GKD-M Baseline Optimizer [Loxx]Giga Kaleidoscope GKD-M Baseline Optimizer is a Metamorphosis module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
The Baseline Optimizer enables traders to backtest over 60 moving averages using variable period inputs. It then exports the baseline with the highest cumulative win rate per candle to any baseline-enabled GKD backtest. To perform the backtesting, the trader selects an initial period input (default is 60) and a skip value that increments the initial period input up to seven times. For instance, if a skip value of 5 is chosen, the Baseline Optimizer will run the backtest for the selected moving average on periods such as 60, 65, 70, 75, and so on, up to 90. If the user selects an initial period input of 45 and a skip value of 2, the Baseline Optimizer will conduct backtests for the chosen moving average on periods like 45, 47, 49, 51, and so forth, up to 57.
The Baseline Optimizer provides a table displaying the output of the backtests for a specified date range. The table output represents the cumulative win rate for the given date range.
On the Metamorphosis side of the Baseline Optimizer, a cumulative backtest is calculated for each candle within the date range. This means that each candle may exhibit a different distribution of period inputs with the highest win rate for a particular moving average. The Baseline Optimizer identifies the period input combination with the highest win rates for long and short positions and creates a win-rate adaptive long and short moving average chart. The moving average used for shorts differs from the moving average used for longs, and the moving average for each candle may vary from any other candle. This customized baseline can then be exported to all baseline-enabled GKD backtests.
The backtest employed in the Baseline Optimizer is a Solo Confirmation Simple, allowing only one take profit and one stop loss to be set.
Lastly, the Baseline Optimizer incorporates Goldie Locks Zone filtering, which can be utilized for signal generation in advanced GKD backtests.
█ Moving Averages included in the Baseline Optimizer
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
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
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
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.
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
The Goldie Locks Zone volatility filter is the standard first-pass filter used in all advanced GKD backtests (Complex, Super Complex, and Full GKd). This filter requires the price to fall within a range determined by multiples of volatility. The Goldie Locks Zone is separate from the core Baseline and utilizes its own moving average with Loxx's Exotic Source Types you can read about below.
On the chart, you will find green and red dots positioned at the top, indicating whether a candle qualifies for a long or short trade respectively. Additionally, green and red triangles are located at the bottom of the chart, signifying whether the trigger has crossed up or down and qualifies within the Goldie Locks zone. The Goldie Locks zone is represented by a white color on the mean line, indicating low volatility levels that are not suitable for trading.
█ Volatility Types Included in the Baseline Optimizer
The GKD system utilizes volatility-based take profits and stop losses. Each take profit and stop loss is calculated as a multiple of volatility. Users can also adjust the multiplier values in the settings.
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.
█ Loxx's Expanded Source Types Included in Baseline Optimizer
This indicator allows you to select from 33 source types. They are as follows:
Close
Open
High
Low
Median
Typical
Weighted
Average
Average Median Body
Trend Biased
Trend Biased (Extreme)
HA Close
HA Open
HA High
HA Low
HA Median
HA Typical
HA Weighted
HA Average
HA Average Median Body
HA Trend Biased
HA Trend Biased (Extreme)
HAB Close
HAB Open
HAB High
HAB Low
HAB Median
HAB Typical
HAB Weighted
HAB Average
HAB Average Median Body
HAB Trend Biased
HAB Trend Biased (Extreme)
What are Heiken Ashi "better" candles?
Heiken Ashi "better" candles are a modified version of the standard Heiken Ashi candles, which are a popular charting technique used in technical analysis. Heiken Ashi candles help traders identify trends and potential reversal points by smoothing out price data and reducing market noise. The "better formula" was proposed by Sebastian Schmidt in an article published by BNP Paribas in Warrants & Zertifikate, a German magazine, in August 2004. The aim of this formula is to further improve the smoothing of the Heiken Ashi chart and enhance its effectiveness in identifying trends and reversals.
Standard Heiken Ashi candles are calculated using the following formulas:
Heiken Ashi Close = (Open + High + Low + Close) / 4
Heiken Ashi Open = (Previous Heiken Ashi Open + Previous Heiken Ashi Close) / 2
Heiken Ashi High = Max (High, Heiken Ashi Open, Heiken Ashi Close)
Heiken Ashi Low = Min (Low, Heiken Ashi Open, Heiken Ashi Close)
The "better formula" modifies the standard Heiken Ashi calculation by incorporating additional smoothing, which can help reduce noise and make it easier to identify trends and reversals. The modified formulas for Heiken Ashi "better" candles are as follows:
Better Heiken Ashi Close = (Open + High + Low + Close) / 4
Better Heiken Ashi Open = (Previous Better Heiken Ashi Open + Previous Better Heiken Ashi Close) / 2
Better Heiken Ashi High = Max (High, Better Heiken Ashi Open, Better Heiken Ashi Close)
Better Heiken Ashi Low = Min (Low, Better Heiken Ashi Open, Better Heiken Ashi Close)
Smoothing Factor = 2 / (N + 1), where N is the chosen period for smoothing
Smoothed Better Heiken Ashi Open = (Better Heiken Ashi Open * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Open * (1 - Smoothing Factor))
Smoothed Better Heiken Ashi Close = (Better Heiken Ashi Close * Smoothing Factor) + (Previous Smoothed Better Heiken Ashi Close * (1 - Smoothing Factor))
The smoothed Better Heiken Ashi Open and Close values are then used to calculate the smoothed Better Heiken Ashi High and Low values, resulting in "better" candles that provide a clearer representation of the market trend and potential reversal points.
Heiken Ashi "better" candles, as mentioned previously, provide a clearer representation of market trends and potential reversal points by reducing noise and smoothing out price data. When using these candles in conjunction with other technical analysis tools and indicators, traders can gain valuable insights into market behavior and make more informed decisions.
To effectively use Heiken Ashi "better" candles in your trading strategy, consider the following tips:
-Trend Identification: Heiken Ashi "better" candles can help you identify the prevailing trend in the market. When the majority of the candles are green (or another color, depending on your chart settings) and there are no or few lower wicks, it may indicate a strong uptrend. Conversely, when the majority of the candles are red (or another color) and there are no or few upper wicks, it may signal a strong downtrend.
-Trend Reversals: Look for potential trend reversals when a change in the color of the candles occurs, especially when accompanied by longer wicks. For example, if a green candle with a long lower wick is followed by a red candle, it could indicate a bearish reversal. Similarly, a red candle with a long upper wick followed by a green candle may suggest a bullish reversal.
-Support and Resistance: You can use Heiken Ashi "better" candles to identify potential support and resistance levels. When the candles are consistently moving in one direction and then suddenly change color with longer wicks, it could indicate the presence of a support or resistance level.
-Stop-Loss and Take-Profit: Using Heiken Ashi "better" candles can help you manage risk by determining optimal stop-loss and take-profit levels. For instance, you can place your stop-loss below the low of the most recent green candle in an uptrend or above the high of the most recent red candle in a downtrend.
-Confirming Signals: Heiken Ashi "better" candles should be used in conjunction with other technical indicators, such as moving averages, oscillators, or chart patterns, to confirm signals and improve the accuracy of your analysis.
In this implementation, you have the choice of AMA, KAMA, or T3 smoothing. These are as follows:
Kaufman Adaptive Moving Average (KAMA)
The Kaufman Adaptive Moving Average (KAMA) is a type of adaptive moving average used in technical analysis to smooth out price fluctuations and identify trends. The KAMA adjusts its smoothing factor based on the market's volatility, making it more responsive in volatile markets and smoother in calm markets. The KAMA is calculated using three different efficiency ratios that determine the appropriate smoothing factor for the current market conditions. These ratios are based on the noise level of the market, the speed at which the market is moving, and the length of the moving average. The KAMA is a popular choice among traders who prefer to use adaptive indicators to identify trends and potential reversals.
Adaptive Moving Average
The Adaptive Moving Average (AMA) is a type of moving average that adjusts its sensitivity to price movements based on market conditions. It uses a ratio between the current price and the highest and lowest prices over a certain lookback period to determine its level of smoothing. The AMA can help reduce lag and increase responsiveness to changes in trend direction, making it useful for traders who want to follow trends while avoiding false signals. The AMA is calculated by multiplying a smoothing constant with the difference between the current price and the previous AMA value, then adding the result to the previous AMA value.
T3
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.
█ 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: Full GKD Backtest
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Kase Peak Oscillator
Confirmation 2: uf2018
Continuation: Vortex
Exit: Rex Oscillator
Metamorphosis: Baseline Optimizer as shown on the chart above
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:
Cerca negli script per "trendline"
Kitchen [ilovealgotrading]
OVERVIEW:
Kitchen is a strategy that aims to trade in the direction of the trend by using supertrend and stochRsi data by calculating at different time values.
IMPLEMENTATION DETAILS – SETTINGS:
First of all, let's understand the supertrend and stocrsi indicators.
How do you read and use Super Trend for trading ?
The price is often going upwards when it breaks the super trend line while keeping its position above the indication level.
When the market is in a bullish trend, the indicator becomes green. The indicator level will act as trendline support in such a scenario. The color of the indicator changes to red to indicate a negative trend once the price crosses the support line. The price uses the super trend level as a trendline resistance during a bearish move.
In our strategy, if our 1-hour and 4-hour supertrend lines show the up or down train in the same direction at the same time, we can assume that a train is forming here.
Why do I use the time of 1 hour and 4 hours ?
When I did a backtest from the past to the present, I discovered that the most accurate and consistent time zones are the 1 hour and 4 hour time zones.
By the way we can change our short term timeframe(1H) and long term timeframe(4H) from settings panel.
How do you read and use the Stoch-RSI Indicator?
This indicator analyzes price dynamics automatically to detect overbought and oversold locations.
The indicator includes:
- The primary line, which typically has values between 0 and 100;
- Two dynamic levels for overbought and oversold conditions.
IF our stoch-rsi indicator value has fallen below our lower boundary line, the oversold event has been observed in the price, if our stoch-rsi value breaks up our bottom line after becoming oversold, we think that the price will start the recovery phase.(The case is also true for the opposite.)
However, this does not always apply and we need additional approvals, Therefore, our 1H and 4H supertrrend indicator provides us with additional confirmation.
Buy Condition:
Our 1H(short term) and 4H(long term) supertrrend indicator, has given the buy signal(green line and yellow line), and if our stochrsi indicator has broken our oversold line up on the past 15 bars, the buy signal is formed here.
Sell Condition:
Our 1H(short term) and 4H(long term) supertrrend indicator, has given the sell signal(red line and orange line), and if our stochrsi indicator has broken our overbuy line down on the past 15 bars, the sell signal is formed here.
Stop Loss or Take Profit Conditions:
Exit Long Senerio:
All conditions are completed, the buy signal has arrived and we have entered a LONG trade, the 1-hour supertrend line follows the price rise(yellow line), if the price breaks below the 1-hour super trend line and a sell condition occurs for 1H timeframe for supertrend indcator, LONG trade will exit here.
Exit Short Senerio:
All conditions are completed, the Sell signal has arrived and we have entered a SHORT trade, the 1-hour supertrend line follows the price down(orange line), if the price breaks up the 1-hour super trend line and a buy condition occurs for 1H timeframe for supertrend indcator, SHORT trade will exit here.
What can you change in the settings panel?
1-We can set Start and End date for backtest and future alarms
2-We can set ATR length and Factor for supertrend indicator
3-We can set our short term and long term timeframe value
4-We can set StochRsi Up and Low limit to confirm buy and sell conditions
5-We can set stochrsi retroactive approval length
6-We can set stochrsi values or the length
7-We can set Dollar cost for per position
8- We can choose the direction of our positions, we can set only LONG, only SHORT or both directions.
9-IF you want to place automatic buy and sell orders with this strategy, you can paste your codes into the Long open-close or Short open-close message sections.
For example
IF you write your alert window this code {{strategy.order.alert_message}}.
When trigger Long signal you will get dynamically what you pasted here for Long Open Message
ALSO:
Please do not open trades without properly managing your risk and psychology!!!
If you have any ideas what to add to my work to add more sources or make calculations cooler, suggest in DM .
LT Elliott Wave 2.0LT Elliott Waves 2.0 Indicator:
According to Elliott Wave Theory, price moves in 5 waves in the direction of the major trend and moves in 3 waves (ABC) when it moves against the major trend. The key purpose and value of elliott wave theory (EWT) is to provide context for chart analysis. According to the book The Elliott Wave Principle by Frost & Prechter: “This context provides both a basis for disciplined thinking and a perspective on the market's general position and outlook.” The benefit of having context is that one can identify and anticipate changes in direction.
In Elliott Wave theory, waves 1, 3, 5 and C are impulse waves (a five wave pattern that makes progress) whereas waves 2, 4, A and B are corrective waves (a three wave pattern – or combination of three waves - that moves against the direction of the larger trend). Although wave A can also be formed of 5 waves, it is commonly formed of 3 waves. Here is a brief summary of the waves:
Wave 3 tends to be the strongest and most dynamic wave – it is usually (but not always) the longest wave but it is never the shortest. Wave 4 is a corrective wave that is typically composed of 3 smaller waves (ABC) and is notorious for being messy and unpredictable in nature. Wave 5 is the final wave before a significant correction or reversal in trend and is often accompanied by divergences (e.g. negative divergences in an uptrend) and exhaustions in momentum. It is also possible for a wave 5 to form after a “blow-off top” pattern. Wave 2 is composed of 3 smaller waves (ABC) and is a retracement of wave 1 – the retracement can be shallow to moderate (23.6% to 38.2%) or deep (50%, 61.8% to 78.6%). Wave 1 is the first wave of a trend and is composed of 5 smaller waves – it usually occurs after divergences (in the prior move) and extremes in both sentiment and momentum. For example, the wave 1 of an uptrend can often begin after capitulation in the price (after a major decline), extremely pessimistic sentiment, extremely oversold momentum readings, positive divergences and sometimes accompanied by a volume breadth thrust. Waves A and C are often equal in measure. Wave A can be formed of either 5 waves or 3 waves - but more commonly it is composed of 3 waves. Wave B is always corrective and composed of 3 smaller waves. Wave C is a five wave impulse pattern.
The Elliott Wave 2.0 (or EW2) chart indicator seeks to simplify elliott wave theory (EWT) in that its main purpose is to identify the potential major trends and corrections. The indicator takes a more simple and direct approach to EWT in that it focuses more on trying to identify whether price is trending or not, and if there is a trend, then the probable wave pattern. It does this by mainly using the structure of the price chart as well as other factors such as momentum, fibonacci retracements & extensions and the relationship of price to its key averages. The indicator then takes its best guess at whether price is in a trending environment, and if so, which wave it is probably forming. This means that the wave count can often depend on the chart timeframe chosen. For example, what may appear as a major downtrend on a lower timeframe chart may potentially be a corrective drop on a much higher timeframe, due to the different price structure of the charts. To keep things simple and to avoid complexity, the indicator does not display the minor sub-waves within the major waves.
The main feature and benefit of the Elliott Wave 2.0 (EW2) indicator is that it can remove most of the subjectivity in chart and wave analysis. It also allows for flexibility in that it allows the chartist to alter the wave count and the position of the wave counts if they choose to do so (within the parameters and rules set by the indicator). As with all of technical analysis, the wave counts shown by the elliott wave indicator are NOT certain or absolute – they are only a possibility or a probability. So the risk always exists of an alternative wave count. It is for the chartist to determine the probable wave counts and limit or control the risks based on their knowledge of technical analysis and risk management.
The settings of the Elliott Wave 2.0 indicator (EW2) are fairly self-explanatory but here is a brief summary:
In the Trend Analysis Switch, the indicator is set by default to a “moderate” trend setting in that it waits for moderately significant changes in momentum before a probable wave 5 is shown (i.e. the fifth wave within the elliott five wave pattern). So for example, in an uptrend, the indicator may show a probable “wave 3” (a blue-coloured wave 3) if the path of least resistance and the likely trend is still to the upside. Once a change in momentum and trend direction occurs, then the indicator may change the wave count to a “wave 5” (a green-coloured wave 5) provided the parameters for this wave count have been met. This default “moderate” setting can be changed by the user or chartist. So if the user wants to change the wave count from a probable “wave 5” to a potential “wave 3”, then it may be possible to do so by changing the trend analysis switch from “moderate” to “strict”. The indicator will likely then display a “wave 3” count until the price reverses some more and breaks below a key support level (assuming the prior trend was up), thus changing the wave count from a “wave 3” (in blue) to a probable “wave 5” (in green). (The opposite of this example applies in downtrends.)
If the chartist decides to delay the changing of the wave count, such as delaying the change of wave 3 to a wave 5, then the “strict” option can be enabled in the trend analysis switch. If the user prefers a slightly more aggressive (or quicker) change in the trend and wave count, then the “aggressive” option can be applied. This is provided the chartist decides it is reasonable or “logical” to make the change. The EW2 indicator will only make the change IF doing so is allowed within its set parameters and rules. The trend analysis switch settings (moderate, aggressive and strict) are largely based on the relative position of price to certain key averages and crossovers, such as both short-term & long-term moving averages which can act as support and resistance.
It is important to mention here is that if any changes are made to the settings of the EW2 indicator (such as moving or modifying the wave counts), it is essential to click on “Reset settings” when changing the chart to a different symbol or timeframe. So whenever a new chart symbol or timeframe is chosen, it is recommended to apply the “reset settings” function in the indicator defaults at the bottom left of the settings section. This will refresh the settings of the indicator back to defaults.
The Elliott Wave 2.0 indicator has greater flexibility options within the settings to change the positions of certain wave counts based on the structure of the chart. This can be achieved by manually moving the wave counts in the first top-section of the EW2 settings, where it says “Move Wave Forward/Back”. By clicking the up or down arrows (on the box provided) the position of the wave count can be moved, based on the zigzag patterns of the chart. So for example, if we wanted to move the position of the historical wave 5 (shown in green on the chart), then we would hover over the box next to “Move hist wave 5 forward/back” and click up or down on the arrows. Clicking the UP arrow would move the wave count position forward on the chart while clicking the DOWN arrow would move it backward. The same can be done with other waves such as the positions of waves 1, 2, 3 and 4 – provided this is permitted by the structure on the chart and the rules set by the indicator.
In the elliott wave indicator, the potential major wave counts are shown in blue and sometimes yellow. The blue wave counts have a slightly higher probability than the yellow as the yellow will need further confirmation by the price structure and momentum.
The starting point for the wave counts is shown as a green “wave 5” – this is referred to as the “historical wave 5” as it is the likely fifth wave of the prior wave (e.g. a prior impulse or corrective wave). The historical wave 5 is the starting point where the indicator starts “counting” the waves. The indicator makes its best guess as to where to start counting from the historical wave 5, but the user has the option to change its position, if required as per the parameters set by the indicator. As a general rule, in an uptrend, the green historical wave 5 should ideally be positioned at the lowest point on the chart (such as the lowest point in the past 300 bars). The opposite applies in downtrends where the historical wave 5 should ideally be at the highest point on the chart (e.g. in the past 300 bars). It should be noted that when the position of the green historical wave 5 is changed, this usually affects the entire wave count. The position of the historical wave 5 (green) can be changed in the settings of the elliott wave indicator (as discussed above). Additionally, if needed, we can also change the label of the green historical wave 5 within the settings to a pink “C-wave” (i.e. the “C-wave” of the prior corrective wave).
As mentioned, the EW2 indicator does its best to make the optimal “guess” as to the probable position of the wave counts, using the structure and momentum on the chart. However, just like any other chart indicator, it is not perfect and it can get the position wrong at times. This is to be expected as we are dealing with an uncertain, chaotic and probabilistic environment when doing chart analysis. Therefore, where it is deemed suitable, the position of the waves - such as waves 1, 2, 3, 4 and the historical wave 5 (or “C-wave”) – can be changed in the settings. The “wider jumps” option can be enabled in the settings for bigger skips in the position of the waves when toggling the positions using the up and down arrows.
The position of most waves can also be altered by modifying the major structure (in the zigzag) using the option in the settings called “Modify major structure”. Please note that using this can sometimes change the wave count as well. This specific setting provides a drop-down menu (labelled A to F) that allows several structures to be chosen in the zigzag (within certain limits). However, in the majority of cases, only the first four options (A, B, C and D) will be required to change the structure of the zigzag. Options C and D provide the greatest variability in the zigzag structure.
One useful method to remember is that often the most effective way to modify the wave count is to adjust the positions of waves 1 and 3 (assuming the starting position of the historical green wave 5 has been decided). As long as we can place wave 1 (and by default wave 2) to a reasonable or “logical” level, the remaining wave counts and projections can usually take care of themselves. Adjusting the position of wave 3 to a logical position can also be useful in this respect. A good way to correctly determine the “wave 1” and “wave 3” of an impulse is to look at its internal structure. If both are composed of five sub-waves (i.e. if each have an internal structure of five smaller waves) then the probability is higher that we have identified the waves 1 and 3 correctly, as both waves are impulsive. The same rule can apply for wave 5. Another rule to remember is that wave 3 can never be the shortest impulse wave but it is often the longest.
One of the new and major features of the Elliott Wave 2.0 indicator are the wave 3, wave 4 and wave 5 projections. The indicator uses a number of fibonacci extensions and ratios of certain waves (e.g. waves 1 and 2) to calculate the probable wave 3 projections as well as the potential wave 5 projections. The wave 3 projections are labelled as “3” and are shown as blue horizontal lines. Wave 5 projections are labelled as “5” and are shown as cyan, green, brown or purple lines. EW2 also makes use of mainly fibonacci retracements (such as the golden ratio) for calculating the probable wave 4 projections. Wave 4 projections are shown as “4” in orange, dark blue and red. Wave 4 has a number of alternate settings which make use of RSI momentum and fibonacci levels. The alternate settings for wave 4 can be used if the user believes that wave 3 has completed and that a wave 4 correction is likely in progress or coming to an end. The setting “Wave 5 in progress” can also be used for this purpose, if the chartist believes that the wave 4 has likely completed (or coming to an end) and that a potential “wave 5” is taking shape.
The “Logarithmic fibs” setting is an option for certain charts and timeframes that require a logarithmic chart to be used. For example, higher timeframe charts (such as weekly or monthly) and very volatile price charts may benefit from a logarithmic setting and therefore logarithmic fibonacci levels. Generally, if the chartist is using a log chart for a specific symbol (or timeframe), it would be preferable to apply logarithmic fibonacci levels as well. So this function can be selected in the EW2 settings accordingly.
While most wave projections (such as waves 3, 4 and 5) will show automatically on the chart, the user can decide to remove certain projections (e.g. waves 4 and 5) to reduce the amount of text or lines on a chart. This can be done by selecting the specific “Hide wave projections” function in the settings for the waves 4 and 5. Extra projections for waves 3, 4 or 5 can be shown by selecting the option for “More projections” for each specific wave.
Another useful feature is the “Wave 3 probability decrease level” function. This draws a horizontal magenta line at a specific fibonacci extension which can act as a key level of support (or resistance) for a probable wave 3. This could be helpful when a new trend is starting and we have the beginning of what appears to be a wave 3. For example, in a new uptrend, the probable wave 3 would have to stay above this key level (shown as a magenta line) if the probability of a wave 3 is going to remain high or intact. In other words, if price were to drop below this key level (in an uptrend) then the odds of a wave 3 would be lowered significantly and downside risk could increase. The opposite applies in downtrends.
The lookback period can be decreased to make the EW2 indicator focus on the much more recent data, such as the previous 100 bars. This can be done by selecting “Use short term” in the settings. This function can be used in situations where the trend may have changed very suddenly and the user wants to focus on the more immediate chart structure.
The setting also provides an “Aggressive wave 3” option for situations that may require the wave 3 to be shown sooner, such as at the start of a new trend. This option as well as others are included for further flexibility in the wave count. When this “aggressive wave 3” option enabled, the EW2 will display a yellow “wave 3” provided the conditions have been met based on fibonacci extensions.
As mentioned, the elliott wave indicator is programmed to look for and identify potential trending or impulsive patterns, and when appropriate, corrective ABC patterns. In this sense, we are looking to simplify elliott wave theory by taking a more flexible and common-sense approach to the wave patterns. So if the price action has broken key levels of support or resistance, momentum is increasing and price is moving deliberately in a specific direction, it becomes more likely that price is in a trending environment (rather than just a correction). Therefore, the EW2 indicator will likely start by showing the initial impulsive waves 1, 2 and 3 (in blue or blue/yellow) instead of the corrective waves ABC (in pink). However, the user has the choice to change the waves from 123 to ABC by selecting “ABC corrective waves” in the settings.
The EW2 indicator also allows the option to “reverse” the probable trend (and wave count) if required. For example, if the EW2 indicator is showing a probable wave 3 or wave 5 (in blue) and price begins to pullback or move in the opposite direction to the main trend of the wave 3 or 5 – e.g. if price starts to break key support levels (e.g. after an uptrend) and then reverse lower in the opposite direction to the primary trend - then the user has the option to change the wave count in the opposite direction (e.g. downward) a bit quicker. This can be achieved by selecting the option in the settings called “Reverse probable trend”. Applying this setting will reverse the original wave count of the primary trend as set by the indicator (e.g. from up to down or vice versa) provided it is permitted by the rules of the indicator. The colours of the wave counts will change to grey instead of blue. The user can also choose where to manually place the historical wave 5 (if required). However, although this “reverse” option is provided for extra flexibility, it should NOT be used very often. It should only be applied for certain special circumstances where it is deemed appropriate to change the wave count from an uptrend to a downtrend (or vice versa). The EW2 indicator does a reasonably good job on its own of identifying most trend changes without the need for this special “reverse trend” setting.
The chartist can apply other methods of chart analysis – such as trendline breaks, oscillators, regression channels, breaks of support/resistance – to determine when a probable wave (or wave count) has likely completed. For example, technical analysis methods such as trendline breaks and support/resistance breaks can be used by the chartist to determine the probability of whether wave 4 has potentially completed or not. In an uptrend, confirmation that a probable wave such as wave 4 has completed will not come until price has taken out the highs prior to the decline (i.e. the highs before the pullback in the probable “wave 4” correction). The same applies in reverse for a downtrend: confirmation that the probable wave 4 has completed will not come until price has taken out the lows prior to the rally (in a probable wave 4 correction).
It should be remembered that the appearance of the most recent wave counts (or wave labels) shown by the indicator, by themselves do NOT mean that the specific waves in question have definitely completed or finished. The same applies with wave projections as they do NOT imply that price has to necessarily move to those specific projection levels. They are merely to provide helpful guidance and education in chart analysis. Nothing in chart analysis is certain or definite as we are dealing with a system that is chaotic, unpredictable and probabilistic. The wave label itself is simply an indication that the most recent wave is probably still in progress, not necessarily that it has completed. Chartists can apply other technical analysis tools and methods (e.g. trend lines, support/resistance breaks, moving averages and regression channels etc.) to increase the probability of when a specific wave has probably completed. The same also applies to past or “completed” wave counts (or past wave labels): they do NOT mean that the specific waves have definitely completed or finished – it is merely a possibility or probability. So the risk always exists that the wave counts may potentially be wrong, and that an alternative wave count interpretation may exist.
In certain circumstances where there are volatile conditions and charts, it is possible that the elliott wave indicator may show an “unusual” wave count. For example, it is possible that the positions of certain wave counts (such as waves 1, 2, 3 and 5) may be in the “wrong” order. This happens rarely so it is not an issue that happens very often. However, if this issue occurs, the chartist can rectify the matter by applying one of the following options: (a) moving and adjusting the position of the historical wave 5 (in green) to a more logical position, (b) applying the “use short term” setting or (c) wait a bit longer until the volatility resolves itself in time. These options can usually resolve the issue and show the wave counts in a “proper” manner. Changing to a slightly lower (or higher) timeframe can also usually resolve this issue. If any changes are made to the settings of the indicator, please reset the indicator settings back to “default” when changing to a different symbol or timeframe.
The user can also choose to enable the zigzags of the waves to be shown on the chart. This can display the wave structures and zigzags, if enabled. By default it is set to off. As mentioned previously, it may also be a good idea to reset the settings of the indicators whenever a new chart or timeframe is chosen. This then refreshes the settings back to its default.
It is important to appreciate that the elliott wave indicator generally requires between 1,500 to 2000 bars of data on the chart in order to display the wave counts adequately and appropriately. So if a chart or timeframe has less than the minimum number of historical data or bars on the chart, the wave counts may not display properly or not appear at all. Certain chart symbols and timeframes (such as the monthly timeframe) may have very limited amount of data on them. Therefore, the elliott wave indicator will likely not appear on these charts or may not display properly. In these situations, a different chart symbol or a lower timeframe with more data on it can be chosen. For example, instead of a monthly timeframe, a weekly or daily timeframe can be chosen. Similarly, if a “study error” message appears on the EW2 indicator, this can be remedied by switching to a slightly lower (or higher) timeframe. However, usually such study errors are temporary and often get resolved after a brief time.
We have allowed for further flexibility in the EW2 indicator so that the user can move the wave counts manually, if required. The chartist can manually move the position of a wave count to a specific bar (or candle) on the chart if they choose to do so. For example, if we want to move the position of wave 1 to a specific bar in the past, we would first tick the box in the indicator settings called “Manually Place Wave 1”. Then we would use the “date range” tool to find out the distance of that past bar from the current bar (e.g. 50 bars) and then input that number (50) into the box next to “Manually Place Wave 1”.
Price action, markets and their charts are non-linear and chaotic, which means that they are subject to uncertainty, variable change and being unpredictable in nature. So we must maintain a probabilistic mindset and attitude to technical analysis. Nothing is certain. Therefore, no wave count is certain or “set in stone”. Wave counts, just like the actions and emotions of human beings, are subject to change. Elliott Wave theory, just like all of technical analysis is about what is possible, what is probable and what the risks are of a particular outcome. The advantage of elliott wave theory, as explained previously, is about gaining an understanding of context and the likely big picture. The indicator is provided in good faith but we do not vouch for its accuracy.
As mentioned previously, chartists should be aware of the probabilistic and uncertain nature of price action and the markets, and therefore prepare to limit and control any potential risks.
The chart indicator can be used on the charts of the majority of markets (e.g. stocks, indices, ETFs, currencies, cryptocurrencies, precious metals, commodities etc.) and any timeframe. Nothing in this indicator, its signals or labels should be construed as a recommendation to buy or sell any market (e.g. stocks, securities, indices, ETFs, currencies, cryptocurrencies, metals, commodities etc.). The indicator is provided solely for educational purposes, to gain a better understanding of technical analysis and elliott wave theory. It should be noted that the degree of noise and randomness increases significantly on lower timeframes. So the lower the timeframe that is chosen (e.g. 15-min or lower) the greater the degree of noise and randomness and therefore the higher the frequency of false signals or whipsaws. The indicator can be applied to candlestick charts and bar charts.
If you would like access, please send me a PM on Tradingview.
Average Trend with Deviation BandsTL,DR: A trend indicator with deviation bands using a modified Donchian calculation
This indicator plots a trend using the average of the lowest and highest closing price and the lowest low and highest high of a given period. This is similar to Donchian channels which use an average of the lowest and highest value (of a given period). This might sound like a small change but imho it provides a better "average" when lows/highs and lowest/highest closing prices are considered in the average calculation as well.
I also added the option to show 2 deviation bands (one is deactivated by default but can be activated in the options menu). The deviation band uses the standard deviation (of the average trend) and can be used to determine if a price movement is still in a "normal" range or not. Based on my testing it is fine to use one band with a standard deviation of 1 but it is also possible to show a second band with a different deviation value if needed. The bands (and trendline) can also be used as dynamic support/resistance zones.
Trendline without deviation bands
Trop BandsTrop Bands is a tool that uses an exponential moving average (EMA) as its central trendline and upper and lower bands to identify potential buying and selling opportunities in the market. The bands are calculated based on recent moves away from the EMA, and they are plotted around the central trendline to provide a visual representation of market trends and conditions. When the price moves outside of these bands, it can be seen as a signal that the security is overbought or oversold and may be ready for a reversal, just like Bollinger Bands.
In addition to providing signals when the price moves outside of the bands, the indicator can also show triangles outside/inside the bands. These triangles are based on the Demand Index developed by James Sibbet and are intended to provide additional confirmation of potential trading opportunities. They can be used in conjunction with other technical analysis tools to help identifying potential trading opportunities in the market.
Stripped Baseline [Loxx]Stripped Baseline is a stripped down version of Loxx's Baseline indicator. This version includes the core baseline only to reduce processing overhead.
What is Baseline?
A core moving average used as part of a volatility-based trading system. This baseline includes 41 moving average types to choose from. See details here:
Also included are 35 different source types for price input. Read more about these source types here:
The full Baseline trading system can be found here:
v1.0 Included Moving Averages
Adaptive Moving Average - AMA
ADXvma - Average Directional Volatility Moving Average
Ahrens Moving Average
Alexander Moving Average - ALXMA
Double Exponential Moving Average - DEMA
Double Smoothed Exponential Moving Average - DSEMA
Exponential Moving Average - EMA
Fast Exponential Moving Average - FEMA
Fractal Adaptive Moving Average - FRAMA
Hull Moving Average - HMA
IE /2 - Early maout 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
Parabolic Weighted Moving Average
Recursive Moving Trendline
Simple Moving Average - SMA
Sine Weighted Moving Average
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
Volume Weighted EMA - VEMA
Zero-Lag DEMA - Zero Lag Double Exponential Moving Average
Zero-Lag Moving Average
Zero Lag TEMA - Zero Lag Triple Exponential Moving Average
Volatility Types
v1.0 Included Volatility
Average True Range (ATR)
True Range Double ( TRD )
Trading Rules
Post Baseline Cross Qualifier (PBCQ): If price crosses the baseline but the trade is invalid due to additional qualifiers, then the strategy doesn't enter a trade on that candle. This setting allows you override this disqualification in the following manner: If price crosses XX bars ago and is now qualified by other qualifiers, then the strategy enters a trade.
Volatility: If price crosses the baseline, we check to see how far it has moved in terms of multiples of volatility denoted in price (ATR x multiple). If price has moved by at least "Qualifier multiplier" and less than "Range Multiplier", then the strategy enters a trade. This range is shown on the chart with yellow area that tracks price above/blow the baseline. Also, see the dots at the top of the chart. If the dots are green, then price passes the volatility test for a long. If the dots are red, then price passes the volatility test for a short.
Additional moving averages, volatility types, qualifiers, and other advanced features will be added in future releases.
Baseline Backtest
TALibrary "TA"
General technical analysis functions
div_bull(pS, iS, cp_length_after, cp_length_before, pivot_length, lookback, no_broken, pW, iW, hidW, regW)
Test for bullish divergence
Parameters:
pS : Price series (float)
iS : Indicator series (float)
cp_length_after : Bars after current (divergent) pivot low to be considered a valid pivot (optional int)
cp_length_before : Bars before current (divergent) pivot low to be considered a valid pivot (optional int)
pivot_length : Bars before and after prior pivot low to be considered valid pivot (optional int)
lookback : Bars back to search for prior pivot low (optional int)
no_broken : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)
pW : Weight of change in price, used in degree of divergence calculation (optional float)
iW : Weight of change in indicator, used in degree of divergence calculation (optional float)
hidW : Weight of hidden divergence, used in degree of divergence calculation (optional float)
regW : Weight of regular divergence, used in degree of divergence calculation (optional float)
Returns:
flag = true if divergence exists (bool)
degree = degree (strength) of divergence (float)
type = 1 = regular, 2 = hidden (int)
lx1 = x coordinate 1 (int)
ly1 = y coordinate 1 (float)
lx2 = x coordinate 2 (int)
ly2 = y coordinate 2 (float)
div_bear(pS, iS, cp_length_after, cp_length_before, pivot_length, lookback, no_broken, pW, iW, hidW, regW)
Test for bearish divergence
Parameters:
pS : Price series (float)
iS : Indicator series (float)
cp_length_after : Bars after current (divergent) pivot high to be considered a valid pivot (optional int)
cp_length_before : Bars before current (divergent) pivot highto be considered a valid pivot (optional int)
pivot_length : Bars before and after prior pivot high to be considered valid pivot (optional int)
lookback : Bars back to search for prior pivot high (optional int)
no_broken : Flag to only consider divergence valid if the pivot-to-pivot trendline is unbroken (optional bool)
pW : Weight of change in price, used in degree of divergence calculation (optional float)
iW : Weight of change in indicator, used in degree of divergence calculation (optional float)
hidW : Weight of hidden divergence, used in degree of divergence calculation (optional float)
regW : Weight of regular divergence, used in degree of divergence calculation (optional float)
Returns:
flag = true if divergence exists (bool)
degree = degree (strength) of divergence (float)
type = 1 = regular, 2 = hidden (int)
lx1 = x coordinate 1 (int)
ly1 = y coordinate 1 (float)
lx2 = x coordinate 2 (int)
ly2 = y coordinate 2 (float)
TTP Kent Strat PROKent Strat PRO trades breakouts using Bollinger Bands together with SuperTrend.
PRO features:
- 3commas bot alerts for long/short bots
- Custom JSON bots alerts
Features:
- Risk/reward ratio parameter
- Longs, shorts and combined positions.
- Breakout settings
- Trailing SL, trailing TP
- Use of latest candles to place the SL using a lookback parameter (how many candles to look back for a low/high price)
- Select your SL between the ATR trendline and the latest candle: the closest or furthest away value
- Show the trendline
- Backtest mode for accurate backtests
- Signal mode for live price accurate signals
- Date range backtesting
Filters:
- EMA 200 filter and timeframe selector. This filter can be used to trade with the trend: open longs on an uptrend and shorts on a downtrend.
- ADX filter using threshold. This filter can be used to filter entries where the trend is not very strong.
- ADX pointing up. ADX values pointing up and above certain threshold can improve entries.
- Relative volume filter based on the volume being X% above the MA of the Volume. Trading with volume can help filtering out bad trades.
Example setup:
1) pick BINANCE:ETHUSDT chart, 15 min chart
2) trade longs + shorts
3) pick ratio 3
4) trailing SL checked
5) trailing TP unchecked
7) stop loss "furthest"
8) candle loopback 30
9) BB period 21, dev 1, ATR filter on, atr period 5
10) EMA filter on, 15 min
11) ADX off
12) Volume filter on set to 60%
TTP Kent StratKent Strat trades breakouts using Bollinger Bands together with SuperTrend.
Features:
- Risk/reward ratio parameter
- Longs, shorts and combined positions.
- Breakout settings
- Trailing SL, trailing TP
- Use of latest candles to place the SL using a lookback parameter (how many candles to look back for a low/high price)
- Select your SL between the ATR trendline and the latest candle: the closest or furthest away value
- Show the trendline
- Backtest mode for accurate backtests
- Signal mode for live price accurate signals
- Date range backtesting
Filters:
- EMA 200 filter and timeframe selector. This filter can be used to trade with the trend: open longs on an uptrend and shorts on a downtrend.
- ADX filter using threshold. This filter can be used to filter entries where the trend is not very strong.
- ADX pointing up. ADX values pointing up and above certain threshold can improve entries.
- Relative volume filter based on the volume being X% above the MA of the Volume. Trading with volume can help filtering out bad trades.
Example setup:
1) pick BINANCE:ETHUSDT chart, 15 min chart
2) trade longs + shorts
3) pick ratio 3
4) trailing SL checked
5) trailing TP unchecked
7) stop loss "furthest"
8) candle loopback 30
9) BB period 21, dev 1, ATR filter on, atr period 5
10) EMA filter on, 15 min
11) ADX off
12) Volume filter on set to 60%
BTMM|TDIThis is the trader's dynamic index inspired by Steve Mauro's BTMM strategy.
In addition to the RSI, Trendline, Baseline, Volatility Bands I have also included additional trend biases that are painted in the background to provide more confluence when the markets break out in either direction.
For convenience, a position size calculator is included for all users to quickly calculate lot sizes on forex pairs with difference account balance currencies. The calculator works accurately on forex pairs. DO NOT USE for crypto or indices as some brokers have unique contract sizes that could not be fully incorporated into the tool.
There is also data table that displays historical values of the RSI, Trendline, Baseline, and an EMA vs Price scoring procedure that covers the current candle (t0) and up to 3 candles back. The table is meant to provide a snapshot view of either bullish or bearish dominance that can be deciphered with a quick glance.
loxxmas - moving averages used in Loxx's indis & stratsLibrary "loxxmas"
TODO:loxx moving averages used in indicators
kama(src, len, kamafastend, kamaslowend)
KAMA Kaufman adaptive moving average
Parameters:
src : float
len : int
kamafastend : int
kamaslowend : int
Returns: array
ama(src, len, fl, sl)
AMA, adaptive moving average
Parameters:
src : float
len : int
fl : int
sl : int
Returns: array
t3(src, len)
T3 moving average, adaptive moving average
Parameters:
src : float
len : int
Returns: array
adxvma(src, len)
ADXvma - Average Directional Volatility Moving Average
Parameters:
src : float
len : int
Returns: array
ahrma(src, len)
Ahrens Moving Average
Parameters:
src : float
len : int
Returns: array
alxma(src, len)
Alexander Moving Average - ALXMA
Parameters:
src : float
len : int
Returns: array
dema(src, len)
Double Exponential Moving Average - DEMA
Parameters:
src : float
len : int
Returns: array
dsema(src, len)
Double Smoothed Exponential Moving Average - DSEMA
Parameters:
src : float
len : int
Returns: array
ema(src, len)
Exponential Moving Average - EMA
Parameters:
src : float
len : int
Returns: array
fema(src, len)
Fast Exponential Moving Average - FEMA
Parameters:
src : float
len : int
Returns: array
hma(src, len)
Hull moving averge
Parameters:
src : float
len : int
Returns: array
ie2(src, len)
Early T3 by Tim Tilson
Parameters:
src : float
len : int
Returns: array
frama(src, len, FC, SC)
Fractal Adaptive Moving Average - FRAMA
Parameters:
src : float
len : int
FC : int
SC : int
Returns: array
instant(src, float)
Instantaneous Trendline
Parameters:
src : float
float : alpha
Returns: array
ilrs(src, int)
Integral of Linear Regression Slope - ILRS
Parameters:
src : float
int : len
Returns: array
laguerre(src, float)
Laguerre Filter
Parameters:
src : float
float : alpha
Returns: array
leader(src, int)
Leader Exponential Moving Average
Parameters:
src : float
int : len
Returns: array
lsma(src, int, int)
Linear Regression Value - LSMA (Least Squares Moving Average)
Parameters:
src : float
int : len
int : offset
Returns: array
lwma(src, int)
Linear Weighted Moving Average - LWMA
Parameters:
src : float
int : len
Returns: array
mcginley(src, int)
McGinley Dynamic
Parameters:
src : float
int : len
Returns: array
mcNicholl(src, int)
McNicholl EMA
Parameters:
src : float
int : len
Returns: array
nonlagma(src, int)
Non-lag moving average
Parameters:
src : float
int : len
Returns: array
pwma(src, int, float)
Parabolic Weighted Moving Average
Parameters:
src : float
int : len
float : pwr
Returns: array
rmta(src, int)
Recursive Moving Trendline
Parameters:
src : float
int : len
Returns: array
decycler(src, int)
Simple decycler - SDEC
Parameters:
src : float
int : len
Returns: array
sma(src, int)
Simple Moving Average
Parameters:
src : float
int : len
Returns: array
swma(src, int)
Sine Weighted Moving Average
Parameters:
src : float
int : len
Returns: array
slwma(src, int)
linear weighted moving average
Parameters:
src : float
int : len
Returns: array
smma(src, int)
Smoothed Moving Average - SMMA
Parameters:
src : float
int : len
Returns: array
super(src, int)
Ehlers super smoother
Parameters:
src : float
int : len
Returns: array
smoother(src, int)
Smoother filter
Parameters:
src : float
int : len
Returns: array
tma(src, int)
Triangular moving average - TMA
Parameters:
src : float
int : len
Returns: array
tema(src, int)
Tripple exponential moving average - TEMA
Parameters:
src : float
int : len
Returns: array
vwema(src, int)
Volume weighted ema - VEMA
Parameters:
src : float
int : len
Returns: array
vwma(src, int)
Volume weighted moving average - VWMA
Parameters:
src : float
int : len
Returns: array
zlagdema(src, int)
Zero-lag dema
Parameters:
src : float
int : len
Returns: array
zlagma(src, int)
Zero-lag moving average
Parameters:
src : float
int : len
Returns: array
zlagtema(src, int)
Zero-lag tema
Parameters:
src : float
int : len
Returns: array
threepolebuttfilt(src, int)
Three-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
threepolesss(src, int)
Three-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
twopolebutter(src, int)
Two-pole Ehlers Butterworth
Parameters:
src : float
int : len
Returns: array
twopoless(src, int)
Two-pole Ehlers smoother
Parameters:
src : float
int : len
Returns: array
Trend Lines & TimeA simple but powerful script that allows you to set levels that, when a trendline (user set with 2 points) intersects those levels, will produce a vertical line to show the timing of that event
Useful not only for price action but also for use on indicators like a relative strength index (RSI)
Instructions: Plot the trendline down as you wish - choose the price levels & the margin of error for the particular market you are looking at
It's pretty simple
The script is not perfect:
1. Try to make sure when you are projecting a line that it is hitting the level you would like within 500 bars of the last bar on screen (this is a limit within Tradingview & although I've implemented some error checking and avoidance... it still gives errors sometimes when projected too far
Advice work with trend lines that project timing events that aren't so far in the future & use a steeper trendline generally (less horizontal)
2. You MUST use the "Margin of error" or "Orb" setting here - if your market is in a high price range (like 5 digits ..BTC, DJI, NIFTY ETC) then you would want to choose a high number like 100 or even 1000 ... if your price range is very small using smaller numbers or even decimals -- always try 1 first! -- experiment!
There ARE going to be some setups that cause the script to produce an error - I'll release an instructional video that shows the standard use of this tool, it will help you direct your use of it when trying to project to levels of interest in the future BUT generally it works as I think you would expect and is pretty intuitive
Enjoy!
50% Retracement - Support & ResistanceRetracement refers to price reversal after reaching a recent high or low, finding an area of support or resistance, and then continuing in the direction of the bigger picture trend. The concept of 50% retracement is based on the work of W.D. Gann.
Gann was born in 1878 in Texas. Over his trading career, it's been stated he was one of the most successful traders who ever lived. With that said, there is no irrefutable proof he made great fortunes in the market. However, it's a fact that his trading ideas and principles are still in use today, many years after his death in 1955.
Gann believed there was a natural order that exists for everything in the universe, including the stock market. He theorized that price movements occurred in a manner that can be pre-determined based on historical precedent and the influence of mathematical equations and relationships. The end result was predictable movement of prices between areas of support and resistance.
The idea of 50% retracement is best explained in this quote from Gann:
"After an initial, sustained price move, either up or down, prices retrace to 50% of their initial move."
What's important here is the idea that retracement applies in both directions. When price is heading up, it may be approaching an area of resistance. When price is declining, it may be heading towards an area of support.
Continuation of the Trend
The primary reason we are interested to gauge levels of retracement is that once a retracement is complete, there is often a continuation of the previous trend. For example, if moving from a recent low to a new high, if price retraces 50%, at that point we look for a bounce and a continuation of the upward trend.
Retracement to Area of Support
When moving a recent low to a recent high, one can anticipate a price to move down 50% of the original move up.
For example, if a stock climbed from $50 to $100, a 50% retracement of the move from low to high would result in a price of $75. We now look for this $75 price area to be an area of support.
Retracement to Area of Resistance
Retracement is also applicable in the other direction. If price moves from a recent high to a new low and starts moving back up, look for price to regain 50% of the original move down. This retracement is often an area of resistance.
For example, if the recent high was $100 and price bounced off a low of $50, look for resistance near $75.
Additional Retracements - 33% and 66%
Gann also focused on other incremental retracements that he calculated based on various geometric angles believed to balance price and time. What I've found most helpful is to keep things simple and focus on no more than three retracements, 33% 50% and 66%.
Direction of Retracement
When moving from a recent low to a new high, the retracement will be downward. If multiple retracement percentages are shown, they will be smallest to largest going from the top to bottom.
When moving from a recent high to a new low, the retracement will be upward. If multiple retracement percentages are shown, they will be smallest to largest going from the bottom to top.
Retracement Versus Reversal
As described above, retracement refers to retracing a move back down towards a recent low after hitting a new high, or moving back up from a recent low towards a previous high.
The difference between a retracement and reversal is that the latter breaks the uptrend as shown in the chart that follows:
■ With retracements, the upward trendline acted as support of the upward trend.
■ With a reversal, the upward trendline was broken and the price continued to move down.
Additional Retracement Examples
Features
■ Choose up to three retracement levels: 33%, 50% and 66%.
■ Configure price and line color at each retracement level.
■ Show/hide retracements on intraday, daily, weekly and monthly.
■ Set preferred lookback count for Marked Highs/Lows.
■ Show Marked Highs/Lows as price or symbols.
■ Show lines of support and/or resistance.
BBPBΔ(OBV-PVT)BB - Time Series Decomposition & Volume WeightedThis is an indicator that shows 5 different points of information:
#1 The Trendline is uses a time-series decomposition to remove noise and seasonality data to provide a trendline without using moving averages. This is then further processed by a custom VWAP block that weights it based on the time frame you're currently using.
#2 BB%B - This is the blue histogram that's partially transparent. This is used to find when a security is overbought or oversold.
#3 BB%B of the Δ(OBV-PVT). This is the green histogram. We took the OBV and subtracted the PVT from it, then we found the delta of that compared to the previous candle. This output a line, which we wrapped in bollinger bands to find the BB%B of this line. This line is represented as a histogram, for visual clarity.
#4 Long and Short Indicators: Long is represented by a green dot, and short is represented by a red dot.
#5 Zones - there are multiple zones, which are used to identify overbought and oversold zones.
How to use the indicator:
Simple way: Long on green dot, Short on red dot. Use stop losses and take profits.
Slightly More Complex: Same as above, but also close out longs, when the green histogram drops but the blue does not. As this means price action hasn't caught up with volume. Use stop losses and take profits.
Full Usage: Long only when both the green, blue and yellow lines are below 0, and sell when the blue or green histogram rises above 1. Perform the opposite for the shorting. Ignore the dots if you use this method, they are for simple reference points til you get used to this indicator. Use stop losses and take profits.
KTA - ALT/BTC Strength DetectorThis is an indicator that displays the Relative Strength Index (RSI) of Total Crypto Market Capitalization Excluding BTC (ALT MCAP.) and BTC ONLY Market Capitalization (BTC MCAP.). Both RSI's have a lookback of 60Days, and the timeframe is hardcoded to daily. So it's a lookback of 60 days calculation for both RSI's.
The histogram is a visual representation of the difference between the BTC MCAP. RSI and the ALT MCAP. RSI If the color of the histogram bars is blue, this indicates that the BTC MCAP. RSI is higher than the ALT MCAP. RSI (BTC MCAP RSI crossed over ALT MCAP RSI) and visa versa for the green bars (ALT MCAP RSI crossed over BTC MCAP RSI).
This indicator can show the strength of the cryptocurrency market (excluding bitcoin) capitalization versus the Bitcoin-only market capitalization.
Yellow trendline = ALT MCAP. 60 day RSI
Purple trendline = BTC MCAP. 60 day RSI
Note: Histogram base has been set to 50 to fit the histogram in the same area as the RSI's so the calculation for the histogram is ( (ALT MCAP. 60day RSI / BTC MCAP. 60day RSI) *50 ).
Please note I am not a finical advisor, and I do not intend to give financial advice; I am only making this chart as I love scripting here on Trading View, and I would like to give something back to this fantastic community.
I hope you enjoy it.
Best,
KTA
LT Elliott WavesLT Elliott Waves Indicator:
According to Elliott Wave Theory, price moves in 5 waves in the direction of the major trend and moves in 3 waves (ABC) when it moves against the major trend. The key purpose and value of elliott wave theory (EWT) is to provide context for chart analysis. According to the book The Elliott Wave Principle by Frost & Prechter: “This context provides both a basis for disciplined thinking and a perspective on the market's general position and outlook.” The benefit of having context is that one can identify and anticipate changes in direction.
In Elliott Wave theory, waves 1, 3, 5 and C are impulse waves (a five wave pattern that makes progress) whereas waves 2, 4, A and B are corrective waves (a three wave pattern – or combination of three waves - that moves against the direction of the larger trend). Although wave A can also be formed of 5 waves, it is commonly formed of 3 waves. Here is a brief summary of the waves:
Wave 3 tends to be the strongest and most dynamic wave – it is usually (but not always) the longest wave but it is never the shortest. Wave 4 is a corrective wave that is typically composed of 3 smaller waves (ABC) and is notorious for being messy and unpredictable in nature. Wave 5 is the final wave before a significant correction or reversal in trend and is often accompanied by divergences (e.g. negative divergences in an uptrend) and exhaustions in momentum. It is also possible for a wave 5 to form after a “blow-off top” pattern. Wave 2 is composed of 3 smaller waves (ABC) and is a retracement of wave 1 – the retracement can be shallow to moderate (23.6% to 38.2%) or deep (50%, 61.8% to 78.6%). Wave 1 is the first wave of a trend and is composed of 5 smaller waves – it usually occurs after divergences (in the prior move) and extremes in both sentiment and momentum. For example, the wave 1 of an uptrend can often begin after capitulation in the price (after a major decline), extremely pessimistic sentiment, extremely oversold momentum readings, positive divergences and sometimes accompanied by a volume breadth thrust. Waves A and C are often equal in measure. Wave A can be formed of either 5 waves or 3 waves - but more commonly it is composed of 3 waves. Wave B is always corrective and composed of 3 smaller waves. Wave C is a five wave impulse pattern.
The Elliott Wave indicator (and addendum) seeks to simplify elliott wave theory (EWT) in that its main purpose is to identify the potential major trends and corrections. The indicator takes a more simple and direct approach to EWT in that it focuses more on trying to identify whether price is trending or not and if so, the probable wave pattern. It does this by mainly using the structure of the price chart and sometimes other factors such as divergences, momentum and the relationship of price to its key averages. The indicator then takes its best guess at whether price is in a trending environment, and if so, which wave it is probably forming. The wave count can therefore depend on the chart timeframe chosen. For example, what may appear as a major downtrend on a lower timeframe chart may potentially be a corrective drop on a much higher timeframe, due to the different price structure of the charts. To keep things simple and to avoid complexity, the indicator does not display the minor sub-waves within the major waves (probably with the exception of wave 4).
The main feature and benefit of the Elliott Wave indicator is that it can remove subjectivity in chart and wave analysis. It also for flexibility in that it allows the chartist to alter the wave count and the position of the wave counts if they choose to do so (within the parameters and rules set by the indicator). As with all of technical analysis, the wave counts shown by the elliott wave indicator are NOT certain – they are only a possibility or a probability. So the risk always exists of an alternative wave count. It is for the chartist to determine the probable wave counts and limit or control the risks based on their knowledge of technical analysis and risk management.
The settings of the Elliott Wave indicator are fairly self-explanatory but here is a brief summary:
By default the indicator is set to a strict setting (“Alt 8”) in that it waits for divergences or exhaustions in momentum before a probable wave 5 is shown (i.e. the fifth wave within the elliott five wave pattern). So for example, in an uptrend, the indicator may show a probable “wave 3” if there are no negative divergences. Once a divergence appears then the indicator may change the wave count to a “wave 5” provided the parameters for this wave count have been met. This default setting can be changed and removed if the chartist wishes to do so. So if the user wants to change the wave count from a probable “wave 5” to a potential “wave 3”, then the Alt 8 setting can be unselected (i.e. unticked) in the settings and then the Alt 1 can be selected. The indicator will then display a “wave 3” count until the price reverses and breaks below a key support level, thus changing the wave count from a “3” to a probable “5”. (The opposite of this example applies in downtrends.) A strict criteria setting is provided for charts of crypto.
The Elliott Wave indicator has options in the settings to change the positions of certain wave counts based on the structure of the chart. This is achieved by choosing the different major and minor structures based on the zigzag patterns of the chart. So the user can alter the positions of certain wave counts (if needed) by modifying the zigzag structure on the chart.
The lookback period in the settings can be increased (or decreased) to include more data on the chart, when needed. In the majority of situations the lookback period can remain at the default setting of 200 bars – but the user can decide to take into account more (or less) data by changing the lookback period to 300 (or 100 if less data is required).
In the elliott wave indicator, the potential major wave counts are shown in blue and the likely ABC counts (for wave 4) are shown in yellow. The starting point for the wave counts is shown as a green “wave 5” – this is referred to as the “historical wave 5” as it is the likely fifth wave of the prior wave. (For the most recent probable wave counts, such as ABC or 123, this is covered in the elliott wave addendum indicator).
The position of the wave counts, such as waves 1 & 2, 3, 4 and the historical wave 5 (in green) can be changed and modified to a reasonable degree. The historical wave 5 is the starting point where the indicator starts “counting” the waves. The indicator makes its best guess as to where to start counting from the historical wave 5, but the user has the option to change its position, if required as per the parameters set by the indicator. When the position of the green historical wave 5 is changed, this usually affects the entire wave count. The position of the historical wave 5 (green) can be changed by Alt6 in the settings of the elliott wave indicator. Alt4 can change the positions of waves 1 and 2 in the indicator. Alt5 can modify the position of the ABC waves within wave 4 (although by default they are set to major points on the chart). The position of wave 3 can be changed by Alt7. For wider “jumps” in the position of wave 3 and wave 5, the wider jump option can be enabled in the settings. For example, “Alt7Jump” has three ways of moving (or “jumping”) the wave 3 called J1, J2 and J3. The position of most wave counts can also be altered by modifying the major and minor structures or zigzag (which can sometimes change the wave count as well).
If the chartist decides to delay the changing of the wave count, such as delaying the change of wave 3 to a wave 5, then the option Alt10 “Delayed wave count” can be enabled. For example, if the indicator displays a probable “wave 5” on the chart, Alt10 can be enabled to change the wave count to a probable “wave 3” if the chartist decides it is reasonable or “logical” to do so. The Alt10 is similar to Alt1 in that both affect waves 3 and 5. However, Alt10 is less strict than Alt1 so it can often change the wave 5 to a wave 3 in the majority of situations. If Alt10 is enabled, it may be a good idea to ensure that the elliott wave addendum indicator is set to display an ABC wave count (instead of the 123) within the settings.
In certain circumstances where there are volatile conditions and charts, it is possible that the elliott wave indicator may show an “unusual” wave count. For example, it is possible that the positions of certain wave counts (such as waves 1, 2, 3 and 5) may be in the “wrong” order. This happens rarely so it is not an issue that happens very often. However, if this issue occurs, the chartist can rectify the matter by first increasing the lookback period (e.g. to 300) to see if this resolves the issue. If it does not, then Alt9 “temporary wave shift” in the elliott wave addendum can be enabled as this can usually resolve the issue and show the wave counts in a “proper” manner. Changing to a slightly lower timeframe can also usually resolve this issue. If Alt9 is enabled, care should be taken to unselect this option at a later date (as it is only a temporary solution).
The aggressive wave count setting (called “Aggressive 123”) is mainly for the addendum of the elliott wave indicator (i.e. EW addendum). Enabling this option can often change the wave count from an ABC to a 123 provided this is permitted by the parameters of the indicator. This option as well as others are included for further flexibility in the wave count.
The user can also choose to enable the zigzags of the waves to be shown on the chart. This can display the minor and major wave structures and zigzags, if enabled. By default it is set to off. It may also be a good idea to reset the settings of the indicators whenever a new chart or timeframe is chosen. This then refreshes the settings back to its default.
It is important to appreciate that the elliott wave indicator generally requires between 1,500 to 2000 bars of data on the chart in order to display the wave counts adequately and appropriately. So if a chart or timeframe has less than the minimum number of historical data or bars on the chart, the wave counts may not display properly or not appear at all. Certain chart symbols and timeframes (such as the monthly timeframe) may have very limited amount of data on them. Therefore, the elliott wave indicator will likely not appear on these charts or may not display properly. In these situations, a different chart symbol or a lower timeframe with more data on it can be chosen. For example, instead of a monthly timeframe, a weekly or daily timeframe can be chosen.
As mentioned, the elliott wave indicator is programmed to look for and identify potential trending patterns (as well as corrective patterns). In this sense, we are looking to simplify elliott wave theory by taking a more flexible and common-sense approach to the wave patterns. So if the price action has broken key levels of support or resistance, momentum is increasing and price is moving deliberately in a specific direction, it becomes more likely that price is in a trending environment (rather than just a correction).
If the main elliott wave indicator (i.e. LT Elliott Waves) is showing a probable wave 3, and price begins to pullback or move in the opposite direction to the main trend of the wave 3, the EW addendum may be used to display the probable ABC wave counts. These ABC wave counts could be for the likely wave 4 correction. However, if price starts to break key support levels (e.g. after an uptrend) and then reverse lower in the opposite direction (to the mentioned wave 3), then it is likely that the main indicator will change the wave count from a wave 3 to a wave 5. This can indicate that the main uptrend may have probably ended and that we are in either a large correction or a trend reversal, as shown by the EW addendum. This example can also apply in reverse for downtrends e.g. if price starts to break resistance levels and move higher after a downtrend.
We have allowed for further flexibility in the main elliott wave indicator i.e. LT Elliott Waves (and the EW addendum) so that the user can change the wave counts, if required. For example, the chartist can change the wave count from a probable wave 5 to a potential wave 3 – or a probable 123 to ABC (or vice versa) if they choose to do so. Further explanation and information is provided in the description for EW addendum. The position of the wave counts can be changed as well to a reasonable degree.
The chartist can apply other methods of chart analysis – such as trendline breaks, oscillators, regression channels, breaks of support/resistance – to determine when a probable wave (or wave count) has likely completed. For example, technical analysis methods such as trendline breaks and support/resistance breaks can be used by the chartist to determine the probability of whether wave 4 has potentially completed or not. In an uptrend, confirmation that a probable wave such as wave 4 has completed will not come until price has taken out the highs prior to the decline (i.e. the highs before the pullback in the probable “wave 4” correction). The same applies in reverse for a downtrend: confirmation that the probable wave 4 has completed will not come until price has taken out the lows prior to the rally (in a probable wave 4 correction).
It should be remembered that the appearance of the most recent wave counts (or wave labels) shown by the indicator, by themselves do NOT mean that the specific waves in question have definitely completed or finished. Nothing in chart analysis is certain or definite. The wave label itself is simply an indication that the most recent wave is probably still in progress, not necessarily that it has completed. Chartists can apply other technical analysis tools and methods (e.g. trend lines, support/resistance breaks, moving averages and regression channels etc.) to increase the probability of when a specific wave has probably completed. The same also applies to past or “completed” wave counts (or past wave labels): they do NOT mean that the specific waves have definitely completed or finished – it is merely a possibility or probability. So the risk always exists that the wave counts may potentially be wrong, and that an alternative wave count interpretation may exist.
Price action, markets and their charts are non-linear and chaotic, which means that they are subject to uncertainty, variable change and being unpredictable in nature. So we must maintain a probabilistic mindset and attitude to technical analysis. Nothing is certain. Therefore, no wave count is certain or “set in stone”. Wave counts, just like the actions and emotions of human beings, are subject to change. Elliott Wave theory, just like all of technical analysis is about what is possible, what is probable and what the risks are of a particular outcome. The advantage of elliott wave theory, as explained previously, is about gaining an understanding of context and the likely big picture. The indicator is provided in good faith but we do not vouch for its accuracy.
As mentioned previously, chartists should be aware of the probabilistic and uncertain nature of price action and the markets, and therefore prepare to limit and control any potential risks.
The indicator can be used on the charts of the majority of markets (e.g. stocks, indices, ETFs, currencies, cryptocurrencies, precious metals, commodities etc.) and any timeframe. Nothing in this indicator, its signals or labels should be construed as a recommendation to buy or sell any market (e.g. stocks, securities, indices, ETFs, currencies, cryptocurrencies, metals, commodities etc.). The indicator is provided solely for educational purposes, to gain a better understanding of technical analysis and elliott wave theory. It should be noted that the degree of noise and randomness increases significantly on lower timeframes. So the lower the timeframe that is chosen (e.g. 15-min or lower) the greater the degree of noise and randomness and therefore the higher the frequency of false signals or whipsaws. The indicator can be applied to candlestick charts and bar charts.
If you would like access, please send me a PM on Tradingview.
Rate Of Change [SIDD]This Oscillator is helping identify rate of change in Price.
Basic Definition :-
The Rate of Change ( ROC ) is a momentum technical indicator.
It measures the percentage change in price between the current price and the price a certain number of periods ago.
This indicator is plotted against zero, with the indicator moving upwards into positive territory if price changes are to the upside, and moving into negative territory if price changes are to the downside.
Customization of inbuilt ROC:- I have created EMA of ROC with 9 days exponential moving average and Coloring the plot of 9 EMA of ROC Green and RED. Green line indicates that Price change rate is positive in last 9 time period on selected resolution (time frame) and Red line indicated that negative price change rate.
I have identified the zone like +5 and -5 line area in study where some resistance or support is there for 9 EMA ROC line. and if 9 EMA ROC crosses those line then intensity of previous trend get increased.
I have drawn here breakout trendline from lower high candle with hand mark up and same time ROC is above 5 marked with hand up. Similarly I have drawn hand mark down where breakdown trendline is drawn for higher low candle breakdown.
You can see clearly ROC 9 EMA is sync correctly with breakout and breakdown candle when ROC 9 EMA
is above 5 and below 5.
I able to observed that ROC 9 EMA is helping in finding correct breakout and breakdown candles with proper trendline breakout and breakdown.
above all my observation is with daily time frame and 1 Hr time frame candles mostly. If you are changing time frame then see the difference and post same in comment so I can watch those changes as well,
You can modify this study and lets create better than this as well. As I think nothing is perfect in this world always there is scope of improvement.
This study to see how the price are getting changing and what is the rate of change .
This study doesn't give any buy and sell recommendation.
I have other indicator which is given in my signature below that you can check.
Profit HunterThis is a simple indicator working on the basis of trend optimized method, when price crosses and goes up the trendline it goes for long signal and when it crosses down the trendline it goes for short signal. The risk reward ratio plays a significant role in the succcess rate. Intraday square off timing is also given in the settings.
In indicator settings you will have access to
Entry Time Flexibility
Exit Time Flexibility
Take Profit Value
Stop Loss Value
Trailing Stop Losses
Contact us using the link given below to obtain access to this indicator.
This is for charting purpose and following an indicator bliendly involves risk. The historical results don't imply its future success.
gap winnerConnect day open to day close of the orange line , after that connect day open to day close of nifty or banknifty . If the trendline conflict against each other, gap winner's trend wins. For example - gap winner's trend is bullish , nifty trend is bearish , we can expect GAP UP on very next day.
Note - Trendline you need to draw by yourself.
Easy TrendThis signal is completely based on analysis and transformation of a single simple moving average. As with all signals and indicators, it should be combined with others.
This is how the signal is built:
1. First it takes the SMA of the closing price.
2. It then takes the ROC of that SMA using a length of 1.
3. It takes an 8-period SMA and also a 64-period SMA of that ROC.
4. These are plotted as follows:
- the ROC is plotted in green when above 0 (trending up) and red when below 0 (trending down).
- the 8-period SMA is plotted as a thin white line within the ROC signal
- the 64-period SMA is plotted as a thick white line within the ROC signal
When the trendline is green, this is a bullish zone. When the trendline is red, this is a bearish zone.
Moving averages (all types of moving averages) are inherently lagging signals. To compensate for that, I am offsetting each SMA series by half of its period. This may be confusing to some, but the end result is a mathematically accurate SMA signal, centered on the signal that it is providing the moving average of. It doesn't stop the lag, but it directly and obviously shows how lagged each signal is, which I personally find better to trade against.
Symbols on the top and bottom of indicator:
Yellow triangle at bottom of indicator shows where a downward trend is starting to bottom out and a buy/long opening may be available soon.
Green triangle at bottom of indicator shows that a downward trend has switched to an upward trend. This indicates a good time to buy.
Yellow triangle at top of indicator shows where an upward trend is starting to plateau and a sell/short opening may be available soon.
Red triangle at top of indicator shows that an upward trend has switched to a downward trend. This indicates a good time to sell.
Note: You may see multiple yellow triangles before seeing a green or red triangle. This can happen when multiple trend accelerations or decelerations occur within an overall green or red zone.
In addition there is a dotted line connecting the end of the 64-period SMA to the end of the 8-period SMA. This indicates the direction the trend is moving towards. When the dotted line crosses the zero line, this portrays a rough estimate of where the trend may switch from a downtrend to an uptrend or vice versa. This is the "best" time to buy or sell, depending on your strategy.
I recommend placing a SMA on your candles set to the same window size as this indicator, and also to offset that SMA to the left by half its window size. For example, a 90-period SMA should be offset by -45 periods. That will cause it to be correctly aligned with this trend signal.
OH2B Take Profit IndicatorOH2B Trading Indicator
The Take-Profit Indicator gives you exit points for taking profits based on the Average Directional Index and Relative Strength Index.
It also gives you possible swing-high and swing-low in a trend .
The OH2B Trading Indicator is an indicator that gives you entry points for BUY and SELL based on Trend Channel and Ema-Crossover.
Both indicators work better in tandem with the best result.
Trading with Both Indicators
Orange Zone : Wait for the next signal from OH2B Trading Indicator.
The OH2B Trading Indicator gives you entry points for BUY or SELL .
When the ema1 crosses above ema2, the trend channel will be painted GREEN in color, and a BUY signal will appear. = BUY
When the ema1 crosses below ema2, the trend channel will be painted RED in color, and a SELL signal will appear. = SELL
When a BUY or SELL signal appears on the chart, don't rush into any trade.
Please allow a few hours for confirmation of signals .
To take profit, please follow the Take-Profit Indicator at the bottom.
RED dots are the Take-Profit targets for the BUY (long) signal
GREEN dots are the Take-Profit targets for the SELL (short) signal
*After taking profit, please set a Stoploss to at least break-even level to protect your profits.
When the price enters the Orange Zone again, you may close your trade and wait for the next signal.
If you like to keep your positions at Orange Zone, please remember to set a stop-loss alert when an opposite signal pops up on the chart.
Often the price goes to the Trendline after the signal, so keep some funds to increase the position or even open all position near the Trendline , or use it for re-enter with SL after you closed position on TPs.
OH2B Trading Indicator for Swing TradingOH2B Trading Indicator
The OH2B Trading Indicator is an indicator built for advanced traders.
It gives you entry points for BUY and SELL based on Trend Channel and Ema-Crossover.
It offers you a higher frequency of signals for swing trading.
The Take-Profit Indicator gives you exit points for taking profits based on the Average Directional Index and Relative Strength Index.
Trading with Both Indicators
Orange Zone : Wait for the next signal from OH2B Trading Indicator.
The OH2B Trading Indicator gives you entry points for BUY or SELL .
When the ema1 crosses above ema2, the trend channel will be painted GREEN in color, and a BUY signal will appear. = BUY
When the ema1 crosses below ema2, the trend channel will be painted RED in color, and a SELL signal will appear. = SELL
When a BUY or SELL signal appears on the chart, don't rush into any trade.
Please allow a few hours for confirmation of signals .
To take profit, please follow the Take-Profit Indicator at the bottom.
RED dots are the Take-Profit targets for the BUY (long) signal
GREEN dots are the Take-Profit targets for the SELL (short) signal
*After taking profit, please set a Stoploss to at least break-even level to protect your profits.
When the price enters the Orange Zone again, you may close your trade and wait for the next signal.
If you like to keep your positions at Orange Zone, please remember to set a stop-loss alert when an opposite signal pops up on the chart.
Often the price goes to the Trendline after the signal, so keep some funds to increase the position or even open all position near the Trendline , or use it for re-enter with SL after you closed position on TPs.
Tips for the traders:
Recommended using the indicators at 1H Timeframe .
Recommended using the indicators for Spot Trading at both Crypto and Stock Market.
Do not use leverage above 5x.
No more than 5% of the deposit in one trade.
Try to enter/exit a position by limit orders.
Do not rush into any trade when a new signal pops up, please allow a few hours for confirmation of signals.
OH2B Trading Indicator for Long-Term TradingOH2B Trading Indicator
The OH2B Trading Indicator is an indicator built for beginners.
It gives you entry points for BUY and SELL based on Trend Channel and Ema-Crossover.
The Take-Profit Indicator gives you exit points for taking profits based on the Average Directional Index and Relative Strength Index.
Trading with Both Indicators
Orange Zone : Wait for the next signal from OH2B Trading Indicator.
The OH2B Trading Indicator gives you entry points for BUY or SELL .
When the ema1 crosses above ema2, the trend channel will be painted GREEN in color, and a BUY signal will appear. = BUY
When the ema1 crosses below ema2, the trend channel will be painted RED in color, and a SELL signal will appear. = SELL
When a BUY or SELL signal appears on the chart, don't rush into any trade.
Please allow a few hours for confirmation of signals .
To take profit, please follow the Take-Profit Indicator at the bottom.
RED dots are the Take-Profit targets for the BUY (long) signal
GREEN dots are the Take-Profit targets for the SELL (short) signal
*After taking profit, please set a Stoploss to at least break-even level to protect your profits.
When the price enters the Orange Zone again, you may close your trade and wait for the next signal.
If you like to keep your positions at Orange Zone, please remember to set a stop-loss alert when an opposite signal pops up on the chart.
Often the price goes to the Trendline after the signal, so keep some funds to increase the position or even open all position near the Trendline , or use it for re-enter with SL after you closed position on TPs.
Tips for the beginners:
Recommended using the indicators at 4H Timeframe .
Recommended using the indicators for Spot Trading at both Crypto and Stock Market.
Do not use leverage above 5x.
No more than 5% of the deposit in one trade.
Try to enter/exit a position by limit orders.
Do not rush into any trade when a new signal pops up, please allow a few hours for confirmation of signals.
