Bollinger Bands with user selection options to calculate the moving average basis and bands from a variety of different moving averages. The user selects their choice of moving average, and the bands automatically adjust. The user may select a MA that reacts faster to volatility or slower/smoother. Added additional options to color the bands or basis based on...

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This is based on a video I watched while searching for good indicators to use for scanning pumps across the crypto market. You can probably find the video by searching for "Pump Finder On 15 Minute Chart With Best Trading Indicators". The approach presented uses LSMA and BB B% to detect pumps. Results: It does detect many pumps, it also detects many...

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Rᴀꜰꜰ Rᴇɢʀᴇꜱꜱɪᴏɴ Cʜᴀɴɴᴇʟ (RRC) This study aims to automate Raff Regression Channel drawing either based on ZigZag Indicator or optionally User Preference The Raff Regression Channel , developed by Gilbert Raff, is based on a linear regression, which is the least-squares line-of-best-fit for a price series, with evenly spaced trend lines above and below . The...

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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...

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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...

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Introduction 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...

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Introduction 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 ...

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Introduction 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...

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Introduction The estimation of a least squares moving average of any degree isn't an interesting goal, this is due to the fact that lsma of high degrees would highly overshoot as well as overfit the closing price, which wouldn't really appear smooth. However i proposed an estimate of an lsma of any degree using convolution and a new sine wave series, all the...

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Introduction 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 The indicator aim to provide fast and smooth results. length control the...

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Introduction The ability to reduce lag while keeping a good level of stability has been a major challenge for smoothing filters in technical analysis. Stability involve many parameters, one of them being overshoots. Overshoots are a common effect induced by low-lagging filters, they are defined as the ability of a signal output to exceed a target input. This...

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Thank 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...

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Strategy based on going long on bottom turning point of a user-definable MA, and short at the top turning point. Can set any length of MA, and choose between SMA or EMA.

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Introduction I already mentioned various problems associated with the lsma, one of them being overshoots, so here i propose to use an lsma using a developed and adaptive form of 1st order polynomial to provide several improvements to the lsma. This indicator will adapt to various coefficient of determinations while also using various recursions. More In Depth ...

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Quick script made by reusing some functions written for other projects. This is a variation on the least squares moving average, but with custom weights on the linear regression. This gives higher weights to recent values and values with high volume. Behaves very similarly to my volume weighted Hull moving average, especially with the hull smoothing option turned on.

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Introduction Another lsma estimate, i don't think you are surprised, the lsma is my favorite low-lag filter and i derived it so many times that our relationship became quite intimate. So i already talked about the classical method, the line-rescaling method and many others, but we did not made to many IIR estimate, the only one was made using a general filter...

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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...

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Similar to Bollinger Bands but adjusted for momentum. Instead of having the centerline be a simply moving average and the bands showing the rolling variance, this does a linear regression, and shows the LSMA at the center, while the band width is the average deviation from the regression line instead of from the SMA. This means that unlike for normal Bollinger...

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