Dip & Rip Patterns - The Quant Science🇺🇸
GENERAL OVERVIEW
This indicator detects Dip and Rip patterns by quickly highlighting them on the chart.
These patterns have become popular during the pandemic period mainly in the stock, ETF and cryptocurrency markets on which traders use two interesting strategies:
Buy The Dip
Sell The Rip
Before going into the merits of this technical indicator, let's understand what these two patterns mean and what they identify precisely.
Rip (Rise In Price) : wants to identify a market condition in which the price rises rapidly, for example from $100 to $110 in a few minutes or hours.
Dip (Drop In Price) : wants to identify a market condition in which the price drops rapidly, for example from $100 to $90 in a few minutes or hours.
HOW TO USE
For a better user experience, we recommend choosing a neutral colour for the candles while analysing with this indicator. You can quickly change the colour in Chart Settings > Symbol > Candles .
Depending on the configuration set by the user, the indicator will show Dip (Dip In Price) patterns in red and Rip (Rise In Price) patterns in green.
When the pattern forms, a circle will be displayed and a vertical line will be coloured on the chart along with the body of the candle. The user will then be able to quickly and easily track the configured market conditions.
In this example, we decided to use a 4H timeframe on the BTC/USDT pair (Binance).
Set in the user interface:
Period: 20
Dip (%): -25
Rip (%): 20
Price falls by 25% or more in 80 hours (Dip Pattern).
Price rise by 25% or more in 80 hours (Rip Pattern).
The user can easily configure the parameters via the user interface in the Inputs section (A) and change the indicator design in the Properties section (B).
🇮🇹
PANORAMICA GENERALE
Questo indicatore rileva i Dip e Rip patterns evidenziandoli velocemente sul grafico.
Questi patterns sono diventati famosi durante il periodo pandemico principalmente nel mercato delle azioni, ETF e Criptovalute su cui i trader utilizzano due interessanti strategie:
Buy The Dip
Sell The Rip
Prima di entrare nel merito di questo indicatore tecnico, comprendiamo il significato di questi due pattern e cosa identificano precisamente.
Rip (Rise In Price) : vuole identificare una condizione di mercato in cui il prezzo sale rapidamente, per esempio passando da 100$ a 110$ in pochi minuti o poche ore.
Dip (Drop In Price) : vuole identificare una condizione di mercato in cui il prezzo cala rapidamente, per esempio passando da 100$ a 90$ in pochi minuti o poche ore.
UTILIZZO
Per una migliore esperienza utente consigliamo di scegliere un colore neutro per le candele mentre si analizza con questo indicatore. Puoi cambiare velocemente il colore in Chart Settings > Symbol > Candles .
In base alla configurazione impostata dall'utente l'indicatore mostrerà in rosso i pattern Dip (Dip In Price) e in verde i pattern Rip (Rise In Price).
Quando il pattern si forma verrà visualizzato un cerchio e una linea verticale sul grafico che sarà colorata insieme al corpo della candela. L'utente quindi potrà tracciare facilmente e velocemente le condizioni di mercato configurate.
In questo esempio abbiamo deciso di utilizzare un timeframe 4H con l'obbiettivo di ricercare i patterns sul pair BTC/USDT (Binance).
Impostiamo nell'interfaccia utente:
Period: 20
Dip (%): -25
Rip (%): 20
Il prezzo diminuisce del 25% o più in 80 ore (Dip Pattern).
Il prezzo aumenta del 25% o più in 80 ore (Rip Pattern).
L' utente può configurare facilmente i parametri attraverso l'interfaccia utente nella sezione Inputs (A) e modificare il design dell'indicatore nella sezione Properties (B).
Recherche dans les scripts pour "欧易PI币开盘价格"
BTC - Hotness Index### Script Description
#### BTC - Hotness Index
This Pine Script, version 4, aims to generate a "Hotness Index" for Bitcoin (BTC) trading by utilizing a Pi Cycle Top Indicator. The script operates in a daily (`1D`) time frame and involves calculating two Simple Moving Averages (SMA) based on `close` prices:
- 111-day SMA (`D_111SMA`)
- 350-day SMA (`D_350SMA`) multiplied by 2
The primary indicator (`pi_indicator`) is derived by dividing `D_111SMA` by `D_350SMA`.
##### Sell Signal
A sell signal is plotted as a histogram if `pi_indicator` crosses above 1 (`pi_plot` variable).
##### Buy Signal
A buy signal is plotted as a histogram if `pi_indicator` crosses below 0.35 (`pi_plot_buy` variable).
##### Horizontal Lines
Two horizontal lines are included to denote the "Buy Zone" and "Sell Zone":
- "Sell Zone" at `pi_indicator` level of 1
- "Buy Zone" at `pi_indicator` level of 0.35
##### Plotting
Histogram plots are used for visualizing the signals:
- Sell signals are colored red (`RGB: 255, 59, 59`)
- Buy signals are colored green (`RGB: 82, 255, 59`)
This script provides traders a visual guide for potential buy/sell opportunities based on the Pi Cycle Top Indicator and the Hotness Index for Bitcoin. It operates under the terms of the Mozilla Public License 2.0.
Filtered, N-Order Power-of-Cosine, Sinc FIR Filter [Loxx]Filtered, N-Order Power-of-Cosine, Sinc FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that allows up to 50 values for alpha, orders, of depth. This one differs from previous Power-of-Cosine filters I've published in that it this uses Windowed-Sinc filtering. I've also included a Dual Element Lag Reducer using Kalman velocity, a standard deviation filter, and a clutter filter. You can read about each of these below.
Impulse Response
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.
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 .
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
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related indicators
Variety, Low-Pass, FIR Filter Impulse Response Explorer
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
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
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
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
Bitcoin CycleThis script displays 4 different Moving Averages:
2 Year Moving Average (White)
1 Year Moving Average (Doubled in value, Red)
116 Day Moving Average (Transparent, Red)
232 Day Moving Average (Transparent, White)
For the last cycles: once the 2 year MA crossed the 232 Day MA, it marked the cycle bottom within a few days and once the 1 year MA (x2) crossed the 116 Day MA, it marked the cycle top within a few days.
It is interesting to note that both 365/116 and 730/232 equal 3.1465, which is very close to Pi (3.142). It is actually the closest we can get to Pi when dividing 365 by another whole number.
MathExtensionLibrary "MathExtension"
Math Extension.
log2(_value) calculate log base 2
Parameters:
_value : float, number.
Returns: float, base 2 logarithm of value.
fmod(numerator, denominator) float remainder of x divided by y.
Parameters:
numerator : float, division numerator.
denominator : float, division denuminator.
Returns: float
fractional(value) computes the fractional part of the argument value.
Parameters:
value : float, value to compute.
Returns: float, fractional part.
integral(value) Find the integral of value.
Parameters:
value : float, value.
Returns: float.
atan2(value_x, value_y) Approximation to atan2 calculation, arc tangent of y/ x in the range (-pi,pi) radians.
Parameters:
value_x : float, value x.
value_y : float, value y.
Returns: float, value with angle in radians. (negative if quadrante 3 or 4)
hypotenuse(value_x, value_y) Multidimensional euclidean distance from the origin to a point.
Parameters:
value_x : float, value x.
value_y : float, value y.
Returns: float
near_equal(value_a, value_b, relative_tolerance, absolute_tolerance) Determine whether two floating point numbers are near in value.
Parameters:
value_a : float, value to compare with.
value_b : float, value to be compared against.
relative_tolerance : float, default (1.0e-09).
absolute_tolerance : float, default (0.0).
Returns: bool
factorize(value) Factorize a number.
Parameters:
value : int, positive number.
Returns: int
permutations(options_size, combo_size) Number of ways to choose k items from n items without repetition and with order.
Parameters:
options_size : int, number of items to pool from
combo_size : int, number of items to be chosen
Returns: int
combinations(options_size, combo_size) Find the total number of possibilities to choose k things from n items
Parameters:
options_size : int, number of items to pool from
combo_size : int, number of items to be chosen
Returns: int
MathConstantsUniversalLibrary "MathConstantsUniversal"
Mathematical Constants
SpeedOfLight() Speed of Light in Vacuum: c_0 = 2.99792458e8 (defined, exact; 2007 CODATA)
MagneticPermeability() Magnetic Permeability in Vacuum: mu_0 = 4*Pi * 10^-7 (defined, exact; 2007 CODATA)
ElectricPermittivity() Electric Permittivity in Vacuum: epsilon_0 = 1/(mu_0*c_0^2) (defined, exact; 2007 CODATA)
CharacteristicImpedanceVacuum() Characteristic Impedance of Vacuum: Z_0 = mu_0*c_0 (defined, exact; 2007 CODATA)
GravitationalConstant() Newtonian Constant of Gravitation: G = 6.67429e-11 (2007 CODATA)
PlancksConstant() Planck's constant: h = 6.62606896e-34 (2007 CODATA)
DiracsConstant() Reduced Planck's constant: h_bar = h / (2*Pi) (2007 CODATA)
PlancksMass() Planck mass: m_p = (h_bar*c_0/G)^(1/2) (2007 CODATA)
PlancksTemperature() Planck temperature: T_p = (h_bar*c_0^5/G)^(1/2)/k (2007 CODATA)
PlancksLength() Planck length: l_p = h_bar/(m_p*c_0) (2007 CODATA)
PlancksTime() Planck time: t_p = l_p/c_0 (2007 CODATA)
MathConstantsAtomicLibrary "MathConstantsAtomic"
Mathematical Constants
FineStructureConstant() Fine Structure Constant: alpha = e^2/4*Pi*e_0*h_bar*c_0 (2007 CODATA)
RydbergConstant() Rydberg Constant: R_infty = alpha^2*m_e*c_0/2*h (2007 CODATA)
BohrRadius() Bor Radius: a_0 = alpha/4*Pi*R_infty (2007 CODATA)
HartreeEnergy() Hartree Energy: E_h = 2*R_infty*h*c_0 (2007 CODATA)
QuantumOfCirculation() Quantum of Circulation: h/2*m_e (2007 CODATA)
FermiCouplingConstant() Fermi Coupling Constant: G_F/(h_bar*c_0)^3 (2007 CODATA)
WeakMixingAngle() Weak Mixin Angle: sin^2(theta_W) (2007 CODATA)
ElectronMass() Electron Mass: (2007 CODATA)
ElectronMassEnergyEquivalent() Electron Mass Energy Equivalent: (2007 CODATA)
ElectronMolarMass() Electron Molar Mass: (2007 CODATA)
ComptonWavelength() Electron Compton Wavelength: (2007 CODATA)
ClassicalElectronRadius() Classical Electron Radius: (2007 CODATA)
ThomsonCrossSection() Thomson Cross Section: (2002 CODATA)
ElectronMagneticMoment() Electron Magnetic Moment: (2007 CODATA)
ElectronGFactor() Electon G-Factor: (2007 CODATA)
MuonMass() Muon Mass: (2007 CODATA)
MuonMassEnegryEquivalent() Muon Mass Energy Equivalent: (2007 CODATA)
MuonMolarMass() Muon Molar Mass: (2007 CODATA)
MuonComptonWavelength() Muon Compton Wavelength: (2007 CODATA)
MuonMagneticMoment() Muon Magnetic Moment: (2007 CODATA)
MuonGFactor() Muon G-Factor: (2007 CODATA)
TauMass() Tau Mass: (2007 CODATA)
TauMassEnergyEquivalent() Tau Mass Energy Equivalent: (2007 CODATA)
TauMolarMass() Tau Molar Mass: (2007 CODATA)
TauComptonWavelength() Tau Compton Wavelength: (2007 CODATA)
ProtonMass() Proton Mass: (2007 CODATA)
ProtonMassEnergyEquivalent() Proton Mass Energy Equivalent: (2007 CODATA)
ProtonMolarMass() Proton Molar Mass: (2007 CODATA)
ProtonComptonWavelength() Proton Compton Wavelength: (2007 CODATA)
ProtonMagneticMoment() Proton Magnetic Moment: (2007 CODATA)
ProtonGFactor() Proton G-Factor: (2007 CODATA)
ShieldedProtonMagneticMoment() Proton Shielded Magnetic Moment: (2007 CODATA)
ProtonGyromagneticRatio() Proton Gyro-Magnetic Ratio: (2007 CODATA)
ShieldedProtonGyromagneticRatio() Proton Shielded Gyro-Magnetic Ratio: (2007 CODATA)
NeutronMass() Neutron Mass: (2007 CODATA)
NeutronMassEnegryEquivalent() Neutron Mass Energy Equivalent: (2007 CODATA)
NeutronMolarMass() Neutron Molar Mass: (2007 CODATA)
NeutronComptonWavelength() Neuron Compton Wavelength: (2007 CODATA)
NeutronMagneticMoment() Neutron Magnetic Moment: (2007 CODATA)
NeutronGFactor() Neutron G-Factor: (2007 CODATA)
NeutronGyromagneticRatio() Neutron Gyro-Magnetic Ratio: (2007 CODATA)
DeuteronMass() Deuteron Mass: (2007 CODATA)
DeuteronMassEnegryEquivalent() Deuteron Mass Energy Equivalent: (2007 CODATA)
DeuteronMolarMass() Deuteron Molar Mass: (2007 CODATA)
DeuteronMagneticMoment() Deuteron Magnetic Moment: (2007 CODATA)
HelionMass() Helion Mass: (2007 CODATA)
HelionMassEnegryEquivalent() Helion Mass Energy Equivalent: (2007 CODATA)
HelionMolarMass() Helion Molar Mass: (2007 CODATA)
Avogadro() Avogadro constant: (2010 CODATA)
Vector2OperationsLibrary "Vector2Operations"
functions to handle vector2 operations.
math_fractional(_value) computes the fractional part of the argument value.
Parameters:
_value : float, value to compute.
Returns: float, fractional part.
atan2(_a) Approximation to atan2 calculation, arc tangent of y/ x in the range radians.
Parameters:
_a : vector2 in the form of a array .
Returns: float, value with angle in radians. (negative if quadrante 3 or 4)
set_x(_a, _value) Set the x value of vector _a.
Parameters:
_a : vector2 in the form of a array .
_value : value to replace x value of _a.
Returns: void Modifies vector _a.
set_y(_a, _value) Set the y value of vector _a.
Parameters:
_a : vector in the form of a array .
_value : value to replace y value of _a.
Returns: void Modifies vector _a.
get_x(_a) Get the x value of vector _a.
Parameters:
_a : vector in the form of a array .
Returns: float, x value of the vector _a.
get_y(_a) Get the y value of vector _a.
Parameters:
_a : vector in the form of a array .
Returns: float, y value of the vector _a.
get_xy(_a) Return the tuple of vector _a in the form
Parameters:
_a : vector2 in the form of a array .
Returns:
length_squared(_a) Length of vector _a in the form. , for comparing vectors this is computationaly lighter.
Parameters:
_a : vector in the form of a array .
Returns: float, squared length of vector.
length(_a) Magnitude of vector _a in the form.
Parameters:
_a : vector in the form of a array .
Returns: float, Squared length of vector.
vmin(_a) Lowest element of vector.
Parameters:
_a : vector in the form of a array .
Returns: float
vmax(_a) Highest element of vector.
Parameters:
_a : vector in the form of a array .
Returns: float
from(_value) Assigns value to a new vector x,y elements.
Parameters:
_value : x and y value of the vector. optional.
Returns: float vector.
new(_x, _y) Creates a prototype array to handle vectors.
Parameters:
_x : float, x value of the vector. optional.
_y : float, y number of the vector. optional.
Returns: float vector.
down() Vector in the form . Returns: float vector.
left() Vector in the form . Returns: float vector.
one() Vector in the form . Returns: float vector.
right() Vector in the form . Returns: float vector
up() Vector in the form . Returns: float vector
zero() Vector in the form . Returns: float vector
add(_a, _b) Adds vector _b to _a, in the form
.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns:
subtract(_a, _b) Subtract vector _b from _a, in the form
.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns:
multiply(_a, _b) Multiply vector _a with _b, in the form
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns:
divide(_a, _b) Divide vector _a with _b, in the form
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns:
negate(_a) Negative of vector _a, in the form
Parameters:
_a : vector in the form of a array .
Returns:
perp(_a) Perpendicular Vector of _a.
Parameters:
_a : vector in the form of a array .
Returns:
vfloor(_a) Compute the floor of argument vector _a.
Parameters:
_a : vector in the form of a array .
Returns:
fractional(_a) Compute the fractional part of the elements from vector _a.
Parameters:
_a : vector in the form of a array .
Returns:
vsin(_a) Compute the sine of argument vector _a.
Parameters:
_a : vector in the form of a array .
Returns:
equals(_a, _b) Compares two vectors
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: boolean value representing the equality.
dot(_a, _b) Dot product of 2 vectors, in the form
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float
cross_product(_a, _b) cross product of 2 vectors, in the form
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float
scale(_a, _scalar) Multiply a vector by a scalar.
Parameters:
_a : vector in the form of a array .
_scalar : value to multiply vector elements by.
Returns: float vector
normalize(_a) Vector _a normalized with a magnitude of 1, in the form.
Parameters:
_a : vector in the form of a array .
Returns: float vector
rescale(_a) Rescale a vector to a new Magnitude.
Parameters:
_a : vector in the form of a array .
Returns:
rotate(_a, _radians) Rotates vector _a by angle value
Parameters:
_a : vector in the form of a array .
_radians : Angle value.
Returns:
rotate_degree(_a, _degree) Rotates vector _a by angle value
Parameters:
_a : vector in the form of a array .
_degree : Angle value.
Returns:
rotate_around(_center, _target, _degree) Rotates vector _target around _origin by angle value
Parameters:
_center : vector in the form of a array .
_target : vector in the form of a array .
_degree : Angle value.
Returns:
vceil(_a, _digits) Ceils vector _a
Parameters:
_a : vector in the form of a array .
_digits : digits to use as ceiling.
Returns:
vpow(_a) Raise both vector elements by a exponent.
Parameters:
_a : vector in the form of a array .
Returns:
distance(_a, _b) vector distance between 2 vectors.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float, distance.
project(_a, _axis) Project a vector onto another.
Parameters:
_a : vector in the form of a array .
_axis : float vector2
Returns: float vector
projectN(_a, _axis) Project a vector onto a vector of unit length.
Parameters:
_a : vector in the form of a array .
_axis : vector in the form of a array .
Returns: float vector
reflect(_a, _b) Reflect a vector on another.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float vector
reflectN(_a, _b) Reflect a vector to a arbitrary axis.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float vector
angle(_a) Angle in radians of a vector.
Parameters:
_a : vector in the form of a array .
Returns: float
angle_unsigned(_a, _b) unsigned degree angle between 0 and +180 by given two vectors.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float
angle_signed(_a, _b) Signed degree angle between -180 and +180 by given two vectors.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float
angle_360(_a, _b) Degree angle between 0 and 360 by given two vectors
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
Returns: float
clamp(_a, _vmin, _vmax) Restricts a vector between a min and max value.
Parameters:
_a : vector in the form of a array .
_vmin : vector in the form of a array .
_vmax : vector in the form of a array .
Returns: float vector
lerp(_a, _b, _rate_of_move) Linearly interpolates between vectors a and b by _rate_of_move.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
_rate_of_move : float value between (a:-infinity -> b:1.0), negative values will move away from b.
Returns: vector in the form of a array
herp(_a, _b, _rate_of_move) Hermite curve interpolation between vectors a and b by _rate_of_move.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
_rate_of_move : float value between (a-infinity -> b1.0), negative values will move away from b.
Returns: vector in the form of a array
area_triangle(_a, _b, _c) Find the area in a triangle of vectors.
Parameters:
_a : vector in the form of a array .
_b : vector in the form of a array .
_c : vector in the form of a array .
Returns: float
to_string(_a) Converts vector _a to a string format, in the form "(x, y)"
Parameters:
_a : vector in the form of a array .
Returns: string in "(x, y)" format
vrandom(_max) 2D random value
Parameters:
_max : float vector, vector upper bound
Returns: vector in the form of a array
noise(_a) 2D Noise based on Morgan McGuire @morgan3d
thebookofshaders.com
www.shadertoy.com
Parameters:
_a : vector in the form of a array .
Returns: vector in the form of a array
array_new(_size, _initial_vector) Prototype to initialize a array of vectors.
Parameters:
_size : size of the array.
_initial_vector : vector to be used as default value, in the form of array .
Returns: _vector_array complex Array in the form of a array
array_size(_id) number of vector elements in array.
Parameters:
_id : ID of the array.
Returns: int
array_get(_id, _index) Get the vector in a array, in the form of a array
Parameters:
_id : ID of the array.
_index : Index of the vector.
Returns: vector in the form of a array
array_set(_id, _index, _a) Sets the values vector in a array.
Parameters:
_id : ID of the array.
_index : Index of the vector.
_a : vector, in the form .
Returns: Void, updates array _id.
array_push(_id, _a) inserts the vector at the end of array.
Parameters:
_id : ID of the array.
_a : vector, in the form .
Returns: Void, updates array _id.
array_unshift(_id, _a) inserts the vector at the begining of array.
Parameters:
_id : ID of the array.
_a : vector, in the form .
Returns: Void, updates array _id.
array_pop(_id, _a) removes the last vector of array and returns it.
Parameters:
_id : ID of the array.
_a : vector, in the form .
Returns: vector2, updates array _id.
array_shift(_id, _a) removes the first vector of array and returns it.
Parameters:
_id : ID of the array.
_a : vector, in the form .
Returns: vector2, updates array _id.
array_sum(_id) Total sum of all vectors.
Parameters:
_id : ID of the array.
Returns: vector in the form of a array
array_center(_id) Finds the vector center of the array.
Parameters:
_id : ID of the array.
Returns: vector in the form of a array
array_rotate_points(_id) Rotate Array vectors around origin vector by a angle.
Parameters:
_id : ID of the array.
Returns: rotated points array.
array_scale_points(_id) Scale Array vectors based on a origin vector perspective.
Parameters:
_id : ID of the array.
Returns: rotated points array.
array_tostring(_id, _separator) Reads a array of vectors into a string, of the form " ""
Parameters:
_id : ID of the array.
_separator : string separator for cell splitting.
Returns: string Translated complex array into string.
line_new(_a, _b) 2 vector line in the form.
Parameters:
_a : vector, in the form .
_b : vector, in the form .
Returns:
line_get_a(_line) Start vector of a line.
Parameters:
_line : vector4, in the form .
Returns: float vector2
line_get_b(_line) End vector of a line.
Parameters:
_line : vector4, in the form .
Returns: float vector2
line_intersect(_line1, _line2) Find the intersection vector of 2 lines.
Parameters:
_line1 : line of 2 vectors in the form of a array .
_line2 : line of 2 vectors in the form of a array .
Returns: vector in the form of a array .
draw_line(_line, _xloc, _extend, _color, _style, _width) Draws a line using line prototype.
Parameters:
_line : vector4, in the form .
_xloc : string
_extend : string
_color : color
_style : string
_width : int
Returns: draw line object
draw_triangle(_v1, _v2, _v3, _xloc, _color, _style, _width) Draws a triangle using line prototype.
Parameters:
_v1 : vector4, in the form .
_v2 : vector4, in the form .
_v3 : vector4, in the form .
_xloc : string
_color : color
_style : string
_width : int
Returns: tuple with 3 line objects.
draw_rect(_v1, _size, _angle, _xloc, _color, _style, _width) Draws a square using vector2 line prototype.
Parameters:
_v1 : vector4, in the form .
_size : float
_angle : float
_xloc : string
_color : color
_style : string
_width : int
Returns: tuple with 3 line objects.
Filter Amplitude Response Estimator - A Simple CalculationIn digital signal processing knowing how a system interact with the frequency content of an input signal is extremely important, the mathematical tool that give you this information is called "frequency response". The frequency response regroup two elements, the amplitude response, and the phase response. The amplitude response tells you how the system modify the amplitude of the frequency components in the input signal, the phase response tells you how the system modify the phase of the frequency components in the signal, each being a function of the frequency.
The today proposed tool aim to give a low resolution representation of the amplitude response of any filter.
What Is The Amplitude Response Of A Filter ?
Remember that filters allow to interact with the frequency content of a signal by amplifying, attenuating and/or removing certain frequency components in the input signal, the amplitude (also called magnitude) response of a filter let you know exactly how your filter change the amplitude of the frequency components in the input signal, another way to see the amplitude response is as a tool that tell you what is the peak amplitude of a filter using a sinusoid of a certain frequency as input signal.
For example if the amplitude response of a filter give you a value of 0.9 at frequency 0.5, it means that the filter peak amplitude using a sinusoid of frequency 0.5 is equal to 0.9.
There are several ways to calculate the frequency response of a filter, when our filter is a FIR filter (the filter impulse response is finite), the frequency response of the filter is the absolute value of the discrete Fourier transform (DFT) of the filter impulse response.
If you are curious about this process, know that the DFT of a N samples signal return N values, so if our FIR filter coefficients are composed of only 5 values we would get a frequency response of 5 values...which would not be useful, this is why we "pad" our coefficients with zeros, that is we add zeros to the start and end of our series of coefficients, this process is called "zero-padding", so if our series of coefficients is: (1,2,3,4,5), applying zero padding would give (0,0...1,2,3,4,5,...0,0) while keeping a certain symmetry. This is related to the concept of resolution, a low resolution amplitude response would be composed of a low number of values and would not be useful, this is why we use zero-padding to add more values thus increasing the resolution.
Making a Fourier transform in Pinescript is not doable, as you need the complex number i for computing a DFT, but thats not even the only problem, a DFT would not be that useful anyway (as the processes to make it useful in a trading context would be way too complex) . So how can we calculate a filter amplitude response without using a DFT ? The simple answer is by taking the peak amplitude of a filter using a sinusoid of a certain frequency as input, this is what the proposed tool do.
Using The Tool
The proposed tool give you a 50 point amplitude response from frequency 0.005 to 0.25 by default. the setting "Range Divisor" allow you to see the amplitude response by using a different range of frequency, for example if the range divisor is equal to 2 the filter amplitude response will be evaluated from frequency 0.0025 to 0.125.
In the script, filt hold the filter you want to see the frequency response, by default a simple moving average.
The position of the frequency response is defined by the "Show Amplitude Response At Bar Number" setting, if you want the frequency response to start at bar number 5000 then enter 5000, by default 10000. If you are not a premium set the number at 4000 and it should work.
amplitude response of a simple moving average of period 14, res = 2.
By default the amplitude response use an amplitude scale, a value of 1 represent an unchanged amplitude. You can use Dbfs (decibel full scale) instead by checking the "To Decibels (Full Scale)" setting.
Dbfs amplitude response, a value of 0 represent an unchanged amplitude.
Some Amplitude Responses
In order to prove the accuracy of the proposed tool we can compare the amplitude response given by the proposed tool with the mathematical function of the amplitude response of a simple moving average, that is:
abs(sin(pi*f*length)/(length*sin(pi*f)))
In cyan the amplitude response given by the proposed tool and in blue the above function. Below are the amplitude responses of some moving averages with period 14.
Amplitude response of an EMA, the EMA is a IIR filter, therefore the amplitude response can't be made by taking the DFT of the impulse response (as this ones has infinite length), however our tool can give its frequency response.
Amplitude response of the Hull MA, as you can see some frequencies are amplified, this is common with low-lag filters.
Gaussian moving average (ALMA), with offset = 0.5 and sigma = 6.
Simple moving average high-pass filter amplitude response
Center of gravity bandpass filter amplitude response
Center of gravity bandreject filter.
IMPORTANT!: The amplitude response of adaptive moving averages is not stationary and might change over time.
Conclusion
A tool giving the amplitude response of any filter has been presented, of course this method is not efficient at all and has a low resolution of 50 points (the common resolution is of 512 points) and is difficult to work with, but has the merit to work on Tradingview and can give the frequency response of IIR filters, if you really need to see the frequency response of a filter then i recommend you to use the function freqz from the scipy package.
I still hope you will enjoy using this tool to have a look at the amplitude responses of your favorite moving averages.
I'am aware of the current situation, however i'am somehow feeling left out from the pinescript community, let me know via PM if i have done something to you and i'll do my best to fix any problems i might have caused (or i might be being parano xD)
Template For Custom FIR Filters - Make Your Moving AverageIntroduction
FIR filters (finite impulse response) are widely used in technical analysis, there is the simple or arithmetic moving average, the triangular, the weighted, the least squares...etc. A FIR filter is characterized by the fact that its impulse response (the output of a filter using an impulse as input) is finite, this mean that the impulse response won't have infinite outputs unlike IIR filters.
They are extremely simple to design to, even without the Fourier transform, this is why i post this template that will let you create custom filters from step responses. Don't hesitate to post your results.
How It Works
Originally you create your filters from the frequency response you want your filter to have, this is because the inverse Fourier transform of the frequency response is the filter impulse response.
After that step you use convolution (convolution is the sum of the product between the signal and the impulse response) and you will have your filter. But we don't have Fourier transforms in pine so how can we possibly make FIR filters from convolution ? Well here the thing, the impulse response is the derivative of the step response and the step response is the sum of the impulse response, this mean we can create filters from step responses.
Step response of a moving average.
Step responses are easy to design, you just need a function that start at 0 and end up at 1.
How To Use The Template
All the work is done for you, the only thing you need to do is to enter your function at line 5 :
f(x)=> your function
For example if you want your filter to have a step response equal to sqrt(x) just enter :
f(x)=> sqrt(x)
This will give the following filter output :
You can create custom step responses from online graphing tools like fooplot or wolfram alpha, i recommend fooplot.
You can also design your filter step response from the line 14/15/16, b will be your filter step response, just use a , for example b = pow(a,2) , then replace output in plot by b and use overlay false, you can also plot step , if you like your step response copy the content of b and paste after f(x) => .
Filter Characteristics
The impulse response determine how many of a certain signal you want in your filter, this is also called weighting, you can think of filter design as cooking where your ingredients are the the signal at different periods and the impulse response determine how many of an ingredient you must include in the recipe. The step response can also tell you about your filter characteristics, for example :
This one converge faster to the step function, this mean that the filter will have less lag.
However this one converge slower to the step function, this mean the filter might have more lag but could be smoother.
Be aware that you must find a good weighting balance, else you can have output equals to the signal or just a delayed version of the signal without smoothing.
Real Case
Lets design a sine weighted moving average (swma), this FIR filter use the first 180 degrees of a sine wave function as impulse response.
Impulse response of the swma.
We can design it from the step response without much problems, remember that the impulse response is the derivative of the step response, therefore the derivative of the step response is equal to the first 180 degrees of a sine wave, the derivative of the cosine function is a sine function, therefore :
f(x)=> .5*(1 - cos(x*pi))
And voila.
Designing A BandPass Filter
The bandpass filter like a low-pass and high pass filter, you can think of it as a smooth oscillator.
To design a bandpass filter your step response must be bell shaped, or starting at 0 and ending at 0, for example :
f(x)=>sin(x*pi) give :
Conclusion
Just use fooplot and experiment, you could get nice filters, i will try to post some using this template but it would be really nice to have other people use it. If you need further help pm me.
Thanks for reading !
CCI-MACD Strategy 4.2
I cerchi si basano sull'oscillatore CCI (Commodity Channel Index).
L’indicatore CCI ci permette di osservare se il livello attuale del prezzo è particolarmente al di sopra o al di sotto di una certa media mobile, avente un numero di periodi scelto da noi.
Più la deviazione dal prezzo medio nel breve termine è forte, e maggiormente l’indicatore si allontanerà dallo 0: verso l’alto in caso di uptrend, o verso il basso in caso di downtrend.
Il segnale viene dato quando il valore del CCI supera la linea dello zero.
Il tutto è filtrato con un altro indicatore, il MACD, acronimo di "Moving Average Convergence Divergence", usato per identificare cambiamenti nel momentum del prezzo.
The circles are based on the CCI (Commodity Channel Index) oscillator.
The CCI indicator allows us to observe whether the current price level is significantly above or below a certain moving average, with a number of periods chosen by us.
The greater the deviation from the short-term average price, the further the indicator will deviate from 0: upwards in the case of an uptrend, or downwards in the case of a downtrend.
The signal is given when the CCI value crosses the zero line.
This is all filtered through another indicator, the MACD, which stands for "Moving Average Convergence Divergence," used to identify changes in price momentum.
Setup Score OscillatorSetup Score Oscillator – Full Description
🎯 Purpose of the Script
This script is a manual trading setup scoring tool, designed to help traders quantify the quality of a trade setup by combining multiple technical, cyclical, and contextual signals.
Instead of relying on a single indicator, the trader manually selects which signals are present, and the script calculates a total score (0–100%), displayed as an oscillator in a separate panel (like RSI or MACD).
🔧 How it works in practice
1. Manual signal inputs
The script presents a set of checkboxes in the settings, where the trader can enable/disable the following signals:
✅ Confirmed Support/Resistance
✅ Aligned Volume Profile
✅ Favorable Cyclic Timing
✅ Valid Trend Line
✅ Aligned Cyclical Moving Averages
✅ Relevant Fibonacci Level
✅ Classic Volume Signal (spike, dry-up, etc.)
✅ Oscillator confirmation (e.g., divergences)
✅ Extreme Sentiment
✅ Relevant or incoming News
Each selected signal contributes to the total score based on its weight.
2. Scoring system
Each signal has a default weight (e.g., 20% for support/resistance, 15% for cycles, etc.).
Optionally, the trader can enable the “custom weights” checkbox and adjust each signal’s weight directly in the settings.
3. Score visualization
The final score (sum of all active weights) is plotted as an oscillator ranging from 0 to 100%, with dynamic coloring:
Range Color Meaning
0–39% Red No valid setup
40–54% Yellow Watchlist only
55–69% Orange Good setup
70–100% Green Strong setup
Several horizontal threshold lines are displayed:
50% → neutral threshold
40%, 55%, 70% → operational levels
4. Optional background coloring
When the score exceeds 55% or 70%, the oscillator background lightly changes color to highlight stronger setups (non-intrusive).
📌 Practical benefits
Objectifies subjective analysis: each decision becomes a number.
Prevents overtrading: no entries if the score is too low.
Adaptable to any trading style: swing, intraday, positional.
User-friendly: no coding needed – just tick boxes.
Italiano:
Setup Score Oscillator – Descrizione completa
🎯 Obiettivo dello script
Lo script è uno strumento manuale di valutazione dei setup di trading, pensato per aiutare il trader a quantificare la qualità di un'opportunità operativa basandosi su più segnali tecnici, ciclici e contestuali.
Invece di affidarsi a un solo indicatore, il trader seleziona manualmente quali segnali sono presenti, e lo script calcola un punteggio complessivo percentuale (0–100%), rappresentato come oscillatore in una finestra separata (tipo RSI, MACD, ecc.).
🔧 Come funziona operativamente
1. Input manuale dei segnali
Lo script mostra una serie di checkbox nelle impostazioni, dove il trader può attivare o disattivare i seguenti segnali:
✅ Supporto/Resistenza confermata
✅ Volume Profile allineato
✅ Cicli o timing favorevole
✅ Trend line valida
✅ Medie mobili cicliche allineate
✅ Livello di Fibonacci rilevante
✅ Volume classico significativo (spike, dry-up)
✅ Conferme da oscillatori (es. divergenze)
✅ Sentiment estremo (es. euforia o panico)
✅ News importanti imminenti o appena uscite
Ogni casella attiva contribuisce al punteggio totale, con un peso specifico.
2. Sistema di punteggio
Ogni segnale ha un peso predefinito (es. 20% per supporti/resistenze, 15% per cicli, ecc.).
Facoltativamente, il trader può attivare la funzione “Enable custom weights” per personalizzare i pesi di ciascun segnale direttamente da input.
3. Visualizzazione del punteggio
Il punteggio complessivo (somma dei pesi attivati) viene tracciato come oscillatore da 0 a 100%, con colori dinamici:
Range Colore Significato
0–39% Rosso Nessun setup valido
40–54% Giallo Osservazione
55–69% Arancione Setup buono
70–1005 Verde Setup forte
Sono tracciate anche delle linee guida orizzontali a:
50% → soglia neutra
40%, 55%, 70% → soglie operative
4. Colorazione dello sfondo (facoltativa)
Quando il punteggio supera 55% o 70%, lo sfondo dell’oscillatore cambia leggermente colore per evidenziare il segnale (non invasivo).
📌 Vantaggi pratici
Oggettivizza l’analisi soggettiva: ogni decisione manuale si trasforma in un numero.
Evita overtrading: se il punteggio è troppo basso, non si entra.
Adattabile a ogni stile: swing, intraday, position.
Facile da usare anche senza codice: basta spuntare le caselle.
Open Interest-RSI + Funding + Fractal DivergencesIndicator — “Open Interest-RSI + Funding + Fractal Divergences”
A multi-factor oscillator that fuses Open-Interest RSI, real-time Funding-Rate data and price/OI fractal divergences.
It paints BUY/SELL arrows in its own pane and directly on the price chart, helping you spot spots where crowd positioning, leverage costs and price action contradict each other.
1 Purpose
OI-RSI – measures conviction behind position changes instead of price momentum.
Funding Rate – shows who pays to hold positions (longs → bull bias, shorts → bear bias).
Fractal Divergences – detects HH/LL in price that are not confirmed by OI-RSI.
Optional Funding filter – hides signals when funding is already extreme.
Together these elements highlight exhaustion points and potential mean-reversion trades.
2 Inputs
RSI / Divergence
RSI length – default 14.
High-OI level / Low-OI level – default 70 / 30.
Fractal period n – default 2 (swing width).
Fractals to compare – how many past swings to scan, default 3.
Max visible arrows – keeps last 50 BUY/SELL arrows for speed.
Funding Rate
mode – choose FR, Avg Premium, Premium Index, Avg Prem + PI or FR-candle.
Visual scale (×) – multiplies raw funding to fit 0-100 oscillator scale (default 10).
specify symbol – enable only if funding symbol differs from chart.
use lower tf – averages 1-min premiums for smoother intraday view.
show table – tiny two-row widget at chart edge.
Signal Filter
Use Funding filter – ON hides long signals when funding > Buy-threshold and short signals when funding < Sell-threshold.
BUY threshold (%) – default 0.00 (raw %).
SELL threshold (%) – default 0.00 (raw %).
(Enter funding thresholds as raw percentages, e.g. 0.01 = +0.01 %).
3 Visual Outputs
Sub-pane
Aqua OI-RSI curve with 70 / 50 / 30 reference lines.
Funding visualised according to selected mode (green above 0, red below 0, or other).
BUY / SELL arrows at oscillator extremes.
Price chart
Identical BUY / SELL arrows plotted with force_overlay = true above/below candles that formed qualifying fractals.
Optional table
Shows current asset ticker and latest funding value of the chosen mode.
4 Signal Logic (Summary)
Load _OI series and compute RSI.
Retrieve Funding-Rate + Premium Index (optionally from lower TF).
Find fractal swings (n bars left & right).
Check divergence:
Bearish – price HH + OI-RSI LH.
Bullish – price LL + OI-RSI HL.
If Funding-filter enabled, require funding < Buy-thr (long) or > Sell-thr (short).
Plot arrows and trigger two built-in alerts (Bearish OI-RSI divergence, Bullish OI-RSI divergence).
Signals are fixed once the fractal bar closes; they do not repaint afterwards.
5 How to Use
Attach to a liquid perpetual-futures chart (BTC, ETH, major Binance contracts).
If _OI or funding series is missing you’ll see an error.
Choose timeframe:
15 m – 4 h for intraday;
1 D+ for swing trades.
Lower TFs → more signals; raise Fractals to compare or use Funding filter to trim noise.
Trade checklist
Funding positive and rising → longs overcrowded.
Price makes higher high; OI-RSI makes lower high; Funding above Sell-threshold → consider short.
Reverse logic for longs.
Combine with trend filter (EMA ribbon, SuperTrend, etc.) so you fade only when price is stretched.
Automation – set TradingView alerts on the two alertconditions and send to webhooks/bots.
Performance tips
Keep Max visible arrows ≤ 50.
Disable lower-TF premium aggregation if script feels heavy.
6 Limitations
Some symbols lack _OI or funding history → script stops with a console message.
Binance Premium Index begins mid-2020; older dates show na.
Divergences confirm only after n bars (no forward repaint).
7 Changelog
v1.0 – 10 Jun 2025
Initial public release.
Added price-chart arrows via force_overlay.
Fibonacci Circle Zones🟩 The Fibonacci Circle Zones indicator is a technical visualization tool, building upon the concept of traditional Fibonacci circles. It provides configurable options for analyzing geometric relationships between price and time, used to identify potential support and resistance zones derived from circle-based projections. The indicator constructs these Fibonacci circles based on two user-selected anchor points (Point A and Point B), which define the foundational price range and time duration for the geometric analysis.
Key features include multiple mathematical Circle Formulas for radius scaling and several options for defining the circle's center point, enabling exploration of complex, non-linear geometric relationships between price and time distinct from traditional linear Fibonacci analysis. Available formulas incorporate various mathematical constants (π, e, φ variants, Silver Ratio) alongside traditional Fibonacci ratios, facilitating investigation into different scaling hypotheses. Furthermore, selecting the Center point relative to the A-B anchors allows these circular time-price patterns to be constructed and analyzed from different geometric perspectives. Analysis can be further tailored through detailed customization of up to 12 Fibonacci levels, including their mathematical values, colors, and visibility..
📚 THEORY and CONCEPT 📚
Fibonacci circles represent an application of Fibonacci principles within technical analysis, extending beyond typical horizontal price levels by incorporating the dimension of time. These geometric constructions traditionally use numerical proportions, often derived from the Fibonacci sequence, to project potential zones of price-time interaction, such as support or resistance. A theoretical understanding of such geometric tools involves considering several core components: the significance of the chosen geometric origin or center point , the mathematical principles governing the proportional scaling of successive radii, and the fundamental calculation considerations (like chart scale adjustments and base radius definitions) that influence the resulting geometry and ensure its accurate representation.
⨀ Circle Center ⨀
The traditional construction methodology for Fibonacci circles begins with the selection of two significant anchor points on the chart, usually representing a key price swing, such as a swing low (Point A) and a subsequent swing high (Point B), or vice versa. This defined segment establishes the primary vector—representing both the price range and the time duration of that specific market move. From these two points, a base distance or radius is derived (this calculation can vary, sometimes using the vertical price distance, the time duration, or the diagonal distance). A center point for the circles is then typically established, often at the midpoint (time and price) between points A and B, or sometimes anchored directly at point B.
Concentric circles are then projected outwards from this center point. The radii of these successive circles are calculated by multiplying the base distance by key Fibonacci ratios and other standard proportions. The underlying concept posits that markets may exhibit harmonic relationships or cyclical behavior that adheres to these proportions, suggesting these expanding geometric zones could highlight areas where future price movements might decelerate, reverse, or find equilibrium, reflecting a potential proportional resonance with the initial defining swing in both price and time.
The Fibonacci Circle Zones indicator enhances traditional Fibonacci circle construction by offering greater analytical depth and flexibility: it addresses the origin point of the circles: instead of being limited to common definitions like the midpoint or endpoint B, this indicator provides a selection of distinct center point calculations relative to the initial A-B swing. The underlying idea is that the geometric source from which harmonic projections emanate might vary depending on the market structure being analyzed. This flexibility allows for experimentation with different center points (derived algorithmically from the A, B, and midpoint coordinates), facilitating exploration of how price interacts with circular zones anchored from various perspectives within the defining swing.
Potential Center Points Setup : This view shows the anchor points A and B , defined by the user, which form the basis of the calculations. The indicator dynamically calculates various potential Center points ( C through N , and X ) based on the A-B structure, representing different geometric origins available for selection in the settings.
Point X holds particular significance as it represents the calculated midpoint (in both time and price) between A and B. This 'X' point corresponds to the default 'Auto' center setting upon initial application of the indicator and aligns with the centering logic used in TradingView's standard Fibonacci Circle tool, offering a familiar starting point.
The other potential center points allow for exploring circles originating from different geometric anchors relative to the A-B structure. While detailing the precise calculation for each is beyond the scope of this overview, they can be broadly categorized: points C through H are derived from relationships primarily within the A-B time/price range, whereas points I through N represent centers projected beyond point B, extrapolating the A-B geometry. Point J, for example, is calculated as a reflection of the A-X midpoint projected beyond B. This variety provides a rich set of options for analyzing circle patterns originating from historical, midpoint, and extrapolated future anchor perspectives.
Default Settings (Center X, FibCircle) : Using the default Center X (calculated midpoint) with the default FibCircle . Although circles begin plotting only after Point B is established, their curvature shows they are geometrically centered on X. This configuration matches the standard TradingView Fib Circle tool, providing a baseline.
Centering on Endpoint B : Using Point B, the user-defined end of the swing, as the Center . This anchors the circular projections directly to the swing's termination point. Unlike centering on the midpoint (X) or start point (A), this focuses the analysis on geometric expansion originating precisely from the conclusion of the measured A-B move.
Projected Center J : Using the projected Point J as the Center . Its position is calculated based on the A-B swing (conceptually, it represents a forward projection related to the A-X midpoint relationship) and is located chronologically beyond Point B. This type of forward projection often allows complete circles to be visualized as price develops into the corresponding time zone.
Time Symmetry Projection (Center L) : Uses the projected Point L as the Center . It is located at the price level of the start point (A), projected forward in time from B by the full duration of the A-B swing . This perspective focuses analysis on temporal symmetry , exploring geometric expansions from a point representing a full time cycle completion anchored back at the swing's origin price level.
⭕ Circle Formula
Beyond the center point , the expansion of the projected circles is determined by the selected Circle Formula . This setting provides different mathematical methods, or scaling options , for scaling the circle radii. Each option applies a distinct mathematical constant or relationship to the base radius derived from the A-B swing, allowing for exploration of various geometric proportions.
eScaled
Mathematical Basis: Scales the radius by Euler's number ( e ≈ 2.718), the base of natural logarithms. This constant appears frequently in processes involving continuous growth or decay.
Enables investigation of market geometry scaled by e , exploring relationships potentially based on natural exponential growth applied to time-price circles, potentially relevant for analyzing phases of accelerating momentum or volatility expansion.
FibCircle
Mathematical Basis: Scales the radius to align with TradingView’s built-in Fibonacci Circle Tool.
Provides a baseline circle size, potentially emulating scaling used in standard drawing tools, serving as a reference point for comparison with other options.
GoldenFib
Mathematical Basis: Scales the radius by the Golden Ratio (φ ≈ 1.618).
Explores the fundamental Golden Ratio proportion, central to Fibonacci analysis, applied directly to circular time-price geometry, potentially highlighting zones reflecting harmonic expansion or retracement patterns often associated with φ.
GoldenContour
Mathematical Basis: Scales the radius by a factor derived from Golden Ratio geometry (√(1 + φ²) / 2 ≈ 0.951). It represents a specific geometric relationship derived from φ.
Allows analysis using proportions linked to the geometry of the Golden Rectangle, scaled to produce circles very close to the initial base radius. This explores structural relationships often associated with natural balance or proportionality observed in Golden Ratio constructions.
SilverRatio
Mathematical Basis: Scales the radius by the Silver Ratio (1 + √2 ≈ 2.414). The Silver Ratio governs relationships in specific regular polygons and recursive sequences.
Allows exploration using the proportions of the Silver Ratio, offering a significant expansion factor based on another fundamental metallic mean for comparison with φ-based methods.
PhiDecay
Mathematical Basis: Scales the radius by φ raised to the power of -φ (φ⁻ᵠ ≈ 0.53). This unique exponentiation explores a less common, non-linear transformation involving φ.
Explores market geometry scaled by this specific phi-derived factor which is significantly less than 1.0, offering a distinct contractile proportion for analysis, potentially relevant for identifying zones related to consolidation phases or decaying momentum.
PhiSquared
Mathematical Basis: Scales the radius by φ squared, normalized by dividing by 3 (φ² / 3 ≈ 0.873).
Enables investigation of patterns related to the φ² relationship (a key Fibonacci extension concept), visualized at a scale just below 1.0 due to normalization. This scaling explores projections commonly associated with significant trend extension targets in linear Fibonacci analysis, adapted here for circular geometry.
PiScaled
Mathematical Basis: Scales the radius by Pi (π ≈ 3.141).
Explores direct scaling by the fundamental circle constant (π), investigating proportions inherent to circular geometry within the market's time-price structure, potentially highlighting areas related to natural market cycles, rotational symmetry, or full-cycle completions.
PlasticNumber
Mathematical Basis: Scales the radius by the Plastic Number (approx 1.3247), the third metallic mean. Like φ and the Silver Ratio, it is the solution to a specific cubic equation and relates to certain geometric forms.
Introduces another distinct fundamental mathematical constant for geometric exploration, comparing market proportions to those potentially governed by the Plastic Number.
SilverFib
Mathematical Basis: Scales the radius by the reciprocal Golden Ratio (1/φ ≈ 0.618).
Explores proportions directly related to the core 0.618 Fibonacci ratio, fundamental within Fibonacci-based geometric analysis, often significant for identifying primary retracement levels or corrective wave structures within a trend.
Unscaled
Mathematical Basis: No scaling applied.
Provides the base circle defined by points A/B and the Center setting without any additional mathematical scaling, serving as a pure geometric reference based on the A-B structure.
🧪 Advanced Calculation Settings
Two advanced settings allow further refinement of the circle calculations: matching the chart's scale and defining how the base radius is calculated from the A-B swing.
The Chart Scale setting ensures geometric accuracy by aligning circle calculations with the chart's vertical axis display. Price charts can use either a standard (linear) or logarithmic scale, where vertical distances represent price changes differently. The setting offers two options:
Standard : Select this option when the price chart's vertical axis is set to a standard linear scale.
Logarithmic : It is necessary to select this option if the price chart's vertical axis is set to a logarithmic scale. Doing so ensures the indicator adjusts its calculations to maintain correct geometric proportions relative to the visual price action on the log-scaled chart.
The Radius Calc setting determines how the fundamental base radius is derived from the A-B swing, offering two primary options:
Auto : This is the default setting and represents the traditional method for radius calculation. This method bases the radius calculation on the vertical price range of the A-B swing, focusing the geometry on the price amplitude.
Geometric : This setting provides an alternative calculation method, determining the base radius from the diagonal distance between Point A and Point B. It considers both the price change and the time duration relative to the chart's aspect ratio, defining the radius based on the overall magnitude of the A-B price-time vector.
This choice allows the resulting circle geometry to be based either purely on the swing's vertical price range ( Auto ) or on its combined price-time movement ( Geometric ).
🖼️ CHART EXAMPLES 🖼️
Default Behavior (X Center, FibCircle Formula) : This configuration uses the midpoint ( Center X) and the FibCircle scaling Formula , representing the indicator's effective default setup when 'Auto' is selected for both options initially. This is designed to match the output of the standard TradingView Fibonacci Circle drawing tool.
Center B with Unscaled Formula : This example shows the indicator applied to an uptrend with the Center set to Point B and the Circle Formula set to Unscaled . This configuration projects the defined levels (0.236, 0.382, etc.) as arcs originating directly from the swing's termination point (B) without applying any additional mathematical scaling from the formulas.
Visualization with Projected Center J : Here, circles are centered on the projected point J, calculated from the A-B structure but located forward in time from point B. Notice how using this forward-projected origin allows complete inner circles to be drawn once price action develops into that zone, providing a distinct visual representation of the expanding geometric field compared to using earlier anchor points. ( Unscaled formula used in this example).
PhiSquared Scaling from Endpoint B : The PhiSquared scaling Formula applied from the user-defined swing endpoint (Point B). Radii expand based on a normalized relationship with φ² (the square of the Golden Ratio), creating a unique geometric structure and spacing between the circle levels compared to other formulas like Unscaled or GoldenFib .
Centering on Swing Origin (Point A) : Illustrates using Point A, the user-defined start of the swing, as the circle Center . Note the significantly larger scale and wider spacing of the resulting circles. This difference occurs because centering on the swing's origin (A) typically leads to a larger base radius calculation compared to using the midpoint (X) or endpoint (B). ( Unscaled formula used).
Center Point D : Point D, dynamically calculated from the A-B swing, is used as the origin ( Center =D). It is specifically located at the price level of the swing's start point (A) occurring precisely at the time coordinate of the swing's end point (B). This offers a unique perspective, anchoring the geometric expansion to the initial price level at the exact moment the defining swing concludes. ( Unscaled formula shown).
Center Point G : Point G, also dynamically calculated from the A-B swing, is used as the origin ( Center =G). It is located at the price level of the swing's endpoint (B) occurring at the time coordinate of the start point (A). This provides the complementary perspective to Point D, anchoring the geometric expansion to the final price level achieved but originating from the moment the swing began . As observed in the example, using Point G typically results in very wide circle projections due to its position relative to the core A-B action. ( Unscaled formula shown).
Center Point I: Half-Duration Projection : Using the dynamically calculated Point I as the Center . Located at Point B's price level but projected forward in time by half the A-B swing duration , Point I's calculated time coordinate often falls outside the initially visible chart area. As the chart progresses, this origin point will appear, revealing large, sweeping arcs representing geometric expansions based on a half-cycle temporal projection from the swing's endpoint price. ( Unscaled formula shown).
Center Point M : Point M, also dynamically calculated from the A-B swing, serves as the origin ( Center =M). It combines the midpoint price level (derived from X) with a time coordinate projected forward from Point B by the full duration of the A-B swing . This perspective anchors the geometric expansion to the swing's balance price level but originates from the completion point of a full temporal cycle relative to the A-B move. Like other projected centers, using M allows for complete circles to be visualized as price progresses into its time zone. ( SilverFib formula shown).
Geometric Validation & Functionality : Comparing the indicator (red lines), using its default settings ( Center X, FibCircle Formula ), against TradingView's standard Fib Circle tool (green lines/white background). The precise alignment, particularly visible at the 1.50 and 2.00 levels shown, validates the core geometry calculation.
🛠️ CONFIGURATION AND SETTINGS 🛠️
The Fibonacci Circle Zones indicator offers a range of configurable settings to tailor its functionality and visual representation. These options allow customization of the circle origin, scaling method, level visibility, visual appearance, and input points.
Center and Formula
Settings for selecting the circle origin and scaling method.
Center : Dropdown menu to select the origin point for the circles.
Auto : Automatically uses point X (the calculated midpoint between A and B).
Selectable points including start/end (A, B), midpoint (X), plus various points derived from or projected beyond the A-B swing (C-N).
Circle Formula : Dropdown menu to select the mathematical method for scaling circle radii.
Auto : Automatically selects a default formula ('FibCircle' if Center is 'X', 'Unscaled' otherwise).
Includes standard Fibonacci scaling ( FibCircle, GoldenFib ), other mathematical constants ( PiScaled, eScaled ), metallic means ( SilverRatio ), phi transformations ( PhiDecay, PhiSquared ), and others.
Fib Levels
Configuration options for the 12 individual Fibonacci levels.
Advanced Settings
Settings related to core calculation methods.
Radius Calc : Defines how the base radius is calculated (e.g., 'Auto' for vertical price range, 'Geometric' for diagonal price-time distance).
Chart Scale : Aligns circle calculations with the chart's vertical axis setting ('Standard' or 'Logarithmic') for accurate visual proportions.
Visual Settings
Settings controlling the visual display of the indicator elements.
Plots : Dropdown controlling which parts of the calculated circles are displayed ( Upper , All , or Lower ).
Labels : Dropdown controlling the display of the numerical level value labels ( All , Left , Right , or None ).
Setup : Dropdown controlling the visibility of the initial setup graphics ( Show or Hide ).
Info : Dropdown controlling the visibility of the small information table ( Show or Hide ).
Text Size : Adjusts the font size for all text elements displayed by the indicator (Value ranges from 0 to 36).
Line Width : Adjusts the width of the circle plots (1-10).
Time/Price
Inputs for the anchor points defining the base swing.
These settings define the start (Point A) and end (Point B) of the price swing used for all calculations.
Point A (Time, Price) : Input fields for the exact time coordinate and price level of the swing's starting point (A).
Point B (Time, Price) : Input fields for the exact time coordinate and price level of the swing's ending point (B).
Interactive Adjustment : Points A and B can typically be adjusted directly by clicking and dragging their markers on the chart (if 'Setup' is set to 'Show'). Changes update settings automatically.
📝 NOTES 📝
Fibonacci circles begin plotting only once the time corresponding to Point B has passed and is confirmed on the chart. While potential center locations might be visible earlier (as shown in the setup graphic), the final circle calculations require the complete geometry of the A-B swing. This approach ensures that as new price bars form, the circles are accurately rendered based on the finalized A-B relationship and the chosen center and scaling.
The indicator's calculations are anchored to user-defined start (A) and end (B) points on the chart. When switching between charts with significantly different price scales (e.g., from an index at 5,000 to a crypto asset at $0.50), it is typically necessary to adjust these anchor points to ensure the circle elements are correctly positioned and scaled.
⚠️ DISCLAIMER ⚠️
The Fibonacci Circle Zones indicator is a visual analysis tool designed to illustrate Fibonacci relationships through geometric constructions incorporating curved lines, providing a structured framework for identifying potential areas of price interaction. Like all technical and visual indicators, these visual representations may visually align with key price zones in hindsight, reflecting observed price dynamics. It is not intended as a predictive or standalone trading signal indicator.
The indicator calculates levels and projections using user-defined anchor points and Fibonacci ratios. While it aims to align with TradingView’s standard Fibonacci circle tool by employing mathematical and geometric formulas, no guarantee is made that its calculations are identical to TradingView's proprietary methods.
🧠 BEYOND THE CODE 🧠
The Fibonacci Circle Zones indicator, like other xxattaxx indicators , is designed with education and community collaboration in mind. Its open-source nature encourages exploration, experimentation, and the development of new Fibonacci and grid calculation indicators and tools. We hope this indicator serves as a framework and a starting point for future Innovation and discussions.
Math Art with Fibonacci, Trigonometry, and Constants-AYNETScientific Explanation of the Code
This Pine Script code is a dynamic visual representation that combines mathematical constants, trigonometric functions, and Fibonacci sequences to generate geometrical patterns on a TradingView chart. The code leverages Pine Script’s drawing functions (line.new) and real-time bar data to create evolving shapes. Below is a detailed scientific explanation of its components:
1. Inputs and User-Defined Parameters
num_points: Specifies the number of points used to generate the geometrical pattern. Higher values result in more complex and smoother shapes.
scale: A scaling factor to adjust the size of the shape.
rotation: A dynamic rotation factor that evolves the shape over time based on the bar index (bar_index).
shape_color: Defines the color of the drawn shapes.
2. Mathematical Constants
The script employs essential mathematical constants:
Phi (ϕ): Known as the golden ratio
(
1
+
5
)
/
2
(1+
5
)/2, which governs proportions in Fibonacci spirals and natural growth patterns.
Pi (π): Represents the ratio of a circle's circumference to its diameter, crucial for trigonometric calculations.
Euler’s Number (e): The base of natural logarithms, incorporated in exponential growth modeling.
3. Geometric and Trigonometric Calculations
Fibonacci-Based Radius: The radius for each point is determined using a Fibonacci-inspired formula:
𝑟
=
scale
×
𝜙
⋅
𝑖
num_points
r=scale×
num_points
ϕ⋅i
Here,
𝑖
i is the point index. This ensures the shape grows proportionally based on the golden ratio.
Angle Calculation: The angular position of each point is calculated as:
𝜃
=
𝑖
⋅
Δ
𝜃
+
rotation
⋅
bar_index
100
θ=i⋅Δθ+rotation⋅
100
bar_index
where
Δ
𝜃
=
2
𝜋
num_points
Δθ=
num_points
2π
. This generates evenly spaced points along a circle, with dynamic rotation.
Coordinates: Cartesian coordinates
(
𝑥
,
𝑦
)
(x,y) for each point are derived using:
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=
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⋅
cos
(
𝜃
)
,
𝑦
=
𝑟
⋅
sin
(
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x=r⋅cos(θ),y=r⋅sin(θ)
These coordinates describe a polar-to-Cartesian transformation.
4. Dynamic Line Drawing
Connecting Points: For each pair of consecutive points, a line is drawn using:
line.new
(
𝑥
1
,
𝑦
1
,
𝑥
2
,
𝑦
2
)
line.new(x
1
,y
1
,x
2
,y
2
)
The coordinates are adjusted by:
bar_index: Aligns the x-axis to the chart’s time-based bar index.
int() Conversion: Ensures x-coordinates are integers, as required by line.new.
Line Properties:
Color: Set by the user.
Width: Fixed at 1 for simplicity.
5. Real-Time Adaptation
The shapes evolve dynamically as new bars form:
Rotation Over Time: The rotation parameter modifies angles proportionally to bar_index, creating a rotating effect.
Bar Index Alignment: Shapes are positioned relative to the current bar on the chart, ensuring synchronization with market data.
6. Visualization and Applications
This script generates evolving geometrical shapes, which have both aesthetic and educational value. Potential applications include:
Mathematical Visualization: Demonstrating the interplay of Fibonacci sequences, trigonometry, and geometry.
Technical Analysis: Serving as a visual overlay for price movement patterns, highlighting cyclical or wave-like behavior.
Dynamic Art: Creating visually appealing and evolving patterns on financial charts.
Scientific Relevance
This code synthesizes principles from:
Mathematical Analysis: Incorporates constants and formulas central to calculus, trigonometry, and algebra.
Geometry: Visualizes patterns derived from polar coordinates and Fibonacci scaling.
Real-Time Systems: Adapts dynamically to market data, showcasing practical applications of mathematics in financial visualization.
If further optimization or additional functionality is required, let me know! 😊
VWAP Bands [TradingFinder] 26 Brokers Data (Forex + Crypto)🔵 Introduction
Indicators are tools that help analysts predict the price trend of a stock through mathematical calculations on price or trading volume. It is evident that trading volume significantly impacts the price trend of a stock symbol.
The Volume-Weighted Average Price (VWAP) indicator combines the influence of trading volume and price, providing technical analysts with a practical tool.
This technical indicator determines the volume-weighted average price of a symbol over a specified time period. Consequently, this indicator can be used to identify trends and entry or exit points.
🟣 Calculating the VWAP Indicator
Adding the VWAP indicator to a chart will automatically perform all calculations for you. However, if you wish to understand how this indicator is calculated, the following explains the steps involved.
Consider a 5-minute chart. In the first candle of this chart (which represents price information in the first 5 minutes), sum the high, low, and close prices, and divide by 3. Multiply the resulting number by the volume for the period and call it a variable (e.g., X).
Then, divide the resulting output by the total volume for that period to calculate your VWAP. To maintain the VWAP sequence throughout the trading day, it is necessary to add the X values obtained from each period to the previous period and divide by the total volume up to that time. It is worth noting that the calculation method is the same for intervals shorter than a day.
The mathematical formula for this VWAP indicator : VWAP = ∑ (Pi×Vi) / ∑ Vi
🔵 How to Use
Traders might consider the VWAP indicator as a tool for predicting trends. For example, they might buy a stock when the price is above the VWAP level and sell it when the price is below the VWAP.
In other words, when the price is above the VWAP, the price is rising, and when it is below the VWAP, the price is falling. Major traders and investment funds also use the VWAP ratio to help enter or exit stocks with the least possible market impact.
It is important to note that one should not rely solely on the VWAP indicator when analyzing symbols. This is because if prices rise quickly, the VWAP indicator may not adequately describe the conditions. This indicator is generally used for daily or shorter time frames because using longer intervals can distort the average.
Since this indicator uses past data in its calculations, it can be considered a lagging indicator. As a result, the more data there is, the greater the delay.
🟣 Difference Between VWAP and Simple Moving Average
On a chart, the VWAP and the simple moving average may look similar, but these two indicators have different calculations. The VWAP calculates the total price considering volume, while the simple moving average does not consider volume.
In simpler terms, the VWAP indicator measures each day's price change relative to the trading volume that occurred that day. In contrast, the simple moving average implicitly assumes that all trading days have the same volume.
🟣 Reasons Why Traders Like the VWAP Indicator
The VWAP Considers Volume: Since VWAP takes volume into account, it can be more reliable than a simple arithmetic average of prices. Theoretically, one person can buy 200,000 shares of a symbol in one transaction at a single price.
However, during the same time frame, 100 other people might place 200 different orders at various prices that do not total 100,000 shares. In this case, if you only consider the average price, you might be mistaken because trading volume is ignored.
The Indicator Can Help Day Traders: While reviewing your trades, you might notice that the shares you bought at market price are trading below the VWAP indicator.
In this case, there's no need to worry because with the help of VWAP, you always get a price below the average. By knowing the volume-weighted average price of a stock, you can easily make an informed decision about paying more or less than other traders for the stock.
VWAP Can Signal Market Trend Changes: Buying low and selling high can be an excellent strategy for individuals. However, you are looking to buy when prices start to rise and sell your shares when prices start to fall.
Since the VWAP indicator simulates a balanced price in the market, when the price crosses above the VWAP line, one can assume that traders are willing to pay more to acquire shares, and as a result, the market will grow. Conversely, when the price crosses below the line, this can be considered a sign of a downward movement.
🔵 Setting
Period : Indicator calculation time frame.
Source : The Price used for calculations.
Market Ultra Data : If you turn on this feature, 26 large brokers will be included in the calculation of the trading volume.
The advantage of this capability is to have more reliable volume data. You should be careful to specify the market you are in, FOREX brokers and Crypto brokers are different.
Multiplier : Coefficient of band lines.
Trend Forecasting - The Quant Science🌏 Trend Forecasting | ENG 🌏
This plug-in acts as a statistical filter, adding new information to your chart that will allow you to quickly verify the direction of a trend and the probability with which the price will be above or below the average in the future, helping you to uncover probable market inefficiencies.
🧠 Model calculation
The model calculates the arithmetic mean in relation to positive and negative events within the available sample for the selected time series. Where a positive event is defined as a closing price greater than the average, and a negative event as a closing price less than the average. Once all events have been calculated, the probabilities are extrapolated by relating each event.
Example
Positive event A: 70
Negative event B: 30
Total events: 100
Probabilities A: (100 / 70) x 100 = 70%
Probabilities B: (100 / 30) x 100 = 30%
Event A has a 70% probability of occurring compared to Event B which has a 30% probability.
🔍 Information Filter
The data on the graph show the future probabilities of prices being above average (default in green) and the probabilities of prices being below average (default in red).
The information that can be quickly retrieved from this indicator is:
1. Trend: Above-average prices together with a constant of data in green greater than 50% + 1 indicate that the observed historical series shows a bullish trend. The probability is correlated proportionally to the value of the data; the higher and increasing the expected value, the greater the observed bullish trend. On the other hand, a below-average price together with a red-coloured data constant show quantitative data regarding the presence of a bearish trend.
2. Future Probability: By analysing the data, it is possible to find the probability with which the price will be above or below the average in the future. In green are classified the probabilities that the price will be higher than the average, in red are classified the probabilities that the price will be lower than the average.
🔫 Operational Filter .
The indicator can be used operationally in the search for investment or trading opportunities given its ability to identify an inefficiency within the observed data sample.
⬆ Bullish forecast
For bullish trades, the inefficiency will appear as a historical series with a bullish trend, with high probability of a bullish trend in the future that is currently below the average.
⬇ Bearish forecast
For short trades, the inefficiency will appear as a historical series with a bearish trend, with a high probability of a bearish trend in the future that is currently above the average.
📚 Settings
Input: via the Input user interface, it is possible to adjust the periods (1 to 500) with which the average is to be calculated. By default the periods are set to 200, which means that the average is calculated by taking the last 200 periods.
Style: via the Style user interface it is possible to adjust the colour and switch a specific output on or off.
🇮🇹Previsione Della Tendenza Futura | ITA 🇮🇹
Questo plug-in funge da filtro statistico, aggiungendo nuove informazioni al tuo grafico che ti permetteranno di verificare rapidamente tendenza di un trend, probabilità con la quale il prezzo si troverà sopra o sotto la media in futuro aiutandoti a scovare probabili inefficienze di mercato.
🧠 Calcolo del modello
Il modello calcola la media aritmetica in relazione con gli eventi positivi e negativi all'intero del campione disponibile per la serie storica selezionata. Dove per evento positivo si intende un prezzo alla chiusura maggiore della media, mentre per evento negativo si intende un prezzo alla chiusura minore della media. Calcolata la totalità degli eventi le probabilità vengono estrapolate rapportando ciascun evento.
Esempio
Evento positivo A: 70
Evento negativo B: 30
Totale eventi : 100
Formula A: (100 / 70) x 100 = 70%
Formula B: (100 / 30) x 100 = 30%
Evento A ha una probabilità del 70% di realizzarsi rispetto all' Evento B che ha una probabilità pari al 30%.
🔍 Filtro informativo
I dati sul grafico mostrano le probabilità future che i prezzi siano sopra la media (di default in verde) e le probabilità che i prezzi siano sotto la media (di default in rosso).
Le informazioni che si possono rapidamente reperire da questo indicatore sono:
1. Trend: I prezzi sopra la media insieme ad una costante di dati in verde maggiori al 50% + 1 indicano che la serie storica osservata presenta un trend rialzista. La probabilità è correlata proporzionalmente al valore del dato; tanto più sarà alto e crescente il valore atteso e maggiore sarà la tendenza rialzista osservata. Viceversa, un prezzo sotto la media insieme ad una costante di dati classificati in colore rosso mostrano dati quantitativi riguardo la presenza di una tendenza ribassista.
2. Probabilità future: analizzando i dati è possibile reperire la probabilità con cui il prezzo si troverà sopra o sotto la media in futuro. In verde vengono classificate le probabilità che il prezzo sarà maggiore alla media, in rosso vengono classificate le probabilità che il prezzo sarà minore della media.
🔫 Filtro operativo
L' indicatore può essere utilizzato a livello operativo nella ricerca di opportunità di investimento o di trading vista la capacità di identificare un inefficienza all'interno del campione di dati osservato.
⬆ Previsione rialzista
Per operatività di tipo rialzista l'inefficienza apparirà come una serie storica a tendenza rialzista, con alte probabilità di tendenza rialzista in futuro che attualmente si trova al di sotto della media.
⬇ Previsione ribassista
Per operatività di tipo short l'inefficienza apparirà come una serie storica a tendenza ribassista, con alte probabilità di tendenza ribassista in futuro che si trova attualmente sopra la media.
📚 Impostazioni
Input: tramite l'interfaccia utente Input è possibile regolare i periodi (da 1 a 500) con cui calcolare la media. Di default i periodi sono impostati sul valore di 200, questo significa che la media viene calcolata prendendo gli ultimi 200 periodi.
Style: tramite l'interfaccia utente Style è possibile regolare il colore e attivare o disattivare un specifico output.
Stock Rating [TrendX_]# OVERVIEW
This Stock Rating indicator provides a thorough evaluation of a company (NON-FINANCIAL ONLY) ranging from 0 to 5. The rating is the average of six core financial metrics: efficiency, profitability, liquidity, solvency, valuation, and technical ratings. Each metric encompasses several financial measurements to ensure a robust and holistic evaluation of the stock.
## EFFICIENCY METRICS
1. Asset-to-Liability Ratio : Measures a company's ability to cover its liabilities with its assets.
2. Equity-to-Liability Ratio : Indicates the proportion of equity used to finance the company relative to liabilities.
3. Net Margin : Shows the percentage of revenue that translates into profit.
4. Operating Expense : Reflects the costs required for normal business operations.
5. Operating Expense Ratio : Compares operating expenses to total revenue.
6. Operating Profit Ratio : Measures operating profit as a percentage of revenue.
7. PE to Industry Relative PE/PB : Compares the company's PE ratio to the industry average.
## PROFITABILITY METRICS
1. ROA : Indicates how efficiently a company uses its assets to generate profit.
2. ROE : Measures profitability relative to shareholders' equity.
3. EBITDA : Reflects a company's operational profitability.
4. Free Cash Flow Margin : Shows the percentage of revenue that remains as free cash flow.
5. Revenue Growth : Measures the percentage increase in revenue over a period.
6. Gross Margin : Reflects the percentage of revenue exceeding the cost of goods sold.
7. Net Margin : Percentage of revenue that is net profit.
8. Operating Margin : Measures the percentage of revenue that is operating profit.
## LIQUIDITY METRICS
1. Current Ratio : Indicates the ability to cover short-term obligations with short-term assets.
2. Interest Coverage Ratio : Measures the ability to pay interest on outstanding debt.
3. Debt-to-EBITDA : Compares total debt to EBITDA.
4. Debt-to-Equity Ratio : Indicates the relative proportion of debt and equity financing.
## SOLVENCY METRICS
1. Altman Z-score : Predicts bankruptcy risk
2. Beneish M-score : Detects earnings manipulation.
3. Fulmer H-factor : Predicts business failure risk.
## VALUATION METRICS
1. Industry Relative PE/PB Comparison : Compares the company's PE and PB ratios to industry averages.
2. Momentum of PE, PB, and EV/EBITDA Multiples : Tracks the trends of PE, PB, and EV/EBITDA ratios over time.
## TECHNICAL METRICS
1. Relative Strength Index (RSI) : Measures the speed and change of price movements.
2. Supertrend : Trend-following indicator that identifies market trends.
3. Moving Average Golden-Cross : Occurs when a short-term MA crosses above mid-term and long-term MA which are determined by half-PI increment in smoothing period.
4. On-Balance Volume Golden-Cross : Measures cumulative buying and selling pressure.
Trend Angle IndicatorTrend Angle Indicator
Description
The Trend Angle Indicator is designed to measure the strength of a trend by calculating the angle of the trend.
Specifically, it computes the angle of a Simple Moving Average (SMA) over a specified length and then applies
an Exponential Moving Average (EMA) to the angle for smoothing.
This approach provides a clear indication of the trend's direction and intensity.
It also includes customizable alerts for significant changes in the trend angle and zero-line crossings,
making it a robust tool for traders seeking to gauge market momentum.
Key Features
- **Trend Angle Calculation**: Measures the trend's angle, providing insights into trend direction and strength.
- **SMA and EMA**: Uses SMA for the base calculation and EMA for smoothening the angle values.
- **Visual Trend Indication**: Visually indicates uptrends and downtrends with customizable colors - red and green.
- **Alerts**: Configurable alerts for significant changes in trend angle and zero-line crossings.
Calculation Methodology
1. **Simple Moving Average (SMA):**
- The script calculates the SMA of the close price over a user-defined `input_length`.
2. **Angle Calculation:**
- The height of the trend is calculated by subtracting the SMA value from the SMA value `input_length` bars ago. A higher angle value indicates a stronger trend.
- The angle in degrees is obtained using the arctangent function: \
3. **Exponential Moving Average (EMA):**
- Applies an EMA to the calculated angle to smooth out the values based on a user-defined `input_ma_length`.
4. **Trend Detection:**
The color of the angle plot and filled area provide a quick visual representation of the current trend direction
- The trend angle changes are monitored and visualized with color-coded plots.
- Uptrend: Angle >= 0 uses `upColor` (green).
- Downtrend: Angle < 0 uses `downColor` (red).
#### Using the Indicator
1. **Adding the Indicator:**
- Add the indicator to your TradingView chart by selecting it from the Pine Script library or by pasting the script into the Pine Script editor.
2. **Inputs:**
- **Length**: Defines the period for the SMA calculation.
- **MA Length**: Sets the period for the EMA smoothing.
- **Angle Change Threshold (degrees)**: Defines the threshold for significant angle change alerts.
- **Color Candles**: Optionally colorizes the price candles based on the angle's trend direction.
3. **Customizing Plots:**
- **Angle Plot**: Displays the EMA of the trend angle. The color changes based on whether the trend is up or down.
- **Zero Line**: A horizontal line at zero to easily visualize crossings that signify a change in trend direction.
- **Fill Color**: Fills the area above/below the zero line with colors representing the direction of the trend.
4. **Setting Alerts:**
- **Cross Above Zero**: Triggers an alert when the trend angle crosses above zero, indicating a potential start of an uptrend.
- **Cross Below Zero**: Triggers an alert when the trend angle crosses below zero, indicating a potential start of a downtrend.
- **Significant Angle Change**: Alerts when the angle change exceeds the user-defined threshold, highlighting significant trend changes.
#### Example Usage
To use and customize the Trend Angle Indicator on your chart:
1. **Add to Chart**: Apply the indicator from the TradingView library or by pasting the script into the Pine Script editor.
2. **Configure Inputs**:
- Adjust the `Length` to set the period for the SMA.
- Set the `MA Length` for the EMA smoothing.
- Define the `Angle Change Threshold` for receiving alerts on significant changes.
3. **Display Customization**:
- Enable `Color Candles` to have the price candles reflect the trend direction.
4. **Set Alerts**:
- Use the alert conditions provided to get notified about critical events like zero line crossings or significant angle changes.
