pyfinance | Python package | Cryptocurrency library

Well... You didn't import _read_files, you imported pyfinance. Here's what you probably should do:

You said you'd tried a loop but not what the issue was. Here's a simple loop based on your example - does this work for you?

You can convert your dates to an integer using datetime.datetime.strptime and time.mktime, and then build models for desired subsets of your dataframe using statsmodels and a custom function to handle the rolling windows:

Output:

I realized to sync jupyter notebook you just have to do:

conda install jupyter

I suggest you have a look at the source code in order to get into the nitty gritty of what rolling does. In particular I suggest you have a look at the rolling functions in generic.py and window.py. From there you can have a look at the Window class which is used if you specify a window type or the default Rolling class. The last one inherits from _Rolling_and_Expanding and ultimately _Rolling and _Window.

That said, I'll give my two cents: Pandas' whole rolling mechanism relies on the numpy function apply_along_axis. In particular it is used here in pandas. It is used in conjunction with the windows.pyx cython module. In goes your series, out comes the aggregated rolling window. For typical aggregation functions it handles them for you efficiently, but for custom ones (using apply()) it uses a roll_generic() in windows.pyx.

The rolling function in pandas operates on pandas data frame columns independently. It is not a python iterator, and is lazy loaded, meaning nothing is computed until you apply an aggregation function to it. The functions which actually apply the rolling window of data aren't used until right before an aggregation is done.

A source of confusion might be that you're thinking of the rolling object as a dataframe. (You have named the rolling object df in your last code snippet). It really isn't. It is an object which can produce dataframes by applying aggregations over the window logic it houses.

The lambda you are supplying is applied for each cell of your new dataframe. It takes a window backwards (along each column) in your old dataframe, and it aggregates it to one single cell in the new dataframe. The aggregation can be things like sum, mean, something custom you've made, etc., over some window size, say 3. Here are some examples: