transaction prices.
When given --infer-costs, hledger will now separately infer transaction
prices for different prices. Given a pair of adjacent conversion
postings, hledger will check if there is a single posting with a
transaction price which matches both the amounts. If so, it associates
those conversion postings to that priced post.
If it can't find any transaction price postings which match, it will
find the first non-transaction price post which matches one of the two
amounts, and will add a transaction price to that, and associate them.
When given --infer-equity, hledger will change transaction prices to balancing equity postings. This introduces the inverse operation, --infer-costs, which will match balancing equity postings and transform them into a transaction price, allowing --cost to work properly with them. This is only a partial inverse as it needs to use some heuristics to match the postings which will not work in complicated cases.
Specifically, when hledger finds exactly two conversion postings in a transaction (by default, subaccounts of equity:conversion or equity:trad(e|ing)), it will find the first posting in the transaction whose amount is negative one of the conversion posting amounts, and inserts the corresponding transaction price.
May also fix#1154, #1033, #708, #536, #73: testing is needed.
This aims to solve all problems where misconfigured locales lead to
parsers failing on utf8-encoded data. This should hopefully avoid
encoding issues, but since it fundamentally alters how encoding is dealt
with it may lead to unexpected outcomes. Widespread testing on a number
of different platforms would be useful.
total prices. (#1813)
Also reduce duplication for inferring market prices (previously it was
done separately in both Hledger.Data.Journal and
Hledger.Cli.Commands.Prices), and remove *TotalPriceToUnitPrice
functions, since unit prices cannot represent all total prices.
Add a helper function numDigitsInt to get the number of digits in an
integer, which has a surprising number of ways to get it wrong.
^assets?(:.+)?:(cash|bank|che(ck|que?)(ing)?|savings?|current)(:|$)
This should have no effect outside the cash report.
The rule for auto-detecting "Cash" (liquid assets) from account names
has changed in this release - see [Account types]. This is to simplify
how account types are detected and configured. If you use the cashflow
report without explicitly declaring accounts as Cash, you might notice a
change, and might need to add explicit account type: tags.
This requires checking parent accounts for any new accounts introduced by auto postings which do not exist in the original journal.
Also refactor journalFinalise to only call journalPostingsAddAccountTags once, and use fewer intermediate variables.
type:TYPES, where TYPES is any of the (case insensitive) letters
ALERXCV, matches accounts by their declared or inferred type.
(See https://hledger.org/hledger.html#account-types.)
This should work with most commands, eg:
hledger bal type:al
hledger reg type:x
API changes:
Journal has a new jaccounttypes map.
The journalAccountType lookup function makes it easy to check an account's type.
The journalTags and journalInheritedTags functions look up an account's tags.
Functions like journalFilterPostings and journalFilterTransactions,
and new matching functions matchesAccountExtra, matchesPostingExtra
and matchesTransactionExtra, use these to allow more powerful matching
that is aware of account types and tags.
Accounts, postings, and transactions can now all be filtered by the
tags in an account's declaration. In particular it's now possible to
more reliably select accounts by type, using their type: tag rather
than their name:
account myasset ; type:Asset
account myliability ; type:Liability
$ hledger accounts tag:type=^a
myasset
Accounts inherit tags from their parents.
API changes:
A finalised Journal has a new jdeclaredaccounttags field
for easy lookup of account tags.
Query.matchesTaggedAccount is a tag-aware version of matchesAccount.
Previously the helper functions splitspan and splitspan' would calculate
each span from the start point of the previous span. This meant we had
to be very careful not to lose any relevant information (e.g. what day
of the week it was) about the original start date. We now calculate each
span from the original start date, so there's no risk of losing
information. This simplifies many of the calculations.
