Get Customer Transaction Aggregations

Get aggregated financial summaries for customer transactions.

Returns categorized spending and income data organized into tables, subtables, and rows with time-series values.

Endpoint: GET https://dev.walkerstdata.com.au/api/v1/customer/{customerId}/transactions/aggregate

Request

cURL

# Get aggregated transaction data for a customer
curl -X GET "https://dev.walkerstdata.com.au/api/v1/customer/f9e8d7c6-b5a4-3210-9876-543210abcdef/transactions/aggregate" \
  -H "x-api-key: YOUR_API_KEY"

JavaScript

const customerId = 'f9e8d7c6-b5a4-3210-9876-543210abcdef';

const response = await fetch(`https://dev.walkerstdata.com.au/api/v1/customer/${customerId}/transactions/aggregate`, {
  method: 'GET',
  headers: {
    'x-api-key': 'YOUR_API_KEY',
  },
});

const result = await response.json();
const aggregatedData = result.data.tables;

Python

import requests

customer_id = 'f9e8d7c6-b5a4-3210-9876-543210abcdef'

response = requests.get(
    f'https://dev.walkerstdata.com.au/api/v1/customer/{customer_id}/transactions/aggregate',
    headers={'x-api-key': 'YOUR_API_KEY'}
)

result = response.json()
aggregated_data = result['data']['tables']

Response

{
  "data": {
    "tables": [
      {
        "id": "income",
        "hierarchy": "table",
        "type": "objectArray",
        "values": [
          {
            "id": "sales volume",
            "hierarchy": "row",
            "type": "timeseries",
            "values": {
              "2025-01": 53512.8,
              "2025-02": 1094617.8,
              "total": 1435732.86
            }
          }
        ]
      },
      {
        "id": "expenses",
        "hierarchy": "table",
        "type": "objectArray",
        "values": [
          {
            "id": "ffc by type",
            "hierarchy": "subtable",
            "type": "objectArray",
            "values": [
              {
                "id": "asset-finance",
                "hierarchy": "row",
                "type": "timeseries",
                "values": {
                  "2025-04": -1145.1,
                  "total": -1145.1
                }
              }
            ]
          }
        ]
      }
    ]
  },
  "message": null
}

Hierarchy Structure

The aggregation data follows a 3-level hierarchy:

• Table: Top-level categories (income, expenses)
• Subtable: Mid-level groupings within tables
• Row: Individual data series with time-series values

Each level contains:

• id: Identifier for the category
• hierarchy: Level type (table, subtable, row)
• type: Data structure type (objectArray, timeseries)
• values: Either nested objects or time-series data with totals

Time-Series Data

Row-level entries include monthly breakdowns and totals:

• Monthly values: YYYY-MM format keys with amounts
• Total: Sum across all time periods
• Positive values: Income/credits
• Negative values: Expenses/debits

Use Cases

• Financial dashboards: Display categorized spending summaries
• Cash flow analysis: Track income vs expenses over time
• Business insights: Understand customer financial patterns
• Credit assessment: Evaluate spending behaviors and trends

Next Steps

With aggregated data, you can:

• Build visualizations: Create charts and graphs from time-series data
• Generate reports: Use hierarchical structure for organized summaries
• Export summaries: Combine with download endpoint for file exports