Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

https://github.com/jbms/beancount-import

Web UI for semi-automatically importing external data into beancount
https://github.com/jbms/beancount-import

beancount

Last synced: 12 days ago
JSON representation

Web UI for semi-automatically importing external data into beancount

Lists

README 

        
Beancount-import is a tool for semi-automatically importing financial data from

external data sources into the [Beancount](https://beancount.github.io/)

bookkeeping system, as well as merging and reconciling imported transactions with

each other and with existing transactions.



[![License: GPL v2](https://img.shields.io/badge/License-GPL%20v2-blue.svg)](LICENSE)

[![PyPI](https://img.shields.io/pypi/v/beancount-import)](https://pypi.org/project/beancount-import)

[![Build](https://github.com/jbms/beancount-import/workflows/Build/badge.svg)](https://github.com/jbms/beancount-import/actions?query=workflow%3ABuild)

[![Coverage Status](https://coveralls.io/repos/github/jbms/beancount-import/badge.svg?branch=master)](https://coveralls.io/github/jbms/beancount-import?branch=master)



# Key features



- Pluggable data source architecture, including existing support for OFX (cash,

  investment, and retirement accounts), Mint.com, Amazon.com, and Venmo.



- Supports [beancount importers](https://beancount.github.io/docs/importing_external_data.html) so it's easier to write your own,

  and existing beancount and fava users can hop right on with no hustle.



- Robustly associates imported transactions with the source data, to

  automatically avoid duplicates.



- Automatically predicts unknown legs of imported transactions based on a

  learned classifier (currently decision tree-based).



- Sophisticated transaction matching/merging system that can semi-automatically

  combine and reconcile both manually entered and imported transactions from

  independent sources.



- Easy-to-use, powerful web-based user interface.



# Basic operation



From the data source modules, beancount-import obtains a list of *pending*

imported transactions.  (Balance and price entries may also be provided.)

Depending on the external data source, pending transactions may fully specify

all of the Beancount accounts (e.g. an investment transaction from an OFX source

where shares of a stock are bought using cash in the same investment account),

or may have some postings to unknown accounts, indicated by the special account

name `Expenses:FIXME`.  For example, pending transactions obtained from bank

account/credit card account data (e.g. using the Mint.com data source) always

have exactly two postings, one to the known Beancount account corresponding to

the bank account from which the data was obtained, and the other to an unknown

account.



For each pending transaction, beancount-import attempts to find matches to both

existing transactions and to other pending transactions, and computes a set of

candidate merged transactions.  For each unknown account posting,

Beancount-import predicts the account based on a learned classifier.  Through a

web interface, the user can view the pending transactions, select the original

transaction or one of the merged candidates, and confirm or modify any predicted

accounts.  The web interface shows the lines in the journal that would be added

or removed for each candidate.  Once the user accepts a candidate, the candidate

is inserted or merged into the Beancount journal, and the user is then presented

with the next pending entry.



The imported transactions include metadata fields on the transaction and on the

postings that serve several purposes:

 - indicating to the data source module which entries in its external

   representation have already been imported and should not be imported again;

 - indicating which postings are *cleared*, meaning they have been confirmed by

   the authoritative source, which also constrains matching (a cleared posting

   can only match an uncleared posting);

 - providing necessary information for training the classifier used for

   predicting unknown accounts;

 - providing information to the user that may be helpful for identifying and

   understanding the transaction.



# Installation



1. Ensure you have activated a suitable Python 3 virtualenv if desired.



2. To install the most recent published package from PyPi, simply type:



   ```shell

   pip install beancount-import

   ```



   Alternatively, to install from a clone of the repository, type:

   ```shell

   pip install .

   ```



   or for development:

   ```shell

   pip install -e .

   ```



   The published PyPI package includes pre-built copy of the frontend and no

   further building is required.  When installing from the git repository, the

   frontend is built automatically by the above installation commands, but

   [Node.js](https://nodejs.org) is required.  If you don't already have it

   installed, follow the instructions in the [frontend](frontend/README.md)

   directory to install it.



# Demo



To see Beancount-import in action on test data, refer to the instructions in the

[examples](examples/) directory.



# Data sources



Data sources are defined by implementing the Source interface defined by the

[beancount_import.source](beancount_import/source/__init__.py) module.



The data sources provide a way to import and reconcile already-downloaded data.

To retrieve financial data automatically, you can use the

[finance_dl](https://github.com/jbms/finance-dl) package.  You can also use any

other mechanism, including manually downloading the data from a financial

institution's website, provided that it is in the format required by the data

source.



The currently supported set of data sources is:



- [beancount_import.source.ofx](beancount_import/source/ofx.py): most versatile

  data source, supports checking, savings, credit card, investment, and

  retirement accounts.

- [beancount_import.source.mint](beancount_import/source/mint.py): supports all

  cash account types supported by [Mint.com](https://www.mint.com).

- [beancount_import.source.venmo](beancount_import/source/venmo.py)

- [beancount_import.source.amazon](beancount_import/source/amazon.py): supports

  regular and digital order invoices.

- [beancount_import.source.healthequity](beancount_import/source/healthequity.py):

  supports [HealthEquity.com](https://healthequity.com) HSA accounts, including

  both cash and investment transactions.

- [beancount_import.source.google_purchases](beancount_import/source/google_purchases.py):

  imports purchases from that Google has [heuristically extracted from Gmail

  messages](https://myaccount.google.com/purchases).

- [beancount_import.source.paypal](beancount_import/source/paypal.py):

  imports Paypal transactions.

- [beancount_import.source.waveapps](beancount_import/source/waveapps.py):

  imports receipts from [Wave](https://waveapps.com), a free receipt-scanning

  website/mobile app.

- [beancount_import.source.stockplanconnect](beancount_import/source/stockplanconnect.py):

  imports release/trade transactions from

  [Morgan Stanley StockPlan Connect](https://stockplanconnect.com).

- [beancount_import.source.schwab_csv](beancount_import/source/schwab_csv.py):

  imports transactions and positions CSV exports from Schwab brokerage accounts.

- [beancount_import.source.ultipro_google](beancount_import/source/ultipro_google.py):

  imports Google employee Ultipro payroll statements.

- [beancount_import.source.generic_importer_source](beancount_import/source/generic_importer_source.py):

  imports from `beancount.ingest.importer.ImporterProtocol` subclass Importers. See [beancount's documentation](https://beancount.github.io/docs/importing_external_data.html#the-importing-process) on how to write one and checkout the [examples](examples/) directory for a simple csv importer



Refer to the individual data source documentation for details on configuration.



# Usage



To run Beancount-import, create a Python script that invokes the

`beancount_import.webserver.main` function.  Refer to the examples

[fresh](examples/fresh/run.py) and

[manually_entered](examples/manually_entered/run.py).



## Errors



Any errors either from Beancount itself or one of the data sources are shown in

the `Errors` tab.  It is usually wise to manually resolve any errors, either

using the built-in editor or an external editor, before proceeding, as some

errors may result in incorrect behavior.  Balance errors, however, are generally

safe to ignore.



## Viewing candidates



Select the `Candidates` tab to view the current pending imported entry, along

with all proposed matches with existing and other pending transactions.  The

original unmatched entry is always listed last, and the proposal that

includes the most matched postings is listed first.  The list with checkboxes at

the top indicates which existing or pending transactions are used in each

proposed match; the current pending transaction is always listed first.  If many

incorrect matches were found, you can deselect the checkboxes to filter the

matches.



You can select one of the proposed entries by clicking on it, or using the

up/down arrow keys.  To accept a proposed entry as is, you can press Enter or

double click it.  This immediately modifies the journal to reflect the change,

and also displays the relevant portion of the journal in the Journal tab, so

that you may easily make manual edits.



## Specifying unknown accounts



If a proposed entry includes unknown accounts, they are highlighted with a

distinctive background color and labeled with a group number.  The account shown

is the one that was automatically predicted, or `Expenses:FIXME` if automatic

prediction was not possible (e.g. because of lack of training data).  There are

several ways to correct any incorrectly-predicted accounts:

 - To change an individual account, you can Shift+click on it, type in the new

   account name, and then press Enter.  If you press Escape while typing in the

   account name, the account will be left unchanged.  A fuzzy matching algorithm

   is used for autocompletion: if you type "ex:co", for example, it will match

   any accounts for which there is a subsequence of 2 components, where the

   first starts (case-insensitively) with "ex" and the second starts with "co",

   such as an `Expenses:Drinks:Coffee` account.

 - To change all accounts within a proposed entry that share the same group

   number, you can click on one of the accounts without holding shift, or press

   the digit key corresponding to the group number.  Once you type in an account

   and press Enter, the specified account will be substituted for all postings

   in the group.

 - To change all accounts within a proposed entry, you can click the `Change

   account` button or press the `a` key.  Once you type in an account and press

   Enter, the specified account will be substituted for all unknown accounts in

   the current entry.

 - If you wish to postpone specifying the correct account, you can click the

   `Fixme later` button or press the `f` key.  This will substitute the original

   unknown account names for all unknown accounts in the current entry.  If you

   then accept this entry, the transaction including these FIXME accounts will

   be added to your journal, and the next time you start Beancount-import the

   transaction will be treated as a pending entry.



## Viewing associated source data



Data sources may indicate that additional source data is associated with

particular candidate entries, typically based on the metadata fields and/or

links that are included in the transaction.  For example, the

`beancount_import.source.amazon` data source associates the order invoice HTML

page with the transaction, and the `beancount_import.source.google_purchases`

data source associates the purchase details HTML page.  Other possible source

data types include PDF statements and receipt images.



You can view any associated source data for the currently selected candidate by

selecting the `Source data` tab.



## Changing the narration, payee, links or tags



To modify the narration of an entry, you can click on it, click the `Narration`

button, or press the `n` key.  This actually lets you modify the payee, links,

and tags as well.  If you introduce a syntax error in the first line of

transaction, the text box will be highlighted in red and focus will remain until

you either correct it or press Escape, which will revert the first line of the

transaction back to its previous value.



## Checking for uncleared postings



The `Uncleared` tab displays the list of postings to accounts for which there is

an authoritative source and which have not been cleared.  Normally, postings are

marked as cleared by adding the appropriate source-specific metadata fields that

associate it with the external data representation, such as an `ofx_fitid` field

in the case of the OFX source.



This list may be useful for finding discrepancies that need manual correction.

Typical causes of uncleared postings include:

1. The source data for the posting has not yet been downloaded.

2. The transaction is a duplicate of another transaction already in the journal,

   and needs to be manually merged/deleted.

3. The posting is from before the earliest date for which source data was

   imported, and no earlier data is available.  Such postings can be ignored by

   adding a `cleared_before: ` metadata field to the `open` directive for

   the account or one of its ancestor accounts.

4. The source data is missing or cannot be imported, but the posting was

   manually verified.  Such postings can be ignored by adding a `cleared: TRUE`

   metadata field to them.



## Skipping and ignoring imported entries



If you are presented with a pending entry that you don't wish to import, you

have several options:



1. You can skip past it by selecting a different transaction in the `Pending`

   tab, or can skip to the next pending entry by clicking on the button labeled

   `⏩` or pressing the `]` key.  This skips it in the current session, but it

   remains as a pending entry and will be included again if you restart

   beancount-import.



2. You can click on the button labeled `Fixme later` or press the `f` key to

   reset all unknown accounts, and then accept the candidate.  This will add the

   transaction to your journal, but with the unknown accounts left as

   `Expenses:FIXME`.  This is useful for transactions for which you don't know

   how to assign an account, or which you expect to match to another transaction

   that will be generated from data that hasn't yet been downloaded.  Any

   transactions in the journal with `Expenses:FIXME` accounts will be included

   at the end of the list of pending entries the next time you start

   beancount-import.



3. You can click on the button labeled `Ignore` or press the `i` key to add the

   selected candidate to the special "ignored" journal file.  This is useful for

   transactions that are erroneous, such as actual duplicates.  Entries that are

   ignored will not be presented again if you restart beancount-import.

   However, if you manually delete them from the "ignored" journal file, they

   will return as pending entries.



## Usage with a reverse proxy



If you want to run Beancount-import with features like TLS or authentication,

then you can run it behind a reverse proxy that provides this functionality.

For instance, an NGINX location configuration like the following can route

traffic to a local instance of Beancount-import:



```

location /some/url/prefix/ {

    proxy_pass_header Server;

    proxy_set_header Host $http_host;

    proxy_redirect off;

    proxy_set_header X-Real-IP $remote_addr;

    proxy_set_header X-Scheme $scheme;

    proxy_http_version 1.1;

    proxy_set_header Upgrade $http_upgrade;

    proxy_set_header Connection "Upgrade";

    proxy_pass http://localhost:8101/;

}

```



Replace `/some/url/prefix/` with your desired URL path (retaining the trailing

slash), or even just `/` to make Beancount-import available at the URL root.



# Use with an existing Beancount journal



If you start using Beancount-import with an existing beancount journal

containing transactions that are also referenced in the external data supplied

to a data sources, the data source will not know to skip those transactions,

because they will not have the requisite metadata indicating the association.

Therefore, they will all be presented to you as new pending imported

transactions.



However, the matching mechanism will very likely have determined the correct

match to an existing transaction, which will be presented as the default option.

Accepting these matches will simply have the effect of inserting the relevant

metadata into your journal so that the transactions are considered "cleared" and

won't be imported again next time you run Beancount-import.  It should be a

relatively quick process to do this even for a large number of transactions.



# Development



For development of this package, make sure to install Beancount-import using the

`pip install -e .` command rather than the `pip install .` command.  If you

previously ran the `pip install` command without the `-e` option, you can simply

re-run the `pip install -e .` command.



## Testing



You can run the tests using the `pytest` command.



Many of the tests are "golden" tests, which work by creating a textual

representation of some state and comparing it with the contents of a particular

file in the [testdata/](testdata/) directory.  If you change one of these tests

or add a new one, you can have the tests automatically generate the output by

setting the environment variable `BEANCOUNT_IMPORT_GENERATE_GOLDEN_TESTDATA=1`,

e.g.:



```shell

BEANCOUNT_IMPORT_GENERATE_GOLDEN_TESTDATA=1 pytest

```



Make sure to commit to at least stage any changes you've made to the relevant

`testdata` files prior to running the tests with this environment variable set.

That way you can manually verify any changes between the existing output and the

new output using `git diff`.



## Web frontend



The web frontend source code is in the [frontend/](frontend/) directory.  Refer

to the README.md file there for how to rebuild and run the frontend after making

changes.



# Basic workflow



## Simple expense transaction from Mint.com data source



Suppose the user has purchased a coffee at Starbucks on 2016-08-09 using a

credit card, and has set up Mint.com to retrieve the transaction data for this

credit card.



Given the following CSV entry:



```csv

"Date","Description","Original Description","Amount","Transaction Type","Category","Account Name","Labels","Notes"

"8/10/2016","Starbucks","STARBUCKS STORE 12345","2.45","debit","Coffee Shops","My Credit Card","",""

```



and the following open account directive:



```

1900-01-01 open Liabilities:Credit-Card  USD

  mint_id: "My Credit Card"

```



the Mint data source will generate the following pending transaction:



```

2016-08-10 * "STARBUCKS STORE 12345"

  Liabilities:Credit-Card             -2.45 USD

    date: 2016-08-10

    source_desc: "STARBUCKS STORE 12345"

  Expenses:FIXME                       2.45 USD

```



The user might manually specify that the unknown account is `Expenses:Coffee`.

The web interface will then show the updated changeset:



```

+2016-08-10 * "STARBUCKS STORE 12345"

+  Liabilities:Credit-Card             -2.45 USD

+    date: 2016-08-10

+    source_desc: "STARBUCKS STORE 12345"

+  Expenses:Coffee                      2.45 USD

```



If the `Expenses:Coffee` account does not already exist, Beancount-import will

additionally include an `open` directive in the changeset:



```

+2016-08-10 * "STARBUCKS STORE 12345"

+  Liabilities:Credit-Card             -2.45 USD

+    date: 2016-08-10

+    source_desc: "STARBUCKS STORE 12345"

+  Expenses:Coffee                      2.45 USD

+ 2016-08-10 open Expenses:Coffee USD

```



Once the user accepts this change, the changeset is applied to the journal.  The

presence of the `date` and `source_desc` metadata fields indicate to the Mint

data source that the `Liabilities:Credit-Card` posting is cleared.  The

combination of the words in the `source_desc`, the *source* account of

`Liabilities:Credit-Card`, and the *target* account of `Expenses:Coffee` serves

as a training example for the classifier.  A subsequent pending transaction with

a `source_desc` field containing the word `STARBUCKS` is likely to be

automatically classified as `Expenses:Coffee`.  Note that while in this case the

narration matches the `source_desc` field, the narration has no effect on the

automatic prediction.  The user must not delete or modify these metadata fields,

but additional metadata fields may be added.



Mint.com has its own heuristics for computing the `Description` and `Category`

fields from the `Original Description` provided by the financial institution.

However, these are ignored by the Mint data source as they are not stable (can

change if the data is re-downloaded) and not particularly reliable.



## Match to a manually entered transaction



Considering the same transaction as shown in the previous example, suppose the

user has already manually entered the transaction prior to running the import:



```

2016-08-09 * "Coffee"

  Liabilities:Credit-Card             -2.45 USD

  Expenses:Coffee

```



When running Beancount-import, the user will be presented with two candidates:



```

 2016-08-09 * "Coffee"

   Liabilities:Credit-Card             -2.45 USD

+    date: 2016-08-10

+    source_desc: "STARBUCKS STORE 12345"

   Expenses:Coffee



+2016-08-10 * "STARBUCKS STORE 12345"

+  Liabilities:Credit-Card             -2.45 USD

+    date: 2016-08-10

+    source_desc: "STARBUCKS STORE 12345"

+  Expenses:FIXME                       2.45 USD

```



The user should select the first one; selecting the second one would yield a

duplicate transaction (but which could later be diagnosed as an uncleared

transaction).  The `Expenses:FIXME` account in the second candidate would in

general actually be some other, possibly incorrect, predicted account, but which

is clearly indicated as an prediction that can be changed.



As is typically the case, the date on the manually entered transaction (likely

the date on which the transaction actually occurred) is not exactly the same as

the date provided by the bank.  To handle this discrepancy, Beancount-import

allows matches between postings that are up to 5 days apart.  The `date`

metadata field allows the posting to be reliably matched to the corresponding

entry in the CSV file, even though the overall transaction date differs.



Note that even though this transaction was manually entered, once it is matched

with the pending transaction and the `source_desc` and `date` metadata fields

are added, it functions as a training example exactly the same as in the

previous example.



## Credit card payment transaction



Suppose the user pays the balance of a credit card using a bank account, and

Mint.com is set up to retrieve the transactions from both the bank account and

the credit card.



Given the following CSV entries:



```csv

"Date","Description","Original Description","Amount","Transaction Type","Category","Account Name","Labels","Notes"

"11/27/2013","Transfer from My Checking","CR CARD PAYMENT ALEXANDRIA VA","66.88","credit","Credit Card Payment","My Credit Card","",""

"12/02/2013","National Federal Des","NATIONAL FEDERAL DES:TRNSFR","66.88","debit","Transfer","My Checking","",""

```



and the following open account directives:



```

1900-01-01 open Liabilities:Credit-Card  USD

  mint_id: "My Credit Card"



1900-01-01 open Assets:Checking  USD

  mint_id: "My Checking"

```



the Mint data source will generate 2 pending transactions, and for the first one

will present two candidates:



```

+2013-11-27 * "CR CARD PAYMENT ALEXANDRIA VA"

+  Liabilities:Credit-Card             66.88 USD

+    date: 2013-11-27

+    source_desc: "CR CARD PAYMENT ALEXANDRIA VA"

+  Assets:Checking                    -66.88 USD

+    date: 2013-12-02

+    source_desc: "NATIONAL FEDERAL DES:TRNSFR"



+2013-11-27 * "CR CARD PAYMENT ALEXANDRIA VA"

+  Liabilities:Credit-Card             66.88 USD

+    date: 2013-11-27

+    source_desc: "CR CARD PAYMENT ALEXANDRIA VA"

+  Expenses:FIXME                     -66.88 USD

```



Note that the `Expenses:FIXME` account in the second transaction will actually

be whichever account was predicted automatically.  If there have been prior

similar transactions, it is likely to be correct predicted as `Assets:Checking`.



The user should accept the first candidate to import both transactions at once.

In that case, both postings are considered cleared, and the new transaction will

result in two training examples for automatic prediction, corresponding to each

of the two combinations of `source_desc`, source account, and target account.



However, if the user accepts the second candidate (perhaps because the

transaction hasn't yet been posted to the checking account and the pending

transaction derived from the checking account data is not yet available), and

either leaves the account as `Expenses:FIXME`, manually specifies

`Assets:Checking`, or relies on the automatic prediction to choose

`Assets:Checking`, then when importing the transaction from the checking

account, the user will be presented with the following candidates and will have

another chance to accept the match:



```

 2013-11-27 * "CR CARD PAYMENT ALEXANDRIA VA"

   Liabilities:Credit-Card             66.88 USD

     date: 2013-11-27

     source_desc: "CR CARD PAYMENT ALEXANDRIA VA"

   Assets:Checking                    -66.88 USD

+    date: 2013-12-02

+    source_desc: "NATIONAL FEDERAL DES:TRNSFR"



+2013-12-02 * "NATIONAL FEDERAL DES:TRNSFR"

+  Assets:Checking                    -66.88 USD

+    date: 2013-12-02

+    source_desc: "NATIONAL FEDERAL DES:TRNSFR"

+  Expenses:FIXME                      66.88 USD

```



License

==



Copyright (C) 2014-2018 Jeremy Maitin-Shepard.



Distributed under the GNU General Public License, Version 2.0 only.

See [LICENSE](LICENSE) file for details.