This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications...
Elder-Ray Bear and Bull Power
Dr. Alexander Elder cleverly named his first indicator Elder-Ray because of its function, which is designed to see through the market like an X-ray machine. Developed in 1989, the Elder-Ray indicator can be applied to the chart of any security and helps traders determine the strength of competing groups of bulls and bears by gazing...
This script has been based on ProwdClown's instructions of usage.
GM settings 9, 6, 3 should be used, LSMA 25, 0 has been implemented.
Original author for main script: LazyBear, xSilas and Ni6HTH4wK, modified By sco77m4r7in and oh92, later modified By...
This is an experimental study which calculates a linear regression channel over a specified period or interval using custom moving average types for its calculations.
Linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables.
In linear regression, the relationships are modeled using...
The "AC-P" version of Jaggedsoft's RSX Divergence and Everget's RSX script is my personal customized version of RSX with the following additions and modifications:
LSMA-D line that averages in three LSMA components to form a composite, the LSMA-D line. Offset for the LSMA-D line is set to -2 to offset latency from averaging togther the LSMA components to form...
Compute a rolling linear regression channel, the value of the bands at a precise point in time is equal to the last value of the corresponding extremity of a regression channel of equal length and mult at that point. The bands are made by adding/subtracting the RMSE of a linear regression to a least-squares moving average.
Length : Period of...
You can choose one of these MA types in params:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Arnaud Legoux Moving Average (ALMA)
Hull Moving Average (HMA)
Volume-weighted Moving Average (VWMA)
Least Square Moving Average (LSMA)
Smoothed Moving Average (SMMA)
Double Exponential Moving Average...
This is an experimental study that takes a moving average of price, then offsets the average by up to 11 consecutive Fibonacci numbers from 1 to 144.
Choose between Kaufman's Adaptive Moving Average, Hull Moving Average, Fractal Adaptive Moving Average, Geometric Moving Average, or Exponential Moving Average.
You can choose one of these MA types in params:
Simple Moving Average ( SMA )
Exponential Moving Average ( EMA )
Weighted Moving Average ( WMA )
Arnaud Legoux Moving Average ( ALMA )
Hull Moving Average ( HMA )
Volume-weighted Moving Average ( VWMA )
Least Square Moving Average ( LSMA )
Smoothed Moving Average ( SMMA )
Double Exponential Moving Average (...
An adaptive filtering technique allowing permanent re-evaluation of the filter parameters according to price volatility. The construction of this filter is based on the formula of moving ordinary least squares or lsma , the period parameter is estimated by dividing the true range with its highest. The filter will react faster during high volatility periods and...
At the start of 2019 i published my first post "Approximating A Least Square Moving Average In Pine", who aimed to provide alternatives calculation of the least squares moving average (LSMA), a moving average who aim to estimate the underlying trend in the price without excessive lag.
The LSMA has the form of a linear regression ax + b where x ...
Forecasting is a blurry science that deal with lot of uncertainty. Most of the time forecasting is made with the assumption that past values can be used to forecast a time series, the accuracy of the forecast depend on the type of time series, the pre-processing applied to it, the forecast model and the parameters of the model.
In tradingview we...
This is an experimental study designed using data from Bollinger Bands to determine price squeeze ranges and active levels of support and resistance.
First, a set of Bollinger Bands using a Coefficient of Variation weighted moving average as the basis is calculated.
Then, the relative percentage of current bandwidth to maximum bandwidth over the specified sampling...
The trend step indicator family has produced much interest in the community, those indicators showed in certain cases robustness and reactivity. Their ease of use/interpretation is also a major advantage. Although those indicators have a relatively good fit with the input price, they can still be improved by introducing least-squares fitting on...
Plot a linear regression channel through the last length closing prices, with the possibility to use another source as input. The line is fit by using linear combinations between the WMA and SMA thus providing both an interesting and efficient method. The results are the same as the one provided by the built-in linear regression, only the computation differ.
The ability of the least squares moving average to provide a great low lag filter is something i always liked, however the least squares moving average can have other uses, one of them is using it with the z-score to provide a fast smoothing oscillator.
The indicator aim to provide fast and smooth results. length control the...
Lots of moving averages are based on a weighted sum, the most common ones being the simple (arithmetic) and linearly weighted moving average. The problems with the weighted sum approach is that when your moving average is a FIR filter then the number of operations increase with higher values of length, and when the weights are based on a complex calculation this...
This is an experimental study inspired by Goichi Hosoda's Ichimoku Kinkō Hyō.
In this study, a McGinley Dynamic replaces the Tenkan-Sen and Kaufman's Adaptive Moving Average replaces the Kijun-Sen.
The cloud is calculated by taking the mean of the highest high and lowest low, adding a golden mean standard deviation above and below, and offsetting it over the...