Introduction A simple oscillator using a modified lowess architecture, good in term of smoothness and reactivity. Lowess Regression Lowess or local regression is a non-parametric (can be used with data not fitting a normal distribution) smoothing method. This method fit a curve to the data using least squares. In order to have a lowess regression one must...
Introduction Roughness of a signal is often non desired since smooth signals are easier to analyse, its logical to say that anything interacting with rough price is subject to decrease in accuracy/efficiency and can induce non desired effects such as whipsaws. Being able to measure it can give useful information and potentially avoid errors in an analysis. It...
Introduction The last indicators i posted where about estimating the least squares moving average, the task of estimating a filter is a funny one because its always a challenge and it require to be really creative. After the last publication of the 1LC-LSMA , who estimate the lsma with 1 line of code and only 3 functions i felt like i could maybe make something...
Even Shorter Estimation I know that i'am insistent with the lsma but i really like it and i'm happy to deconstruct it like a mad pinescript user. But if you have an idea about some kind of indicator then dont hesitate to contact me, i would be happy to help you if its feasible. My motivation for such indicator was to use back the correlation function (that i...
This type of moving average was originally developed by Bruno Pio in 2010. I just ported the original code from MetaTrader 5. The method uses a linear combination of EMA cascades to achieve better smoothness. Well, actually you can create your own X-uple EMA, but be sure that the combination' coefficients are valid.
This type of moving average was originally developed by Bruno Pio in 2010. I just ported the original code from MetaTrader 5.
The weights of this moving average are powers of the weights of the standard weighted moving average WMA . Remember: When parameter Power = 0, you will get SMA . When parameter Power = 1, you will get WMA . Good luck!
Adapt To The Right Situation There are already some Adaptive Stochastic scripts out there, but i didn't see the concept of using different periods highest/lowest for their calculations. What we want for such oscillator is to be active when price is trending and silent during range periods. Like that the information we will see will be clear and easy to...
Another Adaptive Filter This indicator share the same structure as a classic adaptive filter using an exponential window with a smoothing constant. However the smoothing constant used is different than any previously made (Kalman Gain, Efficiency ratio, Scaled Fractal Dimension Index) , here the smoothing constant is inspired by the different formulations for...
This is a sample script I coded during one of my mentoring videos about my methodology, the goal of that video is to teach my students how they can do their R&D !
Hey there! This tool will help you to choose a moving average/filter that has the lowest lag throughout the whole history for the specified period. What does it do? It calculates the mean absolute errors for each moving average or filter and shows histogram with results. The lower error the lower lag of the moving average. So, the best average will be at the...
Trading The Movements That Matters Inspired by the Price Volume Trend indicator the Efficient Price aim to create a better version of the price containing only the information a trend trader must need. Calculation This indicator use the Efficiency Ratio as a smoothing constant, it is calculated as follow : ER =...
The Self Referencing Stochastic Oscillator The stochastic oscillator bring values in range of (0,100). This process is called Feature scaling or Unity-Based Normalization When a function use recursion you can highlights cycles or create smoother results depending on various factors, this is the goal of a recursive stochastic. For example : k =...
Developed by Emily Karobein, the Karobein oscillator is an oscillator that aim to rescale smoothed values with more reactivity in a range of (0,1) Calculation The scaling method is similar to the one used in a kalman filter for the kalman gain. We first average the up/downs x, those calculations are similar to the ones used for calculating the average...
A One Dimensional Kalman Filter, the particularity of Kalman Filtering is the constant recalculation of the Error between the measurements and the estimate.This version is modified to allow more/less filtering using an alternative calculation of the error measurement. Camparison of the Kalman filter Red with a moving average Black of both period 50 Can...
🆓 Smoothed Chande Trend Score w/ Signal Line by Cryptorhythms 👀Did not see this one in the public library yet, so here you go! I added an ema signal line that you can configure the length on. Also dressed it up a little with OB/OS zones and some purdy colors. Here are long + short charts: 👍Enjoying this indicator or find it useful? Please give me a like...
Mean Reversion and Momentum Interpretation: - Divergence means trend reversal - Parallel movement means trend continuation Squares above serve as a confirming signal
A quadratic regression is the process of finding the equation that best fits a set of data.This form of regression is mainly used for smoothing data shaped like a parabola. Because we can use short/midterm/longterm periods we can say that we use a Quadratic Least Squares Moving Average or a Moving Quadratic Regression. Like the Linear Regression (LSMA) a...