In developing the "Likelihood of Winning - Probability Density Function (PDF)" indicator, my aim was to offer traders a statistical tool to quantify the probability of reaching target prices. This indicator, grounded in risk assessment principles, enables users to analyze potential outcomes based on the normal distribution, providing insights into market...
Library "NormalDistributionFunctions" The NormalDistributionFunctions library encompasses a comprehensive suite of statistical tools for financial market analysis. It provides functions to calculate essential statistical measures such as mean, standard deviation, skewness, and kurtosis, alongside advanced functionalities for computing the probability density...
Shows the z-Score of log-return (blue line) and volatility (black line). In statistics, the z-score is the number of standard deviations by which a value of a raw score is above or below the mean value. This indicator aggregates z-score based on two indicators: MeanReversion by Logarithmic Returns MeanReversion by Volatility Change the time period in...
Normal Distribution Asymmetry & Volatility Zones Indicator provides insights into the skewness of a price distribution and identifies potential volatility zones in the market. The indicator calculates the skewness coefficient, indicating the asymmetry of the price distribution, and combines it with a measure of volatility to define buy and sell zones. The key...
The Apeiron Fair Value Bands take into account a given MA and determine a Fair Value Area (FVA) for the price of a certain asset. The script plots a MA and a tolerance ribbon for it, as well as 2 bands (preset to 1 Standard deviations and 2 Standard deviations respectively, which can be manually changed) with a tolerance ribbon as well. This creates 3 areas of...
When doing machine learning using oscillators, it would be better if the oscillators were normally distributed. So I analyzed the distribution of oscillators. The value of the oscillator was divided into 50 groups each from 0 to 100. ex) if rsi value is 45.43 -> group_44, 58.23 -> group_58 Ocscillators : RSI, Stoch, MFI, WT, RVI, etc.... Caution: The normal...
Does RSI Follow a Normal Distribution? The value of RSI was converted to a value between 0~2, 2~4, ..., 98~100, and the number of samples was graphed. The Z values are expressed so that the values corresponding to 30 and 70 of the RSI can be compared with the standard normal distribution. Additionally, when using the RSI period correction function of the 'RSI...
The Return Abnormality Score indicator is designed to help traders identify potential reversals in price by detecting abnormal daily returns beyond a certain significance level. The indicator uses a normal cumulative distribution function to calculate the probability of the daily return and flags it when it exceeds the specified significance level. Traders can...
This indicator shows the expected range of movement of price given the assumption that price is log-normally distributed. This includes 3 multiples of standard deviation and 1 user selected level input as a multiple of standard deviation. Expected assumes that volatility remains static on the next bar. In reality, this may or may not be the case, so use caution...
Multi-Panel: Trade-Volatility-Probability shows user selected and volatility-based price levels and probabilities on the chart. This is useful for both options and all styles of up/down trading methods that rely on volatility. Trading Panel: Shows trading information to take profits and stop-loss based on multiples of volatility. Also shows equity inputs by...
Library "normsinv" Description: Returns the inverse of the standard normal cumulative distribution. The distribution has a mean of zero and a standard deviation of one; i.e., normsinv seeks that value z such that a normal distribtuion of mean of zero and standard deviation one is equal to the input probability. Reference: github.com normsinv(y0)...
Library "cndev" This function returns the inverse of cumulative normal distribution function Reference: The Full Monte, by Boris Moro, Union Bank of Switzerland . RISK 1995(2) CNDEV(U) Returns the inverse of cumulative normal distribution function Parameters: U : float, Returns: float.
Library "ctnd" Description: Double precision algorithm to compute the cumulative trivariate normal distribution found in A.Genz, Numerical computation of rectangular bivariate and trivariate normal and t probabilities”, Statistics and Computing, 14, (3), 2004. The cumulative trivariate normal is needed to price window barrier options, see G.F. Armstrong,...
Library "norminv" Description: An inverse normal distribution is a way to work backwards from a known probability to find an x-value. It is an informal term and doesn't refer to a particular probability distribution. Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation. Reference: ...
Library "cnd" Cumulative Normal Distribution CND1(x) Returns the Cumulative Normal Distribution (CND) using the Hart (1968) method. (preferred method, 14-18 decimal accuracy) Parameters: x : float, Returns: float. CND2(x) Returns the Cumulative Normal Distribution (CND) using the Abromowitz and Stegun (1974) Polynomial...
One-Sided Gaussian Filter w/ Channels is a Gaussian Moving Average that is calculated using a Fibonacci weighting function. Keltner channels have been added to show zones of exhaustion. A better name would be "Half Gaussian bell weighted" or "Half normal distribution weighted" indicator, since the weights for calculation of the average (similar to linear...
Bollinger Bands are the result of the assumption that closing prices will follow a normal distribution. However, when I actually calculated the probability, the closing price does not follow a normal distribution. According to the normal distribution, the probability that Z > 2 should be 2.2%, but on the chart, the probability is 6~9%. Can we get a useful...
Probability Distribution Histogram During data exploration it is often useful to plot the distribution of the data one is exploring. This indicator plots the distribution of data between different bins. Essentially, what we do is we look at the min and max of the entire data set to determine its range. When we have the range of the data, we decide how many...