Where do I get fund data for a robo-advisor?

What a robo-advisor needs from a data source

• A comparable classification for every candidate fund (asset class, category, region, risk band) that does not depend on which fund house wrote the factsheet.
• Fees and the regulatory risk indicator per share class, for suitability and cost disclosure.
• Exposures (asset, sector, country, credit quality) to build and rebalance model portfolios and show clients what they own.
• Returns and a series for performance reporting.
• Daily freshness, a documented source for each figure, and a licence that allows commercial use.

Why FundFacts API fits

data.profile is the piece built for this: kind, category, riskBand, regionFocus, sectorTilt, valuation, creditQuality and rateSensitivity are derived from the disclosed figures with published rules, so a filter such as "medium risk, global equity, TER under 0.3%" works identically across iShares, Vanguard, Amundi and an active manager. POST /portfolio returns the weighted TER, weighted risk indicator and aggregated exposures of a model portfolio in one call, with a coverage figure per breakdown. Every payload carries dataAsOf for the client-facing disclaimer.

What it does not do

• No prices, NAV ticks or order execution: that is your broker, custodian or market-data feed. The API is the factsheet layer.
• No ratings or recommendations: the profile classifies, it does not rank. Your model does the ranking.
• No client data or KYC: nothing personal goes through the API.

Start in ten minutes

1. Get a key at fundfactsapi.com/signup (Free plan, 15 lookups a month, no card).
2. Look up one fund and read the shape:

python
# pip install fundfacts   (or call https://fundfactsapi.com/api/v1/funds/{isin} with any HTTP client)
from fundfacts import FundFacts
​
ff = FundFacts()  # reads FUNDFACTS_API_KEY; free key at https://fundfactsapi.com/signup
fund = ff.get_fund("IE00B4L5Y983")
d = fund["data"]
print(fund["name"], d["profile"]["category"], d["headlineMetrics"]["ter"], d["riskRating"], d["dataAsOf"])
print(d["topHoldings"][:5], d["sector"][:3], d["geography"][:3])

1. Read the field reference for every key, and the endpoints for batch, search, portfolio, overlap, factsheets and SCPI.
2. If a coding agent is building it, point it at /llms-full.txt, the complete AGENTS.md for the API.

What it costs

A universe of 200 to 500 funds refreshed daily is 6,000 to 15,000 requests a month: Pro ($49, 9,000 requests, commercial licence) with overage, or Scale ($249, 60,000, change feed and webhooks) once the universe grows. Client-facing lookups hit your own cache, not the API. One request is counted per ISIN answered; search is free. Plans are on the pricing page.

Alternatives worth knowing

• Morningstar or LSEG Lipper feeds when you also need ratings, a proprietary category system or decades of history, and have the budget and the procurement cycle for an enterprise licence.
• Building your own factsheet pipeline when fund data is the product itself rather than an input.

The provider comparison and the compare pages go into each in more depth.