Value At Risk Channel [AstrideUnicorn]The Value at Risk Channel (VaR Channel) is a trading indicator designed to help traders control the level of risk exposure in their positions. The user can select a time period and a probability value, and the indicator will plot the upper and lower limits that the price can reach during the selected time period with the given probability.
CONCEPTS
The indicator is based on the Value at Risk (VaR) calculation. VaR is an important metric in risk management that quantifies the degree of potential financial loss within a position, portfolio or company over a specific period of time. It is widely used by financial institutions like banks and investment companies to forecast the extent and likelihood of potential losses in their portfolios.
We use the so-called “historical method” to compute VaR. The algorithm looks at the history of past returns and creates a histogram that represents the statistical distribution of past returns. Assuming that the returns follow a normal distribution, one can assign a probability to each value of return. The probability of a specific return value is determined by the distribution percentile to which it belongs.
HOW TO USE
Let’s assume you want to plot the upper and lower limits that price will reach within 4 hours with 5% probability. To do this, go to the indicator Settings tab and set the Timeframe parameter to "4 hours'' and the Probability parameter to 5.0.
You can use the indicator to set your Stop-Loss at the price level where it will trigger with low probability. And what's more, you can measure and control the probability of triggering.
You can also see how likely it is that the price will reach your Take-Profit within a specific period of time. For example, you expect your target level to be reached within a week. To determine this probability, set the Timeframe parameter to "1 week" and adjust the Probability parameter so that the upper or lower limit of your VaR channel is close to your Take-Profit level. The resulting Probability parameter value will show the probability of reaching your target in the expected time.
The indicator can be a useful tool for measuring and managing risk, as well as for developing and fine-tuning trading strategies. If you find other uses for the indicator, feel free to share them in the comments!
SETTINGS
Timeframe - sets the time period, during which the price can reach the upper or lower bound of the VaR channel with the probability, set by the Probability parameter.
Probability - specifies the probability with which the price can reach the upper or lower bound of the VaR channel during the time period specified by the Timeframe parameter.
Window - specifies the length of history (number of historical bars) used for VaR calculation.
Bands
DEMA Supertrend Bands [Misu]█ Indicator based on DEMA (Double Exponential Moving Average) & Supertrend to show Bands .
DEMA attempts to remove the inherent lag associated with Moving Averages by placing more weight on recent values.
Supertrend aims to detect price trends, it's also used to set protective stops.
█ Usages:
Combining Dema to calculate Supertrend results in nice lower and upper bands.
This can be used to identify potential supports and resistances and set protective stops.
█ Parameters:
Length DEMA: Double Ema lenght used to calculate DEMA. Dema is used by Supertrend indicator.
Length Atr: Atr lenght used to calculate Atr. Atr is used by Supertrend indicator.
Band Mult: Used to calculate Supertrend Bands width.
█ Other Applications:
The mid band can be used to filter bad signals in the manner of a more classical Moving Average.
VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones [Loxx]VHF-Adaptive, Digital Kahler Variety RSI w/ Dynamic Zones is an RSI indicator with adaptive inputs, Digital Kahler filtering, and Dynamic Zones. This indicator uses a Vertical Horizontal Filter for calculating the adaptive period inputs and allows the user to select from 7 different types of RSI.
What is VHF Adaptive Cycle?
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
What is Digital Kahler?
From Philipp Kahler's article for www.traders-mag.com, August 2008. "A Classic Indicator in a New Suit: Digital Stochastic"
Digital Indicators
Whenever you study the development of trading systems in particular, you will be struck in an extremely unpleasant way by the seemingly unmotivated indentations and changes in direction of each indicator. An experienced trader can recognise many false signals of the indicator on the basis of his solid background; a stupid trading system usually falls into any trap offered by the unclear indicator course. This is what motivated me to improve even further this and other indicators with the help of a relatively simple procedure. The goal of this development is to be able to use this indicator in a trading system with as few additional conditions as possible. Discretionary traders will likewise be happy about this clear course, which is not nerve-racking and makes concentrating on the essential elements of trading possible.
How Is It Done?
The digital stochastic is a child of the original indicator. We owe a debt of gratitude to George Lane for his idea to design an indicator which describes the position of the current price within the high-low range of the historical price movement. My contribution to this indicator is the changed pattern which improves the quality of the signal without generating too long delays in giving signals. The trick used to generate this “digital” behavior of the indicator. It can be used with most oscillators like RSI or CCI .
First of all, the original is looked at. The indicator always moves between 0 and 100. The precise position of the indicator or its course relative to the trigger line are of no interest to me, I would just like to know whether the indicator is quoted below or above the value 50. This is tantamount to the question of whether the market is just trading above or below the middle of the high-low range of the past few days. If the market trades in the upper half of its high-low range, then the digital stochastic is given the value 1; if the original stochastic is below 50, then the value –1 is given. This leads to a sequence of 1/-1 values – the digital core of the new indicator. These values are subsequently smoothed by means of a short exponential moving average . This way minor false signals are eliminated and the indicator is given its typical form.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
4 signal types
Alerts
Loxx's Expanded Source Types
Loxx's Moving Averages
Loxx's Variety RSI
Loxx's Dynamic Zones
Williams %R on Chart w/ Dynamic Zones [Loxx]Williams %R on Chart w/ Dynamic Zones is a Williams %R indicator but instead of being an oscillator it appears on chart. The WPR calculation used here leverages T3 moving average for its calculation. In addition, the WPR is bound by Dynamic Zones.
What is Williams %R?
Williams %R , also known as the Williams Percent Range, is a type of momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. The Williams %R may be used to find entry and exit points in the market. The indicator is very similar to the Stochastic oscillator and is used in the same way. It was developed by Larry Williams and it compares a stock’s closing price to the high-low range over a specific period, typically 14 days or periods.
What is T3 moving average?
Developed by Tim Tillson, the T3 Moving Average is considered superior to traditional moving averages as it is smoother, more responsive and thus performs better in ranging market conditions as well.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included
Bar coloring
Channels fill
Loxx's Expanded Source Types
35+ moving average types
McNichollBandsLibrary "McNichollBands"
This is a library which only functions to make the McNicholl's Bollinger Bands modifications. It's also my first library, so I'll probably screw some things up.
mcNichollBands(alpha, useLogScale, widthMultiplier)
Calculates the McNicholl's Bollinger Bands modifications.
Parameters:
alpha : The alpha constant to be used on the EMA calculations.
useLogScale : Whether to use the log version of the prices or not.
widthMultiplier : The number that shall be multiplied by the volatility to form the bands.
Returns: A tuple containing the lower band, the center line, and the upper band.
Rollin' pseudo-Bollinger Bands 5 linear regression curves and new highs/lows mixed together from the basis for this indicator. Using slightly different logic an upper boundary and lower boundary are formed. Then the boundary's are built upon to show price channels within the band using variations of fib levels and the distance between the initial boundary's. Dots plotted show the inverse of the close price relative to either the upper or lower boundary depending on where the close is relative to the center of the band. This shows the market's tendency for symmetry which is useful when looking for reversals etc. If it's too cluttered feel free to turn off some things in the options and keep what you feel is helpful.
Weight Gain 4000 - (Adjustable Volume Weighted MA) - [mutantdog]Short Version:
This is a fairly self-contained system based upon a moving average crossover with several unique features. The most significant of these is the adjustable volume weighting system, allowing for transformations between standard and weighted versions of each included MA. With this feature it is possible to apply partial weighting which can help to improve responsiveness without dramatically altering shape. Included types are SMA, EMA, WMA, RMA, hSMA, DEMA and TEMA. Potentially more will be added in future (check updates below).
In addition there are a selection of alternative 'weighted' inputs, a pair of Bollinger-style deviation bands, a separate price tracker and a bunch of alert presets.
This can be used out-of-the-box or tweaked in multiple ways for unusual results. Default settings are a basic 8/21 EMA cross with partial volume weighting. Dev bands apply to MA2 and are based upon the type and the volume weighting. For standard Bollinger bands use SMA with length 20 and try adding a small amount of volume weighting.
A more detailed breakdown of the functionality follows.
Long Version:
ADJUSTABLE VOLUME WEIGHTING
In principle any moving average should have a volume weighted analogue, the standard VWMA is just an SMA with volume weighting for example. Actually, we can consider the SMA to be a special case where volume is a constant 1 per bar (the value is somewhat arbitrary, the important part is that it's constant). Similar principles apply to the 'elastic' EVWMA which is the volume weighted analogue of an RMA. In any case though, where we have standard and weighted variants it is possible to transform one into the other by gradually increasing or decreasing the weighting, which forms the basis of this system. This is not just a simple multiplier however, that would not work due to the relative proportions being the same when set at any non zero value. In order to create a meaningful transformation we need to use an exponent instead, eg: volume^x , where x is a variable determined in this case by the 'volume' parameter. When x=1, the full volume weighting applies and when x=0, the volume will be reduced to a constant 1. Values in between will result in the respective partial weighting, for example 0.5 will give the square root of the volume.
The obvious question here though is why would you want to do this? To answer that really it is best to actually try it. The advantages that volume weighting can bring to a moving average can sometimes come at the cost of unwanted or erratic behaviour. While it can tend towards much closer price tracking which may be desirable, sometimes it needs moderating especially in markets with lower liquidity. Here the adjustability can be useful, in many cases i have found that adding a small amount of volume weighting to a chosen MA can help to improve its responsiveness without overpowering it. Another possible use case would be to have two instances of the same MA with the same length but different weightings, the extent to which these diverge from each other can be a useful indicator of trend strength. Other uses will become apparent with experimentation and can vary from one market to another.
THE INCLUDED MODES
At the time of publication, there are 7 included moving average types with plans to add more in future. For now here is a brief explainer of what's on offer (continuing to use x as shorthand for the volume parameter), starting with the two most common types.
SMA: As mentioned above this is essentially a standard VWMA, calculated here as sma(source*volume^x,length)/sma(volume^x,length). In this case when x=0 then volume=1 and it reduces to a standard SMA.
RMA: Again mentioned above, this is an EVWMA (where E stands for elastic) with constant weighting. Without going into detail, this method takes the 1/length factor of an RMA and replaces it with volume^x/sum(volume^x,length). In this case again we can see that when x=0 then volume=1 and the original 1/length factor is restored.
EMA: This follows the same principle as the RMA where the standard 2/(length+1) factor is replaced with (2*volume^x)/(sum(volume^x,length)+volume^x). As with an RMA, when x=0 then volume=1 and this reduces back to the standard 2/(length+1).
DEMA: Just a standard Double EMA using the above.
TEMA: Likewise, a standard Triple EMA using the above.
hSMA: This is the same as the SMA except it uses harmonic mean calculations instead of arithmetic. In most cases the differences are negligible however they can become more pronounced when volume weighting is introduced. Furthermore, an argument can be made that harmonic mean calculations are better suited to downtrends or bear markets, in principle at least.
WMA: Probably the most contentious one included. Follows the same basic calculations as for the SMA except uses a WMA instead. Honestly, it makes little sense to combine both linear and volume weighting in this manner, included only for completeness and because it can easily be done. It may be the case that a superior composite could be created with some more complex calculations, in which case i may add that later. For now though this will do.
An additional 'volume filter' option is included, which applies a basic filter to the volume prior to calculation. For types based around the SMA/VWMA system, the volume filter is a WMA-4, for types based around the RMA/EVWMA system the filter is a RMA-2.
As and when i add more they will be listed in the updates at the bottom.
WEIGHTED INPUTS
The ohlc method of source calculations is really a leftover from a time when data was far more limited. Nevertheless it is still the method used in charting and for the most part is sufficient. Often the only important value is 'close' although sometimes 'high' and 'low' can be relevant also. Since we are volume weighting however, it can be useful to incorporate as much information as possible. To that end either 'hlc3' or 'hlcc4' tend to be the best of the defaults (in the case of 24/7 charting like crypto or intraday trading, 'ohlc4' should be avoided as it is effectively the same as a lagging version of 'hlcc4'). There are many other (infinitely many, in fact) possible combinations that can be created, i have included a few here.
The premise is fairly straightforward, by subtracting one value from another, the remaining difference can act as a kind of weight. In a simple case consider 'hl2' as simply the midrange ((high+low)/2), instead of this using 'high+low-open' would give more weight to the value furthest from the open, providing a good estimate of the median. An even better estimate can be achieved by combining that with 'high+low-close' to give the included result 'hl-oc2'. Similarly, 'hlc3' can be considered the basic mean of the three significant values, an included weighted version 'hlc2-o2' combines a sum with subtraction of open to give an estimated mean that may be more accurate. Finally we can apply a similar principle to the close, by subtracting the other values, this one potentially gets more complex so the included 'cc-ohlc4' is really the simplest. The result here is an overbias of the close in relation to the open and the midrange, while in most cases not as useful it can provide an estimate for the next bar assuming that the trend continues.
Of the three i've included, hlc2-o2 is in my opinion the most useful especially in this context, although it is perhaps best considered to be experimental in nature. For that reason, i've kept 'hlcc4' as the default for both MAs.
Additionally included is an 'aux input' which is the standard TV source menu and, where possible, can be set as outputs of other indicators.
THE SYSTEM
This one is fairly obvious and straightforward. It's just a moving average crossover with additional deviation (bollinger) bands. Not a lot to explain here as it should be apparent how it works.
Of the two, MA1 is considered to be the fast and MA2 is considered to be the slow. Both can be set with independent inputs, types and weighting. When MA1 is above, the colour of both is green and when it's below the colour of both is red. An additional gradient based fill is there and can be adjusted along with everything else in the visuals section at the bottom. Default alerts are available for crossover/crossunder conditions along with optional marker plots.
MA2 has the option for deviation bands, these are calculated based upon the MA type used and volume weighted according to the main parameter. In the case of a unweighted SMA being used they will be standard Bollinger bands.
An additional 'source direct' price tracker is included which can be used as the basis for an alert system for price crossings of bands or MAs, while taking advantage of the available weighted inputs. This is displayed as a stepped line on the chart so is also a good way to visualise the differences between input types.
That just about covers it then. The likelihood is that you've used some sort of moving average cross system before and are probably still using one or more. If so, then perhaps the additional functionality here will be of benefit.
Thanks for looking, I welcome any feedack
Adaptive Price ZoneThe Adaptive Price Zone was developed by Lee Leibfarth in 2006, and it attempts to create a band for mean-reversal strategies. It works by taking the double-smoothed average of the volatility from 5 days and adding/subtracting it from the average price of the day (hl2).
If you are planning to use it, remember that it changes throughout the day , so you might want to use an offset. You can also choose to use the true range for the volatility instead of the high and low difference.
Waddah Attar Hidden Levels [Loxx]Waddah Attar Hidden Levels is a dynamic indicator of support of resistance built by Ahmad Waddah Attar
Details
-Uses data from the Daily time frame only
-Used for intraday trading, restricted to timeframes 1 hour and below
-Best Time Frames 15, 30, 60 minutes
-Draws support and resistance lines on chart inside a boundary of fibonacci levels
How to use Waddah Attar Hidden Levels
-Breakout trading indicator
-Buy at the broken red line, or insert pending buy order
-Sell at the broken green line, or insert pending buy order
-Take profit/Stop-loss at blue lines
Bollinger CloudsThis indicator plots Bollinger Bands for your current timeframe (e.g 5 minutes) and also plots the Bollinger Bands for a higher timeframe (15 minutes for 5 minute timeframe). Then the gaps between the current and higher timeframe upper and lower bands is filled to create clouds which can be used as entry zones. Like Bollinger Bands, this indicator shouldn't be solely used for entries, use it in conjunction with other indicators.
Bollinger Band Timeframes
Current / Higher
1 minute / 5 minutes
3 minutes / 10 minutes
5 minutes / 15 minutes
10 minutes / 30 minutes
15 minutes / 1 hour
30 minutes / 2 hours
45 minutes / 1.5 hours
1 hour / 4 hours
2 hours / 8 hours
2.5 hours / 10 hours
4 hours / 1 Day
1 Day / 3 Days
3 Days / 9 Days
5 Days / 2 Weeks
1 Week / 1 Month
CommonMarkupLibrary "CommonMarkup"
Provides functions for chart markup, such as indicating recession bands.
markRecessionBands(showBands, lineY, labelY)
Mark vertical bands and show recession band labels if argument showBands is true. Example "markRecessionBands(bar_index ,3.0"
Parameters:
showBands : - show vertical recession bands when true. Functionally equiv to no op when false
lineY : - y-axis value for line positioning
labelY : - y-axis value for label positioning
@return true - always answers the value of showBands
TL WavesI created this indicator inspired by the miyuki waves indicator by eto_miyuki. In my indicator we have 17 types of moving averages which can be selected in the settings.
It is a trend indicator, the base of the wave is a moving average and 4 Average True Range (ATR) Bands derived from the baseline are formed.
There are also 3 moving averages in a guppy style, these 3 moving averages can also be configured.
The moving average options are:
SMA ---> Simple
WMA ---> Weighted
VWMA ---> Volume Weighted
EMA ---> Exponential
DEMA ---> Double EMA
ALMA ---> Arnaud Legoux
HMA ---> Hull MA
SMMA ---> Smoothed
LSMA ---> Least Squares
KAMA ---> Kaufman Adaptive
TEMA ---> Triple EMA
ZLEMA ---> Zero Lag
FRAMA ---> Fractal Adaptive
VIDYA ---> Variable Index Dynamic Average
JMA ---> Jurik Moving Average
T3 ---> Tillson
TRIMA ---> Triangular
All settings are available for changing inputs.
Root mean squared error range (RMSER)Similarly to Bollinger bands, the RMSER gives a support and resistance areas for the trading price. Unlike bollinger bands, which use standard deviation, this support and resistance is calculated with 2 * the root mean squared error away from the moving average. This works very well with indices, like $SPX, and prices only fall outside the range during black swan events like the 2020 crash.
wnG - VWAP MOD Modified version of VWAP :
Classic VWAP with 6 levels based on the Average True Range to identify the distance and distribution of the prices around the VWAP.
There are 2 calcul methodologies for the bands
- Last 24 Hours Average True Range
- Progressive Average True Range starting from 00:00
As prices tend to move around the VWAP level, favor LONG positions in the GREEN ZONE (and SHORT in the RED ZONE).
How to use it :
Avoid taking long position when price is in the RED ZONE
Avoid taking short position when price is in the GREEN ZONE
==> Adjust the settings depending on your timeframe and asset
ATR BandsIn many strategies, it's quite common to use a scaled ATR to help define a stop-loss, and it's not uncommon to use it for take-profit targets as well. While it's possible to use the built-in ATR indicator and manually calculate the offset value, we felt this wasn't particularly intuitive or efficient, and could lead to the potential for miscalculations. And while there are quite a few indicators that plot ATR bands in some form or another already on TV, we could not find one that actually performed the exact way that we wanted. They all had at least one of the following gaps:
The ATR offset was not configurable (usually hard-coded to be based off the high or low, while we generally prefer to use close)
It would only print a single band (either the upper or lower), which would require the same indicator to be added twice
The ATR scaling factor was either not configurable or only stepped in whole numbers (often time fractional factors like 1.5 yield better results)
To that end, we took to making this enhanced version to meet all of the above requirements. While we were doing so, we decided to take this opportunity to also make some non-functional enhancements as well:
Updated the indicator to the most recent version of Pine
Updated the indicator definition to allow alternate (non-chart) timeframe usage
Made the input types explicitly defined to improve consistency
Updated the inputs with appropriate minimum values and step sizes where appropriate
Separated settings into logical groups
Added helptext to the indicator settings noting usage and common settings values
Explicitly titled the on-chart plots of the ATR bands so that they can more easily be identified and referenced in other indicators/scripts, as well as the Data Window
Food for thought : When looking at some of the behaviors of these ATR bands, you can see that when price first levels out, you can draw a "consolidation zone" from the first peak of the upper ATR band to the first valley of the lower ATR band that price will generally respect. Look for price to break and close outside of that zone. When that happens, price will usually (but not always) make a notable move in that direction, which can be used as either a potential trigger or as an additional confluence with other indicators/price action.
Finally, while we have made what we feel are some noteworthy updates and enhancements to this indicator, and have every intention of continuing to do so as we find worthy opportunities for enhancement, credit is still due to the original author: AlexanderTeaH
Supertrend Channels [LuxAlgo]The Supertrend is one of the most used indicators by traders when it comes to determining whether the market is up-trending or down-trending.
This indicator is displayed as a trailing stop, showing a lower monotonic extremity during up-trends and an upper monotonic extremity during down-trends. Today we propose a channel indicator based on the Supertrend trailing stop using trailing maximas/minimas.
Settings
Length: Atr length used by the Supertrend indicator.
Mult: Multiplicative factor for the Atr used by the Supertrend indicator.
Usage
The ability of the indicator to show an up-trend or down-trend is the same as the Supertrend, with rising channels when an up-trend is detected by the Supertrend and declining channels when a down-trend is detected by the Supertrend.
The look of the channels can remind of the Donchian channels indicator, and as such a similar usage can be appropriate. The extremities can for example be used as supports and resistances.
Additionally, the channel's average can be used to filter out noisy variations in the price while keeping a good distance from the price.
Rudy's BB with MartingaleMy first strategy script that uses Bollinger Bands and Martingale to increase contract size after negative profit.
Mobo BandsThis indicator is the Mobo Bands (Momentum Breakout Bands). These bands are bollinger bands that have an adjusted standard deviation. There are Buy signals when it has momentum breakouts above the bands for moves to the upside and Sell signals when it has momentum breakouts below the bands for moves to the downside. The bands simply suggest that all markets have periods of chop which we all know to be true. While the price is inside the bands it is said to be trendless. Once the breakouts happen you can take trades in the breakout direction. I like to use these to swing trade options on the hourly timeframe but the bands should work on most instruments and timeframes. I like to use it to take swings on SPY on the 1 hour chart for entries and use the Daily chart for trend confirmation.
Weighted Standard Deviation BandsLinearly weighted standard deviations over linearly weighted mean.
The rationale of the study can be deduced from my latest publications where I go deeper into explaining the benefits of linear weighting, but in short, I can remind that by using linear weighting we are able to increase the information gain by communicating the sequential nature of time series to the calculations via linear weighting.
Note, that multiplier parameters can take both negative and positive values resulting in ability to have, for example, 1st and 6th weighted standard deviations higher than the weighted mean.
Despite the modification of the classic standard deviation formula, I assume that mathematical qualities of standard deviation will hold due to the fact we can alternately weight the window itself, and then apply the classic standard deviation over the weighted window. In both cases, the results will be the same.
Aight that was too formal, but your short strangles should be happy
Here is it, for you
Anchored TWAP with StDev Bands [MrShadow]TWAP with:
- Anchoring: Custom, Day, Week, Month, Quarter, Year (custom anchoring can be selected by dragging a vertical line through the chart)
- Standard Devation Bands
- Auto-coloring depending on the trend
Volatility ChannelThis script is based on an idea I have had for bands that react better to crypto volatility. It calculates a Donchian Channel, SMMA-Smoothed True Range, Bollinger Bands (standard deviation), and a Keltner Channel (average true range) and averages the components to construct its bands/envelopes. This way, hopefully band touches are a more reliable indicator of a temporary bottom, and so on. Secondary coloring for strength of trend is given as a gradient based on RSI.
BBands ChannelsBased on the Bollinger Bands system. This shows outer channels to the bollinger bands .
BEAM DCA Strategy MonthlyThis strategy is based on BEAM bands for BTC. The space between the original BEAM bands is broken up into 10 bands representing levels of risk for investing fresh capital.
The strategy will buy bitcoin when the price is in the bottom 5 bands, increasing the amount investmented as the price approaches the 1400 D SMA.
The strategy will limit sell bitcoin when the price is in the top 5 bands, increasing the amount sold as the price approaches the upper BEAM band.
Best used on Daily timeframe and on a chart with history of price data, i.e. INDEX:BTCUSD or BITSTAMP:BTCUSD
To use the strategy:
Set start date
Set day of month to invest
Set the maximum amount to be invested on any given month
Toggle buy/sell orders
Observe the backtest
You can see how the strategy backtests via the information boxes in the bottom right.
There is also functionality to adjust the bands for diminishing returns. Note, this should be used with great skepticism, as the adjustments were made by simple function fitting and not rigorous statistical processes.
That about sums it up! As you can see, even with just a small amount of capital invested at regular intervals can lead to huge realised gains using this version of BEAM bands!
