Smoothing R-Squared ComparisonIntroduction
Heyo guys, here I made a comparison between my favorised smoothing algorithms.
I chose the R-Squared value as rating factor to accomplish the comparison.
The indicator is non-repainting.
Description
In technical analysis, traders often use moving averages to smooth out the noise in price data and identify trends. While moving averages are a useful tool, they can also obscure important information about the underlying relationship between the price and the smoothed price.
One way to evaluate this relationship is by calculating the R-squared value, which represents the proportion of the variance in the price that can be explained by the smoothed price in a linear regression model.
This PineScript code implements a smoothing R-squared comparison indicator.
It provides a comparison of different smoothing techniques such as Kalman filter, T3, JMA, EMA, SMA, Super Smoother and some special combinations of them.
The Kalman filter is a mathematical algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement.
The input parameters for the Kalman filter include the process noise covariance and the measurement noise covariance, which help to adjust the sensitivity of the filter to changes in the input data.
The T3 smoothing technique is a popular method used in technical analysis to remove noise from a signal.
The input parameters for the T3 smoothing method include the length of the window used for smoothing, the type of smoothing used (Normal or New), and the smoothing factor used to adjust the sensitivity to changes in the input data.
The JMA smoothing technique is another popular method used in technical analysis to remove noise from a signal.
The input parameters for the JMA smoothing method include the length of the window used for smoothing, the phase used to shift the input data before applying the smoothing algorithm, and the power used to adjust the sensitivity of the JMA to changes in the input data.
The EMA and SMA techniques are also popular methods used in technical analysis to remove noise from a signal.
The input parameters for the EMA and SMA techniques include the length of the window used for smoothing.
The indicator displays a comparison of the R-squared values for each smoothing technique, which provides an indication of how well the technique is fitting the data.
Higher R-squared values indicate a better fit. By adjusting the input parameters for each smoothing technique, the user can compare the effectiveness of different techniques in removing noise from the input data.
Usage
You can use it to find the best fitting smoothing method for the timeframe you usually use.
Just apply it on your preferred timeframe and look for the highlighted table cell.
Conclusion
It seems like the T3 works best on timeframes under 4H.
There's where I am active, so I will use this one more in the future.
Thank you for checking this out. Enjoy your day and leave me a like or comment. 🧙♂️
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Credits to:
▪@loxx – T3
▪@balipour – Super Smoother
▪ChatGPT – Wrote 80 % of this article and helped with the research
Recherche dans les scripts pour "N+credit最新动态"
Local Model Kalman Market ModeIntroduction
Heyo guys, I made a new (repainting) indicator called Local Model Kalman Market Mode.
I created it, because I wanted a reliable market mode filter for a potential mean-reversion strategy (e. g. BB Scalping).
On the screenshot you can see an example of how to use it in a BB strategy.
E.g. you would enter long when you have bullish divergence, price is under lower BB, price is under PoC and this indicator here shows range-bound market phase.
You would exit long on cross of the middle band.
Description
The indicator attempts to model the underlying market using different local models (i.e., trending, range-bound, and choppy) and combines them using the T3 Six Pole Kalman Filter to generate an overall estimate of the market.
The Fisher Transform is applied on the price to reach a Gaussian distribution, which increases the accuracy of the indicator itself.
The script first defines state variables for each local model, which include trend direction, trend strength, upper and lower bounds of the range, volatility of the range, level of choppiness, and strength of noise.
Then, likelihood functions are defined for each local model based on the state variables.
Next, the script calculates weights for each local model based on their likelihoods and uses them to calculate state variables for the overall estimate.
Finally, the script combines the state variables using the T3 Six Pole Kalman Filter to generate the overall estimate of the market, which is plotted in blue.
Fundamental Knowledge
To understand the explanation of the indicator and the script, there are a few fundamental concepts that you need to know:
Market: A market is a place where buyers and sellers come together to exchange goods or services.
In the context of trading, the market refers to the exchange where financial instruments such as stocks, currencies, and commodities are bought and sold.
Local models: Local models are statistical models that attempt to capture the characteristics of a particular market regime.
For example, a trending market may have different characteristics than a range-bound market or a choppy market.
The indicator uses different local models to capture the different market regimes.
Trend direction and strength: The trend direction refers to the direction in which the market is moving, either up or down.
The trend strength refers to the magnitude of the trend and how likely it is to continue.
Range-bound market: A range-bound market is a market where prices are trading within a specific range, with a clear upper and lower bound.
Choppiness: Choppiness refers to the degree of irregularity in price movements, often seen in sideways or range-bound markets.
Volatility: Volatility refers to the degree of variation in the price of an asset over time. High volatility implies larger price swings, while low volatility implies smaller price swings.
Kalman filter: A Kalman filter is a mathematical algorithm used to estimate an unknown variable from a series of noisy measurements.
In the context of the indicator, the Kalman filter is used to generate an overall estimate of the market by combining the local models.
T3 Six Pole Kalman Filter: The T3 Six Pole Kalman Filter is a specific type of Kalman filter that is used to smooth and filter time-series data, such as the price data of a financial instrument.
Fisher Transform: The Fisher Transform is a mathematical formula used to transform any probability distribution into a Gaussian normal distribution. It is commonly used in technical analysis to transform non-Gaussian indicators into ones that are more suitable for statistical analysis.
By understanding these fundamental concepts, you should have a basic understanding of how the indicator works and how it generates an overall estimate of the market.
Usage
You can use this indicator on every timeframe.
Users can customize the parameters of the T3 Six Pole Kalman Filter (T3 length, alpha, beta, gamma, and delta) using input functions.
Try out different parameter combinations and use the one you like most.
Thank you for checking this out. Leave me a comment or boost the script, when you wanna support me! 👌
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Credits to:
▪@HPotter - Fisher Transform
▪@loxx - T3
▪ChatGPT - Helped me to make the research for this indicator and helped to build the core algorithm.
Adaptive Fusion ADX VortexIntroduction
The Adaptive Fusion ADX DI Vortex Indicator is a powerful tool designed to help traders identify trend strength and potential trend reversals in the market. This indicator uses a combination of technical analysis (TA) and mathematical concepts to provide accurate and reliable signals.
Features
The Adaptive Fusion ADX DI Vortex Indicator has several features that make it a powerful tool for traders. The Fusion Mode combines the Vortex Indicator and the ADX DI indicator to provide a more accurate picture of the market. The Hurst Exponent Filter helps to filter out choppy markets (inspired by balipour). Additionally, the indicator can be customized with various inputs and settings to suit individual trading strategies.
Signals
The enterLong signal is generated when the algorithm detects that it's a good time to buy a stock or other asset. This signal is based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossover between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterLong signal.
Similarly, the enterShort signal is generated when the algorithm detects that it's a good time to sell a stock or other asset. This signal is also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value is above a certain threshold and there is a crossunder between the plus and minus lines of the ADX indicator, then the algorithm will generate an enterShort signal.
The exitLong and exitShort signals are generated when the algorithm detects that it's a good time to close a long or short position, respectively. These signals are also based on certain conditions such as the values of technical indicators like ADX, Vortex, and Fusion. For example, if the ADX value crosses above a certain threshold or there is a crossover between the minus and plus lines of the ADX indicator, then the algorithm will generate an exitLong signal.
Usage
Traders can use this indicator in a variety of ways, depending on their trading strategy and style. Short-term traders may use it to identify short-term trends and potential trade opportunities, while long-term traders may use it to identify long-term trends and potential investment opportunities. The indicator can also be used to confirm other technical indicators or trading signals. Personally, I prefer to use it for short-term trades.
Strengths
One of the strengths of the Adaptive Fusion ADX DI Vortex Indicator is its accuracy and reliability. The indicator uses a combination of TA and mathematical concepts to provide accurate and reliable signals, helping traders make informed trading decisions. It is also versatile and can be used in a variety of trading strategies.
Weaknesses
While this indicator has many strengths, it also has some weaknesses. One of the weaknesses is that it can generate false signals in choppy or sideways markets. Additionally, the indicator may lag behind the market, making it less effective in fast-moving markets. That's a reason why I included the Hurst Exponent Filter and special smoothing.
Concepts
The Adaptive ADX DI Vortex Indicator with Fusion Mode and Hurst Filter is based on several key concepts. The Average Directional Index (ADX) is used to measure trend strength, while the Vortex Indicator is used to identify trend reversals. The Hurst Exponent is used to filter out noise and provide a more accurate picture of the market.
In conclusion, the Adaptive Fusion ADX DI Vortex Indicator is a versatile and powerful tool for traders. By combining technical analysis and mathematical concepts, this indicator provides accurate and reliable signals for identifying trend strength and potential trend reversals. While it has some weaknesses, its many strengths and features make it a valuable addition to any trader's toolbox.
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Credits to:
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@balipour – Hurst Exponent Filter
Stochastic Momentum Index (SMI) Refurbished▮Introduction
Stochastic Momentum Index (SMI) Indicator is a technical indicator used in technical analysis of stocks and other financial instruments.
It was developed by William Blau in 1993 and is considered to be a momentum indicator that can help identify trend reversal points.
Basically, it's a combination of the True Strength Index with a signal line to help identify turning points in the market.
SMI uses the stochastic formula to compare the current closing price of an asset with the maximum and minimum price range over a specific period.
He then compares this ratio to a short-term moving average to create an indicator that oscillates between -100 and +100.
When the SMI is above 0, it is considered positive, indicating that the current price is above the short-term moving average.
When it is below 0, it is considered negative, indicating that the current price is below the short-term moving average.
Traders use the SMI to identify potential trend reversal points.
When the indicator reaches an extreme level above +40 or below -40, a trend reversal is possible.
Furthermore, traders also watch for divergences between the SMI and the asset price to identify potential trading opportunities.
It is important to remember that the SMI is a technical indicator and as such should be used in conjunction with other technical analysis tools to get a complete picture of the market situation.
▮ Improvements
The following features were added:
1. 7 color themes, for TSI, Signal and Histogram.
2. Possibility to customize moving average type for TSI/Signal.
3. Dynamic Zones.
4. Crossing Alerts.
5. Alert points on specific ranges.
5. Coloring of bars according to TSI/Signal/Histogram.
▮ Themes
Examples:
▮ About Dynamic Zones
'Most indicators use a fixed zone for buy and sell signals.
Here's a concept based on zones that are responsive to the past levels of the indicator.'
The concept of Dynamic Zones was described by Leo Zamansky ( Ph .D.) and David Stendahl, in the magazine of Stocks & Commodities V15:7 (306-310).
Basically, a statistical calculation is made to define the extreme levels, delimiting a possible overbought/oversold region.
Given user-defined probabilities, the percentile is calculated using the method of Nearest Rank.
It is calculated by taking the difference between the data point and the number of data points below it, then dividing by the total number of data points in the set.
The result is expressed as a percentage.
This provides a measure of how a particular value compares to other values in a data set, identifying outliers or values that are significantly higher or lower than the rest of the data.
▮ What to look for
1. Divergences/weakening of a trend/reversal:
2. Supports, resistances, pullbacks:
3. Overbought/Oversold Points:
▮ Thanks and Credits
- TradingView and PineCoders: for SMI and Moving Averages
- allanster: for Dynamic Zones
Strong Demands & Supplies + Liquidity | Zonas de Compra e VendaThis indicator is inspired on the Smart Money Concepts indicator (Credits to @LuxAlgo) and it was optimized to show only the most relevant demand and supply zones (premium) on every time frame - but on higher time frames (1H and above) the zones are more relevant and stronger, meaning these zones can handle the price for longer time.
I've added a new feature that includes the Liquidity lines in order to add more confluence and importance to a demand or supply zone: when a demand or supply zone has strong liquidity (like weekly or monthly) next to it means that zone can be a strongest price target.
- Blue Line: Daily liquidity
- Yellow Line: Weekly Liquidity
- Purple Line: Monthly Liquidity
Main Features:
- Displays the most relevant demand and supply zones (green and red boxes) and which ones are strong and weak
- Displays the relevant change of character and break of structure
- Displays the previous day highest price and previous day lowest price
- Display imbalances between sell and buy orders (purple boxes)
- Displays the liquidity areas with lines on each point.
- It works for Forex and Cryptocurrency as well.
Portuguese:
Este indicador é inspirado no Smart Money Concepts (Créditos para @LuxAlgo) e foi otimizado para mostrar apenas as zonas de procura e oferta mais relevantes em cada time frame - mas em time frames maiores as zonas são mais relevantes e mais fortes.
Adicionei uma nova funcionalidade que inclui as linhas de Liquidez de forma a adicionar mais confluência e importância a uma zona de procura ou oferta: quando uma zona de procura ou oferta tem forte liquidez (como semanal/linha amarela ou mensal/linha roxa) junto a ela significa que aquela zona pode ser um alvo de preço mais forte.
- Linha Azul: Liquidez diária
- Linha Amarela: Liquidez Semanal
- Linha Roxa: Liquidez Mensal
Principais características:
- Exibe as zonas de procura e oferta mais relevantes (zonas a verde e zonas a vermelho) e quais delas são fortes e fracas
- Exibe a mudança relevante de caráter e quebra de estrutura
- Exibe o preço mais alto do dia anterior e o preço mais baixo do dia anterior
- Exibe as imbalances entre as ordens de venda e compra (zonas a roxo)
- Exibe as zonas de maior liquidez através de linhas no gráfico
- Funciona tanto para Forex como para Criptomoedas
MVRV Z Score and MVRV Free Float Z-ScoreIMPORTANT: This script needs as much historic data as possible. Please run it on INDEX:BTCUSD , BNC:BLX or another chart of sufficient length.
MVRV
The MVRV (Market Value to Realised Value Ratio) simply divides bitcoins market cap by bitcoins realized market cap. This was previously impossible on Tradingview but has now been made possible thanks to Coinmetrics providing us with the realized market cap data.
In the free float version, the free float market cap is used instead of the regular market cap.
Z-Score
The MVRV Z-score divides the difference between Market cap and realized market cap by the historic standard deviation of the market cap.
Historically, this has been insanely accurate at detecting bitcoin tops and bottoms:
A Z-Score above 7 means bitcoin is vastly overpriced and at a local top.
A Z-Score below 0.1 means bitcoin is underpriced and at a local bottom.
In the free float version, the free float market cap is used instead of the regular market cap.
The Z-Score, also known as the standard score is hugely popular in a wide range of mathematical and statistical fields and is usually used to measure the number of standard deviations by which the value of a raw score is above or below the mean value of what is being observed or measured.
Credits
MVRV Z Score initially created by aweandwonder
MVRV initially created by Murad Mahmudov and David Puell
Smart QQE ModSmart QQE - Chart Overlay
Smart QQE shows QQE Trend and RSI plot on chart to determine the trend direction and eliminate false signals.
QQE is obtained from original code by Glaz and rescaled to fit on chart. RSI 50 level acts as Zero which is plotted as a Bollinger on chart.
This is not a Bollinger band . its an RSI channel with levels 0-100 plotted around the mid band. The RSI Mid Band is calculated based on RSI value.
Trend:
Price above RSI Mid band is uptrend
Price below RSI Mid band is Down Trend
The Green line - Discount Zone - 0-RSI level - Oversold Zone
The Red Line - Premium Zone - 100 - RSI level - Overbought Zone
Buy / Sell signals
QQE Buy and Sell signals are plotted based on crossovers of RSI and Fast RSI crossovers.
QQE trend is colored based on the crossover.
Candle color:
candle color determines the Original QQE Trend.
Blue - QQE line above Threshold level in Buy Zone
Pink - QQE line below Threshold level in Sell Zone
Entries are to be made with proper confirmation.
HULL MA is provided as a MA Ribbon for additional confirmation. This MA can be changed to various forms Like EMA , SMA , WMA , HMA , RMA the open and close of the MA are plotted so it determines the exact Trend reversal of the price.
Credits to @Glaz QQE Threshold
VHF Adaptive Linear Regression KAMAIntroduction
Heyo, in this indicator I decided to add VHF adaptivness, linear regression and smoothing to a KAMA in order to squeeze all out of it.
KAMA:
Developed by Perry Kaufman, Kaufman's Adaptive Moving Average (KAMA) is a moving average designed to account for market noise or volatility. KAMA will closely follow prices when the price swings are relatively small and the noise is low. KAMA will adjust when the price swings widen and follow prices from a greater distance. This trend-following indicator can be used to identify the overall trend, time turning points and filter price movements.
VHF:
Vertical Horizontal Filter (VHF) was created by Adam White to identify trending and ranging markets. VHF measures the level of trend activity, similar to ADX DI. Vertical Horizontal Filter does not, itself, generate trading signals, but determines whether signals are taken from trend or momentum indicators. Using this trend information, one is then able to derive an average cycle length.
Linear Regression Curve:
A line that best fits the prices specified over a user-defined time period.
This is very good to eliminate bad crosses of KAMA and the pric.
Usage
You can use this indicator on every timeframe I think. I mostly tested it on 1 min, 5 min and 15 min.
Signals
Enter Long -> crossover(close, kama) and crossover(kama, kama )
Enter Short -> crossunder(close, kama) and crossunder(kama, kama )
Thanks for checking this out!
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Credits to
▪️@cheatcountry – Hann Window Smoohing
▪️@loxx – VHF and T3
▪️@LucF – Gradient
Hurst Spectral Analysis Oscillator"It is a true fact that any given time history of any event (including the price history of a stock) can always be considered as reproducible to any desired degree of accuracy by the process of algebraically summing a particular series of sine waves. This is intuitively evident if you start with a number of sine waves of differing frequencies, amplitudes, and phases, and then sum them up to get a new and more complex waveform." (Spectral Analysis chapter of J M Hurst's book, Profit Magic )
Background: A band-pass filter or bandpass filter is a device that passes frequencies within a certain range and rejects (attenuates) frequencies outside that range. Bandpass filters are widely used in wireless transmitters and receivers. Well-designed bandpass filters (having the optimum bandwidth) maximize the number of signal transmitters that can exist in a system while minimizing the interference or competition among signals. Outside of electronics and signal processing, other examples of the use of bandpass filters include atmospheric sciences, neuroscience, astronomy, economics, and finance.
About the indicator: This indicator will accept float/decimal length inputs to display a spectrum of 11 bandpass filters. The trader can select a single bandpass for analysis that includes future high/low predictions. The trader can also select which bandpasses contribute to a composite model of expected price action.
10 Statements to describe the 5 elements of Hurst's price-motion model:
Random events account for only 2% of the price change of the overall market and of individual issues.
National and world historical events influence the market to a negligible degree.
Foreseeable fundamental events account for about 75% of all price motion. The effect is smooth and slow changing.
Unforeseeable fundamental events influence price motion. They occur relatively seldom, but the effect can be large and must be guarded against.
Approximately 23% of all price motion is cyclic in nature and semi-predictable (basis of the "cyclic model").
Cyclicality in price motion consists of the sum of a number of (non-ideal) periodic cyclic "waves" or "fluctuations" (summation principle).
Summed cyclicality is a common factor among all stocks (commonality principle).
Cyclic component magnitude and duration fluctuate slowly with the passage of time. In the course of such fluctuations, the greater the magnitude, the longer the duration and vice-versa (variation principle).
Principle of nominality: an element of commonality from which variation is expected.
The greater the nominal duration of a cyclic component, the larger the nominal magnitude (principle of proportionality).
Shoutouts & Credits for all the raw code, helpful information, ideas & collaboration, conversations together, introductions, indicator feedback, and genuine/selfless help:
🏆 @TerryPascoe
🏅 DavidF at Sigma-L, and @HPotter
👏 @Saviolis, parisboy, and @upslidedown
Simple Zigzag UDT█ OVERVIEW
This indicator displays zigzag based on high and low, which is using user-defined types (UDT) or objects .
█ CREDITS
LonesomeTheBlue
█ FEATURES
1. Label can be resized.
2. Label can be display either short (Eg : HH, LL, H, L, etc) and long (Eg : Higher Low, etc)
3. Color can be customized either contrast color of chart background, trend color or customized color.
█ EXAMPLES / USAGES
ROC (Rate of Change) Refurbished▮ Introduction
The Rate of Change indicator (ROC) is a momentum oscillator.
It was first introduced in the early 1970s by the American technical analyst Welles Wilder.
It calculates the percentage change in price between periods.
ROC takes the current price and compares it to a price 'n' periods (user defined) ago.
The calculated value is then plotted and fluctuates above and below a Zero Line.
A technical analyst may use ROC for:
- trend identification;
- identifying overbought and oversold conditions.
Even though ROC is an oscillator, it is not bounded to a set range.
The reason for this is that there is no limit to how far a security can advance in price but of course there is a limit to how far it can decline.
If price goes to $0, then it obviously will not decline any further.
Because of this, ROC can sometimes appear to be unbalanced.
(TradingView)
▮ Improvements
The following features were added:
1. Eight moving averages for the indicator;
2. Dynamic Zones;
3. Rules for coloring bars/candles.
▮ Motivation
Averages have been added to improve trend identification.
For finer tuning, you can choose the type of averages.
You can hide them if you don't need them.
The Dynamic Zones has been added to make it easier to identify overbought/oversold regions.
Unlike other oscillators like the RSI for example, the ROC does not have a predetermined range of oscillations.
Therefore, a fixed line that defines an overbought/oversold range becomes unfeasible.
It is in this matter that the Dynamic Zone helps.
It dynamically adjusts as the indicator oscillates.
▮ About Dynamic Zones
'Most indicators use a fixed zone for buy and sell signals.
Here's a concept based on zones that are responsive to the past levels of the indicator.'
The concept of Dynamic Zones was described by Leo Zamansky (Ph.D.) and David Stendahl, in the magazine of Stocks & Commodities V15:7 (306-310).
Basically, a statistical calculation is made to define the extreme levels, delimiting a possible overbought/oversold region.
Given user-defined probabilities, the percentile is calculated using the method of Nearest Rank.
It is calculated by taking the difference between the data point and the number of data points below it, then dividing by the total number of data points in the set.
The result is expressed as a percentage.
This provides a measure of how a particular value compares to other values in a data set, identifying outliers or values that are significantly higher or lower than the rest of the data.
▮ Thanks and Credits
- TradingView: for ROC and Moving Averages
- allanster: for Dynamic Zones
Harmonic Pattern Table UDT█ OVERVIEW
This table indicator was intended as helper / reference for using XABCD Pattern drawing tool.
The values shown in table was based on Harmonic Trading Volume 3: Reaction vs. Reversal written by Scott M Carney.
Code upgrade from Harmonic Pattern Table (Source Code) and based on latest User-Defined Type (UDT) .
As a result, code appeared more cleaner.
█ FEATURES
1. List Harmonic Patterns.
2. Font size small for mobile app and font size normal for desktop.
3. Options to show Animal name in text, emoji or both.
█ USAGE
Similar to Harmonic Pattern Table (Source Code).
█ CREDITS
Scott M Carney, Trading Volume 3: Reaction vs. Reversal
CMO with ATR and LagF Filtering - RevNR - 12-27-22Rev NR of the CMO ATR, with LagF Filtering - Released 12-27-22 by @Hockeydude84
This code takes Chande Momentum Oscillator (CMO), adds a coded ATR option and then filters the result through a Laguerre Filter (LagF) to reduce erroneous signals.
This code also has an option for self adjusting alpha on the Lag, via a lookback table and monitoring the price rate of change (ROC) in the lookback length.
Faster ROC will allow the LagF to move faster, slower price action will slow down LagF reaction. Pausing of signals is also present based on Rate of Change of the LagF Curve
Aggressive signals and Base signaling is allowed - aggressive bases signals on increase/decrease of previous LagF curve value point, Base is greater or less than 0
Original Code credits; Lost some of this due to time and multiple script manipulations, I believe the CMO origin code is from @TradingView House Code, and the LagF from @KıvançÖzbilgiç
CM_Williams_Vix_Fix - Market Top and Bottom with multi-timeframeThis is a modification of CM_Williams_Vix_Fix indicator to include both market tops and bottoms with multi-timeframe support. The original indicator only finds market bottoms.
All credits go to the original author ChrisMoody.
Original script link
Working:
The histogram above 0 signifies the trend of market going UP and the histogram below 0 signifies the trend of market going DOWN.
The histogram bar is calculated using "LookBack Period Standard Deviation High" number of candles. A threshold is calculated using bollinger bands and based on percentile of "Look Back Period Percentile High" number of candles.
If the histogram bar above 0 crosses the up threshold then we have market top which is signified by histogram bar having the color green. If the histogram bar below 0 crosses the down threshold then we have market bottom which is signified by histogram bar having the color red.
The market tops and bottoms can also be calculated across multiple timeframes.
Sample usage:
Suppose the market is in an uptrend and the indicator displays red market bottom bar, this might be an indication that the market has reached the end of a pullback. We can use additional indicators like stochastic or rsi to get additional confluence.
This indicator does not repaint but you need to wait for the candle to close.
MAGIC MACDMAGIC MACD ( MACD Indicator with Trend Filter and EMA Crossover confirmation and Momentum). This MACD uses Default Trading view MACD
from Technical indicators library and adding a second MACD along with 3 EMA's to detect Trend and confirm MACD Signal.
Eliminates usage of 3different indicators (Default MACD , MACD-2,EMA5, EMA20, EMA50)
Basic IDEA.
Idea is to filter Histogram when price is above or below 50EMA. Similar to QQE -mod oscillator but Has a EMA Filter
1.Take DEFAULT MACD crossover signals with lower period
2.check with a Higher MACD Histogram.
3.Enter upon EMA crossover signal and Histogram confirmation.
Histogram changes to GRAY when price is below EMA 50 or above EMA 50 (Follows Trend)
4.Exit on next Default MACD crossover signal.
Overview :
Moving Average Convergence Divergence Indicator Popularly Known as MACD is widely used. MACD Usually generates a lots of False signals
and noise in Lower Time Frames, making it difficult to enter a trade in sideways market. Divergence is a major issue along with sideways
movement and tangling of MACD and Signal Lines. There is no way to confirm a Default MACD signal, except to switch time frames and
verify.
Magic MACD Can be used to in combination with other signals.
This MACD uses two MACD Signals to verify the signal given by Default MACD . The Histogram Plot shown is of a higher period
MACD (close,5,50,30) values. When a signal is generated on a lower MACD it is verified by the histogram with higher time period.
Technicals Used:
1. Lower MACD-1 values 12,26 and signal-9 (crossover Signals)
2. Higher MACD-2 values 5,50 and signal-30 (Histogram)
3. EMA 50 (Histogram Filter to allow only if price above or below Ema 50)
4. EMA 5 and EMA 20 for crossover confirmation of trend
What's is in this Indicator?
1.Histogram-(higher period 5,50 and 30signal)
2. MACD crossover Signals-(lower period Default MACD setting)
3.Signal Lines-( EMA 5 & 20)
Implemented & Removed in this Indicator
1. Default MACD and Signal Lines are removed completely
2. MACD crossover are taken on lower periods and plotted as signals(Blue Triangle or Red Triangle)
3. Histogram is plotted from a higher Period providing a clear picture with Higher Time period
4. EMA 5 and EMA 20 are used for MACD signal confirmation
How to use?
Up Signal
1. MACD Default (12,26,30) up signals are shown in Blue
2. Wait till the Histogram changes Blue
3. Look for EMA signals crossover near by
Down Signal
1. MACD Default (12,26,30) up signals are shown in Red
2. Wait till the Histogram changes Red
3. Look for EMA signals crossover near by
Do's
Consider only opposite color as signals
1. Red Triangle on Blue Histogram(likely to move down direction)
2. Blue Triangle on Red Histogram (Likely to move up direction)
Don'ts
1.Ignore Blue Signal on Blue Histogram (pull back signals can be used to enter trade if you miss first crossover)
2.Ignore Red Signal on Red Histogram(pull back signals can be used to enter trade if you miss first crossover)
3.Ignore Up and Down signals till Gray or Blacked out area is finished in Histogram
Tips:
1. EMA plot also shows pull back areas along with signals
2.side by side opposite signals shows sides ways movement
3. EMA 5,20 is plotted on MACD Histogram for Additional Benefit
Thanks & Credits
To Tradingview Team for allowing me to use their default MACD version and coding it in to a MAGIC MACD by adding a few lines of code that
makes it more enhanced.
Warning...!
This is purely for Educational purpose only. Not to be used as a stand alone indicator. Usage is at your own Risk. Please get familiar with its working before implementing. Its not a Financial Advice or Suggestion . Any losses or gains is at your own risk.
WelcomeUDT█ OVERVIEW
This is a simplest example of user-defined types (UDT) or objects , which simplify as alternative to hello world.
█ CREDITS
Tradingview
█ USAGE
These are the types used during initializations, commonly variables.
export type Settings
int bar
float price
string phrase
...
Example of library function to print out label.
export printLabel(Settings setup) =>
if setup.variable
var label lab = na
label.delete(lab)
lab := label.new(setup.bar, setup.price, setup.phrase, color = setup.bg)
else
label.new(setup.bar, setup.price, setup.phrase, color = setup.bg)
Usage of types
Settings setup = Settings.new(bar_index , priceInput, phraseInput, colorInput, variableInput)
Alternative way to write types
Settings setup = Settings.new(
bar = bar_index ,
price = priceInput,
phrase = phraseInput,
variable = variableInput)
Usage of types into custom function / library function.
printLabel(setup)
printLabel(Settings)
Print out label
Parameters:
Settings : types
Returns: Label object
Settings
Initialize type values
Fields:
bar : X position for label
price : Y position for label
phrase : Text for label
bg : Color for label
variable : Boolean for enable new line and delete line
Simple OHLC Custom Range Interactive█ OVERVIEW
This indicator show lines of OHLC which can be commonly used as support and resistance zones.
OHLC can be shown table with candlestick visual.
Color of candlestick depends on direction of bullish / bearish of the chosen candlestick.
█ INSPIRATION
Inspired by design, code and usage of CAGR . Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView .
█ FEATURES
Table can positioned by any position and font size can be resized.
OHLC can be in full or simple name.
Lines can be extend either right, left, both or none.
█ HOW TO USE
Only 1 point is required.
Dont worry about magnet, point will attached depends on High or Low of the candle.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
Chandelier Exit ZLSMA StrategyIntroduction
Heyo guys, I recently checked out some eye-catching trading strategy videos on YT and found one to test.
This indicator is based on the video.
Usage
The recommended timeframe is 5 min.
Signals
Long Entry => L Label
Price crosses above ZLSMA and Chandelier Exit shows Buy
Long Exit => green circle
Price crosses below ZLSMA
Short Entry => S Label
Price crosses below ZLSMA and Chandelier Exit shows Sell
Short Exit => orange circle
Prices crosses above ZLSMA
Ty for checking this out. Enjoy!
--
Credits to
@netweaver2011 - ZLSMA
@everget – Chandelier Exit
Adaptive Fisherized KSTIntroduction
Heyo guys, here is a new adaptive fisherized indicator of me.
I applied Inverse Fisher Transform, Ehlers dominant cycle analysis,
smoothing and divergence analysis on the Know Sure Thing (KST) indicator.
Moreover, the indicator doesn't repaint.
Usage
I didn't backtest the indicator, but I recommend the 5–15 min timeframe.
It can be also used on other timeframs, but I have no experience with that.
The indicator has no special filter system, so you need to find an own combo in order to build a trading system.
A trend filter like KAMA or my Adaptive Fisherized Trend Intensity Index could fit well.
If you find a good combo, let me know it in the comments pls.
Signals
Zero Line
KST crossover 0 => Enter Long
KST crossunder 0 => Enter Short
Cross
KST crossover KST MA => Enter Long
KST crossunder KST MA => Enter Short
Cross Filtered
KST crossover KST MA and KST above 0 => Enter Long
KST crossunder KST MA and KST under 0 => Enter Short
KST crossunder 0 => Exit Long
KST crossover 0 => Exit Short
More to read: KST Explanation
Enjoy and let me know your opinion!
--
Credits to
- @tista
- @blackcat1402
- @DasanC
- @cheatcountry
Bitcoin Miner Extreme SellingThis script is for identifying extreme selling. Judging by the chart, Bitcoin miners often (not always) sell hard for two reasons: to take profit into parabolic price rises, or to stay solvent when the price is very low.
Extreme selling thus often coincides with long-term tops and bottoms in Bitcoin price. This can be a useful EXTRA data point when trying to time long-term Bitcoin spot or crypto equity investment (NOT advice, you remain responsible, etc). The difference between selling measured in BTC and in USD gives a reasonable idea of whether miners are selling to make a profit or to stay solvent.
CREDITS
The idea for using the ratio of miner outflows to reserves comes from the "Bitcoin Miner Sell Pressure" script by the pioneering capriole_charles.
The two request.security calls are identical. Another similarity is that you have to sum the outflows to make it make sense. But it doesn't make much difference, it turns out from testing, to use an average of the reserves, so I didn't. All other code is different.
The script from capriole_charles uses Bollinger bands to highlight periods when sell pressure is high, uses a rolling 30-day sum, and only uses the BTC metrics.
My script uses a configurable 2-6 week rolling sum (there's nothing magical about one month), uses different calculations, and uses BTC, USD, and composite metrics.
INPUTS
Rolling Time Basis : Determines how much data is rolled up. At the lowest level, daily data is too volatile. If you choose, e.g., 1 week, then the indicator displays the relative selling on a weekly basis. Longer time periods, obviously, are smoother but delayed, while shorter time periods are more reactive. There is no "real" time period, only an explicit interpretation.
Show Data > Outflows : Displays the relative selling data, along with a long-term moving average. You might use this option if you want to compare the "real" heights of peaks across history.
Show Data > Delta (the default): Only the difference between the relative selling and the long-term moving average is displayed, along with an average of *that*. This is more signal and less noise.
Base Currency : Configure whether the calculations use BTC or USD as the metric. This setting doesn't use the BTC price at all; it switches the data requested from INTOTHEBLOCK.
If you choose Composite (the default), the script combines BTC and USD together in a relative way (you can't simply add them, as USD is a much bigger absolute value).
In Composite mode, the peaks are coloured red if BTC selling is higher than USD, which usually indicates forced selling, and green if USD is higher, which usually indicates profit-taking. This categorisation is not perfectly accurate but it is interesting insomuch as it is derived from block data and not Bitcoin price.
In BTC or USD mode, a gradient is used to give a rough visual idea of how far from the average the current value is, and to make it look pretty.
USAGE NOTES
Because of the long-term moving averages, the length of the chart does make a difference. I recommend running the script on the longest Bitcoin chart, ticker BLX.
To use it to compare selling with pivots in crypto equities, use a split chart: one BLX with the indicator applied, and one with the equity of your choice. Sync Interval, Crosshair, Time, and Date Range, but not Symbol.
Adaptive Fisherized Trend Intensity Index Introduction
Here, I modified the script "Trend Intensity Index" (TII) of @everyget.
TTI was developed by M.H. Pee, who also published other trend analysis indicators like the Trend Trigger/Continuation Factor
It helps to determine how strong the current trend is.
The stronger the trend, the higher the chance the price may continue moving in the current direction.
Features
Adaptive mode (based on Ehlers dominant cycle determination) => automatically determines the length
Inverse Fisher Transform => gives sharper signals
Customizable MA Types => discover the impact of different ma bases
Hann Window and NET smoothing => state-of-the-art smoothing
Trend Visualization => shows you the up/down/side trend
Usage
This indicator here offers a perfect trend filtering system. It is capable of up/down/side trend detection.
There are a lot of trend indicators which don't respect sidetrends, which makes this indicator pretty useful.
A lot of traders use trend-following trading systems.
A trader will usually make his/her entry in the market during a strong trend and ride it, until the TII provides an indication of a reversal.
For mean-revertive trading systems, you could use TII to just trade in side trend.
A lot of mean-revertive signal emitters like Bollinger Bands or RSI work most of the times better in side trend.
Furthermore, every timeframe could be used, but higher timeframes have more impact because trends are stronger there.
Signals
Green zone (Top) => Etablished bullish trend
"Peachy" Zone (Middle) => Sidetrend/flat market
Red Zone (Bottom) => Etablished bearish trend
Enjoy guys!
(Let me know your opinions!)
--
Credits to:
@blackcat1402
@DasanC
@cheatcountry
@everget
Adaptive VWAP Stdev BandsIntroduction
Heyo, here are some adaptive VWAP Standard Deviation Bands with nice colors.
I used Ehlers dominant cycle theories and ZLSMA smoothing to create this indicator.
You can choose between different algorithms to determine the dominant cycle and this will be used as reset period.
Everytime bar_index can be divided through the dominant cycle length and the result is zero VWAP resets if have chosen an adaptive mode in the settings.
The other reset event you can use is just a simple time-based event, e.g. reset every day.
Usage
I think people buy/sell when it reaches extreme zones.
Enjoy!
---
Credits to:
@SandroTurriate - VWAP Stdev Bands
@blackcat1402 - Dominant Cycle Analysis
@DasanC - Dominant Cycle Analysis
@veryfid - ZLSMA
(Sry, too lazy for linking)
I took parts of their code. Ty guys for your work! Just awesome.
Zig Zag Ratio Simplified█ OVERVIEW
This indicator was to show ratio between zig zag. Ideally to find Fibonacci Retracement / Projection, Harmonic Patterns, ABCD, Elliot Wave and etc.
█ CREDITS
LonesomeTheBlue
█ FEATURES
Table can positioned by any position and font size can be resized.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
