Variety, Low-Pass, FIR Filter Impulse Response Explorer [Loxx]Variety Low-Pass FIR Filter, Impulse Response Explorer is a simple impulse response explorer of 16 of the most popular FIR digital filtering windowing techniques. Y-values are the values of the coefficients produced by the selected algorithms; X-values are the index of sample. This indicator also allows you to turn on Sinc Windowing for all window types except for Rectangular, Triangular, and Linear. This is an educational indicator to demonstrate the differences between popular FIR filters in terms of their coefficient outputs. This is also used to compliment other indicators I've published or will publish that implement advanced FIR digital filters (see below to find applicable indicators).
Inputs:
Number of Coefficients to Calculate = Sample size; for example, this would be the period used in SMA or WMA
FIR Digital Filter Type = FIR windowing method you would like to explore
Multiplier (Sinc only) = applies a multiplier effect to the Sinc Windowing
Frequency Cutoff = this is necessary to smooth the output and get rid of noise. the lower the number, the smoother the output.
Turn on Sinc? = turn this on if you want to convert the windowing function from regular function to a Windowed-Sinc filter
Order = This is used for power of cosine filter only. This is the N-order, or depth, of the filter you wish to create.
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What's a Low-Pass Filter?
A low-pass filter is the type of frequency domain filter that is used for smoothing sound, image, or data. This is different from a high-pass filter that is used for sharpening data, images, or sound.
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included Windowing Functions
N-Order Power-of-Cosine (this one is really N-different types of FIR filters)
Hamming
Hanning
Blackman
Blackman Harris
Blackman Nutall
Nutall
Bartlet Zero End Points
Bartlet-Hann
Hann
Sine
Lanczos
Flat Top
Rectangular
Linear
Triangular
If you wish to dive deeper to get a full explanation of these windowing functions, see here: en.wikipedia.org
Related indicators
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Cari dalam skrip untuk "10年期国债+交易单位+价格"
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands [Loxx]STD-Filtered, Variety FIR Digital Filters w/ ATR Bands is a FIR Digital Filter indicator with ATR bands. This indicator contains 12 different digital filters. Some of these have already been covered by indicators that I've recently posted. The difference here is that this indicator has ATR bands, allows for frequency filtering, adds a frequency multiplier, and attempts show causality by lagging price input by 1/2 the period input during final application of weights. Period is restricted to even numbers.
The 3 most important parameters are the frequency cutoff, the filter window type and the "causal" parameter.
Included filter types:
- Hamming
- Hanning
- Blackman
- Blackman Harris
- Blackman Nutall
- Nutall
- Bartlet Zero End Points
- Bartlet Hann
- Hann
- Sine
- Lanczos
- Flat Top
Frequency cutoff can vary between 0 and 0.5. General rule is that the greater the cutoff is the "faster" the filter is, and the smaller the cutoff is the smoother the filter is.
You can read more about discrete-time signal processing and some of the windowing functions in this indicator here:
Window function
Window Functions and Their Applications in Signal Processing
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What is a Standard Deviation Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related indicators
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Variety FIR Filters
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
TNT_UpgradedThe background of the indicator to show TrendingUp (Green) / TrendingDown (Red) / Range Bound (Blue) Regions.
The concept is very simple, at each candle we look at the size of the candle and use a moving average of these candle body size (ABS (close-open)) and compare it agains a double smoothened average, i.e. moving average of this average to find trending or not trending periods.
In the upgrade the moving average is now looking only at the current day for intraday timeframe, i.e. in the first 5 bars it is an average of last 5 values, for last 10 candles it is an average of 10 values with the max limited to 28 that is for candle 28 onwards the average is always for 28 candles for default values or as defined by user.
I find it useful primarily for entry in options, a green background is more favourable for call option buying, a red background is favourable for put option buying and blue background is more favourable for option selling.
The coloured ranges show the direction bias, this has been designed using RSI on 3 timeframes with different weight-ages, all customisable by the user.
PS, I only trade Bank Nifty for intraday, all my observations are driven only by Bank Nifty.
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter [Loxx]STD/Clutter-Filtered, Kaiser Window FIR Digital Filter is an is FIR digital filter using Kaiser Windowing. I've also included a clutter filter to reduce signal noise.
What is a Kaiser Window?
The Kaiser window, also known as the Kaiser–Bessel window, was developed by James Kaiser at Bell Laboratories. It is a one-parameter family of window functions used in finite impulse response filter design and spectral analysis. The Kaiser window approximates the DPSS window which maximizes the energy concentration in the main lobe but which is difficult to compute. Kaiser windowing strikes a balance among the various conflicting goals of amplitude accuracy, side lobe distance, and side lobe height. Choosing this window will often reveal signals close to the noise floor that other windows may obscure. For this reason, many spectrum analyzers default to this window. For our purposes here, we use a the Kaiser–Bessel-derived (KBD) window, which is designed to be suitable for use with the modified discrete cosine transform (MDCT).
You can read more here: The Io-sinh function, calculation of Kaiser windows and design of FIR filters
Kaiser Window Amplitudes (not the default settings)
What is a Finite Impulse Response Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Realed Indicators
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
STD- and Clutter-Filtered, Non-Lag Moving Average
Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter
STD-Filtered, Ultra Low Lag Moving Average
Scalping The Bull - BTC Chart for Trend AnalysisName: BTC Chart for Trend Analysis
Category: Scalping, Trend Analysis .
Timeframe: 1M, 5M, 30M, 1D depending on the specific technique.
Technical Analysis: The indicator supports the operations of the trader named "Scalping The Bull" who uses BTC as an Index for Crypto trading.
Suggested usage: When trading on altcoins, to check whether or not they are trending with Bitcoin and whether those anticipate its movements.
It is therefore possible to see Bitcoin specifically if it makes red or green candles and how it is positioned with respect to the EMA 5, 10, 60, 223, however configurable from the panel.
Used in conjunction with Scalping The Bull Indicator or PRO Indicator, on the main panel.
Configuration:
EMA Length:
- EMA 1: by default 5, configurable
- EMA 2: by default 10, configurable
- EMA 3: by default 60, configurable
- EMA 4: by default 223, configurable
Colors can be modified from "Settings" > "Style"
Designed to be used with the following the indicator:
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt [Loxx]STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt is a normalized Cardinal Sine Filter Kernel Weighted Fir Filter that uses Ehler's FIR filter calculation instead of the general FIR filter calculation. This indicator has Kalman Velocity lag reduction, a standard deviation filter, a clutter filter, and a kernel noise filter. When calculating the Kernels, the both sides are calculated, then smoothed, then sliced to just the Right side of the Kernel weights. Lastly, blackman windowing is used for our purposes here. You can read about blackman windowing here:
Blackman window
Advantages of Blackman Window over Hamming Window Method for designing FIR Filter
The Kernel amplitudes are shown below with their corresponding values in yellow:
This indicator is intended to be used with Heikin-Ashi source inputs, specially HAB Median. You can read about this here:
Moving Average Filters Add-on w/ Expanded Source Types
What is a Finite Impulse Response Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
Ehlers FIR Filter
Ehlers Filter (EF) was authored, not surprisingly, by John Ehlers. Read all about them here: Ehlers Filters
What is Normalized Cardinal Sine?
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
STD- and Clutter-Filtered, Non-Lag Moving Average [Loxx]STD- and Clutter-Filtered, Non-Lag Moving Average is a Weighted Moving Average with a minimal lag using a damping cosine wave as the line of weight coefficients. The indicator has two filters. They are static (in points) and dynamic (expressed as a decimal). They allow cutting the price noise giving a stepped shape to the Moving Average. Moreover, there is the possibility to highlight the trend direction by color. This also includes a standard deviation and clutter filter. This filter is a FIR filter.
What is a Generic or Direct Form FIR Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter [Loxx]Clutter-Filtered, D-Lag Reducer, Spec. Ops FIR Filter is a FIR filter moving average with extreme lag reduction and noise elimination technology. This is a special instance of a static weight FIR filter designed specifically for Forex trading. This is not only a useful indictor, but also a demonstration of how one would create their own moving average using FIR filtering weights. This moving average has static period and weighting inputs. You can change the lag reduction and the clutter filtering but you can't change the weights or the numbers of bars the weights are applied to in history.
Plot of weighting coefficients used in this indicator
These coefficients were derived from a smoothed cardinal sine weighed SMA on EURUSD in Matlab. You can see the coefficients in the code.
What is Normalized Cardinal Sine?
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
What is a Generic or Direct Form FIR Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Things to note
Due to the computational demands of this indicator, there is a bars back input modifier that controls how many bars back the indicator is calculated on. Because of this, the first few bars of the indicator will sometimes appear crazy, just ignore this as it doesn't effect the calculation.
Related Indicators
STD-Filtered, Ultra Low Lag Moving Average
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
STD-Filtered, Ultra Low Lag Moving Average [Loxx]STD-Filtered, Ultra Low Lag Moving Average is a FIR filter that smooths price using a low-pass filtering with weights derived from a normalized cardinal since function. This indicator attempts to reduce lag to an extreme degree. Try this on various time frames with various Type inputs, 0 is the default, so see where the sweet spot is for your trading style.
What is a Finite Impulse Response Filter?
In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
Ultra Low Lag Moving Average's weights are designed to have MAXIMUM possible smoothing and MINIMUM possible lag compatible with as-flat-as-possible phase response.
What is Normalized Cardinal Sine?
The sinc function sinc(x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
How this works, (easy mode)
1. Use a HA or HAB source type
2. The lower the Type value the smoother the moving average
3. Standard deviation stepping is added to further reduce noise
Included
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Ehlers Linear Extrapolation Predictor [Loxx]Ehlers Linear Extrapolation Predictor is a new indicator by John Ehlers. The translation of this indicator into PineScript™ is a collaborative effort between @cheatcountry and I.
The following is an excerpt from "PREDICTION" , by John Ehlers
Niels Bohr said “Prediction is very difficult, especially if it’s about the future.”. Actually, prediction is pretty easy in the context of technical analysis. All you have to do is to assume the market will behave in the immediate future just as it has behaved in the immediate past. In this article we will explore several different techniques that put the philosophy into practice.
LINEAR EXTRAPOLATION
Linear extrapolation takes the philosophical approach quite literally. Linear extrapolation simply takes the difference of the last two bars and adds that difference to the value of the last bar to form the prediction for the next bar. The prediction is extended further into the future by taking the last predicted value as real data and repeating the process of adding the most recent difference to it. The process can be repeated over and over to extend the prediction even further.
Linear extrapolation is an FIR filter, meaning it depends only on the data input rather than on a previously computed value. Since the output of an FIR filter depends only on delayed input data, the resulting lag is somewhat like the delay of water coming out the end of a hose after it supplied at the input. Linear extrapolation has a negative group delay at the longer cycle periods of the spectrum, which means water comes out the end of the hose before it is applied at the input. Of course the analogy breaks down, but it is fun to think of it that way. As shown in Figure 1, the actual group delay varies across the spectrum. For frequency components less than .167 (i.e. a period of 6 bars) the group delay is negative, meaning the filter is predictive. However, the filter has a positive group delay for cycle components whose periods are shorter than 6 bars.
Figure 1
Here’s the practical ramification of the group delay: Suppose we are projecting the prediction 5 bars into the future. This is fine as long as the market is continued to trend up in the same direction. But, when we get a reversal, the prediction continues upward for 5 bars after the reversal. That is, the prediction fails just when you need it the most. An interesting phenomenon is that, regardless of how far the extrapolation extends into the future, the prediction will always cross the signal at the same spot along the time axis. The result is that the prediction will have an overshoot. The amplitude of the overshoot is a function of how far the extrapolation has been carried into the future.
But the overshoot gives us an opportunity to make a useful prediction at the cyclic turning point of band limited signals (i.e. oscillators having a zero mean). If we reduce the overshoot by reducing the gain of the prediction, we then also move the crossing of the prediction and the original signal into the future. Since the group delay varies across the spectrum, the effect will be less effective for the shorter cycles in the data. Nonetheless, the technique is effective for both discretionary trading and automated trading in the majority of cases.
EXPLORING THE CODE
Before we predict, we need to create a band limited indicator from which to make the prediction. I have selected a “roofing filter” consisting of a High Pass Filter followed by a Low Pass Filter. The tunable parameter of the High Pass Filter is HPPeriod. Think of it as a “stone wall filter” where cycle period components longer than HPPeriod are completely rejected and cycle period components shorter than HPPeriod are passed without attenuation. If HPPeriod is set to be a large number (e.g. 250) the indicator will tend to look more like a trending indicator. If HPPeriod is set to be a smaller number (e.g. 20) the indicator will look more like a cycling indicator. The Low Pass Filter is a Hann Windowed FIR filter whose tunable parameter is LPPeriod. Think of it as a “stone wall filter” where cycle period components shorter than LPPeriod are completely rejected and cycle period components longer than LPPeriod are passed without attenuation. The purpose of the Low Pass filter is to smooth the signal. Thus, the combination of these two filters forms a “roofing filter”, named Filt, that passes spectrum components between LPPeriod and HPPeriod.
Since working into the future is not allowed in EasyLanguage variables, we need to convert the Filt variable to the data array XX . The data array is first filled with real data out to “Length”. I selected Length = 10 simply to have a convenient starting point for the prediction. The next block of code is the prediction into the future. It is easiest to understand if we consider the case where count = 0. Then, in English, the next value of the data array is equal to the current value of the data array plus the difference between the current value and the previous value. That makes the prediction one bar into the future. The process is repeated for each value of count until predictions up to 10 bars in the future are contained in the data array. Next, the selected prediction is converted from the data array to the variable “Prediction”. Filt is plotted in Red and Prediction is plotted in yellow.
The Predict Extrapolation indicator is shown above for the Emini S&P Futures contract using the default input parameters. Filt is plotted in red and Predict is plotted in yellow. The crossings of the Predict and Filt lines provide reliable buy and sell timing signals. There is some overshoot for the shorter cycle periods, for example in February and March 2021, but the only effect is a late timing signal. Further reducing the gain and/or reducing the BarsFwd inputs would provide better timing signals during this period.
ADDITIONS
Loxx's Expanded source types:
Library for expanded source types:
Explanation for expanded source types:
Three different signal types: 1) Prediction/Filter crosses; 2) Prediction middle crosses; and, 3) Filter middle crosses.
Bar coloring to color trend.
Signals, both Long and Short.
Alerts, both Long and Short.
Weighted percentile nearest rankYo, posting it for the whole internet, took the whole day to find / to design the actual working solution for weighted percentile 'nearest rank' algorithm, almost no reliable info online and a lot of library-style/textbook-style solutions that don't provide on real world production level.
The principle:
0) initial data
data = 22, 33, 11, 44, 55
weights = 5 , 3 , 2 , 1 , 4
array(s) size = 5
1) sort data array, apply the sorting pattern to the weights array, resulting:
data = 11, 22, 33, 44, 55
weights = 2 , 5 , 3 , 1 , 4
2) get weights cumsum and sum:
weights = 2, 5, 3 , 1 , 4
weights_cum = 2, 7, 10, 11, 15
weights_sum = 15
3) say we wanna find 50th percentile, get a threshold value:
n = 50
thres = weights_sum / 100 * n
7.5 = 15 / 100 * 50
4) iterate through weights_cum until you find a value that >= the threshold:
for i = 0 to size - 1
2 >= 7.5 ? nah
7 >= 7.5 ? nah
10 >= 7.5 ? aye
5) take the iteration index that resulted "aye", and find the data value with the same index, that's gonna be the resulting percentile.
i = 2
data = 33
This one is not an approximation, not an estimator, it's the actual weighted percentile nearest rank as it is.
I tested the thing extensively and it works perfectly.
For the skeptics, check lines 40, 41, 69 in the code, you can comment/uncomment dem to switch for unit (1) weights, resulting in the usual non-weighted percentile nearest rank that ideally matches the TV's built-in function.
Shoutout for @wallneradam for the sorting function mane
...
Live Long and Prosper
HPI for crypto [ptt]The Herrick Payoff Index is designed to show the amount of money flowing into or out of a futures contract.
This indicator uses open interest (from Binance PERP like this BTCUSDTPERP_OI) from during its calculations, therefore, the pairs being analyzed must contain open interest data on Binance.
The indicator only works with USDT pairs! Like RVNUSDT, BTCUSDT... does not work with USD pairs!
The indicator works in two mode.
Index mode - when the values moving 0-100
In this case, if the value below 10, it shows the money is flowing out of the futures contract and near the local bottom. If the value above 90, it shows the money is flowing into the futures contract and near the local top.
(The two trigger can be modified, the default is low:10 and high:90)
Oscillator mode - when the values moving around the origo (0)
In this case, if the value above 0 (green), it shows the money is flowing into the futures contract, this is bullish
If the value below 0 (red), it shows the money is flowing out of the futures contract, this is bearish
scalping with market facilitationThis strategy is for scalping low timeframes for 10 pips. I have yet to see a strategy with this unique combo of indicators.
First we have volume indicator market facilitation, where we are looking for volume and mfi to be up, then we want the adx 5 to be above level 30 and above its ema period 3, then if these conditions are good we take shorts when ema 8 is below ema 100 and longs when ema8 is above ema 100 with parabolic sar in its propet place, also to verify trend we have obv over or under its ema of 55 and macd line over its signal line.
I have heikenashi bars on with the regular priceline showing so j see actual price levels, when i get a buy signal i set a buystop above the high of that bar and have a stoploss of 7.5 pips and a take profit of 10 pips, reverse for sells, i have to use metatrader to trade so i use this as my signals to trade.
Note this is not advice trade at your own risk no guarantees in anything in life, but i wanted to share this for it is helping me with my trades to be more strict and semi mechanical. I use it for forex time frames 1 3 5 15 mjn
DMI StrategyThis strategy is based on DMI indicator. It helps me to identify base or top of the script. I mostly use this script to trade in Nifty bank options, even when the signal comes in nifty . It can be used to trade in other scripts as well. Pivot points can also be used to take entry. Long entry is taken when DI+(11) goes below 10 and DI-(11) goes above 40 , whereas short entry is taken when DI-(11) goes below 10 and DI+(11) goes above 40.
For bank nifty , I take the trade in the strike price for which the current premium is nearby 300, with the SL of 20%. If premium goes below 10% I buy one more lot to average, but exit if the premium goes below 20% of the first entry. If the trade moves in the correct direction, we need to start trailing our stoploss or exit at the pre-defined target.
As this a strategy, there is one problem. While we are in the phase of "long", if again the "long" phase comes, it will not be shown on chart until a "short" phase has come, and vice versa. This has been resolved by creating an indicator instead of strategy with the name of "DMI Buy-sell on chart". Please go through that to get more entry points.
Please have a look at strategy tester to back test
DMI StrategyThis strategy is based on DMI indicator. It helps me to identify base or top of the script. I mostly use this script to trade in Nifty bank options, even when the signal comes in nifty. It can be used to trade in other scripts as well. Pivot points can also be used to take entry. Long entry is taken when DI+(11) goes below 10 and DI-(11) goes above 40, whereas short entry is taken when DI-(11) goes below 10 and DI+(11) goes above 40.
For bank nifty, I take the trade in the strike price for which the current premium is nearby 300, with the SL of 20%. If premium goes below 10% I buy one more lot to average, but exit if the premium goes below 20% of the first entry. If the trade moves in the correct direction, we need to start trailing our stoploss or exit at the pre-defined target.
Please have a look at strategy tester to back test.
Mark MinerviniHi everyone,
Just sharing a script that I made when I began to be interested in Mark Minervini, Wiliam O'Neil, Nicolas Darvas,.. trading style.
This script displays :
- 10 EMA (orange)
- 20 EMA (blue light)
- 50 SMA (blue)
-150 SMA (green)
-200 SMA (red)
- Shows when the stock is "extended" from EMA10 to high of the candle (Works in Daily, you can adjust the % to make it match with the stock's volatility)
(Shows a red area between price and 10 EMA)
- Shows when Mark Minervini's trend template is respected by highlighting green between 150 & 200 SMA.
I Although added bollinger bands and 5 EMA for very strong stocks. (I never use them)
Have fun
itrade buy/sellThe indicator was written based on several types of other indicators.
I took ema, rsi ema and an augmented version of qqe rsi.
The indicator checks for oversold or overbought on all of these indicators and, based on this, issues a buy or sell signal.
In the indicator, you can adjust the length of each point for yourself, so you can set rsi to 10 or 100, as it suits you.
The indicator works better on higher timeframes 4h-1w
But it can also be used on smaller timeframes, but the lower the timeframe, the higher the risk.
_________________________________________________________________________________________________________
Индикатор был написан на основе нескольких видов других индикаторов.
Я взял ema,rsi ema идополненую версию qqe rsi.
Индикатор проверяет перепроданость или перекупленость на этих всех индикаторах и изходя из этого выдаёт сигнал на покупку или продажу.
В индикаторе можно настроить длинну каждого пункта под себя,так вы можете поставить rsi на 10 или же на 100,как вам будет удобно.
Индикатор работает лучше на больших таймфреймах 4ч-1w
Но так же его можно использовать на более мелких таймфреймах,но чем ниже таймфрейм,тем выше риск.
Goertzel Cycle Period [Loxx]Goertzel Cycle Period is an indicator that uses Goertzel algorithm to extract the cycle period of ticker's price input to then be injected into advanced, adaptive indicators and technical analysis algorithms.
The following information is extracted from: "MESA vs Goertzel-DFT, 2003 by Dennis Meyers"
Background
MESA which stands for Maximum Entropy Spectral Analysis is a widely used mathematical technique designed to find the frequencies present in data. MESA was developed by J.P Burg for his Ph.D dissertation at Stanford University in 1975. The use of the MESA technique for stocks has been written about in many articles and has been popularized as a trading technique by John Ehlers.
The Fourier Transform is a mathematical technique named after the famed French mathematician Jean Baptiste Joseph Fourier 1768-1830. In its digital form, namely the discrete-time Fourier Transform (DFT) series, is a widely used mathematical technique to find the frequencies of discrete time sampled data. The use of the DFT has been written about in many articles in this magazine (see references section).
Today, both MESA and DFT are widely used in science and engineering in digital signal processing. The application of MESA and Fourier mathematical techniques are prevalent in our everyday life from everything from television to cell phones to wireless internet to satellite communications.
MESA Advantages & Disadvantage
MESA is a mathematical technique that calculates the frequencies of a time series from the autoregressive coefficients of the time series. We have all heard of regression. The simplest regression is the straight line regression of price against time where price(t) = a+b*t and where a and b are calculated such that the square of the distance between price and the best fit straight line is minimized (also called least squares fitting). With autoregression we attempt to predict tomorrows price by a linear combination of M past prices.
One of the major advantages of MESA is that the frequency examined is not constrained to multiples of 1/N (1/N is equal to the DFT frequency spacing and N is equal to the number of sample points). For instance with the DFT and N data points we can only look a frequencies of 1/N, 2/N, Ö.., 0.5. With MESA we can examine any frequency band within that range and any frequency spacing between i/N and (i+1)/N . For example, if we had 100 bars of price data, we might be interested in looking for all cycles between 3 bars per cycle and 30 bars/ cycle only and with a frequency spacing of 0.5 bars/cycle. DFT would examine all bars per cycle of between 2 and 50 with a frequency spacing constrained to 1/100.
Another of the major advantages of MESA is that the dominant spectral (frequency) peaks of the price series, if they exist, can be identified with fewer samples than the DFT technique. For instance if we had a 10 bar price period and a high signal to noise ratio we could accurately identify this period with 40 data samples using the MESA technique. This same resolution might take 128 samples for the DFT. One major disadvantage of the MESA technique is that with low signal to noise ratios, that is below 6db (signal amplitude/noise amplitude < 2), the ability of MESA to find the dominant frequency peaks is severely diminished.(see Kay, Ref 10, p 437). With noisy price series this disadvantage can become a real problem. Another disadvantage of MESA is that when the dominant frequencies are found another procedure has to be used to get the amplitude and phases of these found frequencies. This two stage process can make MESA much slower than the DFT and FFT . The FFT stands for Fast Fourier Transform. The Fast Fourier Transform(FFT) is a computationally efficient algorithm which is a designed to rapidly evaluate the DFT. We will show in examples below the comparisons between the DFT & MESA using constructed signals with various noise levels.
DFT Advantages and Disadvantages.
The mathematical technique called the DFT takes a discrete time series(price) of N equally spaced samples and transforms or converts this time series through a mathematical operation into set of N complex numbers defined in what is called the frequency domain. Why would we what to do that? Well it turns out that we can do all kinds of neat analysis tricks in the frequency domain which are just to hard to do, computationally wise, with the original price series in the time domain. If we make the assumption that the price series we are examining is made up of signals of various frequencies plus noise, than in the frequency domain we can easily filter out the frequencies we have no interest in and minimize the noise in the data. We could then transform the resultant back into the time domain and produce a filtered price series that hopefully would be easier to trade. The advantages of the DFT and itís fast computation algorithm the FFT, are that it is extremely fast in calculating the frequencies of the input price series. In addition it can determine frequency peaks for very noisy price series even when the signal amplitude is less than the noise amplitude. One of the disadvantages of the FFT is that straight line, parabolic trends and edge effects in the price series can distort the frequency spectrum. In addition, end effects in the price series can distort the frequency spectrum. Another disadvantage of the FFT is that it needs a lot more data than MESA for spectral resolution. However this disadvantage has largely been nullified by the speed of today's computers.
Goertzel algorithm attempts to resolve these problems...
What is the Goertzel algorithm?
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of the individual terms of the discrete Fourier transform (DFT). It is useful in certain practical applications, such as recognition of dual-tone multi-frequency signaling (DTMF) tones produced by the push buttons of the keypad of a traditional analog telephone. The algorithm was first described by Gerald Goertzel in 1958.
Like the DFT, the Goertzel algorithm analyses one selectable frequency component from a discrete signal. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. For covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms, but for computing a small number of selected frequency components, it is more numerically efficient. The simple structure of the Goertzel algorithm makes it well suited to small processors and embedded applications.
The main calculation in the Goertzel algorithm has the form of a digital filter, and for this reason the algorithm is often called a Goertzel filter
Where is Goertzel algorithm used?
This package contains the advanced mathematical technique called the Goertzel algorithm for discrete Fourier transforms. This mathematical technique is currently used in today's space-age satellite and communication applications and is applied here to stock and futures trading.
While the mathematical technique called the Goertzel algorithm is unknown to many, this algorithm is used everyday without even knowing it. When you press a cell phone button have you ever wondered how the telephone company knows what button tone you pushed? The answer is the Goertzel algorithm. This algorithm is built into tiny integrated circuits and immediately detects which of the 12 button tones(frequencies) you pushed.
Future Additions:
Bartels test for cycle significance, testing output cycles for utility
Hodrick Prescott Detrending, smoothing
Zero-Lag Regression Detrending, smoothing
High-pass or Double WMA filtering of source input price data
References:
1. Burg, J. P., ëMaximum Entropy Spectral Analysisî, Ph.D. dissertation, Stanford University, Stanford, CA. May 1975.
2. Kay, Steven M., ìModern Spectral Estimationî, Prentice Hall, 1988
3. Marple, Lawrence S. Jr., ìDigital Spectral Analysis With Applicationsî, Prentice Hall, 1987
4. Press, William H., et al, ìNumerical Receipts in C++: the Art of Scientific Computingî,
Cambridge Press, 2002.
5. Oppenheim, A, Schafer, R. and Buck, J., ìDiscrete Time Signal Processingî, Prentice Hall,
1996, pp663-634
6. Proakis, J. and Manolakis, D. ìDigital Signal Processing-Principles, Algorithms and
Applicationsî, Prentice Hall, 1996., pp480-481
7. Goertzel, G., ìAn Algorithm for he evaluation of finite trigonometric seriesî American Math
Month, Vol 65, 1958 pp34-35.
Zero-lag, 3-Pole Super Smoother [Loxx]Zero-lag, 3-Pole Super Smoother is an Ehlers 3-pole smoother with lag reduction
What is 3-pole Super Smoother?
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter. Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
Included:
-Color bars
-Loxx's Expanded Source Types
3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping [Loxx]3-Pole Super Smoother w/ EMA-Deviation-Corrected Stepping is an Ehlers 3-pole smoother with EMA deviations corrective stepping. This allows for greater response to volatility.
What is 3-pole Super Smoother?
A SuperSmoother filter is used anytime a moving average of any type would otherwise be used, with the result that the SuperSmoother filter output would have substantially less lag for an equivalent amount of smoothing produced by the moving average. For example, a five-bar SMA has a cutoff period of approximately 10 bars and has two bars of lag. A SuperSmoother filter with a cutoff period of 10 bars has a lag a half bar larger than the two-pole modified Butterworth filter. Therefore, such a SuperSmoother filter has a maximum lag of approximately 1.5 bars and even less lag into the attenuation band of the filter. The differential in lag between moving average and SuperSmoother filter outputs becomes even larger when the cutoff periods are larger.
What is EMA Deviation Corrected?
Dr. Alexander Uhl invented a method that he used to filter the moving average and to check for signals.
By definition, the Standard Deviation (SD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values. In technical analysis we usually use it to measure the level of current volatility.
Standard Deviation is based on Simple Moving Average calculation for mean value. The built-in MetaTrader 5 Standard Deviation can change that and can use one of the 4 basic types of averages for calculations. This version is not doing that. It is, instead, using the properties of EMA to calculate what can be called a new type of deviation, and since it is based on EMA, we shall call it EMA deviation.
It is similar to Standard Deviation, but on a first glance you shall notice that it is "faster" than the Standard Deviation and that makes it useful when the speed of reaction to volatility is expected from any code or trading system.
Included:
-Color bars
-Loxx's Expanded Source Types
percentageLibLibrary "percentageLib"
: every thing need anout percentage
getPercentage(entry, exit)
: get percentage change of of two value
Parameters:
entry : : value of entry price
exit : : value of exit price
Returns: : negative or positive value
applyPercentageNoAddUp(price, percentage)
: apply percentage change on value decrease or increase
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
applyPercentageAddUp(price, percentage)
: apply percentage change on value decrease or increase
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
reversePercentage(percentage)
: get percentage (positive or negative) and return the percentage need to back to previous price
Parameters:
percentage : : percentage change can be negative or positive
Returns: : return positive/negative value
@example : reversePercentage(10) =>11.11111111111111111111111 , reversePercentage(10) =>9.0909090909090909
getReversePercentage(price, percentage)
: get two prices and return the percentage need to back to previous price
Parameters:
price : : value of price
percentage : : percentage change can be negative or positive
Returns: : return only positive value
@example : getReversePercentage(100,90) =>11.11111111111111111111111
multipeBarTotalPercentage()
Super Sniper - Screener - MTF- Multi Asset -Multi IndicatorSuper Sniper Screener with Multi Indicator EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE. Trade Entry/Exit with multiple Indicator for Multiple Assets and Multiple Timeframe. if you are on a Higher / Lower Time frame and are looking for Entry/Exit based on a lower timeframe then this indicator is the best. The all in one screener for EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE
What is Sniper Entry?
if you are on a Higher / Lower Time frame and are looking for Entry/Exit based on a lower timeframe then this indicator is the best.
About this indicator.
horizontal Lines are in pink (down) and blue(up) color grey (neutral)
10 horizontal lines are plotted each for a timeframe + Asset / Currency / Stock
Back ground represents UP/DOWN Trend based on EMA 200 with the current time frame.
RED-Dot and GREEN-Dot are buy sell signals of SSL Channel for current Time Frame.
You can choose only one type of indicator (EMA Crossover, Trend, ADX, MACD, SSL, CCI, STOCHASTIC, RSI, HMA, QQE, WAE) for all the 10-lines
Select Asset and Timeframe for Each Line on the Graph.
Based on the color of the line and changes on Multi Timeframe you can ENTER/EXIT a Trade on current Timeframe.
Hence a Sniper Entry with multiple Indicator Screener
Keltner Channel Width Oscillator (KingThies)Definition
The Keltner Channel Width oscillator is a technical analysis indicator derived originally from the same relationship the Bollinger Band Width indicator takes on Bollinger Bands.
Similar to the Bollinger Bands, Kelts measure volatility in relation to price, and factor in various range calculations to create three bands around the price of a given stock or digital asset. The Middle Line is typically a 20 Day Exponential Moving Average while the upper and lower bands highlight price at different range variations around its basis. Keltner Channel Width serve as a way to quantitatively measure the width between the Upper and Lower Bands and identify opportunities for entires and exits, based on the relative range price is experiencing that day.
Calculation
Kelt Channel Width = (Upper Band - Lower Band) / Middle Band
More on Keltner Channels
Keltner channel was first described by a Chicago grain trader called Chester W. Keltner in his 1960 book How to Make Money in Commodities. Though Keltner claimed no ownership of the original idea and simply called it the ten-day moving average trading rule, his name was applied by those who heard of this concept through his books.
Similarly to the Bollinger Bands, Keltner channel is a technical analysis tool based on three parallel lines. In fact, the Keltner indicator consists of a central moving average in addition to channel lines spread above and below it. The central line represents a 10-day simple moving average of what Chester W. Keltner called typical price. The typical price is defined as the average of the high, low and close. The distance between the central line and the upper, or lower line, is equivalent to the simple moving average of the preceding 10 days' trading ranges.
One way to interpret the Keltner Channel would be to consider the price breakouts outside of the channel. A trader would track price movement and consider any close above the upper line as a strong buy signal. Equivalently, any close below the lower line would be considered a strong sell signal. The trader would follow the trend emphasized by the indicator while complementing his analysis with the use of other indicators as well. However, the breakout method only works well when the market moves from a range-bound setting to an established trend. In a trend-less configuration, the Keltner Channel is better used as an overbought/oversold indicator. Thus, as the price breaks out below the lower band, a trader waits for the next close inside the Keltner Channel and considers this price behavior as an oversold situation indicating a potential buy signal. Similarly, as the price breaks out above the upper band, the trader waits for the next close inside the Keltner Channel and considers this price action as an overbought situation indicating a potential sell signal. By waiting for the price to close within the Channel, the trader avoids getting caught in a real upside or downside breakout.
