Unfortunately, I need to generate a couple of hundred of these values on each of the worksheets, and with the recalculating the weighted mean for each row, performance is prohibitively slow. With Sierra Chart, they plot 0.5, 1, 1.5 and 2 standard deviation bands from the VWAP, as default. Exactly how you create the VWAP bands will be dependent on your charting package. So to isolate to a particular question number, a filter on Q and Q is needed (which is the point of the use of earlier() in the formula - it is restoring the filters from slicers on the output pivot/cubefunctions). Using a mathematical formula to determine standard deviations from the VWAP, it is possible to create additional indicators to help determine market movements. is just a simple measure that is linked to a slicer to select which weighting scheme to use (unweigthed, using Q, or using Q.Įach row of Q is a question response for a single respondent of the survey. Q - Numeric - An alternative response, and what the above metric is actually determining the SD on Q - Numeric - A numeric response to the question What is a Weighted Standard Deviation The standard deviation provides information about the distribution between observations in a sample or population. Q - numeric - Some questions have multiple iterations, so this is storing this The resulting weighted standard deviation is approximately 5.9. YSD (Q):=SQRT(SUMX(ADDCOLUMNS(ADDCOLUMNS(ADDCOLUMNS(Q,"Mean",Calculate(,All(Q),Q=Earlier(Q),Q=Earlier(Q))),"Delta",(Q - )^2),"wDelta", * SWITCH(,1,1,2,3,)),AVERAGE())/)Ī few things to call out on the data structure: It often occurs, however, that one must combine two or more measurements of the same quantity with differing errors.
I did manage to get a measure that yields the proper value, however it is incredibly slow at calculating, likely due to it effectively generating the weighted mean for each row of data to calculate the variance. We have thus far discussed the estimation of the mean and standard deviation from a series of measurements of the same quantity with the same instrument.
Just in case you're interested in the relation between the standard error and the standard deviation: The standard error is (for ddof = 0) calculated as the weighted standard deviation divided by the square root of the sum of the weights minus 1 ( corresponding source for statsmodels version 0.Thanks again, GDRIII - I read through that article earlier, however not sure there is much I can use for this application. std_mean the standard error of weighted mean: > weighted_stats.std_mean var the weighted variance: > weighted_stats.var Financial analyst often uses it for measuring and managing risk for a specific portfolio or fund. Investors most commonly use it to measure the risk of a stock (a measure of stock volatility over a period of time). std the weighted standard deviation: > weighted_stats.std Standard Deviation function can be used as a worksheet function & can also be applied by using VBA code.
You initialize the class (note that you have to pass in the correction factor, the delta degrees of freedom at this point): weighted_stats = DescrStatsW(array, weights=weights, ddof=0) If the standard deviation of the random errors in the data is not constant across all levels of the explanatory variables, using weighted least squares with weights that are inversely proportional to the variance at each level of the explanatory variables yields the most precise parameter estimates possible. There is a class in statsmodels that makes it easy to calculate weighted statistics: .Īssuming this dataset and weights: import numpy as npįrom import DescrStatsW