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Statistics

Uptrend Downtrend Loopback Candle Identification LibThis library is for identifying uptrends and downtrends using a loopback candle analysis method. Which contains two functions: uptrendLoopbackCandleIdentification() and downtrendLoopbackCandleIdentification() . These functions check if the current candle is part of an uptrend or downtrend, respectively, based on the specified lookback period. The uptrendLoopbackCandleIdentification() takes two arguments: index , which is the index of the current bar, and lookbackPeriod , which is the number of previous candles to check for an uptrend. The function returns false if the index is less than the lookback period. Otherwise, it initializes a boolean variable isHigherHigh as true and loops through the previous candles. If any of the previous candles have a higher high than the current candle, isHigherHigh is set to false , and the loop breaks. Finally, the function returns the value of isHigherHigh . The downtrendLoopbackCandleIdentification() takes the same arguments and returns false if the index is less than the lookback period. The function initializes a boolean variable isHigherLow as true and loops through the previous candles. If any of the previous candles have a higher low than the current candle, isHigherLow is set to false , and the loop breaks. The function returns the value of isHigherLow .
مكتبة Pine Script™
من ‎AugustoErni‎
MathEasingFunctionsLibrary "MathEasingFunctions" A collection of Easing functions. Easing functions are commonly used for smoothing actions over time, They are used to smooth out the sharp edges of a function and make it more pleasing to the eye, like for example the motion of a object through time. Easing functions can be used in a variety of applications, including animation, video games, and scientific simulations. They are a powerful tool for creating realistic visual effects and can help to make your work more engaging and enjoyable to the eye. --- Includes functions for ease in, ease out, and, ease in and out, for the following constructs: sine, quadratic, cubic, quartic, quintic, exponential, elastic, circle, back, bounce. --- Reference: easings.net learn.microsoft.com ease_in_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine_unbound(v)   Sinusoidal function, the position over elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_sine(v)   Sinusoidal function, the position over elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad_unbound(v)   Quadratic function, the position equals the square of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quad(v)   Quadratic function, the position equals the square of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic_unbound(v)   Cubic function, the position equals the cube of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_cubic(v)   Cubic function, the position equals the cube of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart_unbound(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quart(v)   Quartic function, the position equals the formula `f(t)=t^4` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint_unbound(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_quint(v)   Quintic function, the position equals the formula `f(t)=t^5` of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo_unbound(v)   Exponential function, the position equals the exponential formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_expo(v)   Exponential function, the position equals the exponential formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ_unbound(v)   Circular function, the position equals the circular formula of elapsed time (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_circ(v)   Circular function, the position equals the circular formula of elapsed time (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back_unbound(v)   Back function, the position retreats a bit before resuming (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_back(v)   Back function, the position retreats a bit before resuming (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic_unbound(v)   Elastic function, the position oscilates back and forth like a spring (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_elastic(v)   Elastic function, the position oscilates back and forth like a spring (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce_unbound(v)   Bounce function, the position bonces from the boundery (unbound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. ease_in_out_bounce(v)   Bounce function, the position bonces from the boundery (bound).   Parameters:      v (float) : `float` Elapsed time.   Returns: Ratio of change. select(v, formula, effect, bounded)   Parameters:      v (float)      formula (string)      effect (string)      bounded (bool)
مكتبة Pine Script™
من ‎RicardoSantos‎
5
UtilityLibrary "Utility" dema(src, length)   Parameters:      src (float)      length (simple int) tema(src, length)   Parameters:      src (float)      length (simple int) hma(src, length)   Parameters:      src (float)      length (int) zlema(src, length)   Parameters:      src (float)      length (simple int) stochRSI(src, lengthRSI, lengthStoch, smoothK, smoothD)   Parameters:      src (float)      lengthRSI (simple int)      lengthStoch (int)      smoothK (int)      smoothD (int) slope(src, length)   Parameters:      src (float)      length (int)
مكتبة Pine Script™
من ‎derekren321‎
loxxfftLibrary "loxxfft" This code is a library for performing Fast Fourier Transform (FFT) operations. FFT is an algorithm that can quickly compute the discrete Fourier transform (DFT) of a sequence. The library includes functions for performing FFTs on both real and complex data. It also includes functions for fast correlation and convolution, which are operations that can be performed efficiently using FFTs. Additionally, the library includes functions for fast sine and cosine transforms. Reference: www.alglib.net fastfouriertransform(a, nn, inversefft)   Returns Fast Fourier Transform   Parameters:      a (float ) : float , An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices.      nn (int) : int, The number of function values. It must be a power of two, but the algorithm does not validate this.      inversefft (bool) : bool, A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT.   Returns: float , Modifies the input array a in-place, which means that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The transformed data will have real and imaginary parts interleaved, with the real parts at even indices and the imaginary parts at odd indices. realfastfouriertransform(a, tnn, inversefft)   Returns Real Fast Fourier Transform   Parameters:      a (float ) : float , A float array containing the real-valued function samples.      tnn (int) : int, The number of function values (must be a power of 2, but the algorithm does not validate this condition).      inversefft (bool) : bool, A boolean flag that indicates the direction of the transformation (True for inverse, False for direct).   Returns: float , Modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. fastsinetransform(a, tnn, inversefst)   Returns Fast Discrete Sine Conversion   Parameters:      a (float ) : float , An array of real numbers representing the function values.      tnn (int) : int, Number of function values (must be a power of two, but the code doesn't validate this).      inversefst (bool) : bool, A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST.   Returns: float , The output is the transformed array 'a', which will contain the result of the transformation. fastcosinetransform(a, tnn, inversefct)   Returns Fast Discrete Cosine Transform   Parameters:      a (float ) : float , This is a floating-point array representing the sequence of values (time-domain) that you want to transform. The function will perform the Fast Cosine Transform (FCT) or the inverse FCT on this input array, depending on the value of the inversefct parameter. The transformed result will also be stored in this same array, which means the function modifies the input array in-place.      tnn (int) : int, This is an integer value representing the number of data points in the input array a. It is used to determine the size of the input array and control the loops in the algorithm. Note that the size of the input array should be a power of 2 for the Fast Cosine Transform algorithm to work correctly.      inversefct (bool) : bool, This is a boolean value that controls whether the function performs the regular Fast Cosine Transform or the inverse FCT. If inversefct is set to true, the function will perform the inverse FCT, and if set to false, the regular FCT will be performed. The inverse FCT can be used to transform data back into its original form (time-domain) after the regular FCT has been applied.   Returns: float , The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array. fastconvolution(signal, signallen, response, negativelen, positivelen)   Convolution using FFT   Parameters:      signal (float ) : float , This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1.      signallen (int) : int, This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array.      response (float ) : float , This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively.      negativelen (int) : int, This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero.      positivelen (int) : int, This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero.   Returns: float , The resulting convolved values are stored back in the input signal array. fastcorrelation(signal, signallen, pattern, patternlen)   Returns Correlation using FFT   Parameters:      signal (float ) : float ,This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1.      signallen (int) : int, This is an integer representing the length of the input signal array.      pattern (float ) : float , This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1.      patternlen (int) : int, This is an integer representing the length of the pattern array.   Returns: float , The signal array containing the correlation values at points from 0 to SignalLen-1. tworealffts(a1, a2, a, b, tn)   Returns Fast Fourier Transform of Two Real Functions   Parameters:      a1 (float ) : float , An array of real numbers, representing the values of the first function.      a2 (float ) : float , An array of real numbers, representing the values of the second function.      a (float ) : float , An output array to store the Fourier transform of the first function.      b (float ) : float , An output array to store the Fourier transform of the second function.      tn (int) : float , An integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition.   Returns: float , The a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b. █ Detailed explaination of each function Fast Fourier Transform The fastfouriertransform() function takes three input parameters: 1. a: An array of real and imaginary parts of the function values. The real part is stored at even indices, and the imaginary part is stored at odd indices. 2. nn: The number of function values. It must be a power of two, but the algorithm does not validate this. 3. inversefft: A boolean value that indicates the direction of the transformation. If True, it performs the inverse FFT; if False, it performs the direct FFT. The function performs the FFT using the Cooley-Tukey algorithm, which is an efficient algorithm for computing the discrete Fourier transform (DFT) and its inverse. The Cooley-Tukey algorithm recursively breaks down the DFT of a sequence into smaller DFTs of subsequences, leading to a significant reduction in computational complexity. The algorithm's time complexity is O(n log n), where n is the number of samples. The fastfouriertransform() function first initializes variables and determines the direction of the transformation based on the inversefft parameter. If inversefft is True, the isign variable is set to -1; otherwise, it is set to 1. Next, the function performs the bit-reversal operation. This is a necessary step before calculating the FFT, as it rearranges the input data in a specific order required by the Cooley-Tukey algorithm. The bit-reversal is performed using a loop that iterates through the nn samples, swapping the data elements according to their bit-reversed index. After the bit-reversal operation, the function iteratively computes the FFT using the Cooley-Tukey algorithm. It performs calculations in a loop that goes through different stages, doubling the size of the sub-FFT at each stage. Within each stage, the Cooley-Tukey algorithm calculates the butterfly operations, which are mathematical operations that combine the results of smaller DFTs into the final DFT. The butterfly operations involve complex number multiplication and addition, updating the input array a with the computed values. The loop also calculates the twiddle factors, which are complex exponential factors used in the butterfly operations. The twiddle factors are calculated using trigonometric functions, such as sine and cosine, based on the angle theta. The variables wpr, wpi, wr, and wi are used to store intermediate values of the twiddle factors, which are updated in each iteration of the loop. Finally, if the inversefft parameter is True, the function divides the result by the number of samples nn to obtain the correct inverse FFT result. This normalization step is performed using a loop that iterates through the array a and divides each element by nn. In summary, the fastfouriertransform() function is an implementation of the Cooley-Tukey FFT algorithm, which is an efficient algorithm for computing the DFT and its inverse. This FFT library can be used for a variety of applications, such as signal processing, image processing, audio processing, and more. Feal Fast Fourier Transform The realfastfouriertransform() function performs a fast Fourier transform (FFT) specifically for real-valued functions. The FFT is an efficient algorithm used to compute the discrete Fourier transform (DFT) and its inverse, which are fundamental tools in signal processing, image processing, and other related fields. This function takes three input parameters: 1. a - A float array containing the real-valued function samples. 2. tnn - The number of function values (must be a power of 2, but the algorithm does not validate this condition). 3. inversefft - A boolean flag that indicates the direction of the transformation (True for inverse, False for direct). The function modifies the input array a in-place, meaning that the transformed data (the FFT result for direct transformation or the inverse FFT result for inverse transformation) will be stored in the same array a after the function execution. The algorithm uses a combination of complex-to-complex FFT and additional transformations specific to real-valued data to optimize the computation. It takes into account the symmetry properties of the real-valued input data to reduce the computational complexity. Here's a detailed walkthrough of the algorithm: 1. Depending on the inversefft flag, the initial values for ttheta, c1, and c2 are determined. These values are used for the initial data preprocessing and post-processing steps specific to the real-valued FFT. 2. The preprocessing step computes the initial real and imaginary parts of the data using a combination of sine and cosine terms with the input data. This step effectively converts the real-valued input data into complex-valued data suitable for the complex-to-complex FFT. 3. The complex-to-complex FFT is then performed on the preprocessed complex data. This involves bit-reversal reordering, followed by the Cooley-Tukey radix-2 decimation-in-time algorithm. This part of the code is similar to the fastfouriertransform() function you provided earlier. 4. After the complex-to-complex FFT, a post-processing step is performed to obtain the final real-valued output data. This involves updating the real and imaginary parts of the transformed data using sine and cosine terms, as well as the values c1 and c2. 5. Finally, if the inversefft flag is True, the output data is divided by the number of samples (nn) to obtain the inverse DFT. The function does not return a value explicitly. Instead, the transformed data is stored in the input array a. After the function execution, you can access the transformed data in the a array, which will have the real part at even indices and the imaginary part at odd indices. Fast Sine Transform This code defines a function called fastsinetransform that performs a Fast Discrete Sine Transform (FST) on an array of real numbers. The function takes three input parameters: 1. a (float array): An array of real numbers representing the function values. 2. tnn (int): Number of function values (must be a power of two, but the code doesn't validate this). 3. inversefst (bool): A boolean flag indicating the direction of the transformation. If True, it performs the inverse FST, and if False, it performs the direct FST. The output is the transformed array 'a', which will contain the result of the transformation. The code starts by initializing several variables, including trigonometric constants for the sine transform. It then sets the first value of the array 'a' to 0 and calculates the initial values of 'y1' and 'y2', which are used to update the input array 'a' in the following loop. The first loop (with index 'jx') iterates from 2 to (tm + 1), where 'tm' is half of the number of input samples 'tnn'. This loop is responsible for calculating the initial sine transform of the input data. The second loop (with index 'ii') is a bit-reversal loop. It reorders the elements in the array 'a' based on the bit-reversed indices of the original order. The third loop (with index 'ii') iterates while 'n' is greater than 'mmax', which starts at 2 and doubles each iteration. This loop performs the actual Fast Discrete Sine Transform. It calculates the sine transform using the Danielson-Lanczos lemma, which is a divide-and-conquer strategy for calculating Discrete Fourier Transforms (DFTs) efficiently. The fourth loop (with index 'ix') is responsible for the final phase adjustments needed for the sine transform, updating the array 'a' accordingly. The fifth loop (with index 'jj') updates the array 'a' one more time by dividing each element by 2 and calculating the sum of the even-indexed elements. Finally, if the 'inversefst' flag is True, the code scales the transformed data by a factor of 2/tnn to get the inverse Fast Sine Transform. In summary, the code performs a Fast Discrete Sine Transform on an input array of real numbers, either in the direct or inverse direction, and returns the transformed array. The algorithm is based on the Danielson-Lanczos lemma and uses a divide-and-conquer strategy for efficient computation. Fast Cosine Transform This code defines a function called fastcosinetransform that takes three parameters: a floating-point array a, an integer tnn, and a boolean inversefct. The function calculates the Fast Cosine Transform (FCT) or the inverse FCT of the input array, depending on the value of the inversefct parameter. The Fast Cosine Transform is an algorithm that converts a sequence of values (time-domain) into a frequency domain representation. It is closely related to the Fast Fourier Transform (FFT) and can be used in various applications, such as signal processing and image compression. Here's a detailed explanation of the code: 1. The function starts by initializing a number of variables, including counters, intermediate values, and constants. 2. The initial steps of the algorithm are performed. This includes calculating some trigonometric values and updating the input array a with the help of intermediate variables. 3. The code then enters a loop (from jx = 2 to tnn / 2). Within this loop, the algorithm computes and updates the elements of the input array a. 4. After the loop, the function prepares some variables for the next stage of the algorithm. 5. The next part of the algorithm is a series of nested loops that perform the bit-reversal permutation and apply the FCT to the input array a. 6. The code then calculates some additional trigonometric values, which are used in the next loop. 7. The following loop (from ix = 2 to tnn / 4 + 1) computes and updates the elements of the input array a using the previously calculated trigonometric values. 8. The input array a is further updated with the final calculations. 9. In the last loop (from j = 4 to tnn), the algorithm computes and updates the sum of elements in the input array a. 10. Finally, if the inversefct parameter is set to true, the function scales the input array a to obtain the inverse FCT. The resulting transformed array is stored in the input array a. This means that the function modifies the input array in-place and does not return a new array. Fast Convolution This code defines a function called fastconvolution that performs the convolution of a given signal with a response function using the Fast Fourier Transform (FFT) technique. Convolution is a mathematical operation used in signal processing to combine two signals, producing a third signal representing how the shape of one signal is modified by the other. The fastconvolution function takes the following input parameters: 1. float signal: This is an array of real numbers representing the input signal that will be convolved with the response function. The elements are numbered from 0 to SignalLen-1. 2. int signallen: This is an integer representing the length of the input signal array. It specifies the number of elements in the signal array. 3. float response: This is an array of real numbers representing the response function used for convolution. The response function consists of two parts: one corresponding to positive argument values and the other to negative argument values. Array elements with numbers from 0 to NegativeLen match the response values at points from -NegativeLen to 0, respectively. Array elements with numbers from NegativeLen+1 to NegativeLen+PositiveLen correspond to the response values in points from 1 to PositiveLen, respectively. 4. int negativelen: This is an integer representing the "negative length" of the response function. It indicates the number of elements in the response function array that correspond to negative argument values. Outside the range , the response function is considered zero. 5. int positivelen: This is an integer representing the "positive length" of the response function. It indicates the number of elements in the response function array that correspond to positive argument values. Similar to negativelen, outside the range , the response function is considered zero. The function works by: 1. Calculating the length nl of the arrays used for FFT, ensuring it's a power of 2 and large enough to hold the signal and response. 2. Creating two new arrays, a1 and a2, of length nl and initializing them with the input signal and response function, respectively. 3. Applying the forward FFT (realfastfouriertransform) to both arrays, a1 and a2. 4. Performing element-wise multiplication of the FFT results in the frequency domain. 5. Applying the inverse FFT (realfastfouriertransform) to the multiplied results in a1. 6. Updating the original signal array with the convolution result, which is stored in the a1 array. The result of the convolution is stored in the input signal array at the function exit. Fast Correlation This code defines a function called fastcorrelation that computes the correlation between a signal and a pattern using the Fast Fourier Transform (FFT) method. The function takes four input arguments and modifies the input signal array to store the correlation values. Input arguments: 1. float signal: This is an array of real numbers representing the signal to be correlated with the pattern. The elements are numbered from 0 to SignalLen-1. 2. int signallen: This is an integer representing the length of the input signal array. 3. float pattern: This is an array of real numbers representing the pattern to be correlated with the signal. The elements are numbered from 0 to PatternLen-1. 4. int patternlen: This is an integer representing the length of the pattern array. The function performs the following steps: 1. Calculate the required size nl for the FFT by finding the smallest power of 2 that is greater than or equal to the sum of the lengths of the signal and the pattern. 2. Create two new arrays a1 and a2 with the length nl and initialize them to 0. 3. Copy the signal array into a1 and pad it with zeros up to the length nl. 4. Copy the pattern array into a2 and pad it with zeros up to the length nl. 5. Compute the FFT of both a1 and a2. 6. Perform element-wise multiplication of the frequency-domain representation of a1 and the complex conjugate of the frequency-domain representation of a2. 7. Compute the inverse FFT of the result obtained in step 6. 8. Store the resulting correlation values in the original signal array. At the end of the function, the signal array contains the correlation values at points from 0 to SignalLen-1. Fast Fourier Transform of Two Real Functions This code defines a function called tworealffts that computes the Fast Fourier Transform (FFT) of two real-valued functions (a1 and a2) using a Cooley-Tukey-based radix-2 Decimation in Time (DIT) algorithm. The FFT is a widely used algorithm for computing the discrete Fourier transform (DFT) and its inverse. Input parameters: 1. float a1: an array of real numbers, representing the values of the first function. 2. float a2: an array of real numbers, representing the values of the second function. 3. float a: an output array to store the Fourier transform of the first function. 4. float b: an output array to store the Fourier transform of the second function. 5. int tn: an integer representing the number of function values. It must be a power of two, but the algorithm doesn't validate this condition. The function performs the following steps: 1. Combine the two input arrays, a1 and a2, into a single array a by interleaving their elements. 2. Perform a 1D FFT on the combined array a using the radix-2 DIT algorithm. 3. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b. Here is a detailed breakdown of the radix-2 DIT algorithm used in this code: 1. Bit-reverse the order of the elements in the combined array a. 2. Initialize the loop variables mmax, istep, and theta. 3. Enter the main loop that iterates through different stages of the FFT. a. Compute the sine and cosine values for the current stage using the theta variable. b. Initialize the loop variables wr and wi for the current stage. c. Enter the inner loop that iterates through the butterfly operations within each stage. i. Perform the butterfly operation on the elements of array a. ii. Update the loop variables wr and wi for the next butterfly operation. d. Update the loop variables mmax, istep, and theta for the next stage. 4. Separate the FFT results of the two input functions from the combined array a and store them in output arrays a and b. At the end of the function, the a and b arrays will contain the Fourier transform of the first and second functions, respectively. Note that the function overwrites the input arrays a and b. █ Example scripts using functions contained in loxxfft Real-Fast Fourier Transform of Price w/ Linear Regression Real-Fast Fourier Transform of Price Oscillator Normalized, Variety, Fast Fourier Transform Explorer Variety RSI of Fast Discrete Cosine Transform STD-Stepped Fast Cosine Transform Moving Average
مكتبة Pine Script™
من ‎loxx‎
3
Console📕 Console Library 🔷 Introduction This script is an adaptation of the classic JavaScript console script. It provides a simple way to display data in a console-like table format for debugging purposes. While there are many nice console/logger scripts out there, my personal goal was to achieve inline functionality and visual object (label, lines) logging . 🔷 How to Use ◼ 1. Import the Console library into your script: import cryptolinx/Console/1 - or - Instead of the library namespace, you can define a custom namespace as alias. import cryptolinx/Console/1 as c ◼ 2. Create and init a new `` object. The `init()` method is used to initialize the console object with default settings. It can be used to customize it. // When using the `var` keyword in a declaration, the logs will act as ever-forwarding. // Without `var`, the `console` variable will be redeclared every time `bar` is called. // var console = Console.terminal.new(log_position=position.bottom_left, prefix = '> ', show_no = true) - or - If you has set up an alias before. var console = c.terminal.new().init() ◼ 3. Logging // inline ✨ array testArray = array.new(3, .0).log(console) // basic console.log(testArray) // inline ✨ var testLabel = label.new(bar_index, close, 'Label Text').log(console) // basic console.log(testLabel) // It is also possible to use `().` for literals ✨. int a = 100 testCalc = (5 * 100).log(console) + a.log(console) // SUM: 600 console. .empty() .log('SUM' + WS + testCalc.tostring()) ◼ 4. Visibility Finally, we need to call the `show()` method to display the logged messages in the console. console.show(true) // True by default. Simply turn it on or off
مكتبة Pine Script™
من ‎cryptolinx‎
3
HarmonicPatternTrackingLibrary "HarmonicPatternTracking" Library contains few data structures and methods for tracking harmonic pattern trades via pinescript. method draw(this)   Creates and draws HarmonicDrawing object for given HarmonicPattern   Namespace types: HarmonicPattern   Parameters:      this (HarmonicPattern) : HarmonicPattern object   Returns: current HarmonicPattern object method addTrade(this)   calculates HarmonicTrade and sets trade object for HarmonicPattern   Namespace types: HarmonicPattern   Parameters:      this (HarmonicPattern) : HarmonicPattern object   Returns: bool true if pattern trades are valid, false otherwise method delete(this)   Deletes drawing objects of HarmonicDrawing   Namespace types: HarmonicDrawing   Parameters:      this (HarmonicDrawing) : HarmonicDrawing object   Returns: current HarmonicDrawing object method delete(this)   Deletes drawings of harmonic pattern   Namespace types: HarmonicPattern   Parameters:      this (HarmonicPattern) : HarmonicPattern object   Returns: current HarmonicPattern object HarmonicDrawing   Drawing objects of Harmonic Pattern   Fields:      xa (series line) : xa line      ab (series line) : ab line      bc (series line) : bc line      cd (series line) : cd line      xb (series line) : xb line      bd (series line) : bd line      ac (series line) : ac line      xd (series line) : xd line      x (series label) : label for pivot x      a (series label) : label for pivot a      b (series label) : label for pivot b      c (series label) : label for pivot c      d (series label) : label for pivot d      xabRatio (series label) : label for XAB Ratio      abcRatio (series label) : label for ABC Ratio      bcdRatio (series label) : label for BCD Ratio      xadRatio (series label) : label for XAD Ratio HarmonicTrade   Trade tracking parameters of Harmonic Patterns   Fields:      initialEntry (series float) : initial entry when pattern first formed.      entry (series float) : trailed entry price.      initialStop (series float) : initial stop when trade first entered.      stop (series float) : current stop updated as per trailing rules.      target1 (series float) : First target value      target2 (series float) : Second target value      target3 (series float) : Third target value      target4 (series float) : Fourth target value      status (series int) : Trade status referenced as integer      retouch (series bool) : Flag to show if the price retouched after entry HarmonicProperties   Display and trade calculation properties for Harmonic Patterns   Fields:      fillMajorTriangles (series bool) : Display property used for using linefill for harmonic major triangles      fillMinorTriangles (series bool) : Display property used for using linefill for harmonic minor triangles      majorFillTransparency (series int) : transparency setting for major triangles      minorFillTransparency (series int) : transparency setting for minor triangles      showXABCD (series bool) : Display XABCD pivot labels      lblSizePivots (series string) : Pivot label size      showRatios (series bool) : Display Ratio labels      useLogScaleForScan (series bool) : Use log scale to determine fib ratios for pattern scanning      useLogScaleForTargets (series bool) : Use log scale to determine fib ratios for target calculation      base (series string) : base on which calculation of stop/targets are made.      entryRatio (series float) : fib ratio to calculate entry      stopRatio (series float) : fib ratio to calculate initial stop      target1Ratio (series float) : fib ratio to calculate first target      target2Ratio (series float) : fib ratio to calculate second target      target3Ratio (series float) : fib ratio to calculate third target      target4Ratio (series float) : fib ratio to calculate fourth target HarmonicPattern   Harmonic pattern object to track entire pattern trade life cycle   Fields:      id (series int) : Pattern Id      dir (series int) : pattern direction      x (series float) : X Pivot      a (series float) : A Pivot      b (series float) : B Pivot      c (series float) : C Pivot      d (series float) : D Pivot      xBar (series int) : Bar index of X Pivot      aBar (series int) : Bar index of A Pivot      bBar (series int) : Bar index of B Pivot      cBar (series int) : Bar index of C Pivot      dBar (series int) : Bar index of D Pivot      przStart (series float) : Start of PRZ range      przEnd (series float) : End of PRZ range      patterns (bool ) : array representing the patterns      patternLabel (series string) : string representation of list of patterns      patternColor (series color) : color assigned to pattern      properties (HarmonicProperties) : HarmonicProperties object containing display and calculation properties      trade (HarmonicTrade) : HarmonicTrade object to track trades      drawing (HarmonicDrawing) : HarmonicDrawing object to manage drawings
مكتبة Pine Script™
من ‎Trendoscope‎
3
OHLC📕 LIBRARY OHLC 🔷 Introduction This library is a custom library designed to work with real-time bars. It allows to easily calculate OHLC values for any source. Personally, I use this library to accurately display the highest and lowest values on visual indicators such as my progress bars. 🔷 How to Use ◼ 1. Import the OHLC library into your TradingView script: import cryptolinx/OHLC/1 - or - Instead of the library namespace, you can define a custom namespace as alias. import cryptolinx/OHLC/1 as src ◼ 2. Create a new OHLC source using the `new()` function. varip mySrc = OHLC.new() // It is required to use the `varip` keyword to init your `` - or - If you has set up an alias before. varip mySrc = src.new() === In that case, your `` needs to be `na`, define your object like that varip mySrc = na ◼ 3. Call the `hydrateOHLC()` method on your OHLC source to update its values: Basic float rsi = ta.rsi(close, 14) mySrc.hydrateOHLC(rsi) - or - Inline rsi = ta.rsi(close, 14).hydrateOHLC(mySrc) ◼ 4. The data is accessible under their corresponding names. mySrc.open mySrc.high mySrc.low mySrc.close 🔷 Note: This library only works with real-time bars and will not work with historical bars.
مكتبة Pine Script™
من ‎cryptolinx‎
1
MarkovChainLibrary "MarkovChain" Generic Markov Chain type functions. --- A Markov chain or Markov process is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. --- reference: Understanding Markov Chains, Examples and Applications. Second Edition. Book by Nicolas Privault. en.wikipedia.org www.geeksforgeeks.org towardsdatascience.com github.com stats.stackexchange.com timeseriesreasoning.com www.ris-ai.com github.com gist.github.com github.com gist.github.com writings.stephenwolfram.com kevingal.com towardsdatascience.com spedygiorgio.github.io github.com www.projectrhea.org method to_string(this)   Translate a Markov Chain object to a string format.   Namespace types: MC   Parameters:      this (MC) : `MC` . Markov Chain object.   Returns: string method to_table(this, position, text_color, text_size)   Namespace types: MC   Parameters:      this (MC)      position (string)      text_color (color)      text_size (string) method create_transition_matrix(this)   Namespace types: MC   Parameters:      this (MC) method generate_transition_matrix(this)   Namespace types: MC   Parameters:      this (MC) new_chain(states, name)   Parameters:      states (state )      name (string) from_data(data, name)   Parameters:      data (string )      name (string) method probability_at_step(this, target_step)   Namespace types: MC   Parameters:      this (MC)      target_step (int) method state_at_step(this, start_state, target_state, target_step)   Namespace types: MC   Parameters:      this (MC)      start_state (int)      target_state (int)      target_step (int) method forward(this, obs)   Namespace types: HMC   Parameters:      this (HMC)      obs (int ) method backward(this, obs)   Namespace types: HMC   Parameters:      this (HMC)      obs (int ) method viterbi(this, observations)   Namespace types: HMC   Parameters:      this (HMC)      observations (int ) method baumwelch(this, observations)   Namespace types: HMC   Parameters:      this (HMC)      observations (int ) Node   Target node.   Fields:      index (series int) : . Key index of the node.      probability (series float) : . Probability rate of activation. state   State reference.   Fields:      name (series string) : . Name of the state.      index (series int) : . Key index of the state.      target_nodes (Node ) : . List of index references and probabilities to target states. MC   Markov Chain reference object.   Fields:      name (series string) : . Name of the chain.      states (state ) : . List of state nodes and its name, index, targets and transition probabilities.      size (series int) : . Number of unique states      transitions (matrix) : . Transition matrix HMC   Hidden Markov Chain reference object.   Fields:      name (series string) : . Name of thehidden chain.      states_hidden (state ) : . List of state nodes and its name, index, targets and transition probabilities.      states_obs (state ) : . List of state nodes and its name, index, targets and transition probabilities.      transitions (matrix) : . Transition matrix      emissions (matrix) : . Emission matrix      initial_distribution (float )
مكتبة Pine Script™
من ‎RicardoSantos‎
12
FunctionProbabilityViterbiLibrary "FunctionProbabilityViterbi" The Viterbi Algorithm calculates the most likely sequence of hidden states *(called Viterbi path)* that results in a sequence of observed events. viterbi(observations, transitions, emissions, initial_distribution)   Calculate most probable path in a Markov model.   Parameters:      observations (int ) : array . Observation states data.      transitions (matrix) : matrix . Transition probability table, (HxH, H:Hidden states).      emissions (matrix) : matrix . Emission probability table, (OxH, O:Observed states).      initial_distribution (float ) : array . Initial probability distribution for the hidden states.   Returns: array. Most probable path.
مكتبة Pine Script™
من ‎RicardoSantos‎
2
FunctionBaumWelchLibrary "FunctionBaumWelch" Baum-Welch Algorithm, also known as Forward-Backward Algorithm, uses the well known EM algorithm to find the maximum likelihood estimate of the parameters of a hidden Markov model given a set of observed feature vectors. --- ### Function List: > `forward (array pi, matrix a, matrix b, array obs)` > `forward (array pi, matrix a, matrix b, array obs, bool scaling)` > `backward (matrix a, matrix b, array obs)` > `backward (matrix a, matrix b, array obs, array c)` > `baumwelch (array observations, int nstates)` > `baumwelch (array observations, array pi, matrix a, matrix b)` --- ### Reference: > en.wikipedia.org > github.com > en.wikipedia.org > www.rdocumentation.org > www.rdocumentation.org forward(pi, a, b, obs)   Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.   Parameters:      pi (float ) : Initial probabilities.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states.      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations.      obs (int ) : List with actual state observation data.   Returns: - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. forward(pi, a, b, obs, scaling)   Computes forward probabilities for state `X` up to observation at time `k`, is defined as the probability of observing sequence of observations `e_1 ... e_k` and that the state at time `k` is `X`.   Parameters:      pi (float ) : Initial probabilities.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states.      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. Given state matrix is size (M x O) where M is number of states and O is number of different possible observations.      obs (int ) : List with actual state observation data.      scaling (bool) : Normalize `alpha` scale.   Returns: - #### Tuple with: > - `matrix _alpha`: Forward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. > - `array _c`: Array with normalization scale. backward(a, b, obs)   Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.   Parameters:      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations      obs (int ) : Array with actual state observation data.   Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. backward(a, b, obs, c)   Computes backward probabilities for state `X` and observation at time `k`, is defined as the probability of observing the sequence of observations `e_k+1, ... , e_n` under the condition that the state at time `k` is `X`.   Parameters:      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations      obs (int ) : Array with actual state observation data.      c (float ) : Array with Normalization scaling coefficients.   Returns: - `matrix _beta`: Backward probabilities. The probabilities are given on a logarithmic scale (natural logarithm). The first dimension refers to the state and the second dimension to time. baumwelch(observations, nstates)   **(Random Initialization)** Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.   Parameters:      observations (int ) : List of observed states.      nstates (int)   Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor` baumwelch(observations, pi, a, b)   Baum–Welch algorithm is a special case of the expectation–maximization algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.   Parameters:      observations (int ) : List of observed states.      pi (float ) : Initial probaility distribution.      a (matrix) : Transmissions, hidden transition matrix a or alpha = transition probability matrix of changing states given a state matrix is size (M x M) where M is number of states      b (matrix) : Emissions, matrix of observation probabilities b or beta = observation probabilities. given state matrix is size (M x O) where M is number of states and O is number of different possible observations   Returns: - #### Tuple with: > - `array _pi`: Initial probability distribution. > - `matrix _a`: Transition probability matrix. > - `matrix _b`: Emission probability matrix. --- requires: `import RicardoSantos/WIPTensor/2 as Tensor`
مكتبة Pine Script™
من ‎RicardoSantos‎
7
Commission-aware Trade LabelsCommission-aware Trade Labels Description: This library provides an easy way to visualize take-profit and stop-loss levels on your chart, taking into account trading commissions. The library calculates and displays the net profit or loss, along with other useful information such as risk/reward ratio, shares, and position size. Features: Configurable take-profit and stop-loss prices or percentages. Set entry amount or shares. Calculates and displays the risk/reward ratio. Shows net profit or loss, considering trading commissions. Customizable label appearance. Usage: Add the script to your chart. Create an Order object for take-profit and stop-loss with desired configurations. Call target_label() and stop_label() methods for each order object. Example: target_order = Order.new(take_profit_price=27483, stop_loss_price=28000, shares=0.2) stop_order = Order.new(stop_loss_price=29000, shares=1) target_order.target_label() stop_order.stop_label() This script is a powerful tool for visualizing your trading strategy's performance and helps you make better-informed decisions by considering trading commissions in your profit and loss calculations. Library "tradelabels" entry_price(this)   Parameters:      this : Order object @return entry_price take_profit_price(this)   Parameters:      this : Order object @return take_profit_price stop_loss_price(this)   Parameters:      this : Order object @return stop_loss_price is_long(this)   Parameters:      this : Order object @return entry_price is_short(this)   Parameters:      this : Order object @return entry_price percent_to_target(this, target)   Parameters:      this : Order object      target : Target price @return percent risk_reward(this)   Parameters:      this : Order object @return risk_reward_ratio shares(this)   Parameters:      this : Order object @return shares position_size(this)   Parameters:      this : Order object @return position_size commission_cost(this, target_price)   Parameters:      this : Order object @return commission_cost      target_price net_result(this, target_price)   Parameters:      this : Order object      target_price : The target price to calculate net result for (either take_profit_price or stop_loss_price) @return net_result create_take_profit_label(this, prefix, size, offset_x, bg_color, text_color)   Parameters:      this      prefix      size      offset_x      bg_color      text_color create_stop_loss_label(this, prefix, size, offset_x, bg_color, text_color)   Parameters:      this      prefix      size      offset_x      bg_color      text_color create_entry_label(this, prefix, size, offset_x, bg_color, text_color)   Parameters:      this      prefix      size      offset_x      bg_color      text_color create_line(this, target_price, line_color, offset_x, line_style, line_width, draw_entry_line)   Parameters:      this      target_price      line_color      offset_x      line_style      line_width      draw_entry_line Order   Order   Fields:      entry_price : Entry price      stop_loss_price : Stop loss price      stop_loss_percent : Stop loss percent, default 2%      take_profit_price : Take profit price      take_profit_percent : Take profit percent, default 6%      entry_amount : Entry amount, default 5000$      shares : Shares      commission : Commission, default 0.04%
مكتبة Pine Script™
من ‎bunulu‎
4
Bitwise, Encode, DecodeLibrary "Bitwise, Encode, Decode" Bitwise, Encode, Decode, and more Library docs() Hover-Over Documentation for inside Text Editor bAnd(a, b)   Returns the bitwise AND of two integers   Parameters:      a : `int` - The first integer      b : `int` - The second integer   Returns: `int` - The bitwise AND of the two integers bOr(a, b)   Performs a bitwise OR operation on two integers.   Parameters:      a : `int` - The first integer.      b : `int` - The second integer.   Returns: `int` - The result of the bitwise OR operation. bXor(a, b)   Performs a bitwise Xor operation on two integers.   Parameters:      a : `int` - The first integer.      b : `int` - The second integer.   Returns: `int` - The result of the bitwise Xor operation. bNot(n)   Performs a bitwise NOT operation on an integer.   Parameters:      n : `int` - The integer to perform the bitwise NOT operation on.   Returns: `int` - The result of the bitwise NOT operation. bShiftLeft(n, step)   Performs a bitwise left shift operation on an integer.   Parameters:      n : `int` - The integer to perform the bitwise left shift operation on.      step : `int` - The number of positions to shift the bits to the left.   Returns: `int` - The result of the bitwise left shift operation. bShiftRight(n, step)   Performs a bitwise right shift operation on an integer.   Parameters:      n : `int` - The integer to perform the bitwise right shift operation on.      step : `int` - The number of bits to shift by.   Returns: `int` - The result of the bitwise right shift operation. bRotateLeft(n, step)   Performs a bitwise right shift operation on an integer.   Parameters:      n : `int` - The int to perform the bitwise Left rotation on the bits.      step : `int` - The number of bits to shift by.   Returns: `int`- The result of the bitwise right shift operation. bRotateRight(n, step)   Performs a bitwise right shift operation on an integer.   Parameters:      n : `int` - The int to perform the bitwise Right rotation on the bits.      step : `int` - The number of bits to shift by.   Returns: `int` - The result of the bitwise right shift operation. bSetCheck(n, pos)   Checks if the bit at the given position is set to 1.   Parameters:      n : `int` - The integer to check.      pos : `int` - The position of the bit to check.   Returns: `bool` - True if the bit is set to 1, False otherwise. bClear(n, pos)   Clears a particular bit of an integer (changes from 1 to 0) passes if bit at pos is 0.   Parameters:      n : `int` - The integer to clear a bit from.      pos : `int` - The zero-based index of the bit to clear.   Returns: `int` - The result of clearing the specified bit. bFlip0s(n)   Flips all 0 bits in the number to 1.   Parameters:      n : `int` - The integer to flip the bits of.   Returns: `int` - The result of flipping all 0 bits in the number. bFlip1s(n)   Flips all 1 bits in the number to 0.   Parameters:      n : `int` - The integer to flip the bits of.   Returns: `int` - The result of flipping all 1 bits in the number. bFlipAll(n)   Flips all bits in the number.   Parameters:      n : `int` - The integer to flip the bits of.   Returns: `int` - The result of flipping all bits in the number. bSet(n, pos, newBit)   Changes the value of the bit at the given position.   Parameters:      n : `int` - The integer to modify.      pos : `int` - The position of the bit to change.      newBit : `int` - na = flips bit at pos reguardless 1 or 0 | The new value of the bit (0 or 1).   Returns: `int` - The modified integer. changeDigit(n, pos, newDigit)   Changes the value of the digit at the given position.   Parameters:      n : `int` - The integer to modify.      pos : `int` - The position of the digit to change.      newDigit : `int` - The new value of the digit (0-9).   Returns: `int` - The modified integer. bSwap(n, i, j)   Switch the position of 2 bits of an int   Parameters:      n : `int` - int to manipulate      i : `int` - bit pos to switch with j      j : `int` - bit pos to switch with i   Returns: `int` - new int with bits switched bPalindrome(n)   Checks to see if the binary form is a Palindrome (reads the same left to right and vice versa)   Parameters:      n : `int` - int to check   Returns: `bool` - result of check bEven(n)   Checks if n is Even   Parameters:      n : `int` - The integer to check.   Returns: `bool` - result. bOdd(n)   checks if n is Even if not even Odd   Parameters:      n : `int` - The integer to check.   Returns: `bool` - result. bPowerOfTwo(n)   Checks if n is a Power of 2.   Parameters:      n : `int` - number to check.   Returns: `bool` - result. bCount(n, to_count)   Counts the number of bits that are equal to 1 in an integer.   Parameters:      n : `int` - The integer to count the bits in.      to_count `string` - the bits to count   Returns: `int` - The number of bits that are equal to 1 in n. GCD(a, b)   Finds the greatest common divisor (GCD) of two numbers.   Parameters:      a : `int` - The first number.      b : `int` - The second number.   Returns: `int` - The GCD of a and b. LCM(a, b)   Finds the least common multiple (LCM) of two integers.   Parameters:      a : `int` - The first integer.      b : `int` - The second integer.   Returns: `int` - The LCM of a and b. aLCM(nums)   Finds the LCM of an array of integers.   Parameters:      nums : `int ` - The list of integers.   Returns: `int` - The LCM of the integers in nums. adjustedLCM(nums, LCM)   adjust an array of integers to Least Common Multiple (LCM)   Parameters:      nums : `int ` - The first integer      LCM : `int` - The second integer   Returns: `int ` - array of ints with LCM charAt(str, pos)   gets a Char at a given position.   Parameters:      str : `string` - string to pull char from.      pos : `int` - pos to get char from string (left to right index).   Returns: `string` - char from pos of string or "" if pos is not within index range decimalToBinary(num)   Converts a decimal number to binary   Parameters:      num : `int` - The decimal number to convert to binary   Returns: `string` - The binary representation of the decimal number decimalToBinary(num, to_binary_int)   Converts a decimal number to binary   Parameters:      num : `int` - The decimal number to convert to binary      to_binary_int : `bool` - bool to convert to int or to string (true for int, false for string)   Returns: `string` - The binary representation of the decimal number binaryToDecimal(binary)   Converts a binary number to decimal   Parameters:      binary : `string` - The binary number to convert to decimal   Returns: `int` - The decimal representation of the binary number decimal_len(n)   way of finding decimal length using arithmetic   Parameters:      n `float` - floating decimal point to get length of.   Returns: `int` - number of decimal places int_len(n)   way of finding number length using arithmetic   Parameters:      n : `int`- value to find length of number   Returns: `int` - lenth of nunber i.e. 23 == 2 float_decimal_to_whole(n)   Converts a float decimal number to an integer `0.365 to 365`.   Parameters:      n : `string` - The decimal number represented as a string.   Returns: `int` - The integer obtained by removing the decimal point and leading zeroes from s. fractional_part(x)   Returns the fractional part of a float.   Parameters:      x : `float` - The float to get the fractional part of.   Returns: `float` - The fractional part of the float. form_decimal(a, b, zero_fix)   helper to form 2 ints into 1 float seperated by the decimal   Parameters:      a : `int` - a int      b : `int` - b int      zero_fix : `bool` - fix for trailing zeros being truncated when converting to float   Returns: ` ` - float = float decimal of ints | string = string version of b for future use to ref length bEncode(n1, n2)   Encodes two numbers into one using bit OR. (fastest)   Parameters:      n1 : `int` - The first number to Encodes.      n2 : `int` - The second number to Encodes.   Returns: `int` - The result of combining the two numbers using bit OR. bDecode(n)   Decodes an integer created by the bCombine function.(fastest)   Parameters:      n : `int` - The integer to decode.   Returns: ` ` - A tuple containing the two decoded components of the integer. Encode(a, b)   Encodes by seperating ints into left and right of decimal float   Parameters:      a : `int` - a int      b : `int` - b int   Returns: `float` - new float of encoded ints one on left of decimal point one on right Decode(encoded)   Decodes float of 2 ints seperated by decimal point   Parameters:      encoded : `float` - the encoded float value   Returns: ` ` - tuple of the 2 ints from encoded float encode_heavy(a, b)   Encodes by combining numbers and tracking size in the decimal of a floating number (slowest)   Parameters:      a : `int` - a int      b : `int` - b int   Returns: `float` - new decimal of encoded ints decode_heavy(encoded)   Decodes encoded float that tracks size of ints in float decimal   Parameters:      encoded : `float` - encoded float   Returns: ` ` - tuple of decoded ints decimal of float (slowest)   Parameters:      encoded : `float` - the encoded float value   Returns: ` ` - tuple of the 2 ints from encoded float Bitwise, Encode, Decode Docs In the documentation you may notice the word decimal not used as normal this is because when referring to binary a decimal number is a number that can be represented with base 10 numbers 0-9 (the wiki below explains better) A rule of thumb for the two integers being encoded it to keep both numbers less than 65535 this is because anything lower uses 16 bits or less this will maintain 100% accuracy when decoding although it is possible to do numbers up to 2147483645 with this library doesnt seem useful enough to explain or demonstrate. The functions provided work within this 32-bit range, where the highest number is all 1s and the lowest number is all 0s. These functions were created to overcome the lack of built-in bitwise functions in Pinescript. By combining two integers into a single number, the code can access both values i.e when indexing only one array index for a matrices row/column, thus improving execution time. This technique can be applied to various coding scenarios to enhance performance. Bitwise functions are a way to use integers in binary form that can be used to speed up several different processes most languages have operators to perform these function such as `<<, >>, &, ^, |, ~` en.wikipedia.org
مكتبة Pine Script™
من ‎FFriZz‎
6
DataChartLibrary "DataChart" Library to plot scatterplot or heatmaps for your own set of data samples draw(this)   draw contents of the chart object   Parameters:      this : Chart object   Returns: current chart object init(this)   Initialize Chart object.   Parameters:      this : Chart object to be initialized   Returns: current chart object addSample(this, sample, trigger)   Add sample data to chart using Sample object   Parameters:      this : Chart object      sample : Sample object containing sample x and y values to be plotted      trigger : Samples are added to chart only if trigger is set to true. Default value is true   Returns: current chart object addSample(this, x, y, trigger)   Add sample data to chart using x and y values   Parameters:      this : Chart object      x : x value of sample data      y : y value of sample data      trigger : Samples are added to chart only if trigger is set to true. Default value is true   Returns: current chart object addPriceSample(this, priceSampleData, config)   Add price sample data - special type of sample designed to measure price displacements of events   Parameters:      this : Chart object      priceSampleData : PriceSampleData object containing event driven displacement data of x and y      config : PriceSampleConfig object containing configurations for deriving x and y from priceSampleData   Returns: current chart object Sample   Sample data for chart   Fields:      xValue : x value of the sample data      yValue : y value of the sample data ChartProperties   Properties of plotting chart   Fields:      title : Title of the chart      suffix : Suffix for values. It can be used to reference 10X or 4% etc. Used only if format is not format.percent      matrixSize : size of the matrix used for plotting      chartType : Can be either scatterplot or heatmap. Default is scatterplot      outliersStart : Indicates the percentile of data to filter out from the starting point to get rid of outliers      outliersEnd : Indicates the percentile of data to filter out from the ending point to get rid of outliers.      backgroundColor      plotColor : color of plots on the chart. Default is color.yellow. Only used for scatterplot type      heatmapColor : color of heatmaps on the chart. Default is color.red. Only used for heatmap type      borderColor : border color of the chart table. Default is color.yellow.      plotSize : size of scatter plots. Default is size.large      format : data representation format in tooltips. Use mintick.percent if measuring any data in terms of percent. Else, use format.mintick      showCounters : display counters which shows totals on each quadrants. These are single cell tables at the corners displaying number of occurences on each quadrant.      showTitle : display title at the top center. Uses the title string set in the properties      counterBackground : background color of counter table cells. Default is color.teal      counterTextColor : text color of counter table cells. Default is color.white      counterTextSize : size of counter table cells. Default is size.large      titleBackground : background color of chart title. Default is color.maroon      titleTextColor : text color of the chart title. Default is color.white      titleTextSize : text size of the title cell. Default is size.large      addOutliersToBorder : If set, instead of removing the outliers, it will be added to the border cells.      useCommonScale : Use common scale for both x and y. If not selected, different scales are calculated based on range of x and y values from samples. Default is set to false.      plotchar : scatter plot character. Default is set to ascii bullet. ChartDrawing   Chart drawing objects collection   Fields:      properties : ChartProperties object which determines the type and characteristics of chart being plotted      titleTable : table containing title of the chart.      mainTable : table containing plots or heatmaps.      quadrantTables : Array of tables containing counters of all 4 quandrants Chart   Chart type which contains all the information of chart being plotted   Fields:      properties : ChartProperties object which determines the type and characteristics of chart being plotted      samples : Array of Sample objects collected over period of time for plotting on chart.      displacements : Array containing displacement values. Both x and y values      displacementX : Array containing only X displacement values.      displacementY : Array containing only Y displacement values.      drawing : ChartDrawing object which contains all the drawing elements PriceSampleConfig   Configs used for adding specific type of samples called PriceSamples   Fields:      duration : impact duration for which price displacement samples are calculated.      useAtrReference : Default is true. If set to true, price is measured in terms of Atr. Else is measured in terms of percentage of price.      atrLength : atrLength to be used for measuring the price based on ATR. Used only if useAtrReference is set to true. PriceSampleData   Special type of sample called price sample. Can be used instead of basic Sample type   Fields:      trigger : consider sample only if trigger is set to true. Default is true.      source : Price source. Default is close      highSource : High price source. Default is high      lowSource : Low price source. Default is low      tr : True range value. Default is ta.tr
مكتبة Pine Script™
من ‎Trendoscope‎
20
Feature ScalingLibrary "Feature_Scaling" FS: This library helps you scale your data to certain ranges or standarize, normalize, unit scale or min-max scale your data in your prefered way. Mostly used for normalization purposes. minmaxscale(source, min, max, length)   minmaxscale: Min-max normalization scales your data to set minimum and maximum range   Parameters:      source      min      max      length   Returns: res: Data scaled to the set minimum and maximum range meanscale(source, length)   meanscale: Mean normalization of your data   Parameters:      source      length   Returns: res: Mean normalization result of the source standarize(source, length, biased)   standarize: Standarization of your data   Parameters:      source      length      biased   Returns: res: Standarized data unitlength(source, length)   unitlength: Scales your data into overall unit length   Parameters:      source      length   Returns: res: Your data scaled to the unit length
مكتبة Pine Script™
من ‎serkany88‎
1
TableBuilderLibrary "TableBuilder" A helper library to make it simpler to create tables in pinescript This is a simple table building library that I created because I personally feel that the built-in table building method is too verbose. It features chaining methods and variable arguments. There are many features that are lacking because the implementation is early, and there may be antipatterns because I am not familiar with the runtime behavior like pinescript. If you have any comments on code improvements or features you want, please comment :D
مكتبة Pine Script™
من ‎ironpark‎
2
SILLibrary "SIL" mean_src(x, y)   calculates moving average : x is the source of price (OHLC) & y = the lookback period   Parameters:      x      y stan_dev(x, y, z)   calculates standard deviation, x = source of price (OHLC), y = the average lookback, z = average given prior two float and intger inputs, call the f_avg_src() function in f_stan_dev()   Parameters:      x      y      z vawma(x, y)   calculates volume weighted moving average, x = source of price (OHLC), y = loookback period   Parameters:      x      y gethurst(x, y, z)   calculates the Hurst Exponent and Hurst Exponent average, x = source of price (OHLC), y = lookback period for Hurst Exponent Calculation, z = lookback period for average Hurst Exponent   Parameters:      x      y      z
مكتبة Pine Script™
من ‎Amjad_S‎
libKageMiscLibrary "libKageMisc" Kage's Miscelaneous library print(_value)   Print a numerical value in a label at last historical bar.   Parameters:      _value : (float) The value to be printed.   Returns: Nothing. barsBackToDate(_year, _month, _day)   Get the number of bars we have to go back to get data from a specific date.   Parameters:      _year : (int) Year of the specific date.      _month : (int) Month of the specific date. Optional. Default = 1.      _day : (int) Day of the specific date. Optional. Default = 1.   Returns: (int) Number of bars to go back until reach the specific date. bodySize(_index)   Calculates the size of the bar's body.   Parameters:      _index : (simple int) The historical index of the bar. Optional. Default = 0.   Returns: (float) The size of the bar's body in price units. shadowSize(_direction)   Size of the current bar shadow. Either "top" or "bottom".   Parameters:      _direction : (string) Direction of the desired shadow.   Returns: (float) The size of the chosen bar's shadow in price units. shadowBodyRatio(_direction)   Proportion of current bar shadow to the bar size   Parameters:      _direction : (string) Direction of the desired shadow.   Returns: (float) Ratio of the shadow size per body size. bodyCloseRatio(_index)   Proportion of chosen bar body size to the close price   Parameters:      _index : (simple int) The historical index of the bar. Optional. Default = 0.()   Returns: (float) Ratio of the body size per close price. lastDayOfMonth(_month)   Returns the last day of a month.   Parameters:      _month : (int) Month number.   Returns: (int) The number (28, 30 or 31) of the last day of a given month. nameOfMonth(_month)   Return the short name of a month.   Parameters:      _month : (int) Month number.   Returns: (string) The short name ("Jan", "Feb"...) of a given month. pl(_initialValue, _finalValue)   Calculate Profit/Loss between two values.   Parameters:      _initialValue : (float) Initial value.      _finalValue : (float) Final value = Initial value + delta.   Returns: (float) Profit/Loss as a percentual change. gma(_Type, _Source, _Length)   Generalist Moving Average (GMA).   Parameters:      _Type : (string) Type of average to be used. Either "EMA", "HMA", "RMA", "SMA", "SWMA", "WMA" or "VWMA".      _Source : (series float) Series of values to process.      _Length : (simple int) Number of bars (length).   Returns: (float) The value of the chosen moving average. xFormat(_percentValue, _minXFactor)   Transform a percentual value in a X Factor value.   Parameters:      _percentValue : (float) Percentual value to be transformed.      _minXFactor : (float) Minimum X Factor to that the conversion occurs. Optional. Default = 10.   Returns: (string) A formated string. isLong()   Check if the open trade direction is long.   Returns: (bool) True if the open position is long. isShort()   Check if the open trade direction is short.   Returns: (bool) True if the open position is short. lastPrice()   Returns the entry price of the last openned trade.   Returns: (float) The last entry price. barsSinceLastEntry()   Returns the number of bars since last trade was oppened.   Returns: (series int) getBotNameFrosty()   Return the name of the FrostyBot Bot.   Returns: (string) A string containing the name. getBotNameZig()   Return the name of the FrostyBot Bot.   Returns: (string) A string containing the name. getTicksValue(_currencyValue)   Converts currency value to ticks   Parameters:      _currencyValue : (float) Value to be converted.   Returns: (float) Value converted to minticks. getSymbol(_botName, _botCustomSymbol)   Formats the symbol string to be used with a bot   Parameters:      _botName : (string) Bot name constant. Either BOT_NAME_FROSTY or BOT_NAME_ZIG. Optional. Default is empty string.      _botCustomSymbol : (string) Custom string. Optional. Default is empy string.   Returns: (string) A string containing the symbol for the bot. If all arguments are empty, the current symbol is returned in Binance format. showProfitLossBoard()   Calculates and shows a board of Profit/Loss through the years.   Returns: Nothing.
مكتبة Pine Script™
من ‎marcelokg‎
2
Liquidation_linesLibrary "Liquidationline" f_calculateLeverage(_leverage, _maintainance, _value, _direction)   Parameters:      _leverage      _maintainance      _value      _direction f_liqline_update(_Liqui_Line, _killonlowhigh)   Parameters:      _Liqui_Line      _killonlowhigh f_liqline_draw(_Liqui_Line, _priceorliq)   Parameters:      _Liqui_Line      _priceorliq f_liqline_add(_Liqui_Line, linetoadd, _limit)   Parameters:      _Liqui_Line      linetoadd      _limit Liquidationline   Fields:      creationtime      stoptime      price      leverage      maintainance      line_active      line_color      line_thickness      line_style      line_direction      line_finished      text_active      text_size      text_color this library can draw typical liquidation lines, which can be called e.g. by indicator signals You can see the default implementation in the lower part of the code, starting with RUNTIME Don't forget to increase max lines to 500 in your script. It can look like this screenshot here, with only minor changes to your executing script. The base is the same
مكتبة Pine Script™
من ‎djmad‎
25
DistributionsLibrary "Distributions" Library with price distribution zones calculation helpers. Based on research from "Trading Systems and Methods, 5th Edition" by Perry J. Kaufman getZones(h, l, c, window)   Returns price distribution zones based on HLC and for some period   Parameters:      h : high price      l : low price      c : close price      window : period to calculate distributions   Returns: tuple of 5 price zones in descent order, from highest to lowest
مكتبة Pine Script™
من ‎miivanov‎
PerformanceTableLibrary "PerformanceTable" TODO: add library description here This library was created as a library because adding a performance table to an existing strategy script made the strategy script lengthy and inconvenient to manage. The monthly table script referenced @QuantNomad's code. The performance table script referenced @myncrypto's code. To use, copy and paste the code below at the bottom of the strategy script you are using, and the table for strategy performance will be displayed on a chart. //------------Copy & Paste --------------------------------------// import Cube_Lee/PerformanceTable/1 as PT PT = input.bool(true, "Show Performance Table", tooltip = "전략의 성과를 우측상단에 테이블로 표시합니다.", group = "Performance Table") MT = input.bool(true, "Show Monthly Table", tooltip = "전략의 월별 수익률을 우측하단에 테이블로 표시합니다.", group = "Performance Table") if PT PT.PerformanceTable() if MT PT.MonthlyTable() //------------Copy & Paste---------------------------------------// PerformanceTable() MonthlyTable()
مكتبة Pine Script™
من ‎Cube_Lee‎
3
MLExtensionsLibrary "MLExtensions" normalizeDeriv(src, quadraticMeanLength)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src : The input series (i.e., the first-order derivative for price).      quadraticMeanLength : The length of the quadratic mean (RMS).   Returns: nDeriv The normalized derivative of the input series. normalize(src, min, max)   Rescales a source value with an unbounded range to a target range.   Parameters:      src : The input series      min : The minimum value of the unbounded range      max : The maximum value of the unbounded range   Returns: The normalized series rescale(src, oldMin, oldMax, newMin, newMax)   Rescales a source value with a bounded range to anther bounded range   Parameters:      src : The input series      oldMin : The minimum value of the range to rescale from      oldMax : The maximum value of the range to rescale from      newMin : The minimum value of the range to rescale to      newMax : The maximum value of the range to rescale to   Returns: The rescaled series color_green(prediction)   Assigns varying shades of the color green based on the KNN classification   Parameters:      prediction : Value (int|float) of the prediction   Returns: color color_red(prediction)   Assigns varying shades of the color red based on the KNN classification   Parameters:      prediction : Value of the prediction   Returns: color tanh(src)   Returns the the hyperbolic tangent of the input series. The sigmoid-like hyperbolic tangent function is used to compress the input to a value between -1 and 1.   Parameters:      src : The input series (i.e., the normalized derivative).   Returns: tanh The hyperbolic tangent of the input series. dualPoleFilter(src, lookback)   Returns the smoothed hyperbolic tangent of the input series.   Parameters:      src : The input series (i.e., the hyperbolic tangent).      lookback : The lookback window for the smoothing.   Returns: filter The smoothed hyperbolic tangent of the input series. tanhTransform(src, smoothingFrequency, quadraticMeanLength)   Returns the tanh transform of the input series.   Parameters:      src : The input series (i.e., the result of the tanh calculation).      smoothingFrequency      quadraticMeanLength   Returns: signal The smoothed hyperbolic tangent transform of the input series. n_rsi(src, n1, n2)   Returns the normalized RSI ideal for use in ML algorithms.   Parameters:      src : The input series (i.e., the result of the RSI calculation).      n1 : The length of the RSI.      n2 : The smoothing length of the RSI.   Returns: signal The normalized RSI. n_cci(src, n1, n2)   Returns the normalized CCI ideal for use in ML algorithms.   Parameters:      src : The input series (i.e., the result of the CCI calculation).      n1 : The length of the CCI.      n2 : The smoothing length of the CCI.   Returns: signal The normalized CCI. n_wt(src, n1, n2)   Returns the normalized WaveTrend Classic series ideal for use in ML algorithms.   Parameters:      src : The input series (i.e., the result of the WaveTrend Classic calculation).      n1      n2   Returns: signal The normalized WaveTrend Classic series. n_adx(highSrc, lowSrc, closeSrc, n1)   Returns the normalized ADX ideal for use in ML algorithms.   Parameters:      highSrc : The input series for the high price.      lowSrc : The input series for the low price.      closeSrc : The input series for the close price.      n1 : The length of the ADX. regime_filter(src, threshold, useRegimeFilter)   Parameters:      src      threshold      useRegimeFilter filter_adx(src, length, adxThreshold, useAdxFilter)   filter_adx   Parameters:      src : The source series.      length : The length of the ADX.      adxThreshold : The ADX threshold.      useAdxFilter : Whether to use the ADX filter.   Returns: The ADX. filter_volatility(minLength, maxLength, useVolatilityFilter)   filter_volatility   Parameters:      minLength : The minimum length of the ATR.      maxLength : The maximum length of the ATR.      useVolatilityFilter : Whether to use the volatility filter.   Returns: Boolean indicating whether or not to let the signal pass through the filter. backtest(high, low, open, startLongTrade, endLongTrade, startShortTrade, endShortTrade, isStopLossHit, maxBarsBackIndex, thisBarIndex)   Performs a basic backtest using the specified parameters and conditions.   Parameters:      high : The input series for the high price.      low : The input series for the low price.      open : The input series for the open price.      startLongTrade : The series of conditions that indicate the start of a long trade.`      endLongTrade : The series of conditions that indicate the end of a long trade.      startShortTrade : The series of conditions that indicate the start of a short trade.      endShortTrade : The series of conditions that indicate the end of a short trade.      isStopLossHit : The stop loss hit indicator.      maxBarsBackIndex : The maximum number of bars to go back in the backtest.      thisBarIndex : The current bar index.   Returns: A tuple containing backtest values init_table()   init_table()   Returns: tbl The backtest results. update_table(tbl, tradeStatsHeader, totalTrades, totalWins, totalLosses, winLossRatio, winrate, stopLosses)   update_table(tbl, tradeStats)   Parameters:      tbl : The backtest results table.      tradeStatsHeader : The trade stats header.      totalTrades : The total number of trades.      totalWins : The total number of wins.      totalLosses : The total number of losses.      winLossRatio : The win loss ratio.      winrate : The winrate.      stopLosses : The total number of stop losses.   Returns: Updated backtest results table.
مكتبة Pine Script™
من ‎jdehorty‎
6
DataCorrelationLibrary "DataCorrelation" Implementation of functions related to data correlation calculations. Formulas have been transformed in such a way that we avoid running loops and instead make use of time series to gradually build the data we need to perform calculation. This allows the calculations to run on unbound series, and/or higher number of samples 🎲 Simplifying Covariance Original Formula //For Sample Covₓᵧ = ∑ ((xᵢ-x̄)(yᵢ-ȳ)) / (n-1) //For Population Covₓᵧ = ∑ ((xᵢ-x̄)(yᵢ-ȳ)) / n Now, if we look at numerator, this can be simplified as follows ∑ ((xᵢ-x̄)(yᵢ-ȳ)) => (x₁-x̄)(y₁-ȳ) + (x₂-x̄)(y₂-ȳ) + (x₃-x̄)(y₃-ȳ) ... + (xₙ-x̄)(yₙ-ȳ) => (x₁y₁ + x̄ȳ - x₁ȳ - y₁x̄) + (x₂y₂ + x̄ȳ - x₂ȳ - y₂x̄) + (x₃y₃ + x̄ȳ - x₃ȳ - y₃x̄) ... + (xₙyₙ + x̄ȳ - xₙȳ - yₙx̄) => (x₁y₁ + x₂y₂ + x₃y₃ ... + xₙyₙ) + (x̄ȳ + x̄ȳ + x̄ȳ ... + x̄ȳ) - (x₁ȳ + x₂ȳ + x₃ȳ ... xₙȳ) - (y₁x̄ + y₂x̄ + y₃x̄ + yₙx̄) => ∑xᵢyᵢ + n(x̄ȳ) - ȳ∑xᵢ - x̄∑yᵢ So, overall formula can be simplified to be used in pine as //For Sample Covₓᵧ = (∑xᵢyᵢ + n(x̄ȳ) - ȳ∑xᵢ - x̄∑yᵢ) / (n-1) //For Population Covₓᵧ = (∑xᵢyᵢ + n(x̄ȳ) - ȳ∑xᵢ - x̄∑yᵢ) / n 🎲 Simplifying Standard Deviation Original Formula //For Sample σ = √(∑(xᵢ-x̄)² / (n-1)) //For Population σ = √(∑(xᵢ-x̄)² / n) Now, if we look at numerator within square root ∑(xᵢ-x̄)² => (x₁² + x̄² - 2x₁x̄) + (x₂² + x̄² - 2x₂x̄) + (x₃² + x̄² - 2x₃x̄) ... + (xₙ² + x̄² - 2xₙx̄) => (x₁² + x₂² + x₃² ... + xₙ²) + (x̄² + x̄² + x̄² ... + x̄²) - (2x₁x̄ + 2x₂x̄ + 2x₃x̄ ... + 2xₙx̄) => ∑xᵢ² + nx̄² - 2x̄∑xᵢ => ∑xᵢ² + x̄(nx̄ - 2∑xᵢ) So, overall formula can be simplified to be used in pine as //For Sample σ = √(∑xᵢ² + x̄(nx̄ - 2∑xᵢ) / (n-1)) //For Population σ = √(∑xᵢ² + x̄(nx̄ - 2∑xᵢ) / n) 🎲 Using BinaryInsertionSort library Chatterjee Correlation and Spearman Correlation functions make use of BinaryInsertionSort library to speed up sorting. The library in turn implements mechanism to insert values into sorted order so that load on sorting is reduced by higher extent allowing the functions to work on higher sample size. 🎲 Function Documentation chatterjeeCorrelation(x, y, sampleSize, plotSize)   Calculates chatterjee correlation between two series. Formula is - ξnₓᵧ = 1 - (3 * ∑ |rᵢ₊₁ - rᵢ|)/ (n²-1)   Parameters:      x : First series for which correlation need to be calculated      y : Second series for which correlation need to be calculated      sampleSize : number of samples to be considered for calculattion of correlation. Default is 20000      plotSize : How many historical values need to be plotted on chart.   Returns: float correlation - Chatterjee correlation value if falls within plotSize, else returns na spearmanCorrelation(x, y, sampleSize, plotSize)   Calculates spearman correlation between two series. Formula is - ρ = 1 - (6∑dᵢ²/n(n²-1))   Parameters:      x : First series for which correlation need to be calculated      y : Second series for which correlation need to be calculated      sampleSize : number of samples to be considered for calculattion of correlation. Default is 20000      plotSize : How many historical values need to be plotted on chart.   Returns: float correlation - Spearman correlation value if falls within plotSize, else returns na covariance(x, y, include, biased)   Calculates covariance between two series of unbound length. Formula is Covₓᵧ = ∑ ((xᵢ-x̄)(yᵢ-ȳ)) / (n-1) for sample and Covₓᵧ = ∑ ((xᵢ-x̄)(yᵢ-ȳ)) / n for population   Parameters:      x : First series for which covariance need to be calculated      y : Second series for which covariance need to be calculated      include : boolean flag used for selectively including sample      biased : boolean flag representing population covariance instead of sample covariance   Returns: float covariance - covariance of selective samples of two series x, y stddev(x, include, biased)   Calculates Standard Deviation of a series. Formula is σ = √( ∑(xᵢ-x̄)² / n ) for sample and σ = √( ∑(xᵢ-x̄)² / (n-1) ) for population   Parameters:      x : Series for which Standard Deviation need to be calculated      include : boolean flag used for selectively including sample      biased : boolean flag representing population covariance instead of sample covariance   Returns: float stddev - standard deviation of selective samples of series x correlation(x, y, include)   Calculates pearson correlation between two series of unbound length. Formula is r = Covₓᵧ / σₓσᵧ   Parameters:      x : First series for which correlation need to be calculated      y : Second series for which correlation need to be calculated      include : boolean flag used for selectively including sample   Returns: float correlation - correlation between selective samples of two series x, y
مكتبة Pine Script™
من ‎Trendoscope‎
9