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Efficient Data Structures

Hello everyone,

the title says it all: what is the most efficient data structure for local backtests?

Some background information regarding the question:
I wanted to do some backtests offline and installed the zipline package using an anaconda environment. However, if I want to use local data instead of quandl (structured the data as a panel according to the zipline documentation), I always get an error. This seems to be a known problem on Github. As a result I can't use the zipline package for local backtesting.

So I was wondering if I should still use the panel data structure or rather some sort of a multiindex dataframe?

My local data contains daily price data and fundamental data (annual & quarterly) of around 35k of stocks (listed & delisted).

Appreciate any helpful comment.

Best,
Zugzwang