HC_V2Description:
Detailed Clarity Signal is a sophisticated TradingView indicator designed to provide traders with enhanced trading signals based on Bollinger Bands and the consistency of price actions. This script is particularly useful for identifying high-probability entry points in volatile markets. It combines the power of Bollinger Bands with a unique scoring system that assesses the clarity of buy and sell signals.
Features:
Dynamic Bollinger Bands: Utilizes a standard deviation multiplier to dynamically adjust Bollinger Bands, providing a flexible approach to volatility.
Clarity Scoring System: Each trading signal is scored based on its clarity, which is determined by how significantly the price exceeds the Bollinger Bands and the consistency of similar signals over a short span of time. This helps in distinguishing stronger signals from the weaker ones.
Consecutive Signal Tracking: Tracks consecutive buy or sell signals, allowing for gaps of up to three bars, to enhance the reliability of the trading signals.
Alert Conditions: Includes conditions for setting alerts when signals of high clarity (levels 4 and 5) are detected, making it easier for traders to act promptly on significant trading opportunities.
Visual and Sound Alerts: Designed to integrate seamlessly with TradingView's alert system, providing both visual markers and sound alerts to ensure that traders do not miss important trading signals.
How It Works:
The indicator calculates Bollinger Bands and measures the current close price in relation to these bands. When the price closes significantly beyond the bands coupled with consistent behavior in previous sessions, the signal clarity increases. This clarity is quantified from levels 1 to 5, with higher levels indicating stronger signals. Traders can set alerts to be notified when signals of substantial clarity are detected, aiding in decision-making during fast-moving market conditions.
Usage:
Entry Signal: A high clarity level (4 or 5) suggests a strong buy or sell opportunity, depending on whether the signal is above or below the Bollinger Bands.
Exit Signal: Traders may consider closing positions as the clarity level decreases or as opposing signals begin to form, providing a methodical approach to capturing gains and managing risks.
Conclusion:
The Detailed Clarity Signal indicator is an invaluable tool for traders looking to leverage the volatility of the markets with a higher degree of precision. By focusing on the quality of signals, it provides a robust method to enhance trading strategies, ensuring that traders can make informed decisions backed by a comprehensive analysis of price movements and trend strength.
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Khaled Tamim's Avellaneda-Stoikov StrategyDescription:
This strategy applies the Avellaneda-Stoikov (A-S) model to generate buy and sell signals for underlying assets based on option pricing theory. The A-S model estimates bid and ask quotes for options contracts considering factors like volatility (sigma), time to expiration (T), and risk aversion (gamma).
Key Concepts:
Avellaneda-Stoikov Model: A mathematical framework for option pricing that incorporates volatility, time decay, and risk tolerance.
Bid-Ask Quotes: The theoretical buy and sell prices for an option contract.
Inventory Management: The strategy tracks its long or short position based on signals.
How it Works:
A-S Model Calculation: The avellanedaStoikov function calculates bid and ask quotes using the underlying asset's closing price, user-defined parameters (gamma, sigma, T, k, and M), and a small fee (adjustable).
Signal Generation: The strategy generates long signals when the closing price falls below the adjusted bid quote and short signals when it exceeds the adjusted ask quote.
Trade Execution: Buy and sell orders are triggered based on the generated signals (long for buy, short for sell).
Inventory Tracking: The strategy's net profit reflects the current inventory level (long or short position).
Customization:
Gamma (γ): Controls risk aversion in the A-S model (higher values imply lower risk tolerance).
Sigma (σ): Represents the underlying asset's expected volatility.
T: Time to expiration for the hypothetical option (defaults to a short-term option).
k: A constant factor in the A-S model calculations.
M: Minimum price buffer for buy/sell signals (prevents excessive churn).
Important Note:
This strategy simulates option pricing behavior for a theoretical option and does not directly trade options contracts. Backtesting results may not reflect actual market conditions.
Further Considerations:
The 0.1% fee is a placeholder and may need adjustment based on real-world trading costs.
Consider using realistic timeframes for T (e.g., expiry for a real option)
Disclaimer: This strategy is for educational purposes only and does not constitute financial advice.
GKD-B Multi-Ticker Stepped Baseline [Loxx]Giga Kaleidoscope GKD-B Multi-Ticker Stepped Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
This version of the GKD-B Baseline is designed specifically to support traders who wish to conduct GKD-BT Multi-Ticker Backtests with multiple tickers. This functionality is exclusive to the GKD-BT Multi-Ticker Backtests.
Traders have the capability to apply a filter to the selected moving average, leveraging various volatility metrics to enhance trend identification. This feature is tailored for traders favoring a gradual and consistent approach, enabling them to discern more sustainable trends. The system permits filtering for both the input data and the moving average results, requiring price movements to exceed a specific threshold—defined as multiples of the volatility—before acknowledging a trend change. This mechanism effectively reduces false signals caused by market noise and lateral movements. A distinctive aspect of this tool is its ability to adjust both price and moving average data based on volatility indicators like VIX, EUVIX, BVIV, and EVIV, among others. Understanding the time frame over which a volatility index is measured is crucial; for instance, VIX is measured on an annual basis, whereas BVIV and EVIV are based on a 30-day period. To accurately convert these measurements to a daily scale, users must input the correct "days per year" value: 252 for VIX and 30 for BVIV and EVIV. Future updates will introduce additional functionality to extend analysis across various time frames, but currently, this feature is solely available for daily time frame analysis.
█ GKD-B Multi-Ticker Stepped Baseline includes 65+ different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
One More Moving Average - OMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Geometric Mean Moving Average
Coral
Tether Lines
Range Filter
Triangle Moving Average Generalized
Ultinate Smoother
Adaptive Moving Average - AMA
The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Kaufman Adaptive Moving Average - 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.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
One More Moving Average (OMA)
The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
█ Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types.
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Volatility Ticker Selection
Import volatility tickers like VIX, EUVIX, BVIV, and EVIV.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, and the Average Directional Index (ADX).
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker CC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Advance Trend Pressure as shown on the chart above
Confirmation 2: uf2018
Continuation: Coppock Curve
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
Standardized Orderflow [AlgoAlpha]Introducing the Standardized Orderflow indicator by AlgoAlpha. This innovative tool is designed to enhance your trading strategy by providing a detailed analysis of order flow and velocity. Perfect for traders who seek a deeper insight into market dynamics, it's packed with features that cater to various trading styles. 🚀📊
Key Features:
📈 Order Flow Analysis: At its core, the indicator analyzes order flow, distinguishing between bullish and bearish volume within a specified period. It uses a unique standard deviation calculation for normalization, offering a clear view of market sentiment.
🔄 Smoothing Options: Users can opt for a smoothed representation of order flow, using a Hull Moving Average (HMA) for a more refined analysis.
🌪️ Velocity Tracking: The indicator tracks the velocity of order flow changes, providing insights into the market's momentum.
🎨 Customizable Display: Tailor the display mode to focus on either order flow, order velocity, or both, depending on your analysis needs.
🔔 Alerts for Critical Events: Set up alerts for crucial market events like crossover/crossunder of the zero line and overbought/oversold conditions.
How to Use:
1. Setup: Easily configure the indicator to match your trading strategy with customizable input parameters such as order flow period, smoothing length, and moving average types.
2. Interpretation: Watch for bullish and bearish columns in the order flow chart, utilize the Heiken Ashi RSI candle calculation, and look our for reversal notations for additional market insights.
3. Alerts: Stay informed with real-time alerts for key market events.
Code Explanation:
- Order Flow Calculation:
The core of the indicator is the calculation of order flow, which is the sum of volumes for bullish or bearish price movements. This is followed by normalization using standard deviation.
orderFlow = math.sum(close > close ? volume : (close < close ? -volume : 0), orderFlowWindow)
orderFlow := useSmoothing ? ta.hma(orderFlow, smoothingLength) : orderFlow
stdDev = ta.stdev(orderFlow, 45) * 1
normalizedOrderFlow = orderFlow/(stdDev + stdDev)
- Velocity Calculation:
The velocity of order flow changes is calculated using moving averages, providing a dynamic view of market momentum.
velocityDiff = ma((normalizedOrderFlow - ma(normalizedOrderFlow, velocitySignalLength, maTypeInput)) * 10, velocityCalcLength, maTypeInput)
- Display Options:
Users can choose their preferred display mode, focusing on either order flow, order velocity, or both.
orderFlowDisplayCond = displayMode != "Order Velocity" ? display.all : display.none
wideDisplayCond = displayMode != "Order Flow" ? display.all : display.none
- Reversal Indicators and Divergences:
The indicator also includes plots for potential bullish and bearish reversals, as well as regular and hidden divergences, adding depth to your market analysis.
bullishReversalCond = reversalType == "Order Flow" ? ta.crossover(normalizedOrderFlow, -1.5) : (reversalType == "Order Velocity" ? ta.crossover(velocityDiff, -4) : (ta.crossover(velocityDiff, -4) or ta.crossover(normalizedOrderFlow, -1.5)) )
bearishReversalCond = reversalType == "Order Flow" ? ta.crossunder(normalizedOrderFlow, 1.5) : (reversalType == "Order Velocity" ? ta.crossunder(velocityDiff, 4) : (ta.crossunder(velocityDiff, 4) or ta.crossunder(normalizedOrderFlow, 1.5)) )
In summary, the Standardized Orderflow indicator by AlgoAlpha is a versatile tool for traders aiming to enhance their market analysis. Whether you're focused on short-term momentum or long-term trends, this indicator provides valuable insights into market dynamics. 🌟📉📈
GKD-B Multi-Ticker Baseline [Loxx]Giga Kaleidoscope GKD-B Multi-Ticker Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
This is a special implementation of GKD-B Baseline that allows the trader to input multiple tickers to be passed onto a GKD-BT Multi-Ticker Backtest. This baseline can only be used with the GKD-BT Multi-Ticker Backtests.
GKD-B Multi-Ticker Baseline includes 64 different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
One More Moving Average - OMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - 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.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
One More Moving Average (OMA)
The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
█ Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types.
█ Volatility Types included
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. You can change the values of the multipliers in the settings as well.
This module includes 17 types of volatility:
Close-to-Close
Parkinson
Garman-Klass
Rogers-Satchell
Yang-Zhang
Garman-Klass-Yang-Zhang
Exponential Weighted Moving Average
Standard Deviation of Log Returns
Pseudo GARCH(2,2)
Average True Range
True Range Double
Standard Deviation
Adaptive Deviation
Median Absolute Deviation
Efficiency-Ratio Adaptive ATR
Mean Absolute Deviation
Static Percent
Various volatility estimators and indicators that investors and traders can use to measure the dispersion or volatility of a financial instrument's price. Each estimator has its strengths and weaknesses, and the choice of estimator should depend on the specific needs and circumstances of the user.
Close-to-Close
Close-to-Close volatility is a classic and widely used volatility measure, sometimes referred to as historical volatility.
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a larger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility is calculated using only a stock's closing prices. It is the simplest volatility estimator. However, in many cases, it is not precise enough. Stock prices could jump significantly during a trading session and return to the opening value at the end. That means that a considerable amount of price information is not taken into account by close-to-close volatility.
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. This is useful as close-to-close prices could show little difference while large price movements could have occurred during the day. Thus, Parkinson's volatility is considered more precise and requires less data for calculation than close-to-close volatility.
One drawback of this estimator is that it doesn't take into account price movements after the market closes. Hence, it systematically undervalues volatility. This drawback is addressed in the Garman-Klass volatility estimator.
Garman-Klass
Garman-Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing prices. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change follows a continuous diffusion process (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremes.
Researchers Rogers and Satchell have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates a drift term (mean return not equal to zero). As a result, it provides better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. This leads to an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
Yang-Zhang volatility can be thought of as a combination of the overnight (close-to-open volatility) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility. It is considered to be 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator incorporates the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e., it assumes that the underlying asset follows a Geometric Brownian Motion (GBM) process with zero drift. Therefore, the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, with the main applications being technical analysis and volatility modeling.
The moving average is designed such that older observations are given lower weights. The weights decrease exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility. It's the standard deviation of ln(close/close(1)).
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by ?.
?avg(var;M) + (1 ? ?) avg(var;N) = 2?var/(M+1-(M-1)L) + 2(1-?)var/(M+1-(M-1)L)
Solving for ? can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg(var; N) against avg(var; M) - avg(var; N) and using the resulting beta estimate as ?.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma ? or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis, we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we can call it EMA deviation. Additionally, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to the standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, a manual recreation of the quantile function in Pine Script is used. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is a widely used indicator for many occasions in technical analysis. It is calculated as the RMA of the true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range.
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation (SD). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
8. Metamorphosis - a technical indicator that produces a compound signal from the combination of other GKD indicators*
*(not part of the NNFX algorithm)
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
What is an Metamorphosis indicator?
The concept of a metamorphosis indicator involves the integration of two or more GKD indicators to generate a compound signal. This is achieved by evaluating the accuracy of each indicator and selecting the signal from the indicator with the highest accuracy. As an illustration, let's consider a scenario where we calculate the accuracy of 10 indicators and choose the signal from the indicator that demonstrates the highest accuracy.
The resulting output from the metamorphosis indicator can then be utilized in a GKD-BT backtest by occupying a slot that aligns with the purpose of the metamorphosis indicator. The slot can be a GKD-B, GKD-C, or GKD-E slot, depending on the specific requirements and objectives of the indicator. This allows for seamless integration and utilization of the compound signal within the GKD-BT framework.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v2.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
6. GKD-M - Metamorphosis module (Metamorphosis, Number 8 in the NNFX algorithm, but not part of the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data to A backtest module wherein the various components of the GKD system are combined to create a trading signal.
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Multi-Ticker SCC Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Trasnform
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, GKD-M, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD system.
█ Giga Kaleidoscope Modularized Trading System Signals
Standard Entry
1. GKD-C Confirmation gives signal
2. Baseline agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
1-Candle Standard Entry
1a. GKD-C Confirmation gives signal
2a. Baseline agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Baseline Entry
1. GKD-B Basline gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Volatility/Volume agrees
7. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
1-Candle Baseline Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSBC Bars Back' prior
Next Candle
1b. Price retraced
2b. Baseline agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Volatility/Volume Entry
1. GKD-V Volatility/Volume gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Confirmation 2 agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Volatility/Volume Entry
1a. GKD-V Volatility/Volume gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSVVC Bars Back' prior
Next Candle
1b. Price retraced
2b. Volatility/Volume agrees
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Baseline agrees
Confirmation 2 Entry
1. GKD-C Confirmation 2 gives signal
2. Confirmation 1 agrees
3. Price inside Goldie Locks Zone Minimum
4. Price inside Goldie Locks Zone Maximum
5. Volatility/Volume agrees
6. Baseline agrees
7. Confirmation 1 signal was less than 7 candles prior
1-Candle Confirmation 2 Entry
1a. GKD-C Confirmation 2 gives signal
2a. Confirmation 1 agrees
3a. Price inside Goldie Locks Zone Minimum
4a. Price inside Goldie Locks Zone Maximum
5a. Confirmation 1 signal was less than 'Maximum Allowable PSC2C Bars Back' prior
Next Candle
1b. Price retraced
2b. Confirmation 2 agrees
3b. Confirmation 1 agrees
4b. Volatility/Volume agrees
5b. Baseline agrees
PullBack Entry
1a. GKD-B Baseline gives signal
2a. Confirmation 1 agrees
3a. Price is beyond 1.0x Volatility of Baseline
Next Candle
1b. Price inside Goldie Locks Zone Minimum
2b. Price inside Goldie Locks Zone Maximum
3b. Confirmation 1 agrees
4b. Confirmation 2 agrees
5b. Volatility/Volume agrees
Continuation Entry
1. Standard Entry, 1-Candle Standard Entry, Baseline Entry, 1-Candle Baseline Entry, Volatility/Volume Entry, 1-Candle Volatility/Volume Entry, Confirmation 2 Entry, 1-Candle Confirmation 2 Entry, or Pullback entry triggered previously
2. Baseline hasn't crossed since entry signal trigger
4. Confirmation 1 agrees
5. Baseline agrees
6. Confirmation 2 agrees
█ Connecting to Backtests
All GKD indicators are chained indicators meaning you export the value of the indicators to specialized backtest to creat your GKD trading system. Each indicator contains a proprietary signal generation algo that will only work with GKD backtests. You can find these backtests using the links below.
GKD-BT Giga Confirmation Stack Backtest
GKD-BT Giga Stacks Backtest
GKD-BT Full Giga Kaleidoscope Backtest
GKD-BT Solo Confirmation Super Complex Backtest
GKD-BT Solo Confirmation Complex Backtest
GKD-BT Solo Confirmation Simple Backtest
GKD-M Baseline Optimizer
GKD-M Accuracy Alchemist
GKD-B Stepped Baseline [Loxx]Giga Kaleidoscope GKD-B Stepped Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ GKD-B Stepped Baseline
This is a special implementation of GKD-B Baseline in that it allows the user to filter the selected moving average using the various types of volatility listed below. This additional filter allows the trader to identify longer trends that may be more confucive to a slow and steady trading style.
GKD Stepped Baseline includes 64 different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
One More Moving Average - OMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
The T3 moving average is a type of technical indicator used in financial analysis to identify trends in price movements. It is similar to the Exponential Moving Average (EMA) and the Double Exponential Moving Average (DEMA), but uses a different smoothing algorithm.
The T3 moving average is calculated using a series of exponential moving averages that are designed to filter out noise and smooth the data. The resulting smoothed data is then weighted with a non-linear function to produce a final output that is more responsive to changes in trend direction.
The T3 moving average can be customized by adjusting the length of the moving average, as well as the weighting function used to smooth the data. It is commonly used in conjunction with other technical indicators as part of a larger trading strategy.
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - 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.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
One More Moving Average (OMA)
The One More Moving Average (OMA) is a technical indicator that calculates a series of Jurik-style moving averages in order to reduce noise and provide smoother price data. It uses six exponential moving averages to generate the final value, with the length of the moving averages determined by an adaptive algorithm that adjusts to the current market conditions. The algorithm calculates the average period by comparing the signal to noise ratio and using this value to determine the length of the moving averages. The resulting values are used to generate the final value of the OMA, which can be used to identify trends and potential changes in trend direction.
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. The green and red dots at the top of the chart denote whether a candle qualifies for a either or long or short respectively. The green and red triangles at the bottom of the chart denote whether the trigger has crossed up or down and qualifies inside the Goldie Locks zone. White coloring of the Goldie Locks Zone mean line is where volatility is too low to trade.
Volatility Types Included
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Standard Deviation
Standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation.
Adaptive Deviation
By definition, the Standard Deviation (STD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility .
Standard Deviation is based on Simple Moving Average calculation for mean value. This version of standard deviation uses the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA , we can call it EMA deviation. And added to that, Perry Kaufman's efficiency ratio is used to make it adaptive (since all EMA type calculations are nearly perfect for adapting).
The difference when compared to standard is significant--not just because of EMA usage, but the efficiency ratio makes it a "bit more logical" in very volatile market conditions.
Median Absolute Deviation
The median absolute deviation is a measure of statistical dispersion. Moreover, the MAD is a robust statistic, being more resilient to outliers in a data set than the standard deviation. In the standard deviation, the distances from the mean are squared, so large deviations are weighted more heavily, and thus outliers can heavily influence it. In the MAD, the deviations of a small number of outliers are irrelevant.
Because the MAD is a more robust estimator of scale than the sample variance or standard deviation, it works better with distributions without a mean or variance, such as the Cauchy distribution.
For this indicator, I used a manual recreation of the quantile function in Pine Script. This is so users have a full inside view into how this is calculated.
Efficiency-Ratio Adaptive ATR
Average True Range (ATR) is widely used indicator in many occasions for technical analysis . It is calculated as the RMA of true range. This version adds a "twist": it uses Perry Kaufman's Efficiency Ratio to calculate adaptive true range
Mean Absolute Deviation
The mean absolute deviation (MAD) is a measure of variability that indicates the average distance between observations and their mean. MAD uses the original units of the data, which simplifies interpretation. Larger values signify that the data points spread out further from the average. Conversely, lower values correspond to data points bunching closer to it. The mean absolute deviation is also known as the mean deviation and average absolute deviation.
This definition of the mean absolute deviation sounds similar to the standard deviation ( SD ). While both measure variability, they have different calculations. In recent years, some proponents of MAD have suggested that it replace the SD as the primary measure because it is a simpler concept that better fits real life.
For Pine Coders, this is equivalent of using ta.dev()
Additional features will be added in future releases.
█ Giga Kaleidoscope Modularized Trading System
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Hurst Exponent
Confirmation 1: Fisher Transform
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
]█ Setting up the GKD
The GKD system involves chaining indicators together. These are the steps to set this up.
Use a GKD-C indicator alone on a chart
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
Use a GKD-V indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Use a GKD-B indicator alone on a chart
**nothing, it's already useable on the chart without any settings changes
Baseline (Baseline, Backtest)
1. Import the GKD-B Baseline into the GKD-BT Backtest: "Input into Volatility/Volume or Backtest (Baseline testing)"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline"
Volatility/Volume (Volatility/Volume, Backte st)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Solo"
2. Inside the GKD-V indicator, change the "Signal Type" setting to "Crossing" (neither traditional nor both can be backtested)
3. Import the GKD-V indicator into the GKD-BT Backtest: "Input into C1 or Backtest"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Volatility/Volume"
5. Inside the GKD-BT Backtest, a) change the setting "Backtest Type" to "Trading" if using a directional GKD-V indicator; or, b) change the setting "Backtest Type" to "Full" if using a directional or non-directional GKD-V indicator (non-directional GKD-V can only test Longs and Shorts separately)
6. If "Backtest Type" is set to "Full": Inside the GKD-BT Backtest, change the setting "Backtest Side" to "Long" or "Short
7. If "Backtest Type" is set to "Full": To allow the system to open multiple orders at one time so you test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Solo Confirmation Simple (Confirmation, Backtest)
1. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Simple"
1. Import the GKD-C indicator into the GKD-BT Backtest: "Input into Backtest"
2. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Solo Confirmation Simple"
Solo Confirmation Complex without Exits (Baseline, Volatility/Volume, Confirmation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
6. Import the GKD-C into the GKD-BT Backtest: "Input into Exit or Backtest"
Solo Confirmation Complex with Exits (Baseline, Volatility/Volume, Confirmation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C indicator, change the "Confirmation Type" setting to "Solo Confirmation Complex"
4. Import the GKD-V indicator into the GKD-C indicator: "Input into C1 or Backtest"
5. Import the GKD-C indicator into the GKD-E indicator: "Input into Exit"
6. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
7. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Full GKD without Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full wo/ Exits"
9. Import the GKD-E into the GKD-BT Backtest: "Input into Exit or Backtest"
Full GKD with Exits (Baseline, Volatility/Volume, Confirmation 1, Confirmation 2, Continuation, Exit, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Chained"
2. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
3. Inside the GKD-C 1 indicator, change the "Confirmation Type" setting to "Confirmation 1"
4. Import the GKD-V indicator into the GKD-C 1 indicator: "Input into C1 or Backtest"
5. Inside the GKD-C 2 indicator, change the "Confirmation Type" setting to "Confirmation 2"
6. Import the GKD-C 1 indicator into the GKD-C 2 indicator: "Input into C2"
7. Inside the GKD-C Continuation indicator, change the "Confirmation Type" setting to "Continuation"
8. Import the GKD-C Continuation indicator into the GKD-E indicator: "Input into Exit"
9. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "GKD Full w/ Exits"
10. Import the GKD-E into the GKD-BT Backtest: "Input into Backtest"
Baseline + Volatility/Volume (Baseline, Volatility/Volume, Backtest)
1. Inside the GKD-V indicator, change the "Testing Type" setting to "Baseline + Volatility/Volume"
2. Inside the GKD-V indicator, make sure the "Signal Type" setting is set to "Traditional"
3. Import the GKD-B Baseline into the GKD-V indicator: "Input into Volatility/Volume or Backtest (Baseline testing)"
4. Inside the GKD-BT Backtest, change the setting "Backtest Special" to "Baseline + Volatility/Volume"
5. Import the GKD-V into the GKD-BT Backtest: "Input into C1 or Backtest"
6. Inside the GKD-BT Backtest, change the setting "Backtest Type" to "Full". For this backtest, you must test Longs and Shorts separately
7. To allow the system to open multiple orders at one time so you can test all Longs or Shorts, open the GKD-BT Backtest, click the tab "Properties" and then insert a value of something like 10 orders into the "Pyramiding" settings. This will allow 10 orders to be opened at one time which should be enough to catch all possible Longs or Shorts.
Requirements
Outputs
Chained or Standalone: GKD-BT or GKC-V
Stack 1: GKD-C Continuation indicator
Stack 2: GKD-C Continuation indicator
GKD-B Baseline [Loxx]Giga Kaleidoscope Baseline is a Baseline module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is an NNFX algorithmic trading strategy?
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend (such as "Baseline" shown on the chart above)
3. Confirmation 1 - a technical indicator used to identify trend. This should agree with the "Baseline"
4. Confirmation 2 - a technical indicator used to identify trend. This filters/verifies the trend identified by "Baseline" and "Confirmation 1"
5. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown.
6. Exit - a technical indicator used to determine when trend is exhausted.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 module (Confirmation 1/2, Numbers 3 and 4 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 5 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 6 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average as shown on the chart above
Volatility/Volume: Jurik Volty
Confirmation 1: Vortex
Confirmation 2: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Now that you have a general understanding of the NNFX algorithm and the GKD trading system. let's go over what's inside the GKD-B Baseline itself.
GKD Baseline Special Features and Notable Inputs
GKD Baseline v1.0 includes 63 different moving averages:
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Deviation Scaled Moving Average - DSMA
Donchian
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Double Smoothed FEMA - DSFEMA
Double Smoothed Range Weighted EMA - DSRWEMA
Double Smoothed Wilders EMA - DSWEMA
Double Weighted Moving Average - DWMA
Ehlers Optimal Tracking Filter - EOTF
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Generalized DEMA - GDEMA
Generalized Double DEMA - GDDEMA
Hull Moving Average (Type 1) - HMA1
Hull Moving Average (Type 2) - HMA2
Hull Moving Average (Type 3) - HMA3
Hull Moving Average (Type 4) - HMA4
IE /2 - Early T3 by Tim Tilson
Integral of Linear Regression Slope - ILRS
Instantaneous Trendline
Kalman Filter
Kaufman Adaptive Moving Average - KAMA
Laguerre Filter
Leader Exponential Moving Average
Linear Regression Value - LSMA ( Least Squares Moving Average )
Linear Weighted Moving Average - LWMA
McGinley Dynamic
McNicholl EMA
Non-Lag Moving Average
Ocean NMA Moving Average - ONMAMA
Parabolic Weighted Moving Average
Probability Density Function Moving Average - PDFMA
Quadratic Regression Moving Average - QRMA
Regularized EMA - REMA
Range Weighted EMA - RWEMA
Recursive Moving Trendline
Simple Decycler - SDEC
Simple Jurik Moving Average - SJMA
Simple Moving Average - SMA
Sine Weighted Moving Average
Smoothed LWMA - SLWMA
Smoothed Moving Average - SMMA
Smoother
Super Smoother
T3
Three-pole Ehlers Butterworth
Three-pole Ehlers Smoother
Triangular Moving Average - TMA
Triple Exponential Moving Average - TEMA
Two-pole Ehlers Butterworth
Two-pole Ehlers smoother
Variable Index Dynamic Average - VIDYA
Variable Moving Average - VMA
Volume Weighted EMA - VEMA
Volume Weighted Moving Average - VWMA
Zero-Lag DEMA - Zero Lag Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Adaptive Moving Average - AMA
Description. The Adaptive Moving Average (AMA) is a moving average that changes its sensitivity to price moves depending on the calculated volatility. It becomes more sensitive during periods when the price is moving smoothly in a certain direction and becomes less sensitive when the price is volatile.
ADXvma - Average Directional Volatility Moving Average
Linnsoft's ADXvma formula is a volatility-based moving average, with the volatility being determined by the value of the ADX indicator.
The ADXvma has the SMA in Chande's CMO replaced with an EMA , it then uses a few more layers of EMA smoothing before the "Volatility Index" is calculated.
A side effect is, those additional layers slow down the ADXvma when you compare it to Chande's Variable Index Dynamic Average VIDYA .
The ADXVMA provides support during uptrends and resistance during downtrends and will stay flat for longer, but will create some of the most accurate market signals when it decides to move.
Ahrens Moving Average
Richard D. Ahrens's Moving Average promises "Smoother Data" that isn't influenced by the occasional price spike. It works by using the Open and the Close in his formula so that the only time the Ahrens Moving Average will change is when the candlestick is either making new highs or new lows.
Alexander Moving Average - ALXMA
This Moving Average uses an elaborate smoothing formula and utilizes a 7 period Moving Average. It corresponds to fitting a second-order polynomial to seven consecutive observations. This moving average is rarely used in trading but is interesting as this Moving Average has been applied to diffusion indexes that tend to be very volatile.
Deviation Scaled Moving Average - DSMA
The Deviation-Scaled Moving Average is a data smoothing technique that acts like an exponential moving average with a dynamic smoothing coefficient. The smoothing coefficient is automatically updated based on the magnitude of price changes. In the Deviation-Scaled Moving Average, the standard deviation from the mean is chosen to be the measure of this magnitude. The resulting indicator provides substantial smoothing of the data even when price changes are small while quickly adapting to these changes.
Donchian
Donchian Channels are three lines generated by moving average calculations that comprise an indicator formed by upper and lower bands around a midrange or median band. The upper band marks the highest price of a security over N periods while the lower band marks the lowest price of a security over N periods.
Double Exponential Moving Average - DEMA
The Double Exponential Moving Average ( DEMA ) combines a smoothed EMA and a single EMA to provide a low-lag indicator. It's primary purpose is to reduce the amount of "lagging entry" opportunities, and like all Moving Averages, the DEMA confirms uptrends whenever price crosses on top of it and closes above it, and confirms downtrends when the price crosses under it and closes below it - but with significantly less lag.
Double Smoothed Exponential Moving Average - DSEMA
The Double Smoothed Exponential Moving Average is a lot less laggy compared to a traditional EMA . It's also considered a leading indicator compared to the EMA , and is best utilized whenever smoothness and speed of reaction to market changes are required.
Double Smoothed FEMA - DSFEMA
Same as the Double Exponential Moving Average (DEMA), but uses a faster version of EMA for its calculation.
Double Smoothed Range Weighted EMA - DSRWEMA
Range weighted exponential moving average (EMA) is, unlike the "regular" range weighted average calculated in a different way. Even though the basis - the range weighting - is the same, the way how it is calculated is completely different. By definition this type of EMA is calculated as a ratio of EMA of price*weight / EMA of weight. And the results are very different and the two should be considered as completely different types of averages. The higher than EMA to price changes responsiveness when the ranges increase remains in this EMA too and in those cases this EMA is clearly leading the "regular" EMA. This version includes double smoothing.
Double Smoothed Wilders EMA - DSWEMA
Welles Wilder was frequently using one "special" case of EMA (Exponential Moving Average) that is due to that fact (that he used it) sometimes called Wilder's EMA. This version is adding double smoothing to Wilder's EMA in order to make it "faster" (it is more responsive to market prices than the original) and is still keeping very smooth values.
Double Weighted Moving Average - DWMA
Double weighted moving average is an LWMA (Linear Weighted Moving Average). Instead of doing one cycle for calculating the LWMA, the indicator is made to cycle the loop 2 times. That produces a smoother values than the original LWMA
Ehlers Optimal Tracking Filter - EOTF
The Elher's Optimum Tracking Filter quickly adjusts rapid shifts in the price and yet is relatively smooth when the price has a sideways action. The operation of this filter is similar to Kaufman’s Adaptive Moving
Average
Exponential Moving Average - EMA
The EMA places more significance on recent data points and moves closer to price than the SMA ( Simple Moving Average ). It reacts faster to volatility due to its emphasis on recent data and is known for its ability to give greater weight to recent and more relevant data. The EMA is therefore seen as an enhancement over the SMA .
Fast Exponential Moving Average - FEMA
An Exponential Moving Average with a short look-back period.
Fractal Adaptive Moving Average - FRAMA
The Fractal Adaptive Moving Average by John Ehlers is an intelligent adaptive Moving Average which takes the importance of price changes into account and follows price closely enough to display significant moves whilst remaining flat if price ranges. The FRAMA does this by dynamically adjusting the look-back period based on the market's fractal geometry.
Generalized DEMA - GDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages.". Instead of using fixed multiplication factor in the final DEMA formula, the generalized version allows you to change it. By varying the "volume factor" form 0 to 1 you apply different multiplications and thus producing DEMA with different "speed" - the higher the volume factor is the "faster" the DEMA will be (but also the slope of it will be less smooth). The volume factor is limited in the calculation to 1 since any volume factor that is larger than 1 is increasing the overshooting to the extent that some volume factors usage makes the indicator unusable.
Generalized Double DEMA - GDDEMA
The double exponential moving average (DEMA), was developed by Patrick Mulloy in an attempt to reduce the amount of lag time found in traditional moving averages. It was first introduced in the February 1994 issue of the magazine Technical Analysis of Stocks & Commodities in Mulloy's article "Smoothing Data with Faster Moving Averages''. This is an extension of the Generalized DEMA using Tim Tillsons (the inventor of T3) idea, and is using GDEMA of GDEMA for calculation (which is the "middle step" of T3 calculation). Since there are no versions showing that middle step, this version covers that too. The result is smoother than Generalized DEMA, but is less smooth than T3 - one has to do some experimenting in order to find the optimal way to use it, but in any case, since it is "faster" than the T3 (Tim Tillson T3) and still smooth, it looks like a good compromise between speed and smoothness.
Hull Moving Average (Type 1) - HMA1
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMA for smoothing.
Hull Moving Average (Type 2) - HMA2
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses EMA for smoothing.
Hull Moving Average (Type 3) - HMA3
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses LWMA for smoothing.
Hull Moving Average (Type 4) - HMA4
Alan Hull's HMA makes use of weighted moving averages to prioritize recent values and greatly reduce lag whilst maintaining the smoothness of a traditional Moving Average. For this reason, it's seen as a well-suited Moving Average for identifying entry points. This version uses SMMA for smoothing.
IE /2 - Early T3 by Tim Tilson and T3 new
T3 is basically an EMA on steroids, You can read about T3 here:
Integral of Linear Regression Slope - ILRS
A Moving Average where the slope of a linear regression line is simply integrated as it is fitted in a moving window of length N (natural numbers in maths) across the data. The derivative of ILRS is the linear regression slope. ILRS is not the same as a SMA ( Simple Moving Average ) of length N, which is actually the midpoint of the linear regression line as it moves across the data.
Instantaneous Trendline
The Instantaneous Trendline is created by removing the dominant cycle component from the price information which makes this Moving Average suitable for medium to long-term trading.
Kalman Filter
Kalman filter is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies. This means that the filter was originally designed to work with noisy data. Also, it is able to work with incomplete data. Another advantage is that it is designed for and applied in dynamic systems; our price chart belongs to such systems. This version is true to the original design of the trade-ready Kalman Filter where velocity is the triggering mechanism.
Kalman Filter is a more accurate smoothing/prediction algorithm than the moving average because it is adaptive: it accounts for estimation errors and tries to adjust its predictions from the information it learned in the previous stage. Theoretically, Kalman Filter consists of measurement and transition components.
Kaufman Adaptive Moving Average - 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.
Laguerre Filter
The Laguerre Filter is a smoothing filter which is based on Laguerre polynomials. The filter requires the current price, three prior prices, a user defined factor called Alpha to fill its calculation.
Adjusting the Alpha coefficient is used to increase or decrease its lag and its smoothness.
Leader Exponential Moving Average
The Leader EMA was created by Giorgos E. Siligardos who created a Moving Average which was able to eliminate lag altogether whilst maintaining some smoothness. It was first described during his research paper "MACD Leader" where he applied this to the MACD to improve its signals and remove its lagging issue. This filter uses his leading MACD's "modified EMA" and can be used as a zero lag filter.
Linear Regression Value - LSMA ( Least Squares Moving Average )
LSMA as a Moving Average is based on plotting the end point of the linear regression line. It compares the current value to the prior value and a determination is made of a possible trend, eg. the linear regression line is pointing up or down.
Linear Weighted Moving Average - LWMA
LWMA reacts to price quicker than the SMA and EMA . Although it's similar to the Simple Moving Average , the difference is that a weight coefficient is multiplied to the price which means the most recent price has the highest weighting, and each prior price has progressively less weight. The weights drop in a linear fashion.
McGinley Dynamic
John McGinley created this Moving Average to track prices better than traditional Moving Averages. It does this by incorporating an automatic adjustment factor into its formula, which speeds (or slows) the indicator in trending, or ranging, markets.
McNicholl EMA
Dennis McNicholl developed this Moving Average to use as his center line for his "Better Bollinger Bands" indicator and was successful because it responded better to volatility changes over the standard SMA and managed to avoid common whipsaws.
Non-lag moving average
The Non Lag Moving average follows price closely and gives very quick signals as well as early signals of price change. As a standalone Moving Average, it should not be used on its own, but as an additional confluence tool for early signals.
Ocean NMA Moving Average - ONMAMA
Created by Jim Sloman, the NMA is a moving average that automatically adjusts to volatility without being programmed to do so. For more info, read his guide "Ocean Theory, an Introduction"
Parabolic Weighted Moving Average
The Parabolic Weighted Moving Average is a variation of the Linear Weighted Moving Average . The Linear Weighted Moving Average calculates the average by assigning different weights to each element in its calculation. The Parabolic Weighted Moving Average is a variation that allows weights to be changed to form a parabolic curve. It is done simply by using the Power parameter of this indicator.
Probability Density Function Moving Average - PDFMA
Probability density function based MA is a sort of weighted moving average that uses probability density function to calculate the weights. By its nature it is similar to a lot of digital filters.
Quadratic Regression Moving Average - QRMA
A quadratic regression is the process of finding the equation of the parabola that best fits a set of data. This moving average is an obscure concept that was posted to Forex forums in around 2008.
Regularized EMA - REMA
The regularized exponential moving average (REMA) by Chris Satchwell is a variation on the EMA (see Exponential Moving Average) designed to be smoother but not introduce too much extra lag.
Range Weighted EMA - RWEMA
This indicator is a variation of the range weighted EMA. The variation comes from a possible need to make that indicator a bit less "noisy" when it comes to slope changes. The method used for calculating this variation is the method described by Lee Leibfarth in his article "Trading With An Adaptive Price Zone".
Recursive Moving Trendline
Dennis Meyers's Recursive Moving Trendline uses a recursive (repeated application of a rule) polynomial fit, a technique that uses a small number of past values estimations of price and today's price to predict tomorrow's price.
Simple Decycler - SDEC
The Ehlers Simple Decycler study is a virtually zero-lag technical indicator proposed by John F. Ehlers. The original idea behind this study (and several others created by John F. Ehlers) is that market data can be considered a continuum of cycle periods with different cycle amplitudes. Thus, trending periods can be considered segments of longer cycles, or, in other words, low-frequency segments. Applying the right filter might help identify these segments.
Simple Loxx Moving Average - SLMA
A three stage moving average combining an adaptive EMA, a Kalman Filter, and a Kauffman adaptive filter.
Simple Moving Average - SMA
The SMA calculates the average of a range of prices by adding recent prices and then dividing that figure by the number of time periods in the calculation average. It is the most basic Moving Average which is seen as a reliable tool for starting off with Moving Average studies. As reliable as it may be, the basic moving average will work better when it's enhanced into an EMA .
Sine Weighted Moving Average
The Sine Weighted Moving Average assigns the most weight at the middle of the data set. It does this by weighting from the first half of a Sine Wave Cycle and the most weighting is given to the data in the middle of that data set. The Sine WMA closely resembles the TMA (Triangular Moving Average).
Smoothed LWMA - SLWMA
A smoothed version of the LWMA
Smoothed Moving Average - SMMA
The Smoothed Moving Average is similar to the Simple Moving Average ( SMA ), but aims to reduce noise rather than reduce lag. SMMA takes all prices into account and uses a long lookback period. Due to this, it's seen as an accurate yet laggy Moving Average.
Smoother
The Smoother filter is a faster-reacting smoothing technique which generates considerably less lag than the SMMA ( Smoothed Moving Average ). It gives earlier signals but can also create false signals due to its earlier reactions. This filter is sometimes wrongly mistaken for the superior Jurik Smoothing algorithm.
Super Smoother
The Super Smoother filter uses John Ehlers’s “Super Smoother” which consists of a Two pole Butterworth filter combined with a 2-bar SMA ( Simple Moving Average ) that suppresses the 22050 Hz Nyquist frequency: A characteristic of a sampler, which converts a continuous function or signal into a discrete sequence.
Three-pole Ehlers Butterworth
The 3 pole Ehlers Butterworth (as well as the Two pole Butterworth) are both superior alternatives to the EMA and SMA . They aim at producing less lag whilst maintaining accuracy. The 2 pole filter will give you a better approximation for price, whereas the 3 pole filter has superior smoothing.
Three-pole Ehlers smoother
The 3 pole Ehlers smoother works almost as close to price as the above mentioned 3 Pole Ehlers Butterworth. It acts as a strong baseline for signals but removes some noise. Side by side, it hardly differs from the Three Pole Ehlers Butterworth but when examined closely, it has better overshoot reduction compared to the 3 pole Ehlers Butterworth.
Triangular Moving Average - TMA
The TMA is similar to the EMA but uses a different weighting scheme. Exponential and weighted Moving Averages will assign weight to the most recent price data. Simple moving averages will assign the weight equally across all the price data. With a TMA (Triangular Moving Average), it is double smoother (averaged twice) so the majority of the weight is assigned to the middle portion of the data.
Triple Exponential Moving Average - TEMA
The TEMA uses multiple EMA calculations as well as subtracting lag to create a tool which can be used for scalping pullbacks. As it follows price closely, its signals are considered very noisy and should only be used in extremely fast-paced trading conditions.
Two-pole Ehlers Butterworth
The 2 pole Ehlers Butterworth (as well as the three pole Butterworth mentioned above) is another filter that cuts out the noise and follows the price closely. The 2 pole is seen as a faster, leading filter over the 3 pole and follows price a bit more closely. Analysts will utilize both a 2 pole and a 3 pole Butterworth on the same chart using the same period, but having both on chart allows its crosses to be traded.
Two-pole Ehlers smoother
A smoother version of the Two pole Ehlers Butterworth. This filter is the faster version out of the 3 pole Ehlers Butterworth. It does a decent job at cutting out market noise whilst emphasizing a closer following to price over the 3 pole Ehlers .
Variable Index Dynamic Average - VIDYA
Variable Index Dynamic Average Technical Indicator ( VIDYA ) was developed by Tushar Chande. It is an original method of calculating the Exponential Moving Average ( EMA ) with the dynamically changing period of averaging.
Variable Moving Average - VMA
The Variable Moving Average (VMA) is a study that uses an Exponential Moving Average being able to automatically adjust its smoothing factor according to the market volatility.
Volume Weighted EMA - VEMA
An EMA that uses a volume and price weighted calculation instead of the standard price input.
Volume Weighted Moving Average - VWMA
A Volume Weighted Moving Average is a moving average where more weight is given to bars with heavy volume than with light volume. Thus the value of the moving average will be closer to where most trading actually happened than it otherwise would be without being volume weighted.
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
John Ehlers's Zero Lag DEMA's aim is to eliminate the inherent lag associated with all trend following indicators which average a price over time. Because this is a Double Exponential Moving Average with Zero Lag, it has a tendency to overshoot and create a lot of false signals for swing trading. It can however be used for quick scalping or as a secondary indicator for confluence.
Zero-Lag Moving Average
The Zero Lag Moving Average is described by its creator, John Ehlers , as a Moving Average with absolutely no delay. And it's for this reason that this filter will cause a lot of abrupt signals which will not be ideal for medium to long-term traders. This filter is designed to follow price as close as possible whilst de-lagging data instead of basing it on regular data. The way this is done is by attempting to remove the cumulative effect of the Moving Average.
Zero-Lag TEMA - Zero Lag Triple Exponential Moving Average
Just like the Zero Lag DEMA , this filter will give you the fastest signals out of all the Zero Lag Moving Averages. This is useful for scalping but dangerous for medium to long-term traders, especially during market Volatility and news events. Having no lag, this filter also has no smoothing in its signals and can cause some very bizarre behavior when applied to certain indicators.
Exotic Triggers
This version of Baseline allows the user to select from exotic or source triggers. An exotic trigger determines trend by either slope or some other mechanism that is special to each moving average. A source trigger is one of 32 different source types from Loxx's Exotic Source Types. You can read about these source types here:
Volatility Goldie Locks Zone
This volatility filter is the standard first pass filter that is used for all NNFX systems despite the additional volatility/volume filter used in step 5. For this filter, price must fall into a range of maximum and minimum values calculated using multiples of volatility. Unlike the standard NNFX systems, this version of volatility filtering is separated from the core Baseline and uses it's own moving average with Loxx's Exotic Source Types. The green and red dots at the top of the chart denote whether a candle qualifies for a either or long or short respectively. The green and red triangles at the bottom of the chart denote whether the trigger has crossed up or down and qualifies inside the Goldie Locks zone. White coloring of the Goldie Locks Zone mean line is where volatility is too low to trade.
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (Geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman-Klass-Yang-Zhang (GKYZ) volatility estimator consists of using the returns of open, high, low, and closing prices in its calculation.
GKYZ volatility estimator takes into account overnight jumps but not the trend, i.e. it assumes that the underlying asset follows a GBM process with zero drift. Therefore the GKYZ volatility estimator tends to overestimate the volatility when the drift is different from zero. However, for a GBM process, this estimator is eight times more efficient than the close-to-close volatility estimator.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
The average true range (ATR) is a technical analysis indicator, introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems, that measures market volatility by decomposing the entire range of an asset price for that period.
The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Additional features will be added in future releases.
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Smart Money Concepts(v0.01) - SoldiSmart Money Concepts
We are very pleased to be releasing our latest addition to the Soldi tools, called Smart Money Concepts. What this indicator was built to be is a guideline and tool to help a trader develop the mental mind state of a Smart Money Trader. Picking up on the digital footprints that they might have missed! This is our first iteration of this tool but we have so so much more coming to bring to this tool! So much that we might need to release 2 scripts to be able to efficiently fit it all in. As always Soldi/MMCFX always try to raise the bar on what is possible with PineScript and what advanced concepts we can bring to the retail market with ease, this project was insanely fun trying to get together and we spent a lot of months talking with and doing sessions with very well versed traders who only specialize and solely trade live with Smart Money/ICT Concepts. After many months of talking with and working with these traders we believe we have put together a very unique tool that any SMC trader would love to have in their tool belt.
What is Smart Money Concepts?
Smart Money Concepts (SMC) is the practice of trying to track the digital footprints left by Market Makers and large money traders like Institutional bodies and brokers. I believe this concept was originally developed by Inner Circle Trading (ICT), who has some great great content for free on YouTube. To my knowledge he was the father of the concepts being taken mainstream to retail individuals. Since then, there has been many other who have released content on these theories. For the sake of congruency we have only developed these tools based off the knowledge and practices taught by ICT.
What is Included within this tool?
What is currently Included with this tool are the following.
Market Structure - This includes Break of Structures (BOS) and Change of Characters (CHoCH), It was really important for us to define the different shifts that SMC traders track and follow so we built a unique customizable system that allows the traders to track these Market Structure shifts in real-time. Part of this module includes the option to plot the High/Low labels, by putting this settings on you will mark out the swing points as their respective Higher High(HH), High Low(HL), Lower Low(LL) and Lower High(LH) . This feature is a great way to help familiarize yourself with spotting these instances, there is a slight lag due to the nature of the calculations for tracking the Swing Points. By default we track 4 left bars and 4 right bars, on the 5th bar if the swing point returns true you will see the label plot itself. If you have a higher bar count you will need to wait till x+1 to see the label be plotted. eg. 7 bar count on the left and right, you will need to wait till the 8th bar to see the label be plotted.
By changing the bar counts you also change how the Market Structure module picks up the Market shifts (BOS/CHoCH)
4 bar left, 4 bar right example:
7 bar left, 7 bar right example:
Liquidity Sweep - This part of the Market Structure module is still being worked on and built out, this feature is meant to help a trader identify potential liquidity sweeps that have taken place past or present by switching the bar color to the user defined color (default yellow). There are many different types of liquidity sweeps that can take place and we are still working on the different profiles of these! More profiles will be added to the the updates in the future to help identify these potential trade areas
Liquidity Sweep example:
Trend Bars - This part of the Market Structure module helps traders identify structure trends based on the breaks of existing structure. Again this will shift as you play with the bar count settings, low bar count will identify faster swing points and shifts where as higher bar counts will identify longer term structures. By having this setting on it will change the bar colors to Red(Bearish) or Blue(Bullish) by default, we recommend to change your candles border settings to make this more visible.
7 bar left, 7 bar right. With High Low Labels and Trend Bars
Fair Value Gaps - This module will track the Fair Value Gaps and Imbalances that will take place in real-time. Once the final candle closes it will plot the FVG. Unlike other FVG indicators on TradingView we hold and store ALL the FVG's that take place, other indicators will only hold on to x amount of the FVG's and as new ones enter the list the old ones get bumped out. We didn't like this idea, so what we did was instead store all of the FVG's but create a threshold to where they would be plotted, eg. if you set the threshold to 4% it will only show you the FVG's within a 4% range from the current price. This way you still have access to all the data with out compromising but it helps you focus on the current data at hand.
Fair Value Gap/Imbalance - 3% threshold example
Fair Value Gap/Imbalance - 8% threshold example
Order Blocks - This was an especially interesting module to build, just like the FVG's we found that a lot if not all the authors on TradingView haven't actually been coming close to tracking and plotting true ICT style Order blocks. We set out to change that though, again through a unique approach we have built this Order Block indicator. To also comment on the other scripts out there that claim to track Order Blocks, not a SINGLE script mentions anything about Validated Order Blocks , which was especially important to all the SMC traders I have talked to and had help from building this indicator. Just like the FVG piece this also has a 'threshold' plot, but not only that it gives you the option to look at "No Validation" and "Validated" Order Blocks. With soon another style of Validation to choose from. If you choose the "Validated" option the script will actively seek Order Blocks that have a POI/liquidity sitting above it. I also want to make it clear that based on your bar count settings the order blocks will differ, as they are also based from structure breaks!
Order Blocks with "No Validation" example
Order Blocks with "Validation" example
Advanced Session Tracking - We always seek to out do what has been done and what we have already done, that being said we built our Advanced Session Tracking module to follow each user define Session's Open, High, Low, Close, Liquidity threshold and extend that into the next session . As per our last KillZone indicator we also included the Forward Plotting feature which will plot the defined sessions 24 hours in advance vs only showing you real time. Many if not all Session tracking tools on TradingView only show you real-time and in the past when the define sessions are but we find that to be a very silly practice because as SMC traders you know how important it is the relation between time and price. Instead of reacting to the sessions you and prepare for the sessions ahead of time anticipating when price might react to time.
note: There is a small bug with tracking the crypto based sessions, this is working to be fixed for the next update, check the release notes to see when the fix occurs
Session Background plots with forward plotting example
Session Backgrounds with High/Lows and Liquidity range example
What is to come with the updates?
We are always looking to improve anything, even if it is just a fraction better. That is why we are continuing to work with our SMC traders to refine the concepts, profiles, coding as well as the logic behind the calculations.
Here is a list of what we are planning and working on to be released in the updates to come!
Intra-Day Profiling - Each day has a profile, what we want to achieve is to track and predict these profiles
Liquidity Scanner - There are different types of liquidity that form and we want to be able to find and track these
Smart Trend Alerts - We want to combine quant methods into SMC to provide high probability trade ideas
User Suggestions - We are always open to work with the community to bring features they want
If it's not Soldi, it isn't money
Weekly Open + Monday High/Low (After Monday Close)b]Description
This indicator marks key weekly reference levels based on Monday’s price behavior.
It automatically detects each trading week and tracks:
• Weekly Open – the first traded price of the new week
• Monday High – the highest price reached on Monday
• Monday Low – the lowest price reached on Monday
Logic
The Monday range is fully captured only after Monday has closed .
No levels are plotted during Monday.
Starting from Tuesday, the indicator displays thin dots showing the completed Monday High, Monday Low, and Weekly Open for the remainder of the week.
When a new week begins, the indicator resets automatically and begins tracking the new week’s Monday.
Customization
The user can choose colors for:
• Monday High/Low
• Weekly Open
Purpose
This indicator helps traders visualize weekly structure, monitor weekly opening levels, and quickly identify Monday’s range for weekly bias analysis or strategy development.
It can also be used to manually backtest Monday range strategies .
Global View V6 - QuantSyOverview:
Global View V5 is an advanced macro analysis tool that synthesizes multiple global economic indicators to determine market risk regimes and sentiment. This indicator provides traders and analysts with a real-time assessment of macroeconomic conditions by tracking liquidity flows, monetary policy, currency strength, safe assets, and economic activity metrics.
Key Features:
1. Multi-Component Risk Analysis
Tracks seven critical macro indicators:
FED Liquidity
Global M2 Money Supply
Gold
DXY
Trade Balance
Manufacturin
Shipping Activity
2. Risk Regime Classification
Calculates weighted risk scores to classify market conditions into five distinct regimes:
Risk-On: Strong expansionary macro conditions
Slight Risk-On: Moderately positive conditions
Neutral: Balanced environment
Slight Risk-Off: Moderately negative conditions
Risk-Off: Strong contractionary macro conditions
3. Comprehensive Dashboard
Features a professional, customizable dashboard displaying:
Current macro regime with color-coded visualization
Risk-On vs Risk-Off percentage breakdown
Individual metric status with weekly/monthly/yearly rate of change
Market metrics including volatility index, risk sentiment, and macro momentum
4. Advanced Analytics
Volatility Index: Aggregates rate-of-change volatility across key metrics
Macro Momentum: Composite momentum score across all tracked indicators
Risk Sentiment: Net difference between risk-on and risk-off signals
5. Customization Options
Adjustable risk weights for each component
Configurable risk-on/risk-off thresholds
Fine-tunable parameters for all technical calculations
**⚠️ Disclaimer**
Educational tool only - does not constitute investment advice. The developer assumes no liability for any trading profits or losses incurred through the use/misuse of this indicator.
This indicator does not include any features related to interest, leverage, or gambling. Users are fully responsible for making sure their assets and trading practices align with Islamic guidelines.
RT-Mali Pivots & STD Liquidation LevelsIntroduction
The Mali Pivots indicator is a tool that is able to highlight support and resistance levels in relation to the 9,10,11,12,13 and extended Mali Counts of the trending price action. While numerous traders trade these counts, this tool is unique in that it continues counting past these base levels and will mark extended runs coming to an end with a vertical Red or Green Column.
The indicator identifies levels based on both 9,10,11,12,13 and extended Mali Counts, but splits them into both Support and Resistance levels and marks each level with a horizontal pivot. The level being Support/Resistance can be determined by the colors of the pivot. In addition, note that these pivot styles and colors can be edited in the tool settings.
How It Works
This calculation is based on the value of the current close compared to the close that happened four candles back.
MaliCount:= close > close When this condition is true for 9,10,11,12 or 13 consecutive closes in a row, the indicator will mark the candle with the current number count as well as drawing a horizontal pivot to indicate the importance of the Mali Count price levels.
When a count reaches important thresholds for the Mali Count, the indicator will: Label the candle with the current Mali Count.
Draw a horizontal pivot at those important Mali Count price levels.
Mark extended runs with a vertical red or green column when trends keep pushing beyond the minimum Mali Counts. The result is a visual map of where prior trend Mali Counts have completed and where price has historically reacted in the past. These levels are not trade signals by themselves, but they can be used as context when planning entries, exits, and risk.
Patterns of Mali Counts
At the core of the Mali Pivots Indicator is a tool that counts candles of a trend. Conceptually, it: Compares the current close to the close a few candles back to determine if the trend is still pressing in the same direction.
Increments an internal Mali Count as long as the trend condition continues.
Resets the Mali Count when the condition fails.
When the Mali Count reaches the minimum consecutive qualifying closes, the indicator marks that candle with the Mali Count number and establishes a pivot on the important price levels. Because up and down trends are tracked separately, traders can see both bullish and bearish runs and where they completed. Pivots
Each completed important Mali Count leaves behind a horizontal pivot that acts as a reference level on the chart. These pivots are split into support and resistance: Bullish Mali Counts leave pivots that often behave as support when price trades back into them from above.
Bearish Mali Counts leave pivots that often behave as resistance when price trades back into them from below. Pivot colors indicate whether the level is currently treated as support or resistance, and these colors can be adjusted in the settings to match the trader’s chart style.
Extended Trends Ending and Exhaustion
One of the unique aspects of Mali Pivots is that it continues counting beyond the typical 9,10,11,12 and 13 thresholds. When a trend keeps running and the internal Mali Count reaches extended levels, the indicator can highlight that area with a vertical red or green column.
These extended markers are designed to draw attention to trend legs that have pushed well beyond the usual Mali Counts. In many cases, these zones coincide with areas where trends begin to stall, consolidate, or reverse, but they should still be treated as context rather than automatic reversal signals.
Scalp Trading Zone
Some traders use Mali Pivots to frame shorter term trades between nearby support and resistance pivots. On example charts, this is sometimes called a “scalp trading zone” where: Active support and resistance pivots outline a defined price range.
Traders can study how price reacts inside that zone before deciding if they want to participate. This is an optional usage pattern meant for review and study. The indicator itself does not define specific scalp entry or exit rules. Settings
Mali Pivots Indicator includes a focused set of settings so traders can tune its visuals to their own charts:
Standard Liquidation Levels
- Liquidations On/Off - Toggles the tool on and off.
- Dark Mode On/Off - Allows users to use settings for light/dark backgrounds.
Mali Pivots - General Settings
- Pivots Toggle - Toggles the Mali Pivots on and off.
- Candle Color Toggle - Toggles the Candle Marker on and off.
Mali Pivots - Mali Count Settings
- 9/10/11/12/13 Number Toggle - Allows traders to turn off and on specific number displays on their chart.
Mali Pivots - Extended Run Exhaustion
- Extended Bearish/Bullish Trend Stop Toggle - Allows traders to control which extended run markers they want displayed on their chart.
Mali Pivots - Color Settings
- All Color Options - Allows traders to customize the coloring of the tool to match their specific requirements.
How Traders Commonly Use Mali Pivots Tracking where important Mali Counts have completed in the past and how price reacts when it revisits those levels.
Using pivots and extended columns as context when combining Mali Pivots with other tools such as RT-Main Indicator, volume, or broader market structure.
Reviewing historical Mali Counts in Replay mode to better understand how different assets behave around completed Mali Counts and extended runs. In all cases, the Mali Pivots Indicator is designed to highlight structure and potential exhaustion zones, while trade execution remains at the trader’s discretion.
What Makes This Tool Different
Mali Pivots differs from simple counting tools in a few key ways: It tracks both the standard Mali Counts and extended Mali Counts that continue beyond the normal levels.
It converts Mali Count completions into horizontal pivots that can act as long lived support or resistance references.
It visually distinguishes extended trends with vertical columns so unusual runs are easy to spot on the chart. Standard Liquidation Levels
This indicator also includes a simple liquidation level tool that projects where potential Longs and Shorts are vulnerable to being liquidated. This tool measures the base of the volume in the displayed candles and projects the following levels:
5X Liquidation Level - Longs & Shorts
10X Liquidation Level - Longs & Shorts
25X Liquidation Level - Longs & Shorts
50X Liquidation Level - Longs & Shorts
Important Note
Mali Pivots and Standard Liquidation Levels are intended to provide additional context around the trend's Mali Counts, support, and resistance. It is not a standalone signal generator and should always be used together with your own analysis, testing, and risk management. Historical trends and pivot reactions do not guarantee future results.
🐋 Tight lines and happy trading!
RT-Main IndicatorThe RT-Main Indicator is the core indicator that started it all. Developed over more than 5 years, this all in one tool helps traders identify when market participants are buying and selling using multi-colored candles that update in real time. It also identifies key support and resistance levels with Rainbow Pivots and highlights unusual price movements with Whale Print arrows. At its core, the RT-Main Indicator tracks buying and selling with eight colors instead of two, because real world markets are complex and order flow should not be treated as purely binary(Red vs Green).
Introduction
The RT-Main Indicator is designed as a primary Rainbow Theory Tool. It uses color coded candles to show changes in strength, Rainbow Pivots to mark important support and resistance areas, and Whale Prints to flag abnormal buy and sell activity. The goal is to bring these components together into a single framework so traders can read trend, structure, and larger player behavior without stacking many separate indicators.
This tutorial will cover each aspect of the tool:
Colored Candles
Whales are stealth experts and their strength is their ability to not be detected as they move the market. Rainbow Theory illuminates them from the shadows with a spectrum of specifically coded colors to display their unique strengths/weaknesses. In practice, this means the RT-Main Indicator uses internal strength and exhaustion metrics to color candles so that shifts in buying and selling pressure are easier to see.
The base of the RT-Main Indicator is the colored candles it paints onto the chart. These colors automatically tune to the chart based on the timeframe the trader is currently using (1D, H12, H1, 15M, etc). Instead of painting charts with a single Bullish Color (Green) and a single Bearish Color (Red), Rainbow Theory breaks out and identifies these moves into four Bearish Colors (Red|Orange|Yellow|White) and four Bullish Colors (Green|Blue|Purple|Pink). Each color tells a different story of the trend and helps traders better understand the nature of the current trend.
Bullish Colors
#4 - Green Candles - Weakest bullish color, these trends can sustain for extended periods of time.
#3 - Blue Candles - Strong bullish color, a move is starting to develop and can sustain.
#2 - Purple Candles - Second strongest bullish color, Whales are committed to the move but cannot sustain this level of momentum for long durations and a top is near.
#1 - Pink Candles - Strongest bullish color, Whales are using every single ounce of energy they have to push price up, the trend cannot be sustained and its time to take profits.
Bearish Colors
#4 - Red Candles - Weakest bearish color, these trends can sustain for extended periods of time.
#3 - Orange Candles - Strong bearish color, a move is starting to develop and can sustain.
#2 - Yellow Candles - Second strongest bearish color, Whales are committed to the move but cannot sustain this level of momentum for long durations and a bottom is near.
#1 - White Candles - Strongest bearish color, Whales are using every single ounce of energy they have to push price down into all out capitulation, the trend cannot be sustained and its time to look for entries.
How To Enable Colored Candles
By default, the Indicator’s Candles are placed behind the default candles. To properly display them, you must bring them forward. To do this, click the settings icon on the indicator, click visual order and then click bring to front:
Example - Bringing all the colors together into a Bearish Trend that reverses into a Bullish Trend:
The color thresholds can be tuned using the following options:
Automatic Tuning On/Off - Enables or disables the automatic color tuning that adjusts for each timeframe.
Auto Tuning Gain (Inc/Dec) - Increases or decreases how aggressive the automatic tuning algorithm adjusts color tuning.
Manual Fine Tuning - Linear Color Shift - Manually controls the linear sensitivity for color candle thresholds. This can be visualized as a setting being adjusted up or down in a straight, linear fashion. Linear Color Shift
Manual Fine Tuning - Exponential Color Shift - Manually controls the exponential sensitivity for color candle thresholds. This can be visualized as a setting being adjusted in an exponential manner where each level moves in an exponential shift instead of all moving equally. Exponential Color Shift Dark Mode
Some traders prefer light colored backgrounds for their charting, which can make white candles difficult to see. The RT-Main Indicator includes a Dark Mode toggle so colors stay readable on both dark and light charts.
Dark Mode Candles On/Off - Forces the indicator to use the second color set stored in the Style tab in the RT-Main Indicator settings when using light backgrounds. The White/Black Candle can also have a custom color applied if the trader is not content with these two default options.
Custom Candle Colors
In addition to toggling between light and dark modes, each individual color used by the RT-Main Indicator can be edited in the Style tab. This allows traders to keep the same logic while adjusting the visual palette to match their own chart layout.
Rainbow Rotations
Rainbow Rotations are a feature traders use to catch reversals or reversions when a trend fully blows out. The algorithm triggers on the first weaker candle that closes after a Pink or White candle prints. The general idea of this event is to show peaks and valleys of an asset.
In a strong bearish move, White candles mark extreme selling. If a weaker Yellow candle appears after a White candle, that first weaker candle is where the rotation event triggers and a Rainbow Rotation marker is placed on the chart. In a strong bullish move, Pink candles mark extreme buying. The first weaker bullish candle after a Pink candle triggers the opposite side rotation marker.
Note that Rainbow Rotations can only be visible for a finite amount of candles. The Replay function in TradingView can be used to review previous triggers.
Rainbow Rotation settings are available near the top of the settings menu:
Rainbow Rotation Alerts On/Off - Toggles these signals on or off with one click.
Rainbow Rotation Symbol - Customizes the symbol that is plotted on the chart for Rainbow Rotations. Both text and emojis can be used instead of the default symbol.
Rainbow Rotation Alerts
Rainbow Rotations can also be automated with standard TradingView alerts. To set this up:
Click the Alert icon on the right side of the screen.
Change Condition to the RT-Main Indicator.
Change the second condition to one of the three options:
Bullish Alerts | Bearish Alerts | Bearish and Bullish Alerts
Set Trigger to Once Per Bar Close.
Once set up, this allows traders to be notified when the RT-Main Indicator detects an extreme bullish or bearish trend that is starting to reverse.
Automated Pivots
One of the RT-Main Indicator's most powerful functions is the automated support and resistance pivots. This logic uses two internal bots that are tuned to look for potential support and resistance order blocks.
The Resistance Pivot Bot prints lines that are painted with red dashes.
The Support Pivot Bot prints lines that are painted with green dashes.
Regardless of the color of the dashed pivot line, any trend that approaches a pivot should be respected. For example, a trend moving up towards a green support pivot should still treat that area as resistance if price is approaching from below.
As the algorithm continues to print additional pivots on the chart, traders can start identifying order blocks that are otherwise hidden in the price action. These order blocks are key support and resistance areas that trends will often interact with and respect. Multiple stacked pivots in the same region are a visual clue that such an order block has formed.
Pivots can be tuned with the following options:
Pivot On/Off - Quickly toggles all pivots on or off.
Pivot Style - Switches between different styles of marking pivots.
Pivot Sensitivity (Inc/Dec) - Tunes the sensitivity of the pivot algorithms. Adjusting this changes how many pivots are printed on the chart.
Pivot Line Drawing Length - Controls how long the indicator draws the pivot lines.
Resistance / Support Pivot Colors - Allows customization of pivot colors to match the rest of the chart.
Whale Prints
One of the most important parts of the RT-Main Indicator is tracking Whale Prints. This portion of the script looks for abnormal buys and sells that are more consistent with large players than typical flow. Under normal circumstances, whales try to avoid being visible when they buy or sell, but there are times where they are forced to come out of hiding and deliberately move the market.
The Whale Print logic is tuned to notify the trader when it detects that this type of unusual activity may be occurring.
Bearish Whale Prints are marked on the chart with a red triangle.
Bullish Whale Prints are marked on the chart with a green triangle.
Whale Print clusters are situations where multiple Whale Prints have been identified in the past 10 candles. While individual Whale Prints are useful, clusters of Whale Prints are particularly important because they often signal that a very large move is potentially being prepared/defended.
The Whale Print table is an active tracker that counts the number of bullish and bearish Whale Prints that have occurred in the past 10 candles. Whale Print settings can be tuned with:
Whale Print Clusters Table On/Off - Toggles the Whale Print table on or off with one click.
Whale Print Clusters Alerts On/Off - Toggles the Whale Print cluster symbol on or off.
Whale Print Cluster Symbol - Changes the symbol on the chart for Whale Clusters. Emojis and text can both be used instead of the default symbol.
Whale Print Cluster Bullish/Bearish Label Color - Customizes the color of the Whale Print cluster labels on the chart. Whale Print Cluster Alerts
Whale Print Cluster alerts can be automated with standard TradingView alerts. To set this up:
Click the Alert icon on the right side of the screen.
Change Condition to the RT-Main Indicator.
Change the second condition to one of the two options:
Bull Whale Cluster Alert | Bear Whale Cluster Alert
Set Trigger to Once Per Bar Close. Once set up, this allows traders to be notified when the RT-Main Indicator detects a Whale Print Cluster.
Bull/Bear Trend Step Line
The inflection point of the colored candles is controlled by the Bull/Bear Trend Step Line. This is the grey stepped line on the chart where the bullish and bearish colors meet. Candles above this line are marked by the four bullish candle colors.
Candles below this line are marked by the four bearish candle colors.
The Bull/Bear Trend Step Line can be tuned with:
Bull/Bear Line Offset - Controls a vertical threshold for the line.
Bull/Bear Line Smoothness - Controls the sensitivity and smoothness of the line so traders can fine tune it for their specific setups. Most traders do not adjust the Bull/Bear Step Line. The small group that does typically only use these settings for lower timeframe trading setups below 5 minute candles. If preferred, the line can be recolored or hidden from the Style tab of the RT-Main Indicator without changing how the core color logic works.
Important Note
The RT-Main Indicator is intended to provide additional context around trend strength, exhaustion, and key areas of support and resistance. It is not a standalone signal generator and should always be used together with your own analysis, testing, and risk management. Historical color patterns, pivots, and Whale Prints do not guarantee future results.
🐋 Tight lines and happy trading!
Ultimate Market Structure + MTF Dashboard [FIXED]Ultimate Market Structure + MTF Dashboard — Indicator Description
🔶 Overview
Ultimate Market Structure + MTF Dashboard is a fully-automated Smart Money Concepts (SMC) market-structure indicator designed to give traders extremely clean and accurate structural mapping on any timeframe.
It intelligently detects:
External (Swing) Structure
Internal Structure
BOS / CHoCH (Break of Structure / Change of Character)
HH/HL/LH/LL swing labels
Strong/Weak Highs & Lows
Equal Highs (EQH) & Equal Lows (EQL)
Internal BOS/CHoCH (micro-structure)
Multi-Timeframe Structure Dashboard (D, H4, H1, M15, M5)
This indicator eliminates clutter, repaints nothing, and provides crystal-clear visual understanding of market direction.
🎯 What This Indicator Solves
Most structure indicators fail because they:
✔ spam BOS/CHoCH everywhere
✔ repaint pivots
✔ mix internal and external structure
✔ draw messy lines
✔ ignore confluence
✔ don’t show higher timeframe structure
This script solves all of that by using:
Gap-proof pivot detection
Strict internal/external structure separation
Proper BOS/CHoCH logic using previous break
“LastBreak memory” system (no double BOS on same leg)
ATR-filtered internal pivots
Candle-context confluence filter
User-controlled filters for BOS/CHoCH only
Everything is designed for clean, reliable structure.
🧠 How It Works (Logic Explained Clearly)
1️⃣ Swing Structure – External
Based on user-defined swing length (default: 50).
Detects major turning points and evaluates:
HH / HL → Bullish structure
LH / LL → Bearish structure
Once a swing high/low is confirmed, the indicator tracks:
Has price crossed that pivot?
If yes → BOS or CHoCH depending on previous break direction.
2️⃣ Internal Structure – Micro Trend
A second layer using small length pivots (default: 5).
Useful for:
Entries
Scalp-level reversals
Early CHoCH detection
Internal structure uses ATR distance from swing pivots to avoid overlap.
3️⃣ BOS / CHoCH Logic
The script uses a very strict rule:
If previous break direction was opposite → CHoCH
If previous break direction was same → BOS
This eliminates false CHoCH spam and improves trend clarity.
4️⃣ Strong & Weak High/Low Detection
Each time a BOS occurs:
In bearish trend → last swing high = Strong High
In bullish trend → last swing low = Strong Low
Opposite becomes Weak High/Weak Low
These are important Smart Money Concepts levels for:
Premium/discount zones
Liquidity targets
Stop hunts
5️⃣ Equal Highs & Equal Lows (EQH/EQL)
The script automatically identifies EQH/EQL using:
Percentage threshold
Confirmation bar count
Useful for:
Liquidity sweep setups
Inducement
Stop runs
6️⃣ Multi-Timeframe Dashboard
Displays Internal & External structure for:
D (Daily)
H4
H1
M15
M5
Each cell is color-coded:
🟢 Bullish
🔴 Bearish
⚪ Neutral
This gives you instant top-down analysis without switching charts.
📌 What You Can Use This Indicator For
✔ Trend Trading
Keep trades aligned with:
Higher timeframe external trend
Lower timeframe internal entries
For example:
Daily → Bullish
H1 → Bullish
M5 → CHoCH bullish
Entry → Pullback to strong low
✔ Scalping
Internal structure (i-BOS, i-CHoCH) gives:
Fast reversals
Micro CHoCH entries
High-frequency trend shifts
Works extremely well on 1M–5M.
✔ Smart Money Concepts Trading
This indicator gives every SMC component you need:
Liquidity (EQH/EQL)
Swing structure
Internal structure
BOS/CHoCH
Strong/Weak High/Low
Multi-TF context
Perfect for ICT/SMC trading style.
✔ Institutional Order Flow Mapping
Using strong/weak highs/lows and BOS, you can easily determine:
Where smart money targets your stops
Where displacement started
Where structure shifted
Where mitigation may occur
✔ High-Timeframe Confirmation
The dashboard prevents you from trading against:
Daily trend
H4 liquidity levels
H1 structure direction
📈 Who Is This Indicator For?
Beginners
Learn structure visually instead of guessing.
Advanced Traders
Combine structure with:
Liquidity sweeps
FVG
OB
Breaker blocks
Momentum shifts
Scalpers
Use internal BOS/CHoCH for sniper entries.
Swing Traders
Use swing BOS to hold trades for large R:R moves.
ICT / SMC Traders
Perfect for order-block & FVG models.
📌 Recommended Settings
Swing Structure
Length: 50–100
Best for BTC, FX, XAU
Internal Structure
Length: 3–7
Best for scalping
EQ Threshold
FX: 0.10% – 0.25%
Crypto: 0.35% – 0.5%
Confirm Method
Close = safer
Wick = aggressive (scalping)
🧩 Unique Features (Compared to Other Indicators)
✔ Advanced gap-proof pivot engine
✔ Proper historical vs. present structure mode
✔ ATR-filtered internal pivots
✔ Smart confluence filter (detect candle context)
✔ Chart remains clean & minimal
✔ Works on all timeframes including 1-second
✔ No repaint structure
✔ Optimised for high-volatility assets like XAUUSD
🔚 Final Notes
This indicator was engineered to give traders a complete structure toolkit with professional-grade accuracy normally found only in premium paid tools.
With:
Clean BOS/CHoCH
Perfect swing tracking
Full multi-TF dashboard
Smart liquidity detection
Strong/weak level mapping
You can analyse any market with clarity and confidence.
Market Echo Screener [BigBeluga]
The Market Echo Screener is a structured multi-asset dashboard capable of tracking up to 15 symbols simultaneously .
Designed to condense complex market data into an actionable format. Each column represents a specialized calculation, giving traders insight into signals, phases, retests, and volatility — all updated in real time.
For each symbol, it displays a full set of analytics: trend signals, take profit progression, wave structure, equilibrium pulls, volatility-adjusted flows, smart band retests, volatility regimes, and live price context — all condensed into one unified table.
Instead of flipping through multiple charts, traders get an instant overview of market dynamics across an entire watchlist, making it easier to spot alignment and high-probability opportunities.
⬤ Trend Signals
This column is powered by a low-pass digital trend filter that smooths short-term fluctuations and isolates directional momentum.
It produces Buy and Sell signals when price crosses adaptive thresholds relative to the smoothed baseline. Stronger “＋” signals appear when slope acceleration or momentum divergence confirms additional conviction.
• Uses recursive filtering to eliminate noise.
• Signal strength is determined by the magnitude of deviation from the baseline.
• Tracks how many bars back the signal occurred, using a bar-counting algorithm.
• Combines both normal and power signals to reflect phases of market conviction.
⬤ TPs (Take Profits)
The take profit ladder is generated through an adaptive volatility-projection model .
When a signal fires, projected levels are based on volatility-weighted extensions. Each level (TP1–TP6) represents an incrementally wider confidence band, dynamically recalculated with every new bar.
• Uses volatility-normalized ranges for TP distances.
• Level activation is sequential, progressing as price reaches thresholds.
• Reset occurs when opposite signals are detected.
• Higher TPs imply extended momentum runs, while early TP triggers highlight conservative exits.
⬤ ActionWave
The ActionWave column applies a dual-smoothing algorithm combining custom MA stacks and polynomial regression to capture the underlying wave structure.
It identifies macro phases (Bullish ∆ / Bearish ∇) and flags retests when price folds back into the average after expansion.
• Wave slope is calculated using gradient differentials.
• Retests are confirmed within a bar-window threshold (e.g., 20–25 bars).
• Distinguishes continuation from exhaustion by analyzing whether slope remains positive/negative.
• Provides a clean map of trend rhythm without intrabar noise.
⬤ Magnet
The Magnet measure calculates a dynamic equilibrium band around price.
By averaging the midpoints of recent high–low ranges and weighting them by volatility, it defines a “fair zone” where price tends to trend and mean-revert.
• Bullish/Bearish status is derived from price position relative to the equilibrium mean.
• Retests occur when price leaves the zone and then re-enters within a tolerance band.
• Incorporates a mean-reversion index to highlight strength of pull.
• Acts as a gravitational anchor, showing when price is likely to snap back.
⬤ FlowTrend
FlowTrend is calculated using volatility and noise adjusted envelope bands .
It determines the active market flow by testing whether price consistently holds above or below the smoothed envelope. Retests are logged when price touches the envelope and respects trend direction.
• Bands expand/contract based on ATR and rolling variance.
• Flow state = Bullish if closing above upper envelope, Bearish if below.
• Retests validated only if trend slope and band alignment remain intact.
• Helps identify continuation setups by filtering false flips.
⬤ Smart Bands
Smart Bands employ an adaptive trailing stop framework that shifts with volatility and momentum.
Price interaction with these bands is tracked for bullish (∆) or bearish (∇) retests, highlighting whether the current move has revalidated at its volatility boundary.
• Bands derived from trailing volatility-adjusted stops.
• Upward retest fires when price tests support bands during uptrend.
• Downward retest occurs when resistance bands are tapped in downtrend.
• Provides structured “confirmation points” that validate signals.
⬤ Volatility
Volatility is measured via a hybrid standard deviation logic .
First, the standard deviation of closing prices over 10 bars is scaled by a factor, then normalized against its own 20-bar rolling standard deviation. The result is converted into a 0–100 index, producing three regimes:
❄️ Calm (<50): low dispersion, mean-reversion conditions dominate.
⚠️ Elevated (50–70): directional expansion likely, watch for breakout tension.
💥 Explosive (>70): strong dispersion, trend-following setups favored.
• Uses layered smoothing to dampen noise.
• Normalization ensures comparability across different assets.
• Acts as a meta-filter for selecting strategy type (range vs. momentum).
⬤ Price
The price column displays the latest close rounded to the nearest tick size.
It is color-coded by candle bias: green for bullish closes, red for bearish closes.
• Tick normalization ensures clean display across assets with different decimal precision.
• Color-coding gives instant sentiment context.
• Serves as the anchor reference for all other metrics in the row.
The Market Echo Screener is not a simple signal table — it’s a layered analytics framework.
Each column is driven by technical calculations: smoothing filters, volatility projections, equilibrium models, and adaptive band logic. Together, they create a unified lens on multiple assets, allowing traders to rapidly identify alignment, filter out noise, and focus on the clearest opportunities.
Multi Market Structure TrendOVERVIEW
Multi Market Structure Trend is a multi-layered market structure analyzer that detects trend shifts across five independent pivot-based structures . Each pivot uses a different lookback length, offering a comprehensive view of structural momentum from short-term to long-term.
The indicator visually displays the net trend direction using colored candlesticks and a dynamic gauge that tracks how many of the 5 market structure layers are currently bullish or bearish.
⯁ STRUCTURE TRACKING SYSTEM
The indicator tracks five separate market structure layers in parallel using pivot-based breakouts. Each one can be individually enabled or disabled.
Each structure works as follows:
A bullish MSB (Market Structure Break) occurs when price breaks above the most recent swing high.
A bearish MSB occurs when price breaks below the most recent swing low.
Structure breaks are plotted as horizontal lines and labeled with the number (1 to 5) corresponding to their pivot layer.
⯁ CANDLE COLOR GRADIENT SYSTEM
The indicator calculates the average directional bias from all enabled market structures to determine the current trend score.
Each structure contributes a score of +1 for bullish and -1 for bearish.
The total score ranges from -5 (all bearish) to +5 (all bullish) .
Candlesticks are colored using a smooth gradient:
Bright Green: Strong bullish trend (e.g., +5).
Orange: Neutral mixed trend (e.g., 0).
Red: Strong bearish trend (e.g., -5).
⯁ TREND GAUGE PANEL
Displayed at the middle-right side of the chart, the gauge shows the current trend strength in real time.
The bar consists of up to 10 gradient cells (5 up, 5 down).
Each active market structure pushes the score in one direction.
The central cell displays a numeric trend score:
+5 = All 5 market structures bullish
0 = Mixed/neutral trend
-5 = All 5 market structures bearish
Colors of the gauge bars match the candle gradient system.
⯁ USAGE
This indicator is highly effective for traders who want to:
Monitor short- and long-term structure shifts simultaneously on a single chart.
Use structure alignment as a trend confirmation tool — for example, waiting for at least 2 out of 5 structures to align before entering a trade.
Visually filter noise from different time horizons using the gauge and candle gradient.
Track CHoCH (Change of Character) transitions clearly and across multiple scales.
⯁ CONCLUSION
Multi Market Structure Trend offers a unique and powerful way to assess trend direction using stacked market structure logic. With five independently calculated structure layers, colored candle feedback, and a real-time trend gauge, traders can better time entries, filter noise, and confirm multi-timeframe alignment — all within a single chart overlay.
Magic Candle [MMT]The Magic Candle indicator is a dynamic price-action tool that visually and intuitively tracks trend bias shifts, entry signals, stop levels, and reversal triggers based on a custom candle-state logic. It is designed for traders seeking to identify actionable shifts in market structure and trend momentum, blending key elements of trend-following and reversal anticipation into one system.
Core Functionality
1: Flip Candidate and Trigger Logic :
For both bullish and bearish trends, the indicator identifies "flip candidate" candles – the most extreme, recent candle in the direction of the current trend. A "flip trigger" level is set at the high (for bearish) or low (for bullish) of this candidate. If price breaks decisively past this level, it signals a potential trend reversal, switching the indicator's bias and updating the tracked ranges and stop-loss points.
2: Entry and Stop-Loss Visualization :
Before entries, the indicator highlights the tracked high and low as "Entry" levels. Upon bias confirmation (break of the tracked level), a stop-loss line is automatically plotted at the opposite extreme of the tracked move for real-time risk guidance.
3: State Table Panel :
A dedicated on-chart table summarizes, in real time: current state (trend bias), active stop-loss level, entry reference level, and the present flip trigger. This offers at-a-glance status updates for systematic trade management.
Practical Usage
Momentum Confirmation :
Stay synchronized with price momentum by entering long or short once tracked highs or lows are broken, aligning trades with confirmed directional moves. Best to use in a confirmed trend direction.
Reversal Alerts :
The flip trigger logic allows traders to anticipate potential reversals, with visual cues and optional labels that remove guesswork at possible shift points.
Risk Management :
Fully integrated stop-loss plotting supports disciplined trade management, setting stop points based on recent price extremes to correspond with current bias and volatility.
This indicator benefits traders looking to combine the clarity of structured price-action logic with robust automation, suitable for many timeframes and all liquid instruments.
The DTC Indicator The Day Trading Channel EditionOverview
The DTC Indicator is a precision-built engulfing confirmation system developed by The Day Trading Channel to simplify structured, session-based trading.
It identifies high-probability engulfing setups during user-defined sessions, automatically marks entry levels, and visualizes target/invalidation zones in real time.
The tool provides traders with a clean, rules-driven framework to analyze market structure objectively without relying on subjective interpretation or multi-indicator clutter.
The DTC Indicator is designed for day traders who value logic over luck — offering full control over session windows, confirmation filters, and risk parameters.
Core Concept
At its foundation, the DTC Indicator revolves around a straightforward yet powerful principle:
The first few candles of a session often define the directional intent of the market.
The script scans the initial candles of each active session for bullish or bearish engulfing structures — one of the most reliable candlestick confirmations in price action theory.
Once identified, it automatically logs the entry price, stop-loss, and take-profit levels based on the trader’s configured risk-to-reward ratio.
From there, the indicator takes over visual tracking — plotting live boxes for target and invalidation levels, marking outcome labels (TP/SL), and updating the internal statistics dashboard to keep a running log of all observed setups.
Key Features
🎯 Session Control & Customization
• Define up to four unique trading sessions (e.g., London, New York, Sydney, Asian).
• Each session is independently configurable, allowing traders to isolate setups only during high-activity periods.
• Visually differentiated sessions make it easy to monitor which time windows produce the best consistency.
🧩 Engulfing Confirmation Logic
• Detects bullish engulfing when a candle fully engulfs the body of the previous bearish candle.
• Detects bearish engulfing when a candle fully engulfs the body of the previous bullish candle.
• Signal confirmation is session-aware — only triggers within the specified start-window of each session.
• False positives are filtered out automatically if price fails to close beyond the engulfing range.
📊 Dynamic Entry Snapshot System
• Every valid setup is recorded as a “snapshot,” capturing the entry price, target, and invalidation levels.
• Boxes are drawn live on the chart, extending until price hits either the target or invalidation.
• Once resolved, the outcome is logged into the performance dashboard automatically.
🧮 Performance Dashboard
• Displays key stats directly on-chart:
Total setups
true / false
true-rate percentage
Latest signal direction
Last target & invalidation values
• The dashboard automatically filters by date range, letting traders review historical session performance.
🔔 Smart Alerts
• Optional alerts trigger on confirmed setups.
• Each alert message includes symbol, timeframe, direction, target/invalidation values, and timestamp.
• Compatible with TradingView’s webhook system for automation or third-party integration.
🎨 Visual Customization
• Choose between Modern Blue, Classic Green-Red , and Gold Edition color themes.
• Adjustable label size, box opacity, line thickness, and text color.
• Option to toggle boxes, lines, or only retain labels for a minimal layout.
Why It’s Different
The DTC Indicator isn’t another candlestick detector — it’s a structured visual journal of real-time session behavior.
Instead of cluttering the screen with redundant signals, it focuses on clarity: showing you when a session produces genuine intent, and how price reacts to that intent across multiple timeframes.
Each setup becomes a mini “trade story” — logged, tracked, and concluded.
This gives traders powerful visual feedback on how specific sessions behave and how consistent a setup truly is over time.
Recommended Use Cases
• Intraday Forex and Gold (XAUUSD) trading
• Scalping and short-term swing trading on 1 hour charts
• Session-based backtesting for pattern validation
• Visual trade journaling and post-session analysis
Recommended Defaults:
Timeframe: 1-hour (h1)
Risk-Reward Ratio: 1 : 2.5
Primary Sessions: London, New York
Commission & Margin (recommended table display) : 0.02% commission, 1:100 margin
Limitations & Transparency Notice
• The indicator tracks simulated outcomes only; it does not represent executed trades.
• Historical win-rates are observational, not predictive of future performance.
• Non-standard chart types (Heikin-Ashi, Renko, Range) are not supported for engulfing detection.
• All results are based on visual backtesting and should be interpreted as educational data.
Access & Licensing
This invite-only version of the DTC Indicator is maintained and distributed by The Day Trading Channel .
Access may be granted to selected traders, educational partners, or evaluation firms for research and testing purposes.
Unauthorized redistribution, decompilation, or commercial replication of the script is strictly prohibited.
Disclaimer
This indicator is provided for educational and analytical purposes only.
It does not constitute financial advice, investment recommendations, or trade execution signals.
Trading financial markets carries risk — users are solely responsible for their decisions and results.
© 2025 The Day Trading Channel. All Rights Reserved.
ZS Game Changer Pump & Dump DetectorZS GAME CHANGER PUMP AND DUMP DETECTOR - TOP 2 MOMENTUM TRACKER
Created by Zakaria Safri
An intelligent indicator specifically designed to identify and highlight the two most significant pump and dump candles within your selected lookback period. Perfect for traders who want to focus on the game-changing moves that truly matter in volatile markets like cryptocurrency, stocks, and forex.
CORE FEATURES
AUTOMATIC GAME CHANGER DETECTION
The indicator continuously scans your specified lookback period and automatically identifies the top 2 strongest pump candles and top 2 strongest dump candles. These game-changing candles are highlighted with distinctive gold labels and horizontal reference lines, making them instantly visible on your chart. Unlike other indicators that show every small move, this focuses exclusively on the market-moving moments that define trends and create opportunities.
INTELLIGENT PUMP AND DUMP CLASSIFICATION
Uses advanced percentage-based calculations to classify candles as pumps when price surges significantly upward and dumps when price plunges sharply downward. The detection system accounts for candle body size, wick proportions, and volume confirmation to ensure only legitimate momentum moves trigger signals. Customizable thresholds allow adaptation to any market volatility profile from calm stocks to wild altcoins.
ADVANCED WICK EXCLUSION FILTER
Eliminates false signals caused by candles with large wicks and small bodies. This filter focuses analysis exclusively on candles with substantial body sizes that indicate genuine directional conviction rather than temporary spikes followed by rejection. The body to candle ratio is fully adjustable to match your preferred signal quality standards.
VOLUME CONFIRMATION SYSTEM
Optional volume filter ensures detected pumps and dumps are backed by real market participation. The indicator compares current volume against a moving average and only triggers signals when volume exceeds your specified multiplier threshold. This eliminates low-volume noise and focuses on moves supported by institutional or crowd participation.
RALLY SEQUENCE DETECTION
Identifies and highlights consecutive sequences of pump or dump candles with colored background overlays. Green background indicates sustained buying pressure across multiple candles while red background shows sustained selling pressure. The rally detection system includes an optional one-miss allowance that prevents the sequence from breaking due to a single neutral candle.
HORIZONTAL REFERENCE LINES
Draws dashed lines from each game changer candle extending to the current bar, providing constant visual reference to the most significant support and resistance levels created by extreme momentum. The top game changer gets a thick dashed line while the second gets a dotted line for easy differentiation. Labels on the right side display the exact percentage move.
COMPREHENSIVE STATISTICS DASHBOARD
Real-time information panel showing current market status as pumping, dumping, or neutral along with the current candle percentage change. Displays the exact percentage values for top pump number 1, top pump number 2, top dump number 1, and top dump number 2. Shows running totals of all pumps and dumps detected since chart load. Tracks consecutive candle counts during active rally sequences.
TESTING AND VERIFICATION MODE
Built-in debug mode displays percentage change directly on each qualifying pump and dump candle, allowing instant verification that calculations are accurate. Shows which filters are currently active with a simple code in the dashboard. Helps traders understand exactly why certain candles qualified as game changers.
HOW THE GAME CHANGER DETECTION WORKS
SCANNING ALGORITHM
Every bar close, the indicator scans backward through your specified lookback period examining every candle's percentage change from its previous close. For bullish moves, it identifies the two candles with the largest positive percentage change that meet your threshold requirements. For bearish moves, it identifies the two candles with the largest negative percentage change meeting threshold requirements.
RANKING SYSTEM
Candles are ranked purely by their percentage move magnitude. The number 1 game changer is always the single strongest move in the lookback period. The number 2 game changer is the second strongest move. Rankings update dynamically as new candles form and old candles exit the lookback window.
VISUAL IDENTIFICATION
Game changer number 1 for both pumps and dumps receives a large gold label reading GAME CHANGER NUMBER 1 with zero transparency for maximum visibility. Game changer number 2 receives a slightly smaller gold label with partial transparency. The candle bars themselves are colored in gold instead of the standard green or red. Horizontal lines extend from the game changer price level to current bar.
FILTER APPLICATION
Only candles that pass your configured filters qualify for game changer consideration. If wick exclusion is enabled, candles with large wicks and small bodies are ignored. If volume confirmation is enabled, only candles with above-average volume qualify. This ensures game changers represent legitimate market moves rather than aberrations.
PRACTICAL APPLICATIONS
FOR CRYPTOCURRENCY TRADERS
Crypto markets experience extreme volatility with occasional massive pump and dump candles that define entire trends. This indicator instantly identifies which candles represent true market structure shifts versus normal noise. Use the game changer levels as key support and resistance for entries, exits, and stop placement. The top pump often marks the local high to watch for breakouts while the top dump marks the local low for reversal trades.
FOR DAY TRADERS
Intraday charts contain hundreds of candles but only a few truly matter for the session outcome. Game changer detection filters out 98 percent of candles to show you the 2 percent that drove the actual price movement. Enter trades on the side of the strongest recent game changer. Use game changer levels as magnet prices where algorithmic trading often returns.
FOR SWING TRADERS
On daily and four-hour timeframes, game changers represent major institutional activity or news-driven moves. The top dump often marks capitulation selling that creates reversal opportunities. The top pump often marks FOMO buying that creates resistance levels. Swing traders can build positions knowing these levels will be defended or tested multiple times.
FOR VOLATILITY ANALYSIS
Understanding which candles created the most volatility helps assess market risk. Multiple game changers clustered together indicate unstable choppy conditions. Game changers separated by many neutral candles indicate trending stable conditions. Use this context to adjust position sizing and stop distances appropriately.
FOR SUPPORT AND RESISTANCE TRADING
Game changer candles create the strongest support and resistance levels because they represent prices where massive volume transacted in short time periods. These levels have higher probability of holding on retest compared to arbitrary moving averages or pivot points. Trade bounces off game changer levels or breakouts through them.
RECOMMENDED SETTINGS BY MARKET
CRYPTOCURRENCY 15-MINUTE TO 1-HOUR CHARTS
Candle Size Threshold: 2.0 percent
Body to Candle Ratio: 0.5
Volume Multiplier: 1.5 times average
Game Changer Lookback: 100 bars
Extreme Threshold: 3.5 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: Yes
Minimum Rally Candles: 3
STOCKS DAILY CHARTS
Candle Size Threshold: 1.0 percent
Body to Candle Ratio: 0.6
Volume Multiplier: 2.0 times average
Game Changer Lookback: 50 bars
Extreme Threshold: 2.5 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: Yes
Minimum Rally Candles: 2
FOREX 1-HOUR TO 4-HOUR CHARTS
Candle Size Threshold: 0.5 percent
Body to Candle Ratio: 0.5
Volume Multiplier: Not applicable
Game Changer Lookback: 80 bars
Extreme Threshold: 1.0 percent
Enable Wick Filter: Yes
Enable Volume Confirmation: No
Minimum Rally Candles: 3
SCALPING 1-MINUTE TO 5-MINUTE CHARTS
Candle Size Threshold: 0.8 percent
Body to Candle Ratio: 0.4
Volume Multiplier: 1.2 times average
Game Changer Lookback: 50 bars
Extreme Threshold: 1.5 percent
Enable Wick Filter: No
Enable Volume Confirmation: Yes
Minimum Rally Candles: 2
WHAT IS INCLUDED
Automatic identification of top 2 pump candles
Automatic identification of top 2 dump candles
Gold colored game changer labels with size differentiation
Gold colored candle bars for game changers
Horizontal reference lines from game changers to current price
Regular pump and dump detection with green and red candles
Rally sequence detection with background highlighting
Extreme move detection and labeling system
Real-time statistics dashboard with all key metrics
Percentage change debug mode for verification
Volume confirmation filter with adjustable multiplier
Wick exclusion filter with adjustable body ratio
Customizable lookback period from 20 to 500 bars
Consecutive candle counter for rally tracking
Alert system for game changers, pumps, dumps, and rallies
Works on all timeframes from 1 minute to monthly
Compatible with stocks, forex, cryptocurrency, and futures
UNDERSTANDING GAME CHANGERS
WHAT MAKES A CANDLE A GAME CHANGER
A game changer is not just a large move but the largest move within context. In a volatile crypto market, a 5 percent pump might not rank in the top 2. In a stable stock, a 2 percent pump could be the number 1 game changer. The indicator adapts to your specific instrument and timeframe to find what truly matters in that context.
WHY FOCUS ON TOP 2 ONLY
Markets are driven by a small number of significant moves rather than the average of all moves. By focusing exclusively on the top 2 in each direction, traders can ignore noise and concentrate on the price levels that actually matter for support, resistance, and momentum. This creates clarity in decision making.
GAME CHANGERS AS MARKET STRUCTURE
The top pump often marks the recent high that bulls must break to continue uptrend. The top dump often marks the recent low that bears must break to continue downtrend. These become the key levels around which all other price action rotates. Understanding this structure is essential for profitable trading.
GAME CHANGERS AS SENTIMENT INDICATORS
Consecutive pump game changers signal strong bullish sentiment and FOMO conditions. Consecutive dump game changers signal fear and capitulation. Alternating pump and dump game changers signal indecision and range conditions. Read the pattern of game changers to gauge market psychology.
VERIFICATION AND TESTING
HOW TO VERIFY ACCURACY
Enable Show Debug Info on Chart in the Testing and Debug settings group. This displays the percentage change calculation directly on every qualifying pump and dump candle. Manually verify by calculating open minus close divided by close multiplied by 100. The debug percentage should match your manual calculation exactly.
HOW TO TEST FILTERS
Toggle wick exclusion filter on and off while watching how many candles qualify. With filter on, candles with long wicks and small bodies should disappear. Toggle volume confirmation on and off to see how low-volume candles get excluded. Adjust the thresholds and watch the real-time impact on signal count.
HOW TO VERIFY GAME CHANGERS
Look at your chart and visually identify which candle had the biggest green body in the lookback period. The game changer number 1 pump label should be on that exact candle. Repeat for the biggest red candle to verify game changer number 1 dump. The rankings should match your visual assessment.
LOOKBACK PERIOD EFFECTS
Decrease the lookback period to 20 bars and watch game changers update to only recent moves. Increase to 500 bars and watch game changers potentially change to older historic moves. The optimal lookback balances recency with significance. Too short misses important levels, too long includes irrelevant history.
DASHBOARD INFORMATION GUIDE
STATUS ROW
Shows PUMPING when current candle qualifies as a pump, DUMPING when current candle qualifies as a dump, or NEUTRAL when current candle does not meet threshold requirements. This updates in real-time on every bar close.
CURRENT CHANGE ROW
Displays the percentage change of the current candle from its previous close. Positive percentages indicate bullish candle, negative indicate bearish candle. This number may or may not meet your threshold to qualify as pump or dump.
TOP PUMP NUMBER 1
The highest positive percentage change found in your lookback period. This candle is marked with the large gold GAME CHANGER NUMBER 1 label below it. Shows N/A if no pumps exist in the lookback period.
TOP PUMP NUMBER 2
The second highest positive percentage change found in your lookback period. Marked with smaller gold GAME CHANGER NUMBER 2 label. Shows N/A if only one or zero pumps exist.
TOP DUMP NUMBER 1
The highest negative percentage change magnitude found in your lookback period. This candle is marked with the large gold GAME CHANGER NUMBER 1 label above it. Shows N/A if no dumps exist.
TOP DUMP NUMBER 2
The second highest negative percentage change magnitude found in your lookback period. Marked with smaller gold GAME CHANGER NUMBER 2 label. Shows N/A if only one or zero dumps exist.
TOTAL PUMPS
Running count of all pump candles detected since you loaded the indicator on this chart. This number continuously increases as new qualifying pumps form. Resets when you reload the chart.
TOTAL DUMPS
Running count of all dump candles detected since chart load. Increases as new qualifying dumps form and resets on chart reload.
CONSECUTIVE
Shows the current count of consecutive pump or dump candles during an active rally. Displays 3 UP during a 3-candle pump rally or 5 DN during a 5-candle dump rally. Shows 0 when no rally is active.
ALERT SYSTEM
GAME CHANGER DETECTED ALERT
Triggers whenever the current candle becomes one of the top 2 pumps or top 2 dumps. This is the highest priority alert indicating a market-moving event just occurred. Use this alert for immediate notification of significant opportunities.
PUMP DETECTED ALERT
Triggers on every candle that qualifies as a pump according to your threshold and filter settings. This includes regular pumps and extreme pumps but excludes game changers which have their separate alert. Use for general upward momentum monitoring.
DUMP DETECTED ALERT
Triggers on every candle that qualifies as a dump according to your settings. Includes regular and extreme dumps but excludes game changers. Use for general downward momentum monitoring.
PUMP RALLY STARTED ALERT
Triggers when consecutive pump candles reach your minimum rally threshold. Indicates the beginning of a sustained upward movement sequence. Use to catch trends early.
DUMP RALLY STARTED ALERT
Triggers when consecutive dump candles reach your minimum rally threshold. Indicates the beginning of a sustained downward movement sequence. Use for trend following or reversal timing.
ALERT MESSAGE FORMAT
All alerts include the ticker symbol and current price using TradingView placeholders. Messages are descriptive and specify which type of signal triggered. Alerts work with TradingView notification system including email, SMS, webhook, and app notifications.
TECHNICAL SPECIFICATIONS
CALCULATION METHODOLOGY
Percentage change calculated as current close minus previous close divided by previous close multiplied by 100. Body ratio calculated as absolute value of close minus open divided by high minus low. Volume elevation calculated as current volume divided by 20-period simple moving average of volume. Game changer ranking uses absolute value comparison across entire lookback array.
PERFORMANCE CHARACTERISTICS
Lightweight calculations optimized for speed on all timeframes. No repainting of signals ensuring all triggers are final on bar close. Variables properly scoped with var keyword for memory efficiency. Maximum bars back set to 500 to prevent excessive historical loading. Updates in real-time on every bar close without lag.
COMPATIBILITY
Works on all TradingView plans including free, pro, and premium. Compatible with stocks, forex, cryptocurrency, futures, indices, and commodities. Functions correctly on all timeframes from 1 second to monthly. No external data requests ensuring fast loading. Overlay true setting places directly on price chart.
RISK DISCLAIMER
This indicator is a technical analysis tool for identifying momentum and should not be used as the sole basis for trading decisions. Game changer levels can be broken during strong trends and are not guaranteed support or resistance. Pump and dump detection does not predict future price direction. Always use proper risk management with stop losses on every trade. Combine this indicator with other forms of analysis including fundamentals, market context, and risk assessment. Practice on demo accounts before live trading. Past performance of game changer signals does not guarantee future results. Trading carries substantial risk of loss and is not suitable for all investors. The creator is not responsible for trading losses incurred while using this tool.
SUPPORT AND UPDATES
Regular updates based on user feedback and market evolution. Built following PineCoders industry standards and best practices for code quality. Clean well-documented code structure for transparency and auditability. Optimized performance across all timeframes and instruments. Active development with continuous improvements and feature additions.
WHY CHOOSE ZS GAME CHANGER PUMP AND DUMP DETECTOR
Focuses on what matters by highlighting only the top 2 moves in each direction instead of cluttering your chart with every small fluctuation. Saves time by automatically identifying the most significant candles rather than requiring manual scanning. Provides clarity through visual gold labels and reference lines that make game changers unmistakable. Adapts to any market with customizable thresholds for volatility and volume. Eliminates noise with advanced wick and volume filters ensuring signal quality. Offers verification through debug mode proving calculations are accurate and trustworthy. Includes comprehensive statistics showing exact percentages and counts. Works everywhere across all markets, timeframes, and instruments without modification.
Transform your chart analysis by focusing exclusively on the game-changing moments that define trends and create opportunities.
Version 1.1 | Created by Zakaria Safri | Pine Script Version 5 | PineCoders Compliant
SSMT [TakingProphets]SSMT (Sequential SMT) — multi-cycle intermarket divergence with quarter-based timing
Purpose
Informational overlay that detects intermarket SMT divergences between the chart symbol and a user-selected correlated symbol. It does not generate buy/sell signals and is not financial advice. Use it to structure analysis and alerts, not to automate trades.
What it does
Scans for SMT on five coordinated cycles: Micro, 90-Minute, Daily (Q1–Q4), Weekly, Monthly.
Draws anchored lines and labels where divergences occur and keeps them after the period ends so you can use historical SMTs as context.
Offers per-cycle alerts (high-side/bearish, low-side/bullish).
Optional session/quarter boxes for timing context.
Time base uses America/New_York to align with common session conventions (with a 17:00–18:00 ET pause guard for CME instruments).
Why these modules belong together (more than a mashup)
All cycles share a single time-partitioning framework (quarters/sessions → day → week → month). That common clock means:
Comparability: divergences on Micro/90m/D/W/M are directly comparable because they’re computed with the same boundaries for both instruments.
Sequencing: higher-cycle context can gate lower-cycle events (e.g., a Daily Q3 divergence framing how you treat a Micro divergence).
Persistence: drawings retain the cycle identity (e.g., , ) so prior signals remain interpretable as the market progresses.
This is a coherent engine—not separate indicators pasted together—because detection, labeling, alerts, and persistence are all driven by the same quarter/period state machine.
How it works (high-level mechanics)
Time partitioning
Daily quarters (ET):
Q1: 18:00–00:00
Q2: 00:00–06:00
Q3: 06:00–12:00
Q4: 12:00–18:00
90-Minute cycle: four 90-minute blocks inside the active session.
Micro cycle: finer 20–22 minute blocks inside the session for granular timing.
Weekly/Monthly: tracked by calendar periods (Mon–Fri, and calendar month).
Pause guard: 17:00–18:00 ET to avoid false transitions during CME’s daily maintenance window.
State tracking (per cycle)
Tracks previous vs. current highs/lows for the chart symbol and the correlated symbol (fetched at the same timeframe).
Maintains cycle IDs (e.g., year*100 + weekofyear for weekly) so drawings remain tied to the originating period.
Divergence condition (SMT)
High-side (bearish): one instrument makes a higher high vs. its previous period while the other does not.
Low-side (bullish): one instrument makes a lower low vs. its previous period while the other does not.
When detected, the script plots a labeled span/line (e.g., SSMT w/ES) and records it for persistence.
Alerts
Two per cycle: High-side (bearish) and Low-side (bullish).
Fire on the bar where the condition first becomes true.
Inputs & customization
Correlated symbol (default can be an index future).
Cycle toggles: Micro, 90m, Daily (Q1–Q4), Weekly, Monthly.
Styling: line color/width, label text/size.
Session/quarter boxes: on/off.
Alerts: per-cycle SMT events on/off.
How to use
Add the indicator to your chart (e.g., NQ, ES) and select a correlated symbol.
Turn on the cycles you want to monitor; optionally enable quarter/session boxes.
Interpret SMTs by side:
High-side (bearish): chart makes HH, correlated does not.
Low-side (bullish): chart makes LL, correlated does not.
Set alerts for the cycles that matter to your workflow.
Combine with your higher-timeframe narrative and risk rules.
Repainting, timing, and limitations
Uses higher-timeframe data without look-ahead; values can update intrabar until the period closes.
SMTs may form and resolve within a period; conservative users may wait for period close.
Assumes America/New_York timing; very thin markets may yield fewer or noisier signals.
SMT quality depends on the benchmark you select; correlations vary across regimes.
Educational tool only. No performance claims; not a signal generator.
Originality & scope (for protected/invite-only publications)
A multi-cycle SMT engine built on a shared quarter/period state machine (Micro → 90m → Daily Q1–Q4 → Weekly → Monthly).
Quarter-aware persistence keeps divergence drawings tied to their source cycle for durable context.
CME pause handling and stable calendar IDs make detections consistent across sessions and rollovers.
Implements SMT through extremum sequencing and cross-instrument comparison rather than wrapping generic divergence indicators.
TradeScope: MA Reversion • RVOL • Trendlines • GAPs • TableTradeScope is an all-in-one technical analysis suite that brings together price action, momentum, volume dynamics, and trend structure into one cohesive and fully customizable indicator.
An advanced, modular trading suite that combines moving averages, reversion signals, RSI/CCI momentum, relative volume, gap detection, trendline analysis, and dynamic tables — all within one powerful dashboard.
Perfect for swing traders, intraday traders, and analysts who want to read price strength, volume context, and market structure in real time.
⚙️ Core Components & Inputs
🧮 Moving Average Settings
Moving Average Type & Length:
Choose between SMA or EMA and set your preferred period for smoother or more reactive trend tracking.
Multi-MA Plotting:
Up to 8 customizable moving averages (each with independent type, color, and length).
Includes a “window filter” to show only the last X bars, reducing chart clutter.
MA Reversion Engine:
Detects when price has extended too far from its moving average.
Reversion Lookback: Number of bars analyzed to determine historical extremes.
Reversion Threshold: Sensitivity multiplier—lower = more frequent signals, higher = stricter triggers.
🔄 Trend Settings
Short-Term & Long-Term Trend Lookbacks:
Uses linear regression to detect the slope and direction of the short- and long-term trend.
Results are displayed in the live table with color-coded bias:
🟩 Bullish | 🟥 Bearish
📈 Momentum Indicators
RSI (Relative Strength Index):
Adjustable period; displays the current RSI value, overbought (>70) / oversold (<30) zones, and trending direction.
CCI (Commodity Channel Index):
Customizable length with color-coded bias:
🟩 Oversold (< -100), 🟥 Overbought (> 100).
Tooltip shows whether the CCI is trending up or down.
📊 Volume Analysis
Relative Volume (RVOL):
Estimates end-of-day projected volume using intraday progress and compares it against the 20-day average.
Displays whether today’s volume is expected to exceed yesterday’s, and highlights color by strength.
Volume Trend (Short & Long Lookbacks):
Visual cues for whether current volume is above or below short-term and long-term averages.
Estimated Full-Day Volume & Multiplier:
Converts raw volume into “X” multiples (e.g., 2.3X average) for quick interpretation.
🕳️ Gap Detection
Automatically identifies and plots bullish and bearish price gaps within a defined lookback period.
Gap Lookback: Defines how far back to search for gaps.
Gap Line Width / Visibility: Controls the thickness and display of gap lines on chart.
Displays the closest open gap in the live table, including its distance from current price (%).
🔍 ATR & Volatility
14-day ATR (% of price):
Automatically converts the Average True Range into a percent, providing quick volatility context:
🟩 Low (<3%) | 🟨 Moderate (3–5%) | 🟥 High (>5%)
💬 Candlestick Pattern Recognition
Auto-detects popular reversal and continuation patterns such as:
Bullish/Bearish Engulfing
Hammer / Hanging Man
Shooting Star / Inverted Hammer
Doji / Harami / Kicking / Marubozu / Morning Star
Each pattern is shown with contextual color coding in the table.
🧱 Pivot Points & Support/Resistance
Optional Pivot High / Pivot Low Labels
Adjustable left/right bar lengths for pivot detection
Theme-aware text and label color options
Automatically drawn diagonal trendlines for both support and resistance
Adjustable line style, color, and thickness
Detects and tracks touches for reliability
Includes breakout alerts (with optional volume confirmation)
🚨 Alerts
MA Cross Alerts:
Triggers when price crosses the fast or slow moving average within a tolerance band (default ±0.3%).
Diagonal Breakout Alerts:
Detects and alerts when price breaks diagonal trendlines.
Volume-Confirmed Alerts:
Filters breakouts where volume exceeds 1.5× the 20-bar average.
🧾 Live Market Table
A fully dynamic table displayed on-chart, customizable via input toggles:
Choose which rows to show (e.g., RSI, ATR, RVOL, Gaps, CCI, Trend, MA info, Diff, Low→Close%).
Choose table position (top-right, bottom-left, etc.) and text size.
Theme selection: Light or Dark
Conditional background colors for instant visual interpretation:
🟩 Bullish or Oversold
🟥 Bearish or Overbought
🟨 Neutral / Moderate
🎯 Practical Uses
✅ Identify confluence setups combining MA reversion, volume expansion, and RSI/CCI extremes.
✅ Track trend bias and gap proximity directly in your dashboard.
✅ Monitor relative volume behavior for intraday strength confirmation.
✅ Automate MA cross or breakout alerts to stay ahead of key price action.
🧠 Ideal For
Swing traders seeking confluence-based setups
Intraday traders monitoring multi-factor bias
Analysts looking for compact market health dashboards
💡 Summary
TradeScope is designed as a single-pane-of-glass market view — combining momentum, trend, volume, structure, and reversion into one clear visual system.
Fully customizable. Fully dynamic.
Use it to see what others miss — clarity, confluence, and confidence in every trade.
Heikin Ashi Overlay SuiteHeikin Ashi Overlay Suite is designed to give traders more control and clarity when working with Heikin Ashi candles — whether you're analyzing trend strength, reducing chart noise, or simply improving your visual read of market momentum. It works by layering multiple types of HA overlays and color systems on top of your standard candlestick chart — without switching chart types. With dynamic gradient coloring, smoothing options, and a predictive line tool, this script helps you see not just what the current trend is, but how strong it is, and what it would take to reverse it.
Heikin Ashi candles help reduce noise but this script goes further by:
➡️adding color intelligence that shows trend strength using a streak counter
➡️uses smoothing logic to clean up chop and whipsaws
➡️introduces a predictive close line — a subtle but powerful guide for anticipating trend flips before they happen
Everything is configurable: colors, candle sources, overlays, predictive tools, and line styles. It’s built for traders who want visual speed, but don’t want to sacrifice signal quality.
At its core, the script offers two powerful dropdown controls:
💥HA Color Scheme (Colors Regular Candles) — Applies Heikin Ashi-derived coloring to your regular candles based on trend direction or streak strength. This gives you instant visual context without switching to a separate chart type.
💥HA Candle Overlay Mode — Overlays actual Heikin Ashi-style candles directly on top of your chart, using your preferred source:
➡️Custom HA candles using internal formula logic
➡️TradingView’s built-in Heikin Ashi source with your own colors
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
🎨 Custom + Gradient HA Coloring🎨
See trend strength at a glance:
➡️1–4 bar streaks → lighter tone
➡️5–8 bars → medium tone
➡️9+ bars → bold tone, ideal for momentum-based entries, exits, or scaling strategies
→ Choose from:
➡️Your own custom color set
➡️A simple 2-color base mode
➡️Or a 3-level gradient for progressive trend analysis (using the streak counter)
🏛️ TradingView Official Heikin Ashi Overlay
Prefer native HA candles but want your own colors?
This mode plots TradingView's Heikin Ashi source, with your personal bullish/bearish color scheme.
➡️Ensures consistency with built-in charts while still leveraging your visual style.
🌊 Smoothed Heikin Ashi Candles — Clarity in Chaos🌊
These aren’t your standard HA candles. Smoothed Heikin Ashi uses a two-step EMA process to transform chaotic price action into a cleaner, slower-moving trend structure:
🔹 First, it smooths the raw OHLC data using EMA — filtering out minor price fluctuations.
🔹 Then, it applies the Heikin Ashi transformation on top of the smoothed data.
🔹 Finally, it applies a second EMA smoothing pass to the HA values — creating ultra-smooth candles.
📈 What You See:
Trends appear more fluid and consistent.
Choppy ranges and fakeouts are visually suppressed.
Minor pullbacks within a trend are de-emphasized, helping you avoid premature exits.
🎯 Best For:
Swing traders looking to stay in positions longer.
Intraday traders dealing with volatile or noisy instruments.
Anyone who wants a "trend map" overlay without the distractions of raw price action.
✅ Reduces whipsaws
✅ Delivers high-contrast trend zones
✅ Makes reversals more visually apparent (but with a slight lag)
📍 Predictive Close Line📍
Shows where the real close must land to flip the current HA candle's color.
✅ Use it like predictive support/resistance
✅ Know if the trend is actually at risk
✅Visualize potential fakeouts or confirmation
Color-coded based on current HA direction (bullish, bearish, or neutral).
📈 Tick by tick & bar-to-bar Plots📈
Provides 2 plot types:
1)1 plot that tracks a bar tick by tick
2)another plot that tracks the close from bar to bar
For the bar to bar plot, you can choose between 2 options:
✅Full Plot — continuous line colored by HA trend
✅Recent Segments — color just the last few bars (configurable) to reduce chart clutter
✅ Customize width, number of bars, and visibility
➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖
📘 How to Use this script📘
Imagine you're watching a choppy 15-minute chart on a volatile crypto pair — price action is messy, and it’s hard to tell if a trend is forming or just noise.
Here’s how to cut through the chaos using Heikin Ashi Overlay Suite:
🔹 Step 1: Enable "Smoothed HA Candles"
Start by turning on the smoothed candles. You’ll immediately notice the noise fades, and broader directional moves become easier to follow. It's like switching from static to clean trend zones.
🧠 Why: Smoothed HA uses a double EMA process that filters out small reversals and lets larger moves stand out. Perfect for sideways or jittery charts.
🔹 Step 2: Watch the Color Gradient Build
As the smoothed candles begin to align in one direction, the gradient coloring (1–4, 5–8, 9+ streaks) gives you an at-a-glance visual of how strong the trend is.
✅ If you see 9+ same-colored candles? You’re likely in a mature trend.
✅ If it resets often? You’re in chop — consider staying out.
🔹 Step 3: Use the Predictive Close Line for Anticipation
Now here’s the edge — this line tells you where the candle would have to close to flip colors.
📉 If price is hovering just above it during a bullish run — momentum may be weakening.
📈 If price bounces off it — the trend may be strengthening.
This is excellent for confirming entries, exits, or spotting early warning signs.
🔹 Step 4: Switch Between Candle Modes as Needed
You can flip between:
✅ Custom HA: Gradient candles with your colors
✅ TradingView HA: The official source with your styling
✅ None: Just color regular candles using the HA logic
Use what fits your style — everything is modular.
🔹 Step 5: Tune It to Your Chart
Lastly, tweak streak thresholds (currently only can do this within the source code), smoothing lengths, and line styles to match your timeframe and strategy.
🎯 Tailor The Settings to Fit Your Trading Style🎯
🔹 🧪 Scalper (1–5 min charts)
If you’re trading fast intraday moves, you want quicker responsiveness and less lag.
Try these settings:
🔸Smoothing Lengths: Use lower values (e.g. len = 3, len2 = 5)
🔸Candle Mode: Use Custom HA or TV’s HA for real-time color flips
🔸Predictive Close Line: Great for ultra-fast anticipation of color reversals
🔸Line Mode: Use Recent Segments mode to track short bursts of trend
🔸Colors: Use high-contrast, opaque colors for clarity
✅ These settings help you catch micro-trends and flip signals faster, while still filtering out the worst of the noise.
🔹 🧪 Swing Trader (30m–4h charts and beyond)
If you’re looking for multi-hour or multi-day trend confirmation, prioritize clarity and staying in moves longer.
Recommended setup:
🔸Smoothing Lengths: Medium to high values (e.g. len = 8, len2 = 21)
🔸Candle Mode: Use Smoothed HA Candles to block out intrabar chop
🔸Gradient Colors: Enable to visualize trend maturity and strength
🔸Predictive Close Line: Helps confirm trend continuation or spot early reversals
🔸Line Mode: Use Full Plot Line for clean HA-based trend tracking
✅ These settings give you a calm, clean view of the bigger picture — ideal for holding positions longer and avoiding early exits.
🔧 This script isn’t just a chart overlay — it’s a visual trend engine.🔧
Ideal For:
🔶 Trend-followers who want clean, color-coded confirmation
🔶 Reversal traders spotting exhaustion via predictive flips
🔶 Scalpers filtering noise with lighter smoothing
🔶 Swing traders using smoothed visuals to hold longer
📌 Final Note
Heikin Ashi Overlay Pro is designed to help you see momentum, trend shifts, and market structure with greater clarity — not to predict price on its own. For best results:
✔️ Combine with support/resistance, moving averages, or price action patterns
✔️ Use Predictive Close as a confirmation tool, not a signal generator
✔️ Pair gradient colors with structure to gauge trend maturity
✔️ Always zoom out and check higher timeframes for context
🧠 Use this as part of a layered approach — not a standalone system.
🙏 Credits🙏
⚡HA logic based on SimpleCryptoLife
⚡Smoothed HA concept adapted from a script by Jackvmk
💡💡💡Turn logic into clarity. Structure into trades. And uncertainty into confidence.💡💡💡
Killzones SMT + IFVG detectorKillzones SMT + IFVG Detector
Summary
This strategy implements a specific intraday workflow inspired by ICT-style concepts.
It combines:
Killzone session levels (recording untouched highs/lows)
SMT divergence between NQ and ES (exclusive sweep logic)
IFVG confirmation (3-bar imbalance + width filter + inversion guard)
and an optional smart exit engine
The components are not simply mashed together: they interact in sequence.
A setup only confirms if all conditions line up (time window → untouched level sweep → divergence → valid IFVG → confirmation candle → risk filter).
Workflow
Killzones & session levels
Tracks highs/lows inside default killzones (19:00–23:00, 01:00–04:00, 08:30–10:00, 11:00–12:00, 12:30–15:00, chart timezone).
Stores untouched levels forward; sweeps trigger candidate signals.
SMT divergence (exclusive sweep)
Bullish SMT : one index sweeps its low while the other remains above its session low.
Bearish SMT : one index sweeps its high while the other remains below its session high.
Detection supports “Sweep (Cross)” or “Exact Tick.”
Session IDs are tracked so once a side has fired, later re-touches can’t re-trigger .
IFVG confirmation
Locks the first valid 3-bar IFVG after SMT.
Confirmation requires a candle close beyond the IFVG boundary in the direction of the close.
IFVGs must meet a minimum width filter (default 1.0 point).
Inversion guard: ignores IFVGs already inverted before SMT.
Optional “re-lock” keeps tracking the latest IFVG until confirmation/expiry.
Smart exit engine
Initial stop from opposite wick (+ buffer).
Fixed TP (default 40 points).
Dynamic stop escalation at progress thresholds (BE → 50% → 80% of target).
Safety gates
Weekend lockout (Fri 16:40 → Sun 18:00).
Same-bar sweep of high & low cancels setups.
Max initial stop filter skips oversized setups.
Optional cooldown bars.
Alerts
SMT Bullish/Bearish : divergence detected this bar.
Confirm Long/Short : IFVG confirmation triggered.
Default Strategy Properties (used in screenshots/backtests)
Initial capital: $25,000
Order size: 1 contract
Commission: $1.25 per contract per side
Slippage: 2 ticks
Backtest window: Jun 16, 2025 – Sep 14, 2025
These settings are intentionally conservative. If you change them, your results will differ.
How to use
Apply on an NQ or ES futures chart (1–5 min).
Choose your killzones and detection mode.
Select confirmation symbol (NQ, ES, or “Sweeper”).
Enable/disable IFVG re-lock.
Review signals and use alerts for automation if desired.
Limitations
Strict filters reduce trade count; extend backtest window for more samples.
Works best on NQ/ES; not validated elsewhere.
Past performance is not indicative of future results.
This is an educational tool ; not financial advice.
MomentumQ DashMomentumQ Dash – Multi-Timeframe & Watchlist Dashboard
The MomentumQ Dash is a professional dashboard-style indicator designed to help traders quickly evaluate market conditions across multiple timeframes and assets.
Unlike single-signal tools, MomentumQ Dash consolidates market regime, buy/sell conditions, and pre-signal alerts into an easy-to-read table, allowing traders to stay focused on actionable setups without flipping between charts.
All signals displayed in MomentumQ Dash are derived from the MomentumQ Oscillator (MoQ Osci) , our proprietary tool designed to identify momentum shifts and adaptive buy/sell conditions. By integrating these signals into a dashboard format, MomentumQ Dash provides a structured overview of the market that is both comprehensive and easy to interpret.
A unique advantage of this tool is the dual-table system:
A timeframe table that tracks the current symbol across five user-defined timeframes.
A watchlist table that monitors up to five different assets on the same timeframe.
This combination gives traders a complete market overview at a glance, supporting both intraday and higher-timeframe strategies.
Key Features
1. Multi-Timeframe Signal Dashboard
Tracks buy, sell, pre-buy, and pre-sell conditions for up to 5 configurable timeframes.
Highlights market regime (Bull/Bear) with background colors for quick visual recognition.
Displays the last detected signal and how many bars ago it occurred.
2. Watchlist Asset Table
Monitor up to 5 custom symbols (e.g., indices, commodities, crypto pairs) in one view.
Independent timeframe selection for the watchlist table.
Clean symbol display with exchange prefixes automatically removed.
3. Flexible Layout & Theme Integration
Choice of table position (Top Right, Middle Right, Bottom Right) for each table.
Light/Dark mode setting for seamless chart integration.
Compact, minimal design to avoid clutter.
4. MoQ Osci Signal Engine
Signals are powered by the MomentumQ Oscillator (MoQ Osci), which uses adaptive momentum analysis.
Identifies early pre-signals (potential setup zones) as well as confirmed buy/sell events.
Helps traders recognize transitions in market structure without lagging indicators.
How It Works
Timeframe Analysis
The indicator calculates MoQ Osci signals on each timeframe.
When price deviates beyond upper/lower adaptive thresholds, buy/sell signals are generated.
Pre-signals are displayed when price approaches these zones, offering early alerts.
Trend Regime Detection
Regime is derived from MoQ Osci’s momentum distance relative to its adaptive mean.
Bull regime = positive momentum bias; Bear regime = negative momentum bias.
This provides a simple but reliable context for trade direction.
Watchlist Tracking
Signals are calculated identically for each custom symbol selected by the user.
Results are presented in a compact table, making it easy to spot alignment or divergence across markets.
How to Use This Indicator
Use the Timeframe Table to align intraday setups with higher-timeframe context.
Monitor the Watchlist Table to track correlated assets (e.g., SPX, NDX, VIX, Oil, Gold).
Pay attention to pre-buy / pre-sell warnings for early setup confirmation.
Use the “Last” column to quickly check the most recent signal and its timing.
Combine with your existing price action strategy to validate entries and exits.
This indicator works on all TradingView markets: Forex, Stocks, Crypto, Futures, and Commodities.
Why Is This Indicator Valuable?
Provides a complete dashboard view of market conditions in one place.
Combines multi-timeframe confirmation with multi-asset monitoring .
Signals are based on the proven MoQ Osci tool , ensuring consistency across strategies.
Saves time and reduces the need to constantly switch charts.
Fully customizable to match any trading workflow.
Example Trading Approaches
1. Multi-Timeframe Alignment
Wait for a buy signal on the lower timeframe (e.g., 15m) while the higher timeframe (1h/4h) is in Bull regime.
Enter long with higher-timeframe confirmation, improving trade probability.
2. Cross-Market Confirmation
If SPX and NDX both trigger sell signals while VIX shows a buy, this may confirm risk-off sentiment.
Use this confluence to support trade decisions in equities or correlated markets.
3. Pre-Signal Monitoring
Watch for PB (Pre-Buy) or PS (Pre-Sell) warnings before confirmed signals.
These can highlight potential breakout or reversal zones before they occur.
Disclaimer
This indicator is a technical analysis tool and does not guarantee profits.
It should be used as part of a complete trading plan that includes risk management.
Past performance is not indicative of future results.
