Distance from the High/Low priceThis indicator shows how far the price is from the Top and Bottom over a set period of time.
The basic purpose of this indicator is to quickly compare how many symbols have risen over a certain period of time.
For example,
For example, let's say I want to see what the maximum increase is from the December, and how much it's currently down from there.
Then, let's set the "Length" to approximately 1500 and check it from December 18th.
So now you can see that bitcoin is up to about 44%, and it's down 6.9% from its peak.
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For the second example, let's say I want to see what the maximum increase in ALPHA is and how far it is currently from that maximum.
So, as you can see in the chart above, the maximum increase over the period was about 120%, and now it's down by 22.8%.
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In addition, if you check 'Retracement' in the indicator setting, you can see the ratio of the currently located returns based on Top and Bottom.
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이 지표는, 특정 기간동안 여러개의 symbol들이 얼마만큼의 상승을 했는지 빠르게 비교하기 위해 만들었습니다.
위에 첨부한 사진을 기준으로 말씀드리겠습니다.
2022년 12월 말부터 올라온 상승의 최대폭이 얼마인지, 그리고 그 최대 상승으로부터 현재 얼마나 떨어졌는지를 확인하고 싶은 상황이라고 하겠습니다.
그렇다면 'Length'를 대략 1500으로 설정하여 12월 18일부터 확인해보겠습니다.
그러면 비트코인은 최대 약 44%만큼 상승하였고, 현재 최고점으로부터 6.9% 떨어진 상황이라는 것을 확인할 수 있습니다.
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두 번째 예시로, ALPHA의 최대 상승폭이 얼마인지, 그리고 그 최댓값으로부터 현재 얼마만큼 떨어져 있는 상황인지를 확인하고 싶다고 가정해보겠습니다.
그렇다면 위의 차트에서 보이는 바와 같이, 해당 기간동안 최대 상승폭이 약 120%였고, 현재 그 최댓값으로부터 22.8%정도 하락한 상황이라는 것을 확인할 수 있습니다.
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번외로, 지표 설정에서 'Retracement'를 체크하시면, Top과 Bottom을 기준으로 현재 위치한 되돌림의 비율을 확인할 수 있습니다.
Regressions
Premium Linear Regression - The Quant ScienceThis script calculates the average deviation of the source data from the linear regression. When used with the indicator, it can plot the data line and display various pieces of information, including the maximum average dispersion around the linear regression.
The code includes various user configurations, allowing for the specification of the start and end dates of the period for which to calculate linear regression, the length of the period to use for the calculation, and the data source to use.
The indicator is designed for multi-timeframe use and to facilitate analysis for traders who use regression models in their analysis. It displays a green linear regression line when the price is above the line and a red line when the price is below. The indicator also highlights areas of dispersion around the regression using circles, with bullish areas shown in green and bearish areas shown in red.
XYZ Super Fibonacci Channel Cluster
Simple setups
Just input two different ema, X and Y.
Multiple = input Phi factor (ex: 0.38 , 0.618 , 1.618 , 3.14)
Usage
Grouping movements into channels to identify trend acceleration and deceleration
Example usability in the BTC/USD trading pair (timeframe = 1D) =>
Input Setups
Source = hlc3
Multiplier = 2
X Ema = 13
Y Ema = 21
How to identify acceleration and deceleration?
H_1 to H_2 => Bullish but no acceleration (because at same top level border).
H_2 to H_3 => Bullish with acceleration (go up to another top level border).
H_3 to H_4 / H_4 to H_5 => Bullish deceleration (because drop to another top level border).
L_1 to L_2 => Bearish signal (because fall below EMA-super and touch the bottom border of Super Channel).
L_2 to L_3 => Bearish acceleration (drop to another bottom level border).
L_3 to L_4 => Bearish deceleration (go up to another bottom level border).
Pivot and Price DiscoveryA Population Sampled linear regression model that provides additional detail about the distribution moments (skew, kurtosis, variance and mean) as well as providing indicators that track when a pivot has enough momentum to trade on as well as expected ranges of future price action based on Std Devs.
For the momentum lines -- red indicates that there has been a reducing pivot with momentum, this continues as a grey line for continuation, and will be cancelled when an increasing pivot with momentum is encountered.
Forward looking trend triangle captures the +/- stated standard deviation from the latest bar_index over 2 periods. Movements that trace outside of this can be considered a precursor to an upcoming pivot, and by analyzing skewness and kurtosis, the probability of an upcoming pivot should be better understood.
I have really only looked at this for timescales greater than 5 minutes. Adjust the lookback length accordingly when moving to different timescales:
For example, 1 hr at 10m timescale will be a lookback length of 6 which is too low for accurate analysis, so keep the lookback length appropriate for the timescales being used.
Also realize that trade volume will skew the deviations and regression if you are including data outside of regular trading hours (futures are different, but also experience volume sensitivity -- I maylook into accounting for this in future versions.)
© TheGeeBee
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
Ac Full Scalping 1.0These unified indicators are used for a 5-minute scalping strategy.
We regularly look for the RSI to be overbought and the price to be outside the bollinger bands as the main analysis.
This serves as a search protocol, to then analyze the price action by visually assisting us with 4 exponential moving averages to see wear or breakout of a move.
It also adds the distance from the price close to the 10-period exponential moving average, developed in two modes where you can mark a background color where the event occurs, or you can choose a shadow that is drawn from the exponential moving average to the closing price.
These two modes can be activated or deactivated so that each person can choose the most visually comfortable way to observe that distance, it is recommended to use one at a time and not both at the same time.
The distance indicator can also be used to change the distance percentage. The percentage as a minimum value admits 0.50%, but it is recommended to use it above 0.80% to make the analysis more effective.
People can also change colors of exponential moving averages, but it is not recommended, and the period cannot be changed to keep the analysis more specific.
The RSI indicator should be added separately, as it is used to see overbought values and divergences.
The other indicators are unified but can be turned on or off for better analysis.
As a summary, what is sought with this type of unified indicators is the attrition, break or retracement in 5-minute time frame to open only short trades.
Back to the FutureHallo, very simple indicator in order to view trends
we have two linear regressions
one is the regular one that we know at length 100
the other one is lagging or past linear which is shorter at length 30
the basic idea is that when we combine both we can see trend of the current and the past linear when they cross each other and from this we can make signals.
Assuming that past shorter trend has the value of resistance or threshold values, so cross of current linear of those points can show if the trend is to buy or to sell by signals seen in the arrows .
So past and present mix and give us the future.
need to solve issue when market goes sideways but it easy to see how the trend look by the signals .
past linear seen in concave lines the current is the other one.
signals of positive trends are arrow up green or blue. negative trend red or orange arrow down
R Squared - MomentumThis little oscillator just returns the R Squared Value of current price action.
It is designed to show trend direction momentum. Great for confluence!
Market Crashes/Chart Timeframes HighlightThis extremely helpful indicator allows you to highlight 7 custom date-based timeframes on your charts.
The default dates selected are what I consider to be the most significant 7 most recent market declines, including and since the 87 flash crash.
Note: The default dates are approximate but good enough to highlight the key timeframes of these pullbacks/crashes/corrections.
It's simple to use and does exactly what it should.
I created this indicator to make it easier when looking at the overall story of a chart. I found it helpful to highlight these areas to see how a market or equity has responded during these significant market pullbacks.
The highlight alone I’ve found helpful, and it becomes more powerful if you combine it with your own trusted trade system.
Also, to get the most out of using the default dates it’s important to understand the narrative behind each pullback/crash. Here’s the list of what I consider significant pullbacks:
Black Monday - Oct 87
1990s Recession - Jul 90 to Mar 91
Dot Com Bubble - 2000 to 2002 or so
Real Estate 2008 Crisis - I choose 2007-2009 to cover full insider knowledge and aftermath
2016 - 2018 - This isn't seen as a pullback, but I have it as significant because in many markets and equities, this was an almost equal percentage pullback as 2008. See Notes below
2020 Crash - Covid-19 and related shenanigans pullback
April 2021 to August 2022 - I believe we are in a current SHORT cycle so I've highlighted April 2021 as the start of what might be the start of a major decline testing Dot Com or lower levels.
A few notes on the above.
You'll find on most of the pullbacks listed above most equities and related markets behave similarly or have similar patterns.
The 2016-18 pullback is the most difficult to track. For instance, GE in this timeframe had a -80% decline, whereas BA depending on how you want to measure it had a 50-110% gain.
Correlated ATR Bands | AdulariHow do I use it?
Never use this indicator as standalone trading signal, it should be used as confluence.
It is highly recommended to use this indicator on the 15m timeframe and above, try experimenting with the inverse feature and multipliers as well.
When the price is above the moving average this shows the bullish trend is strong.
When the price is below the moving average this shows the bearish trend is strong.
When the moving average is purple, the trend is bullish , when it is gray, the trend is bearish.
When price is above the upper band this may indicate a bearish reversal.
When price is below the lower band this may indicate a bullish reversal.
Features:
Purple line for bullish trend and gray line for bearish trend.
Custom formula combining an ATR and Hull MA to clearly indicate trend strength and direction.
Unique approach to moving averages and bands by taking the average of 2 types of MA's combined with custom ATR's, then multiplying these by correlation factors.
Bands to indicate possible trend reversals when price crosses them.
How does it work?
1 — ATR value is calculated, then the correlation between the source and ATR is calculated.
2 — Final value is calculated using the following formula:
correlation * atr + (1 - correlation) * nz(atr , atr)
3 — Moving average is calculated with the following formula:
ta.hma((1-(correlation/100*(1+weight/10)))*(ta.sma(source+value, smoothing)+ta.sma(source-value,smoothing))/2,flength)
4 — Bands calculation using multipliers.
Correlated ATR MA | AdulariHow do I use it?
Never use this indicator as standalone trading signal, it should be used as confluence.
When the price is above the moving average this shows the bullish trend is strong.
When the price is below the moving average this shows the bearish trend is strong.
When the moving average is purple, the trend is bullish, when it is gray, the trend is bearish.
Features:
Purple line for bullish trend and gray line for bearish trend.
Custom formula combining an ATR and Hull MA to clearly indicate trend strength and direction.
Unique approach to moving averages by taking the average of 3 types of MA's combined with custom ATR's.
How does it work?
1 — ATR value is calculated, then the correlation between the source and ATR is calculated.
2 — Signal value is calculated from the difference between the previous source and ATR values.
3 — Final value is being calculated using the following formula:
cor * target + (1 - cor) * nz(atr , target)
4 — Moving average is calculated by getting the average of 3 values: a normal HMA, HMA plus final value, and HMA minus final value.
WaveTrend 3D█ OVERVIEW
WaveTrend 3D (WT3D) is a novel implementation of the famous WaveTrend (WT) indicator and has been completely redesigned from the ground up to address some of the inherent shortcomings associated with the traditional WT algorithm.
█ BACKGROUND
The WaveTrend (WT) indicator has become a widely popular tool for traders in recent years. WT was first ported to PineScript in 2014 by the user @LazyBear, and since then, it has ascended to become one of the Top 5 most popular scripts on TradingView.
The WT algorithm appears to have origins in a lesser-known proprietary algorithm called Trading Channel Index (TCI), created by AIQ Systems in 1986 as an integral part of their commercial software suite, TradingExpert Pro. The software’s reference manual states that “TCI identifies changes in price direction” and is “an adaptation of Donald R. Lambert’s Commodity Channel Index (CCI)”, which was introduced to the world six years earlier in 1980. Interestingly, a vestige of this early beginning can still be seen in the source code of LazyBear’s script, where the final EMA calculation is stored in an intermediate variable called “tci” in the code.
█ IMPLEMENTATION DETAILS
WaveTrend 3D is an alternative implementation of WaveTrend that directly addresses some of the known shortcomings of the indicator, including its unbounded extremes, susceptibility to whipsaw, and lack of insight into other timeframes.
In the canonical WT approach, an exponential moving average (EMA) for a given lookback window is used to assess the variability between price and two other EMAs relative to a second lookback window. Since the difference between the average price and its associated EMA is essentially unbounded, an arbitrary scaling factor of 0.015 is typically applied as a crude form of rescaling but still fails to capture 20-30% of values between the range of -100 to 100. Additionally, the trigger signal for the final EMA (i.e., TCI) crossover-based oscillator is a four-bar simple moving average (SMA), which further contributes to the net lag accumulated by the consecutive EMA calculations in the previous steps.
The core idea behind WT3D is to replace the EMA-based crossover system with modern Digital Signal Processing techniques. By assuming that price action adheres approximately to a Gaussian distribution, it is possible to sidestep the scaling nightmare associated with unbounded price differentials of the original WaveTrend method by focusing instead on the alteration of the underlying Probability Distribution Function (PDF) of the input series. Furthermore, using a signal processing filter such as a Butterworth Filter, we can eliminate the need for consecutive exponential moving averages along with the associated lag they bring.
Ideally, it is convenient to have the resulting probability distribution oscillate between the values of -1 and 1, with the zero line serving as a median. With this objective in mind, it is possible to borrow a common technique from the field of Machine Learning that uses a sigmoid-like activation function to transform our data set of interest. One such function is the hyperbolic tangent function (tanh), which is often used as an activation function in the hidden layers of neural networks due to its unique property of ensuring the values stay between -1 and 1. By taking the first-order derivative of our input series and normalizing it using the quadratic mean, the tanh function performs a high-quality redistribution of the input signal into the desired range of -1 to 1. Finally, using a dual-pole filter such as the Butterworth Filter popularized by John Ehlers, excessive market noise can be filtered out, leaving behind a crisp moving average with minimal lag.
Furthermore, WT3D expands upon the original functionality of WT by providing:
First-class support for multi-timeframe (MTF) analysis
Kernel-based regression for trend reversal confirmation
Various options for signal smoothing and transformation
A unique mode for visualizing an input series as a symmetrical, three-dimensional waveform useful for pattern identification and cycle-related analysis
█ SETTINGS
This is a summary of the settings used in the script listed in roughly the order in which they appear. By default, all default colors are from Google's TensorFlow framework and are considered to be colorblind safe.
Source: The input series. Usually, it is the close or average price, but it can be any series.
Use Mirror: Whether to display a mirror image of the source series; for visualizing the series as a 3D waveform similar to a soundwave.
Use EMA: Whether to use an exponential moving average of the input series.
EMA Length: The length of the exponential moving average.
Use COG: Whether to use the center of gravity of the input series.
COG Length: The length of the center of gravity.
Speed to Emphasize: The target speed to emphasize.
Width: The width of the emphasized line.
Display Kernel Moving Average: Whether to display the kernel moving average of the signal. Like PCA, an unsupervised Machine Learning technique whereby neighboring vectors are projected onto the Principal Component.
Display Kernel Signal: Whether to display the kernel estimator for the emphasized line. Like the Kernel MA, it can show underlying shifts in bias within a more significant trend by the colors reflected on the ribbon itself.
Show Oscillator Lines: Whether to show the oscillator lines.
Offset: The offset of the emphasized oscillator plots.
Fast Length: The length scale factor for the fast oscillator.
Fast Smoothing: The smoothing scale factor for the fast oscillator.
Normal Length: The length scale factor for the normal oscillator.
Normal Smoothing: The smoothing scale factor for the normal frequency.
Slow Length: The length scale factor for the slow oscillator.
Slow Smoothing: The smoothing scale factor for the slow frequency.
Divergence Threshold: The number of bars for the divergence to be considered significant.
Trigger Wave Percent Size: How big the current wave should be relative to the previous wave.
Background Area Transparency Factor: Transparency factor for the background area.
Foreground Area Transparency Factor: Transparency factor for the foreground area.
Background Line Transparency Factor: Transparency factor for the background line.
Foreground Line Transparency Factor: Transparency factor for the foreground line.
Custom Transparency: Transparency of the custom colors.
Total Gradient Steps: The maximum amount of steps supported for a gradient calculation is 256.
Fast Bullish Color: The color of the fast bullish line.
Normal Bullish Color: The color of the normal bullish line.
Slow Bullish Color: The color of the slow bullish line.
Fast Bearish Color: The color of the fast bearish line.
Normal Bearish Color: The color of the normal bearish line.
Slow Bearish Color: The color of the slow bearish line.
Bullish Divergence Signals: The color of the bullish divergence signals.
Bearish Divergence Signals: The color of the bearish divergence signals.
█ ACKNOWLEDGEMENTS
@LazyBear - For authoring the original WaveTrend port on TradingView
@PineCoders - For the beautiful color gradient framework used in this indicator
@veryfid - For the inspiration of using mirrored signals for cycle analysis and using multiple lookback windows as proxies for other timeframes
Nadaraya-Watson: Envelope (Non-Repainting)Due to popular request, this is an envelope implementation of my non-repainting Nadaraya-Watson indicator using the Rational Quadratic Kernel. For more information on this implementation, please refer to the original indicator located here:
What is an Envelope?
In technical analysis, an "envelope" typically refers to a pair of upper and lower bounds that surrounds price action to help characterize extreme overbought and oversold conditions. Envelopes are often derived from a simple moving average (SMA) and are placed at a predefined distance above and below the SMA from which they were generated. However, envelopes do not necessarily need to be derived from a moving average; they can be derived from any estimator, including a kernel estimator such as Nadaraya-Watson.
How to use this indicator?
Overall, this indicator offers a high degree of flexibility, and the location of the envelope's bands can be adjusted by (1) tweaking the parameters for the Rational Quadratic Kernel and (2) adjusting the lookback window for the custom ATR calculation. In a trending market, it is often helpful to use the Nadaraya-Watson estimate line as a floating SR and/or reversal zone. In a ranging market, it is often more convenient to use the two Upper Bands and two Lower Bands as reversal zones.
How are the Upper and Lower bounds calculated?
In this indicator, the Rational Quadratic (RQ) Kernel estimates the price value at each bar in a user-defined lookback window. From this estimation, the upper and lower bounds of the envelope are calculated based on a custom ATR calculated from the kernel estimations for the high, low, and close series, respectively. These calculations are then scaled against a user-defined multiplier, which can be used to further customize the Upper and Lower bounds for a given chart.
How to use Kernel Estimations like this for other indicators?
Kernel Functions are highly underrated, and when calibrated correctly, they have the potential to provide more value than any mundane moving average. For those interested in using non-repainting Kernel Estimations for technical analysis, I have written a Kernel Functions library that makes it easy to access various well-known kernel functions quickly. The Rational Quadratic Kernel is used in this implementation, but one can conveniently swap out other kernels from the library by modifying only a single line of code. For more details and usage examples, please refer to the Kernel Functions library located here:
[MAD] Fibonacci retracementThis is just a Fibonacci Retracement tool with some interactive information based on the actual closing price
How to use:
add the script,
input left bottom with the 1st click,
input top with the 2nd click
Informations you can see than:
Fiblevel (Price) %till_this_point = pricedifference
additional:
Bottom of the fib
Range Up in % + Price-Range
Range Down in %
you can shift the comma with the decimal functions for trading shitcoins as example
if looking into the past, level/price will follow, liveinfo using the close is than hidden
what will follow:
reverse
log/linear
autogrow when range will be wicked
maybe alerts on levels... have to think about how to capture correctly
Spider Lines For Bitcoin (Daily And Weekly)I haven't seen any indicator do this, so I decided to publish this to form automatic Spider Charts without actually going through the effort of drawing them!
This script charts dotted lines (spider lines) all over, depicting support and resistance levels.
It works by connecting some candles from the 2018 bear market to the candle from 1st July 2019, followed by extending the lines to the right, making support and resistance levels for the future. The script works only for the daily and weekly charts for Bitcoin.
The levels are accurate to a good extent.
If the lines don't load, zoom out until the 2018 bear market top and it should load then.
Have fun with this indicator!
Three Linear Regression ChannelsPlot three linear regression channels using alexgrover 's Computing The Linear Regression Using The WMA And SMA indicator for the linear regression calculations.
Settings
Length : Number of inputs to be used
Source : Source input of the indicator
Midline Colour : The colour of the midline
Channel One, Two, and Three Multiplicative Factor : Multiplication factor for the RMSE, determine the distance between the upper and lower level
Channel One, Two, and Three Colour : The channel's lines colour
Usage
For usage details, please refer to alexgrover 's Computing The Linear Regression Using The WMA And SMA indicator.
Attrition Scalper v2.0Green/Red Arrowed Buy/Sell signals are just simple buy sell signals based on SuperTrend, VWAP, Bollinger, Linear Regression
Purple Arrowed Buy/Sell Signals happen when the price/candle cross over or under the yellow outer lines (4.236 fib lines) It's extremely rare and hard for price to stay above these lines therefore we can usually and comfortably buy/sell it, a key information here though when price pumps or dumps super fast and hard to the point of crossing these borders, the trend might also be extremely strong and continous so even if the price temporarily goes back inside the borders as the lines expand over time price can continue riding or crossing these lines back again and continue the uptrend/downtrend, therefore crossing these outer borders doesn't necessarilly and always mean a reversal is due.
When analyzing the instrument you're trading the important factors for support/resistance areas are usually the outer lines like i said previously it's super hard for price to be outside these and will almost always get back inside quickly. The Middle thicker green/red line which is Variable Index Dynamic Average should also be a nice pivot line for major support and resistance . All the other lines are also important dynamic support/resistance lines.
Their Importance Order
1- Outer Yellow Line (4.236 Fibs)
2- Thicker Middle Green/Red Line (VIDYA)
3- Thinner Upper/Lower Green/Red Line (VIDYA +3, VIDYA -3)
4- The Rest Of The Lines (Fib Lines)
You can use this indicator in any market condition in any market to determine key support/resistance levels, use it for mean reversion through price expanding to outside of the most outer line therefore being overbought/oversold basically using the purple buy/sell signals or only follow the normal buy/sell signals or use it in confluence with each other. You can also use this indicator in confluence with your own manual technical analysis or other indicators/strategies you are already using and are comfortable with.
A good part is the support/resistance lines from timeframe to timeframe pictures the whole situation quite well, you can use lower timeframe to find your entry/exit positions and higher timeframe to find your key support/resistance points, they all should be somewhat in confluence from timeframe to timeframe anyways. My recommendation would be to look at 1HR, 4HR and 1D charts for swing trading and 5-15 Min for quick scalping/day trading
You should still probably at least take a look to higher timeframes so that you don't get burned when you realize there is a huge resistance line at price XXXXX on the 4 hour chart but you're expecting it to go above it on the 5 minute chart, it can go above it temporarily but we analyze everything on a closing basis so it most likely won't close above it. Again don't take a position or FOMO when price breaks a support/resistance line, we're looking for a CLOSE above/below them and a retest to see if S/R flip happened would even be better.
Sometimes the most outer line won't be the 4.236 (Yellow) lines as when it gets quite volatile the Thinner Upper/Lower Green/Red Lines (VIDYA +3, VIDYA-3) might cross them to be the most outer line, in this case i have observed that the trend is extremely strong this time price almost always doesn't go above or below the VIDYA line but can stay outside of the Yellow 4.236 Fib line for an extended amount of time (price will still get back inside the channel relatively quickly, just not as fast as the normal condition)
With Proper Risk Management and Discipline this indicator can be of great use to you as it's surprisingly successful especially at mean reversion and pointing out the support/resistance lines, they are so much more successful than your average MA/EMA lines.
Multi-Optimized Linear Regression ChannelA take on alexgrover 's Optimized Linear Regression Channel script which allows users to apply multiple linear regression channel with unique multiplicative factors.
Multiplicative Factors
Adjust the amount of channels and multiplicative factors of existing or additional channels using the "Mults" input.
An input of "1" creates a single linear regression channel with the multiplicative factor of one.
An input of "4" creates a single linear regression channel with the multiplicative factor of four.
An input of "1,4" creates two linear regression channels with multiplicative factors of one and four.
An input of "1,2,3" creates three linear regression channels with multiplicative factors of one, two, and three.
DB Change Forecast ProDB Change Forecast Pro
What does the indicator do?
The DB Change Forecast Pro is a unique indicator that uses price change on HLC3 to detect buy and sell periods along with plotting a linear regression price channel with oversold and undersold zones. It also has a linear regression change forecast mode to optionally project market direction.
Change is calculated by taking a two-bar change of HLC3 and dividing that by the price or, optionally, a fixed divisor.
A fast-moving change cloud is then calculated and displayed as the "regular version" plot (shown in light gray). When the cloud bottom is above low, a buy zone is detected. When the cloud top is below the high, a sell zone is detected.
The linear regression price channel is calculated similarly but using a much slower change rate. The linear regression price channel shows reasonable high, low and HLC3 ranges. At the bar's opening, the channel will be more compact and come fairly accurate about 1/4 into the bar timeframe.
The change forecasted price is projected on the right side of the current bar to indicate the current timeframe direction. Please note this forecasting feature is shown in orange when it's early in the timeframe and gray when the timeframe is more likely to produce an accurate direction forecast for the upcoming bar.
You can use these projected dashed lines to see possible market movements for the Current bar and possible market direction for the next bar. Kindly note these projects change; they should be used to understand possible extreme highs/lows for the current bar or market direction.
The indicator includes an optional change forecast projection feature hidden by default. It will project the market forecast channel with an offset of 1. The forecast is defaulted to an offset of 1 to show market direction. However, you can modify to zero the offset to show the current bar forecast and forecast history.
How should this indicator be used?
First, very important,
1. Settings > Set Symbol to Desired
2. Settings > Set High Timeframe to "Chart"
3. Settings > Ensure "Use price as divisor" is checked.
It's recommended to use this indicator in higher timeframes. Buy and sell signals are displayed in real-time. However, waiting until 1/4 to 1/2 into the current bar is recommended before taking action, and change can happen.
The buy/sell signals (zones) provide recommendations on playing a long vs. a short. When in a buy sone, only play longs. When in a sell zone, only play shorts.
Then use the linear regression price channel oversold and undersold zones to optionally open and close positions within the buy/sell zones.
For example, consider opening a long in a buy zone when the linear regression price channel shows undersold. Then consider closing the long when the price moves into the linear regression oversold or higher. Then repeat as long as it's in the buy zone. Then vice versa for sell zones and shorting.
At basic design, buy in the buy zone, sell or short in the sell zone. If you are up for higher trading frequencies, use the linear regression price channel as described in the example above.
Please note, as, with all indicators, you may need to adjust to fit the indicator to your symbol and desired timeframe.
This is only an example of use. Please use this indicator as your own risk and after doing your due diligence.
Does the indicator include any alerts?
Yes,
"DB CFHLC3: Signal BUY" - Is triggered when a buy signal is fired.
"DB CFHLC3: Signal SELL" - Is triggered when a sell signal is fired.
"DB CFHLC3: Zone BUY" - Is triggered when a buy zone is detected.
"DB CFHLC3: Zeon SELL" - Is triggered when a sell zone is detected.
"DB CFHLC3: Oversold SELL" - Is triggered when the price exceeds the oversold level.
"DB CFHLC3: Undersold BUY" - Is triggered when the price goes below the undersold level.
Any other tips?
Once you have configured the indicator for your symbol and chart timeframe. Meaning the plots are displayed over the price. Check out larger timeframes such as W, 2W, 3W, 4W, M, and 4M. It works wonderfully for showing market lows and highs for long-term investing too!
Another, tip is to combine it with your favorite indicator, such as TTM Squeeze or MACD for confirmation purposes. You may be surprised how fast the indicator shows market direction changes on higher timeframes.
You can just as easily use a high timeframe such as D, 2D, or 3D for day trading due to how the linear price channel works.
Why am I not selling this indicator?
I would like to bless the TradingView community, and I enjoy publishing custom indicators.
If you enjoy this indicator, please consider leaving a thumbs up or a comment for others to know about your experience or recommendations.
Enjoy!
Converging Pullbacks and PeaksMulti Timeframe Converging Lines Indicator. Using the highest/lowest Values at 2 different lengths. Convergence created by taking the highest/lowest value and subtracting/adding the # of barssince the highest/lowest bar was set multiplied by the price multiplied by the float. Curves are created from averaging out the emas of the center lines of the extremeties.
Helps show trendlines automatically most of the time but can be tweaked by changing the floats or Fast/Slow lengths to you liking.
LinReg-MACD AlertsThis is the LinReg-MACD indicator. It issues Buy and Sell signals based on linear regression candles along with a SMA slope filter. It also uses the MACD as confluence for these signals. It also has a LSMA filter. All values are adjustable and there are check boxes for use on different candles. I find it works better for me when swinging higher timeframes like the 1 hour.
Nadaraya-Watson CombineThis is a combination of the Lux Algo Nadaraya-Watson Estimator and Envelope. Please note the repainting issue.
In addition, I've added a plot of the actual values of the current barstate of
the Nadaraya-Watson windows as they are computed (lines 92-95). It only plots values for the current data at
each time update. It is interesting to compare the trajectory of the end points of the Estimator and
Envelope to the smoothing function at each time update. Due to the kernel smoothing at each update the
history is lost at each update (repaint).
I've added a feature to allow adjustment to the kernel smoothing algorithm as suggested by thomsonraja (line 59).
The settings and usage are repeated from Lux Algo below.
Settings
Window Size: Determines the number of recent price observations to be used to fit the Nadaraya-Watson Estimator.
Bandwidth: Controls the degree of smoothness of the envelopes , with higher values returning smoother results.
Mult: Controls the envelope width.
Src: Input source of the indicator.
Kernel power: See line 59, adjusts the exponential power (powh) as suggested by thomsonraja
Kernel denominator: See line 59, adjusts the denominator (den) as suggested by thomsonraja
Usage
This tool outlines extremes made by the prices within the selected window size.
This is achieved by estimating the underlying trend in the price using kernel smoothing,
calculating the mean absolute deviations from it, and adding/subtracting it
from the estimated underlying trend.
I repeat Lux Algo's caution: 'we do not recommend this tool to be used alone
or solely for real time applications.'
Nadaraya-Watson: Rational Quadratic Kernel (Non-Repainting)What is Nadaraya–Watson Regression?
Nadaraya–Watson Regression is a type of Kernel Regression, which is a non-parametric method for estimating the curve of best fit for a dataset. Unlike Linear Regression or Polynomial Regression, Kernel Regression does not assume any underlying distribution of the data. For estimation, it uses a kernel function, which is a weighting function that assigns a weight to each data point based on how close it is to the current point. The computed weights are then used to calculate the weighted average of the data points.
How is this different from using a Moving Average?
A Simple Moving Average is actually a special type of Kernel Regression that uses a Uniform (Retangular) Kernel function. This means that all data points in the specified lookback window are weighted equally. In contrast, the Rational Quadratic Kernel function used in this indicator assigns a higher weight to data points that are closer to the current point. This means that the indicator will react more quickly to changes in the data.
Why use the Rational Quadratic Kernel over the Gaussian Kernel?
The Gaussian Kernel is one of the most commonly used Kernel functions and is used extensively in many Machine Learning algorithms due to its general applicability across a wide variety of datasets. The Rational Quadratic Kernel can be thought of as a Gaussian Kernel on steroids; it is equivalent to adding together many Gaussian Kernels of differing length scales. This allows the user even more freedom to tune the indicator to their specific needs.
The formula for the Rational Quadratic function is:
K(x, x') = (1 + ||x - x'||^2 / (2 * alpha * h^2))^(-alpha)
where x and x' data are points, alpha is a hyperparameter that controls the smoothness (i.e. overall "wiggle") of the curve, and h is the band length of the kernel.
Does this Indicator Repaint?
No, this indicator has been intentionally designed to NOT repaint. This means that once a bar has closed, the indicator will never change the values in its plot. This is useful for backtesting and for trading strategies that require a non-repainting indicator.
Settings:
Bandwidth. This is the number of bars that the indicator will use as a lookback window.
Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel.
Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes.
