I've written similar in Julia, you can see the record type used in https://www.juliapackages.com/p/namedtuples. The full library, not in the open source, uses this type for time series analysis. It's all type safe and allowed expressions such as x = vwap( ts, 5) - l1( vwap( ts, 5)) through to a time moving PCA. Julia makes writing this sort of thing short and quick. The total impl was only a thousand lines or so of code.
I checked your website; do you have an example of how to load data from a file into NamedTuples? Specifically, can NamedTuples infer type from an external source?
Also, do you have an example of what a displayed table looks like? Julia has a DataFrames package that can display a table. I am curious to know how your time-series library displays a table.
unfortunately, I don't have access to that code anymore, I wrote a number of loaders for different data set types including CSV. The time series were all modeled as forward iterating stream of tuples, so there is no specific table abstraction. There is an implicit assumption that the stream is ordered by the join key, in a time series this being the timestamp, though nothing in the implementation enforced that.
Joins are always n-way merge joins, so you can write something like y = 2x^2 - 3z + c and fold that into a single streaming operation y = f( x, z, c ) where y, x, z and c are time streams.
When rendered to screen they looked very similar to your examples. With plugins in the IDE you could directly plot and array of time series as a chart.
