pandas-ta VS static-frame

I do not know what is the difference between MACD and MACDFIX but maybe you can take a look how MACD is implemented in pandas_ta library and modify it a bit to achive a behavior you want.

I use https://github.com/twopirllc/pandas-ta found some indicators that are in the pandas_ta but not in the tulip library though tulip also has thing pandas doesn't have.

Having a look at ta.rsi it looks like it is calculating the value for rsi and returns it.

I feel like this is more of a devops issue but its all local, the `local_cron_job` runs at 11pm each night to update `mcvaluations` which then activates 'PandasTA_Strategy_Examples.ipynb' then `VectorBT_Backtest_with_Pandas_TA.ipynb' then 'backorder.py` and finally `strategy`

Pandas T.A: Biblioteca Pandas para análise técnica.

This is inadequate, as it ignores the types contained within the container. A DataFrame might have string column labels and three columns of integer, string, and floating-point values; these characteristics define the type. A function argument with such type hints provides developers, static analyzers, and runtime checkers with all the information needed to understand the expectations of the interface. StaticFrame 2 now permits this:

StaticFrame is an alternative DataFrame library that offers efficient solutions to this problem, both for in-memory and disk-based memoization.

A NumPy array is a Python object that stores data in a contiguous C-array buffer. The excellent performance of these arrays comes not only from this compact representation, but also from the ability of arrays to share "views" of that buffer among many arrays. NumPy makes frequent use of "no-copy" array operations, producing derived arrays without copying underling data buffers. By taking full advantage of NumPy's efficiency, the StaticFrame DataFrame library offers orders-of-magnitude better performance than Pandas for many common operations.

static-frame. An immutable alternative to pandas.

StaticFrame is an immutable DataFrame library that offers solutions to such problems. In StaticFrame, alternative fill value representations can be used to preserve columnar types in reindexing, shifting, and many other operations that require fill_value arguments. For operations on heterogeneously typed columnar data, one fill value is simply not enough.

For me, one example of high cpu usage is when i visit links like this one (https://github.com/InvestmentSystems/static-frame/blob/master/static_frame/performance/core.py) on GitHub. Safari is extremely laggy when i do nothing more than just scrolling around. Do you have sth like this?