TriexDev - SuperBuySellTrend (PLUS+)Minimal but powerful.
Have been using this for myself, so thought it would be nice to share publicly. Of course no script is correct 100% of the time, but this is one of if not the best in my basic tools. (This is the expanded/PLUS version)
Github Link for latest/most detailed + tidier documentation
Base Indicator - Script Link
TriexDev - SuperBuySellTrend (SBST+) TradingView Trend Indicator
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SBST Plus+
Using the "plus" version is optional, if you only want the buy/sell signals - use the "base" version.
## What are vector candles?
Vector Candles (inspired to add from TradersReality/MT4) are candles that are colour coded to indicate higher volumes, and likely flip points / direction changes, or confirmations.
These are based off of PVSRA (Price, Volume, Support, Resistance Analysis).
You can also override the currency that this runs off of, including multiple ones - however adding more may slow things down.
PVSRA - From MT4 source:
Situation "Climax"
Bars with volume >= 200% of the average volume of the 10 previous chart TFs, and bars
where the product of candle spread x candle volume is >= the highest for the 10 previous
chart time TFs.
Default Colours: Bull bars are green and bear bars are red.
Situation "Volume Rising Above Average"
Bars with volume >= 150% of the average volume of the 10 previous chart TFs.
Default Colours: Bull bars are blue and bear are blue-violet.
A blue or purple bar can mean the chart has reached a top or bottom.
High volume bars during a movement can indicate a big movement is coming - or a top/bottom if bulls/bears are unable to break that point - or the volume direction has flipped.
This can also just be a healthy short term movement in the opposite direction - but at times sets obvious trend shifts.
## Volume Tracking
You can shift-click any candle to get the volume of that candle (in the pair token/stock), if you click and drag - you will see the volume for that range.
## Bollinger Bands
Bollinger Bands can be enabled in the settings via the toggle.
Bollinger Bands are designed to discover opportunities that give investors a higher probability of properly identifying when an asset is oversold (bottom lines) or overbought (top lines).
>There are three lines that compose Bollinger Bands: A simple moving average (middle band) and an upper and lower band.
>The upper and lower bands are typically 2 standard deviations +/- from a 20-day simple moving average, but they can be modified.
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Base Indicator
## What is ATR?
The average true range (ATR) is a technical analysis indicator, which measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following:
- current high - the current low;
- the absolute value of the current high - the previous close;
- and the absolute value of the current low - the previous close.
The ATR is then a moving average, generally using 10/14 days, of the true ranges.
## What does this indicator do?
Uses the ATR and multipliers to help you predict price volatility, ranges and trend direction.
> The buy and sell signals are generated when the indicator starts
plotting either on top of the closing price or below the closing price. A buy signal is generated when the ‘Supertrend’ closes above the price and a sell signal is generated when it closes below the closing price.
> It also suggests that the trend is shifting from descending mode to ascending mode. Contrary to this, when a ‘Supertrend’ closes above the price, it generates a sell signal as the colour of the indicator changes into red.
> A ‘Supertrend’ indicator can be used on equities, futures or forex, or even crypto markets and also on daily, weekly and hourly charts as well, but generally, it will be less effective in a sideways-moving market.
Thanks to KivancOzbilgic who made the original SuperTrend Indicator this was based off
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## Usage Notes
Two indicators will appear, the default ATR multipliers are already set for what I believe to be perfect for this particular (double indicator) strategy.
If you want to break it yourself (I couldn't find anything that tested more accurately myself), you can do so in the settings once you have added the indicator.
Basic rundown:
- A single Buy/Sell indicator in the dim colour; may be setting a direction change, or just healthy movement.
- When the brighter Buy/Sell indicator appears; it often means that a change in direction (uptrend or downtrend) is confirmed.
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You can see here, there was a (brighter) green indicator which flipped down then up into a (brighter) red sell indicator which set the downtrend. At the end it looks like it may be starting to break the downtrend - as the price is hitting the trend line. (Would watch for whether it holds above or drops below at that point)
Another example, showing how sometimes it can still be correct but take some time to play out - with some arrow indicators.
Typically I would also look at oscillators, RSI and other things to confirm - but here it held above the trend lines nicely, so it appeared to be rather obvious.
It's worth paying attention to the trend lines and where the candles are sitting.
Once you understand/get a feel for the basics of how it works - it can become a very useful tool in your trading arsenal.
Also works for traditional markets & commodities etc in the same way / using the same ATR multipliers, however of course crypto generally has bigger moves.
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You can use this and other indicators to confirm likeliness of a direction change prior to the brighter/confirmation one appearing - but just going by the 2nd(brighter) indicators, I have found it to be surprisingly accurate.
Tends to work well on virtually all timeframes, but personally prefer to use it on 5min,15min,1hr, 4hr, daily, weekly. Will still work for shorter/other timeframes, but may be more accurate on mid ones.
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This will likely be updated as I go / find useful additions that don't convolute things. The base indicator may be updated with some limited / toggle-able features in future also.
חפש סקריפטים עבור "one一季度财报"
PrasiGanFanFibntroduction
This is a combination of Fibonacci and Gann fan /retracements.
The script can automatically draw as many:
Fibonacci Retracements
Fibonacci Fan
Gann Retracements
Gann Fan
as the user requires on the chart. Each level set or fan consists of 7 lines based on the most important ratios of Fibonacci/ Gann .
Basics
What are Fibonacci retracements?
Fibonacci retracement levels are horizontal lines that indicate where support and resistance are likely to occur. They stem from Fibonacci’s sequence. Each level is associated with a percentage which is how much of a prior move the price has retraced. The Fibonacci retracement levels are 23.6%, 38.2%, 61.8%, and 78.6%. While not officially a Fibonacci ratio, 50% is also used. The indicator is useful because it can be drawn between any two significant price points, such as a high and a low. The indicator will then create the levels between those two points.
What are Gann retracements?
A developer of technical analysis and trading was W.D. Gann . Gann theory expects a normal retracement of 50 percent. This means that under normal selling pressure, the stock price will decline half the amount of its most recent rise, and vice versa. It also suggests that retracements occur at the halfway point of a move, such as 25 percent (half of 50 percent), 12.5 percent (half of 25 percent), and so on.
What is Fibonacci fan?
Fibonacci fan is a set of sequential trend lines drawn from a trough or peak through a set of points dictated by Fibonacci retracements. The first step to create it is to draw a trend line covering the local lowest and highest prices of a security. To reach retracement levels, the trader divides the difference in price at the low and high end by ratios determined by the Fibonacci series. The lines formed by connecting the starting point for the base trend line and each retracement level create the Fibonacci fan.
What is Gann fan?
A Gann fan consists of a series of lines called Gann angles. These angles are superimposed over a price chart to show potential support and resistance levels. The resulting image is supposed to help technical analysts predict price changes. Gann believed the 45-degree angle to be most important, but the Gann fan also draws angles at degrees like 75, 63.75, 26.25 and 15. The Gann fan originates at a low or high point. The resulting lines show areas of potential future support and resistance . The 45-degree line is known as the 1:1 line because the price will rise or fall at a 45-degree angle when the price moves up/down one unit for each unit of time. All other lines in the Gann fan are drawn above and below the 1:1 line. The other angles are associated with 2:1, 3:1, 4:1, 8:1 and 1:8, 1:4, 1:3, and 1:2 time-to-price moves.
Challenges
The most of the time I dedicated to writing this script has been spent on handling these problems:
1. Finding Local Highest/Lowest Prices
In order to draw Fibonacci and Gann fan /retracements, it's necessary to find local highest and lowest price points (Extrema) on the chart. As this could be so challenging, most traders and coders draw the lines covering the low and high prices over a given period of time or a limited number of bars back instead. I already wrote an indicator using this approach (Auto Fibonacci Combo).
In this new script I tried to find the exact highest and lowest prices based on this idea that: if a high point is formed lower than previous high which was after a lowest point, then that previous one was the local highest point, and vice versa if a low point is formed higher than previous low which was after a highest point, then that previous one was the local lowest point. So logically an extremum price on the chart won't be found until the next high/low point is formed.
2. Finding Proper Chart Scale for Gann Fan
Based on the theory, Gann angles are sensitive to the chart price scale and in order to have the right angles, the chart must be made with the proper scale. J.A. Hyerczyk in his book "Pattern, Price & Time - Using Gann Theory in Technical Analysis" suggests that the easiest way to determine the scale of a market is by taking the difference between top-to-top and bottom-to-bottom and dividing it by the time it took the market to move from top to top and bottom to bottom.
Thus on a properly constructed chart, the basic equation for calculating Gann angles is: Price * Time.
3. Drawing Fans and Relocating Fan Labels at Each New Bar in Pine (A Programming-Related Subject)
To do this, I used linear equations and line slopes. Of course it was so complicated and exhausting, but finally I overcame that thanks to my genius cousin.
Settings and Usage
By default, the script shows detected extremum points plus 1 Fibonacci fan, 1 Gann fan , 1 set of Fibonacci retracements and no Gann retracements on the chart. All of these could be changed in the indicator settings beside the color and transparency of each line.
Feel free to use this and send me your thoughts!
Real-Fast Fourier Transform of Price w/ Linear Regression [Loxx]Real-Fast Fourier Transform of Price w/ Linear Regression is a indicator that implements a Real-Fast Fourier Transform on Price and modifies the output by a measure of Linear Regression. The solid line is the Linear Regression Trend of the windowed data, The green/red line is the Real FFT of price.
What is the Discrete Fourier Transform?
In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.
What is the Complex Fast Fourier Transform?
The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T).
The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward.
What is Real-Fast Fourier Transform?
Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded.
Inputs:
src = source price
uselreg = whether you wish to modify output with linear regression calculation
Windowin = windowing period, restricted to powers of 2: "4", "8", "16", "32", "64", "128", "256", "512", "1024", "2048"
Treshold = to modified power output to fine tune signal
dtrendper = adjust regression calculation
barsback = move window backward from bar 0
mutebars = mute bar coloring for the range
Further reading:
Real-valued Fast Fourier Transform Algorithms IEEE Transactions on Acoustics, Speech, and Signal Processing, June 1987
Related indicators utilizing Fourier Transform
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolation of Variety Moving Averages
Fourier Extrapolator of Price w/ Projection Forecast
Ultimate IndicatorThis is a combination of all the price chart indicators I frequently switch between. It contains my day time highlighter (for day trading), multi-timeframe long-term trend indicator for current commodity in the bottom right, customizable trend EMA which also has multi-timeframe drawing capabilities, VWAP, customizable indicators with separate settings from the trend indicator including: EMA, HL2 over time, Donchian Channels, Keltner Channels, Bollinger Bands, and Super Trend. The settings for these are right below the trend settings and can have their length and multiplier adjusted. All of those also have multi-timeframe capabilities separate from the trend multi-time settings.
The Day Trade Highlight option will draw faint yellow between 9:15-9:25, red between 9:25-9:45, yellow between 9:45-10:05. There will be one white background at 9:30am to show the opening of the market. while the market is open there will be a very faint blue background. For the end of the day there will be yellow between 15:45-15:50, red between 15:50-16:00, and yellow between 16:00-16:05. During the night hours, there is no coloring. The purpose of this highlight is to show the opening / closing times of the market and the hot times for large moves.
The indicators can also be colored in the following ways:
1. Simple = Makes all colors for the indicator Gray
2. Trend = Will use the Donchian Channels to get the short-trend direction and by default will color the short-term direction as Blue or Red. Unless using Super Trend, the Donchian Channel is used to find short-term trend direction.
3. Trend Adv = Will use the Donchian Channels to get the short-trend direction and by default will color the short-term direction as Blue or Red. Unless using Super Trend, the Donchian Channel is used to find short-term trend direction. If there is a short-term up-trend during a long-term down-trend, the Blue will become Navy. If short-term down-trend during long-term up-trend, the Red will be Brown.
4. Squeeze = Compares the Bollinger Bands width to the Keltner Channels width and will color based on relative squeeze of the market: Teal = no squeeze. Yellow = little squeeze. Red = decent squeeze. White = huge squeeze. if you do not understand this one, try drawing the Bollinger Bands while using the Squeeze color option and it should become more apparent how this works. I also recommend leaving the length and multiplier to the default 20 and 2 if using this setting and only changing the timeframe to get longer/shorter lengths as I've seen that changing the length or multiplier can more or less make it not work at all.
Along with the indicator settings are options to draw lines/labels/fills for the indicator. I enjoy having only fills for a cleaner look.
The Labels option will show Buy/Sell signals when the short-term trend flips to agree with the long-term trend.
The Trend Bars option will do the same as the Labels option but instead will color the bars white when a Buy/Sell option is given.
The Range Bars option shows will color a bar white when the Close of a candle is outside of a respective ranging indicator option (Bollinger or Keltner).
The Trend Bars will draw white candles no matter which indicator selection you make (even "Off"). However, Range Bars will only draw white when either Bollinger or Keltner are selected.
The Donchian Channels and Super Trend are trending indicators and should be used during trending markets. I like to use the MACD in conjunction with these indicators for possibly earlier entries.
The Bollinger Bands and Keltner Channel are ranging indicators and should be used during ranging markets. I like to use the RSI in conjunction with these indicators and will use 60/40 for overbought and oversold areas rather than 70/30. During a range, I wait for an overbought or oversold indication and will buy/sell when it crosses back into the middle area and close my position when it touches the opposite band.
I have a MACD/RSI combination indicator if you'd like that as well :D
As always, trade at your own risk. This is not some secret indicator that will 100% win. As always, the trades you see in the picture use a 1:1.5 or 1:2 risk to reward ratio, for today (August 8, 2022) it won 5/6 times with one trade still open at the end of the day. Manage your account correctly and you'll win in the long term. Hit me up with any questions or suggestions. Happy Trading!
Fourier Extrapolation of Variety Moving Averages [Loxx]Fourier Extrapolation of Variety Moving Averages is a Fourier Extrapolation (forecasting) indicator that has for inputs 38 different types of moving averages along with 33 different types of sources for those moving averages. This is a forecasting indicator of the selected moving average of the selected price of the underlying ticker. This indicator will repaint, so past signals are only as valid as the current bar. This indicator allows for up to 1500 bars between past bars and future projection bars. If the indicator won't load on your chart. check the error message for details on how to fix that, but you must ensure that past bars + futures bars is equal to or less than 1500.
Fourier Extrapolation using the Quinn-Fernandes algorithm is one of several (5-10) methods of signals forecasting that I'l be demonstrating in Pine Script.
What is Fourier Extrapolation?
This indicator uses a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
This indicator uses the Quinn-Fernandes algorithm to find the harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
The indicator has the following input parameters:
src - input source
npast - number of past bars, to which trigonometric series is fitted;
Nfut - number of predicted future bars;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
Included:
Loxx's Expanded Source Types
Loxx's Moving Averages
Other indicators using this same method
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
Loxx's Moving Averages: Detailed explanation of moving averages inside this indicator
Loxx's Expanded Source Types: Detailed explanation of source types used in this indicator
Fourier Extrapolator of Price w/ Projection Forecast [Loxx]Due to popular demand, I'm pusblishing Fourier Extrapolator of Price w/ Projection Forecast.. As stated in it's twin indicator, this one is also multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
This indicator uses the Quinn-Fernandes algorithm to find the harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
The indicator has the following input parameters:
src - input source
npast - number of past bars, to which trigonometric series is fitted;
Nfut - number of predicted future bars;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia curve indicates the modeled future values.
The purpose of this indicator is to showcase the Fourier Extrapolator method to be used in future indicators.
PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]PA-Adaptive Polynomial Regression Fitted Moving Average is a moving average that is calculated using Polynomial Regression Analysis. The purpose of this indicator is to introduce polynomial fitting that is to be used in future indicators. This indicator also has Phase Accumulation adaptive period inputs. Even though this first indicator is for demonstration purposes only, its still one of the only viable implementations of Polynomial Regression Analysis on TradingView is suitable for trading, and while this same method can be used to project prices forward, I won't be doing that since forecasting is generally worthless and causes unavoidable repainting. This indicator only repaints on the current bar. Once the bar closes, any signal on that bar won't change.
For other similar Polynomial Regression Fitted methodologies, see here
Poly Cycle
What is the Phase Accumulation Cycle?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
What is Polynomial Regression?
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression.
Things to know
You can select from 33 source types
The source is smoothed before being injected into the Polynomial fitting algorithm, there are 35+ moving averages to choose from for smoothing
The output of the Polynomial fitting algorithm is then smoothed to create the signal, there are 35+ moving averages to choose from for smoothing
Included
Alerts
Signals
Bar coloring
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
True Adaptive-Lookback Phase Change Index [Loxx]Previously I posted a Phase Change Index using Ehlers Autocorrelation Periodogram Algorithm to tease out the adaptive periods. You can find the previous version here: . This new version is also adaptive but uses a different method to derive the adaptive length inputs. This adaptive method derives period inputs by counting pivots from past candles. This version also relies on Jurik Smoothing to generate the final signal. I named this one "true" because I should have specified in the previous PCI's title that it's powered by Ehlers Autocorrelation Periodogram. Additionally, you'll notice the ALB algorithm has changed from other indicators, This is restrict the range of possible ALB period outputs to a specific range so the indicator is usable.
And remember, this is an inverse indicator. This means that small values on the oscillator indicate bullish sentiment and higher values on the oscillator indicate bearish sentiment.
What is the Phase Change Index?
Based on the M.H. Pee's TASC article "Phase Change Index".
Prices at any time can be up, down, or unchanged. A period where market prices remain relatively unchanged is referred to as a consolidation. A period that witnesses relatively higher prices is referred to as an uptrend, while a period of relatively lower prices is called a downtrend.
The Phase Change Index ( PCI ) is an indicator designed specifically to detect changes in market phases.
This indicator is made as he describes it with one deviation: if we follow his formula to the letter then the "trend" is inverted to the actual market trend. Because of that an option to display inverted (and more logical) values is added.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
That's why investors, banks and institutions worldwide ask for the Jurik Research Moving Average ( JMA ). You may apply it just as you would any other popular moving average. However, JMA's improved timing and smoothness will astound you.
What is adaptive Jurik volatility
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
Included:
Bar coloring
2 signal variations w/ alerts
Multiple Moving Avg MTF TableThis script replaces the other script that was just the SMAs that where in a Multi Time Frame Table as this was a redo of that one and this one is SO MUCH MORE!!!!
Not only does this one do the Simple Moving Avg 5, 10, 20, 50, 120, 200 into a table that shows Current/Hourly/Daily/Weekly/Monthly/Quarterly ( 3M )/ Yearly. It now does Exponential Moving Avg , Weighted Moving Avg , and Volume Weight Moving Avg along with Simple Moving Avg.
I still use this script so that you can quickly capture the values so that short-term, and long-term resistance and support can be determined during market hours. Even better now you can select between SMA / EMA / WMA /or VWMA .
imgur.com
The table will change to the values based on the Choice of the type of Moving Avg and if you change the default values.
Now it will take a little bit for the table to show up, so please be patient. I have tested it with stocks, forex, and crypto.
Key Performance IndicatorWe are happy to introduce the Key Performance Indicator by Detlev Matthes. This is an amazing tool to quantify the efficiency of a trading system and identify potential spots of improvement.
Abstract
A key performance indicator with high explanatory value for the quality of trading systems is introduced. Quality is expressed as an indicator and comprises the individual values of qualitative aspects. The work developing the KPI was submitted for the 2017 VTAD Award and won first prize.
Introduction
Imagine that you have a variety of stock trading systems from which to select. During backtesting, each trading system will deliver different results with regard to its indicators (depending on, inter alia, its parameters and the stock used). You will also get different forms of progression for profit development. It requires great experience to select the “best” trading system from this variety of information (provided by several indicators) and significantly varying equity progression forms. In this paper, an indicator will be introduced that expresses the quality of a trading system in just one figure. With such an indicator, you can view the results of one backtest at a glance and also more easily compare a variety of backtesting results with one another.
If you are interested in learning more about the calculations behind this indicator then I have included a link to the english version of his research paper.
Along with this, we now offer indicator development services. If you are interested in learning more then feel free to reach out to get a quote for your project.
**Please note that we have NOT inputted any real strategy into the code and therefore it is not producing any real value. Feel free to change the code as desired to test any strategy!**
drive.google.com
Double CCIWith this variant of the CCI indicator you have 2 CCIs. I call it convenience the fast and the slow.
The slow one has the default period of 20. The fast one has a lower value and will therefore also change his direction much faster.
I don't use this as a decisive indicator, but the fast one does indicate where the standard CCI might go and so you are already prepared for the decisive moment.
I've added a zero line so you can visually track whether the buyers or the sellers are predominant.
Between 0 and +100, as well as between 0 and -100 there is still a battle between buyers and sellers and it is better to wait a little longer before entering a trade.
From +100 to +250 I have colored the zone green; here the buyers are winning and it is a confirmation that you can safer enter the BUY.
From -100 to -250 it's colored red; here the sellers are firmly winning and it is a confirmation to go into a SELL.
Most values are adjustable via the settings and can be switched on or off.
This indicator is not intended to be used as the sole decision element, but rather to fine-tune your entry and exit points . Maybe wait a little longer than you normally would, but then be able to step in at the right time that there is enough volume in your desired direction.
Good luck with it and I would love feedback.
Thank you Tradingview-community.
R-sqrd Adapt. Fisher Transform w/ D. Zones & Divs. [Loxx]The full name of this indicator is R-Squared Adaptive Fisher Transform w/ Dynamic Zones and Divergences. This is an R-squared adaptive Fisher transform with adjustable dynamic zones, signals, alerts, and divergences.
What is Fisher Transform?
The Fisher Transform is a technical indicator created by John F. Ehlers that converts prices into a Gaussian normal distribution.
The indicator highlights when prices have moved to an extreme, based on recent prices. This may help in spotting turning points in the price of an asset. It also helps show the trend and isolate the price waves within a trend.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive an r-squared value that is then modified by a user input "factor"
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 variations w/ alerts
Divergences w/ alerts
Loxx's Expanded Source Types
Dynamic Zones Polychromatic Momentum Candles [Loxx]Dynamic Zones Polychromatic Momentum Candles is a candle coloring, momentum indicator that uses Jurik Filtering and Dynamic Zones to calculate the monochromatic color between two colors.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
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
Loxx's Expanded Source Types
Double Dynamic Zone RSX [Loxx]Double Dynamic Zone RSX is a Juirk RSX RSI indicator using Leo Zamansky and David Stendahl's Dynamic Zones to determine breakouts, breakdowns, and reversals.
What is RSX?
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurik RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag.
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.
VWAP + EMA Analysis [Joshlo]Overview and Use Case
VWAP Analysis gives the possibility to combine multiple time frames of VWAP along with a triplet of exponential moving averages. This can provide insight into potential scalp, swing and longer term trades, depending on your time frame. The use of this indicator with it's setup is based off the the Scalp Setup Alerts provided by Roensch Capital.
The primary use for this script is to help with intraday scalp set ups. Using the Daily VWAP, turned on by default, we can look for price to respect and bounce from one of the VWAP lines (support or resistance) back toward equilibrium, we can also look for price to bounce off of equilibrium and move back toward VWAP support or resistance.
The chart attached shows AMD bouncing off of the Daily time frame VWAP Resistance level multiple times (see yellow boxes), often with confirmation given by an increase in volume which is often far higher than the average volume. In many of these cases a short position could've been opened or put option could have been placed with a profitable outcome.
Every line projected onto the chart via this indicator has the potential to create support or resistance as well as causing 'hang ups', meaning price loses it's momentum, slows down and hangs out in the particular area. This is shown on the chart within the green box.
Chart walkthrough - See attached chart
After a rejection off of the Daily VWAP Resistance line (depicted by the white circle), price starts to move back toward Daily VWAP Equilibrium. In order to reach this line, price needs to move through the 20EMA (white) and 50EMA (purple), the Weekly VWAP Resistance (red circles) and the 200EMA (orange). All of these lines are a part of this single indicator.
The 20EMA seems to offer little resistance but follows the price on it's move, offering some resistance to a volatile move upward. Initially upon contact with the 50EMA, price hangs up and bounces above and below the line whilst finding support on the Weekly VWAP Resistance at the same time. This causes a 'hang up' or sideways movement for around 20 minutes of trading. A potential trade may have entered at the white circle with a VWAP Resistance rejection and exited upon contact with the 50EMA in anticipation of multiple EMAs and support / resistance lines converging which is known to cause price movement to slow.
Eventually with an increase in volume, price breaks below the 20EMA (white), 50EMA (purple) and the Weekly VWAP Resistance level (red circles). Price then finds support on the 200EMA (orange), although there was potential for the price to fall to the Daily VWAP Equilibrium (solid blue). As the Red VWAP lines tend to act more often as resistance as opposed to support (price is rarely above these lines for extended periods), the trade from earlier may have profited more by awaiting contact with the 200EMA before exiting, taking the assumption that the Weekly VWAP Resistance was more likely to act as resistance than support.
A period of consolidation in the green box, around the Weekly VWAP Resistance, 20EMA, 50EMA and with support from the 200EMA eventually resulted in another break out where the price came back up to the Daily VWAP Resistance. Prior to the end of this trading day, there were two more opportunities for scalp setups based off of the price showing consistent rejections off the Daily VWAP Resistance back down to the 50EMA.
In the final example, price breaks above the Daily VWAP Resistance but quickly rejects off of the Monthly VWAP Resistance. For examples where the VWAP Resistance or Support or broken, it can help to look at an indicator such as the RSI to look for bullish divergence or bearish divergence.
Just as this example shows bounces and rejection off of VWAP Resistance, the same applies around the Equilibrium and Support VWAP lines.
The perfect scenario would be to find a ticker where there has already been two or three bounces off of one of these levels, with the goal of taking the trade on the next bounce and either using a percentage price target or technical price target based off of the EMAs or VWAP lines. If there are EMAs close in the direction you want to take the trade, there is a higher chance of hang ups and reversals, so a clear run is the more desired trade set up.
You can also look for these indicator lines to stack up in order to form a stronger support and resistance. For example the 200EMA and Daily VWAP Equilibrium being close to each other may suggest it would take more of an effort to break both of these levels, but one by itself may break more easily.
Indicator Setup
In the settings for the indicator, almost everything you might want to change can be done from the Input tab.
The three options for VWAP (daily, weekly and monthly) allow for analysis on multiple time frames. Daily is turned on as standard.
Standard Deviation Multiplier is set to 2 as standard, this effects the distance of the VWAP support and resistance from the equilibrium line. This seems to be a level that works well with finding support and resistance lines, however if there is excessively high or low volume, occasionally the lines can be thrown off. You can adjust this level if required to find a 'sweet spot' where price likes to reject or find support.
The colors for all VWAPs can be adjusted via the Inputs tab, however if you'd like to change the type of line these are depicted as, this can be done from the Styles tab.
The 3 EMAs (20, 50 and 200) can be toggled on or off and also have their color changed. The style of the lines can be adjusted from with the Styles tab if required.
.srb suiteThe essential suite Indicator.
that are well integrated to ensure visibility of essential items for trading.
it is very cumbersome to put symbol in the Tradingview chart and combine essential individual indicators one by one.
Moreover even with such a combination, the chart is messy and visibility is not good.
This is because each indicator is not designed with the others in mind.
This suite was developed as a composite-solution to that situation, and will make you happy.
designed to work in the same pane with open-source indicator by default.
Recommended visual order ; Back = .srb suite, Front = .srb suite vol & info
individually turn on/off only what you need on the screen.
BTC-agg. Volume
4 BTC-spot & 4 BTC-PERP volume aggregated.
It might helps you don't miss out on important volume flows.
Weighted to spot trading volume when using PERP+spot volume .
If enabled, BTC-agg.Vol automatically applied when selecting BTC-pair.
--> This is used in calculations involving volumes, such as VWAP.
Moving Average
1 x JMA trend ribbon ; Accurately follow short-term trend changes.
3 x EMA ribbon ; zone , not the line.
MA extension line ; It provide high visibility to recognize the direction of the MA.
SPECIAL TOOLS
VWAP with Standard Deviation Bands
VWAP ruler
BB regular (Dev. 2.0, 2.5)
BB Extented (Dev. 2.5, 3.0, 3.5)
Fixed Range Volume Profile ; steamlined one, performace tuned & update.
SPECIAL TOOLS - Auto Fibonacci Retracement - New GUI
'built-in auto FBR ' has been re-born
It shows - retracement Max top/ min bottom ; for higher visibility
It shows - current retracement position ; for higher visibility
The display of the Fib position that exceeds the regular range is auto-determined according to the price.
tradingview | chart setting > Appearance > Top margin 0%, Bottom margin 0% for optimized screen usage
tradingview | chart setting > Appearance > Right margin 57
.srb suite vol & info --> Visual Order > Bring to Front
.srb suite vol & info --> Pin to scale > No scale (Full-screen)
Visual order ; Back = .srb suite, Front = .srb suite vol & info
1. Fib.Retracement core is from tradingview built-in FBR ---> upgrade new-type GUI, and performance tuned.
2. Fixed-range volume-profile core is from the open-source one ---> some update & perf.tuned.
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if you have any questions freely contact to me by message on tradingview.
but please understand that responses may be quite late.
Special thanks to all of contributors of community.
The script may be freely distributed under the MIT license.
Genesis Matrix [Loxx]Over a decade ago, the Genesis Matrix system was one of best strategies for new traders looking to learn how to really trade trends. Fast forward to 2022, a new version of Genesis Matrix has emerged using TVI, CCI, HL Channel & T3
What is T3?
The T3 moving average is an indicator of an indicator since it includes several EMAs of another EMA. Unlike any other moving average, it adds the so-called volume factor, a value between 0 and 1. Like the SMA, traders typically use this indicator to spot trends and trend reversals.
What is CCI?
The Commodity Channel Index ( CCI ) measures the current price level relative to an average price level over a given period of time. CCI is relatively high when prices are far above their average. CCI is relatively low when prices are far below their average. Using this method, CCI can be used to identify overbought and oversold levels.
Genesis matrix uses Jurik-Smoothed CCI w/ MA Deviation--a spin on regular CCI .Usually CCI is calculated as using average ( Simple Moving Average ) and mean deviation. In this version, average is replaced with well known JMA (Jurik Moving Average) instead for the smoothing phase and the deviation is replaced with variety moving average deviation. The result in this one is responsive and fast (as expected) and also it is smoother than the original CCI (as expected).
What is SSL?
Known as the SSL, the Semaphore Signal Level channel chart alert is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. For our purposes here, SSL has been modified to allow for different moving average selection and different closing price look back periods.
What is William Blau Ergodic Tick Volume?
This is one of the techniques described by William Blau in his book "Momentum, Direction and Divergence" (1995). If you like to learn more, we advise you to read this book. His book focuses on three key aspects of trading: momentum, direction and divergence. Blau, who was an electrical engineer before becoming a trader, thoroughly examines the relationship between price and momentum in step-by-step examples. From this grounding, he then looks at the deficiencies in other oscillators and introduces some innovative techniques, including a fresh twist on Stochastics. On directional issues, he analyzes the intricacies of ADX and offers a unique approach to help define trending and non-trending periods.
William Blau's definition of TVI ergodicity is that the indictor is ergodic when periods are set to 32, 5, 1, and the signal is set to 5. Other combinations are not ergodic, according to Blau.
How to use
Long signal: All 4 indicators turn green
Short signal: All 4 indicators turn red
Included
Bar coloring
Phase-Accumulation Adaptive RSX w/ Expanded Source Types [Loxx]Phase-Accumulation Adaptive RSX w/ Expanded Source Types is a Phase Accumulation Adaptive Jurik RSX.
What is RSX?
RSI is a very popular technical indicator, because it takes into consideration market speed, direction and trend uniformity. However, the its widely criticized drawback is its noisy (jittery) appearance. The Jurk RSX retains all the useful features of RSI , but with one important exception: the noise is gone with no added lag.
What is Phase Accumulation?
The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample.
The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio.
Included:
-Toggle on/off bar coloring
Polynomial Regression Extrapolation [LuxAlgo]This indicator fits a polynomial with a user set degree to the price using least squares and then extrapolates the result.
Settings
Length: Number of most recent price observations used to fit the model.
Extrapolate: Extrapolation horizon
Degree: Degree of the fitted polynomial
Src: Input source
Lock Fit: By default the fit and extrapolated result will readjust to any new price observation, enabling this setting allow the model to ignore new price observations, and extend the extrapolation to the most recent bar.
Usage
Polynomial regression is commonly used when a relationship between two variables can be described by a polynomial.
In technical analysis polynomial regression is commonly used to estimate underlying trends in the price as well as obtaining support/resistances. One common example being the linear regression which can be described as polynomial regression of degree 1.
Using polynomial regression for extrapolation can be considered when we assume that the underlying trend of a certain asset follows polynomial of a certain degree and that this assumption hold true for time t+1...,t+n . This is rarely the case but it can be of interest to certain users performing longer term analysis of assets such as Bitcoin.
The selection of the polynomial degree can be done considering the underlying trend of the observations we are trying to fit. In practice, it is rare to go over a degree of 3, as higher degree would tend to highlight more noisy variations.
Using a polynomial of degree 1 will return a line, and as such can be considered when the underlying trend is linear, but one could improve the fit by using an higher degree.
The chart above fits a polynomial of degree 2, this can be used to model more parabolic observations. We can see in the chart above that this improves the fit.
In the chart above a polynomial of degree 6 is used, we can see how more variations are highlighted. The extrapolation of higher degree polynomials can eventually highlight future turning points due to the nature of the polynomial, however there are no guarantee that these will reflect exact future reversals.
Details
A polynomial regression model y(t) of degree p is described by:
y(t) = β(0) + β(1)x(t) + β(2)x(t)^2 + ... + β(p)x(t)^p
The vector coefficients β are obtained such that the sum of squared error between the observations and y(t) is minimized. This can be achieved through specific iterative algorithms or directly by solving the system of equations:
β(0) + β(1)x(0) + β(2)x(0)^2 + ... + β(p)x(0)^p = y(0)
β(0) + β(1)x(1) + β(2)x(1)^2 + ... + β(p)x(1)^p = y(1)
...
β(0) + β(1)x(t-1) + β(2)x(t-1)^2 + ... + β(p)x(t-1)^p = y(t-1)
Note that solving this system of equations for higher degrees p with high x values can drastically affect the accuracy of the results. One method to circumvent this can be to subtract x by its mean.
Jurik CFB Adaptive, Elder Force Index w/ ATR Channels [Loxx]Jurik CFB Adaptive, Elder Force Index w/ ATR Channels is a variation of Elder Force Index that better adapts to trends by calculating dynamic lengths for the traditional Elder Force Index calculation. ATR channels are added to show levels of price extremes or exhaustion of price either up or down. Elder Force Index is typically used for spotting reversals on the weekly timeframe.
What is the Elder Force Index?
Dr. Alexander Elder is one of the contributors to a newer generation of technical indicators. His force index is an oscillator that measures the force, or power, of bulls behind particular market rallies and of bears behind every decline.1
The three key components of the force index are the direction of price change, the extent of the price change, and the trading volume. When the force index is used in conjunction with a moving average, the resulting figure can accurately measure significant changes in the power of bulls and bears.1 In this way, Elder has taken an extremely useful solitary indicator, the moving average, and combined it with his force index for even greater predictive success.
What is Composite Fractal Behavior ( CFB )?
All around you mechanisms adjust themselves to their environment. From simple thermostats that react to air temperature to computer chips in modern cars that respond to changes in engine temperature, r.p.m.'s, torque, and throttle position. It was only a matter of time before fast desktop computers applied the mathematics of self-adjustment to systems that trade the financial markets.
Unlike basic systems with fixed formulas, an adaptive system adjusts its own equations. For example, start with a basic channel breakout system that uses the highest closing price of the last N bars as a threshold for detecting breakouts on the up side. An adaptive and improved version of this system would adjust N according to market conditions, such as momentum, price volatility or acceleration.
Since many systems are based directly or indirectly on cycles, another useful measure of market condition is the periodic length of a price chart's dominant cycle, (DC), that cycle with the greatest influence on price action.
The utility of this new DC measure was noted by author Murray Ruggiero in the January '96 issue of Futures Magazine. In it. Mr. Ruggiero used it to adaptive adjust the value of N in a channel breakout system. He then simulated trading 15 years of D-Mark futures in order to compare its performance to a similar system that had a fixed optimal value of N. The adaptive version produced 20% more profit!
This DC index utilized the popular MESA algorithm (a formulation by John Ehlers adapted from Burg's maximum entropy algorithm, MEM). Unfortunately, the DC approach is problematic when the market has no real dominant cycle momentum, because the mathematics will produce a value whether or not one actually exists! Therefore, we developed a proprietary indicator that does not presuppose the presence of market cycles. It's called CFB (Composite Fractal Behavior) and it works well whether or not the market is cyclic.
CFB examines price action for a particular fractal pattern, categorizes them by size, and then outputs a composite fractal size index. This index is smooth, timely and accurate
Essentially, CFB reveals the length of the market's trending action time frame. Long trending activity produces a large CFB index and short choppy action produces a small index value. Investors have found many applications for CFB which involve scaling other existing technical indicators adaptively, on a bar-to-bar basis.
What is Jurik Volty used in the Juirk Filter?
One of the lesser known qualities of Juirk smoothing is that the Jurik smoothing process is adaptive. "Jurik Volty" (a sort of market volatility ) is what makes Jurik smoothing adaptive. The Jurik Volty calculation can be used as both a standalone indicator and to smooth other indicators that you wish to make adaptive.
What is the Jurik Moving Average?
Have you noticed how moving averages add some lag (delay) to your signals? ... especially when price gaps up or down in a big move, and you are waiting for your moving average to catch up? Wait no more! JMA eliminates this problem forever and gives you the best of both worlds: low lag and smooth lines.
Ideally, you would like a filtered signal to be both smooth and lag-free. Lag causes delays in your trades, and increasing lag in your indicators typically result in lower profits. In other words, late comers get what's left on the table after the feast has already begun.
Buy/Sell on the levelsThis script is generally
My describe is:
There are a lot of levels we would like to buy some crypto.
When the price has crossed the level-line - we buy, but only if we have the permission in array(2)
When we have bought the crypto - we lose the permission for buy for now(till we will sell it on the next higher level)
When we sell some crypto(on the buying level + 1) we have the permission again.
There also are 2 protect indicators. We can buy if these indicators both green only(super trend and PIVOT )
Jun 12
Release Notes: Hello there,
Uncomment this section before use for real trade:
if array.get(price_to_sellBue, i) >= open and array.get(price_to_sellBue, i) <= close// and
//direction < 0 and permission_for_buy != 0
Here is my script.
In general - this is incredible simple script to use and understand.
First of all You can see this script working with only long orders, it means we going to get money if crypto grows only. Short orders we need to close the position on time.
In this script we buy crypto and sell with step 1% upper.
You can simply change the step by changing the price arrays.
Please note, if You want to see where the levels of this script is You Have to copy the next my indicator called LEVEL 1%
In general - if the price has across the price-level we buy some crypto and loose permission for buying for this level till we sell some crypto. There is ''count_of_orders" array field with value 2. When we bought some crypto the value turns to 0. 0 means not allowed to by on this level!!! The script buy if the bar is green only(last tick).
The script check every level(those we can see in "price_to_sellBue" array).
If the price across one of them - full script runs. After buying(if it possible) we check is there any crypto for sell on the level.
We check all levels below actual level( of actual level - ''i'' than we check all levels from 0 to i-1).
If there is any order that has value 0 in count of orders and index <= i-1 - we count it to var SELL amount and in the end of loop sell all of it.
Pay attention - it sells only if price across the level with red bar AND HAS ORDERS TO SELL WHICH WAS BOUGHT BELOW!!!
In Strategy tester it shows not-profitables orders sometimes, because if You have old Long position - it sells it first. First in - first out.
If the price goes down for a long time and You sell after 5 buys You sell the first of it with the highest value.
There is 2 protection from horrible buying in this strategy. The first one - Supertrend. If the supertrend is red - there is no permission for buy.
The second one - something between PIVOT and supertrend but with switcher.
If the price across last minimum - switcher is red - no permission for buy and the actual price becomes last minimum . The last maximum calculated for last 100 bars.
When the price across last maximum - switcher is green, we can buy. The last minimum calculation for last 100 bars, last maximum is actual price.
This two protections will save You from buying if price get crash down.
Enjoy my script.
Should You need the code or explanation, You have any ideas how to improve this crypt, contact me.
Vladyslav.
Jun 12
Release Notes: Here has been uncommented the protection for buy in case of price get down.
5 hours ago
Release Notes: Changed rages up to actual price to make it work
Cipher Twister - Long and ShortINTRO / NOTES:
This script is based on Market Cipher B Oscillator by Falcon
The difference in this script is that only the useful points are printed on the indicator, namely Long and Short Trade Execution signals to be used by a bot, namely the PT Bot.
The script also differs from the original that it has been upgraded to Pinescript v4
This oscillator can be used with ALL time frames, but generally works the best on 15 minute and 1 hour charts on ANY market, no matter, stock, forex, crypto, spot, futures, derivatives, Nasdaq etc...
DEFINITIONS:
This oscillator forms the foundation of Buy and Exit of Long and Short Trades.
There are 2 'Red' Lines at the top of the channel and 2 Green Lines at the bottom of the channel.
These two channels are set at default to be +53 / -53 and +60 / -60 respectively. These two lines will serve as the threshold point if one is to make cautious trades only.
There is a center line which divides the Oscillator into two parts. Above the center line, the market is in over bought territory and Below the center line is in over sold territory.
'Red' dots are drawn by the indicator to represent a potential Short (or a signal to exit from a Long position)
'Green' dots are drawn by the indicator to represent a potential Long (or a signal to exit from a Short position)
The 'Red' and 'Green' dots are draw when a Cross between both wt1 & wt2 cross, thus providing a fantastic indication of potential trend reversal and entry/exit of a position.
STRATEGY NOTES:
The strategy to use this indicator with for realistic and proper results would be to use it with an automated Trading Bot such as Profit Trailer (PT-BOT)
You could use this strategy manually, however it would mean you would need to sit in front of the screen all day and night long and activate the trades immediately after the 'red'/'green' dots are drawn. Usually this will result in non-optimal entries and exits as well as loss on various instances when a 'red' and 'green' dot are printed close together (which is usually when the market goes into correction/consolidation) and slow entries/exits will result in a loss rather than a small profit or exit at BE (Break Even)
ACTUAL STRATEGY (For use with automated bot)
To be used in conjunction with Heikin Ashi Candles for added cautionary measures
For LONGs ONLY
--------------------
1/ When 'Green' dot is drawn, ACTIVATE Long Position
(Use 1.5% Risk Management for each trade)
(Use Lot size based on 1.5% risk management and xLeverage (if any))
2/ Make sure bot Opens an SL (Stop Loss) value based on 1.5% Risk Management
3/ When 'Red' dot is drawn, CLOSE Long Position.
*If you want to add extra caution to your trade, only activate the trade if the 'Green' dot is BELOW the 'Green' Markers
*For added caution, use color coded Heikin Ashi candles to 'confirm' Activation and Closing of a trade in the bot configuration
---------------------------------------------------------------------------------------------------
For SHORTs ONLY
--------------------
1/ When 'Red' dot is drawn, ACTIVATE Short Position
(Use 1.5% Risk Management for each trade)
(Use Lot size based on 1.5% risk management and xLeverage (if any))
2/ Make sure bot Opens an SL (Stop Loss) value based on 1.5% Risk Management
3/ When 'Green' dot is drawn, CLOSE Short Position
*If you want to add extra caution to your trade, only activate the trade if the 'Red' dot is Above the Red Markers
*For added caution, use color coded Heikin Ashi candles to 'confirm' Activation and Closing of a trade in the bot configuration
---------------------------------------------------------------------------------------------------
Supplementary Notes:
Make sure that your bot configuration will only activate ONE TRADE when the 'Green'/'Red' dot appears.
Occasionally during high volatility , 'red'/'green' dots will appear intermittently before remaining drawn, thus the oscillator 'redraws' the dots during market movement.
There will be times where occasionally a 'green' dot or a 'red' dot will appear, the trade will be opened, but the trade will fail due to the market manipulation (algorithm/market maker bots/fake volume etc), to wipe out those trading on derivatives and futures markets using leverage. Do not worry about this, no bot can make 100% wins, no strategy will achieve 100% win ratio and one necessarily doesn't need a high win ratio when using strict money management practices with your trading for SL and lot size.
If you use this method, you will see great results, but again I must stress, using this method with a fully automated bot is the only way to achieve proper results.
