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Experimental

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 Average - Simple Moving Average For a more refined picture of volatility, I've added upper and lower extension channels. They are calculated by adding the upper half range to the channel high, and subtracting the lower half range from the channel low. The new custom bar color scheme indicates trends, midline crosses, MA crosses, and overbought and oversold conditions.
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4
Volume Weighted Average Divergence [DW]This is an experimental study inspired by the volume weighted moving average convergence divergence (VWMACD) concept. In this formula, divergences between two volume weighted moving averages and two simple moving averages over their respective lookback periods are calculated. The difference between the divergences is calculated, then the difference between the result and an exponential moving average of the result are calculated to provide a histogram. Finally, the mean value between the two divergences is calculated to provide the VWAD line. Custom bar colors are also included.
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2
Volume Divergence Polarity Grid [DW]This study is an experiment built off the framework of my Dual Volume Divergence Index indicator. It is designed to gauge polarity over multiple lookback periods of your choice by expressing the data as a two color grid. Positive Volume Divergence and Negative Volume Divergence are calculated, and their relative values are used to gauge polarity. The order of the grid is top to bottom - the top is the first lookback period, and the bottom is the last.
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6
Shift RMS [DW]This is an experimental study designed to track the average magnitude of price movements. First the range between high and low, and the range between open and close are calculated. Then a positive and negative root mean square is taken of both ranges, and the results are smoothed with an exponential moving average. And lastly, the median value between the ranges is calculated and smoothed with an ema to provide a signal line. This study can be used to identify trending and detrending price activity.
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2
Dynamic Range Channel [DW]This is an experimental study that utilizes Kaufman's Adaptive Moving Average and the McGinley Dynamic. First, a fast and slow KAMA based McGinley Dynamic are calculated. The divergence between them is used to indicate wave direction. The channel's bounds are calculated by taking the highest high and lowest low of the slow McGinley Dynamic over a specified channel period. The dynamic midline is calculated by taking the mean of the highest and lowest values over the specified channel period. Custom bar colors are included. Also includes Williams Fractals for additional confirmation signals.
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8
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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2