Peso depreciates against the USD above the upper band .
- Buying : if the Peso appreciates against the USD below the lower band .
Therefore, this indicator can be used as follows:
- If USDARS is above the upper band , it is “expensive” and you may sell .
- If USDARS is below the lower band , it is “cheap” and you may buy .
It can also be applied to other assets such as:
- USDTARS
- Dollar Cable / CCL (Contado con Liquidación) , derived from the BCBA:YPFD / NYSE:YPF ratio.
A mid band —exactly halfway between the upper and lower bands—has also been added.
Once added, the indicator should look like this:
In the following image you can see:
- Upper Floating Band
- Lower Floating Band
- Mid Floating Band
User Configuration
By double-clicking any line you can adjust:
- Start day (Dia de incio), month (Mes de inicio), and year (Año de inicio)
- Initial upper band value (Valor inicial banda superior)
- Initial lower band value (Valor inicial banda inferior)
- Monthly rate Tasa mensual %)
It is recommended not to modify these settings for the Argentine Peso, as they reflect the BCRA’s official framework. However, you may customize them—and the line colors—for other assets or currencies implementing a similar band scheme.
M2 GLI SD BandsHighly customizable M2 Global Liquidity Index with adaptive standard deviation bands.
The SD bands incorporate data from M2 with varying lags to capture M2's full impact on the price of Bitcoin spread across multiple weeks.
EMAs are used for smoothing. Offset, smoothing, and other features are customizable.
Swing BandsThis indicator is a result of experimentation with price action of candle high and lows for quantifying reversals and trend continuation.
The band area shows trend reversal incoming and possible chop.
Middle line is the trend reversal price level. Candle colors change if the close price is above or below the middle line.
Long and short positions can be taken when above or below the bands.
Trend continuations are in effect when price retraces into the bands and breaks above or below in the same direction of the trend.
StDev BandsThis is a "bands"-type indicator. It was developed out of my Sharpe Ratio indicator . It uses the standard deviation of returns as basis for drawing the bands. I'm going to update this indicator as the other indicator evolves. Please be sure you know how to calculate Sharpe Ratio and check out the Sharpe Ratio indicator as well. This will help you understand the purpose of this indicator a bit more.
As a very short introduction. Many investors use the standard deviation of returns as risk measurement . I admit the defaults of this indicator aren't perfect. Normally investors use the standard deviation over a 1 year period. Traditional finance uses 265 days, and because crypto never sleeps, we could use 365. I defaulted it to 20.
Accurate Bollinger Bands mcbw_ [True Volatility Distribution]The Bollinger Bands have become a very important technical tool for discretionary and algorithmic traders alike over the last decades. It was designed to give traders an edge on the markets by setting probabilistic values to different levels of volatility. However, some of the assumptions that go into its calculations make it unusable for traders who want to get a correct understanding of the volatility that the bands are trying to be used for. Let's go through what the Bollinger Bands are said to show, how their calculations work, the problems in the calculations, and how the current indicator I am presenting today fixes these.
--> If you just want to know how the settings work then skip straight to the end or click on the little (i) symbol next to the values in the indicator settings window when its on your chart <--
--------------------------- What Are Bollinger Bands ---------------------------
The Bollinger Bands were formed in the 1980's, a time when many retail traders interacted with their symbols via physically printed charts and computer memory for personal computer memory was measured in Kb (about a factor of 1 million smaller than today). Bollinger Bands are designed to help a trader or algorithm see the likelihood of price expanding outside of its typical range, the further the lines are from the current price implies the less often they will get hit. With a hands on understanding many strategies use these levels for designated levels of breakout trades or to assist in defining price ranges.
--------------------------- How Bollinger Bands Work ---------------------------
The calculations that go into Bollinger Bands are rather simple. There is a moving average that centers the indicator and an equidistant top band and bottom band are drawn at a fixed width away. The moving average is just a typical moving average (or common variant) that tracks the price action, while the distance to the top and bottom bands is a direct function of recent price volatility. The way that the distance to the bands is calculated is inspired by formulas from statistics. The standard deviation is taken from the candles that go into the moving average and then this is multiplied by a user defined value to set the bands position, I will call this value 'the multiple'. When discussing Bollinger Bands, that trading community at large normally discusses 'the multiple' as a multiplier of the standard deviation as it applies to a normal distribution (gaußian probability). On a normal distribution the number of standard deviations away (which trades directly use as 'the multiple') you are directly corresponds to how likely/unlikely something is to happen:
1 standard deviation equals 68.3%, meaning that the price should stay inside the 1 standard deviation 68.3% of the time and be outside of it 31.7% of the time;
2 standard deviation equals 95.5%, meaning that the price should stay inside the 2 standard deviation 95.5% of the time and be outside of it 4.5% of the time;
3 standard deviation equals 99.7%, meaning that the price should stay inside the 3 standard deviation 99.7% of the time and be outside of it 0.3% of the time.
Therefore when traders set 'the multiple' to 2, they interpret this as meaning that price will not reach there 95.5% of the time.
---------------- The Problem With The Math of Bollinger Bands ----------------
In and of themselves the Bollinger Bands are a great tool, but they have become misconstrued with some incorrect sense of statistical meaning, when they should really just be taken at face value without any further interpretation or implication.
In order to explain this it is going to get a bit technical so I will give a little math background and try to simplify things. First let's review some statistics topics (distributions, percentiles, standard deviations) and then with that understanding explore the incorrect logic of how Bollinger Bands have been interpreted/employed.
---------------- Quick Stats Review ----------------
.
(If you are comfortable with statistics feel free to skip ahead to the next section)
.
-------- I: Probability distributions --------
When you have a lot of data it is helpful to see how many times different results appear in your dataset. To visualize this people use "histograms", which just shows how many times each element appears in the dataset by stacking each of the same elements on top of each other to form a graph. You may be familiar with the bell curve (also called the "normal distribution", which we will be calling it by). The normal distribution histogram looks like a big hump around zero and then drops off super quickly the further you get from it. This shape (the bell curve) is very nice because it has a lot of very nifty mathematical properties and seems to show up in nature all the time. Since it pops up in so many places, society has developed many different shortcuts related to it that speed up all kinds of calculations, including the shortcut that 1 standard deviation = 68.3%, 2 standard deviations = 95.5%, and 3 standard deviations = 99.7% (these only apply to the normal distribution). Despite how handy the normal distribution is and all the shortcuts we have for it are, and how much it shows up in the natural world, there is nothing that forces your specific dataset to look like it. In fact, your data can actually have any possible shape. As we will explore later, economic and financial datasets *rarely* follow the normal distribution.
-------- II: Percentiles --------
After you have made the histogram of your dataset you have built the "probability distribution" of your own dataset that is specific to all the data you have collected. There is a whole complicated framework for how to accurately calculate percentiles but we will dramatically simplify it for our use. The 'percentile' in our case is just the number of data points we are away from the "middle" of the data set (normally just 0). Lets say I took the difference of the daily close of a symbol for the last two weeks, green candles would be positive and red would be negative. In this example my dataset of day by day closing price difference is:
week 1:
week 2:
sorting all of these value into a single dataset I have:
I can separate the positive and negative returns and explore their distributions separately:
negative return distribution =
positive return distribution =
Taking the 25th% percentile of these would just be taking the value that is 25% towards the end of the end of these returns. Or akin the 100%th percentile would just be taking the vale that is 100% at the end of those:
negative return distribution (50%) = -5
positive return distribution (50%) = +4
negative return distribution (100%) = -10
positive return distribution (100%) = +20
Or instead of separating the positive and negative returns we can also look at all of the differences in the daily close as just pure price movement and not account for the direction, in this case we would pool all of the data together by ignoring the negative signs of the negative reruns
combined return distribution =
In this case the 50%th and 100%th percentile of the combined return distribution would be:
combined return distribution (50%) = 4
combined return distribution (100%) = 10
Sometimes taking the positive and negative distributions separately is better than pooling them into a combined distribution for some purposes. Other times the combined distribution is better.
Most financial data has very different distributions for negative returns and positive returns. This is encapsulated in sayings like "Price takes the stairs up and the elevator down".
-------- III: Standard Deviation --------
The formula for the standard deviation (refereed to here by its shorthand 'STDEV') can be intimidating, but going through each of its elements will illuminate what it does. The formula for STDEV is equal to:
square root ( (sum ) / N )
Going back the the dataset that you might have, the variables in the formula above are:
'mean' is the average of your entire dataset
'x' is just representative of a single point in your dataset (one point at a time)
'N' is the total number of things in your dataset.
Going back to the STDEV formula above we can see how each part of it works. Starting with the '(x - mean)' part. What this does is it takes every single point of the dataset and measure how far away it is from the mean of the entire dataset. Taking this value to the power of two: '(x - mean) ^ 2', means that points that are very far away from the dataset mean get 'penalized' twice as much. Points that are very close to the dataset mean are not impacted as much. In practice, this would mean that if your dataset had a bunch of values that were in a wide range but always stayed in that range, this value ('(x - mean) ^ 2') would end up being small. On the other hand, if your dataset was full of the exact same number, but had a couple outliers very far away, this would have a much larger value since the square par of '(x - mean) ^ 2' make them grow massive. Now including the sum part of 'sum ', this just adds up all the of the squared distanced from the dataset mean. Then this is divided by the number of values in the dataset ('N'), and then the square root of that value is taken.
There is nothing inherently special or definitive about the STDEV formula, it is just a tool with extremely widespread use and adoption. As we saw here, all the STDEV formula is really doing is measuring the intensity of the outliers.
--------------------------- Flaws of Bollinger Bands ---------------------------
The largest problem with Bollinger Bands is the assumption that price has a normal distribution. This is assumption is massively incorrect for many reasons that I will try to encapsulate into two points:
Price return do not follow a normal distribution, every single symbol on every single timeframe has is own unique distribution that is specific to only itself. Therefore all the tools, shortcuts, and ideas that we use for normal distributions do not apply to price returns, and since they do not apply here they should not be used. A more general approach is needed that allows each specific symbol on every specific timeframe to be treated uniquely.
The distributions of price returns on the positive and negative side are almost never the same. A more general approach is needed that allows positive and negative returns to be calculated separately.
In addition to the issues of the normal distribution assumption, the standard deviation formula (as shown above in the quick stats review) is essentially just a tame measurement of outliers (a more aggressive form of outlier measurement might be taking the differences to the power of 3 rather than 2). Despite this being a bit of a philosophical question, does the measurement of outlier intensity as defined by the STDEV formula really measure what we want to know as traders when we're experiencing volatility? Or would adjustments to that formula better reflect what we *experience* as volatility when we are actively trading? This is an open ended question that I will leave here, but I wanted to pose this question because it is a key part of what how the Bollinger Bands work that we all assume as a given.
Circling back on the normal distribution assumption, the standard deviation formula used in the calculation of the bands only encompasses the deviation of the candles that go into the moving average and have no knowledge of the historical price action. Therefore the level of the bands may not really reflect how the price action behaves over a longer period of time.
------------ Delivering Factually Accurate Data That Traders Need------------
In light of the problems identified above, this indicator fixes all of these issue and delivers statistically correct information that discretionary and algorithmic traders can use, with truly accurate probabilities. It takes the price action of the last 2,000 candles and builds a huge dataset of distributions that you can directly select your percentiles from. It also allows you to have the positive and negative distributions calculated separately, or if you would like, you can pool all of them together in a combined distribution. In addition to this, there is a wide selection of moving averages directly available in the indicator to choose from.
Hedge funds, quant shops, algo prop firms, and advanced mechanical groups all employ the true return distributions in their work. Now you have access to the same type of data with this indicator, wherein it's doing all the lifting for you.
------------------------------ Indicator Settings ------------------------------
.
---- Moving average ----
Select the type of moving average you would like and its length
---- Bands ----
The percentiles that you enter here will be pulled directly from the return distribution of the last 2,000 candles. With the typical Bollinger Bands, traders would select 2 standard deviations and incorrectly think that the levels it highlights are the 95.5% levels. Now, if you want the true 95.5% level, you can just enter 95.5 into the percentile value here. Each of the three available bands takes the true percentile you enter here.
---- Separate Positive & Negative Distributions----
If this box is checked the positive and negative distributions are treated indecently, completely separate from each other. You will see that the width of the top and bottom bands will be different for each of the percentiles you enter.
If this box is unchecked then all the negative and positive distributions are pooled together. You will notice that the width of the top and bottom bands will be the exact same.
---- Distribution Size ----
This is the number of candles that the price return is calculated over. EG: to collect the price return over the last 33 candles, the difference of price from now to 33 candles ago is calculated for the last 2,000 candles, to build a return distribution of 2000 points of price differences over 33 candles.
NEGATIVE NUMBERS(<0) == exact number of candles to include;
EG: setting this value to -20 will always collect volatility distributions of 20 candles
POSITIVE NUMBERS(>0) == number of candles to include as a multiple of the Moving Average Length value set above;
EG: if the Moving Average Length value is set to 22, setting this value to 2 will use the last 22*2 = 44 candles for the collection of volatility distributions
MORE candles being include will generally make the bands WIDER and their size will change SLOWER over time.
I wish you focus, dedication, and earnest success on your journey.
Happy trading :)
Nami Bands with Future Projection [FXSMARTLAB]The Nami Bands ( Inspired by "Nami", meaning "wave" in Japanese) are two dynamic bands around price data: an upper band and a lower band. These bands are calculated based on an Asymmetric Linear Weighted Moving Average of price and a similarly asymmetric weighted standard deviation. This weighting method emphasizes recent data without overreacting to short-term price changes, thus smoothing the bands in line with prevailing market conditions.
Advantages and Benefits of Using the Indicator
* Volatility Analysis: The bands expand and contract with market volatility, helping traders assess periods of high and low volatility. Narrow bands indicate low volatility and potential consolidation, while wide bands suggest increased volatility and potential price movement.
* Dynamic Support and Resistance Levels: By adapting to recent trends, the bands serve as dynamic support (lower band) and resistance (upper band) levels, which traders can use for entry and exit signals.
* Overbought and Oversold Conditions: When prices reach or cross the bands’ outer limits, it may signal overbought (upper band) or oversold (lower band) conditions, suggesting possible reversals or trend slowdowns.
* Trend Confirmation and Continuation: The slope of the central moving average confirms trend direction. An upward slope generally indicates a bullish trend, while a downward slope suggests a bearish trend.
* Anticipating Breakouts and Reversals: The projected bands help identify where price movements may head, allowing traders to anticipate potential breakouts or reversals based on projected support and resistance.
Indicator Parameters
Source (src): The price data used for calculations, by default set to the average of high, low, and close (hlc3).
Length: The period over which calculations are made, defaulted to 50 periods.
Projection Length: The length for future band projection, defaulted to 20 periods.
StdDev Multiplier (mult): A multiplier for the standard deviation, defaulted to 2.0.
Internal Calculations
1. Asymmetric Linear Weighted Moving Average of Price
The indicator uses an Asymmetric Linear Weighted Moving Average (ALWMA) to calculate a central value for the price.
Asymmetric Weighting: This weighting technique assigns the highest weight to the most recent value, with weights decreasing linearly as the data points become older. This structure provides a nuanced focus on recent price trends, while still reflecting historical price levels.
2. Asymmetric Weighted Standard Deviation
The standard deviation in this indicator is also calculated using asymmetric weighting:
Purpose of Asymmetric Weighted Standard Deviation: Rather than aiming for high sensitivity to recent data, this standard deviation measure smooths out volatility by integrating weighted values across the length period, stabilizing the overall measurement of price variability.
This approach yields a balanced view of volatility, capturing broader market trends without being overly reactive to short-lived changes.
3. Upper and Lower Bands
The upper and lower bands are created by adding and subtracting the asymmetric weighted standard deviation from the asymmetric weighted average of price. This creates a dynamic envelope that adjusts to both recent price trends and the smoothed volatility measure:
These bands represent adaptable support and resistance levels that shift with recent market volatility.
Future Band Projection
The indicator provides a projection of the bands based on their current slope.
1. Calculating the Slope of the Bands
The slope for each band is derived from the difference between the current and previous values of each band.
2. Projecting the Bands into the Future
For each period into the future, up to the defined Projection Length, the bands are projected using the current slope.
This feature offers an anticipated view of where support and resistance levels may move, providing insight for future market behavior based on current trends.
PTS - Bollinger Bands with Trailing StopPTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
Thought for 1m 7s
Description for the "PTS - Bollinger Bands with Trailing Stop" Strategy
PTS - Bollinger Bands with Trailing Stop Strategy
Overview
The "PTS - Bollinger Bands with Trailing Stop" strategy is designed to capitalize on strong bullish market movements by combining the Bollinger Bands indicator with a dynamic trailing stop based on the Average True Range (ATR). This strategy aims to enter long positions during upward breakouts and protect profits through an adaptive exit mechanism.
Key Features
1. Bollinger Bands Indicator
Basis Moving Average Type: Choose from SMA, EMA, SMMA, WMA, or VWMA for the Bollinger Bands' basis line. Length: Adjustable period for calculating the moving average and standard deviation (default is 20). Standard Deviation Multiplier (StdDev): Determines the width of the bands (default is 2.0). Source: Select the price data used for calculations (default is the closing price).
2. Entry Conditions
The strategy enters a long position when the closing price crosses above the upper Bollinger Band , indicating strong upward momentum.
3. Exit Conditions
Trailing Stop Loss: Implemented using ATR to adjust dynamically with market volatility. ATR Length: Period for calculating ATR (default is 14). ATR Multiplier for Trailing Stop: Determines how closely the trailing stop follows the price (default is 2.0). Close Below Lower Bollinger Band: The strategy exits the long position if the closing price crosses below the lower Bollinger Band .
4. Risk Management
Commission and Slippage: Commission is set at 0.1%; slippage is set to 3. Position Sizing: Uses 100% of equity per trade (adjustable).
5. Date Range Filter
Specify the time period during which the strategy is active. Start Date: January 1, 2018. End Date: December 31, 2069.
Customizable Inputs
BB Length: Adjust the period for Bollinger Bands calculation. BB StdDev: Modify the standard deviation multiplier. Basis MA Type: Select the moving average type. Source: Choose the price data source. Offset: Shift the Bollinger Bands on the chart. ATR Length: Set the period for ATR calculation. ATR Multiplier for Trailing Stop: Adjust the trailing stop sensitivity.
How the Strategy Works
1. Initialization
Calculates Bollinger Bands and ATR based on selected parameters.
2. Entry Logic
Opens a long position when the closing price exceeds the upper Bollinger Band.
3. Exit Logic
Uses a trailing stop loss based on ATR. Exits if the closing price drops below the lower Bollinger Band.
4. Date Filtering
Executes trades only within the specified date range.
Advantages
Adaptive Risk Management: Trailing stop adjusts to market volatility. Simplicity: Clear entry and exit signals. Customizable Parameters: Tailor the strategy to different assets or conditions.
Considerations
Aggressive Position Sizing: Using 100% equity per trade is high-risk. Market Conditions: Best in trending markets; may produce false signals in sideways markets. Backtesting: Always test on historical data before live trading.
Disclaimer
This strategy is intended for educational and informational purposes only. Trading involves significant risk, and past performance is not indicative of future results. Assess your financial situation and consult a financial advisor if necessary.
Usage Instructions
1. Apply the Strategy: Add it to your TradingView chart. 2. Configure Inputs: Adjust parameters to suit your style and asset. 3. Analyze Backtest Results: Use the Strategy Tester. 4. Optimize Parameters: Experiment with input values. 5. Risk Management: Evaluate position sizing and incorporate risk controls.
Final Notes
The "PTS - Bollinger Bands with Trailing Stop" strategy provides a framework to leverage momentum breakouts while managing risk through adaptive trailing stops. Customize and test thoroughly to align with your trading objectives.
Renko BandsThis is renko without the candles, just the endpoint plotted as a line with bands around it that represent the brick size. The idea came from thinking about what renko actually gives you once you strip away the visual brick format. At its core, renko is a filtered price series that only updates when price moves a fixed amount, which means it's inherently a trend-following mechanism with built-in noise reduction. By plotting just the renko price level and surrounding it with bands at the brick threshold distances, you get something that works like regular volatility bands while still behaving as a trend indicator.
The center line is the current renko price, which trails actual price based on whichever brick sizing method you've selected. When price moves enough to complete a brick in the renko calculation, the center line jumps to the new brick level. The bands sit at plus and minus one brick size from that center line, showing you exactly how far price needs to move before the next brick would form. This makes the bands function as dynamic breakout levels. When price touches or crosses a band, you know a new renko brick is forming and the trend calculation is updating.
What makes this cool is the dual-purpose nature. You can use it like traditional volatility bands where the outer edges represent boundaries of normal price movement, and breaks beyond those boundaries signal potential trend continuation or exhaustion. But because the underlying calculation is renko rather than standard deviation or ATR around a moving average, the bands also give you direct insight into trend state. When the center line is rising consistently and price stays near the upper band, you're in a clean uptrend. When it's falling and price hugs the lower band, downtrend. When the center line is flat and price is bouncing between both bands, you're ranging.
The three brick sizing methods work the same way as standard renko implementations. Traditional sizing uses a fixed price range, so your bands are always the same absolute distance from the center line. ATR-based sizing calculates brick range from historical volatility, which makes the bands expand and contract based on the ATR measurement you chose at startup. Percentage-based sizing scales the brick size with price level, so the bands naturally widen as price increases and narrow as it decreases. This automatic scaling is particularly useful for instruments that move proportionally rather than in fixed increments.
The visual simplicity compared to full renko bricks makes this more practical for overlay use on your main chart. Instead of trying to read brick patterns in a separate pane or cluttering your price chart with boxes and lines, you get a single smoothed line with two bands that convey the same information about trend state and momentum. The center line shows you the filtered trend direction, the bands show you the threshold levels, and the relationship between price and the bands tells you whether the current move has legs or is stalling out.
From a trend-following perspective, the renko line naturally stays flat during consolidation and only moves when directional momentum is strong enough to complete bricks. This built-in filter removes a lot of the whipsaw that affects moving averages during choppy periods. Traditional moving averages continue updating with every bar regardless of whether meaningful directional movement is happening, which leads to false signals when price is just oscillating. The renko line only responds to sustained moves that meet the brick size threshold, so it tends to stay quiet when price is going nowhere and only signals when something is actually happening.
The bands also serve as natural stop-loss or profit-target references since they represent the distance price needs to move before the trend calculation changes. If you're long and the renko line is rising, you might place stops below the lower band on the theory that if price falls far enough to reverse the renko trend, your thesis is probably invalidated. Conversely, the upper band can mark levels where you'd expect the current brick to complete and potentially see some consolidation or pullback before the next brick forms.
What this really highlights is that renko's value isn't just in the brick visualization, it's in the underlying filtering mechanism. By extracting that mechanism and presenting it in a more traditional band format, you get access to renko's trend-following properties without needing to commit to the brick chart aesthetic or deal with the complications of overlaying brick drawings on a time-based chart. It's renko after all, so you get the trend filtering and directional clarity that makes renko useful, but packaged in a way that integrates more naturally with standard technical analysis workflows.
Standard Deviation BandsStandard Deviation Bands
คำอธิบายอินดิเคเตอร์:
อินดิเคเตอร์ SD Bands (Standard Deviation Bands) เป็นเครื่องมือวิเคราะห์ทางเทคนิคที่ออกแบบมาเพื่อวัดความผันผวนของราคาและระบุโอกาสในการเทรดที่อาจเกิดขึ้น อินดิเคเตอร์นี้จะแสดงผลเป็นเส้นขอบ 2 เส้นบนกราฟราคาโดยตรง โดยอ้างอิงจากค่าเฉลี่ยเคลื่อนที่ (Moving Average) และค่าส่วนเบี่ยงเบนมาตรฐาน (Standard Deviation)
* เส้นบน (Upper Band): แสดงระดับที่ราคาเคลื่อนไหวสูงกว่าค่าเฉลี่ย
* เส้นล่าง (Lower Band): แสดงระดับที่ราคาเคลื่อนไหวต่ำกว่าค่าเฉลี่ย
ความกว้างของช่องระหว่างเส้นทั้งสองบ่งบอกถึงระดับความผันผวนของตลาดในปัจจุบัน
วิธีการใช้งานอย่างละเอียด:
คุณสามารถนำอินดิเคเตอร์ SD Bands ไปประยุกต์ใช้ได้หลายวิธีเพื่อประกอบการตัดสินใจ ดังนี้:
1. การใช้เป็นแนวรับ-แนวต้านแบบไดนามิก (Dynamic Support & Resistance)
* แนวรับ: เมื่อราคาวิ่งลงมาแตะหรือเข้าใกล้เส้นล่าง (เส้นสีน้ำเงิน) เส้นนี้อาจทำหน้าที่เป็นแนวรับชั่วคราวและมีโอกาสที่ราคาจะเด้งกลับขึ้นไปหาเส้นกลาง
* แนวต้าน: เมื่อราคาวิ่งขึ้นไปแตะหรือเข้าใกล้เส้นบน (เส้นสีแดง) เส้นนี้อาจทำหน้าที่เป็นแนวต้านชั่วคราวและมีโอกาสที่ราคาจะย่อตัวลงมา
2. การวัดความผันผวนและสัญญาณ Breakout
* ช่วงตลาดสงบ (Low Volatility): เมื่อเส้น SD ทั้งสองเส้นบีบตัวเข้าหากันเป็นช่องที่แคบมาก (คล้ายกับ Bollinger Squeeze) แสดงว่าตลาดมีความผันผวนต่ำมาก ซึ่งมักจะเป็นสัญญาณว่ากำลังจะเกิดการเคลื่อนไหวครั้งใหญ่ (Breakout)
* ช่วงตลาดเป็นเทรนด์ (High Volatility): เมื่อเส้น SD ขยายตัวกว้างออกอย่างรวดเร็ว พร้อมกับที่ราคาวิ่งอยู่นอกขอบ แสดงว่าตลาดเข้าสู่ช่วงเทรนด์ที่แข็งแกร่งและมีโมเมนตัมสูง
3. สัญญาณการกลับตัว (Reversal Signals)
* เมื่อราคาปิดแท่งเทียน นอกเส้น SD Bands อย่างชัดเจน (โดยเฉพาะหลังจากที่เทรนด์นั้นดำเนินมานาน) อาจเป็นสัญญาณว่าแรงซื้อ/แรงขายเริ่มอ่อนกำลังลง และมีโอกาสที่จะเกิดการกลับตัวของราคาในไม่ช้า
การตั้งค่าอินพุต (Input Parameters):
* ระยะเวลา (Length): กำหนดจำนวนแท่งเทียนที่ใช้ในการคำนวณค่าเฉลี่ยและ SD
* 20: สำหรับการวิเคราะห์ระยะสั้นถึงกลาง
* 50 หรือ 100: สำหรับการวิเคราะห์ระยะยาว
* ตัวคูณ (Multiplier): กำหนดระยะห่างของเส้น SD จากค่าเฉลี่ย
* 1.0 - 2.0: เส้นจะอยู่ใกล้ราคามากขึ้น ทำให้เกิดสัญญาณบ่อยขึ้น
* 2.0 - 3.0: เส้นจะอยู่ห่างจากราคามากขึ้น ทำให้เกิดสัญญาณที่น่าเชื่อถือมากขึ้น แต่จะเกิดไม่บ่อย
ข้อควรระวังและคำเตือน:
* อินดิเคเตอร์นี้เป็นเพียง เครื่องมือวิเคราะห์ เพื่อช่วยในการตัดสินใจ ไม่ใช่สัญญาณการซื้อขายที่ถูกต้อง 100%
* ควรใช้ร่วมกับเครื่องมืออื่นๆ เช่น RSI, MACD, หรือ Volume เพื่อยืนยันสัญญาณ
* การเทรดมีความเสี่ยงสูง ควรบริหารจัดการความเสี่ยงและตั้งจุด Stop Loss ทุกครั้ง
คุณสามารถใช้โครงสร้างนี้ในการเขียนโพสต์บน TradingView ได้เลยนะครับ ขอให้ประสบความสำเร็จกับการโพสต์อินดิเคเตอร์ของคุณครับ!
English
Standard Deviation Bands
Indicator Description:
The SD Bands (Standard Deviation Bands) indicator is a powerful technical analysis tool designed to measure price volatility and identify potential trading opportunities. The indicator displays two dynamic bands directly on the price chart, based on a moving average and a customizable standard deviation multiplier.
* Upper Band: Indicates price levels above the moving average.
* Lower Band: Indicates price levels below the moving average.
The width of the channel between these two bands provides a clear picture of current market volatility.
Detailed User Guide:
You can use SD Bands in several ways to enhance your trading decisions:
1. Dynamic Support and Resistance:
These bands can act as dynamic support and resistance levels.
* Support: When the price moves down and touches or approaches the lower band, it can act as support, offering the possibility of a rebound to the average.
* Resistance: When the price moves up and touches or approaches the upper band, it can act as resistance, offering the possibility of a rebound.
2. Volatility Measurement and Breakout Signals:
* Low Volatility (Squeeze): When the two bands converge and form a narrow channel. Indicates very low market volatility. This condition often occurs before significant price movements or breakouts.
* High Volatility (Expansion): When the bands expand and widen rapidly, it indicates that the market is entering a period of strong trending momentum with high momentum.
3. Reversal Signals:
* When the price closes significantly outside the SD Bands (especially after a long-term trend), it may signal that the current momentum has expired and a reversal may be imminent.
Input Parameters:
The indicator's parameters are fully customizable to suit your trading style:
* Length: Defines the number of bars used to calculate the moving average and standard deviation.
* 20: Suitable for short- to medium-term analysis.
* 50 or 100: Suitable for long-term trend analysis.
* Multiplier: Adjusts the sensitivity of the signal bars.
* 1.0 - 2.0: Creates narrower signal bars, leading to more frequent signals.
* 2.0 - 3.0: Creates wider signal bars, providing fewer but potentially more significant signals.
Important Warning:
* This indicator is an analytical tool only. It does not provide guaranteed buy or sell signals.
* Always use it in conjunction with other indicators (such as RSI, MACD, and Volume) for confirmation.
* Trading involves high risk. Proper risk management, including the use of stop-loss orders, is recommended.
You can use this structure for your posts on TradingView. Good luck with your indicators!
Jurik Price Bands and Range Box [BigBeluga]Jurik Price Bands and Range Box
The Jurik Price Bands and Range Box - BigBeluga indicator is an advanced technical analysis tool that combines Jurik Moving Average (JMA) based price bands with a dynamic range box. This versatile indicator is designed to help traders identify trends, potential reversal points, and price ranges over a specified period.
🔵 KEY FEATURES
● Jurik Price Bands
Utilizes Jurik Moving Average for smoother, more responsive bands
//@function Calculates Jurik Moving Average
//@param src (float) Source series
//@param len (int) Length parameter
//@param ph (int) Phase parameter
//@returns (float) Jurik Moving Average value
jma(src, len, ph) =>
var float jma = na
var float e0 = 0.0
var float e1 = 0.0
var float e2 = 0.0
phaseRatio = ph < -100 ? 0.5 : ph > 100 ? 2.5 : ph / 100 + 1.5
beta = 0.45 * (len - 1) / (0.45 * (len - 1) + 2)
alpha = math.pow(beta, phaseRatio)
e0 := (1 - alpha) * src + alpha * nz(e0 )
e1 := (src - e0) * (1 - beta) + beta * nz(e1 )
e2 := (e0 + phaseRatio * e1 - nz(jma )) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * nz(e2 )
jma := e2 + nz(jma )
jma
Consists of an upper band, lower band, and a smooth price line
Bands adapt to market volatility using Jurik MA on ATR
Helps identify potential trend reversal points and overextended market conditions
● Dynamic Range Box
Displays a box representing the price range over a specified period
Calculates high, low, and mid-range prices
Option for adaptive mid-range calculation based on average price
Provides visual representation of recent price action and volatility
● Price Position Indicator
Shows current price position relative to the mid-range
Displays percentage difference from mid-range
Color-coded for quick trend identification
● Dashboard
Displays key information including current price, range high, mid, and low
Shows trend direction based on price position relative to mid-range
Provides at-a-glance market context
🔵 HOW TO USE
● Trend Identification
Use the middle of the Range Box as the primary trend reference point
Price above the middle of the Range Box indicates an uptrend
Price below the middle of the Range Box indicates a downtrend
The bar on the right shows the percentage distance of the close from the middle of the box
This percentage indicates both trend direction and strength
Refer to the dashboard for quick trend direction confirmation
● Potential Reversal Points
Upper and lower Jurik Bands can indicate potential trend reversal points
Price reaching or exceeding these bands may suggest overextended conditions
Watch for price reaction at these levels for possible trend shifts or pullbacks
Range Box high and low can serve as additional reference points for price action
● Range Analysis
Use Range Box to gauge recent price volatility and trading range
Mid-range line can act as a pivot point for short-term price movements
Percentage difference from mid-range helps quantify price position strength
🔵 CUSTOMIZATION
The Jurik Price Bands and Range Box indicator offers several customization options:
Adjust Range Box length for different timeframe analysis
Toggle between standard and adaptive mid-range calculation
Standard:
Adaptive:
Modify Jurik MA length and deviation for band calculation
Toggle visibility of Jurik Bands
By fine-tuning these settings, traders can adapt the indicator to various market conditions and personal trading strategies.
The Jurik Price Bands and Range Box indicator provides a multi-faceted approach to market analysis, combining trend identification, potential reversal point detection, and range analysis in one comprehensive tool. The use of Jurik Moving Average offers a smoother, more responsive alternative to traditional moving averages, potentially providing more accurate signals.
This indicator can be particularly useful for traders looking to understand market context quickly, identify potential reversal points, and assess current market volatility. The combination of dynamic bands, range analysis, and the informative dashboard provides traders with a rich set of data points to inform their trading decisions.
As with all technical indicators, it's recommended to use the Jurik Price Bands and Range Box in conjunction with other forms of analysis and within the context of a well-defined trading strategy. While this indicator provides valuable insights, it should be considered alongside other factors such as overall market conditions, volume, and fundamental analysis when making trading decisions.
Multi Timeframe Bull Market Support BandsMulti Timeframe Bull Market Support Bands (BMSB) Indicator
Concept and Functionality:
The Multi Timeframe Bull Market Support Bands (BMSB) indicator is a powerful tool designed to identify and visualize support levels across multiple timeframes simultaneously. The primary concept behind BMSB is to plot dynamic support bands derived from moving averages (MAs) that adapt to the prevailing bullish conditions across different timeframes. These bands act as support and resistance (S/R) levels, providing traders with critical insights into potential price bounce areas and market direction.
Key Features:
Multi Timeframe Analysis:
- The indicator plots bull market support bands for the following timeframes concurrently: Chart (with price prediction), 5 minutes (5m), 15 minutes (15m), 1 hour (1h or 60), 4 hours (4h or 240), Daily (D), 3 Days (3D), and Weekly (W).
- These bands allow traders to see how the price interacts with different support levels, potentially bouncing between them as it moves across timeframes.
Dynamic Band Visibility:
- Bands from shorter timeframes are only displayed in relevant higher timeframes:
- 5m is shown only in timeframes ≤ 15m.
- 15m is shown only in timeframes ≤ 1h.
- 1h is shown only in timeframes ≤ 4h.
- 4h is shown only in timeframes ≤ D.
- D and 3D are shown only in timeframes ≤ W.
- W is always shown.
Customizable Moving Averages:
- The period of the moving averages used to calculate the support bands can be adjusted. Any changes made will be applied across all bands to maintain consistency.
Future Band Prediction:
- If the current timeframe lacks sufficient bars to calculate a moving average, the indicator shows a blue line on the bar where the band will appear. When a new band appears on the current bar, it is highlighted in purple, allowing traders to notice the first value of the new band.
- These new bands can act as magnets, attracting price action. Knowing when a new band will appear helps traders anticipate whether the price will be drawn to the upcoming band or potentially break through it.
Benefits:
- Enhanced Market Insight: By layering support bands from multiple timeframes, traders gain a comprehensive view of market dynamics and potential bounce areas.
- Improved Decision-Making: The ability to see upcoming support bands and how the price interacts with them aids in making more informed trading decisions.
- Customization and Flexibility: Adjustable moving average periods ensure that the indicator can be tailored to fit various trading strategies and market conditions.
The Multi Timeframe Bull Market Support Bands indicator is a versatile and insightful tool for traders aiming to leverage multi-timeframe analysis to enhance their trading strategies and better understand market behavior.
