That said, this tool *is* still somewhat like an importer in that it needs to

extract transaction details from a third-party statement. Instead of creating

directives, it just checks to see that similar directives are already

present. This is a bit like diff-ing a statement with the books (though we're

only interested in the presence of lines, not so much their order).

Paper checks are entered into the books when written (a.k.a. "posted"), but may

not be cashed until months later sometimes causing reconciliation differences

that live beyond a month. It's worth noting that there are really two dates here

- the posting date and the cleared date. Beancount only allows us to model one,

which is why carrying these reconciliation differences between months feels a

bit awkward.

Problems in scope:

 - errors in the books take hours to find during reconciliation, requiring

   manually comparing statements and the books and are succeptible to mistakes,

   such as not noticing when there are two payments for the same amount on the

   statement, but not in the books (as Bradley likes to quote, "you're entering

   a world of pain")

 - adding statement/reconciliation metadata to books is/was manual and prone to

   mistakes

 - jumping to an individual transaction in a large ledger isn't trivial - Emacs

   grep mode is the current best option

 - not all staff use Emacs

 - auditors would prefer Bradley didn't perform reconciliation, ideally not

   Rosanne either

 - reconciliation reports are created by hand when there are mismatches

Other related problems we're not dealing with here:

 - after updates to the books files, beancount must be restarted to reflect

   updates

 - updates also invalidate the cache meaning restart takes several minutes

 - balance checks are manually updated in

   svn/Financial/Ledger/sanity-check-balances.yaml

 - transactions are entered manually and reconciled after the fact, but

   importing from statements may be useful in some cases

Future possibilities:

 - allow the reconciler to respect manually-applied metadata - not clear how

   this would work exactly

 - allow interactive matching where the user can specifiy a match

 - consider combining this with helper.py into one more complete tool that both

   reconciles and summarises the unreconciled transactions

"""

import argparse

import collections

import copy

import csv

import datetime

import decimal

import io

import itertools

import logging

import os

import re

import sys

from typing import Dict, List, Optional, Sequence, Tuple, TextIO

from beancount import loader

from beancount.query.query import run_query

from colorama import Fore, Style  # type: ignore

from .. import cliutil

from .. import config as configmod

if not sys.warnoptions:

    import warnings

    # Disable annoying warning from thefuzz prompting for a C extension. The

    # current pure-Python implementation isn't a bottleneck for us.

    warnings.filterwarnings('ignore', category=UserWarning, module='thefuzz.fuzz')

from thefuzz import fuzz  # type: ignore

PROGNAME = 'reconcile-statement'

logger = logging.getLogger(__name__)

# Get some interesting feedback on call to RT with this:

# logger.setLevel(logging.DEBUG)

# logger.addHandler(logging.StreamHandler())

JUNK_WORDS = [

    'software',

    'freedom',

    'conservancy',

    'conse',

    'payment',

    'echeck',

    'bill',

    'debit',

    'wire',

    'credit',

    "int'l",

    "in.l",

    'llc',

    'online',

    'donation',

    'usd',

    'inc',

]

JUNK_WORDS_RES = [re.compile(word, re.IGNORECASE) for word in JUNK_WORDS]

ZERO_RE = re.compile('^0+')

def remove_duplicate_words(text: str) -> str:

    unique_words = []

    known_words = set()

    for word in text.split():

        if word.lower() not in known_words:

            unique_words.append(word)

            known_words.add(word.lower())

    return ' '.join(unique_words)

def remove_payee_junk(payee: str) -> str:

    """Clean up payee field to improve quality of fuzzy matching.

    It turns out that bank statement "description" fields are

    difficult to fuzzy match on because they're long and

    noisey. Truncating them (see standardize_XXX_record fns) and

    removing the common junk helps significantly.

    """

    for r in JUNK_WORDS_RES:

        payee = r.sub('', payee)

    payee = ZERO_RE.sub('', payee)

    payee = payee.replace(' - ', ' ')

    payee = re.sub(r'\.0\.\d+', ' ', payee)

    payee = payee.replace('.0', ' ')

    payee = payee.replace('/', ' ')

    payee = re.sub(re.escape('.com'), ' ', payee, flags=re.IGNORECASE)

    payee = re.sub(re.escape('.net'), ' ', payee, flags=re.IGNORECASE)

    payee = payee.replace('*', ' ')

    payee = ' '.join([i for i in payee.split(' ') if len(i) > 2])

    payee = payee.replace('-', ' ')

    payee = remove_duplicate_words(payee)

    payee.strip()

    return payee

def parse_amount(amount: str) -> decimal.Decimal:

    """Parse amounts and handle comma separators as seen in some FR statements."""

    return decimal.Decimal(amount.replace('$', '').replace(',', ''))

def validate_amex_csv(sample: str) -> None:

    required_cols = {'Date', 'Amount', 'Description', 'Card Member'}

    reader = csv.DictReader(io.StringIO(sample))

    if reader.fieldnames and not required_cols.issubset(reader.fieldnames):

        sys.exit(f"This AMEX CSV doesn't seem to have the columns we're expecting, including: {', '.join(required_cols)}. Please use an unmodified statement direct from the institution.")

    if len(a) == 0 or len(b) == 0:

        return 0.0

    first_a = a.split()[0].strip()

    first_b = b.split()[0].strip()

    if first_a.casefold() == first_b.casefold():

        return min(1.0, 0.2 * len(first_a))

    else:

        return 0.0

def payee_match(a: str, b: str) -> float:

    """Score a match between two payees."""

    fuzzy_match = float(fuzz.token_set_ratio(a, b) / 100.00)

    first_word_match = first_word_exact_match(a, b)

    return max(fuzzy_match, first_word_match)

def records_match(r1: Dict, r2: Dict) -> Tuple[float, List[str]]:

    """Do these records represent the same transaction?"""

    date_score = date_proximity(r1['date'], r2['date'])

    if r1['date'] == r2['date']:

        date_message = ''

    elif date_score > 0.0:

        diff = abs((r1['date'] - r2['date']).days)

        date_message = f'+/- {diff} days'

    else:

        date_message = 'date mismatch'

    if r1['amount'] == r2['amount']:

        amount_score, amount_message = 2.0, ''

    else:

        amount_score, amount_message = 0.0, 'amount mismatch'

    # We never consider payee if there's a check_id in the books.

    check_message = ''

    payee_message = ''

    # Sometimes we get unrelated numbers in the statement column with check-ids,

    # so we can't match based on the existence of a statement check-id.

    if r2['check_id']:

        payee_score = 0.0

        if r1['check_id'] and r2['check_id'] and r1['check_id'] == r2['check_id']:

            check_score = 1.0

        else:

            check_message = 'check-id mismatch'

            check_score = 0.0

    else:

        check_score = 0.0

        payee_score = payee_match(r1['payee'], r2['payee'])

            payee_message = ''

            payee_message = 'partial payee match'

        else:

            payee_message = 'payee mismatch'

    overall_score = (date_score + amount_score + check_score + payee_score) / 4

    overall_message = [m for m in [date_message, amount_message, check_message, payee_message] if m]

    return overall_score, overall_message

def match_statement_and_books(statement_trans: List[Dict], books_trans: List[Dict]) -> Tuple[List[Tuple[List, List, List]], List[Dict], List[Dict]]:

    """Match transactions between the statement and books.

    If matched, the books transaction is marked off so that it can

    only be matched once. Some transactions will be matched, some will

    be on the statement but not the books and some on the books but

    not the statement.

    Passes through any unmatched transactions.

    Currently we use the same matching logic for all types of

    statements. It's conceivable that you could have special cases to

    accurately match some types of statements, but that would be more

    work to maintain and test.

    """

    matches = []

    remaining_books_trans = []

    remaining_statement_trans = []

    for r1 in statement_trans:

        best_match_score = 0.0

        best_match_index = None

        best_match_note = []

        matches_found = 0

        for i, r2 in enumerate(books_trans):

            score, note = records_match(r1, r2)

            if score >= 0.5 and score >= best_match_score:

                matches_found += 1

                best_match_score = score

                best_match_index = i

                best_match_note = note

        if best_match_score > 0.5 and matches_found == 1 and 'check-id mismatch' not in best_match_note or best_match_score > 0.8:

            matches.append(([r1], [books_trans[best_match_index]], best_match_note))

            # Don't try to make a second match against this books entry.

            if best_match_index is not None:

                del books_trans[best_match_index]

        else:

            remaining_statement_trans.append(r1)