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متوسط متحرك المربعات الصغرى (LSMA)

General Filter Estimator-An Experiment on Estimating EverythingIntroduction 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 more flexible and less complex with the ability to approximate any filter output. Its possible, but the methods to do so are not some
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من ‎alexgrover‎
11
1LC-LSMA (1 line code lsma with 3 functions)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 had putted aside in the ligh-lsma code) and provide a shorter code than the estimation using the line rescaling method (see : Approxi
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من ‎alexgrover‎
10
Light LSMAEstimating the LSMA Without Classics Parameters I already mentioned various methods in order to estimate the LSMA in the idea i published. The parameter who still appeared on both the previous estimation and the classic LSMA was the sample correlation coefficient. This indicator will use an estimate of the correlation coefficient using the standard score thus providing a totally different approach in the estimation of the LSMA. My motivation for such indicator was to provide a different way
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من ‎alexgrover‎
9
Function for Least Squares Moving AverageThank you to alexgrover for putting me wide to this, after putting up with long conversations and stupid questions. Follow him and behold: www.tradingview.com What is this? This is simply the function for a Least Squares Moving Average. You can render this on the chart by using the linreg() function in Pine. Personally I like to use the slope of the LSMA to help determine what direction to take a trade in, but I'm sure there are other, more exotic ways of using it and, if you know how to g
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من ‎chrysopoetics‎
2
Adaptive Least SquaresAn 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 slower during low volatility ones. High smooth parameter will create smoother results, values inferior to 3 are recommended. You can
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من ‎alexgrover‎
4
All Moving averagesI have added an option to turn on or off any Moving average by choice and if needed, Heikin-ashi used as source (instead of close) List of Moving Averages which you can use T3 - Tillson Moving Average DEMA - Double Exponential Moving Average ALMA - Arnaud Legoux moving average LSMA - Least Squares Moving Average MA - Simple Moving Average EMA - Exponential Moving Average WMA - Weighted Moving Average SMMA -The Smoothed Moving Average TEMA - triple exponential moving average
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من ‎CryptoJoncis‎
2
Kozlod - Yet Another Moving Average Cross Alerts (9 MA types)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 ( DEMA ) Also you can select SL/PT % levels. There are 4 alerts available: LONG/SHORT/EXIT LONG/EXIT SHORT Same script as strategy:
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من ‎QuantNomad‎
7
Multi-Bollinger [DW]This is an experimental study designed to visualize trend activity and volatility using a set of two Bollinger Bands calculated with a basis moving average type of your choice. The available moving averages in this script are: -Exponential Moving Average -Simple Moving Average -Weighted Moving Average -Volume Weighted Moving Average -Hull Moving Average -Least Squares Moving Average -Arnaud Legoux Moving Average -Coefficient of Variation Weighted Moving Average -Fractal Adaptiv
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من ‎DonovanWall‎
Squeeze Box [DW]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 period determines the relative squeeze. The box is outlined by drawing the current highest and lowest source value over the sampling
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من ‎DonovanWall‎
26
Ultra MACD [DW]This is a variation of Gerald Appel's MACD with seven moving average source types to choose from. The MA types I've included in this script are: - Kaufman's Adaptive Moving Average - Geometric Moving Average - Hull Moving Average - Volume Weighted Moving Average - Least Squares Moving Average - Arnaud Legoux Moving Average - Exponential Moving Average Custom bar color scheme is included with two different colorization methods - one based on the MACD, and the other b
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من ‎DonovanWall‎
14
Moving Average Range Channels [DW]This study is an experiment based off the concept used in my Dynamic Range Channel indicator. Rather than using a McGinley Dynamic, a moving average of your choice is used in this calculation. There are eight different moving average types to choose from in this script: - Kaufman's Adaptive Moving Average - Geometric Moving Average - Hull Moving Average - Volume Weighted Moving Average - Least Squares Moving Average - Arnaud Legoux Moving Average - Exponential Moving
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من ‎DonovanWall‎
4
Idō Heikin Ichimoku [DW]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 specified period. The lagging span is calculated by offsetting the closing price by the same amount as the cloud period.
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من ‎DonovanWall‎
2