DEnvelope [Better Bollinger Bands]*** ***
Bollinger Bands (BB) usually expand quickly after a volatility increase but contract more slowly as volatility declines. This extended time it takes for BB to contract after a volatility drop can make trading some instruments using BB alone difficult or less profitable.
In the October 1998 issue of "Futures" there is an article written by Dennis McNicholl called "Better Bollinger Bands", in which the author recommends improving BB by modifying:
- the center line formula &
- different equations for calculating the bands.
These bands, called "DEnvelope", follow price more closely and respond faster to changes in volatility with these modifications.
Fore more indicators, check out my "Master Index of indicators" (Also check my published charts page for new ones I haven't added to that list):
More scripts related to DEnvelope:
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- DEnvelope Bandwidth: pastebin.com
- DEnvelope %B : pastebin.com
Sample chart with above indicators: www.tradingview.com
Поиск скриптов по запросу "band"
Inverse BandsThis was the result of quite some time spent examining how much information could be gleamed by studying the interactions between Keltner Channels, STARC Bands and Bollinger Bands. I was surprised by the results.
First of all, there are four fills that are black. Set the transparency of those to 0 and you'll see this indicator the way that it's meant to be seen. Those fills belong to unused sections of the Bollinger Bands.
There are two clouds which represent STARC Bands and the Keltner Channel. There is some delay when they flip from bullish (green) to bearish (red), but they are indicative of the trend. The space between them is black and the narrower that space is, the greater volatility is. Because of this, we don't need the exterior Bollinger Bands.
The Bollinger Bands remain visible as the yellow interior clouds on the top cloud and the blue interior clouds on the bottom cloud. Often, the thicker the yellow or blue cloud is, the less severe a throwback from a given trend reversal will be. Often the thinner that yellow or blue cloud is, the more severe the trend reversal will be. If price is rising into a thin interior yellow cloud, the following dip will be substantial. If price action dips towards a thicker interior blue cloud, often the pump following that dump will be less enthusiastic.
We preserve the Keltner Channel and STARC bands as our cloud because the way that they interact with the three basis lines yields a lot of information.
The yellow Bollinger basis line tells us about trend strength. The closer the BB basis line is to the top of the top cloud or the bottom of the bottom cloud, the stronger the trend is. When it enters the cloud very close to the bottom of the bottom cloud, you know you're looking at a strong pump, and vice versa when it's close to the top of the top cloud.
The purple Keltner Channel basis line and orange STARC Band basis line can forecast short term trend changes one candlestick in advance by contacting any line in either cloud. The moment either basis line touches or crosses any boundary of the clouds, you know that the next candle will change directions. In an uptrend, a touch or cross means the next candle will have a lower high point. In a downtrend, a cross or touch means the next candle will have a higher high point. This is most useful in scalping.
It'd be pretty easy to slap some crossover alerts on to this and useful considering that they come a candle in advance. Feel free to further explore and develop this.
Resampling Reverse Engineering Bands [DW]This is an experimental study designed to reverse engineer price levels from centered oscillators at user defined sample rates.
This study aims to educate users on the process of oscillator reverse engineering, and to give users an alternative perspective on some of the most commonly used oscillators in the trading game.
Reverse engineering price levels from an oscillator is actually a rather simple, straightforward process.
Rather than plugging price values into a function to solve for oscillator values, we rearrange the function using some basic algebraic operations and plug in a specified oscillator value to solve for price values instead.
This process tells us what price value is needed in order for the oscillator to equal a certain value.
For example, if you wanted to know what price value would be considered “overbought” or “oversold” according to your oscillator, you can do that using this process.
In this study, the reverse engineering functions are used to calculate the price values of user defined high and low oscillator thresholds, and the price values for the oscillator center.
This allows you to visualize what prices will trigger thresholds as a sort of confidence interval, which is information that isn't inherently available when simply analyzing the oscillator directly.
This script is equipped with three reverse engineering functions to choose from for calculating the band values:
-> Reverse Relative Strength Index (RRSI)
-> Reverse Stochastic Oscillator (RStoch)
-> Reverse Commodity Channel Index (RCCI)
You can easily select the function you want to utilize from the "Band Calculation Type" dropdown tab.
These functions are specially designed to calculate at any sample rate (up to 1 bar per sample) utilizing the process of downsampling that I introduced in my Resampling Filter Pack.
The sample rate can be determined with any of these three methods:
-> BPS - Resamples based on the number of bars.
-> Interval - Resamples based on time in multiples of current charting timeframe.
-> PA - Resamples based on changes in price action by a specified size. The PA algorithm in this script is derived from my Range Filter algorithm.
The range for PA method can be sized in points, pips, ticks, % of price, ATR, average change, and absolute quantity.
Utilizing downsampled rates allows you to visualize the reverse engineered values of an oscillator calculated at larger sample scales.
This can be rather beneficial for trend analysis since lower sample rates completely remove certain levels of noise.
By default, the sample rate is set to 1 BPS, which is the same as bar-to-bar calculation. Feel free to experiment with the sample rate parameters and configure them how you like.
Custom bar colors are included as well. The color scheme is based on disparity between sources and the reverse engineered center level.
In addition, background highlights are included to indicate when price is outside the bands, thus indicating "overbought" and "oversold" conditions according to the thresholds you set.
I also included four external output variables for easy integration of signals with other scripts:
-> Trend Signals (Current Resolution Prices) - Outputs 1 for bullish and -1 for bearish based on disparity between current resolution source and the central level output.
-> Trend Signals (Resampled Prices) - Outputs 1 for bullish and -1 for bearish based on disparity between resampled source and the central level output.
-> Outside Band Signal (Current Resolution Prices) - Outputs 1 for overbought and -1 for oversold based on current resolution source being outside the bands. Returns 0 otherwise.
-> Outside Band Signal (Resampled Prices) - Outputs 1 for overbought and -1 for oversold based on resampled source being outside the bands. Returns 0 otherwise.
To use these signals with another script, simply select the corresponding external output you want to use from your script's source input dropdown tab.
Reverse engineering oscillators is a simple, yet powerful approach to incorporate into your momentum or trend analysis setup.
By incorporating projected price levels from oscillators into our analysis setups, we are able to gain valuable insights, make (potentially) smarter trading decisions, and visualize the oscillators we know and love in a totally different way.
I hope you all find this script useful and enjoyable!
Auto-Anchored MA with Deviation BandsAuto-Anchored MA with Deviation Bands
✨ Features
📈 Auto-Anchored MA: Calculates moving averages (EMA, SMA, EWMA, WMA, VWAP, TEMA) anchored to user-defined periods (Hour, Day, Week, etc.).📏 Deviation Bands: Plots upper/lower bands using Percentage or Standard Deviation modes for volatility analysis.⚙️ Customizable Timeframes: Choose anchor periods from Hour to Year for flexible trend analysis.🎨 Visuals: Displays MA and bands with gradient fills, customizable colors, and adjustable display bars.⏱️ Countdown Table: Shows bars since the last anchor for easy tracking.🛠️ Smoothing: Applies smoothing to bands for cleaner visuals.
🛠️ How to Use
Add to Chart: Apply the indicator on TradingView.
Configure Inputs:
Anchor Settings: Select anchor period (e.g., Day, Week).
MA Settings: Choose MA type (e.g., VWAP, TEMA).
Deviation Settings: Set deviation mode (Percentage/Std Dev) and multipliers.
Display Settings: Adjust bars to display, colors, and gradient fill.
Analyze: View MA, deviation bands, and countdown table on the chart.
Track Trends: Use bands as dynamic support/resistance and monitor anchor resets.
🎯 Why Use It?
Dynamic Analysis: Auto-anchors MA to key timeframes for adaptive trend tracking.
Volatility Insight: Deviation bands highlight potential breakouts or reversals.
Customizable: Tailor MA type, timeframe, and visuals to your trading style.
User-Friendly: Clear visuals and countdown table simplify analysis.
📝 Notes
Ensure sufficient bars for accurate MA and deviation calculations.
Gradient fill enhances readability but can be disabled for simplicity.
Best used with complementary indicators like RSI or Bollinger Bands for robust strategies.
Happy trading! 🚀📈
Combo 2/20 EMA & Bandpass Filter by TamarokDescription:
This strategy combines a 2/20 exponential moving average (EMA) crossover with a custom bandpass filter to generate buy and sell signals.
Use the Fast EMA and Slow EMA inputs to adjust trend sensitivity, and the Bandpass Filter Length, Delta, and Zones to fine-tune momentum turns.
Signals occur when both EMA and BPF agree in direction, with optional reversal and time filters.
How to use:
1. Add the script to your chart in TradingView.
2. Adjust the EMA and BP Filter parameters to match your asset’s volatility.
3. Enable ‘Reverse Signals’ to trade counter-trend, or use the time filter to limit sessions.
4. Set alerts on Long Alert and Short Alert for automated notifications.
Inspiration:
Based on HPotter’s original combo strategy (Stocks & Commodities Mar 2010).
Updated to Pine Script v6 with streamlined code and alerts.
WARNING:
For purpose educate only
[COG]Adaptive Volatility Bands# Adaptive Volatility Bands (AVB) Indicator Guide for Traders
## Special Acknowledgment 🙌
This script is inspired by and builds upon the foundational work of **DonovanWall**, a respected contributor to the trading community. His innovative approach to adaptive indicators has been instrumental in developing this advanced trading tool.
## What is the Adaptive Volatility Bands Indicator?
The Adaptive Volatility Bands (AVB) is a sophisticated technical analysis tool designed to help traders understand market dynamics by creating dynamic, responsive price channels that adapt to changing market conditions. Unlike traditional static indicators, this script uses advanced mathematical techniques to create flexible bands that adjust to market volatility in real-time.
## Key Features and Inputs
### 1. Price and Filtering Options
- **Price Source**: Determines the base price used for calculations (default is HLC3 - Average of High, Low, and Close)
- **Filter Poles**: Controls the smoothness of the indicator (1-9 poles)
- Lower values: More responsive, more noise
- Higher values: Smoother, but slower to react
### 2. Volatility and Band Settings
- **Sample Length**: Determines how many bars are used to calculate volatility (default 144)
- **Volatility Multiplier**: Adjusts the width of the main bands (default 1.414)
- **Outer Band Multiplier**: Controls the width of the outer bands (default 2.5)
- **Inner Band Ratio**: Positions the inner bands between the center and outer bands (default 0.25)
### 3. Advanced Processing Options
- **Lag Reduction Mode**: Helps reduce indicator delay
- **Fast Response Mode**: Makes the indicator more responsive to recent price changes
### 4. Signal and Visualization Options
- **Show Entry Signals**: Displays buy and sell signals
- **Signal Display Style**: Choose between labels or shapes
- **Range Filter**: Adds an additional filter for signal validation
## How the Indicator Works
The Adaptive Volatility Bands create a dynamic price channel with three key components:
1. **Center Line**: Represents the core trend direction
2. **Inner Bands**: Closer to the center line
3. **Outer Bands**: Wider bands that show broader price potential
### Color Dynamics
- The indicator uses a smart color gradient system
- Colors change based on price position within the bands
- Helps visualize bullish (green/blue) and bearish (red) market conditions
## Trading Strategies for Beginners
### Basic Entry Signals
- **Buy Signal**:
- Price touches the center line from below
- Candle is bullish (closes higher than it opens)
- Price is above the center line
- Trend is upward
- **Sell Signal**:
- Price touches the center line from above
- Candle is bearish (closes lower than it opens)
- Price is below the center line
- Trend is downward
### Risk Management Tips
1. Use the bands to identify:
- Potential trend changes
- Volatility levels
- Support and resistance areas
2. Combine with other indicators for confirmation
3. Always use stop-loss orders
4. Adjust parameters to match your trading style and asset
## When to Use This Indicator
Best suited for:
- Trending markets
- Swing trading
- Identifying potential entry and exit points
- Understanding market volatility
### Recommended Markets
- Stocks
- Forex
- Cryptocurrencies
- Futures
## Customization
The script offers extensive customization:
- Adjust smoothness
- Change band multipliers
- Modify color schemes
- Enable/disable features like lag reduction
## Important Considerations for Beginners
🚨 **Disclaimer**:
- No indicator guarantees profits
- Always practice with a demo account first
- Learn and understand the indicator before live trading
- Market conditions change, so continually adapt your strategy
## Getting Started
1. Add the script to your TradingView chart
2. Experiment with different settings
3. Backtest on historical data
4. Start with small positions
5. Continuously learn and improve
Happy Trading! 📈🔍
Bitcoin Logarithmic Regression BandsOverview
This indicator displays logarithmic regression bands for Bitcoin. Logarithmic regression is a statistical method used to model data where growth slows down over time. I initially created these bands in 2019 using a spreadsheet, and later coded them in TradingView in 2021. Over time, the bands proved effective at capturing Bitcoin's bull market peaks and bear market lows. In 2024, I decided to share this indicator because I believe these logarithmic regression bands offer the best fit for the Bitcoin chart.
How It Works
The logarithmic regression lines are fitted to the Bitcoin (BTCUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). The two lines in the upper and lower bands share the same 'a' factor, but I adjust the 'b' factor by 0.2 to more accurately capture the bull market peaks and bear market lows. The formula for logaritmic regression is 10^((a * ln) - b).
How to Use the Logarithmic Regression Bands
1. Lower Band (Support Band):
The two lines in the lower band create a potential support area for Bitcoin’s price. Historically, Bitcoin’s price has always found its lows within this band during past market cycles. When the price is within the lower band, it suggests that Bitcoin is undervalued and could be set for a rebound.
2. Upper Band (Resistance Band):
The two lines in the upper band create a potential resistance area for Bitcoin’s price. Bitcoin has consistently reached its highs in this band during previous market cycles. If the price is within the upper band, it indicates that Bitcoin is overvalued, and a potential price correction may be imminent.
Use Cases
- Price Bottoming:
Bitcoin tends to bottom out at the lower band before entering a prolonged bull market or a period of sideways movement.
- Price Topping:
In reverse, Bitcoin tends to top out at the upper band before entering a bear market phase.
- Profitable Strategy:
Buying at the lower band and selling at the upper band can be a profitable trading strategy, as these bands often indicate key price levels for Bitcoin’s market cycles.
Moving Average Band Width [CC]The Moving Average Band Width was created by Vitali Apirine (Stocks and Commodities Aug 2021 pg 25) and this is of course an extension based on my previous Moving Average Bands script. For those of you that aren't aware, the Band Width indicator is an indicator that tells you how close the upper and lower bands are to each other which is a great determination of how volatility is increasing or decreasing. This acts as both a trend indicator and a volatility indicator. I have included strong buy and sell signals in addition to normal ones so stronger signals are darker in color and normal ones are lighter. A great buy signal is when you are in an uptrend (so the line is green already) and the indicator line is moving up which means strong underlying volatility.
Let me know if there are any other indicators or scripts you would like to see me publish!
Mean Reversion w/ Bollinger BandsThis is a more advanced version of my original mean reversion script.
It employs the famous Bollinger Bands.
This robot will buy when price falls below the lower Bollinger Band, and sell when price moves above the upper Bollinger Band.
I've only tested it on the S&P 500, though you could try it out on other assets to see the backtest performance.
During the recent COVID-19 bear market drop, it produced several buy signals on the S&P which I followed, and made some nice gains so far.
I still think this would make a better investing strategy (buy undervalued / sell over-valued), rather than a trading strategy.
I use this robot for my long term portfolio.
Extended Recursive Bands - Maximum Efficiency With Extra OptionsIntroducing A New Calculation For Efficient Bands Calculation !
Here it is ! The Recursive Bands Indicator, an indicator specially created to be extremely efficient, i think you already know that calculation time is extra important in algorithmic trading, and this is the principal motivation for the creation of the proposed indicator. Originally described in my paper "Pierrefeu, Alex (2019): Recursive Bands - A New Indicator For Technical Analysis" , the indicator framework has been widely used in my previous uploaded indicators, however it would have been a shame to not upload it, however user experience being a major concern for me, i decided to add extra options, which explain the term "extended".
On The Indicator Calculation
You can skip this part if it doesn't interest you. The calculation of the indicator is based on recursion, but i want to explain the mathematical formula described in the paper.
I've seen some users trying to remake it from the calculations, however there was always something weird, and i understand, mathematical notations are always a bit weird, even myself don't always write them correctly/understand them, however this one is relatively simple to understand.
First lets explain each elements of the calculation :
α = smoothing constant, or 2/(length+1)
max/min = maximum and minimum function, max return the greatest input value while min return the lowest one, for example :
max(4,2) = 4 while min(4,2) = 2
the "||" notation mean taking the absolute value, for example : |-1| = abs(-1) = 1
The calculation after the max/min function is called the correction factor, and is the core of the indicator. The last two variables are just here to provide an initial value for upper and lower, basically when we start our calculations we will assign the value of the closing price for upper and lower.
The motivation behind using a smoothing constant in range of (0,1) was to tell the reader that the indicator is easily made adaptive, this is what i did on my adaptive trailing stop indicator by using the efficiency ratio as smoothing variable, the user can use 1/length instead of the provided calculation for alpha.
If you interested on the indicator main logic, it is actually really simple, by using upper = max(price,upper) and lower = min(price,lower) we would get the maximum/minimum price value at time t , therefore upper can only be greater or equal than its precedent value, while lower can only be lower or equal than its precedent value, in order to fix that we subtract/sum upper/lower with a value, this allow the upper band to be lower than its precedent value and lower to be greater than its precedent value, this is the role of the correction factor.
The Indicator
The indicator display one upper and one lower band, every common usages applied to bands indicators such as support/resistance, breakout, trailing stop...etc, can also be applied to this one. length control how reactive the bands are, higher values of length will make the bands cross the price less often.
In order to provide more flexibility for the user i added the option to use various methods for the calculation of the indicator, therefore the indicator can use the average true range, standard deviation, average high-low range, and one totally exclusive method specially designed for this indicator.
Classic Method
This option make the indicator use its classical calculation, this is the most efficient method of all.
Atr Method (atr)
This method use the average true range as correction factor, notice that lower values of length can still produce wide band.
Standard Deviation Method (stdev)
This method use a biased estimate of the standard deviation as correction factor.
The method produce smoother bands that converge more slowly toward the price in comparison with the classic correction factor.
Average High-Low Range Method (ahlr)
This method use the average of the high-low range as correction factor, extremely similar to the average true range.
Rising Falling Volatility (rfv) Method
A new method created for this indicator, this correction factor use the absolute prices changes when price value is greater/lower than any length past values of the price, this allow to have more boxy shaped bands, work best with greater values of length.
The bands can be in contact with this method, a possible fix in the future.
Conclusion
The recursive band indicator is one of my greatest indicators in my opinion (i would love to have yours), as you can see the idea behind it is extremely simple and allow for a super efficient band indicator, which was the original motivation behind it, in order to provide more fun for the users i also added more option for the correction factor, this allow the user to be creative and not get stuck with the original calculation.
Like the trend step indicator family we have almost ended our series on the recursive band framework, 1 more trailing stop will be added in the future, and then we'll have more "boring" stuff until i find something cool again, it shouldn't be long ;)
Thanks for reading !
Volume Adaptive BandsIntroduction
I have been asked by @Coppermine and @Verbena to make bands that use volume to provide adaptive results. My first approach was to use exponential averaging, in order to do so i needed to quantify volume movement using rescaling with the objective to make the bands go away from each others when there is low volume, this approach is efficient and can work on any time frame, however i decided at the end to use another method which rely on recursive weighting, cleaner but more parametric. Those bands aim to highlight great breakouts point to go with the trend.
The Indicator
length control the period of the moving averages used in the script, however low length's don't necessarily provide indications for shorter terms breakouts as shown here :
As i said the bands are close to each others when there is high volume and away when there is low volumes.
Low volume period, bands will avoid to cross price
High volume, bands will be close to generate signals.
Correction Factor
Higher time frames will lower the distance between each band, this is because volume is higher during higher time frames, remember that the indicator bands are close to each others when volume is high.
1h chart eurusd.
This is why i added a correction factor, this factor can help you control the distance between each bands, when the correction factor is greater than 1 the bands will be closer to each others, this is useful for low time frames where the average volume is lower. When the time frame is high, use values between 0 and 1 to increase distance between each bands.
Correction factor = 0.2
Conclusion
I presented a new adaptive band indicator that adapt to trading volume by using recursive weighting, volume can be replaced by other indicators but you can have results going nuts, at the end its about experimentation. I hope you will find an use to it, thanks to @Coppermine and @Verbena for the request :)
Thanks for reading !
Bollinger Bands Fibonacci ratiosThe Fibonacci Bollinger Bands indicator is based on the same principles as the standard Bollinger Bands indicator developed by John Bollinger. The Fibonacci Bollinger Bands indicator bases its upper and lower bands on volatility just like the Bollinger Bands indicator does, but instead of using standard deviation as the measure of volatility, a Wilders Smoothed ATR is used in its place.
The middle band is a moving average used to establish the intermediate-term trend. The 3 upper bands are constructed by using the Wilders Smoothed ATR and multiplying it by each of the Fibonacci factors (1.618, 2.618, and 4.236) and then adding the results to the middle band. The 3 lower bands are constructed in the same manner as the upper bands except their results are subtracted from the middle band.
UCS_Value BandsThis Indicator is yet another variation of KC. Inspired from Value Charts webinar. I have seen their videos on youtube. What appears to be a variation of KC.
They use 12 bands Showing the zone, and different MA for different timeframes.
You can get this indicator close to accuracy by changing the inputs (ATR) and (Deviations)
This also can be used with the triple ATR setup - Change the values to EMA to desired value. To obtain the First band to plot @ 1, Change the Band deviation to 0.25.
Also can be used as as the Acceleration Band. With Current Settings, the Third Band will plot the Acceleration band.
List of All my Indicators - www.tradingview.com
Lycka Till
Bollinger Bands SMA 20_2 StrategyMean reversion strategy using Bollinger Bands (20-period SMA with 2.0 standard deviation bands).
Trade Triggers:
🟢 BUY SIGNAL:
When: Price crosses above the lower Bollinger Band
Logic: Price has hit oversold territory and is bouncing back
Action: Places a long position with stop at the lower band
🔴 SELL SIGNAL:
When: Price crosses below the upper Bollinger Band
Logic: Price has hit overbought territory and is pulling back
Action: Places a short position with stop at the upper band
LULD Bands & Trading Halt Detector [Volume Vigilante]📖 LULD Bands & Trading Halt Detector
This advanced tool visualizes official Limit Up / Limit Down (LULD) price bands and detects regulatory trading halts and resumptions based on SEC and NASDAQ rules. It is engineered for high accuracy by anchoring all calculations to the 1-minute timeframe, ensuring reliable signals across any chart resolution.
📌 What Does This Script Do?
- Draws real-time LULD price band estimations and optional buffer (caution) zones directly on the chart.
- Detects trading halt resumptions by monitoring time gaps between candles and other regulatory criteria. (Note: Due to Pine Script limitations, halts cannot be detected in real-time, only resumptions after they occur.)
- Triggers real-time alerts for:
- Trading Resumptions (Limit Up & Limit Down)
- LULD Zone Entries (Caution Zone)
- Band Breaches (Limit Up and Limit Down)
- Plots historical halt resumption markers to analyse past events.
📐 How It Works:
- Implements official SEC/NASDAQ LULD rules for Tier 1 and Tier 2 securities.
- Applies special band adjustments for the final 25 minutes of trading (after 3:35 PM ET).
- Anchors all logic to the 1-minute timeframe for precise calculations, even on higher timeframe charts.
- Includes adjustable volume and volatility filters to eliminate false signals (ghost halts) on low-- liquidity assets, especially Tier 2 securities when TradingView fails to print candles.
⚙️ How to Use It:
1.) Apply the script to any asset or timeframe.
2.) Adjust Volume and Volatility Filters to reduce noise. (Recommended: 500,000+ volume, 10%+ volatility.)
3.) Enable or disable visual components like bands, buffer zones, and halt resumption labels.
4.) Configure alerts directly from the script settings panel.
5.) Apply alerts to individual assets via "Add Alert On..." or to entire watchlists using "Add Alert on the List."
🧩 What Makes This Script Unique?
- True 1-Minute Anchored Calculations: Ensures alerts and visuals match official trading halt criteria regardless of chart timeframe.
- Customisable Buffered Zones: Visualise proximity to regulatory price limits and avoid volatility traps.
- Combines halt resumption detection, limit up/down band visualisation, and real-time alerts into one clean, modular tool.
📚 Disclaimer:
This script is for educational purposes only and does not constitute financial advice. Use at your own discretion and consult a licensed financial advisor before making trading decisions based on it.
Official Resources:
- NASDAQ LULD Regulations (FAQ):
www.nasdaqtrader.com
Current Nasdaq Trading Halts:
www.nasdaqtrader.com
Standard Deviation-Based Fibonacci Band by zdmre This indicator is designed to better understand market dynamics by focusing on standard deviation and the Fibonacci sequence. This indicator includes the following components to assist investors in analyzing price movements:
Weighted Moving Average (WMA) : The indicator creates a central band by utilizing the weighted moving average of standard deviation. WMA provides a more current and accurate representation by giving greater weight to recent prices. This central band offers insights into the general trend of the market, helping to identify potential buying and selling opportunities.
Fibonacci Bands : The Fibonacci bands located above and below the central band illustrate potential support and resistance levels for prices. These bands enable investors to pinpoint areas where the price may exhibit indecisiveness. When prices move within these bands, it may be challenging for investors to discern the market's preferred direction.
Indecisiveness Representation : When prices fluctuate between the Fibonacci bands, they may reflect a state of indecisiveness. This condition is critical for identifying potential reversal points and trend changes. Investors can evaluate these periods of indecisiveness to develop suitable buying and selling strategies.
This indicator is designed to assist investors in better analyzing market trends and supporting their decision-making processes. The integration of standard deviation and the Fibonacci sequence offers a new perspective on understanding market movements.
#DYOR
Robust Bollinger Bands with Trend StrengthThe "Robust Bollinger Bands with Trend Strength" indicator is a technical analysis tool designed assess price volatility, identify potential trading opportunities, and gauge trend strength. It combines several robust statistical methods and percentile-based calculations to provide valuable information about price movements with Improved Resilience to Noise while mitigating the impact of outliers and non-normality in price data.
Here's a breakdown of how this indicator works and the information it provides:
Bollinger Bands Calculation: Similar to traditional Bollinger Bands, this indicator calculates the upper and lower bands that envelop the median (centerline) of the price data. These bands represent the potential upper and lower boundaries of price movements.
Robust Statistics: Instead of using standard deviation, this indicator employs robust statistical measures to calculate the bands (spread). Specifically, it uses the Interquartile Range (IQR), which is the range between the 25th percentile (low price) and the 75th percentile (high price). Robust statistics are less affected by extreme values (outliers) and data distributions that may not be perfectly normal. This makes the bands more resistant to unusual price spikes.
Median as Centerline: The indicator utilizes the median of the chosen price source (either HLC3 or VWMA) as the central reference point for the bands. The median is less affected by outliers than the mean (average), making it a robust choice. This can help identify the center of price action, which is useful for understanding whether prices are trending or ranging.
Trend Strength Assessment: The indicator goes beyond the standard Bollinger Bands by incorporating a measure of trend strength. It uses a robust rank-based correlation coefficient to assess the relationship between the price source and the bar index (time). This correlation coefficient, calculated over a specified length, helps determine whether a trend is strong, positive (uptrend), negative (down trend), or non-existent and weak. When the rank-based correlation coefficient shifts it indicates exhaustion of a prevailing trend. Trend Strength" indicator is designed to provide statistically valid information about trend strength while minimizing the impact of outliers and data distribution characteristics. The parameter choices, including a length of 14 and a correlation threshold of +/-0.7, considered to offer meaningful insights into market conditions and statistical validity (p-value ,0.05 statistically significant). The use of rank-based correlation is a robust alternative to traditional Pearson correlation, especially in the context of financial markets.
Trend Fill: Based on the robust rank-based correlation coefficient, the indicator fills the area between the upper and lower Bollinger Bands with different colors to visually represent the trend strength. For example, it may use green for an uptrend, red for a down trend, and a neutral color for a weak or ranging market. This visual representation can help traders quickly identify potential trend opportunities. In addition the middle line also informs about the overall trend direction of the median.
Ignition Band Angles are Bollinger Bands with numeric angleI developed Bollinger Bands that provide a numeric value indicating their strength. To achieve this, I used the degree of the angle of attack and color-coded the numbers. The top band displays the number in the upper corner of the chart, the bottom band in the bottom corner, and the Basis is in the left middle. These numbers quantify the slope of the bands, which can be difficult to discern on a chart because stretching out the x and y axis can flatten or exaggerate a slope. With my Bollinger Bands, you get a constant reading that provides an accurate measurement of the angle and strength of a trend. I hope this helps.
[EDU] RSI Momentum BandsRSI Momentum Bands serve a purpose to find best RSI momentum for entering a trade.
Indicator is built simply:
1st RSI MA is smoothed RSI by little period, 2nd RSI MA is smoothed RSI by a bigger period.
Then we calculate standard deviation from the 2nd MA and make upper and lower band.
The rules for trades are simple:
When RSI is above higher band - Buy ;
When RSI is below lower band - Sell .
The indicator is for educational purposes only to present trades a momentum bands concepts, widely used across professionals.
Hope you will find it helpful.
Take your profits!
- Tarasenko Fyodor
ATR BandsIt has happened to everybody. You enter the market, the position gets a stop loss, then later the market goes in the direction you originally planned. Worse yet - you enter a position, the market goes in your favor, gets near the target, and then it reverses and you get stopped.
We brazilians call this a "violinado", or getting violinated. It happens either because:
1. You put the stop loss too close, or the target too far
2. You entered in the right direction, but at a wrong time
While the second point cannot be programmly adressed, the first can. One popular way of setting a stop loss is by using the average of the true range, it even has a built-in indicator in TV. The problem with it is that you can still get violinated, since as the trend develops, the stop loss only goes up, never down. So if you enter at the wrong time, one slip can still take you out of the market.
Since I got sick of losing money using a conventional stop loss, I made these ATR bands. When you add this indicator to your graph, 6 lines are going to show up, 3 above the price, 3 below it. These lines are calculated from the ATR of the last 20 periods (can be configurated). The upper lines are the high of the last candle + the ATR * the multiplicator factor, the lower lines are the low - ATR * multiplicator factor. There are three multiplicator factors: 1.0, 1.618 and 2.0, and you change them to be whatever you want.
The logic behind it is that theses bands represents the region in which the market is more likely to stay. So if you enter the market at 50.00, you can't expect it to reach 500.00 in the next hour if the ATR is 5.00. And if you set the stop loss at 49.99, it is very likely that the market is going to stop you. By using the ATR bands, you can get a more reasonable price range, so you would set the stop loss at 45.00 and the take profit level at 60.00.
There are two types os ATR you can use: the regular, calculated with RMA, and another using a custom WMA, which puts greater emphasis on large amplitudes. By default, the average uses the past 20 true ranges. You can also choose to use either the closing price or the extremes of the candle as a basis.
Another thing I've added is the violation statistics, which shows the percentages of the times that a band was violated in the next 5 candles (can be configurated). With this, you can get a broader view on the probability of the bands actually being reached.
You may have notice that the bands are lagged by 1 period. I did this so that there is no way you can use future data. You can disable it or increase it, but I recommend just letting it be 1. These bands are the range in which the price is most likely to stay in, if you change the lag you are essentially breaking it's whole purpose.
Exponential Deviation Bands [ChuckBanger]This is Exponential Deviation Bands. It is a price band indicator based on exponential deviation rather than the more traditional standard deviation, as you find in the well-known Bollinger Bands calculation. As compared to standard deviation bands, exponential deviation bands apply more weight to recent data and generate fewer breakouts. There fore it is a much better tool to identifying trends.
One strategy on the daily can be
Buy next bar if closing price crosses below the lower bands
Sell if price is equal to the current value of the upper bands
Bollinger Bands Trend Model-BuschiEnglish
In general, Bollinger Bands are used as an indicator to visualize the "reversion to the mean". However, in this model, by using smaller variable values (default: 10 time intervals instead of 20, 1 standard deviation instead of 2), they are used as an trend following indicator. Two consecutive closes above the upper band form a buy signal (symbol 'B' above bar) which is reversed by two consecutive closes below the lower band (symbol 'S' under bar) and vice versa. The corresponding buying (green) and selling (red) zones are coloured between the bands.
Deutsch
Im Allgemeinen werden Bollinger-Bänder als ein Indikator verwendet, um die "Rückkehr zum Mittelwert" zu visualisieren. In diesem Modell werden sie durch kleine Variablen-Werte (Standardwert: 10 Zeitintervalle anstatt 20, 1 Standardabweichung anstatt 2) jedoch als Trendfolge-Indikator verwendet. Zwei aufeinanderfolgende Schlusskurse über dem oberen Band (Symbol 'B' über dem Balken) bilden ein Kaufsignal, das durch zwei aufeinanderfolgende Schlusskurse unter dem unteren Band (Symbol 'S' unter Balken) umgekehrt wird. Gleiches gilt umgekehrt. Die entsprechenden Kauf-Zonen (grün) und Verkauf-Zonen (rot) werden zwischen den Bändern eingefärbt.
Kaufman Adaptive BandsIntroduction
Bands are quite efficient in technical analysis, they can provide support and resistance levels, provide breakouts points, trailing stop loss/take profits positions and can show the current market volatility to the user. Most of the time bands are made from a central tendency estimator like a moving average plus/minus a volatility indicator. Therefore bands can be made out of pretty much everything thus allowing for any kind of flavors.
So i propose a band indicator made from a Kaufman adaptive moving average using an estimate of the standard deviation.
Construction
The Kaufman moving average is an exponential averager using the efficiency ratio as smoothing variable, length control the period of kama and in order to provide more smoothness a power parameter has been introduced, higher values of power will return smoother results.
The volatility indicator is made from a biased estimation of the standard deviation by using the square root of the mean of the square minus the square of the mean method, except that we use kama instead of a mean.
The bands are made by adding/subtracting this volatility indicator with kama.
How To Use
The ability of the indicator to adapt to the current market state is what makes him a great tool for avoiding major exposition during ranging market, therefore the indicator will have a greater motion during trending market, or more simply the bands will move during trending markets while staying "flat" during ranging ones. Therefore the indicator might be more suited to breakouts, even if some cases will return what where turning points, this is particularly true during ranging markets.
Of course the efficiency ratio is not an "unbiased" trend metric indicator, it can consider high volatility markets as trending markets. Its one of his downsides.
High values of power will create smoother bands.
When using a low power parameter use an higher mult. In general using a low power value will make the bands move more freely as well as making them closer to each others.
Conclusion
At least the indicator is really nice to the eyes when using high power values, its ability to adapt to the market is a great addition to other more classical bands indicators, i also introduced a volatility estimator based on kama, some might have used the following estimation : kama(abs(price - kama)) which would have created a slower result. A trailing stop might be made from it if i see request about such addition.
If you are curious here are some more images of the indicator performing on different markets. Thanks for reading !
