I recently pushed version 0.2.0 of my
fromo package to
This package implements (relatively) fast, numerically robust
computation of moments via
The big changes in this release are:
- Support for weighted moment estimation.
- Computation of running moments over windows defined by time (or some other increasing index), rather than vector index.
- Some modest improvements in speed for the 'dangerous'
use cases (no checking for
NA, no weights, etc.)
The time-based running moments are supported via the
and we support means, standard deviation, skew, kurtosis, centered and
standardized moments and cumulants, z-score, Sharpe, and t-stat. The
idea is that your observations are associated with some increasing
index, which you can think of as the observation time, and you wish
to compute moments over a fixed time window. To bloat the API, the
times from which you 'look back' can optionally be something other
than the time indices of the input, so the input and output size
can be different.
Some example uses might be:
- Compute the volatility of an asset's returns over the previous 6 months, on every trade day.
- Compute the total monthly sales of a company at month ends.
Because the API also allows you to use weights as implicit time deltas, you can also do weird and unadvisable things like compute the Sharpe of an asset over the last 1 million shares traded.
Speed improvements come from my random walk through c++ design idioms. I also implemented a 'swap' procedure for the running standard deviation which incorporates a Welford's method addition and removal into a single step. I do not believe that Welford's method is the fastest algorithm for a summarizing moment computation: probably a two pass solution to compute the mean first, then the centered moments is faster. However, for the …read more