Polynomialregression — Indicators and Strategies

regressions Library "regressions" This library computes least square regression models for polynomials of any form for a given data set of x and y values. fit(X, y, reg_type, degrees) Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset. Parameters: X (array) : (float ) X inputs for regression fit. y (array) : (float ) y outputs for regression fit. reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial. Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial regress(reg, x) Regress one x input. Parameters: reg (regression) : (regression) The fitted regression which the y_pred will be calulated with. x (float) : (float) The input value cooresponding to the y_pred. Returns: (float) The best fit y value for the given x input and regression. predict(reg, X) Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime Parameters: reg (regression) : (regression) The fitted regression which the y_pred will be calulated with. X (array) Returns: (float ) The best fit y values for the given x input and regression. generate_points(reg, x, y, left_index, right_index) Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels. Parameters: reg (regression) : (regression) Regression which has been fitted to a data set. x (array) : (float ) x values which coorispond to passed y values y (array) : (float ) y values which coorispond to passed x values left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value. right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value. Returns: (chart.point ) Returns an array of chart points plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width) Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function. Parameters: reg (regression) : (regression) Regression which has been fitted to a data set. x (array) y (array) left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value. right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value. curved (bool) : (bool) If the polyline is curved or not. close (bool) : (bool) If true the polyline will be closed. line_color (color) : (color) The color of the line. line_width (int) : (int) The width of the line. Returns: (polyline) The polyline for the regression. series_to_list(src, left_index, right_index) Convert a series to a list. Creates a list of all the cooresponding source values from left_index to right_index. This should be called at the highest scope for consistency. Parameters: src (float) : (float ) The source the list will be comprised of. left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for. right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for. Returns: (float ) An array of size left_index-right_index range_list(start, stop, step) Creates an from the start value to the stop value. Parameters: start (int) : (float ) The true y values. stop (int) : (float ) The predicted y values. step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2: Returns: (float ) An array of size stop-start regression Fields: coeffs (array__float) degrees (array__float) type_linear (series__string) type_quadratic (series__string) type_cubic (series__string) type_custom (series__string) _squared_error (series__float) X (array__float)

Polynomial Regression Derivatives [Loxx]Polynomial Regression Derivatives is an indicator that explores the different derivatives of polynomial position. This indicator also includes a signal line. In a later release, alerts with signal markings will be added. Polynomial Derivatives are as follows 1rst Derivative - Velocity: Velocity is the directional speed of a object in motion as an indication of its rate of change in position as observed from a particular frame of reference and as measured by a particular standard of time (e.g. 60 km/h northbound). Velocity is a fundamental concept in kinematics, the branch of classical mechanics that describes the motion of bodies. 2nd Derivative - Acceleration: In mechanics, acceleration is the rate of change of the velocity of an object with respect to time. Accelerations are vector quantities (in that they have magnitude and direction). The orientation of an object's acceleration is given by the orientation of the net force acting on that object. 3rd Derivative - Jerk: In physics, jerk or jolt is the rate at which an object's acceleration changes with respect to time. It is a vector quantity (having both magnitude and direction). Jerk is most commonly denoted by the symbol j and expressed in m/s3 (SI units) or standard gravities per second (g0/s). 4th Derivative - Snap: Snap, or jounce, is the fourth derivative of the position vector with respect to time, or the rate of change of the jerk with respect to time. Equivalently, it is the second derivative of acceleration or the third derivative of velocity. 5th Derivative - Crackle: The fifth derivative of the position vector with respect to time is sometimes referred to as crackle. It is the rate of change of snap with respect to time. 6nd Derivative - Pop: The sixth derivative of the position vector with respect to time is sometimes referred to as pop. It is the rate of change of crackle with respect to time. Included: Loxx's Expanded Source Types Loxx's Moving Averages

Polynomial Regression Bands w/ Extrapolation of Price [Loxx]Polynomial Regression Bands w/ Extrapolation of Price is a moving average built on Polynomial Regression. This indicator paints both a non-repainting moving average and also a projection forecast based on the Polynomial Regression. I've included 33 source types and 38 moving average types to smooth the price input before it's run through the Polynomial Regression algorithm. This indicator only paints X many bars back so as to increase on screen calculation speed. Make sure to read the tooltips to answer any questions you have. What is Polynomial Regression? In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression . Related indicators Polynomial-Regression-Fitted Oscillator Polynomial-Regression-Fitted RSI PA-Adaptive Polynomial Regression Fitted Moving Average Poly Cycle Fourier Extrapolator of Price w/ Projection Forecast

PA-Adaptive Polynomial Regression Fitted Moving Average [Loxx]PA-Adaptive Polynomial Regression Fitted Moving Average is a moving average that is calculated using Polynomial Regression Analysis. The purpose of this indicator is to introduce polynomial fitting that is to be used in future indicators. This indicator also has Phase Accumulation adaptive period inputs. Even though this first indicator is for demonstration purposes only, its still one of the only viable implementations of Polynomial Regression Analysis on TradingView is suitable for trading, and while this same method can be used to project prices forward, I won't be doing that since forecasting is generally worthless and causes unavoidable repainting. This indicator only repaints on the current bar. Once the bar closes, any signal on that bar won't change. For other similar Polynomial Regression Fitted methodologies, see here Poly Cycle What is the Phase Accumulation Cycle? The phase accumulation method of computing the dominant cycle is perhaps the easiest to comprehend. In this technique, we measure the phase at each sample by taking the arctangent of the ratio of the quadrature component to the in-phase component. A delta phase is generated by taking the difference of the phase between successive samples. At each sample we can then look backwards, adding up the delta phases.When the sum of the delta phases reaches 360 degrees, we must have passed through one full cycle, on average.The process is repeated for each new sample. The phase accumulation method of cycle measurement always uses one full cycle’s worth of historical data.This is both an advantage and a disadvantage.The advantage is the lag in obtaining the answer scales directly with the cycle period.That is, the measurement of a short cycle period has less lag than the measurement of a longer cycle period. However, the number of samples used in making the measurement means the averaging period is variable with cycle period. longer averaging reduces the noise level compared to the signal.Therefore, shorter cycle periods necessarily have a higher out- put signal-to-noise ratio. What is Polynomial Regression? In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in x. Polynomial regression fits a nonlinear relationship between the value of x and the corresponding conditional mean of y, denoted E(y |x). Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, polynomial regression is considered to be a special case of multiple linear regression. Things to know You can select from 33 source types The source is smoothed before being injected into the Polynomial fitting algorithm, there are 35+ moving averages to choose from for smoothing The output of the Polynomial fitting algorithm is then smoothed to create the signal, there are 35+ moving averages to choose from for smoothing Included Alerts Signals Bar coloring