Bollinger Bands %B (ValueRay)One of the key features of this BB%B is its ability to highlight overbought and oversold conditions. This allows you to make informed decisions on when to enter and exit a trade, helping you maximize your profits and minimize your losses.
- Bollinger Bands %B with the ability to change to a different Time Frame.(Defaults to current Chart Time Frame).
- Ability To Turn On/Off Background Highlighting if BB %B is Above/Below 0 / 1 thresholds.
- Ability To Turn On/Off Background Highlighting when BB %B Crosses back above/unser 0/1 thresholds.
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My personal recommandation use: combine with CM_Ultimate RSI Multi Time Frame (ChrisMoody) and have solid oversold/overbought levels, when hes RSI and my BB %B are bot red/green
חפש סקריפטים עבור "band"
SPX Fair Value Bands WSHOSHOThis is a variation of the SPX Fair Value Bands indicator which uses WSHOSHO instead of WALCL.
WSHOSHO only includes the 'Securities Held Outright' portion of the Fed balance sheet. This effectively eliminates the portions related to BTFP (Bank Term Funding Program).
Weighted Deviation Bands [Loxx]What are Weighted Deviation Bands?
Variation of the Bollinger bands but it uses linear weighted average and weighted deviation via Mladen Rakic.
What is Weighted Deviation?
This weighted deviation is a sort of all linear weighted deviation. It uses linear weighting in all the steps calculated (which makes it different from the built in deviation in a case when linear weighted ma is used in the ma method). It is more responsive than the standard deviation
Included
Bar coloring
QQQ Fair Value BandsThis is similar to the SPX Fair Value Bands indicator, but for QQQ.
It is based on the Net Liquidity model:
Net Liquidity = FED - RRP - TGA
Bollinger Band BreakoutThis strategy buys when price crosses above an upper Bollinger Band and sells when the lower band is breached. What makes this strategy different than others:
Long only with filtering for only showing strong tickers
Filter out trades below a moving average on both the current timeframe and a longer period timeframe to keep you out of bear markets
Optional ability to set a tighter initial stop level to increase exposure and decrease downside risk on freshly opened trades while you wait for the lower Bollinger Band trailing stop to catch up
Take entries/exits on wicks/stops or wait for candle closes before entry
Select which dates to backtest
Customize Bollinger Band parameters including the ability to have different values for the upper and lower band standard deviation
MA Band Distance Monitor'MA Band Distance Monitor' indicator is a simple tool for traders who rely on moving averages to make trading decisions. This indicator plots two moving averages of your choice (you can select the type of the moving average), and fills the space between them, creating a "band".
The indicator also generates a table that displays the current price distance from both the fast and slow moving averages, as well as the average of the two. This allows you to quickly assess the strength of the trend and potential entry or exit points.
In addition, the table also shows the average price distance from one to another MA and also the current distance between them, allowing you to compare the current price action to the historical average. This information can help you identify potential trend reversals and assess the overall health of the market.
*** Slow length input must be greater than fast length input, otherwise indicator will produce faulty results
Bollinger Bands SignalsDescription:
This indicator works well in trendy markets on long runs and in mean-reverting markets, at almost any timeframe.
That said, higher timeframes are much preferred for their intrinsic ability to cut out noise. The example chart is in 3H TF.
Be mindful, the script shows somewhat erratic jigsaw-like behaviour during consolidation periods when the price
jumps up and down in indecision which way to go. Fortunately, there are scripts out there that detect such periods.
You can choose between 4 Moving Averages, Vidya being the default. Period, Deviation and Bands Width parameters
all of them affect the signal generation.
For the Pine Script coder this script is pretty obvious.
It uses a standard technical analysis indicator - Bollinger Bands - and appends it with a 'width' parameter and
a signal generation procedure.
The signal generation procedure is the heart of this script that keeps the script pumping signals.
The BB width is used as a filter.
You can use this procedure in your own scripts and it will continue generate signals according to your rules.
Momentum Deviation Bands [Loxx]Momentum Deviation Bands uses a variation of standard deviation. Instead of using price to calculate standard deviation, this uses momentum. This is another type of volatility that will be used in future indicators. This indicator serves more as an educational tool, but can also be used in trading.
You can read about the included moving averages here:
Included
Bar coloring
Bollinger Band strategy with split, limit, stopEntering a short position after breaking the upper Bollinger Band, entering a long position when entering after breaking the lower Bollinger Band
Provides templates for how to display position average price, stop loss, and profit price using the plot function on the chart, and how to buy splits
After entering the position, if the price crosses the mid-band line, the stop loss is adjusted to the mid-band line.
Modified Bollinger BandsThis script has been distributed for learning purposes.
A particular kind of price envelope is "Bollinger Bands" indicator. Upper and lower price range levels are determined by price envelopes. By default, Bollinger Bands are plotted in Tradingview as envelopes at a standard deviation level above and below the price's simple moving average (SMA). I attempted to modify the indicator in this version by adding several kinds of moving averages first. The key feature is that standard deviation should be modified. in Tradingview, SMA calculates the standard deviation. The allocated moving average should be used to calculate the std function when the base line is changed.
Band of Filtered RS by Mustafa ÖZVERBand of Filtered RS by Mustafa ÖZVER
This code shows a range (max-min values) price may get if we get strong movements. These values is based on RSI (Relative Strange Index). And also these are calculated using RSI, if we get trades to make rsi is equal to 25 (or rsi down limit) or 75 (rsi up limit) or any value you set, how much will price value get? This code calculate these and shows these to you on graph.
This price are between these band limits because we expect cross reaction to hard movements on price.
For scalping, we can use these values as
long signal when price under down limit,
short signal when price over up limit,
But only these values can not guarantee good results for trading. BE CAREFUL
Z Score BandThis is a band based on Z Score. What is Z Score? In layman's terms it's a method of finding outliers within a sequence of numbers. It's highly effective to quantify pump and dumps in the crypto market.
The middle line is a simple Exponential Moving Average, you can configure this with whatever period you prefer. It comes default with a period of 247 to which I find suitable for my style of trading. The upper and lower bound are determined by the standard deviation you choose in the settings, it comes with a default of 1.69 although I've heard people saying 2.5 is a better number to really pinpoint outliers.
Trading with this indicator is like trading with any band based indicator. The main difference is that this indicator's sole purpose when I wrote it is to help me find shorting positions in the futures market. On the contrary though, longs are also achievable although I rarely long the futures market.
If prices hit the upper bound and get rejected, it's probably because the move was an outlier, it doesn't happen often and when it does usually it reveals crypto's nature of buying spot and hedging short in the futures market. When prices stay above the upper bound, switch to a higher timeframe until we can see that it's still have some ways upwards.
What's true about using this as a shorting tool is also true with longs. However, it might not be as effective, I'd like to be proven wrong.
Adaptive VWAP Stdev BandsIntroduction
Heyo, here are some adaptive VWAP Standard Deviation Bands with nice colors.
I used Ehlers dominant cycle theories and ZLSMA smoothing to create this indicator.
You can choose between different algorithms to determine the dominant cycle and this will be used as reset period.
Everytime bar_index can be divided through the dominant cycle length and the result is zero VWAP resets if have chosen an adaptive mode in the settings.
The other reset event you can use is just a simple time-based event, e.g. reset every day.
Usage
I think people buy/sell when it reaches extreme zones.
Enjoy!
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Credits to:
@SandroTurriate - VWAP Stdev Bands
@blackcat1402 - Dominant Cycle Analysis
@DasanC - Dominant Cycle Analysis
@veryfid - ZLSMA
(Sry, too lazy for linking)
I took parts of their code. Ty guys for your work! Just awesome.
SPX Fair Value Bands V2An updated version of the SPX Fair Value Bands script from dharmatech and based on the net liquidity concept by MaxJAnderson .
Now with full customization of parameters through the settings (Dialog Box) and allowing the options to the use of
1) Standard Bands based on Offsets of the Fair Value
2) Bollinger Bands
3) Keltner Channels
to better capture buy/sell areas rather than relying on noisy unreliably (and unevenly) updated data from the Treasury/Fed.
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Net Liquidity's importance in the new post-COVID QE to QT regime as described MaxJAnderson
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" In past cycles, size of Fed's balance sheet changed a lot, while TGA and RRP changed relatively little. So size of balance sheet roughly equated Net Liquidity.
(The Treasury General Account) TGA and (Reverse Repo) RRP didn't matter. They were rounding errors by comparison.
But starting in 2020, relative changes in TGA and RRP have been THREE TIMES LARGER than the change in size of the Fed's balance sheet. As result, changes in TGA and RRP have taken over as the primary drivers Net Liquidity.
This is new, and changes the game significantly. Again - the size of the Fed's balance sheet doesn't matter.
What matters is the portion of it that's available to circulate in the economy (Net Liquidity).
And ever since 2020, the Treasury and Reverse Repo have become what controls that. Not the size of Fed's balance sheet.
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The idea that follows is simple,short when $SPX reaches extreme levels of overvaluation, and close out when SPX returns to being undervalued. Here's the formulas I currently use to determine fair value:
Fair Value = (Fed Bal Sheet - TGA - RRP)/1.1 - 1625
And here's the trading rules I currently follow:
Short when diff of $SPX - Fair Value > 350
Close when diff of $SPX - Fair Value < 150
When one of these rules is triggered upon market close on a given day, trades are entered at open of the following day "
HMA w/ SSE-Dynamic EWMA Volatility Bands [Loxx]This indicator is for educational purposes to lay the groundwork for future closed/open source indicators. Some of thee future indicators will employ parameter estimation methods described below, others will require complex solvers such as the Nelder-Mead algorithm on log likelihood estimations to derive optimal parameter values for omega, gamma, alpha, and beta for GARCH(1,1) MLE and other volatility metrics. For our purposes here, we estimate the rolling lambda (λ) value used to calculate EWMA by minimizing of the sum of the squared errors minus the long-run variance--a rolling window of the one year mean of squared log-returns. In practice, practitioners will use a λ equal to a standardized value put out by institutions such as JP Morgan. Even simpler than this, others use a ratio of (per - 1) / (per + 1) to derive λ where per is the lookback period for EWMA. Due to computation limits in Pine, we'll likely not see a true GARCH(1,1) MLE on Pine for quite some time, but future closed source indicators will contain some very interesting industry hacks to get close by employing modifications to EWMA. Enjoy!
Exponentially weighted volatility and its relationship to GARCH(1,1)
Exponentially weighted volatility--also called exponentially weighted moving average volatility (EWMA)--puts more weight on more recent observations. EWMA is calculated as follows:
σ*2 = λσ(n - 1)^2 + (1 − λ)u(n - 1)^2
The estimate, σn, of the volatility for day n (made at the end of day n − 1) is calculated from σn −1 (the estimate that was made at the end of day n − 2 of the volatility for day n − 1) and u^n−1 (the most recent daily percentage change).
The EWMA approach has the attractive feature that the data storage requirements are modest. At any given time, we need to remember only the current estimate of the variance rate and the most recent observation on the value of the market variable. When we get a new observation on the value of the market variable, we calculate a new daily percentage change to update our estimate of the variance rate. The old estimate of the variance rate and the old value of the market variable can then be discarded.
The EWMA approach is designed to track changes in the volatility. Suppose there is a big move in the market variable on day n − 1 so that u2n−1 is large. This causes our estimate of the current volatility to move upward. The value of λ governs how responsive the estimate of the daily volatility is to the most recent daily percentage change. A low value of λ leads to a great deal of weight being given to the u(n−1)^2 when σn is calculated. In this case, the estimates produced for the volatility on successive days are themselves highly volatile. A high value of λ (i.e., a value close to 1.0) produces estimates of the daily volatility that respond relatively slowly to new information provided by the daily percentage change.
The RiskMetrics database, which was originally created by JPMorgan and made publicly available in 1994, used the EWMA model with λ = 0.94 for updating daily volatility estimates. The company found that, across a range of different market variables, this value of λ gives forecasts of the variance rate that come closest to the realized variance rate. In 2006, RiskMetrics switched to using a long memory model. This is a model where the weights assigned to the u(n -i)^2 as i increases decline less fast than in EWMA.
GARCH(1,1) Model
The EWMA model is a particular case of GARCH(1,1) where γ = 0, α = 1 − λ, and β = λ. The “(1,1)” in GARCH(1,1) indicates that σ^2 is based on the most recent observation of u^2 and the most recent estimate of the variance rate. The more general GARCH(p, q) model calculates σ^2 from the most recent p observations on u2 and the most recent q estimates of the variance rate.7 GARCH(1,1) is by far the most popular of the GARCH models. Setting ω = γVL, the GARCH(1,1) model can also be written:
σ(n)^2 = ω + αu(n-1)^2 + βσ(n-1)^2
What this indicator does
Calculate log returns log(close/close(1))
Calculates Lambda (λ) dynamically by minimizing the sum of squared errors. I've restricted this to the daily timeframe so as to not bloat the code with additional logic required to derive an annualized EWMA historical volatility metric.
After the Lambda is derived, EWMA is calculated one last time and the result is the daily volatility
This daily volatility is multiplied by the source and the multiplier +/- the HMA to create the volatility bands
Finally, daily volatility is multiplied by the square-root of days per year to derive annualized volatility. Years are trading days for the asset, for most everything but crypto, its 252, for crypto is 365.
Adaptive Rebound Line Bands (ARL Bands)These bands consist of 4 ARLs (See: Adaptive Rebound Line ('ARL'/AR Line)) that help accurately spot price rebounds.
It is excellent for 15 minute scalping and price-action trading.
See notes in the picture above for more details.
Note: "Top Deviation" is the deviation of the top 'ARL', "High Deviation" is for the high 'ARL', etc.
Z Bollinger BandsThis version of Bollinger Bands measures the average volatility. By taking the 75th percentile of the average absolute value of the difference between the Source and the Mean divided by the Standard Deviation and using that as our multiplier for our Bollinger bands we can have a statistically safe trading zone.
You notice that its dynamic, this is because it take into account the real volatility levels of a window and uses that to determine an appropriate multiplier. As always I hope you enjoy this release.
Polynomial Regression Bands w/ Extrapolation of Price [Loxx]Polynomial Regression Bands w/ Extrapolation of Price is a moving average built on Polynomial Regression. This indicator paints both a non-repainting moving average and also a projection forecast based on the Polynomial Regression. I've included 33 source types and 38 moving average types to smooth the price input before it's run through the Polynomial Regression algorithm. This indicator only paints X many bars back so as to increase on screen calculation speed. Make sure to read the tooltips to answer any questions you have.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression .
Related indicators
Polynomial-Regression-Fitted Oscillator
Polynomial-Regression-Fitted RSI
PA-Adaptive Polynomial Regression Fitted Moving Average
Poly Cycle
Fourier Extrapolator of Price w/ Projection Forecast
Bitcoin Support BandsSMA and EMA support/resistance bands for Bitcoin. Based on 4 week multiples; 1 month, 3 month, 6 month, 1 year, 2 year, 4 year.
Bollinger Bands SqueezeBollinger Bands set to only display when a squeeze is taking place. Squeeze will be highlighted.
SMA EMA Bands [CraftyChaos]This indicator creates bands for SMA and EMA averages and adds an average of the two with the idea that price often touches one of them at support and resistance levels. Saves indicator space by combining all into one indicator
ALMA stdev band with fibsArnaud Legoux Moving Average with standard deviation band and standard deviation Fibonacci levels.
Standard deviation band is alma + stdev and alma - stdev.
Fibonacci levels are alma + stdev * fib ratio and alma - stdev * fib ratio (0.382 / 0.5 / 0.618 / 1.618 / 2.618).
Used like a moving average, but also shows probable price range based on past volatility, and helps to recognize support/resistance levels, trends and trend momentum based on the Fibonacci levels.
EMA 50 HIGH LOW BANDHi
This indicator displays a band of EMA 50 having high and low of the same ema.
This script works well on 5 min chart or lower time frames in intraday.
When any price is above this band, you may consider a buy position and whenever any price is below this band, you may consider a sell position.
You may also take help of EMA 200, which is shown in red color. Whenever price is above EMA200, it is considered bullish and when ever it is below EMA 200, it is considered bearish.
This will remove a lot of noise from your chart.
I hope it helps.
Thanks
