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NORMINV - Inverse of the Normal Cumulative Distribution
NORMINV function is the inverse of the normal cumulative distribution for a specified standard deviation and mean. The normal probability density and the cumulative normal distribution can be calculated using the following function: NORMDIST - Normal Probability Density Function.
This function finds the z value given the probability that a value or variable is within a certain distance from the mean of the distribution.
The function gets three parameters: Probability: A probability corresponding to the normal distribution. The value must be between 0 (0%) and 1 (100%). Mean: The arithmetic mean of the distribution Standard deviation: The standard deviation of the distribution. The value must be positive.
Example:
First, we assume that the one-bar rate of return of a security is normally distributed.
For each bar, we want to calculate the 90% confidence level. We will use the NORMINV statistical function to compute this.
p1 = perf(close, 1);
a = NORMINV(0.9, sma(p1, 500),stddev(p1, 500));
If our function returns 0.05 (5% because we are dealing with daily returns) on a specific bar, then there is 90% change that the one bar rate of return will stay below +5% level in the next bars.
To calculate the lower bound, you should use (10% or 100% - 90%) in the first argument of the NORMINV function.
Trading financial instruments, including foreign exchange on margin, carries a high level of risk and is not suitable for all investors. The high degree of leverage can work against you as well as for you. Before deciding to invest in financial instruments or foreign exchange you should carefully consider your investment objectives, level of experience, and risk appetite. The possibility exists that you could sustain a loss of some or all of your initial investment and therefore you should not invest money that you cannot afford to lose. You should be aware of all the risks associated with trading and seek advice from an independent financial advisor if you have any doubts.
