Transactions API

Overview

The TransactionsAPI provides methods to list and retrieve transaction information. All transaction operations are accessed through the client.transactions property.

Methods

list()

List transactions for a specific account with optional filtering.

def list(
    self,
    account_id: str,
    expand: Optional[list[str]] = None,
    limit: Optional[int] = None,
    since: Optional[Union[datetime, str]] = None,
    before: Optional[datetime] = None,
) -> list[Transaction]:
    """List transactions for an account."""

Parameters:

account_id: The unique account identifier
expand (optional): List of fields to expand (e.g., ["merchant"])
limit (optional): Maximum number of transactions to return (default 100, max 500)
since (optional): Return transactions after this date/time or transaction ID
before (optional): Return transactions before this date/time

Returns: list[Transaction] - List of transaction objects Examples:

Basic Usage
Date Filtering
Expanded Data

from monzoh import MonzoClient

client = MonzoClient()

# Get accounts
accounts = client.accounts.list()
account_id = accounts[0].id

# Get recent transactions
transactions = client.transactions.list(
    account_id=account_id,
    limit=20
)

print(f"Found {len(transactions)} transactions")

for txn in transactions:
    amount = txn.amount / 100
    emoji = "💸" if amount < 0 else "💰"
    print(f"{emoji} {txn.description}: £{amount:.2f}")

from datetime import datetime, timedelta

# Get transactions from the last 7 days
since_date = datetime.now(tz=datetime.timezone.utc) - timedelta(days=7)

recent_transactions = client.transactions.list(
    account_id=account_id,
    since=since_date,
    limit=50
)

print(f"Transactions in last 7 days: {len(recent_transactions)}")

# Get transactions with merchant details expanded
transactions = client.transactions.list(
    account_id=account_id,
    expand=["merchant"],
    limit=10
)

for txn in transactions:
    print(f"Transaction: {txn.description}")
    
    if txn.merchant:
        print(f"  Merchant: {txn.merchant.name}")
        print(f"  Category: {txn.merchant.category}")
        print(f"  Logo: {txn.merchant.logo}")

get()

Retrieve a specific transaction by ID.

def get(
    self,
    transaction_id: str,
    expand: Optional[list[str]] = None
) -> Transaction:
    """Get a specific transaction by ID."""

Parameters:

transaction_id: The unique transaction identifier
expand (optional): List of fields to expand

Returns: Transaction - The transaction object Example:

# Get a specific transaction
transaction = client.transactions.get(
    transaction_id="tx_00009hjzAHHyUp1hAzuJUB",
    expand=["merchant"]
)

print(f"Transaction: {transaction.description}")
print(f"Amount: £{transaction.amount / 100:.2f}")
print(f"Date: {transaction.created}")

if transaction.merchant:
    print(f"Merchant: {transaction.merchant.name}")

Transaction Analysis Examples

Spending Analysis

Analyze spending patterns by category:

from collections import defaultdict
from datetime import datetime, timedelta

def analyze_spending_by_category(account_id, days=30):
    """Analyze spending by category over the last N days."""
    
    client = MonzoClient()
    
    # Get transactions from the last N days
    since = datetime.now(tz=datetime.timezone.utc) - timedelta(days=days)
    transactions = client.transactions.list(
        account_id=account_id,
        since=since,
        limit=200
    )
    
    # Group spending by category
    spending_by_category = defaultdict(list)
    total_spending = 0
    
    for txn in transactions:
        if txn.amount < 0:  # Spending transactions
            amount = abs(txn.amount) / 100
            category = txn.category or "Other"
            
            spending_by_category[category].append({
                'description': txn.description,
                'amount': amount,
                'date': txn.created,
                'merchant': txn.merchant.name if txn.merchant else None
            })
            
            total_spending += amount
    
    # Print analysis
    print(f"Spending Analysis (Last {days} days)")
    print("=" * 40)
    print(f"Total Spent: £{total_spending:.2f}")
    print()
    
    for category, transactions in sorted(
        spending_by_category.items(), 
        key=lambda x: sum(t['amount'] for t in x[1]), 
        reverse=True
    ):
        category_total = sum(t['amount'] for t in transactions)
        percentage = (category_total / total_spending) * 100
        
        print(f"{category}: £{category_total:.2f} ({percentage:.1f}%)")
        print(f"  Transactions: {len(transactions)}")
        print(f"  Average: £{category_total / len(transactions):.2f}")
        
        # Show top transactions in this category
        top_transactions = sorted(
            transactions, 
            key=lambda x: x['amount'], 
            reverse=True
        )[:3]
        
        for txn in top_transactions:
            print(f"    • {txn['description']}: £{txn['amount']:.2f}")
        print()

# Usage
accounts = client.accounts.list()
analyze_spending_by_category(accounts[0].id, days=30)

Monthly Spending Trends

Track spending trends over time:

from datetime import datetime, timedelta
import calendar

def monthly_spending_trends(account_id, months=6):
    """Analyze spending trends over the last N months."""
    
    client = MonzoClient()
    
    # Get transactions from the last N months
    since = datetime.now(tz=datetime.timezone.utc) - timedelta(days=months * 30)
    transactions = client.transactions.list(
        account_id=account_id,
        since=since,
        limit=1000
    )
    
    # Group by month
    monthly_spending = defaultdict(float)
    
    for txn in transactions:
        if txn.amount < 0:  # Spending only
            date = datetime.fromisoformat(txn.created.replace('Z', '+00:00'))
            month_key = date.strftime('%Y-%m')
            monthly_spending[month_key] += abs(txn.amount) / 100
    
    # Print trends
    print("Monthly Spending Trends")
    print("=" * 30)
    
    sorted_months = sorted(monthly_spending.keys())
    
    for i, month in enumerate(sorted_months):
        spending = monthly_spending[month]
        year, month_num = month.split('-')
        month_name = calendar.month_name[int(month_num)]
        
        print(f"{month_name} {year}: £{spending:.2f}")
        
        # Show trend arrow
        if i > 0:
            prev_spending = monthly_spending[sorted_months[i-1]]
            change = ((spending - prev_spending) / prev_spending) * 100
            
            if change > 5:
                trend = "📈 ↗️"
            elif change < -5:
                trend = "📉 ↘️"
            else:
                trend = "➡️"
            
            print(f"    {trend} {change:+.1f}% from previous month")

# Usage
monthly_spending_trends(accounts[0].id, months=6)

Transaction Search and Filtering

Search transactions by various criteria:

def search_transactions(account_id, **criteria):
    """Search transactions by various criteria."""
    
    client = MonzoClient()
    
    # Get recent transactions
    transactions = client.transactions.list(
        account_id=account_id,
        limit=500
    )
    
    filtered_transactions = []
    
    for txn in transactions:
        # Apply filters
        match = True
        
        # Description filter
        if 'description' in criteria:
            if criteria['description'].lower() not in txn.description.lower():
                match = False
        
        # Amount range filter
        if 'min_amount' in criteria:
            if abs(txn.amount) / 100 < criteria['min_amount']:
                match = False
        
        if 'max_amount' in criteria:
            if abs(txn.amount) / 100 > criteria['max_amount']:
                match = False
        
        # Category filter
        if 'category' in criteria:
            if txn.category != criteria['category']:
                match = False
        
        # Merchant filter
        if 'merchant' in criteria and txn.merchant:
            if criteria['merchant'].lower() not in txn.merchant.name.lower():
                match = False
        
        # Date range filter
        if 'after_date' in criteria:
            txn_date = datetime.fromisoformat(txn.created.replace('Z', '+00:00'))
            if txn_date < criteria['after_date']:
                match = False
        
        if match:
            filtered_transactions.append(txn)
    
    return filtered_transactions

# Usage examples
client = MonzoClient()
accounts = client.accounts.list()
account_id = accounts[0].id

# Find all coffee purchases
coffee_transactions = search_transactions(
    account_id,
    description='coffee'
)

# Find large transactions over £100
large_transactions = search_transactions(
    account_id,
    min_amount=100
)

# Find grocery spending
grocery_transactions = search_transactions(
    account_id,
    category='groceries'
)

# Find transactions at specific merchant
tesco_transactions = search_transactions(
    account_id,
    merchant='tesco'
)

print(f"Coffee purchases: {len(coffee_transactions)}")
print(f"Large transactions: {len(large_transactions)}")
print(f"Grocery transactions: {len(grocery_transactions)}")

Duplicate Transaction Detection

Find potential duplicate transactions:

def find_duplicate_transactions(account_id):
    """Find potential duplicate transactions."""
    
    client = MonzoClient()
    
    # Get recent transactions
    transactions = client.transactions.list(
        account_id=account_id,
        limit=200
    )
    
    # Group by amount and description
    grouped = defaultdict(list)
    
    for txn in transactions:
        key = (txn.amount, txn.description.lower().strip())
        grouped[key].append(txn)
    
    # Find potential duplicates
    duplicates = []
    
    for (amount, description), txn_group in grouped.items():
        if len(txn_group) > 1:
            # Check if transactions are close in time (within 24 hours)
            dates = [
                datetime.fromisoformat(txn.created.replace('Z', '+00:00')) 
                for txn in txn_group
            ]
            dates.sort()
            
            for i in range(1, len(dates)):
                time_diff = dates[i] - dates[i-1]
                if time_diff.total_seconds() < 86400:  # 24 hours
                    duplicates.append({
                        'amount': amount / 100,
                        'description': description,
                        'transactions': txn_group,
                        'time_difference': time_diff
                    })
    
    return duplicates

# Usage
duplicates = find_duplicate_transactions(accounts[0].id)

print(f"Found {len(duplicates)} potential duplicates:")
for dup in duplicates:
    print(f"\n💰 £{dup['amount']:.2f} - {dup['description']}")
    print(f"   Time difference: {dup['time_difference']}")
    for txn in dup['transactions']:
        print(f"   • {txn.id} at {txn.created}")

Data Export

Export transaction data to various formats:

import csv
import json
from datetime import datetime

def export_transactions_csv(account_id, filename="transactions.csv"):
    """Export transactions to CSV file."""
    
    client = MonzoClient()
    transactions = client.transactions.list(account_id=account_id, limit=500)
    
    with open(filename, 'w', newline='', encoding='utf-8') as csvfile:
        fieldnames = [
            'id', 'date', 'description', 'amount_gbp', 'category',
            'merchant_name', 'merchant_category', 'notes'
        ]
        
        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
        writer.writeheader()
        
        for txn in transactions:
            writer.writerow({
                'id': txn.id,
                'date': txn.created,
                'description': txn.description,
                'amount_gbp': txn.amount / 100,
                'category': txn.category or '',
                'merchant_name': txn.merchant.name if txn.merchant else '',
                'merchant_category': txn.merchant.category if txn.merchant else '',
                'notes': txn.notes or ''
            })
    
    print(f"Exported {len(transactions)} transactions to {filename}")

def export_transactions_json(account_id, filename="transactions.json"):
    """Export transactions to JSON file."""
    
    client = MonzoClient()
    transactions = client.transactions.list(account_id=account_id, limit=500)
    
    # Convert to serializable format
    export_data = []
    for txn in transactions:
        txn_data = txn.model_dump()
        export_data.append(txn_data)
    
    with open(filename, 'w', encoding='utf-8') as jsonfile:
        json.dump(export_data, jsonfile, indent=2, default=str)
    
    print(f"Exported {len(transactions)} transactions to {filename}")

# Usage
accounts = client.accounts.list()
export_transactions_csv(accounts[0].id)
export_transactions_json(accounts[0].id)

Best Practices

Use pagination: For large date ranges, use the limit parameter and implement pagination
Filter at the API level: Use since and before parameters rather than filtering client-side
Expand selectively: Only expand fields you actually need to reduce response size
Cache results: Cache transaction data to avoid repeated API calls
Handle rate limits: Implement retry logic for rate limit errors

The Transactions API provides powerful capabilities for analyzing spending patterns, detecting anomalies, and building financial insights into your Monzo account activity.

Get Started

Guides

API Reference

Transactions API

Overview

Methods

list()

get()

Transaction Analysis Examples

Spending Analysis

Monthly Spending Trends

Transaction Search and Filtering

Duplicate Transaction Detection

Data Export

Best Practices

Get Started

Guides

API Reference

​Overview

​Methods

​list()

​get()

​Transaction Analysis Examples

​Spending Analysis

​Monthly Spending Trends

​Transaction Search and Filtering

​Duplicate Transaction Detection

​Data Export

​Best Practices

Overview

Methods

list()

get()

Transaction Analysis Examples

Spending Analysis

Monthly Spending Trends

Transaction Search and Filtering

Duplicate Transaction Detection

Data Export

Best Practices