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