I recently started trying out Martin's returns.py[1] and have 1 observation
& 1 question.
1) It seems a bit prickly about needing price entries and doesn't really
work well unless you have them in all the right places. In particular, it
doesn't seem to use prices from the transactions themselves. For instance,
on October 10 I exchanged from VGAVX to VEGBX {23.85 USD}. But there was no
actual price entry for VEGBX. Which leads to returns.py:
1) not knowing how to convert it to USD, so the resulting composite
portfolio is e.g. 100,000 USD + 5 VEGBX.
2) showing a -100% loss in VEGBX. Because eventually there *is* a price
entry for it, so the overall portfolio goes from 100,000 USD + 5 VEGBX to
simply 100,100 USD.
It is easy enough to fix by adding in the more price directives but it took
a bit of debugging to figure *why* it was happening. I'm not sure if
there's a better way to handle this?
Anyway, on to my real question:
The --begin-date and --end-date parameters have no effect right now, so it
always calculates returns over the entire length of time available in the
beancount file. I've temporarily hacked in a fix by changing the first line
of compute_returns() to be:
periods = [(s.begin.date, s.end.date, s.begin.balance, s.end.balance)
for s in timeline if s.begin.date >= date_begin and
s.end.date <= date_end]
This is just a hack. The biggest problem is that the Segments don't really
line up with the dates provided. For instance, if I specify 2017-01-04 as
the date_begin, the first Segment is actually on 2017-01-09.
So my question is: does the segment_periods() function just need to take
the begin_date & end_date into consideration, or is there a better solution?
[1]:
https://bitbucket.org/blais/beancount/src/2b388a853ae4b3eb1cff288e75cdc89afcb24249/experiments/returns/?at=default
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