Variety Distribution Probability Cone forecasts price within a range of confidence using Geometric Brownian Motion (GBM) calculated using selected probability distribution, volatility, and drift. Below is detailed explanation of the inner workings of the indicator and the math involved. While normally this indicator would be used by options traders, this can...
Library "MathStatisticsKernelDensityEstimation" (KDE) Method for Kernel Density Estimation kde(observations, kernel, bandwidth, nsteps) Parameters: observations : float array, sample data. kernel : string, the kernel to use, default='gaussian', options='uniform', 'triangle', 'epanechnikov', 'quartic', 'triweight', 'gaussian', 'cosine', 'logistic',...
a test case for the KDE function on price delta. the KDE function can be used to quickly check or confirm edge cases of the trading systems conditionals.
"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable." from wikipedia.com KDE function with optional kernel: Uniform Triangle Epanechnikov Quartic Triweight Gaussian Cosinus Republishing due to change of function. deprecated script:
"In statistics, kernel density estimation (KDE) is a non-parametric way to estimate the probability density function of a random variable." from wikipedia.com