Initial commit

.gitignore

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+data/
+src/rules.yaml
+venv/

README.md

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+# CSV to Beancount Converter
+
+This project provides a simple utility to convert CSV files into Beancount format. It is designed to facilitate the management of financial data by transforming structured CSV data into a format compatible with Beancount accounting software.
+
+## Project Structure
+
+```
+csv-to-beancount
+├── src
+│   ├── main.py          # Entry point for the application
+│   ├── converter.py     # Logic for converting CSV to Beancount
+│   ├── config.py        # Configuration settings loader
+│   └── utils
+│       └── file_utils.py # Utility functions for file operations
+├── config
+│   └── settings.yaml    # Configuration file with fixed values
+├── data
+│   └── example.csv      # Sample CSV file for conversion
+├── requirements.txt      # Project dependencies
+└── README.md            # Project documentation
+```
+
+## Installation
+
+To set up the project, clone the repository and install the required dependencies:
+
+```bash
+git clone <repository-url>
+cd csv-to-beancount
+pip install -r requirements.txt
+```
+
+## Usage
+
+To convert a CSV file to Beancount format, run the following command:
+
+```bash
+python src/main.py data/example.csv
+```
+
+This command will read the specified CSV file, convert its contents into Beancount format using the defined mappings in the configuration file, and save the output to a Beancount file.
+
+## Configuration
+
+The configuration settings are stored in `config/settings.yaml`. You can modify this file to adjust account mappings and other constants used during the conversion process.
+
+## Contributing
+
+Contributions are welcome! Please feel free to submit a pull request or open an issue for any enhancements or bug fixes.
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for more details.

config/settings.yaml

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+account_mappings:
+  giro_account: "Assets:Bank:Giro"
+
+fixed_values:
+  currency: "EUR"
+  default_transaction_type: "expense"
+
+file_mappings:
+  example.csv: "Assets:Bank:Giro"

requirements.txt

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+PyYAML
+pandas

src/config.py

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+from pathlib import Path
+import yaml
+
+def load_config():
+    config_path = Path(__file__).parent.parent / 'config' / 'settings.yaml'
+    with open(config_path, 'r') as file:
+        config = yaml.safe_load(file)
+    return config

src/converter.py

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+def convert_csv_to_beancount(csv_data, config):
+    beancount_entries = []
+    
+    for row in csv_data:
+        date = row['date']
+        amount = row['amount']
+        description = row['description']
+        
+        # Map the account based on the configuration
+        account = config['account_mappings'].get(row['filename'], 'Assets:Bank:Giro')
+        
+        entry = f"{date} * \"{description}\"\n    {account}  {amount}\n"
+        beancount_entries.append(entry)
+    
+    return ''.join(beancount_entries)

src/converter_new.py

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+import yaml
+
+class BeancountConverter:
+    def __init__(self, rules_file):
+        with open(rules_file, 'r') as f:
+            self.rule_definitions = yaml.safe_load(f)
+        
+        # Compile rules
+        self.rules = self._compile_rules(self.rule_definitions)
+    
+    def _compile_rules(self, rule_defs):
+        compiled_rules = []
+        
+        for rule in rule_defs:
+            conditions = []
+            
+            # Build conditions from rule definition
+            if 'description_contains' in rule:
+                terms = rule['description_contains']
+                conditions.append(
+                    lambda t, terms=terms: any(term.upper() in t.get('description', '').upper() for term in terms)
+                )
+            
+            if 'amount_range' in rule:
+                min_val = rule['amount_range'].get('min', float('-inf'))
+                max_val = rule['amount_range'].get('max', float('inf'))
+                conditions.append(
+                    lambda t, min_val=min_val, max_val=max_val: 
+                        min_val <= float(t.get('amount', 0)) <= max_val
+                )
+            
+            # Combine all conditions
+            compiled_rule = (
+                lambda t, conditions=conditions: all(cond(t) for cond in conditions),
+                rule['target_account']
+            )
+            
+            compiled_rules.append(compiled_rule)
+            
+        # Add default rule
+        compiled_rules.append((lambda t: True, 'Expenses:Uncategorized'))
+        
+        return compiled_rules

src/main.py

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+import pandas as pd
+from src.config import load_config
+from src.converter import convert_csv_to_beancount
+from src.utils.file_utils import write_beancount
+
+def main():
+    # Load configuration settings
+    config = load_config()
+
+    # Read the CSV file
+    csv_file_path = 'data/example.csv'
+    csv_data = pd.read_csv(csv_file_path)
+
+    # Convert CSV data to Beancount format
+    beancount_entries = convert_csv_to_beancount(csv_data, config)
+
+    # Write the Beancount entries to a file
+    output_file_path = 'data/output.beancount'
+    write_beancount(output_file_path, beancount_entries)
+
+if __name__ == "__main__":
+    main()

src/utils/file_utils.py

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+def read_csv(filepath):
+    import pandas as pd
+    return pd.read_csv(filepath)
+
+def write_beancount(filepath, beancount_entries):
+    with open(filepath, 'w') as f:
+        for entry in beancount_entries:
+            f.write(entry + '\n')