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
Cerca negli script per "curve"
loxxdynamiczoneLibrary "loxxdynamiczone"
Dynamic Zones
Derives Leo Zamansky and David Stendahl's Dynamic Zone,
see "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
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.
dZone(type, src, pval, per)
method for retrieving the dynamic zone levels from input source.
Parameters:
type : string, value of either 'buy' or 'sell'.
src : float, source, either regular source type or some other caculated value.
pval : float, probability defined by extension over/under source, a number <= 1.0.
per : int, period lookback.
Returns: float dynamic zone level.
usage:
dZone("buy", close, 0.2, 70)
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.
7-10 flattener tradeIn the budget speech for FY 2023, market borrowing of 14.95 lakh crore from the market. In the Feb MPC meeting, the RBI brought down its estimates of growth and inflation potentially signaling that economy is/will go through a demand slowdown.
Now in a slowing economy, the govt. finances will be affected. Therefore, to bring back the economy on the fiscal consolidation so that sovereign bond ratings are not hit, the Indian govt. must figure out a way
1. Lower its interest payments in the face of increasing public expenditure on creating public infrastructure (read roads/highways etc. ). One simple way is to go down the yield curve in lower maturities to bring down the interest costs.
Keeping in mind (1) above, it was not difficult to expect a borrowing schedule where the shorter tenors will form a bigger percentage of the net issuance by the government.
In fact, if you look at the issuance calendar for securities below the tenor of 10 yrs (which is 2,5,7 yrs), you will find that itself comprises of ~31% of total borrowings.
Therefore, due to increased pressure on the shorter tenors and relatively less pressure on 10 yr bond yield, we can expect the yields spreads to compress in 7-10 yr region of the yield curve.
This script is written to track the same yield spread compression across 7 & 10 yr tenor.
Treasury Yield Spread 10y-2y [TXMC]A simple indicator to show inversions of the US Treasury yield curve, specifically between the 2yr and 10yr yields.
A colored band prints when the 2yr treasury yield surpasses the 10yr, indicating an inversion of the yield curve.
This indicator is for educational purposes only.
Adaptive Ehlers Deviation Scaled Moving Average (AEDSMA)AEDSMA INTRODUCTION
This indicator is a functional enhancement to “Ehlers Deviation Scaled Moving Average (EDSMA / DSMA)”. I’ve used Volume Breakout and Volatility for dynamic length adaption and further Slope too for trend evaluation.
EDSMA was originally developed by John F. Ehlers (Stocks & Commodities V. 36:8: The Deviation-Scaled Moving Average).
IDEA PLACEMENT
I’ve traded almost every kind of market with different volatility conditions using Moving Averages. It was too much of a hassle to select and use different MA length depending upon market trend. So, the journey started with adapting Moving Averages with another parameter and that’s how “MZ SAMA ” came into being where Slope was used to adapt Adaptive Moving Average with trend change. The problem was still pretty much the same as SAMA might not be effective on every market condition. Hence, I worked on Volume to adapt Moving Averages accordingly. I cane up with “MZ RVSI ” which I used in “MZ DVAMA ” to adapt dynamic length in Adaptive Moving Average and also used “MZ RVSI " alongside Slope as confirmation of trend changes.
Meanwhile, I started using DVAMA methodology on different types on Moving Averages that allow dynamic length for example Hull Moving Average, Linear Regression Curve, SMA, WMA, TMA and many more. All of my tested Mas showed too much flexibility because of volume based Adaptive length.
I came across a script of “Adaptive Hull Moving Average” which pretty much used the similar methodology as DVAMA but when I looked into its depth, its volume oscillator wasn’t working at all and only volatility based dynamic length was used. It was an interesting idea so, I decided to use Volume and Volatility alongside for better results but was nearly impossible to achieve what I wanted using only Hull Moving Average.
I had been using EDSMA in “MA MTF Cross Strategy” and “MZ SRSI Strategy V1.0” previously. It was the perfect choice when comparing to usage of slope on it. DSMA works perfectly as support and resistance as its Deviation Scaled. So, I tried using it to adapt dynamic length based on Volume and Volatility and I wasn’t disappointed. It worked like a charm when I adapted dynamic length between 50 and 255.
DYNAMIC LENGTH BENEFITS
Dynamic length adaption methodology works in a way of adapting Relatively Lower Length leading toward overfitting if trend is supported by Volume and Volatility . Similarly, adapting Relatively Higher Length leading toward underfitting if trend isn’t supported by Volume and Volatility .
Dynamic length adaption makes Moving Average to work better for both Bull and Bear-runs avoiding almost every fake break-in and breakouts. Hence, adaptive MA becomes more reliable for breakout trading.
MA would be more useful as it would adapt almost every chart based on its Volume and Volatility data.
DYNAMIC COLORS AND TREND CORRELATION
I’ve used dynamic coloring to identify trends with more detail which are as follows:
Lime Color: Strong Uptrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Fuchsia Color: Weak uptrend only supported by Slope or whatever you’ve selected.
Red Color: Strong Downtrend supported by Volume and Volatility or whatever you’ve chosen from both of them.
Grey Color: Weak Downtrend only supported by Slope or whatever you’ve selected.
Yellow Color: Possible reversal indication by Slope if enabled. Market is either sideways, consolidating or showing choppiness during that period.
SIGNALS
Green Circle: Market good for long with support of Volume and Volatility or whatever you’ve chosen from both of them.
Red Circle: Market good to short with support from Volume and Volatility or whatever you’ve chosen from both of them.
Yellow Cross: Market either touched top or bottom ATR band and can act as good TP or SL.
EDSMA EVELOPE/BANDS: I’ve included ATR based bands to the Adaptive EDSMA which act as good support/resistance despite from main Adaptive EDSMA Curve.
DEFAULT SETTINGS
I’ve set default Minimum length to 50 and Maximum length to 255 which I’ve found works best for almost all timeframes but you can change this delta to adapt your timeframe accordingly with more precision.
Dynamic length adoption is enabled based on both Volume and Volatility but only one or none of them can also be selected.
Trend signals are enabled based on Slope and Volume but Volatility can be enabled for more precise confirmations.
In “ RVSI ” settings TFS Volume Oscillator is set to default but others work good too especially Volume Zone Oscillator. For more details about Volume Breakout you can check “MZ RVSI Indicator".
ATR breakout is set to be positive if period 14 exceeds period 46 but can be changed if more adaption with volatility is required.
EDSMA super smoother filter length is set to 20 which can be increased to 50 or more for better smoothing but this will also change slope results accordingly.
EDSMA super smoother filter poles are set to 2 because found better results with 2 instead of 3.
FURTHER ENHANCEMENTS
So far, I’ve seen better results with Volume Breakout and Volatility but other parameters such as Linear Slope of Particular MA, MACD, “MZ SRSI ”, a Conditional Uptrend MA or simply KDJ can also be used for dynamic length adaption.
I haven't yet gotten used to pine script arrays so, defining and using conditional operators is pretty much lazy programming for me. Would be great redefining everything through truth matrix instead of using if-else conditions.
MathGeometryCurvesChaikinLibrary "MathGeometryCurvesChaikin"
Implements the chaikin algorithm to create a curved path, from assigned points.
chaikin(points_x, points_y, closed) Chaikin algorithm method, uses provided points to generate a smoothed path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
closed : bool, default=false, is the path closed or not.
Returns: tuple with 2 float arrays.
smooth(points_x, points_y, iterations, closed) Iterate the chaikin algorithm, to smooth a sample of points into a curve path.
Parameters:
points_x : float array, the x value of points.
points_y : float array, the y value of points.
iterations : int, number of iterations to apply the smoothing.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
draw(path_x, path_y, closed) Draw the path.
Parameters:
path_x : float array, the x value of the path.
path_y : float array, the y value of the path.
closed : bool, default=false, is the path closed or not.
Returns: array of lines.
[blackcat] L2 Ehlers Adaptive Relative Strength IndexLevel: 2
Background
John F. Ehlers introuced Adaptive Relative Strength Index in his "Rocket Science for Traders" chapter 21 on 2001.
Function
The concept of taking a difference of lagging line from the original function to produce a leading function suggests extending the concept to moving averages. There is no direct theory for this, but it seems to work pretty well. If taking a 7-bar WMA of prices, that average lags the prices by 2 bars. If taking a 7-bar WMA of the first average, this second average is delayed another 2 bars. If taking the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag. Sure, Dr. Ehlers tried to use more lag for the second moving average, which
should produce a better predictive curve. However, remember the lesson of Chapter 3 of the book. An analysis curve cannot precede an event. You cannot predict an event before it occurs. If then taking a 4-bar WMA of the smoothed line to create a 1-bar lag, this lagging line becomes a signal when the lines cross. This is as close to an ideal indicator as we can get.
Key Signal
Predict ---> moving average fast line
Trigger ---> moving average slow line
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 17th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
[blackcat] L2 Ehlers Predictive AverageLevel: 2
Background
John F. Ehlers introuced Predictive Average in his "Rocket Science for Traders" chapter 20 on 2001.
Function
The concept of taking a difference of lagging line from the original function to produce a leading function suggests extending the concept to moving averages. There is no direct theory for this, but it seems to work pretty well. If taking a 7-bar WMA of prices, that average lags the prices by 2 bars. If taking a 7-bar WMA of the first average, this second average is delayed another 2 bars. If taking the difference between the two averages and add that difference to the first average, the result should be a smoothed line of the original price function with no lag. Sure, Dr. Ehlers tried to use more lag for the second moving average, which
should produce a better predictive curve. However, remember the lesson of Chapter 3 of the book. An analysis curve cannot precede an event. You cannot predict an event before it occurs. If then taking a 4-bar WMA of the smoothed line to create a 1-bar lag, this lagging line becomes a signal when the lines cross. This is as close to an ideal indicator as we can get.
Key Signal
Predict ---> moving average fast line
Trigger ---> moving average slow line
Pros and Cons
100% John F. Ehlers definition translation of original work, even variable names are the same. This help readers who would like to use pine to read his book. If you had read his works, then you will be quite familiar with my code style.
Remarks
The 17th script for Blackcat1402 John F. Ehlers Week publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
IBD Relative Strength + Linear RegressionThis is a slight extension to the excellent script by: jamiespips
It shows the Relative Strength of a Stock compared to a suitable Index.
My extension consists of:
- A selection of comparative indices.
- A short EMA-3 to the RS-curve to smooth it out.
- A linear regression trend line to the last part of the RS-curve.
7_Spreads_labels_FTX[Thojdid]This is a light version of the script multi_spreads_labels_FTX aiming to run faster than the latter.
Here we load only 7 spreads. You can choose them by entering coin names in the settings
This script displays spreads between FTX perpetuals contracts and futures contracts.
In the settings, you can also choose which curve to display and enable labels to see the coins names on each curve.
You can also edit the space between labels to make it easier to read.
Press LIKE if you find this helpful.
Multi_Spreads_labels_FTX_V2104_OPEN[Thojdid]This script displays spreads between FTX perpetuals contracts and futures contracts.
In the settings, you can choose which curve to display and enable labels to see the coins names on each curve.
You can also edit the space between labels to make it easier to read.
There are 31 tickers to load so it can take few seconds to appear.
press the lIKE button if you find it useful.
Blackman Filter - The Smoother The BetterIntroduction
Who doesn't like smooth things? I'd like a smooth market price for christmas! But i can't get it, instead its so noisy...so you apply a filter to smooth it, such filters are called low-pass filters, they smooth and its great but they have lag, so nobody really use them, but they are pretty to look at.
Its on a childish note that i will introduce this indicator, so what it is all about? I propose a new FIR filter using a blackman function as filter kernel for financial time-series smoothing, do you prefer the childish tone ? Fear not its surprisingly easy!
The Blackman Function
The blackman function look like a bell shaped curve, look:
The blackman function will produce such curve. This function is called a cosine sum function because she is based on the sum of cosine functions, here only 2.
0.42 - 0.5 * cos(2 * pi * k) + 0.08 * cos(4 * pi * k)
Originally you use this function for windowing , what does it means? In signal processing you have a function called sync function , if you use this function as filter kernel you would get the ideal frequency domain response filter, sometime called brickwall filter, it would be extremely smooth.
Above the optimal low pass filter frequency response.
However the sync function has no ending values and goes on forever, therefore we can't use it for convolution, expect if we apply windowing. Filters using windowing are called windowed-sinc filters, i will describe the procedure below :
1 - Create a sync function = sin(pi*n)/(pi*n)
2 - Truncate it = I only keep the first length points of the sync function.
This create a abrupt end, the frequency of a filter using step 1 as kernel would contain ripples in the pass band and stop band, this is bad! The frequency response would look like this :
3 - I multiply my values of step 2 by a window function, it can the blackman window, i no longer have an abrupt end, its smooth!
The frequency response of the filter using this kernel would no longer have ripples! This is the power of windowing functions.
Here we are not using such thing, but we could in the future. Here instead we use the blackman function as filter kernel, because this function is bell shaped this mean that the filter will certainly be smooth (symmetrical weighting is a rule of thumb for kernels when we want really smooth filters).
The Filter
This filter is quite smooth, unlike the gaussian filter this filter give less weights to recent and past values, this is because the blackman function has fatter tails than the gaussian one. I could make a comparison of both, however they are quite alike, if you often use a gaussian filter its up to you to decide which one you prefer.
The filter can do a better job than the moving average when it comes to preserve the frequency components that constitute the cycles/trend.
We can see that the filter has a greater performance when it comes to keep the shape of the market price, thus it has a slightly better fit.
Conclusion
Ok so in this post you learned a bit about the sync function and windowing, those are basic subjects in signal processing, they allow us to approximate the filter with the ideal frequency response, i also showed you that those windowing function could be used as kernel and that they where pretty smooth on their own, there are many others, but the one i prefer is the blackman windowing function.
I know what you are thinking, "we want trailing stops, alerts, colors, arrows!", and i understand you pal, but sometimes its cool to take a break from all this stuff. However i can tell that i'am working on a side project that aim to estimate rolling maximum/minimum as fast as possible, any experiments will be published here, and i can ensure you that those indicators will make your day quite brighter, we will see that soon.
I hope you learned something from this post! I'am a bit tired (look i'am disappearing !)
Thanks for reading !
The Golden Ratio MultiplierBy Philip Swift
As Bitcoin continues to progress on its adoption journey, we learn more about its growth trajectory.
Rather than Bitcoin price action behaving like a traditional stock market share price, we see it act more like a technology being adopted at an exponential rate.
This is because Bitcoin is a network being adopted by society, and because it is decentralised money with limited supply, its price is a direct representation of that adoption process.
There are a number of regression analysis tools and stock to flow ratio studies that are helping us to understand the direction of Bitcoin’s adoption curve.
The new tool outlined in this paper brings an alternative degree of precision to understanding Bitcoin’s price action over time. It will demonstrate that Bitcoin’s adoption is not only following a broad growth curve but appears to be following established mathematical structures.
In doing so, it also:
Accurately and consistently highlights intracycle highs and lows for Bitcoin’s price.
Picks out every market cycle top in Bitcoin’s history.
Forecasts when Bitcoin will top out in the coming market cycle.
To begin, we will use the 350 day moving average of Bitcoin’s price. It has historically been an important moving average because once price moves above it, a new bull run begins.
more ...
medium.com
All rights reserved to Philip Swift (@PositiveCrypto)
VWMA + SMA BBollinger + RSI Strategy (ChartArt) mod by BiO618This is a script I remade from the original ChartArt's "CA_RSI_Bolling_Strat".
I added a VWMA following the SMA basis curve.
BBand was made with the SMA curve, +2DS.
The point of adding VWMA to the script is to get a fast correlation between price change and volume change.
How to interpret it:
Since 3-Intervals-VWMA = (P1*V1 + P2*V2 + P3*V3) / (V1+V2+V3)
As the volume grows, VWMA get smaller.
If the price goes to the upper band, and the VWMA follows it, Price grew more than Volume, and a correction would happen soon.
If the price goes to the lower band, and the VWMA follows it, Price dipped with a lot of Volume, and a continuation of trend would be expected.
If the price goes to the upper band, and the VWMA stays close to SMA, Price grew with a correspondient Volume, and the continuation of trend would be expected.
If the price goes to the lower band, and the VWMA stays close to SMA, Price dipped with low Volume, a correction would happen soon.
Remember that NO INDICATOR is flawless, support your interpretation with other indicators like RSI and MACD.
Hope you enjoy it!
φ!
RSI Donchian R1 Alerts by JustUncleLThis study is based on an idea by presented by RicardoSantos and JayRogers of using Donchian Channel (DC) on the RSI curve. The idea being that when RSI passes through the DC centre and touches the Highest/Lowest DC then price action tends to follow in the same direction and stay there until the RSI crosses DC centre line again.
This script expands on the original idea by including alert and exit signals based on the above rules. These alerts are also filtered by the rule: they must be within the Oversold and Overbought boundaries of the RSI.
There is also the option of applying MA smoothing to the RSI curve, the HullMA (8) is recommended (default).
Each Entry and Exit signal creates an Alertcondition that can be picked up by the TradingView Alarm system.
TIP: Remember this type of Trading technique only works well in a trending market. Do not try to trade this technique in a ranging/flat market.
Power RSI Segment Runner [CHE] Power RSI Segment Runner — Tracks RSI momentum across higher timeframe segments to detect directional switches for trend confirmation.
Summary
This indicator calculates a running Relative Strength Index adapted to segments defined by changes in a higher timeframe, such as daily closes, providing a smoothed view of momentum within each period. It distinguishes between completed segments, which fix the final RSI value, and ongoing ones, which update in real time with an exponential moving average filter. Directional switches between bullish and bearish momentum trigger visual alerts, including overlay lines and emojis, while a compact table displays current trend strength as a progress bar. This segmented approach reduces noise from intra-period fluctuations, offering clearer signals for trend persistence compared to standard RSI on lower timeframes.
Motivation: Why this design?
Standard RSI often generates erratic signals in choppy markets due to constant recalculation over fixed lookback periods, leading to false reversals that mislead traders during range-bound or volatile phases. By resetting the RSI accumulation at higher timeframe boundaries, this indicator aligns momentum assessment with broader market cycles, capturing sustained directional bias more reliably. It addresses the gap between short-term noise and long-term trends, helping users filter entries without over-relying on absolute overbought or oversold thresholds.
What’s different vs. standard approaches?
- Baseline Reference: Diverges from the classic Wilder RSI, which uses a fixed-length exponential moving average of gains and losses across all bars.
- Architecture Differences:
- Segments momentum resets at higher timeframe changes, isolating calculations per period instead of continuous history.
- Employs persistent sums for ups and downs within segments, with on-the-fly RSI derivation and EMA smoothing.
- Integrates switch detection logic that clears prior visuals on reversal, preventing clutter from outdated alerts.
- Adds overlay projections like horizontal price lines and dynamic percent change trackers for immediate trade context.
- Practical Effect: Charts show discrete RSI endpoints for past segments alongside a curved running trace, making momentum evolution visually intuitive. Switches appear as clean, extendable overlays, reducing alert fatigue and highlighting only confirmed directional shifts, which aids in avoiding whipsaws during minor pullbacks.
How it works (technical)
The indicator begins by detecting changes in the specified higher timeframe, such as a new daily bar, to define segment boundaries. At each boundary, it finalizes the prior segment's RSI by summing positive and negative price changes over that period and derives the value from the ratio of those sums, then applies an exponential moving average for smoothing. Within the active segment, it accumulates ongoing ups and downs from price changes relative to the source, recalculating the running RSI similarly and smoothing it with the same EMA length.
Points for the running RSI are collected into an array starting from the segment's onset, forming a curved polyline once sufficient bars accumulate. Comparisons between the running RSI and the last completed segment's value determine the current direction as long, short, or neutral, with switches triggering deletions of old visuals and creation of new ones: a label at the RSI pane, a vertical dashed line across the RSI range, an emoji positioned via ATR offset on the price chart, a solid horizontal line at the switch price, a dashed line tracking current close, and a midpoint label for percent change from the switch.
Initialization occurs on the first bar by resetting accumulators, and visualization gates behind a minimum bar count since the segment start to avoid early instability. The trend strength table builds vertically with filled cells proportional to the rounded RSI value, colored by direction. All drawing objects update or extend on subsequent bars to reflect live progress.
Parameter Guide
EMA Length — Controls the smoothing applied to the running RSI; higher values increase lag but reduce noise. Default: 10. Trade-offs: Shorter settings heighten sensitivity for fast markets but risk more false switches; longer ones suit trending conditions for stability.
Source — Selects the price data for change calculations, typically close for standard momentum. Default: close. Trade-offs: Open or high/low may emphasize gaps, altering segment intensity.
Segment Timeframe — Defines the higher timeframe for segment resets, like daily for intraday charts. Default: D. Trade-offs: Shorter frames create more frequent but shorter segments; longer ones align with major cycles but delay resets.
Overbought Level — Sets the upper threshold for potential overbought conditions (currently unused in visuals). Default: 70. Trade-offs: Adjust for asset volatility; higher values delay bearish warnings.
Oversold Level — Sets the lower threshold for potential oversold conditions (currently unused in visuals). Default: 30. Trade-offs: Lower values permit deeper dips before signaling bullish potential.
Show Completed Label — Toggles labels at segment ends displaying final RSI. Default: true. Trade-offs: Enables historical review but can crowd charts on dense timeframes.
Plot Running Segment — Enables the curved polyline for live RSI trace. Default: true. Trade-offs: Visualizes intra-segment flow; disable for cleaner panes.
Running RSI as Label — Displays current running RSI as a forward-projected label on the last bar. Default: false. Trade-offs: Useful for quick reads; may overlap in tight scales.
Show Switch Label — Activates RSI pane labels on directional switches. Default: true. Trade-offs: Provides context; omit to minimize pane clutter.
Show Switch Line (RSI) — Draws vertical dashed lines across the RSI range at switches. Default: true. Trade-offs: Marks reversal bars clearly; extends both ways for reference.
Show Solid Overlay Line — Projects a horizontal line from switch price forward. Default: true. Trade-offs: Acts as dynamic support/resistance; wider lines enhance visibility.
Show Dashed Overlay Line — Tracks a dashed line from switch to current close. Default: true. Trade-offs: Shows price deviation; thinner for subtlety.
Show Percent Change Label — Midpoint label tracking percent move from switch. Default: true. Trade-offs: Quantifies progress; centers dynamically.
Show Trend Strength Table — Displays right-side table with direction header and RSI bar. Default: true. Trade-offs: Instant strength gauge; fixed position avoids overlap.
Activate Visualization After N Bars — Delays signals until this many bars into a segment. Default: 3. Trade-offs: Filters immature readings; higher values miss early momentum.
Segment End Label — Color for completed RSI labels. Default: 7E57C2. Trade-offs: Purple tones for finality.
Running RSI — Color for polyline and running elements. Default: yellow. Trade-offs: Bright for live tracking.
Long — Color for bullish switch visuals. Default: green. Trade-offs: Standard for uptrends.
Short — Color for bearish switch visuals. Default: red. Trade-offs: Standard for downtrends.
Solid Line Width — Thickness of horizontal overlay line. Default: 2. Trade-offs: Bolder for emphasis on key levels.
Dashed Line Width — Thickness of tracking and vertical lines. Default: 1. Trade-offs: Finer to avoid dominance.
Reading & Interpretation
Completed segment RSIs appear as static points or labels in purple, indicating the fixed momentum at period close—values drifting toward the upper half suggest building strength, while lower half implies weakness. The yellow curved polyline traces the live smoothed RSI within the current segment, rising for accumulating gains and falling for losses. Directional labels and lines in green or red flag switches: green for running momentum exceeding the prior segment's, signaling potential uptrend continuation; red for the opposite.
The right table's header colors green for long, red for short, or gray for neutral/wait, with filled purple bars scaling from bottom (low RSI) to top (high), topped by the numeric value. Overlay elements project from switch bars: the solid green/red line as a price anchor, dashed tracker showing pullback extent, and percent label quantifying deviation—positive for alignment with direction, negative for counter-moves. Emojis (up arrow for long, down for short) float above/below price via ATR spacing for quick chart scans.
Practical Workflows & Combinations
- Trend Following: Enter long on green switch confirmation after a higher high in structure; filter with table strength above midpoint for conviction. Pair with volume surge for added weight.
- Exits/Stops: Trail stops to the solid overlay line on pullbacks; exit if percent change reverses beyond 2 percent against direction. Use wait bars to confirm without chasing.
- Multi-Asset/Multi-TF: Defaults suit forex/stocks on 1H-4H with daily segments; for crypto, shorten EMA to 5 for volatility. Scale segment TF to weekly for daily charts across indices.
- Combinations: Overlay on EMA clouds for confluence—switch aligning with cloud break strengthens signal. Add volatility filters like ATR bands to debounce in low-volume regimes.
Behavior, Constraints & Performance
Signals confirm on bar close within segments, with running polyline updating live but gated by minimum bars to prevent flicker. Higher timeframe changes may introduce minor repaints on timeframe switches, mitigated by relying on confirmed HTF closes rather than intrabar peeks. Resource limits cap at 500 labels/lines and 50 polylines, pruning old objects on switches to stay efficient; no explicit loops, but array growth ties to segment length—suitable for up to 500-bar histories without lag.
Known limits include delayed visualization in short segments and insensitivity to overbought/oversold levels, as thresholds are inputted but not actively visualized. Gaps in source data reset accumulators prematurely, potentially skewing early RSI.
Sensible Defaults & Quick Tuning
Start with EMA length 10, daily segments, and 3-bar wait for balanced responsiveness on hourly charts. For excessive switches in ranging markets, increase wait bars to 5 or EMA to 14 to dampen noise. If signals lag in trends, drop EMA to 5 and use 1H segments. For stable assets like indices, widen to weekly segments; tune colors for dark/light themes without altering logic.
What this indicator is—and isn’t
This tool serves as a momentum visualization and switch detector layered over price action, aiding trend identification and confirmation in segmented contexts. It is not a standalone trading system, predictive model, or risk calculator—always integrate with broader analysis, position sizing, and stop-loss discipline. View it as an enhancement for discretionary setups, not automated alerts without validation.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
HermesHERMES STRATEGY - TRADINGVIEW DESCRIPTION
OVERVIEW
Hermes is an adaptive trend-following strategy that uses dual ALMA (Arnaud Legoux Moving Average) filters to identify high-quality entry and exit points. It's designed for swing and position traders who want smooth, low-lag signals with minimal whipsaws.
Unlike traditional moving averages that operate on price, Hermes analyzes price returns (percentage changes) to create signals that work consistently across any asset class and price range.
HOW IT WORKS
DUAL ALMA SYSTEM
The strategy uses two ALMA lines applied to price returns:
• Fast ALMA (Blue Line): Short-term trend signal (default: 80 periods)
• Slow ALMA (Black Line): Long-term baseline trend (default: 250 periods)
ALMA is superior to simple or exponential moving averages because it provides:
• Smoother curves with less noise
• Significantly reduced lag
• Natural resistance to outliers and flash crashes
TRADING LOGIC
BUY SIGNAL:
• Fast ALMA crosses above Slow ALMA (bullish regime)
• Price makes new N-bar high (momentum confirmation)
• Optional: Price above 200 EMA (macro trend filter)
• Optional: ALMA lines sufficiently separated (strength filter)
SELL SIGNAL:
• Fast ALMA crosses below Slow ALMA (bearish regime)
• Optional: Price makes new N-bar low (momentum confirmation)
The strategy stays in position during the entire bullish regime, allowing you to ride trends for weeks or months.
VISUAL INDICATORS
LINES:
• Blue Line: Fast ALMA (short-term signal)
• Black Line: Slow ALMA (long-term baseline)
TRADE MARKERS:
• Green Triangle Up: Buy executed
• Red Triangle Down: Sell executed
• Orange "M": Buy blocked by momentum filter
• Purple "W": Buy blocked by weak crossover strength
KEY PARAMETERS
ALMA SETTINGS:
• Short Period (default: 30) - Fast signal responsiveness
• Long Period (default: 250) - Baseline stability
• ALMA Offset (default: 0.90) - Balance between lag and smoothness
• ALMA Sigma (default: 7.5) - Gaussian curve width
ENTRY/EXIT FILTERS:
• Buy Lookback (default: 7) - Bars for momentum confirmation (required)
• Sell Lookback (default: 0) - Exit momentum bars (0 = disabled for faster exits)
• Min Crossover Strength (default: 0.0) - Required ALMA separation (0 = disabled)
• Use Macro Filter (default: true) - Only enter above 200 EMA
BEST PRACTICES
RECOMMENDED ASSETS - Works well on:
• Cryptocurrencies (Bitcoin, Ethereum, etc.)
• Major indices (S&P 500, Nasdaq)
• Large-cap stocks
• Commodities (Gold, Oil)
RECOMMENDED TIMEFRAMES:
• Daily: Primary timeframe for swing trading
• 4-Hour: More active trading (increase trade frequency)
• Weekly: Long-term position trading
PARAMETER TUNING:
• More trades: Lower Short Period (60-80)
• Fewer trades: Raise Short Period (100-120)
• Faster exits: Set Sell Lookback = 0
• Safer entries: Enable Macro Filter (Use Macro Filter = true)
STRATEGY ADVANTAGES
1. Low Lag - ALMA provides faster signals than traditional moving averages
2. Smooth Signals - Minimal whipsaws compared to crossover strategies
3. Asset Agnostic - Same parameters work across different markets
4. Trend Capture - Stays positioned during entire bullish regimes
5. Risk Management - Multiple filters prevent poor entries
6. Visual Clarity - Easy to interpret regime and filter states
WHEN TO USE HERMES
BEST FOR:
• Trending markets (crypto bull runs, equity uptrends)
• Swing trading (hold days to weeks)
• Position trading (hold weeks to months)
• Clear trend identification
• Risk-managed exposure
NOT SUITABLE FOR:
• Ranging/sideways markets
• Scalping or day trading
• High-frequency trading
• Mean reversion strategies
RISK DISCLAIMER
This indicator is for educational purposes only. Past performance does not guarantee future results. Always use proper position sizing and risk management. Test thoroughly on historical data before live trading.
CREDITS
Inspired by Giovanni Santostasi's Power Law Volatility Indicator, generalized for universal application across all assets using adaptive ALMA filtering.
Strategy by Hermes Trading Systems
QUICK START
1. Add indicator to chart
2. Use on daily timeframe for best results
3. Look for green buy signals when blue line crosses above black line
4. Exit on red sell signals when blue line crosses below black line
5. Adjust parameters based on your trading style:
• Conservative: Enable Macro Filter, increase Buy Lookback to 10
• Aggressive: Disable Macro Filter, lower Short Period to 60
• Default settings work well for most assets
[PDR] Daily Rebalance█ OVERVIEW
This indicator is a powerful portfolio backtesting tool designed to simulate the performance of a static-weight, daily rebalancing strategy. It allows you to define a portfolio of up to 10 assets, set their target weights, and track its cumulative return against a user-defined benchmark and a risk-free rate.
The core of the script is its daily rebalancing logic, which calculates and logs every trade needed to bring the portfolio back to its target allocations at the close of each day. This provides a transparent and detailed view of how a static portfolio would have performed historically, including the impact of trading costs.
█ KEY FEATURES
Daily Rebalancing: Simulates a portfolio that is rebalanced at the close of every day to maintain target asset allocations.
Customizable Portfolio: Configure up to 10 different assets with specific weights. If all weights are left at 0, the script automatically creates an equal-weight portfolio from the selected assets.
Performance Comparison: Plots the portfolio's equity curve against a user-defined benchmark (e.g., SET:SET50 ) and a risk-free return, allowing for easy relative performance analysis.
Realistic Simulation: Accounts for trading costs like broker commission and minimum lot sizes for more accurate and grounded backtesting results.
Detailed Performance Metrics: An on-chart table displays real-time statistics, including Current Drawdown, Max Drawdown, and Total Return for both your portfolio and the benchmark.
Trade-by-Trade Logs: For full transparency, every rebalancing trade (BUY/SELL), including shares, price, notional value, and fees, is logged in the Pine Logs panel.
█ HOW TO USE
**Apply to a Daily Chart:** This script is designed to work exclusively on the daily ( 1D ) timeframe. Applying it to any other timeframe will result in a runtime error.
**Configure Settings:** Open the indicator's settings. Set your `Initial Capital`, `Start Time`, and the `Benchmark` symbol you wish to compare against.
**Define Your Assets:** In the 'Assets' group, check the box to enable each asset you want to include, select the symbol, and define its target `Weight (%)`.
**Set Trading Costs:** Adjust the `Broker Commission (%)` and `Minimal Buyable Lot` to match your expected trading conditions.
**Analyze the Results:** The performance curves are plotted in the indicator pane below your main chart. The key metrics table is displayed on the bottom-right of your chart.
**View Rebalancing Trades:** This is a crucial step for understanding the simulation. To see the detailed daily trades, you **must** open the **Pine Logs**. You can find this panel at the bottom of your TradingView window, next to the "Pine Editor" and "Strategy Tester" tabs. The logs provide a complete breakdown of every rebalancing action.
█ DISCLAIMER
This is a backtesting and simulation tool, not a trading signal generator. Its purpose is for research and performance analysis. Past performance is not indicative of future results. Always conduct your own research before making any investment decisions.
Lorentzian Harmonic Flow - Temporal Market Dynamic Lorentzian Harmonic Flow - Temporal Market Dynamic (⚡LHF)
By: DskyzInvestments
What this is
LHF Pro is a research‑grade analytical instrument that models market time as a compressible medium , extracts directional flow in curved time using heavy‑tailed kernels, and consults a history‑based memory bank for context before synthesizing a final, bounded probabilistic score . It is not a mashup; each subsystem is mathematically coupled to a single clock (time dilation via gamma) and a single lens (Lorentzian heavy‑tailed weighting). This script is dense in logic (and therefore heavy) because it prioritizes rigor, interpretability, and visual clarity.
Intended use
Education and research. This tool expresses state recognition and regime context—not guarantees. It does not place orders. It is fully functional as published and contains no placeholders. Nothing herein is financial advice.
Why this is original and useful
Curved time: Markets do not move at a constant pace. LHF Pro computes a Lorentz‑style gamma (γ) from relative speed so its analytical windows contract when the tape accelerates and relax when it slows.
Heavy‑tailed lens: Lorentzian kernels weight information with fat tails to respect rare but consequential extremes (unlike Gaussian decay).
Memory of regimes: A K‑nearest‑neighbors engine works in a multi‑feature space using Lorentz kernels per dimension and exponential age fade , returning a memory bias (directional expectation) and assurance (confidence mass).
One ecosystem: Squeeze, TCI, flow, acceleration, and memory live on the same clock and blend into a single final_score —visualized and documented on the dashboard.
Cognitive map: A 2D heat map projects memory resonance by age and flow regime, making “where the past is speaking” visible.
Shadow portfolio metaphor: Neighbor outcomes act like tiny hypothetical positions whose weighted average forms an educational pressure gauge (no execution, purely didactic).
Mathematical framework (full transparency)
1) Returns, volatility, and speed‑of‑market
Log return: rₜ = ln(closeₜ / closeₜ₋₁)
Realized vol: rv = stdev(r, vol_len); vol‑of‑vol: burst = |rv − rv |
Speed‑of‑market (analog to c): c = c_multiplier × (EMA(rv) + 0.5 × EMA(burst) + ε)
2) Trend velocity and Lorentz gamma (time dilation)
Trend velocity: v = |close − close | / (vel_len × ATR)
Relative speed: v_rel = v / c
Gamma: γ = 1 / √(1 − v_rel²), stabilized by caps (e.g., ≤10)
Interpretation: γ > 1 compresses market time → use shorter effective windows.
3) Adaptive temporal scale
Adaptive length: L = base_len / γ^power (bounded for safety)
Harmonic horizons: Lₛ = L × short_ratio, Lₘ = L × mid_ratio, Lₗ = L × long_ratio
4) Lorentzian smoothing and Harmonic Flow
Kernel weight per lag i: wᵢ = 1 / (1 + (d/γ)²), d = i/L
Horizon baselines: lw_h = Σ wᵢ·price / Σ wᵢ
Z‑deviation: z_h = (close − lw_h)/ATR
Harmonic Flow (HFL): HFL = (w_short·zₛ + w_mid·zₘ + w_long·zₗ) / (w_short + w_mid + w_long)
5) Flow kinematics
Velocity: HFL_vel = HFL − HFL
Acceleration (curvature): HFL_acc = HFL − 2·HFL + HFL
6) Squeeze and temporal compression
Bollinger width vs Keltner width using L
Squeeze: BB_width < KC_width × squeeze_mult
Temporal Compression Index: TCI = base_len / L; TCI > 1 ⇒ compressed time
7) Entropy (regime complexity)
Shannon‑inspired proxy on |log returns| with numerical safeguards and smoothing. Higher entropy → more chaotic regime.
8) Memory bank and Lorentzian k‑NN
Feature vector (5D):
Outcomes stored: forward returns at H5, H13, H34
Per‑dimension similarity: k(Δ) = 1 / (1 + Δ²), weighted by user’s feature weights
Age fading: weight_age = mem_fade^age_bars
Neighbor score: sᵢ = similarityᵢ × weight_ageᵢ
Memory bias: mem_bias = Σ sᵢ·outcomeᵢ / Σ sᵢ
Assurance: mem_assurance = Σ sᵢ (confidence mass)
Normalization: mem_bias normalized by ATR and clamped into band
Shadow portfolio metaphor: neighbors behave like micro‑positions; their weighted net forward return becomes a continuous, adaptive expectation.
9) Blended score and breakout proxy
Blend factor: α_mem = 0.45 + 0.15 × (γ − 1)
Final score: final_score = (1−α_mem)·tanh(HFL / (flow_thr·1.5)) + α_mem·tanh(mem_bias_norm)
Breakout probability (bounded): energy = cap(TCI−1) + |HFL_acc|×k + cap(γ−1)×k + cap(mem_assurance)×k; breakout_prob = sigmoid(energy). Caps avoid runaway “100%” readings.
Inputs — every control, purpose, mechanics, and tuning
🔮 Lorentz Core
Auto‑Adapt (Vol/Entropy): On = L responds to γ and entropy (breathes with regime), Off = static testing.
Base Length: Calm‑market anchor horizon. Lower (21–28) for fast tapes; higher (55–89+) for slow.
Velocity Window (vel_len): Bars used in v. Shorter = more reactive γ; longer = steadier.
Volatility Window (vol_len): Bars used for rv/burst (c). Shorter = more sensitive c.
Speed‑of‑Market Multiplier (c_multiplier): Raises/lowers c. Lower values → easier γ spikes (more adaptation). Aim for strong trends to peak around γ ≈ 2–4.
Gamma Compression Power: Exponent of γ in L. <1 softens; >1 amplifies adaptation swings.
Max Kernel Span: Upper bound on smoothing loop (quality vs CPU).
🎼 Harmonic Flow
Short/Mid/Long Horizon Ratios: Partition L into fast/medium/slow views. Smaller short_ratio → faster reaction; larger long_ratio → sturdier bias.
Weights (w_short/w_mid/w_long): Governs HFL blend. Higher w_short → nimble; higher w_long → stable.
📈 Signals
Squeeze Strictness: Threshold for BB1 = compressed (coiled spring); <1 = dilated.
v/c: Relative speed; near 1 denotes extreme pacing. Diagnostic only.
Entropy: Regime complexity; high entropy suggests caution, smaller size, or waiting for order to return.
HFL: Curved‑time directional flow; sign and magnitude are the instantaneous bias.
HFL_acc: Curvature; spikes often accompany regime ignition post‑squeeze.
Mem Bias: Directional expectation from historical analogs (ATR‑normalized, bounded). Aligns or conflicts with HFL.
Assurance: Confidence mass from neighbors; higher → more reliable memory bias.
Squeeze: ON/RELEASE/OFF from BB
Savitzky-Golay Hampel Filter | AlphaNattSavitzky-Golay Hampel Filter | AlphaNatt
A revolutionary indicator combining NASA's satellite data processing algorithms with robust statistical outlier detection to create the most scientifically advanced trend filter available on TradingView.
"This is the same mathematics that processes signals from the Hubble Space Telescope and analyzes data from the Large Hadron Collider - now applied to financial markets."
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🚀 SCIENTIFIC PEDIGREE
Savitzky-Golay Filter Applications:
NASA: Satellite telemetry and space probe data processing
CERN: Particle physics data analysis at the LHC
Pharmaceutical: Chromatography and spectroscopy analysis
Astronomy: Processing signals from radio telescopes
Medical: ECG and EEG signal processing
Hampel Filter Usage:
Aerospace: Cleaning sensor data from aircraft and spacecraft
Manufacturing: Quality control in precision engineering
Seismology: Earthquake detection and analysis
Robotics: Sensor fusion and noise reduction
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🧬 THE MATHEMATICS
1. Savitzky-Golay Filter
The SG filter performs local polynomial regression on data points:
Fits a polynomial of degree n to a sliding window of data
Evaluates the polynomial at the center point
Preserves higher moments (peaks, valleys) unlike moving averages
Maintains derivative information for true momentum analysis
Originally published in Analytical Chemistry (1964)
Mathematical Properties:
Optimal smoothing in the least-squares sense
Preserves statistical moments up to polynomial order
Exact derivative calculation without additional lag
Superior frequency response vs traditional filters
2. Hampel Filter
A robust outlier detector based on Median Absolute Deviation (MAD):
Identifies outliers using robust statistics
Replaces spurious values with polynomial-fitted estimates
Resistant to up to 50% contaminated data
MAD is 1.4826 times more robust than standard deviation
Outlier Detection Formula:
|x - median| > k × 1.4826 × MAD
Where k is the threshold parameter (typically 3 for 99.7% confidence)
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💎 WHY THIS IS SUPERIOR
vs Moving Averages:
Preserves peaks and valleys (critical for catching tops/bottoms)
No lag penalty for smoothness
Maintains derivative information
Polynomial fitting > simple averaging
vs Other Filters:
Outlier immunity (Hampel component)
Scientifically optimal smoothing
Preserves higher-order features
Used in billion-dollar research projects
Unique Advantages:
Feature Preservation: Maintains market structure while smoothing
Spike Immunity: Ignores false breakouts and stop hunts
Derivative Accuracy: True momentum without additional indicators
Scientific Validation: 60+ years of academic research
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⚙️ PARAMETER OPTIMIZATION
1. Polynomial Order (2-5)
2 (Quadratic): Maximum smoothing, gentle curves
3 (Cubic): Balanced smoothing and responsiveness (recommended)
4-5 (Higher): More responsive, preserves more features
2. Window Size (7-51)
Must be odd number
Larger = smoother but more lag
Formula: 2×(desired smoothing period) + 1
Default 21 = analyzes 10 bars each side
3. Hampel Threshold (1.0-5.0)
1.0: Aggressive outlier removal (68% confidence)
2.0: Moderate outlier removal (95% confidence)
3.0: Conservative outlier removal (99.7% confidence) (default)
4.0+: Only extreme outliers removed
4. Final Smoothing (1-7)
Additional WMA smoothing after filtering
1 = No additional smoothing
3-5 = Recommended for most timeframes
7 = Ultra-smooth for position trading
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📊 TRADING STRATEGIES
Signal Recognition:
Cyan Line: Bullish trend with positive derivative
Pink Line: Bearish trend with negative derivative
Color Change: Trend reversal with polynomial confirmation
1. Trend Following Strategy
Enter when price crosses above cyan filter
Exit when filter turns pink
Use filter as dynamic stop loss
Best in trending markets
2. Mean Reversion Strategy
Enter long when price touches filter from below in uptrend
Enter short when price touches filter from above in downtrend
Exit at opposite band or filter color change
Excellent for range-bound markets
3. Derivative Strategy (Advanced)
The SG filter preserves derivative information
Acceleration = second derivative > 0
Enter on positive first derivative + positive acceleration
Exit on negative second derivative (momentum slowing)
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📈 PERFORMANCE CHARACTERISTICS
Strengths:
Outlier Immunity: Ignores stop hunts and flash crashes
Feature Preservation: Catches tops/bottoms better than MAs
Smooth Output: Reduces whipsaws significantly
Scientific Basis: Not curve-fitted or optimized to markets
Considerations:
Slight lag in extreme volatility (all filters have this)
Requires odd window sizes (mathematical requirement)
More complex than simple moving averages
Best with liquid instruments
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🔬 SCIENTIFIC BACKGROUND
Savitzky-Golay Publication:
"Smoothing and Differentiation of Data by Simplified Least Squares Procedures"
- Abraham Savitzky & Marcel Golay
- Analytical Chemistry, Vol. 36, No. 8, 1964
Hampel Filter Origin:
"Robust Statistics: The Approach Based on Influence Functions"
- Frank Hampel et al., 1986
- Princeton University Press
These techniques have been validated in thousands of scientific papers and are standard tools in:
NASA's Jet Propulsion Laboratory
European Space Agency
CERN (Large Hadron Collider)
MIT Lincoln Laboratory
Max Planck Institutes
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💡 ADVANCED TIPS
News Trading: Lower Hampel threshold before major events to catch spikes
Scalping: Use Order=2 for maximum smoothness, Window=11 for responsiveness
Position Trading: Increase Window to 31+ for long-term trends
Combine with Volume: Strong trends need volume confirmation
Multiple Timeframes: Use daily for trend, hourly for entry
Watch the Derivative: Filter color changes when first derivative changes sign
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⚠️ IMPORTANT NOTICES
Not financial advice - educational purposes only
Past performance does not guarantee future results
Always use proper risk management
Test settings on your specific instrument and timeframe
No indicator is perfect - part of complete trading system
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🏆 CONCLUSION
The Savitzky-Golay Hampel Filter represents the pinnacle of scientific signal processing applied to financial markets. By combining polynomial regression with robust outlier detection, traders gain access to the same mathematical tools that:
Guide spacecraft to other planets
Detect gravitational waves from black holes
Analyze particle collisions at near light-speed
Process signals from deep space
This isn't just another indicator - it's rocket science for trading .
"When NASA needs to separate signal from noise in billion-dollar missions, they use these exact algorithms. Now you can too."
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Developed by AlphaNatt
Version: 1.0
Release: 2025
Pine Script: v6
"Where Space Technology Meets Market Analysis"
Not financial advice. Always DYOR
Global Bond Yields Monitor [MarktQuant]Global Bond Yields Monitor
The Global Bond Yields Monitor is designed to help users track and compare government bond yields across major economies. It provides an at-a-glance view of short- and long-term interest rates for multiple countries, enabling users to observe shifts in global fixed-income markets.
Key Features:
Multi-Country Coverage: Includes major advanced and emerging economies such as the United States, China, Japan, Germany, United Kingdom, Canada, Australia, and more.
Multiple Maturities: Displays yields for the 2-year, 5-year, 10-year, and 30-year maturities (20-year for Russia).
Dynamic Yield Data: Plots real-time yields for the selected country directly from TradingView’s data sources.
Weekly Change Tracking: Calculates and displays the yield change from one week ago ( ) for each maturity.
Table Visualization: Option to display a compact data table showing current yields and weekly changes, color-coded for easier interpretation.
Visual Yield Curve Comparison: Plots yield lines for short- and long-term maturities, with shaded areas between curves for visual clarity.
Customizable Display: Choose table placement and whether to show or hide the weekly change table.
Use Cases
This script is intended for analysts, traders, and investors who want to monitor shifts in sovereign bond markets. Changes in yields can reflect adjustments in monetary policy expectations, inflation outlook, or broader macroeconomic trends.
❗Important Note❗
This indicator is for market monitoring and educational purposes only. It does not generate trading signals, and it should not be interpreted as financial advice. All data is sourced from TradingView’s available market feeds, and accuracy may depend on the source data.
Lorentzian Theory Classifier🧮 Lorentzian Theory Classifier: An Observatory for Market Spacetime
Transcend the flat plane of traditional charting. Enter the curved, dynamic reality of market spacetime. The Lorentzian Theory Classifier (LTC) is not an indicator; it is a computational observatory. It is an instrument engineered to decode the geometry of market behavior, revealing the hidden curvatures and resonant frequencies that precede significant turning points.
We discard the outdated tools of Euclidean simplicity and embrace a more profound truth: financial markets, much like the cosmos described by general relativity, are governed by a fabric that is warped by the mass of participation and the energy of volatility. The LTC is your lens to perceive this fabric, to move beyond predicting lines on a chart and begin reading the very architecture of probability.
The Resonance Manifold: Standard Euclidean models search for historical analogues within a rigid sphere, missing the crucial outliers that define market extremes. The LTC's Lorentzian Resonance engine operates in a curved, non-Euclidean space, allowing it to connect with these "fat-tail" events—the true genesis points of major reversals.
🌌 THE THEORETICAL FRAMEWORK: A new Grand Unified Theory of Market Analysis
The LTC is built upon a revolutionary synthesis of concepts from special relativity, quantum mechanics, and information theory. It reframes market analysis not as a problem of forecasting, but as a problem of state recognition in a non-Euclidean manifold.
1. The Lorentzian Kernel: The Mathematics of Reality
Financial markets are not Gaussian. Their reality is one of "fat tails"—sudden, high-impact events that standard models dismiss as anomalies. The LTC acknowledges this reality by using the mathematically pure and robust Lorentzian kernel as its core engine:
Similarity(x, y) = 1 / (1 + (||x − y||² / γ²))
||x − y||²: The squared distance between the current market state (x) and a historical state (y) in our 8-dimensional feature space.
γ (Gamma): A dynamic bandwidth parameter, our "Lorentz factor," which adapts to market entropy (chaos). In calm markets, gamma is small, demanding precise resonance. In chaotic markets, gamma expands, intelligently seeking broader patterns.
This heavy-tailed function is revolutionary. It correctly assigns profound significance to the rare, extreme events that truly define market structure, while gracefully tuning out the noise of mundane price action. It doesn't just calculate; it understands context.
2. The 8-Dimensional State Vector: The Market's Quantum Fingerprint
To achieve a holistic view, the LTC projects the market onto an 8-dimensional Hilbert space, where each dimension represents a critical "observable":
Momentum & Acceleration (f_rsi, f_roc): The market's velocity and its rate of change.
Cyclical Position (f_stoch, f_cci): The market's location within its recent oscillation cycles.
Energy & Participation (f_vol, f_cor): The force of capital flow and its harmony with price.
Chaos & Uncertainty (f_ent, f_mom): The degree of randomness and the standardized force of price changes.
These are not eight separate indicators. They are entangled properties of a single "market wavefunction." The LTC's genius lies in measuring the geometric distance between these complete quantum states.
3. The k-NN Oracle: A Council of Past Universes
The LTC employs a k-Nearest Neighbors algorithm, but in our curved Lorentzian spacetime. It poses a constant, profound question: " Which moments in history are most geometrically congruent to the present moment across all eight dimensions? "
It then summons a "council" of these historical neighbors. Each neighbor's future outcome (did price ascend or descend?) casts a vote, weighted by its resonant similarity. The result is a probabilistic forecast of stunning clarity:
Prognosis: The final weighted consensus on future direction.
Assurance: The degree of unanimity within the council—a direct measure of the prediction's confidence.
The Funnel of Conviction: The LTC's process is a rigorous distillation of information. Raw, chaotic market data is resolved into a clean 8-dimensional state vector. The Lorentzian Kernel filters these states for resonance, which are then passed to the k-NN Oracle for a vote. Noise is eliminated at each stage, resulting in a single, validated, high-conviction signal.
⚙️ THE COMMAND CONSOLE: A Guide to Calibrating Your Observatory
Mastering the LTC's inputs is to become an architect of your own analytical universe. Each parameter is a dial that tunes the observatory's focus, from galactic structures to subatomic fluctuations. The tooltips in-script—over 6,000 words of documentation—provide immediate reference; this guide provides the philosophy.
A summarized guide to the Core, Signal, Supreme, and Visual controls is included directly in the indicator's code and tooltips. We encourage all users to explore these settings to tune the LTC to their unique analytical style.
🏆 THE SUPREME DASHBOARD: Your Mission Control
The dashboard is not a data table; it is your command interface with market reality. It translates the intricate dance of probabilities and vectors into clear, actionable intelligence.
⚡ ORACLE STATUS
Prognosis: The primary directional vector. Its color, magnitude, and emoji (⚡) reveal the strength and conviction of the Oracle's forward guidance.
Assurance: A real-time gauge of prediction quality, from "LOW" (high uncertainty) to "ELITE" (overwhelming statistical consensus). Interpret this as your core risk metric: trade with conviction when Assurance is ELITE; trade with caution when it is LOW.
🔮 RESONANCE ANALYSIS
Chaos: A direct measurement of market entropy. "LOW CHAOS" signifies a predictable, orderly regime. "HIGH CHAOS" is a warning of randomness and unpredictability, where trend-following logic may fail.
Turbulence: A measure of raw volatility. When the market is "TURBULENT," expect wider price swings and increased risk. Use this metric to adjust stop-loss distances and profit targets dynamically.
🏆 PERFORMANCE & ⚔️ GUARD METRICS
These sections provide illustrative statistics on the script's recent historical behavior. Metrics like Yield Ratio and Guard Index offer a quick heuristic on the prevailing risk-reward environment. Crucially, these are for observational context only and are not a substitute for your own rigorous testing and analysis.
🎨 THE VISUAL MANIFESTATION: Charting the Unseen
The LTC's visuals are designed to transform your chart from a 2D price graph into a 4D informational battlespace.
The Dynamic Aura (Background Color): This is the ambient energy field of the market. A luminous green (Ascend) signifies a bullish resonance field; a deep red (Descend) indicates bearish pressure.
The Assurance Shroud (Blue Bands): A visualization of confidence. When the shroud is wide and expansive , the Oracle's vision is clear and its predictions are robust.
The Prognosis Arc (Curved Line): A geodesic projection of the market's most likely path, based on the current Prognosis.
The Turbulence Cloud (Orange Mist): A visual warning system for market chaos. When this entropic mist expands , it is a clear sign that you are navigating a nebula of high unpredictability.
Oracle Markers (▲▼): The final, validated signals. These are not merely pivot points. They are moments in spacetime where a structural pivot has been confirmed and then ratified by a high-conviction vote from the Lorentzian Oracle. They are the pinnacles of confluence.
The Analyst's Observatory: The LTC transforms your chart into a command center for market analysis, providing a complete, at-a-glance view of market state, risk, and probabilistic trajectory.
🔧 THE ARCHITECT'S VISION: From a Blank Slate to a New Cosmos
The LTC was not assembled; it was derived. It began not with code, but with first principles, asking: "If we were to build an instrument to measure the market today, unbound by the technical dogmas of the 20th century, what would it look like?" The answer was clear: it must be multi-dimensional, it must be adaptive, and it must be built on a mathematical framework that respects the "fat-tailed" nature of reality.
The decision to use a pure Lorentzian kernel was non-negotiable. It represented a commitment to intellectual honesty over computational ease. The development of the Supreme Dashboard was driven by the philosophy of the "glass cockpit"—a belief that a trader's greatest asset is not a black box signal, but a transparent and intuitive flow of high-quality information. This script is the result of that unwavering vision: to create not just another indicator, but a new lens through which to perceive the market.
⚠️ RISK DISCLOSURE & PHILOSOPHY OF USE
The Lorentzian Theory Classifier is an instrument of profound analytical power, intended for the serious, discerning trader. It does not generate infallible signals. It generates high-probability, data-driven hypotheses based on a rigorous and transparent methodology. All trading involves substantial risk, and the future is fundamentally unknowable. Past performance, whether real or simulated, is no guarantee of future results. Use this tool to augment your own skill, to confirm your own analysis, and to manage your own risk within a well-defined trading plan.
"The effort to understand the universe is one of the very few things that lifts human life a little above the level of farce, and gives it some of the grace of tragedy."
— Steven Weinberg, Nobel Laureate in Physics
Trade with rigor. Trade with perspective. Trade with enlightenment. Trade with insight. Trade with anticipation.
— Dskyz, for DAFE Trading Systems
