This is an experimental study designed to identify the underlying trend bias and volatility of an instrument over any custom interval TradingView supports. First, reset points are established at points where the opening price of the interval changes. Next, Volume Weighted Average Price (VWAP) is calculated. It is the cumulative sum of typical price times volume...
This is an experimental study designed to identify underlying price activity using a series of Laguerre Filters. Two different modes are included within this script: -Ribbon Mode - A ribbon of 18 Laguerre Filters with separate Gamma values is calculated. -Band Mode - An average of the 18 filters generates the basis line. Then, Golden Mean ATR over the specified...
This study is an experiment designed to identify market phases using changes in an approximate Hurst Exponent. The exponent in this script is approximated using a simplified Rescaled Range method. First, deviations are calculated for the specified period, then the specified period divided by 2, 4, 8, and 16. Next, sums are taken of the deviations of each period,...
This study is an experimental regression curve built around fractal and ATR calculations. First, Williams Fractals are calculated, and used as anchoring points. Next, high anchor points are connected to negative sloping lines, and low anchor points to positive sloping lines. The slope is a specified percentage of the current ATR over the sampling period. The...
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...
This is an experimental study designed to identify potential areas of support and resistance using a hybrid between Camarilla and Fibonacci pivot calculations. The levels are calculated by taking 110% of the previous interval's range multiplied by 8.33%, 16.67%, 25%, 50%, 61.8%, 78.6%, 100%, 127.2%, 141.4%, and 161.8%, then adding them above and below the interval...
This is an experimental study designed to analyze trend intensity using two Donchian Channels. The DCTI curve is calculated by comparing the differences between Donchian highs and lows over a major an minor period, and expressing them as a positive and negative percentage. The curve is then smoothed with an exponential moving average to provide a signal...
This is an experimental study designed to identify trend activity, and potential support and resistance areas. First, Coefficient of Variation Weighted Moving Average (COVWMA) is calculated, and its intersection points are used as anchor values. Next, a fast period COVWMA calculated for a signal line. For the cloud, its mid level is calculated first by taking the...
This is an experimental study derived from George Lane's Stochastic Oscillator. The %KWMA is calculated by taking a moving average of source with a %K weighting factor over its specified period. The %DWMA is calculated by taking a simple moving average of %KWMA over its specified period. Custom bar color scheme included.
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...
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...
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...
This is a simple experimental study designed to outline trend activity and volatility. In this study, the amount of change between current source and source of a specified lookback is calculated, then added to and subtracted from current source. Next an exponential moving average is taken of the values for smoothing over the specified period. Lastly, a midline is...
This is an experimental study designed to track directional polarities across multiple timeframes and express them as a simple two color grid. The polarity in this calculation is determined by divergence between a fast and slow McGinley Dynamic. Your current resolution's polarity is the top row, the rows below are are for higher timeframes of your choice.
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...
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...
This is an experimental study in which a geometric moving average is taken of price, then the range is multiplied by average annualized volatility based on the current trading timeframe and specified lookback, and by Fibonacci numbers 1 through 21.
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...