Adaptive Squeeze Momentum +Adaptive Squeeze Momentum+ (Auto-Timeframe Version)
Overview
Adaptive Squeeze Momentum+ is an enhanced volatility and momentum indicator designed to identify compression and expansion phases in price action. It is inspired by the classic Squeeze Momentum Indicator by LazyBear but introduces automatic parameter adaptation to any timeframe, making it simpler to use across different markets without manual configuration.
Concepts and Methodology
The script combines Bollinger Bands (BB) and Keltner Channels (KC) to detect periods when volatility contracts (squeeze) or expands (release).
A squeeze occurs when BB are inside KC, suggesting low volatility and potential breakout scenarios.
A squeeze release is detected when BB expand outside KC.
Momentum is derived using a linear regression applied to the difference between price and a midrange reference level.
Original Improvements
Compared to the original Squeeze Momentum Indicator, this version offers several enhancements:
Automatic Adaptation: BB and KC lengths and multipliers are dynamically adjusted based on the chart’s timeframe (from 1 minute up to 1 month), removing the need for manual tuning.
Simplified Visualization: A clean, minimalist histogram and clear squeeze state cross markers allow for faster interpretation.
Flexible Application: Designed to work consistently on intraday, daily, and higher timeframes across crypto, forex, stocks, and indices.
Features
Dynamic Squeeze Detection:
Gray Cross: Neutral (no squeeze detected)
Blue Cross: Active squeeze
Yellow Cross: Squeeze released
Momentum Histogram:
Positive/negative momentum shown with slope-based coloring.
Timeframe-Aware Parameters:
Automatically sets optimal BB/KC configurations.
Usage
Watch for blue crosses indicating an active squeeze phase that may precede a directional move.
Use the histogram color and slope to gauge momentum strength and direction.
Combine squeeze release signals with momentum confirmation for potential entries or exits.
Credits and Licensing
This script was inspired by LazyBear’s OLD “Squeeze Momentum Indicator” (). The implementation here significantly expands upon the original by introducing auto-adaptive parameters, restructured logic, and a new visualization approach. Published under the Mozilla Public License 2.0.
Disclaimer
This indicator is for educational purposes only and does not constitute financial advice. Use at your own risk.
Search in scripts for "KELTNER"
Auto-Length Adaptive ChannelsIntroduction
The key innovation of the ALAC is the implementation of dynamic length identification, which allows the indicator to adjust to the "market beat" or dominant cycle in real-time.
The Auto-Length Adaptive Channels (ALAC) is a flexible technical analysis tool that combines the benefits of five different approaches to market band and price deviation calculations.
Traders often tend to overthink of what length their indicators should use, and this is the main idea behind this script. It automatically calculates length based on pivot points, averaging the distance that is in between of current market highs and lows.
This approach is very helpful to identify market deviations, because deviations are always calculated and compared to previous market behavior.
How it works
The indicator uses a Detrended Rhythm Oscillator (DRO) to identify the dominant cycle in the market. This length information is then used to calculate different market bands and price deviations. The ALAC combines five different methodologies to compute these bands:
1 - Bollinger Bands
2 - Keltner Channels
3 - Envelope
4 - Average True Range Channels
5 - Donchian Channels
By averaging these calculations, the ALAC produces an overall market band that generalizes the approaches of these five methods into a single, adaptive channel.
How to Use
When the price is at the upper band, this might suggest that the asset is overbought and may be due for a price correction. Conversely, when the price is at the lower band, the asset may be oversold and due for a price increase.
The space between the bands represents the market's volatility. Wider bands indicate higher volatility, while narrower bands suggest lower volatility.
Indicator Settings
The settings of the ALAC allow for customization to suit different trading strategies:
Use Autolength?: This allows the indicator to automatically adjust the length of the dominant cycle.
Usual Length: If "Use Autolength?" is disabled, this setting allows the user to manually specify the length of the cycle.
Moving Average Type: This selects the type of moving average to be used in the calculations. Options include SMA, EMA, ALMA, DEMA, JMA, KAMA, SMMA, TMA, TSF, VMA, VAMA, VWMA, WMA, and ZLEMA.
Channel Multiplier: This adjusts the distance between the bands.
Channel Multiplier Step: This changes the step size of the channel multiplier. Each next market band will be multiplied by a previous one. You can potentially use values below 1, which will plot bands inside the first, main channel.
Use DPO instead of source data?: This setting uses the DPO for calculations instead of the source data. Basically, this is how you can add or eliminate trend from calculation of an average leg-up / leg-down move.
Fast: This adjusts the fast length of the DPO.
Slow: This adjusts the slow length of the DPO.
Zig-zag Period: This adjusts the period of the zig-zag pattern used in the DPO.
(!) For more information about DPO visit official TradingView description here: link
Also, I want to say thanks to @StockMarketCycles for initial idea of Detrended Rhythm Oscillator (DRO) that I use in this script.
The Adaptive Average Channel is a powerful and versatile indicator that combines the strengths of multiple technical analysis methods.
In summary, with the ALAC, you can:
1 - Dynamically adapt to any asset and price action with automatic calculation of dominant cycle lengths.
2 - Identify potential overbought and oversold conditions with the adaptive market bands.
3 - Customize your analysis with various settings, including moving average type and channel multiplier.
4 - Enhance your trading strategy by using the indicator in conjunction with other forms of analysis.
Oliver Velez IndicatorOliver Velez is a well-known trader and educator who has developed multiple trading strategies. One of them is the 20-200sma strategy, which is a basic moving average crossover strategy. The strategy involves using two simple moving averages (SMAs) - a short-term SMA with a period of 20 and a long-term SMA with a period of 200 - on a 2-minute timeframe chart.
When the short-term SMA crosses above the long-term SMA, it signals a potential bullish trend and traders may look for opportunities to enter a long position. Conversely, when the short-term SMA crosses below the long-term SMA, it signals a potential bearish trend and traders may look for opportunities to enter a short position.
Traders using this strategy may also look for additional confirmations, such as price action signals or other technical indicators, before entering or exiting a trade. It is important to note that no trading strategy can guarantee profits, and traders should always use risk management techniques to limit potential losses.
This script is an implementation of the 2 SMA's (can also choose other types of MA's), with Elephant Bar Indicator (EBI) and the Tail Bars Indicator in TradingView.
The Elephant Bar Indicator is a technical indicator used in trading to identify potential trend reversals in the market. It is named after the large size of the bullish or bearish candlestick that it represents. The Tail Bars Indicator is a pattern recognition technique that identifies candlestick patterns with long tails or wicks.
The script starts by defining the input parameters for both indicators. For the Elephant Bar Indicator, the user inputs the lookback period and the size multiplier. For the Tail Bars Indicator, the user inputs the tail ratio and opposite wick ratio.
Next, the script calculates the moving averages of the closing price over the defined short and long periods using the Moving Average function. The script then calculates the average candle size and volume over the lookback period.
The script then identifies the Elephant Bars and Tail Bars using the input parameters and additional conditions. For Elephant Bars, the script identifies bullish and bearish bars that meet certain criteria, such as a size greater than the average candle size and volume greater than the average volume.
For Tail Bars, the script identifies bullish and bearish bars that have long tails or wicks and meet certain criteria such as opposite wick size less than or equal to the tail size multiplied by the input opposite wick ratio.
Finally, the script plots the Elephant Bar and Tail Bar signals on the chart using different colors and shapes. The script also plots the moving averages and Keltner Channels to help traders identify potential trend reversals.
It is still under development, so please, if someone has ideas to add, more than welcome
FATL, SATL, RFTL, & RSTL Digital Signal Filter Smoother [Loxx]FATL, SATL, RFTL, & RSTL Digital Signal Filter (DSP) Smoother is is a baseline indicator with DSP processed source inputs
What are digital indicators: distinctions from standard tools, types of filters.
To date, dozens of technical analysis indicators have been developed: trend instruments, oscillators, etc. Most of them use the method of averaging historical data, which is considered crude. But there is another group of tools - digital indicators developed on the basis of mathematical methods of spectral analysis. Their formula allows the trader to filter price noise accurately and exclude occasional surges, making the forecast more effective in comparison with conventional indicators. In this review, you will learn about their distinctions, advantages, types of digital indicators and examples of strategies based on them.
Two non-standard strategies based on digital indicators
Basic technical analysis indicators built into most platforms are based on mathematical formulas. These formulas are a reflection of market behavior in past periods. In other words, these indicators are built based on patterns that were discovered as a result of statistical analysis, which allows one to predict further trend movement to some extent. But there is also a group of indicators called digital indicators. They are developed using mathematical analysis and are an algorithmic spectral system called ATCF (Adaptive Trend & Cycles Following). In this article, I will tell you more about the components of this system, describe the differences between digital and regular indicators, and give examples of 2 strategies with indicator templates.
ATCF - Market Spectrum Analysis Method
There is a theory according to which the market is chaotic and unpredictable, i.e. it cannot be accurately analyzed. After all, no one can tell how traders will react to certain news, or whether some large investor will want to play against the market like George Soros did with the Bank of England. But there is another theory: many general market trends are logical, and have a rationale, causes and effects. The economy is undulating, which means it can be described by mathematical methods.
Digital indicators are defined as a group of algorithms for assessing the market situation, which are based exclusively on mathematical methods. They differ from standard indicators by the form of analysis display. They display certain values: price, smoothed price, volumes. Many standard indicators are built on the basis of filtering the minute significant price fluctuations with the help of moving averages and their variations. But we can hardly call the MA a good filter, because digital indicators that use spectral filters make it possible to do a more accurate calculation.
Simply put, digital indicators are technical analysis tools in which spectral filters are used to filter out price noise instead of moving averages.
The display of traditional indicators is lines, areas, and channels. Digital indicators can be displayed both in the form of lines and in digital form (a set of numbers in columns, any data in a text field, etc.). The digital display of the data is more like an additional source of statistics; for trading, a standard visual linear chart view is used.
All digital models belong to the category of spectral analysis of the market situation. In conventional technical indicators, price indications are averaged over a fixed period of time, which gives a rather rough result. The use of spectral analysis allows us to increase trading efficiency due to the fact that digital indicators use a statistical data set of past periods, which is converted into a “frequency” of the market (period of fluctuations).
Fourier theory provides the following spectral ranging of the trend duration:
low frequency range (0-4) - a reflection of a long trend of 2 months or more
medium frequency range (5-40) - the trend lasts 10-60 days, thus it is referred to as a correction
high frequency range (41-130) - price noise that lasts for several days
The ATCF algorithm is built on the basis of spectral analysis and includes a set of indicators created using digital filters. Its consists of indicators and filters:
FATL: Built on the basis of a low-frequency digital trend filter
SATL: Built on the basis of a low-frequency digital trend filter of a different order
RFTL: High frequency trend line
RSTL: Low frequency trend line
Inclucded:
4 DSP filters
Bar coloring
Keltner channels with variety ranges and smoothing functions
Bollinger bands
40 Smoothing filters
33 souce types
Variable channels
LNL Pullback ArrowsBuying the dip has never been easier! LNL Pullback Arrows are here to pinpoint the best possible entries for the trend following setups. With the Pullback Arrows, trader can pick his own approach and risk level thanks to four different types of arrows. The goal of these arrows is to force the traders to scale in & out of trades which is in my opinion crucial when it comes to trend following strategies. These arrows were designed primarily for the daily & weekly time frame (swing trading).
Four Types of Pullback Arrows:
1. Aggro Arrows - Ideal for aggresive approach during parabolic trends. Sometimes trends are so strong that the price barely revisits the daily 8 EMA. This is where the aggro arrows can perfectly pinpoint the aggresive high risk entries. Ideal for halfsize or 1/4 size of the full position. Aiming for quick 1-2 day moves targeting the recent high/low. These arrows could be also named as scalping arrows for the swing traders. A quick In & Out.
2. HalfSize Arrows - Medium risk approach. First arrows to scale in. HalfSize arrows are the first sign that the pullback might be ending, yet there is still some space left for an even deeper pullback. That is the reason why they are called half-size. Ideally taken with half-sized position. When trading the HalfSize Arrows, It is better to have some "spare ammo in the gun" ready to use.
3. FullSize Arrows - Regular risk approach. These arrows represent a zone where the core of the posititon should be taken. The point of validity for the trend is not that far away, meaning the risk can be kept tight. Ideal for scailing the other halfs or quarters of the full position. Also great for more conservative traders or environments with higher volatility.
4. Rare Arrows - Offer the best risk to reward entries during the trend. Rare Arrows should be the "last kick" of the retracement, therefore stops can be positioned really tight. They either trigger the stop immidiately or they provide another juicy leg up or down in the direction of the trend. However, they really do appear rarely.
Simple EMA Cloud:
A simple cloud based on 21 and 55 exponential moving averages. This default length creates a pullback zone that is wide enough for the conservative traders but also give the opportunities to more aggresive traders. Alternatives such as 8 & 21, or 21 & 34 are forming the zone that is too aggresive and usually too thin. Of course, cloud can be fully adjusted or turned off completely. The only role of the cloud is to gauge the trend.
Tips & Tricks:
1.Importance of the Scailing
- As already stated, scailing is crucial to this since there is no way of knowing the exact level at which the price magically bounce every time. It is hard to tell where and which EMA will be respected. How can we know it will be 21 EMA every time? or 34 EMA or 10 EMA or 100 SMA or 50 DMA ... Single MA does not make a trend. This is the reason why scailing is so important. Scailing can make a difference.
2. Nothing is Perfect
- Same as any other study, nothing works 100% perfectly. Sometimes the setup will go right against you and sometimes the price will fade away sideways and breaks off the structure of the trend. This is not a magic certainty tool. This is just another probability tool.
3. Point of Validity & Other Studies
- Even though the pullback arrows can be a stand-alone strategy. It is important to use other indicators that visualize the actual trend. Whether its EMA Cloud or EMAs or DMI Bars or Keltner Channels, there should be something that validates the trend, something that tells the trend is over. (Pullback Arrows are not showing the actual stops!).
Hope it helps.
PercentX Trend Follower [Trendoscope]"Trendoscope" was born from our trading journey, where we first delved into the world of trend-following methods. Over time, we discovered the captivating allure of pattern analysis and the exciting challenges it presented, drawing us into exploring new horizons. However, our dedication to trend-following methodologies remains steadfast and continues to be an integral part of our core philosophy.
Here we are, introducing another effective trend-following methodology, employing straightforward yet powerful techniques.
🎲 Concepts
Introducing the innovative PercentX Oscillator , a representation of Bollinger PercentB and Keltner Percent K. This powerful tool offers users the flexibility to customize their PercentK oscillator, including options for the type of moving average and length.
The Oscillator Range is derived dynamically, utilizing two lengths - inner and outer. The inner length initiates the calculation of the oscillator's highest and lowest range, while the outer length is used for further calculations, involving either a moving average or the opposite side of the highest/lowest range, to obtain the oscillator ranges.
Next, the Oscillator Boundaries are derived by applying another round of high/low or moving average calculations on the oscillator range values.
Breakouts occur when the close price crosses above the upper boundary or below the lower boundary, signaling potential trading opportunities.
🎲 How to trade a breakout?
To reduce false signals, we employ a simple yet effective approach. Instead of executing market trades, we use stop orders on both sides at a certain distance from the current close price.
In case of an upper side breakout, a long stop order is placed at 1XATR above the close, and a short stop order is placed at 2XATR below the close. Conversely, for a lower side breakout, a short stop order is placed at 1XATR below the close, and a long stop order is placed at 2XATR above the ATR. As a trend following method, our first inclination is to trade on the side of breakout and not to find the reversals. Hence, higher multiplier is used for the direction opposite to the breakout.
The script provides users with the option to specify ATR multipliers for both sides.
Once a trade is initiated, the opposite side of the trade is converted into a stop-loss order. In the event of a breakout, the script will either place new long and short stop orders (if no existing trade is present) or update the stop-loss orders if a trade is currently running.
As a trend-following strategy, this script does not rely on specific targets or target levels. The objective is to run the trade as long as possible to generate profits. The trade is only stopped when the stop-loss is triggered, which is updated with every breakout to secure potential gains and minimize risks.
🎲 Default trade parameters
Script uses 10% equity per trade and up to 4 pyramid orders. Hence, the maximum invested amount at a time is 40% of the equity. Due to this, the comparison between buy and hold does not show a clear picture for the trade.
Feel free to explore and optimize the parameters further for your favorite symbols.
🎲 Visual representation
The blue line represents the PercentX Oscillator, orange and lime colored lines represent oscillator ranges. And red/green lines represent oscillator boundaries. Oscillator spikes upon breakout are highlighted with color fills.
Channel Based Zigzag [HeWhoMustNotBeNamed]🎲 Concept
Zigzag is built based on the price and number of offset bars. But, in this experiment, we build zigzag based on different bands such as Bollinger Band, Keltner Channel and Donchian Channel. The process is simple:
🎯 Derive bands based on input parameters
🎯 High of a bar is considered as pivot high only if the high price is above or equal to upper band.
🎯 Similarly low of a bar is considered as pivot low only if low price is below or equal to lower band.
🎯 Adding the pivot high/low follows same logic as that of regular zigzag where pivot high is always followed by pivot low and vice versa.
🎯 If the new pivot added is of same direction as that of last pivot, then both pivots are compared with each other and only the extreme one is kept. (Highest in case of pivot high and lowest in case of pivot low)
🎯 If a bar has both pivot high and pivot low - pivot with same direction as previous pivot is added to the list first before adding the pivot with opposite direction.
🎲 Use Cases
Can be used for pattern recognition algorithms instead of standard zigzag. This will help derive patterns which are relative to bands and channels.
Example: John Bollinger explains how to manually scan double tap using Bollinger Bands in this video: www.youtube.com This modified zigzag base can be used to achieve the same using algorithmic means.
🎲 Settings
Few simple configurations which will let you select the band properties. Notice that there is no zigzag length here. All the calculations depend on the bands.
With bands display, indicator looks something like this
Note that pivots do not always represent highest/lowest prices. They represent highest/lowest price relative to bands.
As mentioned many times, application of zigzag is not for buying at lower price and selling at higher price. It is mainly used for pattern recognition either manually or via algorithms. Lets build new Harmonic, Chart patterns, Trend Lines using the new zigzag?
One-Sided Gaussian Filter w/ Channels [Loxx]One-Sided Gaussian Filter w/ Channels is a Gaussian Moving Average that is calculated using a Fibonacci weighting function. Keltner channels have been added to show zones of exhaustion. A better name would be "Half Gaussian bell weighted" or "Half normal distribution weighted" indicator, since the weights for calculation of the average (similar to linear weighted average) are taken from a normal distribution curve like function--but only the half of the curve is used for calculation.
Information of the Gaussian distribution can be found here : en.wikipedia.org and once you take a look at the standard normal distribution curve, it will be much clearer what is exactly done in this indicator.
After the Gaussian Filter is applied to the source input, an Ehlers' 2-Pole Super Smoother is applied to reduce noise without significant lag.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Dual Mean Reversion Channel (adjusted lower band)This is a public and open-source lighter version compared to the "Overextended Price Channel" which is provided complimentaty to the Trend Insight System.
Introduction :
Channels are very useful tools to assess overextended price, volatility and upcoming retracement or impulsive moves (such as Bollinger Band squeezes). It is an indispensable addition to any trader using Mean Reversion theory for a scalp-trade or swing-trade.
This script contains :
- 2 channels Keltner-style, using the True Range for volatility
- customizable volatility (channel width) and smoothing period
- a standard selection of moving average ; SMA, EMA, VWMA
- an embedded readjustment of the lower bands to avoid the drop on a logarithmic scale (see explanation below)
Why another channel indicator ?
I have found most conventional channels to be either not based on "proper" volatility (e.g. standard deviation of price action for Bollinger Band), or the bottom channel to be ill adapted to the logarithmic scale and plunges to 0 on some high volatility periods, messing with readability on logarithmic auto-scaled chart.
Also, I find the channels to be most useful when superimposed with another one of longer length; especially a pair of channels with a 50 and 200 period moving average respectively. Mean Reversion traders that mostly trade the 50 and 200 SMA/EMA know what I am talking about as having a channel helps to have a better visual for a proper of entry and exit point.
Disclaimer :
This indicator was originally intended to be used along with the Trend Insight System to improve performance, and the default configuration mostly backtested on BTCUSD.
Please use with caution, proper risk management and along with your favorite oscillator, candlestick reading and signals system.
Some explanation :
Based on Mean Reversion paradigm, everything has a tendency to revert back to the mean :
- when the price enters the upper channel, it is supposed to be (or start getting) overbought as the market is getting overheated, thus prone to correction,
- on the other hand, when the price enters the lower channel, it is supposed to be (or getting) oversold and the market looks favorable for a buy-in.
Depending on the trading style used, a trader will usually either wait until the price leaves the channel towards the mean before taking action (conservative style) or you will set limit orders inside the channel as you expect a reversion to the mean (more agressive/risky style).
With two channels, more complex (and maybe precise) rules can be built to optimize one's trading strategy.
Important notes :
In the end, sticking with 50/200 length and a single setting on volatility might be wiser, be wary of overoptimization which is risky at best and counter productive at worst (according to legendary traders such as Mark Douglas). Even if, needless to say, the volatility needs to be adjusted between a nascent and volatile market (such as crypto) compared to standard call markets that are much less volatile.
End notes :
It will always be considered a work in progress to help bring out the best of trading with channels, any comment and suggestion are welcomed.
MA Streak Change ChannelChange Channel is like KC unless it uses percentage changes in price to set channel distance. Midline is zero-lag smoothed ROC with dynamic period based on MA Streak indicator, if MA Streak shows an ongoing trend, midline going strong and break out the channel.
Consider using ▲ green areas as a signal to buy and ▼ red areas as a sell signal. It works best in a flat market. Use in combination with other indicators.
Relative Channel BandwidthThis indicator uses different volatility channels - Bollinger Band, Donchian Channel and Keltner Channel width to measure volatility.
Indicator plots channel bandwidth percentage with respect to close price.
This is not same as Bollinger Percent B - which is measure of where price is with respect to band. Instead this indicator is similar to ATR Percent indicator published here:
Plotting is color coded to indicate volatility zone:
Red : Extreme volatility
Orange : High volatility
Lime : Low volatility
Green : Extreme low volatility
These levels are again derived by long period bollinger bands
Adoptive Supertrend - BandsAnother adoption of supertrend. This time based on different channels - Bollinger Band, Keltner Channel, Donchian Channel and Pivot point based Donchian channel.
When price hits top of bands, it is considered as start or continuation of uptrend. When price hits bottom of the band it is considered as start or continuation of downtrend. Hence, supertrend is drawn based on these calculations. Use ATR Periods and ATR Multiplier to create stops certain ATR away from band's top and bottom.
Other supertrend adoptions published are here:
Pivot point based donchian channel is published here:
Square Root Moving AverageAbstract
This script computes moving averages which the weighting of the recent quarter takes up about a half weight.
This script also provides their upper bands and lower bands.
You can apply moving average or band strategies with this script.
Introduction
Moving average is a popular indicator which can eliminate market noise and observe trend.
There are several moving average related strategies used by many traders.
The first one is trade when the price is far from moving average.
To measure if the price is far from moving average, traders may need a lower band and an upper band.
Bollinger bands use standard derivation and Keltner channels use average true range.
In up trend, moving average and lower band can be support.
In ranging market, lower band can be support and upper band can be resistance.
In down trend, moving average and upper band can be resistance.
An another group of moving average strategy is comparing short term moving average and long term moving average.
Moving average cross, Awesome oscillators and MACD belong to this group.
The period and weightings of moving averages are also topics.
Period, as known as length, means how many days are computed by moving averages.
Weighting means how much weight the price of a day takes up in moving averages.
For simple moving averages, the weightings of each day are equal.
For most of non-simple moving averages, the weightings of more recent days are higher than the weightings of less recent days.
Many trading courses say the concept of trading strategies is more important than the settings of moving averages.
However, we can observe some characteristics of price movement to design the weightings of moving averages and make them more meaningful.
In this research, we use the observation that when there are no significant events, when the time frame becomes 4 times, the average true range becomes about 2 times.
For example, the average true range in 4-hour chart is about 2 times of the average true range in 1-hour chart; the average true range in 1-hour chart is about 2 times of the average true range in 15-minute chart.
Therefore, the goal of design is making the weighting of the most recent quarter is close to the weighting of the rest recent three quarters.
For example, for the 24-day moving average, the weighting of the most recent 6 days is close to the weighting of the rest 18 days.
Computing the weighting
The formula of moving average is
sum ( price of day n * weighting of day n ) / sum ( weighting of day n )
Day 1 is the most recent day and day k+1 is the day before day k.
For more convenient explanation, we don't expect sum ( weighting of day n ) is equal to 1.
To make the weighting of the most recent quarter is close to the weighting of the rest recent three quarters, we have
sum ( weighting of day 4n ) = 2 * sum ( weighting of day n )
If when weighting of day 1 is 1, we have
sum ( weighting of day n ) = sqrt ( n )
weighting of day n = sqrt ( n ) - sqrt ( n-1 )
weighting of day 2 ≒ 1.414 - 1.000 = 0.414
weighting of day 3 ≒ 1.732 - 1.414 = 0.318
weighting of day 4 ≒ 2.000 - 1.732 = 0.268
If we follow this formula, the weighting of day 1 is too strong and the moving average may be not stable.
To reduce the weighting of day 1 and keep the spirit of the formula, we can add a parameter (we call it as x_1w2b).
The formula becomes
weighting of day n = sqrt ( n+x_1w2b ) - sqrt ( n-1+x_1w2b )
if x_1w2b is 0.25, then we have
weighting of day 1 = sqrt(1.25) - sqrt(0.25) ≒ 1.1 - 0.5 = 0.6
weighting of day 2 = sqrt(2.25) - sqrt(1.25) ≒ 1.5 - 1.1 = 0.4
weighting of day 3 = sqrt(3.25) - sqrt(2.25) ≒ 1.8 - 1.5 = 0.3
weighting of day 4 = sqrt(4.25) - sqrt(3.25) ≒ 2.06 - 1.8 = 0.26
weighting of day 5 = sqrt(5.25) - sqrt(4.25) ≒ 2.3 - 2.06 = 0.24
weighting of day 6 = sqrt(6.25) - sqrt(5.25) ≒ 2.5 - 2.3 = 0.2
weighting of day 7 = sqrt(7.25) - sqrt(6.25) ≒ 2.7 - 2.5 = 0.2
What you see and can adjust in this script
This script plots three moving averages described above.
The short term one is default magenta, 6 days and 1 atr.
The middle term one is default yellow, 24 days and 2 atr.
The long term one is default green, 96 days and 4 atr.
I arrange the short term 6 days to make it close to sma(5).
The other twos are arranged according to 4x length and 2x atr.
There are 9 curves plotted by this script. I made the lower bands and the upper bands less clear than moving averages so it is less possible misrecognizing lower or upper bands as moving averages.
x_src : how to compute the reference price of a day, using 1 to 4 of open, high, low and close.
len : how many days are computed by moving averages
atr : how many days are computed by average true range
multi : the distance from the moving average to the lower band and the distance from the moving average to the lower band are equal to multi * average true range.
x_1w2b : adjust this number to avoid the weighting of day 1 from being too strong.
Conclusion
There are moving averages which the weighting of the most recent quarter is close to the weighting of the rest recent three quarters.
We can apply strategies based on moving averages. Like most of indicators, oversold does not always means it is an opportunity to buy.
If the short term lower band is close to the middle term moving average or the middle term lower band is close to the long term moving average, it may be potential support value.
References
Computing FIR Filters Using Arrays
How to trade with moving averages : the eight trading signals concluded by Granville
How to trade with Bollinger bands
How to trade with double Bollinger bands
Tobacco ChannelThese bands use KAMA for the basis, build Keltner Channels that you might expect high probability reversals to occur from.
I named it Tobacco Channel because I found its idea in Cuban's Reversion Bands — Indicator by cubantobacco.
Elliott Wave Oscillator Signals by DGTElliott Wave Principle , developed by Ralph Nelson Elliott, proposes that the seemingly chaotic behaviour of the different financial markets isn’t actually chaotic. In fact the markets moves in predictable, repetitive cycles or waves and can be measured and forecast using Fibonacci numbers. These waves are a result of influence on investors from outside sources primarily the current psychology of the masses at that given time. Elliott wave predicts that the prices of the a traded currency pair will evolve in waves: five impulsive waves and three corrective waves. Impulsive waves give the main direction of the market expansion and the corrective waves are in the opposite direction (corrective wave occurrences and combination corrective wave occurrences are much higher comparing to impulsive waves)
The Elliott Wave Oscillator (EWO) helps identifying where you are in the 5-3 Elliott Waves, mainly the highest/lowest values of the oscillator might indicate a potential bullish/bearish Wave 3. Mathematically expressed, EWO is the difference between a 5-period and 35-period moving average based on the close. In this study instead 35-period, Fibonacci number 34 is implemented for the slow moving average and formula becomes ewo = ema(source, 5) - ema(source, 34)
The application of the Elliott Wave theory in real time trading gets difficult because the charts look messy. This study (EWO-S) simplifies the visualization of EWO and plots labels on probable reversals/corrections. The good part is that all plotting’s are performed on the top of the price chart including a histogram (optional and supported on higher timeframes). Additionally optional Keltner Channels Cloud added to help confirming the price actions.
What to look for:
Plotted labels can be used to follow the Elliott Wave occurrences and most importantly they can be considered as signals for possible trade setup opportunities. Elliott Wave Rules and Fibonacci Retracement/Extensions are suggested to confirm the patters provided by the EWO-S
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
4x ATR of the Lows by CanUk101Plots 4 Keltner style channels above and below an EMA to allow overbought and oversold interpretation and assess volatility
Jurik Moving Average Crossover Strategy [ChuckBanger]The classic moving average crossover strategy does not work well in markets that, instead of trending, tend to frequently reverse within a trading range. The lag between the actual time the market has reversed direction and when the moving average is signalling a trade, the trend is already over and the market is about to go against your position. In this environment, a more appropriate trading strategy is suggested here using an JMA Keltner Channel.
The idea is to create a channel based of support and resistance. When the market breaks out of the channel, and fails to maintain momentum. It is likely the price will fall back toward the center of the channel. This tendency can be exploited in the following manner.
In the chart above, The aqua and maroon (center line) and the blue lines are part of a channel. The middle line is a slow running JMA of the closing prices, with Length = 30 and phase = 0. The upper blue band is constructed by adding 1.5 times of 30-bar ATR (average true range) to the center JMA line and the lower blue band by deducting the same amount. There is a grey line running through the data- That is a fast running JMA with length = 5 and phase = 100 representing the price.
The red dots indicate that the the price is going back in the channel and the market is retracting from a failed upward breakout, and the green dots mark when price is retracting from a failed downward breakout. These are places where one might want to enter the trade. The orange dots indicate where price crosses the center line, a reasonable place to take profit from or even exit the trade.
The center line also shows the up or down movements if the setting is ticked. This feature is useful to use when exit a trade. For example, you enter a long position on a green dot signal and the color is maroon. You can wait for 3-5 candles (depending of markets). And if the color doesn’t change it can be an indication that the price is going lower. Here it is possible to switch to a short possible or the opposite apply if you enter on a red dot.
The parameter use in this study is for demonstrating purposes only. This is to show how you can use JMA. Do not trade with real money without thoroughly test the strategy. And always use stop-losses.
ENVELOPE BOLLINGER KELTN IMPULSE EMA SMA SAFEZONE SAR CHANDELIERALL THIS ALL IN ONE!
there are many options to check or uncheck to show only the tool that you need at that particular moment.
ENJOY!
ENVELOPE BOLLINGER KELTNER IMPULSE EMA SMA SAFEZONE SAR CHANDELIER
QuarryLake v4As some of you requested, I will make the code for QuarryLake Open for you all.
I have also updated the script in version 4.
This strategy consists of 3 indicators that I found works quite well together.
Keltner Channel, Waddah Attah Explosion, and Volatility Stop .
KC Period = 200
KCATR = 5
Vstop Period = 3
Vstop Mult = 1.5
Long when close > KC, close > Vstop, WAE trendUp
Short when close < KC, close < Vstop, WAE trendDown
Works well on BTCUSD XBTUSD , as well as other major liquid Pair.
This strategy utilized a modified Kelly position sizing for BTCUSD Bitstamp , feel free to modify it to your needs.
And lastly,
Save Hong Kong, the revolution of our times.
Lancelot Band - ATR Reversal+Trending IndicatorThis is an indicator I created recently, with the mind of spotting where price might reverse and where the price is trending. You can see this as the primary indicator for your system, however, it is recommended you use this in conjunction with other confirmation indicators.
This script focus solely on ATR or Average True Range.
This indicator is the combination of the baseline from the Ichimoku cloud and the concept of the Keltner channel.
Baseline period = 14
ATR period = 14
ATR Mult = 1.5
For reversal
Long when price crossover Lower band & Stop loss at xLower band
Sell when price crossunder Upper band & Stop loss at xUpper band
For Trend Following
Long when price crossover xUpper band and Stop loss at Upper band
Short when price crossunder xLower band and Stop loss at Upper band
Again, you will need other indicators to help you to succeed in this system. This indicator will not generate the best exit for your position but will generate a good entry signal when you use it with both volume indicator and exit indicator.
Works well on BTCUSD XBTUSD, as well as other major liquid Pair.
Feel free to follow me on Twitter @Lancelot_Auger for more free Alpha.
Please acknowledge my effort by like and follow.
And lastly,
Save Hong Kong, the revolution of our times.
Alert-QuarryLake Indicator Map - ATR Trend Following Strategy A lot of times I don't like my chart crowding with indicators, thus the reason for creating this script for my strategy QuarryLake.
This script also comes with alert.
Below is the explanation for QuarryLake
I have also updated the script in version 4.
This strategy consists of 3 indicators that I found works quite well together.
Keltner Channel, Waddah Attah Explosion, and Volatility Stop.
You can find WAE here
KC Period = 200
KCATR = 5
Vstop Period = 3
Vstop Mult = 1.5
Long when close > KC, close > Vstop, WAE trendUp
Short when close < KC, close < Vstop, WAE trendDown
Works well on BTCUSD XBTUSD, as well as other major liquid Pair.
Feel free to follow me on Twitter @Lancelot_Auger for more free Alpha.
Please acknowledge my effort by like and follow.
And lastly,
Save Hong Kong, the revolution of our times.
