Alternative Data Overview

Our Alternative Data APIs provide access to non-traditional datasets that complement core market data. These datasets offer unique signals for investment research, competitive analysis, and macro trends that aren't captured by price, volume, or fundamental data alone.

Consumer Spending (EU)

European consumer transaction data powered by Fable Data. Daily credit card, debit card, and open banking transactions across 6 European countries with Bloomberg ticker and industry mapping for ~250 US public companies. Open banking data achieves >85% fill rate at the 7-day mark.

View Consumer Spending endpoints

Normalizing Merchant Aggregates

Growth in the Consumer Spending dataset reflects new account acquisition from data partners, not population or spending growth in any given country. Comparing raw spend totals across time periods will be misleading because the panel of cardholders changes over time. To accurately measure spending trends, you must normalize by the number of active accounts in the period you're analyzing.

Rolling account counts are provided at 8-day and 28-day windows. Choose the window that best aligns with your analysis period. Use spend_out_spend for most analyses; use total_spend for industries with high refund rates (e.g., fast fashion).

Country-level normalization adjusts for overall panel growth in a country. Divide spend by total active accounts to get spend per panelist, then compare across periods.

Example — YoY growth for a UK merchant:

(Q4 2024 UK spend / Q4 2024 active UK accounts) ÷ (Q4 2023 UK spend / Q4 2023 active UK accounts) − 1

Use eight_day_rolling_total_accounts or twenty_eight_day_rolling_total_accounts as the account count.

Category-level normalization provides more precise results when market participation within a category is relatively stable over time (e.g., groceries on a monthly basis). Instead of total accounts, divide by accounts active in the relevant spending category.

Example — YoY growth normalized by category:

(Q4 2024 UK spend / Q4 2024 active UK Category Y accounts) ÷ (Q4 2023 UK spend / Q4 2023 active UK Category Y accounts) − 1

Use eight_day_rolling_category_accounts or twenty_eight_day_rolling_category_accounts as the account count.

Cross-segment comparisons: For cross-country or cross-consumer-type comparisons, calculate spend per panelist within each segment first, then combine using weighted averages based on relative panel sizes.

Sign Convention

Spend values in consumer spending use bank statement convention from the consumer's perspective. Outbound transactions (spend_out_spend) are negative (money leaving the consumer's account), and inbound transactions (spend_in_spend, e.g. refunds) are positive (money returning to the consumer's account). total_spend is the sum of both and is typically negative. When calculating normalized metrics like spend per panelist, use the absolute value if you want a positive magnitude.

Next Steps

Explore our REST API endpoints to access alternative datasets. Our detailed documentation will guide you through integrating this data into your applications for investment research, competitive analysis, and macro trend identification.