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.
Cerca negli script per "track"
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.
Midnight 30min High/LowMidnight 30min High/Low — Overnight Liquidity Range Tracker
Capture the Overnight Session: A Strategic Level Identification Tool from Professional Trading Methodology
This indicator captures the high and low prices during the critical 30-minute midnight session (12:00-12:30 AM EST) and projects these levels forward as key support and resistance zones. These overnight ranges often contain significant liquidity and serve as crucial reference points for intraday price action, representing areas where institutional activity may have established important levels.
🔍 What This Script Does:
Identifies Critical Overnight Session Levels
- Automatically detects the 12:00-12:30 AM EST session window
- Captures the highest and lowest prices during this 30-minute period
- Projects these levels forward for multiple trading days
Creates Dynamic Support/Resistance Zones
- Extends midnight high/low levels as horizontal lines with customizable projection periods
- Fills the area between high and low to create a visual trading range
- Updates automatically each trading day with new overnight levels
Provides Clear Visual Reference Points
- Optional session start markers (●) highlight when the midnight session begins
- Color-coded lines distinguish between high and low levels
- Transparent fill area creates an easy-to-identify trading zone
Real-Time Level Tracking
- Updates levels in real-time during the active midnight session
- Maintains historical levels for reference and backtesting
- Compatible with data window for precise level values
⚙️ Customization Options:
Extend Days (1-30):** Control how many days forward the levels are projected (default: 5 days)
High Line Color:** Customize the midnight high line color (default: blue)
Low Line Color:** Customize the midnight low line color (default: orange)
Fill Color:** Adjust the transparency and color of the range area (default: light aqua, 80% transparency)
Show Session Markers:** Toggle yellow session start indicators on/off (default: enabled)
💡 How to Use:
Deploy on lower timeframes (1m-15m) for precise level identification and reaction monitoring**
Watch for key price interactions:
- Rejection at midnight high levels (potential resistance)
- Bounce from midnight low levels (potential support)
- Range-bound trading between the high and low levels
Combine with liquidity concepts:
- Monitor for stop hunts above/below these levels
- Look for false breakouts that snap back into the range
- Use as confluence with other ICT concepts like FVGs and Order Blocks
Strategic Applications:
- Range trading between midnight levels
- Breakout confirmation when price closes decisively outside the range
- Support/resistance validation for entry and exit planning
🔗 Combine With These Tools for Complete Market Structure Analysis:
✅ First FVG — Opening Range Fair Value Gap Detector.
✅ ICT Turtle Soup (Liquidity Reversal)— Spot stop hunts and false breakout scenarios.
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session.
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
Together, these tools create a comprehensive Smart Money Concepts (SMC) framework — helping traders identify, anticipate, and capitalize on institutional-level price movements with precision and confidence during critical overnight sessions. Also, dont forget to not over-trade.
Midnight 30min High/LowMidnight 30min High/Low — Overnight Liquidity Range Tracker
Capture the Overnight Session: A Strategic Level Identification Tool from Professional Trading Methodology
This indicator captures the high and low prices during the critical 30-minute midnight session (12:00-12:30 AM EST) and projects these levels forward as key support and resistance zones. These overnight ranges often contain significant liquidity and serve as crucial reference points for intraday price action, representing areas where institutional activity may have established important levels.
🔍 What This Script Does:
Identifies Critical Overnight Session Levels
- Automatically detects the 12:00-12:30 AM EST session window
- Captures the highest and lowest prices during this 30-minute period
- Projects these levels forward for multiple trading days
Creates Dynamic Support/Resistance Zones
- Extends midnight high/low levels as horizontal lines with customizable projection periods
- Fills the area between high and low to create a visual trading range
- Updates automatically each trading day with new overnight levels
Provides Clear Visual Reference Points
- Optional session start markers (●) highlight when the midnight session begins
- Color-coded lines distinguish between high and low levels
- Transparent fill area creates an easy-to-identify trading zone
Real-Time Level Tracking
- Updates levels in real-time during the active midnight session
- Maintains historical levels for reference and backtesting
- Compatible with data window for precise level values
⚙️ Customization Options:
Extend Days (1-30):** Control how many days forward the levels are projected (default: 5 days)
High Line Color:** Customize the midnight high line color (default: blue)
Low Line Color:** Customize the midnight low line color (default: orange)
Fill Color:** Adjust the transparency and color of the range area (default: light aqua, 80% transparency)
Show Session Markers:** Toggle yellow session start indicators on/off (default: enabled)
💡 How to Use:
Deploy on lower timeframes (1m-15m) for precise level identification and reaction monitoring**
Watch for key price interactions:
- Rejection at midnight high levels (potential resistance)
- Bounce from midnight low levels (potential support)
- Range-bound trading between the high and low levels
Combine with liquidity concepts:
- Monitor for stop hunts above/below these levels
- Look for false breakouts that snap back into the range
- Use as confluence with other ICT concepts like FVGs and Order Blocks
Strategic Applications:
- Range trading between midnight levels
- Breakout confirmation when price closes decisively outside the range
- Support/resistance validation for entry and exit planning
🔗 Combine With These Tools for Complete Market Structure Analysis:
✅ First FVG — Opening Range Fair Value Gap Detector.
✅ ICT Turtle Soup (Liquidity Reversal)— Spot stop hunts and false breakout scenarios.
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session.
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
Together, these tools create a comprehensive Smart Money Concepts (SMC) framework — helping traders identify, anticipate, and capitalize on institutional-level price movements with precision and confidence during critical overnight sessions. Also, dont forget to not over-trade.
Midnight 30min High/LowMidnight 30min High/Low — Overnight Liquidity Range Tracker
Capture the Overnight Session: A Strategic Level Identification Tool from Professional Trading Methodology
This indicator captures the high and low prices during the critical 30-minute midnight session (12:00-12:30 AM EST) and projects these levels forward as key support and resistance zones. These overnight ranges often contain significant liquidity and serve as crucial reference points for intraday price action, representing areas where institutional activity may have established important levels.
🔍 What This Script Does:
Identifies Critical Overnight Session Levels
- Automatically detects the 12:00-12:30 AM EST session window
- Captures the highest and lowest prices during this 30-minute period
- Projects these levels forward for multiple trading days
Creates Dynamic Support/Resistance Zones
- Extends midnight high/low levels as horizontal lines with customizable projection periods
- Fills the area between high and low to create a visual trading range
- Updates automatically each trading day with new overnight levels
Provides Clear Visual Reference Points
- Optional session start markers (●) highlight when the midnight session begins
- Color-coded lines distinguish between high and low levels
- Transparent fill area creates an easy-to-identify trading zone
Real-Time Level Tracking
- Updates levels in real-time during the active midnight session
- Maintains historical levels for reference and backtesting
- Compatible with data window for precise level values
⚙️ Customization Options:
Extend Days (1-30):** Control how many days forward the levels are projected (default: 5 days)
High Line Color:** Customize the midnight high line color (default: blue)
Low Line Color:** Customize the midnight low line color (default: orange)
Fill Color:** Adjust the transparency and color of the range area (default: light aqua, 80% transparency)
Show Session Markers:** Toggle yellow session start indicators on/off (default: enabled)
💡 How to Use:
Deploy on lower timeframes (1m-15m) for precise level identification and reaction monitoring**
Watch for key price interactions:
- Rejection at midnight high levels (potential resistance)
- Bounce from midnight low levels (potential support)
- Range-bound trading between the high and low levels
Combine with liquidity concepts:
- Monitor for stop hunts above/below these levels
- Look for false breakouts that snap back into the range
- Use as confluence with other ICT concepts like FVGs and Order Blocks
Strategic Applications:
- Range trading between midnight levels
- Breakout confirmation when price closes decisively outside the range
- Support/resistance validation for entry and exit planning
🔗 Combine With These Tools for Complete Market Structure Analysis:
✅ First FVG — Opening Range Fair Value Gap Detector.
✅ ICT Turtle Soup (Liquidity Reversal)— Spot stop hunts and false breakout scenarios
✅ ICT Macro Zones (Grey Box Version)- It tracks real-time highs and lows for each Silver Bullet session
✅ ICT SMC Liquidity Grabs and OBs- Liquidity Grabs, Order Block Zones, and Fibonacci OTE Levels, allowing traders to identify institutional entry models with clean, rule-based visual signals.
Together, these tools create a comprehensive Smart Money Concepts (SMC) framework — helping traders identify, anticipate, and capitalize on institutional-level price movements with precision and confidence during critical overnight sessions.
Zen Lab Checklist - FNSThe Zen Lab Checklist - FNS is a simple yet powerful visual trading assistant designed to help traders maintain discipline and consistency in their trading routines. This provides a customizable on-screen checklist. This indicator allows traders to verify key conditions before entering a trade which will help identify trade quality and promote structured trading habits. This indicator is ideal for discretionary traders who follow a consistent set of entry rules.
✅ Key Features
Customizable Checklist Items:
Define up to 6 checklist labels with on/off toggle switches to track your trade criteria.
Visual Feedback:
Each checklist item displays a ✅ checkmark when conditions are met or a ❌ cross when not. Colors are visually distinct — green for confirmed, red for not confirmed.
Progress Tracker:
A "Trade Score" footer calculates a "trade score" percentage, helping you quickly assess the trade idea quality and readiness.
Table Position Control:
Easily adjust the table’s position on your chart (e.g., top-right, middle-center, bottom-left) using a dropdown menu.
Custom Styling Options:
- Change background and font color of checklist rows.
- Set font size (tiny to huge).
- Set the header and footer colors separately for visual contrast. (default is green background with white font)
📌 How to Use
- Open the indicator settings.
- Label your checklist items to match your personal or strategy-specific rules.
- Toggle the corresponding switches based on your trade setup conditions.
- Review the on-chart checklist and "Trade Score" to confirm your trade decision.
🎯 Why Use This?
- Discipline: Keeps you aligned with your trading plan.
- Clarity: Clear visual indicator of trade readiness.
- Efficiency: Saves time by centralizing your checklist visually on your chart.
- Custom Fit: Adapt the labels and styling to match your strategy or preferences.
⚠️ Notes
This is a manual checklist, meaning you control the toggle switches based on your judgment.
Ideal for discretionary traders who follow a consistent set of entry rules.
ICT HTF Candles [Pro] (fadi)The ICT HTF Candles shows you multi-timeframe price action by plotting up to six higher timeframe candles on your chart, scaled to real price levels. Set candle counts per timeframe or toggle them off for a clean view, saving you time switching between charts. This helps you spot trends and reversals quickly, align trades with the market’s direction, and time setups like sweeps or bounces better. From scalping on the 1m to swinging on the 4H, it simplifies ICT and Smart Money Concepts (SMC), revealing trend shifts and institutional moves clearly. Once you use it, trading without this clarity just won’t feel right.
Key Features:
In-Depth Price Action Levels
These levels track ICT PD arrays and confluences across timeframes, making it easy to see how price action flows from higher timeframes and what your setup faces. Is your 5m trade about to run into a 1H bearish order block? Did it bounce off a higher timeframe FVG and create an SMT with a correlated asset? They make your chart a clear roadmap to market structure, helping you find strong setups, save time, and align with institutional moves:
Change in State of Delivery (CISD): In ICT trading, CISD marks potential reversal levels on each timeframe by showing the open of the highest series of up (green) candles for a bullish shift or the open of the lowest series of down (red) candles for a bearish shift. These levels are set at the opening price of the first candle in those runs, highlighting where the market turns. The indicator makes these levels easy to spot across timeframes, so you can track reversal points clearly. You can set your own confirmation criteria—a close or wick above/below the CISD line (bearish/bullish) or a close or wick above/below the high/low—to verify the CISD level cross. When confirmed, there is a high probability that we have a change in trend, and a reversal order block forms. CISD helps you track these reversal levels and confirm market shifts, making multi-timeframe analysis straightforward.
Order Blocks: When a CISD level cross is confirmed, the price is now below a series of up (green) candles or above a series of down (red) candles, marking these candles as order blocks that usually support the new trend direction. The indicator shows these levels clearly across timeframes, making it easy to spot high-probability reversal or consolidation areas. Keep in mind that price may sometimes move to mitigate an imbalance, so use your best judgment based on your multi-timeframe analysis to confirm they meet your trading criteria.
Trend Bias: Traders often struggle figuring out market bias—guessing the trend wrong, losing on trades against the flow, or missing how lower and higher timeframes line up. The Trend Bias feature tracks order blocks and change in state of delivery, displaying bullish or bearish trends for each timeframe to help you choose trades that go with the market’s direction. The indicator shows these trends clearly across timeframes, so you can quickly see if the 5m matches the 1H or if you’re going against the bigger trend. This makes it easier to avoid bad trades and make decisions faster, keeping you on track with setups that follow the main trend.
Immediate Rebalance: When looking at price action, you’ll see the market doesn’t usually leave behind many Fair Value Gaps (FVGs). That’s because the market is efficient and always rebalancing any inefficiencies. When the market starts a strong move, the last candle will usually close above the previous candle high (for up moves) or below the low (for down moves). At this point, the market will do one of two things: immediately rebalance by retracing first, or have a small retracement but leave behind an FVG. The Immediate Rebalance feature tracks rebalance levels across multiple timeframes, clearly showing where price rebalances. This helps traders have a better expectation of how the market may need to retrace and anticipate Power of Three (PO3) setups by being ready for a Judas swing to rebalance the imbalance.
Fair Value Gaps and Volume Imbalances: If the market fails to immediately rebalance, it will usually attempt to come back and rebalance it at a later time. FVGs and VIs give you a clear area where the price might be heading if it starts breaking structure on lower timeframes. These inefficiencies—price gaps (FVGs) or aggressive moves (VIs)—show where the market’s working to fix imbalances. The Fair Value Gaps and Volume Imbalances feature tracks these levels across timeframes.
Previous Candle Levels: The Previous Candle Levels feature marks the high, low, and middle of the prior candle on each timeframe, helping you identify key price levels for sweeps, bounces, or breakouts. It tracks the candle’s high and low as its extremes and the middle as the 50% mark, which you can set to calculate using the high-to-low range or the open-to-close range. These levels can provide tradable setups on lower timeframes.
Smart Money Techniques (SMT): What’s an ICT indicator without an SMT feature to track cracks in correlated assets? The ICT HTF Candles monitors your chosen correlated assets, like EUR/USD and GBP/USD or SQ and NQ, for signs of strength or weakness to use as confluence with other features and build the case for A+ setups. The SMT feature spots divergences when one asset makes a higher high or lower low while the other doesn’t follow, hinting at potential reversals or market shifts. It tests SMT using two immediate candles, since higher timeframes (HTFs) create larger gaps on lower timeframes. Traders can easily see these divergence levels, like a 15m SMT lining up with a 1H order block or CISD, helping you confirm high-probability setups and strengthen trade entries with multi-timeframe confluence.
Market SurferOverview
If you're ready to surf the charts, Market Surfer is your perfect board 🏄♂️
This is my personal go-to indicator, designed to be a true Swiss Army knife for technical analysis - packed with powerful tools that deliver clear signals straight out of the box.
Market Surfer is heavily inspired by Market Cipher and Traders Reality .
Key Features
Market Waves : Visual representation of cyclical price movements to identify trend strength and potential reversals.
Money Flow : Highlights periods of buying and selling pressure, signaling shifts in market sentiment.
Trend Tracker : Real-time trend detection powered by EMA-based analysis, with color-coded signals for bullish and bearish phases.
Vector Candles : Enhanced candle coloring that indicates when market makers and high-frequency traders join the game, helping to identify significant market moves.
Dynamic Alerts : Configurable alerts for key market events, including trend changes, money flow transitions, and vector candle formations.
How It Works
Wave Theory Analysis : Detects cyclical market movements to highlight potential trend continuations or reversals.
PVSRA Analysis : Identifies vector candles when volume surges significantly relative to historical averages, indicating the presence of large institutional players.
EMA Trend Tracking : Tracks the 50-period EMA to determine overall market momentum and colorizes bars accordingly.
Money Flow Indexing : Uses Heikin-Ashi candle structures to measure buying and selling intensity over time.
Recommendations
Although Market Surfer is versatile and works across all markets and timeframes, I recommend:
Use it on 1H timeframe for mid-term trades and 1D timeframe for long-term ones.
Buy when green and sell when red - keep it simple.
Study vector candles before relying on them - they reveal institutional footprints.
Do not use leverage - trade with clarity and peace of mind.
And most importantly - sleep well.
MARKET SYNERGY ANALYZER# MARKET SYNERGY ANALYZER v2.0
Current Date and Time (UTC): 2025-04-04 00:20:33
Author: Timur İnci
## INTRODUCTION
The Market Synergy Analyzer is an advanced technical analysis tool designed to bridge the gap between traditional market analysis and cross-market correlation studies. This sophisticated indicator provides traders and analysts with a comprehensive view of market relationships, particularly focusing on the synergy between BIST (Borsa Istanbul) indices and cryptocurrency markets.
### Core Purpose
- Identifies market correlations across different asset classes
- Tracks relative strength between markets
- Provides normalized price comparison
- Offers multi-timeframe analysis through customizable EMAs
## DEVELOPMENT
### Technical Implementation
1. **Multi-Market Data Processing**
- Real-time data fetching from BIST indices
- Cryptocurrency market integration
- Cross-market price normalization
2. **Advanced Technical Indicators**
- Four-layer EMA system (5, 14, 34, 233 periods)
- Normalized price ratios
- Percentage difference calculations
- Real-time market synergy detection
3. **Visualization Components**
- Color-coded EMA lines for trend identification
- Normalized candlestick charts
- Visual correlation indicators
### Key Features
- **Market Coverage:**
- 30+ BIST indices including XU100, XU030, XU050
- Major cryptocurrency pairs (BTC/USD, BTC/TRY, BTC/EUR)
- Sector-specific indices
- **Analysis Tools:**
- Relative strength comparison
- Cross-market correlation metrics
- Trend deviation alerts
- Multi-timeframe analysis
## CONCLUSION
### Practical Applications
1. **For Traders:**
- Identify market leading sectors
- Spot divergences between markets
- Time entry and exit points
- Track relative market strength
2. **For Portfolio Managers:**
- Monitor sector rotations
- Assess market correlations
- Optimize portfolio diversification
- Track market breadth
3. **For Risk Managers:**
- Monitor market relationships
- Track systemic risk indicators
- Identify potential market disruptions
- Assess cross-market impacts
### Benefits
- **Enhanced Decision Making:**
- Data-driven market analysis
- Reduced emotional bias
- Systematic approach to market analysis
- Comprehensive market view
- **Risk Management:**
- Early warning system for market changes
- Cross-market risk assessment
- Trend deviation alerts
- Portfolio exposure monitoring
- **Market Insights:**
- Deep market understanding
- Sector rotation identification
- Correlation analysis
- Market leadership tracking
### Target Users
1. Professional Traders
2. Portfolio Managers
3. Market Analysts
4. Risk Managers
5. Institutional Investors
## TECHNICAL REQUIREMENTS
- Platform: TradingView
- Pine Script Version: 6.0
- Data Feed: Real-time market data
- Recommended Timeframes: All
- Memory Usage: Optimized (500 bars back)
## FUTURE DEVELOPMENTS
1. Machine Learning Integration
2. Advanced Pattern Recognition
3. Additional Market Coverage
4. Enhanced Alert System
5. Custom Reporting Features
[AlbaTherium] MTF Volatility Edge Zones Premium for Price Action Volatility Edge Zones Premium for Price Action (HTF)
The MTF Volatility Edge Zones Premium for Price Action is an advanced Multiple Timeframes (MTF) trading indicator that combines the power of volume analysis with price action, designed to reveal key volatility zones and assess market participants’ engagement levels . This tool offers unique insights into the dynamics of higher timeframes (HTF), helping traders identify critical zones of decision-making, such as potential reversals, continuations, or breakout areas.
Introduction to the MTF Volatility Edge Zones Premium
This indicator is built upon a deep understanding of the interaction between price action and volume. By mapping volume data onto price action, Volatility Edge Zones Premium (HTF) pinpoints areas of heightened market engagement. These zones represent where buyers and sellers have shown significant activity, allowing traders to identify market intent and anticipate key movements.
Key Features:
Higher Timeframe Analysis: Focuses on significant price and volume interactions over HTFs (e.g., 4H, Daily, Weekly) for a broader perspective on market trends.
Volatility Zones : Highlights areas where market participants show increased activity, signaling potential market turning points or strong continuations.
Volume-Driven Insights: Tracks the behavior of aggressive buyers and sellers, showing their engagement levels relative to price changes.
Overlayon Price Action: Provides a clear and actionable visual representation of volatility and engagement zones directly on price charts.
Chapter 1: Understanding Volatility and Engagement
1.1 Volatility Edge Zones
Volatility Edge Zones are areas where price and volume interact to signal potential changes in market direction or momentum. These zones are derived from high-volume clusters where significant market activity occurs.
1.2 Participant Engagement
Market participants can be categorized based on their level of engagement in these zones:
Aggressive Buyers: Represented by sharp spikes in volume and upward price action.
Aggressive Sellers: Represented by high volume during downward price movement.
Passive Participants: Identified in zones of consolidation or low volatility.
By isolating these behaviors, traders can gain a clearer picture of market sentiment and the relative strength of buyers versus sellers.
Chapter 2: The Principle of Volume and Price Interplay
2.1 Volume as a Leading Indicator
Volume often precedes price movements, and the Volatility Edge Zones Premium captures this relationship by overlaying volume activity onto price charts. This allows traders to:
Identify where volume supports price movement (trend confirmation).
Spot divergences where price moves without volume support (potential reversals).
2.2 The Role of Higher Timeframes
HTFs filter out market noise, revealing macro trends and key levels of engagement. The indicator uses this perspective to highlight long-term volatility zones, helping traders align their strategies with the broader market context.
Chapter 3: Visualizing Volatility Edge Zones
3.1 Color-Coded Zones for Engagement
The indicator uses a color-coded system to represent volatility zones and market engagement levels. These colors correspond to different market conditions:
Red Zones: High selling pressure and aggressive bearish activity.
Blue Zones: High buying pressure and aggressive bullish activity.
Yellow Zones: Transitional zones, representing indecision or balance between buyers and sellers.
White Zones: Neutral areas, where low engagement is observed but could serve as potential breakout points.
3.2 Key Metrics Tracked
Volume Clusters: Areas of concentrated buying or selling activity.
Directional Bias: Net buying or selling dominance.
Momentum Shifts: Sudden changes in volume relative to price action.
These metrics provide actionable insights into market dynamics, making it easier to predict key movements.
Chapter 4: Practical Applications in Trading
4.1 Identifying High-Impact Zones
By focusing on HTFs, traders can use the Volatility Edge Zones Premium to identify high-impact areas where market participants are most engaged. These zones often align with:
Support and Resistance Levels: High-volume areas that act as barriers or catalysts for price movement.
Breakout Points: Zones of heightened volatility where price is likely to escape consolidation.
4.2 Detecting Bull and Bear Campaigns
The indicator highlights early signs of bullish or bearish campaigns by analyzing volume surges in critical volatility zones. These campaigns often signal the beginning of significant trends.
Chapter 5: Real-World Examples and Strategies
5.1 Spotting Market Reversals
Real-world examples demonstrate how the indicator can identify volatility zones signaling potential reversals, allowing traders to enter positions early.
5.2 Riding the Trend
By tracking volatility zones in alignment with HTF trends, traders can maximize profit potential by entering during periods of high engagement and riding the trend until it weakens.
Conclusion
The MTF Volatility Edge Zones Premium for Price Action is an essential tool for traders looking to master market dynamics through a combination of volume and price action analysis. By focusing on higher timeframes and overlaying volatility zones onto price charts, this indicator provides unparalleled insights into market participant engagement.
Whether you’re trading intraday, swing, or long-term strategies, the MTF Volatility Edge Zones Premium equips you with the information needed to make confident and precise trading decisions. Stay tuned as we continue to enhance this tool for even greater accuracy and usability.
Highest High, Lowest Low, Midpoint for Selected Days [kiyarash]Highest High, Lowest Low, and Midpoint for Selected Days Indicator
This custom TradingView indicator allows you to visualize the highest high, lowest low, and the midpoint (average of the highest high and lowest low) over a custom-defined period. You can choose a starting date and specify how many days ahead you want to track the highest and lowest values. This is useful for identifying key levels in a trend and potential support or resistance zones.
How to Use:
Set the Starting Date:
In the settings, input the starting date from which you want to begin tracking the price range. This will be the reference point for your analysis.
Choose the Number of Days to Track:
Specify how many days you want to analyze from the selected starting date. For example, if you want to see the highest high and lowest low over the next 3 days, enter "3" in the settings.
Visualizing the Levels:
The indicator will automatically calculate the highest price and the lowest price over the selected period and draw three lines:
Red Line: Represents the Highest High within the selected period.
Green Line: Represents the Lowest Low within the selected period.
Blue Line: Represents the Midpoint, which is the average of the Highest High and Lowest Low.
Interpretation:
Highest High is a key resistance level, indicating the highest price reached within the specified period.
Lowest Low is a key support level, showing the lowest price during the same period.
Midpoint provides a reference for the average price, often acting as a neutral level between support and resistance.
This tool can help traders to quickly assess potential market ranges, identify breakout or breakdown points, and make informed decisions based on recent price action.
How to Apply:
Add the indicator to your chart.
Adjust the settings to choose your desired starting date and the number of days you want to analyze.
Observe the drawn lines for the Highest High, Lowest Low, and Midpoint levels, and use them to assist in your trading decisions.
MultiTimeFrame Trends and Candle Bias (by MC) v1This MultiTimeFrame Trends and Candle Bias provides the trader a quick glance on how each timeframe is trending and what the current candle bias is in each timeframe.
Interpreting Candle Bias : Green points to a bullish bias while red, a bearish bias for a given specific timeframe. For instance, if the current 1 hour candle bias is red, it means that the last hour, the bias has been bearish. If the Daily candle bias is red, it means that the day in question has been a bearish for this selected symbol.
Interpreting MTF Trends: Trends for each time frame follows the simple moving average of the closing prices for the X number of candles you enter in the input section. So for example, if you decide to enter 6 for the 1-hour time frame, the trend for the last 6 hours will be shown and tracked; if on the Daily time frame, you enter 7, the trend for the last 7 days or 1 week will be shown and tracked. I have provided below (as well as on tooltips in the input section of this indicator) recommendations of what numbers to use depending on what kind of trader you are.
What is a best setup for MultiTimeFrame Trends?
Considerations Across All Timeframes:
- Trading Style : Scalpers and very short-term intraday traders may prefer fewer candles (like 12 to 20), which allow them to react quickly to price changes. Swing traders or those holding positions for a few hours to a couple of days might prefer more candles (like 50 to 120) to identify more stable trends.
- Market Conditions : In volatile markets, using more candles helps smooth out price fluctuations and provides a clearer trend signal. In trending markets, fewer candles might be sufficient to capture the trend.
- Session-Based Adjustments : Traders may adjust their settings depending on the time of day or session they are trading. For example, during high-volatility periods like market open or close, using fewer candles can help capture quick moves.
The number of preceding candles to use for estimating the recent trend can depend on various factors, including the type of market, the asset being traded, the timeframe, and the specific goals of your analysis. However, here are some general guidelines to help you decide:
### 1. **Short-Term Trends (Fast Moving Averages):**
- **5 to 20 Candles**: If you want to capture a short-term trend, typically in day trading or scalping strategies, you might use 5 to 20 candles. This is common for fast-moving averages like the 9-period or 15-period moving averages. It reacts quickly to price changes, but it can also give more false signals due to market noise.
### 2. **Medium-Term Trends (Moderate Moving Averages):**
- **20 to 50 Candles**: For a more balanced approach that reduces the impact of short-term volatility while still being responsive to trend changes, 20 to 50 candles are commonly used. This range is popular for swing trading strategies, where the goal is to capture trends that last several days to weeks.
### 3. **Long-Term Trends (Slow Moving Averages):**
- **50 to 200 Candles**: To identify long-term trends, such as those seen in position trading or for confirming major trend directions, you might use 50 to 200 candles. The 50-period and 200-period moving averages are particularly well-known and are often used by traders to identify significant trend reversals or confirmations.
### 4. **Adaptive Approach:**
- **Market Conditions**: In trending markets, fewer candles might be needed to identify a trend, while in choppy or range-bound markets, using more candles can help filter out noise.
- **Volatility**: In highly volatile markets, more candles might be necessary to smooth out price action and avoid false signals.
### **Experiment and Backtesting:**
The optimal number of candles can vary significantly based on the asset and strategy. It's often a good idea to backtest different periods to see which provides the best balance between responsiveness and reliability in identifying trends. You can use tools like the strategy tester in TradingView or other backtesting software to compare the performance of different settings.
### **General Recommendation:**
- **For Shorter Timeframes** (e.g., 5m, 15m): 10-20 candles might be effective.
- **For Medium Timeframes** (e.g., 1h, 4h): 20-50 candles are often a good starting point.
- **For Longer Timeframes** (e.g., Daily, Weekly): 50-200 candles help capture major trends.
If you're unsure, a common starting point for many traders is the 20-period moving average, which provides a balance between sensitivity and reliability.
Guidelines for 1-Minute Timeframe:
For the 1-minute (1M) timeframe, trend analysis typically focuses on very short-term price movements, which is crucial for scalping and ultra-short-term trading strategies. Here’s a breakdown of the number of preceding candles you might use:
1. **Very Short-Term Trend:**
- **10 to 20 Candles (10 to 20 Minutes):** Using 10 to 20 candles captures about 10 to 20 minutes of price action. This range is suitable for scalpers who need to identify very short-term trends and make quick trading decisions.
2. **Short-Term Trend:**
- **30 to 60 Candles (30 to 60 Minutes):** This period covers 30 to 60 minutes of trading, making it useful for traders looking to understand the trend over a full trading hour. It helps capture price movements and trends that develop within a single hour.
3. **Intraday Trend:**
- **120 Candles (2 Hours):** Using 120 candles provides a view of the trend over approximately 2 hours. This is useful for traders who want to see how the market is trending throughout a larger portion of the trading day.
4. **Extended Intraday Trend:**
- **240 to 480 Candles (4 to 8 Hours):** This longer period gives a broader view of the intraday trend, covering 4 to 8 hours. It’s helpful for identifying trends that span a significant portion of the trading day, which can be useful for traders looking to align with the broader intraday movement.
**Considerations:**
- **High Sensitivity:** The 1-minute timeframe is highly sensitive to market movements, so shorter periods (10 to 20 candles) can capture rapid price changes but may also generate noise.
- **Market Volatility:** In highly volatile markets, using more candles (like 30 to 60 or more) helps smooth out the noise and provides a clearer trend signal.
- **Trading Style:** Scalpers will typically use shorter periods to make very quick decisions. Traders holding positions for a bit longer, even within the same day, may use more candles to get a clearer picture of the trend.
**Common Approaches:**
- **5-Period Moving Average:** The 5-period moving average on a 1-minute chart can be used for extremely short-term trend signals, reacting quickly to price changes.
- **20-Period Moving Average:** The 20-period moving average is a good choice for capturing short-term trends and can help filter out some of the noise while still being responsive.
- **50-Period Moving Average:** The 50-period moving average provides a broader view of the trend and can help smooth out price movements over a longer intraday period.
**Recommendation:**
- **Start with 10 to 20 Candles:** For the most immediate and actionable signals, especially useful for scalping or very short-term trading.
- **Use 30 to 60 Candles:** For a clearer view of trends that develop over an hour, suitable for those looking to trade within a single trading hour.
- **Consider 120 Candles:** For observing broader intraday trends over 2 hours, helping align trades with more significant intraday movements.
- **Explore 240 to 480 Candles:** For a longer intraday perspective, covering up to 8 hours, which can be useful for strategies that span a larger portion of the trading day.
**Practical Example:**
- **Scalpers:** If you’re executing trades every few minutes, start with 10 to 20 candles to get rapid trend signals.
- **Short-Term Traders:** For trends that last an hour or so, 30 to 60 candles will provide a better sense of direction while still being responsive.
- **Intraday Traders:** For broader trends that span several hours, 120 candles will help you see the overall intraday movement.
Experimentation and backtesting with these settings on historical data will help you fine-tune your approach to the 1-minute timeframe for your specific trading strategy and asset.
Guidelines for 5, 15 and 30 min Timeframes:
For shorter timeframes like 5, 15, and 30 minutes, the number of preceding candles you use will depend on how quickly you want to react to changes in the trend and the specific trading style you’re employing. Here's a breakdown for each:
**5-Minute Timeframe:**
1. **Very Short-Term (Micro Trend):**
- **12 to 20 Candles (60 to 100 Minutes):** Using 12 to 20 candles on a 5-minute chart captures 1 to 1.5 hours of price action. This is ideal for very short-term trades, such as scalping, where quick entries and exits are key.
2. **Short-Term Trend:**
- **30 to 60 Candles (150 to 300 Minutes):** This period covers 2.5 to 5 hours, making it useful for intraday traders who want to identify the trend within a trading session. It helps capture the direction of the market during the most active parts of the day.
3. **Intra-Day Trend:**
- **120 Candles (10 Hours):** Using 120 candles gives you a broad view of the trend over two trading sessions. This is useful for traders who want to understand the trend throughout the entire trading day.
**15-Minute Timeframe:**
1. **Very Short-Term:**
- **12 to 20 Candles (3 to 5 Hours):** On a 15-minute chart, this period covers 3 to 5 hours, making it useful for capturing the morning or afternoon trend within a trading day. It’s often used by intraday traders who need to make quick decisions.
2. **Short-Term Trend:**
- **30 to 60 Candles (7.5 to 15 Hours):** This covers almost a full trading day to a day and a half. It’s popular among day traders who want to align their trades with the trend of the day or the previous trading session.
3. **Intra-Week Trend:**
- **120 Candles (30 Hours):** This period spans about two trading days and is useful for traders looking to capture trends that may extend beyond a single trading day but not necessarily for an entire week.
**30-Minute Timeframe:**
1. **Short-Term Trend:**
- **12 to 20 Candles (6 to 10 Hours):** This period captures the trend over a single trading session. It's useful for day traders who want to understand the market’s direction throughout the day.
2. **Medium-Term Trend:**
- **30 to 50 Candles (15 to 25 Hours):** This period covers about two trading days and is useful for short-term swing traders or intraday traders who are looking for trends that might last a couple of days.
3. **Intra-Week Trend:**
- **100 to 120 Candles (50 to 60 Hours):** This longer period captures about 4 to 5 trading days, making it useful for traders who want to understand the broader trend over the course of the week.
**Summary Recommendations:**
- **5-Minute Chart:**
- **12 to 20 candles** for very short-term trades.
- **30 to 60 candles** for intraday trends within a single session.
- **120 candles** for a broader view of the day’s trend.
- **15-Minute Chart:**
- **12 to 20 candles** for short-term trades within a few hours.
- **30 to 60 candles** for trends lasting a full day or more.
- **120 candles** for trends extending over a couple of days.
- **30-Minute Chart:**
- **12 to 20 candles** for understanding the daily trend.
- **30 to 50 candles** for trends over a couple of days.
- **100 to 120 candles** for an intra-week trend view.
Experimenting with these settings and backtesting on historical data will help you find the optimal number of candles for your specific trading style and the assets you trade.
Guidelines for 1H Timeframes:
When analyzing trends on a 1-hour (1H) timeframe, you're focusing on short to medium-term trends, often used by day traders and short-term swing traders. Here’s how you can approach selecting the number of preceding candles:
1. **Short-Term Trend:**
- **14 to 21 Candles (14 to 21 Hours):** Using 14 to 21 candles on a 1-hour chart captures roughly half a day to a full day of trading activity. This range is ideal for day traders who want to identify short-term momentum and trend changes within a single trading day.
2. **Medium-Term Trend:**
- **50 Candles (2 Days):** A 50-period moving average on a 1-hour chart covers about two days of trading. This period is popular for identifying trends that may last a couple of days, making it useful for short-term swing traders.
3. **Longer-Term Trend:**
- **100 Candles (4 Days):** Using 100 candles gives you a broader view of the trend over about four days of trading. This is helpful for traders who want to align their trades with a more sustained trend that spans the entire week.
4. **Very Short-Term (Micro Trend):**
- **7 to 10 Candles (7 to 10 Hours):** For traders looking to capture micro trends or very short-term price movements, using 7 to 10 candles can provide a quick look at recent price action. This is often used for scalping or very short-term intraday strategies.
**Considerations:**
- **Market Volatility:** In highly volatile markets, using more candles (like 50 or 100) helps smooth out noise and provides a clearer trend signal. In less volatile conditions, fewer candles may suffice to capture trends.
- **Trading Style:** If you are a day trader looking for quick moves, shorter periods (like 7 to 21 candles) might be more suitable. For those who hold positions for a day or two, longer periods (like 50 or 100 candles) can provide better trend confirmation.
- **Asset Class:** The optimal number of candles can vary depending on the asset
Guidelines for 4H Timeframes:
When analyzing trends on a 4-hour (4H) timeframe, you’re generally looking to capture short to medium-term trends. This timeframe is popular among swing traders and intraday traders who want to balance between catching more significant market moves and not being too sensitive to noise. Here's how you can approach selecting the number of preceding candles:
1. **Short-Term Trend:**
- **14 to 21 Candles (2 to 3 Days):** Using 14 to 21 candles on a 4-hour chart covers roughly 2 to 3 days of trading activity. This range is ideal for traders looking to capture short-term momentum, especially in markets where price action can move quickly within a few days.
2. **Medium-Term Trend:**
- **50 Candles (8 to 10 Days):** A 50-period moving average on a 4-hour chart represents approximately 8 to 10 days of trading (considering 6 trading periods per day). This period is popular among swing traders for identifying trends that develop over the course of one to two weeks.
3. **Longer-Term Trend:**
- **100 Candles (16 to 20 Days):** Using 100 candles gives you a broader view of the trend over about 3 to 4 weeks. This is useful for traders who want to align their trades with the more sustained market direction while still remaining responsive to recent changes.
**Considerations:**
- **Market Conditions:** In a trending market, fewer candles (like 14 or 21) may be enough to identify the trend, allowing for quicker responses to price movements. In a more volatile or range-bound market, using more candles (like 50 or 100) can help smooth out noise and avoid false signals.
- **Trading Style:** If you are an intraday trader, shorter periods (14 to 21 candles) may be preferable, as they allow for quick entries and exits. Swing traders might lean towards the 50 to 100 candle range to capture trends that last several days to a few weeks.
- **Volatility:** The higher the volatility of the asset, the more candles you might want to use to ensure that the trend signal is not too erratic.
**Common Approaches:**
- **20-Period Moving Average:** A 20-period moving average on a 4-hour chart is often used by traders to capture short-term trends that align with momentum over the past few days.
- **50-Period Moving Average:** The 50-period moving average is widely used on the 4-hour chart to track medium-term trends. It provides a good balance between reacting to new trends and avoiding too many whipsaws.
- **100-Period Moving Average:** The 100-period moving average offers insight into the longer-term trend on the 4-hour chart, helping to filter out short-term noise and confirm the overall market direction.
**Recommendation:**
- **Start with 20 Candles for Short-Term Trends:** This period is useful for capturing quick movements and short-term trends over a couple of days.
- **Use 50 Candles for Medium-Term Trends:** This is a standard setting that provides a balanced view of the market over about 1 to 2 weeks.
- **Consider 100 Candles for Longer-Term Trends:** This helps to identify more significant trends that have persisted for a few weeks.
**Practical Example:**
- **Intraday Traders:** If you’re focused on shorter-term trades and need to react quickly, using 14 to 21 candles will help you capture the most recent momentum.
- **Swing Traders:** If you’re looking to hold positions for several days to a few weeks, starting with 50 candles will give you a clearer picture of the trend over that period.
- **Position Traders:** For those holding positions for a longer duration within a month, using 100 candles helps to align with the broader trend while still being responsive enough for 4-hour price movements.
Backtesting these settings on your chosen asset and strategy will help refine the optimal number of candles for your specific needs.
Guidelines for Daily Timeframes:
When analyzing trends on a daily timeframe, you're typically focusing on short to medium-term trends. Here’s how you can determine the optimal number of preceding candles:
1. **Short-Term Trend:**
- **10 to 20 Candles (2 to 4 Weeks):** Using 10 to 20 daily candles captures about 2 to 4 weeks of price action. This is commonly used for identifying short-term trends, ideal for swing traders or those looking for quick entries and exits within a month.
2. **Medium-Term Trend:**
- **50 Candles (2 to 3 Months):** The 50-day moving average is a classic choice for capturing medium-term trends. This period covers about 2 to 3 months of trading days and is often used by swing traders and investors to identify the trend over a quarter or a season.
3. **Long-Term Trend:**
- **100 to 200 Candles (4 to 9 Months):** For longer-term trend analysis, using 100 to 200 daily candles gives you a broader perspective, covering approximately 4 to 9 months of price action. The 200-day moving average, in particular, is widely used by investors to determine the overall long-term trend and to assess market health.
**Considerations:**
- **Market Volatility:** In more volatile markets, using a larger number of candles (e.g., 50 or 200) helps smooth out noise and provides a more reliable trend signal. In less volatile markets, fewer candles might be sufficient to capture trends effectively.
- **Trading Style:** Day traders might prefer shorter periods (like 10 or 20 candles) for quicker signals, while position traders and longer-term swing traders might opt for 50 to 200 candles to focus on more sustained trends.
- **Asset Class:** The optimal number of candles can also depend on the asset class. For example, equities might have different optimal settings compared to forex or cryptocurrencies due to different volatility characteristics.
**Common Approaches:**
- **20-Period Moving Average:** The 20-day moving average is a popular choice for short-term trend analysis. It’s widely used by traders to identify the short-term direction and to make quick trading decisions.
- **50-Period Moving Average:** The 50-day moving average is a staple for medium-term trend analysis, often used as a key indicator for both entry and exit points in swing trading.
- **200-Period Moving Average:** The 200-day moving average is crucial for long-term trend identification. It's commonly used by investors and is often seen as a major support or resistance level. When the price is above the 200-day moving average, the market is generally considered to be in a long-term uptrend, and vice versa.
**Recommendation:**
- **Start with 20 Candles for Short-Term Trends:** This period is commonly used for identifying recent trends within the last few weeks.
- **Use 50 Candles for Medium-Term Trends:** This provides a good balance between responsiveness and stability, making it a good fit for most swing trading strategies.
- **Use 200 Candles for Long-Term Trends:** This period is ideal for long-term analysis and is particularly useful for investors looking at the overall market trend.
**Practical Example:**
- If you’re trading equities and want to catch short-term trends, start with 20 candles to identify trends that have developed over the past month.
- If you’re more focused on medium to long-term trends, consider using 50 or 200 candles to ensure you’re aligned with the broader market direction.
Experimenting with these periods and backtesting on historical data will help you determine the best setting for your particular strategy and the asset you're analyzing.
Guidelines for Weekly Timeframes:
When analyzing trends on a weekly timeframe, you're typically looking at intermediate to long-term trends. Here's how you might approach selecting the number of preceding candles:
1. **Intermediate-Term Trend:**
- **13 to 26 Candles (3 to 6 Months):** Using 13 to 26 weekly candles corresponds to a period of 3 to 6 months. This range is effective for identifying intermediate-term trends, which is suitable for swing traders or those looking to hold positions for several weeks to a few months.
2. **Medium-Term Trend:**
- **26 to 52 Candles (6 Months to 1 Year):** For a broader view, you might use 26 to 52 weekly candles. This represents 6 months to 1 year of price data, which is helpful for understanding the market’s behavior over a medium-term period. This range is commonly used by swing traders and position traders who are interested in capturing trends lasting several months.
3. **Long-Term Trend:**
- **104 Candles (2 Years):** Using 104 weekly candles gives you a 2-year perspective. This can be useful for long-term trend analysis, particularly for investors or those looking to identify major trend reversals or continuations over a more extended period.
**Considerations:**
- **Market Type:** In trending markets, fewer candles (like 13 or 26) may work well, capturing the trend more quickly. In choppier or range-bound markets, using more candles can help reduce noise and avoid false signals.
- **Asset Class:** The optimal number of candles can vary depending on the asset class. For example, equities might benefit from a slightly shorter lookback period compared to more volatile assets like commodities or cryptocurrencies.
- **Volatility:** If the market or asset you're analyzing is highly volatile, using a higher number of candles (like 52 or 104) can help smooth out price fluctuations and provide a more stable trend signal.
**Common Approaches:**
- **20-Period Moving Average:** A 20-week moving average is popular among traders for identifying the intermediate trend. It’s responsive enough to capture significant trend changes while filtering out short-term noise.
- **50-Period Moving Average:** The 50-week moving average is often used to identify longer-term trends and is commonly referenced in both technical analysis and by longer-term traders.
- **200-Period Moving Average:** Although less common on weekly charts compared to daily charts, a 200-week moving average can be used to identify very long-term trends, such as multi-year market cycles.
**Recommendation:**
- **Start with 26 Candles:** This gives you a half-year perspective and is a good starting point for most analyses on a weekly timeframe. It balances sensitivity to recent trends with the ability to capture more significant, sustained movements.
- **Adjust Based on Backtesting:** You can increase the number of candles to 52 if you find that you need more stability in the trend signal, or decrease to 13 if you're looking for a more responsive signal.
Experimenting with different periods and backtesting on historical data can help determine the best setting for your specific strategy and asset class.
Guidelines for Monthly Timeframes:
For analyzing trends on monthly timeframes, you would generally be looking at much longer periods to capture the broader, long-term trend. Here's how you can approach it:
1. **Long-Term Trend (Primary Trend):**
- **12 to 24 Candles (1 to 2 Years):** Using 12 to 24 monthly candles corresponds to a period of 1 to 2 years. This is typically sufficient to identify long-term trends and is commonly used by long-term investors or position traders who are interested in the overall direction of the market or asset over multiple years.
2. **Very Long-Term Trend (Secular Trend):**
- **36 to 60 Candles (3 to 5 Years):** To capture very long-term secular trends, you might use 36 to 60 monthly candles. This would represent a time frame of 3 to 5 years and is often used for understanding macroeconomic trends or very long-term investment strategies.
3. **Ultra Long-Term Trend:**
- **120 Candles (10 Years):** In some cases, especially for assets like indices or commodities that are analyzed over decades, using 120 monthly candles can help in identifying ultra long-term trends. This would be appropriate for strategic investors or those looking at generational market cycles.
**Considerations:**
- **Volatility and Stability:** Monthly timeframes generally smooth out short-term volatility, but they can also be slow to react to changes. Using a larger number of candles (e.g., 24 or more) can help ensure that the trend signal is robust and not prone to frequent whipsaws.
- **Asset Class:** The choice of period might also depend on the asset class. For instance, equities might require fewer candles compared to commodities or currencies, which can exhibit different trend dynamics.
- **Market Phases:** In different market phases (bullish, bearish, or sideways), the number of candles might need to be adjusted. For instance, in a strongly trending market, fewer candles might still provide a reliable trend indication, whereas in a more volatile or ranging market, more candles might be needed to smooth out the data.
**Common Approaches:**
- **50-Period Moving Average:** A 50-month moving average is popular among long-term traders and investors for identifying the primary trend. It offers a balance between capturing the overall trend and being responsive enough to significant changes.
- **200-Period Moving Average:** Although rarely used on a monthly chart due to the long timeframe it represents (over 16 years), it can be useful for identifying very long-term secular trends, especially for broad market indices or in macroeconomic analysis.
**Recommendation:**
- **Start with 24 Candles:** This gives you a 2-year perspective on the trend and is a good starting point for most long-term analyses on monthly charts. Adjust upwards if you need a broader trend view, depending on the stability and nature of the asset you're analyzing.
Experimentation and backtesting with your specific asset and strategy can help fine-tune the exact number of candles that work best for your analysis on a monthly timeframe.
Inside Candle and mother candle range with alert++>>This script allows you the inside bar candle and the cnadle is shown in white.
The range of the mother candle is identified and tracked until it breaks.
Once the first range is over ridden then the next similar pattern will be occured and the tracking will be done for the mother candle latest occurrence.
It also has the alert mechanism where you can go and the alert for the indicator in Alerts.
5 min is the most preferrable time frame and while saving the alert Note to save the time frame of the chart. For which ever time frame is saved the Alert will be triggered for the same .
And when th inside bar is triggered it throws an alert condition. this alert condition has to be configured in your alerts and will be buzzing on the screen.
Oct 20
Release Notes: updated with Mother candle top and bottom lines of previous occurrences and tracks the current latest Inside bar mother candle
Release Notes: this script allows you the inside bar cnadle and the cnadle is shown in white. highlighter is configurable and line colors as well.
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
MTF Bitfinex Longs vs. Shorts Support/Resistance [checkm8]Hello and welcome to my multi-timeframe support and resistance indicator based on margin longs and shorts on Bitfinex :D
The premise of the script is simple. It draws support and resistance levels based on large margin movements ( effectively showing the break-even points of those positions ), where:
Longs opening and shorts closing is bullish pressure
Longs closing and shorts opening is bearish pressure
You can select your desired timeframe for the script to show the levels on. The script draws two sets of lines, one based on medium-sized movements and another based on large movements, where you can also manually input the size of the movements for it to track. By default, the script is optimized for 1-hour timeframes on BTCUSD, where the medium sized movements are set to bullish/bearish pressures of over 500 BTC, and large movements based on pressures of over 1000 BTC.
If you choose to use a different currency pair (ex. LTCUSD, ETCUSD, EOSUSD, etc..) you must adjust the volume that the script tracks , as tracking something like a 500 margin long in XRPUSD is useless. This also applies to timeframes , as timeframes lower than 1 hour may require smaller input values, while larger timeframes will require larger movements.
In addition, there is an input for the source. I recommend leaving this setting at hlc3 , because this will capture a more appropriate break-even points for the S/R levels.
A few tips:
If the current price is under a bullish support/resistance level , this implies that the bullish margin positions are underwater (the price is below their long break-evens), ie. shorts closed at the top or longs were entered at the top
If the current price is above a bullish support/resistance level , this implies that the bullish margin positions are in profit and will act as support (they will support their long break-even points)
If the current price is under a bearish support/resistance level , this implies that the bearish margin positions are in profit and will act as resistance (the price is below their short break-evens)
If the current price is above a bearish support/resistance level , this implies that the bearish margin positions are underwater (the price is above their short break-evens), ie. shorts entered at the bottom or longs were closed at the bottom
Happy trading and feel free to reach out with feedback and suggestions! :D
Special thanks goes to oh92 for his input and feedback on the idea. Check out his profile and his vast selection of indicators in the links below!
www.tradingview.com
depthhouse.com
[MAD] FVG with LTF-POC/TPOOverview
The Fair Value Gap (FVG) Detector is a precision tool designed to automatically identify, draw, and track market inefficiencies. These gaps, also known as imbalances, often act as powerful magnets for future price action.
This indicator handles the entire lifecycle of an FVG: from its creation and extension, to the moment it is first touched, and through its entire mitigation process. To add an even deeper layer of analysis, it can now optionally plot two types of micro-analysis lines for the middle candle of the FVG pattern: a volume-based Point of Control (LTF-POC) and a time-based Time Price Opportunity (LTF-TPO). These high-precision lines pinpoint the most significant price levels within the imbalance itself.
By providing a clean and objective visualization of these critical price zones, the FVG Detector gives traders a clear framework for spotting high-probability setups and understanding how the market returns to areas of inefficiency to become balanced once again.
█ How It Works
The indicator’s logic is built on precise detection, dynamic visualization, and intelligent state tracking to provide a comprehensive view of market imbalances.
⚪ The FVG Detection Engine
At its core, the indicator uses a classic three-candle pattern to identify FVGs. This mechanical definition removes all subjectivity:
Bullish FVG: A gap is identified when the high of the first candle is lower than the low of the third candle. The space between these two prices creates the bullish FVG.
Bearish FVG: A gap is identified when the low of the first candle is higher than the high of the third candle. The space between these two prices creates the bearish FVG.
⚪ Dynamic Drawing and Mitigation
Once an FVG is detected, the indicator automatically draws a colored box to represent the gap. This box is then managed through its entire lifecycle:
Extension: If enabled, the FVG box extends forward in time with each new candle, acting as a visible, forward-looking zone of interest.
Partial Mitigation Trigger: The moment price first "touches" the gap, the box changes color to signal that it is no longer a fresh, unmitigated zone. The statistics table counts this as a "Partially Mitigated" event.
Shrinking FVG: As price moves further into the gap, the colored box dynamically shrinks, providing a real-time visual of how much of the imbalance has been filled.
Historical Outline: An optional secondary outline box is drawn to preserve the FVG's original size. This outline stops extending when the FVG is first touched, leaving a permanent historical marker.
⚪ Optional LTF Analysis for Added Precision
The indicator can look "inside" the FVG's middle candle to find its most significant price levels.
LTF-POC (Volume-Based): Using data from a lower timeframe, it analyzes the volume profile of the FVG-creating candle to find the single price level from the lower-timeframe bar with the highest trading volume.
LTF-TPO (Time-Based): It also identifies the Time Price Opportunity by dividing the candle's price range into distinct "bins." The script counts how many lower-timeframe price ticks occurred in each bin, and the TPO line is drawn at the center of the busiest bin.
Visual Confluence: These are drawn as distinct horizontal lines (defaulting to orange for POC and yellow for TPO) that extend and are managed alongside the FVG's historical outline, serving as precise levels of interest within the broader FVG zone.
█ Why This Indicator is Different
While many traders can spot FVGs manually, this indicator offers a significant edge through the possibility of the lowertimeframe analysis and showing the syntetic TPO or POCs for the relevant candles.
⚪ Automated and Objective
The market moves fast, and manually drawing FVGs is impractical and prone to error. This tool automates the entire process.
Never Miss a Gap: The detector impartially scans every three-candle sequence, ensuring no FVG is missed.
No Subjectivity: The rules for detection, mitigation, and LTF analysis are based on fixed mathematical models, removing subjective judgment.
Multi-Timeframe Clarity: The indicator works flawlessly on any timeframe, allowing you to maintain a consistent view of market structure.
⚪ Visualizing Market Memory
This tool does more than just draw boxes; it tells a story. Watching a box change color and shrink provides a visual of market dynamics in action. The optional historical outlines and LTF analysis lines build a "map" on your chart, showing where significant reactions and high-liquidity zones occurred in the past, which provides invaluable context for future price movements.
█ How to Use
⚪ Identifying High-Probability Zones
The primary use of the FVG Detector is to identify high-probability zones where price may react.
Entries: Unmitigated (fresh) FVGs can serve as powerful entry zones. Traders may look for price to return to a bullish FVG to take a long position, or to a bearish FVG to take a short position.
Targets: An FVG in your path can also act as a logical profit target. For example, if you are in a long position, you might take profit as price fills a nearby bearish FVG above you.
⚪ Confluence and Confirmation
FVGs are most powerful when they align with other forms of technical analysis. Look for FVGs that have "confluence" with:
Market Structure: A bullish FVG found at a key support level or after a bullish break of structure is a higher-probability setup.
Order Blocks: An FVG that overlaps with a bullish or bearish order block creates a very potent point of interest.
Premium/Discount Zones: FVGs found deep in a premium (for shorts) or discount (for longs) area of a trading range often yield strong reactions.
The LTF Lines (POC & TPO): Use these lines as a source of internal confluence. While the FVG gives you a zone, the POC and TPO give you precise levels within that zone. The POC shows where the highest volume was traded, while the TPO shows where price spent the most time. Confluence between these two lines can signal an extremely strong level.
█ Settings
Max Number of FVGs to Display: Controls how many active FVGs are kept on the chart to prevent clutter and maintain performance.
Extend Unmitigated FVGs: When enabled, FVG boxes will extend to the right until price touches them.
Show Bullish/Bearish FVGs: Toggles the visibility of bullish or bearish FVGs.
Show FVG Labels: Toggles the visibility of the "FVG" text labels.
Keep Mitigated Outlines: If checked, the historical outline box (and its associated POC/TPO lines) will remain on the chart even after the FVG is completely filled.
Show Statistics: Toggles the visibility of the statistics table, which tracks total, partly mitigated, and fully mitigated FVGs.
Show LTF-TPO (Time-Based): Toggles the calculation and display of the Time Price Opportunity line.
Show LTF-POC (Volume-Based): Toggles the calculation and display of the Point of Control line.
Use Custom LTF for Analysis: Check this to manually select a timeframe for the POC/TPO calculation. If unchecked, the script auto-selects a lower timeframe.
Lower Timeframe: The specific lower timeframe to use when the "Custom LTF" box is checked.
Magnifier (Bars per Slice): Controls how the script auto-selects a lower timeframe (higher number = lower timeframe). Only active when "Custom LTF" is unchecked.
█ The Logic Explained
This indicator uses a clear, rules-based system based on mathematical and conditional principles.
The 3-Candle FVG Pattern
The detection engine precisely identifies FVGs by comparing the price extremes of a three-candle sequence. For a bullish FVG, it confirms that the high of the first candle is strictly below the low of the third candle. For a bearish FVG, the low of the first candle must be strictly above the high of the third. This leaves an objective, unfilled gap in the market.
The Mitigation and Shrinking Process
Once an FVG is created, the indicator monitors it on every subsequent bar. The moment a candle's price action enters the FVG's zone, it's flagged as "partially mitigated," and its color changes. The script then continues to track how far price pushes into the gap, dynamically shrinking the box to visually represent the remaining imbalance.
Lower-Timeframe (LTF) Analysis Explained
To add precision, the indicator performs a micro-analysis of the middle candle of the FVG pattern. This is achieved by mathematically deconstructing that single candle using data from a smaller timeframe.
The lower timeframe is determined either manually or automatically via the Magnifier. The Magnifier works by dividing the chart's current timeframe. For example, on a 60-minute chart, a Magnifier of 60 tells the indicator to perform its analysis using 1-minute data (60÷60=1).
Once the LTF data is obtained, two calculations are performed:
LTF Point of Control (Volume-Based): This method seeks the price of maximum commitment. The indicator analyzes the volume of every single lower-timeframe bar within the main candle and identifies the one bar with the highest trading volume. The closing price of that specific high-volume bar is designated as the POC.
LTF Time Price Opportunity (Time-Based): This method finds the price where the market spent the most time trading. The process is a form of price distribution analysis:
The total price range (high to low) of the main candle is measured.
This range is divided into 40 equal price zones, or "bins". For a candle with a $2 range, each bin would represent a price slice of 5 cents
The indicator then counts how many of the lower-timeframe closing prices fall within each of the 40 bins.
The TPO line is drawn at the midpoint of the single bin that contained the most prices, representing the "busiest" price level.
Time-Based Drawing for Accuracy
To ensure perfect alignment across all historical data and chart reloads, all drawings are anchored to the precise timestamp of the bar, not its sequential position on the chart. This robust method guarantees that all zones remain fixed and accurate regardless of how much historical data is loaded.
█ Disclaimer
Investors are fully responsible for any investment decisions they make.
Have fun trading :-)
52SIGNAL RECIPE CME Gap Support & Resistance Detector═══ 52SIGNAL RECIPE CME Gap Support & Resistance Detector ═══
◆ Overview
The 52SIGNAL RECIPE CME Gap Support & Resistance Detector is an advanced technical indicator that automatically detects and visualizes all types of price gaps occurring in the CME Bitcoin futures market on trading charts. It captures not only gaps formed during weekend and holiday closures, but also those created during the daily 1-hour maintenance period on weekdays, and sudden price gaps resulting from economic indicator releases or news events.
The core value of this indicator lies beyond simply displaying gaps; it visualizes how these price discontinuities act as powerful support and resistance zones that influence future price movements. In real markets, these CME gaps have a high probability of either being "filled" or functioning as important reaction zones, providing traders with valuable entry and exit signals.
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◆ Key Features
• Comprehensive Gap Detection: Detects gaps in all market conditions
- Weekend/holiday closure gaps
- Weekday 1-hour maintenance period gaps
- Gaps from economic indicators/news events causing rapid price changes
• Intuitive Color Coding:
- Blue: When gaps act as support (price is above the gap)
- Red: When gaps act as resistance (price is below the gap)
- Gray: Filled gaps (price has completely passed through the gap area)
• Real-time Role Switching: Automatically changes colors as price moves above/below gaps, visualizing support↔resistance role transitions
• Status Tracking System: Automatically tracks whether gaps are "Filled" or "Unfilled"
• Dynamic Boxes: Clearly marks gap areas with boxes and dynamically changes colors based on price movement
• Precise Labeling: Accurately displays the price range of each gap to support trader decision-making
• Smart Filtering: Improved algorithm that solves consecutive gap detection issues for complete gap tracking
• Key Usage Points:
- Pay special attention when price approaches gap areas
- Color changes in gaps signal important market sentiment shifts
- Areas with multiple clustered gaps are particularly strong reaction zones
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◆ User Guide: Understanding Gap Roles Through Colors
■ Color System Interpretation
• Blue Gaps (Support Role):
▶ Meaning: Current price is above the gap, making the gap act as support
▶ Trading Application: Consider buying opportunities when price approaches blue gap areas
▶ Psychological Meaning: Buying pressure likely to increase at this price level
• Red Gaps (Resistance Role):
▶ Meaning: Current price is below the gap, making the gap act as resistance
▶ Trading Application: Consider selling opportunities when price approaches red gap areas
▶ Psychological Meaning: Selling pressure likely to increase at this price level
• Gray Gaps (Filled Gaps):
▶ Meaning: Price has completely passed through the gap area, filling the gap
▶ Reference Value: Still valuable as reference for past important reaction zones
▶ Trading Application: Used to confirm trend strength and identify key psychological levels
■ Understanding Color Transitions
• Blue → Red Transition:
▶ Meaning: Price has fallen below the gap, changing its role from support to resistance
▶ Market Interpretation: Breakdown of previous support strengthens bearish signals
▶ Trading Application: Consider potential further decline; check gap bottom as resistance during bounces
• Red → Blue Transition:
▶ Meaning: Price has risen above the gap, changing its role from resistance to support
▶ Market Interpretation: Breakout above previous resistance strengthens bullish signals
▶ Trading Application: Consider potential further rise; check gap top as support during pullbacks
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◆ Practical Application Guide
■ Basic Trading Scenarios
• Blue Gap Support Strategy:
▶ Entry Point: When price approaches the top of a blue gap and forms a bounce candle
▶ Stop Loss: Below the gap bottom (if price completely breaks down through the gap)
▶ Take Profit: Previous swing high or next resistance level above
▶ Probability Enhancers: Gap aligned with major moving averages, oversold RSI, strong bounce candle pattern
• Red Gap Resistance Strategy:
▶ Entry Point: When price approaches the bottom of a red gap and forms a rejection candle
▶ Stop Loss: Above the gap top (if price completely breaks up through the gap)
▶ Take Profit: Previous swing low or next support level below
▶ Probability Enhancers: Gap aligned with major moving averages, overbought RSI, strong rejection candle pattern
■ Advanced Pattern Applications
• Multiple Gap Cluster Identification:
▶ Several gaps in close price proximity form extremely powerful support/resistance zones
▶ Same-color gap clusters: Very strong single-direction reaction zones
▶ Mixed-color gap clusters: High volatility zones with bidirectional reactions expected
• Gap Sequence Analysis:
▶ Consecutive same-direction gaps: Strong trend confirmation signal
▶ Increasing gap size pattern: Trend acceleration signal
▶ Decreasing gap size pattern: Trend weakening signal
• News/Indicator Release Gap Utilization:
▶ Gaps formed immediately after economic indicators: Measure market shock intensity
▶ Gap color change observation: Track market reinterpretation of news
▶ Gap filling speed analysis: Evaluate news impact duration
• Key Attention Points:
▶ Pay special attention to the chart whenever price approaches gap areas
▶ Gap color changes signal important market sentiment shifts
▶ Areas with multiple concentrated gaps are likely to show strong price reactions
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◆ Technical Foundation
■ CME Gap Formation Principles
• Key Gap Formation Scenarios:
▶ Weekend Closures (Friday close → Monday open): Most common CME gap formation point
▶ Holiday Closures: Gaps occurring due to CME closures on US holidays
▶ Weekday 1-hour Maintenance: Gaps during daily CME maintenance period (16:00-17:00 CT)
▶ Major Economic Indicator Releases: Gaps from rapid price changes during US employment reports, FOMC decisions, CPI releases, etc.
▶ Significant News Events: Gaps from regulatory announcements, geopolitical events, market shocks, etc.
• Psychological Importance of Gaps:
▶ Zones where price formation did not occur, representing imbalance between buying/selling forces
▶ Gap areas have no actual trading, resulting in accumulated potential orders
▶ Reflect institutional investor positions and liquidity distribution in the CME futures market
■ Support/Resistance Mechanism
• Psychological Level Formation Mechanism:
▶ Unexecuted order accumulation in gap areas: Loss of ordering opportunity at those price levels
▶ Liquidity imbalance: No trading occurred in gap areas, creating liquidity voids
▶ Institutional activity: Institutional participants in CME futures markets pay attention to these gap areas
• Evidence of Support/Resistance Function:
▶ Statistical gap fill phenomenon: Most gaps eventually "fill" (price returns to gap area)
▶ Gap-based reactions: Increased frequency of price reactions (bounces/rejections) when reaching gap areas
▶ Market psychology impact: Influences traders' perceived value and fair price assessment
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◆ Advanced Configuration Options
■ Visualization Settings
• Show Gap Labels (Default: On)
▶ On: Displays price ranges of each gap numerically for precise support/resistance level identification
▶ Off: Hides labels for visual cleanliness
• Color Settings
▶ Filled Gap Color: Gray tones, shows gaps already traversed by price
▶ Unfilled Gap Color - Support: Blue, shows gaps currently acting as support
▶ Unfilled Gap Color - Resistance: Red, shows gaps currently acting as resistance
■ Data Management Settings
• Filled Gap Storage Limit (Default: 10)
▶ Sets maximum number of filled gaps to retain on chart
▶ Recommended settings: Short-term traders (5-8), Swing traders (8-12), Position traders (10-15)
• Maximum Gap Retention Period (Default: 12 months)
▶ Sets period after which old unfilled gaps are automatically removed
▶ Recommended settings: Short-term analysis (3-6 months), Medium-term analysis (6-12 months), Long-term analysis (12-24 months)
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◆ Synergy with Other Indicators
• Volume Profile: Greatly increased reaction probability when CME gaps align with Volume Profile value areas
• Fibonacci Retracements: Formation of powerful reaction zones when major Fibonacci levels coincide with gap areas
• Moving Averages: Areas where major moving averages overlap with CME gaps act as "composite support/resistance"
• Horizontal Support/Resistance: Very strong price reactions expected when historical key price levels align with CME gaps
• Market Sentiment Indicators (RSI/MACD): Assess reaction probability by checking oversold/overbought conditions when price approaches gap areas
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◆ Conclusion
The 52SIGNAL RECIPE CME Gap Support & Resistance Detector is not merely a gap display tool, but an advanced analytical tool that visualizes important support/resistance areas where price may strongly react, using intuitive color codes (blue=support, red=resistance). It detects all types of gaps without omission, whether from weekend and holiday closures, weekday 1-hour maintenance periods, important economic indicator releases, or market shock situations.
The core value of this indicator lies in clearly expressing through intuitive color coding that gaps are not simple price discontinuities, but psychological support/resistance areas that significantly influence future price action. Traders can instantly identify areas where blue gaps act as support and red gaps act as resistance, enabling quick and effective decision-making.
By referencing the color codes when price approaches gap areas to predict possible price reactions, and especially interpreting color transition moments (blue→red or red→blue) as signals of important market sentiment changes and integrating them into trading strategies, traders can capture higher-probability trading opportunities.
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※ Disclaimer: Like all trading tools, the CME Gap Detector should be used as a supplementary indicator and not relied upon alone for trading decisions. Past gap reaction patterns cannot guarantee the same behavior in the future. Always use appropriate risk management strategies.
═══ 52SIGNAL RECIPE CME Gap Support & Resistance Detector ═══
◆ 개요
52SIGNAL RECIPE CME Gap Support & Resistance Detector는 CME 비트코인 선물 시장에서 발생하는 모든 유형의 가격 갭(Gap)을 자동으로 감지하여 트레이딩 차트에 시각화하는 고급 기술적 지표입니다. 주말과 공휴일 휴장은 물론, 평일 1시간 휴장 시간, 그리고 중요 경제지표 발표나 뉴스 이벤트 시 발생하는 급격한 가격 갭까지 누락 없이 포착합니다.
이 인디케이터의 핵심 가치는 단순히 갭을 표시하는 것을 넘어, 이러한 가격 불연속성이 미래 가격 움직임에 영향을 미치는 강력한 지지(Support)와 저항(Resistance) 영역으로 작용한다는 원리를 시각화하는 데 있습니다. 실제 시장에서 이러한 CME 갭은 높은 확률로 미래에 "매꿔지거나" 중요한 반응 구간으로 기능하여 트레이더에게 귀중한 진입/퇴출 신호를 제공합니다.
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◆ 주요 특징
• 전방위 갭 감지: 모든 시장 조건에서 발생하는 갭을 감지
- 주말/공휴일 휴장 갭
- 평일 1시간 휴장 시간 갭
- 경제지표/뉴스 이벤트 시 급격한 가격 변동 갭
• 직관적 색상 구분:
- 파란색: 갭이 지지 역할을 할 때(가격이 갭 위에 있을 때)
- 빨간색: 갭이 저항 역할을 할 때(가격이 갭 아래에 있을 때)
- 회색: 이미 매꿔진 갭(가격이 갭 영역을 완전히 통과)
• 실시간 역할 전환: 가격이 갭 위/아래로 이동함에 따라 지지↔저항 역할 전환을 자동으로 색상 변경으로 시각화
• 상태 추적 시스템: 갭이 "매꿔짐(Filled)" 또는 "매꿔지지 않음(Unfilled)" 상태를 자동 추적
• 다이나믹 박스: 갭 영역을 명확한 박스로 표시하고 가격 움직임에 따라 동적으로 색상 변경
• 정밀 레이블링: 각 갭의 가격 범위를 정확히 표시하여 트레이더의 의사결정 지원
• 스마트 필터링: 연속적 갭 감지 문제를 해결하는 개선된 알고리즘으로 누락 없는 갭 추적
• 핵심 활용 포인트:
- 가격이 갭 영역에 접근할 때 특별히 주목하세요
- 갭 색상 변경 시점은 중요한 시장 심리 변화 신호입니다
- 여러 갭이 밀집된 영역은 특히 강한 반응이 예상되는 구간입니다
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◆ 사용 가이드: 색상으로 이해하는 갭 역할
■ 색상 시스템 해석법
• 파란색 갭 (지지 역할):
▶ 의미: 현재 가격이 갭 위에 있어 갭이 지지선으로 작용
▶ 트레이딩 응용: 가격이 파란색 갭 영역으로 하락 접근 시 매수 기회 고려
▶ 심리적 의미: 매수세력이 이 가격대에서 수요 증가 가능성
• 빨간색 갭 (저항 역할):
▶ 의미: 현재 가격이 갭 아래에 있어 갭이 저항선으로 작용
▶ 트레이딩 응용: 가격이 빨간색 갭 영역으로 상승 접근 시 매도 기회 고려
▶ 심리적 의미: 매도세력이 이 가격대에서 공급 증가 가능성
• 회색 갭 (매꿔진 갭):
▶ 의미: 가격이 갭 영역을 완전히 통과하여 갭이 매꿔진 상태
▶ 참조 가치: 과거 중요 반응 구간으로 여전히 참고 가치 있음
▶ 트레이딩 응용: 추세 강도 확인 및 주요 심리적 레벨 식별에 활용
■ 색상 전환 이해하기
• 파란색 → 빨간색 전환:
▶ 의미: 가격이 갭 아래로 하락하여 갭이 지지에서 저항으로 역할 변경
▶ 시장 해석: 이전 지지선 붕괴로 약세 신호 강화
▶ 트레이딩 응용: 추가 하락 가능성 고려, 반등 시 갭 하단 저항 확인
• 빨간색 → 파란색 전환:
▶ 의미: 가격이 갭 위로 상승하여 갭이 저항에서 지지로 역할 변경
▶ 시장 해석: 이전 저항선 돌파로 강세 신호 강화
▶ 트레이딩 응용: 추가 상승 가능성 고려, 조정 시 갭 상단 지지 확인
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◆ 실전 활용 가이드
■ 기본 트레이딩 시나리오
• 파란색 갭 지지 전략:
▶ 진입 시점: 가격이 파란색 갭 상단에 접근하여 반등 캔들 형성 시
▶ 손절 위치: 갭 하단 아래(갭 완전히 하향 돌파 시)
▶ 이익실현: 이전 스윙 고점 또는 상방 다음 저항선
▶ 확률 증가 조건: 갭과 주요 이동평균선 일치, 과매도 RSI, 강한 반등 캔들
• 빨간색 갭 저항 전략:
▶ 진입 시점: 가격이 빨간색 갭 하단에 접근하여 거부 캔들 형성 시
▶ 손절 위치: 갭 상단 위(갭 완전히 상향 돌파 시)
▶ 이익실현: 이전 스윙 저점 또는 하방 다음 지지선
▶ 확률 증가 조건: 갭과 주요 이동평균선 일치, 과매수 RSI, 강한 거부 캔들
■ 고급 패턴 활용법
• 다중 갭 클러스터 식별:
▶ 여러 갭이 근접한 가격대에 있다면 더욱 강력한 지지/저항 존
▶ 동일 색상 갭 클러스터: 매우 강력한 단일 방향 반응 구간
▶ 색상 혼합 갭 클러스터: 심한 변동성과 양방향 반응 예상 구간
• 갭 시퀀스 분석:
▶ 연속적인 동일 방향 갭: 강한 추세 확인 신호
▶ 갭 크기 증가 패턴: 추세 가속화 신호
▶ 갭 크기 감소 패턴: 추세 약화 신호
• 뉴스/지표 발표 후 갭 활용:
▶ 경제지표 발표 직후 형성된 갭: 시장 충격 강도 측정
▶ 갭 색상 변화 관찰: 시장의 뉴스 재해석 과정 파악
▶ 갭 매꿈 속도 분석: 뉴스 임팩트의 지속성 평가
• 핵심 주목 포인트:
▶ 가격이 갭 영역에 접근할 때마다 차트를 특별히 주목하세요
▶ 갭 색상이 변경되는 시점은 중요한 시장 심리 변화를 의미합니다
▶ 여러 갭이 밀집된 영역은 가격이 강하게 반응할 가능성이 높습니다
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◆ 기술적 기반
■ CME 갭의 발생 원리
• 주요 갭 발생 상황:
▶ 주말 휴장 (금요일 종가 → 월요일 시가): 가장 일반적인 CME 갭 형성 시점
▶ 공휴일 휴장: 미국 공휴일에 따른 CME 휴장 시 발생
▶ 평일 1시간 휴장: CME 시장의 일일 정비 시간(16:00~17:00 CT) 동안 발생
▶ 주요 경제지표 발표: 미 고용지표, FOMC 결정, CPI 등 발표 시 급격한 가격 변동으로 인한 갭
▶ 중요 뉴스 이벤트: 규제 발표, 지정학적 이벤트, 시장 충격 등으로 인한 급격한 가격 변화
• 갭의 심리적 중요성:
▶ 가격 형성이 이루어지지 않은 구간으로, 매수/매도 세력의 불균형 영역
▶ 갭 구간에는 실제 거래가 없었기 때문에 잠재적 주문이 누적되는 영역
▶ 기관 투자자들의 선물 포지션과 유동성 분포가 반영된 중요한 가격 레벨
■ 지지/저항으로 작용하는 원리
• 심리적 레벨 형성 메커니즘:
▶ 갭 구간의 미실행 주문 축적: 갭 발생 시 해당 가격대에 대한 주문 기회 상실
▶ 유동성 불균형: 갭 구간에는 거래가 없었으므로 유동성 공백 발생
▶ 기관 투자자 활동: CME 선물 시장의 기관 참여자들은 이러한 갭 영역에 관심
• 지지/저항 작용 증거:
▶ 통계적 갭 필 현상: 대부분의 갭은 미래에 "매꿔짐"(가격이 갭 구간으로 회귀)
▶ 갭 기반 반응: 갭 영역에 도달 시 가격 반응(반등/거부) 발생 빈도 증가
▶ 시장 심리 영향: 트레이더들의 인지된 가치와 공정가격 평가에 영향
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◆ 고급 설정 옵션
■ 시각화 설정
• 라벨 표시 설정 (Show Gap Labels) (기본값: 켜짐)
▶ 켜짐: 각 갭의 가격 범위를 숫자로 표시하여 정확한 지지/저항 레벨 확인
▶ 꺼짐: 시각적 깔끔함을 위해 라벨 숨김
• 색상 설정
▶ 매꿔진 갭 색상(Filled Gap Color): 회색 계열, 이미 가격이 통과한 갭 표시
▶ 미매꿔진 갭 색상 - 지지(Support): 파란색, 현재 지지 역할을 하는 갭
▶ 미매꿔진 갭 색상 - 저항(Resistance): 빨간색, 현재 저항 역할을 하는 갭
■ 데이터 관리 설정
• 매꿔진 갭 저장 한도 (Filled Gap Storage Limit) (기본값: 10)
▶ 이미 매꿔진 갭을 최대 몇 개까지 차트에 유지할지 설정
▶ 권장 설정: 단기 트레이더(5-8), 스윙 트레이더(8-12), 포지션 트레이더(10-15)
• 최대 갭 보관 기간 (Maximum Gap Retention Period) (기본값: 12개월)
▶ 오래된 미매꿔진 갭을 자동으로 제거하는 기간 설정
▶ 권장 설정: 단기 분석(3-6개월), 중기 분석(6-12개월), 장기 분석(12-24개월)
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◆ 다른 지표와의 시너지
• 볼륨 프로파일: CME 갭과 볼륨 프로파일의 밸류 영역 일치 시 반응 확률 크게 증가
• 피보나치 리트레이스먼트: 주요 피보나치 레벨과 갭 영역 일치 시 강력한 반응 존 형성
• 이동평균선: 주요 이동평균선과 CME 갭이 겹치는 영역은 "복합 지지/저항"으로 작용
• 수평 지지/저항: 과거 중요 가격대와 CME 갭 일치 시 매우 강력한 가격 반응 예상 가능
• 시장 심리 지표(RSI/MACD): 갭 영역 접근 시 과매수/과매도 확인으로 반응 가능성 판단
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◆ 결론
52SIGNAL RECIPE CME Gap Support & Resistance Detector는 단순한 갭 표시 도구가 아닌, 가격이 강하게 반응할 수 있는 중요한 지지/저항 영역을 직관적인 색상 코드(파란색=지지, 빨간색=저항)로 시각화하는 고급 분석 도구입니다. 주말과 공휴일 휴장 시간뿐만 아니라, 평일 1시간 휴장 시간, 중요 경제지표 발표, 그리고 시장 충격 상황에서 발생하는 모든 유형의 갭을 누락 없이 감지합니다.
인디케이터의 핵심 가치는 갭이 단순한 가격 불연속성이 아닌, 미래 가격 행동에 중요한 영향을 미치는 심리적 지지/저항 영역임을 직관적인 색상 코드로 명확히 표현하는 데 있습니다. 파란색 갭은 지지 역할을, 빨간색 갭은 저항 역할을 하는 영역을 즉각적으로 식별할 수 있어 트레이더가 빠르고 효과적인 의사결정을 내릴 수 있도록 도와줍니다.
갭 영역에 접근할 때마다 색상 코드를 참고하여 가능한 가격 반응을 예측하고, 특히 색상 전환이 일어나는 순간(파란색→빨간색 또는 빨간색→파란색)은 중요한 시장 심리 변화 신호로 해석하여 트레이딩 전략에 통합한다면, 더 높은 확률의 거래 기회를 포착할 수 있을 것입니다.
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※ 면책 조항: 모든 트레이딩 도구와 마찬가지로, CME Gap Detector는 보조 지표로 사용되어야 하며 단독으로 거래 결정을 내리는 데 사용해서는 안 됩니다. 과거의 갭 반응 패턴이 미래에도 동일하게 작용한다고 보장할 수 없습니다. 항상 적절한 리스크 관리 전략을 사용하세요.
Break and Retest High Probability StrategyWhat does the script Do:
Script uses Break and Retest strategy on Key Levels like PMH, PDH, PMH, PML and ORB levels. Based on the strength of the candle at these key levels a position is taken and based on Dynamic stop loss, we scale out of the position at key levels. Scale out can also happen based on the QQQ trend.
How it does it:
First the script identifies No Trade Zone - which is higher of PMH and PDH for Highest position of No Trade Zone, and lower of PML and PDL. Any trades within this doesnt take any Trade entries.
Entries are taken in only Regular Trading Hours.
Candle strength is constantly tracked for break out these levels and then wait for retest levels based on Volatality on that day with ATR levels. If it fails to come back to retest - it is ignored else at retest levels strength of the bar is tracked. Scaling out can be done based on various Input parameters given in the strategy. VWAP and 9 EMA is also tracked for taking an entry or not.
How to use it:
Make sure to use various parameters within Inputs like Candle Strength at vwap, QQQ confluence to tweak and see what works best for the time frame and stock.
In the Multi Time frame construct - if you are on 5min time frame the candle stregth can be tracked in lower time frame which can be 1, 2, 3 min etc. This is also configurable within the Inputs.
Make sure to use the levels and values displayed in the table to see real time data.
Also, You can just have the Long entry, Short Entry and Plot variables selected in Style section to declutter the chart. Feel free to reuse the chart
what makes it original.
Strategy Parameters
• Is representative of real world trading conditions.
Break and Retest at key levels while following various confluence set ups makes it completely real world and battle tested indicator. All the parameters used within Inputs and Style are completely known Market variables.
• Is compatible with the markets their strategy is written for.
This is best for doing scalping where momentum and volatality is the king.
• Produces realistic results.
Like any strategy nothing is 100% guaranteed. But the key is to monitor the Profit factor and exit at right positions even if it means lesser number of trades.
This strategy is tested against lot of Tech stocks like nvidia, tesla, amazon against QQQ confluence.
. to help traders interpret the results they publish with their strategy,
Please feel free to tweak the parameters to tweak the strategy and see what works best for the stock you are placing this indicator on.
I primarily take the default parameters of this strategy to do scalping. The Multitime frame restest ( which goes to lower timeframe to check the strength of bars - which is again configurable by Fixed Retest bars and Retest Time Frame. I would recommend you to use Enable candle pattern filter to further refine the trades to be high probability.
This is a high probability set up - so please dont expect many trades from each stock. The strategy only gets triggered when it sees valid signal as per parameters set on the strategy.
HTF Candle Extremes Zigzag (Drawn on LTF)HTF Candle Extremes Zigzag (Drawn on LTF)
This indicator plots zigzag lines connecting the extremes (highs and lows) of Higher Timeframe (HTF) candles directly on your lower timeframe (LTF) chart. It visually highlights trend changes and HTF candle structure by drawing colored lines representing uptrends and downtrends based on HTF candle extremes.
"Key Features"
Higher Timeframe Tracking: Select any HTF to track candle extremes using the built-in security function.
Zigzag Lines: Connects HTF candle lows to highs in an intuitive zigzag pattern.
Trend Indication: Uptrend lines are green, downtrend lines are red (customizable colors).
Customizable Line Width: Adjust the thickness of the zigzag lines for better visibility.
Drawn on Lower Timeframe: All lines appear on your active lower timeframe chart, allowing easy visual correlation.
"How It Works"
The script fetches the open, high, low, close, and time data of the specified HTF candle. It detects new HTF bars and identifies trend direction changes by comparing the highs and lows of consecutive HTF candles.
- When an uptrend is detected, vertical lines are drawn from low to high of the HTF candle, connected to the previous extreme low.
- When a downtrend is detected, vertical lines are drawn from high to low, connected to the previous extreme high.
- Transitions between trends are highlighted by connecting the last extreme of the previous trend to the current extreme, creating a clean zigzag pattern.
Usage Notes:
Ideal for traders who want to visualize HTF market structure and trend changes while analyzing price action on lower timeframes.
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