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johnny's jotter by jotter i mean blog skip to content home (català) spot personal projects ynab debt sync peazip fork ttupf horariglobal timetable planner event scheduler and notifier pyella names carephone about ← older posts efficient algorithm for finding incorrect transactions in double-entry bookkeeping posted on august 16, 2015 by johnny i wanted to find an efficient algorithm for finding missing or problematic transactions with ynab debt sync . the naïve o(n²) approach of comparing all transactions from both budgets would obviously not scale. this post explains how i achieved an average time complexity of o(n·log(n)). thinking about the problem, it occurred to me that i could use the mirrored nature of double-entry bookkeeping to my advantage by sorting each budget's transactions by their amount. the trick is to sort the second budget's transactions in opposite order to the first budget's transactions and, for each pair of transactions, compare the first budget's transaction amount with the inverse of the second budget's transaction amount. for the best-case scenario where the budgets tally up, this results in linear time complexity. you can see an example in the next figure showing two budgets, called "this budget" and "other budget", whose transactions are sorted in this mirrored fashion. this is the best-case scenario: both budgets have the same number of transactions, and each transaction in one budget has its opposite transaction in the other budget. using a common sort algorithm, like quicksort, the complexity for the algorithm's best-case scenario is o(2(n·log(n)) + n), or o(n·log(n)). the best-case scenario isn't of much interest, though, as the use-case for the algorithm is precisely when the budgets don't tally up. if one (or more) transaction(s) is missing from one of the budgets, the previous comparisons will fail at some point, due to the transaction amounts in the incorrect budget "skipping" past the expected transaction amount. you can see this in the next figure, where "other budget" is missing the transaction of amount -9,80. the first comparison is successful: this budget's first transaction of amount 10 is mirrored by other budget's first transaction of amount -10. the comparison fails for the second pair though: this budget's second transaction of amount 9,80 is not mirrored by other budget's second transaction of amount -6,27. looking at the figure, it's easy to see that other budget is the one missing a transaction. specifically, the mirror transaction to this budget's 9,80 transaction. programmatically deciding which budget is missing a transaction is a bit more complicated, and is documented in ynabbudgetcomparer's get_missing_transactions method . the cases depend on the combination of the transaction amount signs and on the absolute value comparison of the amounts. in this case, because this budget's transaction is greater than zero and other budget's transaction is less than zero, coupled with this budget's transaction's absolute value being greater than other budget's transaction's absolute value, we know that other budget is missing a transaction to mirror the transaction of amount 9,80 from this budget. information such as payee or memo (in the case of ynab transactions) will help the user to understand the discrepancy between the budgets, but this budget may have multiple transactions of amount 9,80, each with different information. the next step is to find the best candidate transaction. to do so, first the subset of transactions of amount 9,80 from this budget is extracted and sorted by date. the same is done for transactions of amount -9,80 from other budget. if there are m transactions of this amount, this is o(m·log(m)) operations. it is common that, for n total transactions, n ≫ m, as there are relatively few transactions that share the exact same amount. a transaction subset comparison is triggered each time there is a mismatch in a transaction pair's amounts so, for p skips, the time complexity of this operation is o(pm·log(m)). the value of p depends entirely on how thorough the users have been at recording transactions. in my experience, there is roughly one skip for every ten transactions, so we can also consider n ≫ p, giving n ~ pm. the loosely average time complexity is then o(n·log(n) + n·log(n)), or o(n·log(n)). once the two subsets have been created, the relative complement of other budget transactions in this budget transactions is then calculated, i.e. which transactions exist in this budget but not in other budget. this is a linear operation of, at worst, o(n-1), where n is the amount of transactions from this budget. it is n-1 because if other budget has no transactions of amount -9,80, the whole set of this budget's transactions of amount 9,80 is returned, and is a constant-time o(1) operation. the order of magnitude of this operation doesn't affect the previous time complexity of o(n·log(n)). for the budgets in this example, the subset from this budget contains one transaction of amount 9,80 with date 2015-08-16. the subset from other budget contains zero mirror transactions of amount -9,80, as you can see in the following figure. the transaction from this budget is returned as other budget's missing transaction, required for both budgets to tally up. for extreme cases the algorithm could degenerate to a worst-case scenario of o(mn·log(n)) complexity, for example if none of the transactions were mirrored: each budget containing only its own side of transactions. posted in uncategorized | leave a comment managing short-term lending with two-way ynab posted on august 14, 2015 by johnny this post explains the reason behind my latest personal project, ynab debt sync . shortly after i started using ynab i ran into a problem. how was i meant to borrow cash from —or spot cash to— family and friends? the first few times a friend asked me to spot them some cash to pay for lunch or a beer, i just assigned it to whatever category the activity was related to. that posed a problem though, i wasn't actually spending that money on that category, so my budget wasn't truly reflecting my spending. in any case, i'd (hopefully) be getting that money back the next time i saw them. the solution i came up with was to create a category for each person. if i loaned a person 5€, i'd create an outflow transaction and assign it to that person's category. starting from a zero balance, the category would show a -5€ balance, meaning they owed me that amount. if, another day, i borrowed 7€ from them, i'd record two cash transactions. one inflow of 7€ assigned to that person's category, which would update the balance to +2€ (i.e. i owe them money). the other transaction would be an outflow of 7€, assigned to whatever category made sense (e.g. lunch), decreasing that category's balance. this way my budget would be accurate, as i had actually spent 7€ on lunch. as the two transactions have the same amount, but are opposites of each other (inflow vs. outflow), the balance for my cash account would remain unchanged (usually close to zero, in this case :) to avoid any outstanding loans subtracting from my "available to budget" amount when the loans span two months, for example if i were to borrow money on the last sunday of a month and i wouldn't be able to repay until the following sunday, i "turned the arrow right", as mentioned by this post on ynab's forums . this was great for a while. i could lend and borrow money for short periods of time while being confident my actual spending categories were accurate, and also keeping track of how much i (or they!) owed. then i managed to convince some of these friends and family to use ynab. suddenly, my budget wasn't the sole source of information. i started being a bit lazier when recording money i borrowed; i knew the lender would record the transaction to ensure their budget was up-to-date. the opposite also happened, if i loaned somebody some money, they knew i'd record it and

URL analysis for maian.org

http://maian.org/blog/2010/08/
http://maian.org/blog/author/johnny/
http://maian.org/blog/2010/07/
http://maian.org/blog/peazip.sourceforge.net/peazip_help.pdf
http://maian.org/blog/2015/08/managing-short-term-lending-with-two-way-ynab/#respond
http://maian.org/blog/2009/12/
http://maian.org/blog/2012/02/
http://maian.org/blog/2015/08/managing-short-term-lending-with-two-way-ynab/
http://maian.org/blog/2012/03/irrefutable-proof-two-spaces-after-a-period-on-the-web-is-evil/#respond
http://maian.org/blog/personal-projects/horariglobal/
http://maian.org/blog/2014/01/moodle-sending-text-files-with-incorrect-texthtml-mime-type/
http://maian.org/blog/ca/
http://maian.org/blog/2014/08/how-to-build-peazip-installer-for-windows/
http://maian.org/blog/2009/08/
http://maian.org/blog/feed/

Whois Information

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Domain Name: MAIAN.ORG
Registry Domain ID: D106252782-LROR
Registrar WHOIS Server: whois.enom.com
Registrar URL: http://www.enom.com
Updated Date: 2018-05-10T22:15:54Z
Creation Date: 2005-05-05T22:14:21Z
Registry Expiry Date: 2019-05-05T22:14:21Z
Registrar Registration Expiration Date:
Registrar: eNom, Inc.
Registrar IANA ID: 48
Registrar Abuse Contact Email: [email protected]
Registrar Abuse Contact Phone: +1.4252982646
Reseller:
Domain Status: clientTransferProhibited https://icann.org/epp#clientTransferProhibited
Registrant Organization: WhoisGuard, Inc.
Registrant State/Province: Panama
Registrant Country: PA
Name Server: NS1.DREAMHOST.COM
Name Server: NS2.DREAMHOST.COM
Name Server: NS3.DREAMHOST.COM
DNSSEC: unsigned
URL of the ICANN Whois Inaccuracy Complaint Form: https://www.icann.org/wicf/
>>> Last update of WHOIS database: 2018-06-25T00:27:19Z <<<

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Access to Public Interest Registry WHOIS information is provided to assist persons in determining the contents of a domain name registration record in the Public Interest Registry registry database. The data in this record is provided by Public Interest Registry for informational purposes only, and Public Interest Registry does not guarantee its accuracy. This service is intended only for query-based access. You agree that you will use this data only for lawful purposes and that, under no circumstances will you use this data to (a) allow, enable, or otherwise support the transmission by e-mail, telephone, or facsimile of mass unsolicited, commercial advertising or solicitations to entities other than the data recipient's own existing customers; or (b) enable high volume, automated, electronic processes that send queries or data to the systems of Registry Operator, a Registrar, or Afilias except as reasonably necessary to register domain names or modify existing registrations. All rights reserved. Public Interest Registry reserves the right to modify these terms at any time. By submitting this query, you agree to abide by this policy.

Please query the RDDS service of the Registrar of Record identified in this output for information on how to contact the Registrant, Admin, or Tech contact of the queried domain name.

  REFERRER http://www.pir.org/

  REGISTRAR Public Interest Registry

SERVERS

  SERVER org.whois-servers.net

  ARGS maian.org

  PORT 43

  TYPE domain

DOMAIN

  NAME maian.org

  HANDLE D106252782-LROR

  CREATED 2005-05-05

STATUS
clientTransferProhibited https://icann.org/epp#clientTransferProhibited

NSERVER

  NS1.DREAMHOST.COM 64.90.62.230

  NS2.DREAMHOST.COM 208.97.182.10

  NS3.DREAMHOST.COM 66.33.205.230

OWNER

  ORGANIZATION WhoisGuard, Inc.

ADDRESS

  STATE Panama

  COUNTRY PA

  REGISTERED yes

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