344 lines
11 KiB
Python
Executable File
344 lines
11 KiB
Python
Executable File
#!/usr/bin/env python3
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import os
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import argparse
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import asyncio
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import re
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from claude_agent_sdk import query, ClaudeAgentOptions, ResultMessage
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from docling.document_converter import DocumentConverter
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GET_BEANCOUNT_STATEMENTS_PROMPT = """# System Prompt: Edenred Transactions to Beancount Parser
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You are a specialized financial transaction parser that converts Edenred account movements into Beancount format.
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## Input Format
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You will receive a table with the following columns:
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- **Fecha**: Transaction date
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- **Detalle movimiento**: Transaction description
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- **Importe**: Amount (always negative for expenses)
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You will also receive the source account to use for all transactions.
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Example input:
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```
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| Producto: | Ticket Restaurant |
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| Fecha | Detalle movimiento | Importe |
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2025-10-09 00:00:00 | MCDONALD'S BARCELONA | 12,50
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```
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## Output Format
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Convert each transaction into a Beancount entry with this structure:
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```
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YYYY-MM-DD * "Payee" "Description"
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ExpenseAccount AMOUNT EUR
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SourceAccount
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```
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### Rules for Conversion
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1. **Date**: Use the "Fecha" field in YYYY-MM-DD format
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2. **Flag**: Always use `*` (cleared transaction)
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3. **Payee**: Extract the main payee name from the "Detalle movimiento" field (first recognizable entity/merchant name)
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4. **Description**: Use the full "Detalle movimiento" text as the description
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5. **Amount**: Use the absolute value of "Importe" (remove the negative sign)
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6. **Currency**: Always use EUR
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7. **Source Account**: Use the provided source account as the second posting (the account is automatically debited)
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### Expense Account Classification
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Analyze each transaction and classify it into the most appropriate account based on:
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- The payee/merchant name
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- The transaction description
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- Common spending patterns
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**Available Expense Accounts:**
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Expenses:Supermercat
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Expenses:MenjarFora
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Expenses:Mobilitat
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Expenses:Parking
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Expenses:Gasolina
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Expenses:Altres
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### Guidelines
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- Restaurants, cafes, food delivery: `Expenses:MenjarFora`
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- Supermarkets, grocery stores: `Expenses:Supermercat`
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- Public transport, taxi, ride-sharing: `Expenses:Mobilitat`
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- Parking: `Expenses:Parking`
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- Gas stations: `Expenses:Gasolina`
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- Other/unknown: `Expenses:Altres`
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## Example
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**Input:**
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```
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Source Account: Assets:Benefits:Edenred:TicketsRestaurant
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2025-10-09 00:00:00 | MCDONALD'S BARCELONA | 12,50
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```
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**Output:**
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```
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2025-10-09 * "MCDONALD'S" "MCDONALD'S BARCELONA"
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Expenses:MenjarFora 12.50 EUR
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Assets:Benefits:Edenred:TicketsRestaurant
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```
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## Output Requirements
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- Process all transactions in the input table
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- Maintain chronological order
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- Ensure proper indentation (2 spaces for posting lines)
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- Be consistent with account naming conventions
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- Only output Beancount code, explanations are not needed.
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## Your Task
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Parse the provided account movements data tables and generate the corresponding Beancount statements. Output only the Beancount code.
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"""
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async def get_beancount_statements(markdown_report: str, source_account: str) -> str:
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options = ClaudeAgentOptions(
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system_prompt=GET_BEANCOUNT_STATEMENTS_PROMPT,
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cwd=os.getcwd()
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)
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result = None
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async for message in query(
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prompt=f"Convert this Edenred account movements table to beancount statements.\n\n"
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f"Source Account: {source_account}\n\n{markdown_report}",
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options=options
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):
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if isinstance(message, ResultMessage) and message.subtype == "success":
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result = message.result
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else:
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print(message)
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if result is not None and isinstance(result, str):
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return result
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else:
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raise ValueError(
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"Unable to get Beancount statements from the report!")
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def parse_response(beancount_statements: str):
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"""
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The input beancount statements might be inside a markdown beancount
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code block or in plain text.
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"""
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code_block_pattern = r'```(?:beancount)?\n(.*?)```'
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match = re.search(code_block_pattern, beancount_statements, re.DOTALL)
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if match:
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content = match.group(1)
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else:
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content = beancount_statements
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return content
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def extract_product_type(markdown_report: str) -> str:
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"""
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Extract the Producto field to determine which account to use.
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Returns the appropriate Beancount account.
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"""
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lines = markdown_report.split('\n')
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for line in lines:
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if 'Producto:' in line or 'producto:' in line.lower():
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if 'Ticket Restaurant' in line:
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return 'Assets:Benefits:Edenred:TicketsRestaurant'
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elif 'Edenred Movilidad' in line or 'Movilidad' in line:
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return 'Assets:Benefits:Edenred:TargetaTransport'
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print("Warning: Could not determine product type. Defaulting to TicketsRestaurant")
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return 'Assets:Benefits:Edenred:TicketsRestaurant'
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def extract_balance_and_last_date(markdown_report: str) -> tuple[str, str]:
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"""
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Extract the balance from the Saldo field and the date of the last transaction.
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Returns (last_date, balance) tuple.
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"""
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lines = markdown_report.split('\n')
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balance = ""
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last_date = ""
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for line in lines:
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if '|' not in line:
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continue
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parts = line.split('|')
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if len(parts) >= 3 and 'Saldo:' in parts[1]:
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balance_str = parts[2].strip()
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balance = balance_str.replace(',', '.').replace(' ', '')
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if len(parts) >= 2:
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fecha_col = parts[1].strip()
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date_match = re.search(r'(\d{4})-(\d{2})-(\d{2})', fecha_col)
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if date_match:
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current_date = f"{date_match.group(1)}-{date_match.group(2)}-{date_match.group(3)}"
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if not last_date or current_date > last_date:
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last_date = current_date
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return last_date, balance
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def save_statements(beancount_statements: str, last_date: str, balance: str, source_account: str):
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"""
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The statements are saved in beancount files in
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ledger/transactions/YYYY/MM.beancount.
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Statements are sorted chronologically and split by month if they
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span multiple months.
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A balance assertion is added at the end of the last month's file.
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"""
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from pathlib import Path
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from collections import defaultdict
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if not beancount_statements.strip():
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print("Warning: No valid statements to save")
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return
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lines = beancount_statements.strip().split('\n')
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transactions = []
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current_transaction = []
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for line in lines:
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if re.match(r'^\d{4}-\d{2}-\d{2}', line):
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if current_transaction:
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transactions.append('\n'.join(current_transaction))
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current_transaction = [line]
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elif current_transaction:
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current_transaction.append(line)
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if current_transaction:
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transactions.append('\n'.join(current_transaction))
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transactions.sort(key=lambda t: re.match(
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r'^(\d{4}-\d{2}-\d{2})', t).group(1))
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transactions_by_month = defaultdict(list)
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for transaction in transactions:
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date_match = re.match(r'^(\d{4})-(\d{2})-\d{2}', transaction)
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if date_match:
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year = date_match.group(1)
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month = date_match.group(2)
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key = (year, month)
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transactions_by_month[key].append(transaction)
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last_month_key = max(transactions_by_month.keys()) if transactions_by_month else None
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for (year, month), month_transactions in sorted(transactions_by_month.items()):
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output_dir = Path(f"ledger/transactions/{year}")
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output_dir.mkdir(parents=True, exist_ok=True)
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output_file = output_dir / f"{month}.beancount"
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existing_content = ""
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if output_file.exists():
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with open(output_file, 'r') as f:
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existing_content = f.read()
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with open(output_file, 'w') as f:
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if existing_content:
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f.write(existing_content)
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if not existing_content.endswith('\n'):
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f.write('\n')
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f.write('\n'.join(month_transactions))
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f.write('\n')
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if (year, month) == last_month_key and last_date and balance:
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f.write(f'\n{last_date} balance {source_account} {balance} EUR\n')
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print(f"Saved statements to {output_file}")
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def filter_markdown_by_date(markdown_report: str, from_date: str) -> str:
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"""
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Filter markdown table to only include rows with dates >= from_date.
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"""
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from datetime import datetime
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if not from_date:
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return markdown_report
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try:
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filter_date = datetime.strptime(from_date, "%Y-%m-%d")
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except ValueError:
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print(f"Warning: Invalid date format '{
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from_date}'. Expected YYYY-MM-DD. Ignoring filter.")
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return markdown_report
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lines = markdown_report.split('\n')
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filtered_lines = []
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for line in lines:
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if '|' not in line:
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filtered_lines.append(line)
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continue
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parts = line.split('|')
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if len(parts) < 4:
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filtered_lines.append(line)
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continue
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fecha_col = parts[1].strip()
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date_match = re.search(r'(\d{4})-(\d{2})-(\d{2})', fecha_col)
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if date_match:
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line_date = datetime.strptime(f"{date_match.group(
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1)}-{date_match.group(2)}-{date_match.group(3)}", "%Y-%m-%d")
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if line_date >= filter_date:
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filtered_lines.append(line)
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else:
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filtered_lines.append(line)
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return '\n'.join(filtered_lines)
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def convert_file_to_markdown(path: str):
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converter = DocumentConverter()
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result = converter.convert(path)
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return result.document.export_to_markdown()
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async def main():
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parser = argparse.ArgumentParser(
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description="Parse Edenred report from XLSX format")
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parser.add_argument("source", help="Path to the input XLSX file")
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parser.add_argument("--from", dest="from_date",
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help="Filter transactions from this date (YYYY-MM-DD)")
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args = parser.parse_args()
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if not args.source.endswith(".xlsx"):
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parser.error("Input file must have .xlsx format")
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markdown_report = convert_file_to_markdown(args.source)
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source_account = extract_product_type(markdown_report)
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print(f"Detected source account: {source_account}")
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last_date, balance = extract_balance_and_last_date(markdown_report)
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print(f"Extracted balance: {balance} on date: {last_date}")
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if args.from_date:
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markdown_report = filter_markdown_by_date(
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markdown_report, args.from_date)
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beancount_statements = await get_beancount_statements(
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markdown_report, source_account
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)
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print(f"Final result: \n{beancount_statements}")
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clean_beancount_statements = parse_response(beancount_statements)
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save_statements(clean_beancount_statements, last_date, balance, source_account)
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if __name__ == "__main__":
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asyncio.run(main())
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