This is an experimental study designed to filter out minor price action for a clearer view of trends.
Inspired by the QQE's volatility filter, this filter applies the process directly to price rather than to a smoothed RSI.
First, a smooth average price range is calculated for the basis of the filter and multiplied by a specified amount.
Next, the filter is...
Experimental attemt of applying Logistic Map Equation for some of widly used indicators.
With this study "Awesome Oscillator (AO)", "Rate of Change (ROC)", "Relative Strength Index (RSI)", "Stochastic (STOCH)" and a custom interpretation of Logistic Map Equation is presented
Calculations with Logistic Map Equation makes sense when the calculated results...
This is a study geared toward identifying price trends using Quadratic regression.
Quadratic regression is the process of finding the equation of a parabola that best fits the set of data being analyzed.
In this study, first a quadratic regression curve is calculated, then the slope of the curve is calculated and plotted.
Custom bar colors are included. The...
Calculates VWAP from a fixed point in time as well as standard deviations.
If you find it useful please consider a tip/donation :
BTC - 3BMEXEDyWJ58eXUEALYPadbn1wwWKmf6sA
This is an experimental variation of Paul L. Dysart's Positive Volume Index and Negative Volume Index that tracks the divergences between the PVI and its EMA, and the NVI and its EMA, then plots both together for comparison.
This tool can be used to identify trending price activity.
This is an experimental adaptive trend following study inspired by Giorgos Siligardos's Reverse Engineering RSI and Tushar S. Chande's Variable Moving Average.
In this study, reverse engineered RSI levels are calculated and used to generate a volatility index for VMA calculation.
First, price levels are calculated for when RSI will equal 70 and 30. The...
A Function that returns a linear regression channel using (X,Y) vector points.
_X: Array containing x data points.¹
_Y: Array containing y data points.¹
¹: _X and _Y size must match.
_predictions: Array with adjusted _Y values at _X.
_max_dev: Max deviation from the mean.
Example execution of Monte Carlo Simulation applied to the markets(this is my interpretation of the algo so inconsistencys may appear).
the algorithm is very demanding so performance is limited.
This is an experimental study using z scores of multiple sampling periods to analyze price trends.
Z score measures the number of standard deviations price is from its mean.
In this study, z scores are calculated over a Fibonacci sequence of sampling periods from 3 to 4181.
The scores are then averaged with equal weighting, resulting in a display of long term...
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 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...