---
title: "Fund data for robo-advisors and model portfolios"
description: "Universe construction, model portfolio attributes, rebalancing inputs and KID-compliant fund display for digital advice products, from one call per ISIN."
canonical: https://fundfactsapi.com/use-cases/robo-advisor
published: 2026-09-04
updated: 2026-09-04
publisher: FundFacts API
---

# Fund data for robo-advisors and model portfolios

> Universe construction, model portfolio attributes, rebalancing inputs and KID-compliant fund display for digital advice products, from one call per ISIN.

A robo-advisor maps a questionnaire answer to a model portfolio of funds, then keeps that portfolio on target over time. This page covers the fund data a robo-advisor needs at each stage, from universe construction to the regulated display of each fund, and how to source it from a single endpoint.

## The problem

Digital advice products live or die on operational discipline. The fund universe must be documented, every model must be reproducible, rebalancing must run on consistent inputs, and the fund information shown to a client must match what the fund house currently publishes. Doing this across dozens of share classes from several issuers means either a manual data team or an automated feed.

The regulatory side adds constraints. In Europe a retail client must be able to see the [KID](https://fundfactsapi.com/glossary/kid) risk indicator, costs and objective for each fund before investing, and the [MiFID II](https://fundfactsapi.com/glossary/mifid-ii) suitability assessment relies on those same attributes.

## What you need from the data

Three layers of data support a robo-advisor:

- **Universe.** Asset class, region, structure, distribution policy, currency, fees and size for every eligible share class, so the investment committee can define inclusion rules.
- **Models.** For each model portfolio, the target weights plus the per-fund attributes needed to explain the model: risk rating, benchmark, headline volatility, weighted cost.
- **Client display.** Investment objective, risk rating on the 1–7 scale, ongoing charges, top holdings and an as-of date, presented in a way that mirrors the KID.

Rebalancing itself uses positions and prices from your custodian; fund data supplies the static and semi-static attributes that decide which fund to buy when a sleeve drifts.

## How it works with FundFacts API

One request per ISIN returns all three layers. Keep a `funds` table refreshed daily and build models and client pages on top of it.

```typescript
type Sleeve = { isin: string; target: number };

export async function describeModel(sleeves: Sleeve[]) {
  const rows = await Promise.all(
    sleeves.map(async ({ isin, target }) => {
      const res = await fetch(`https://fundfactsapi.com/api/v1/funds/${isin}`, {
        headers: { Authorization: `Bearer ${process.env.FUNDFACTS_API_KEY}` },
      });
      if (!res.ok) throw new Error(`${res.status} loading ${isin}`);
      const { name, data, expiresAt } = await res.json();
      return {
        isin,
        name,
        target,
        riskRating: data.riskRating as number | undefined,
        ter: data.headlineMetrics?.ter as number | undefined,
        objective: data.investmentObjective as string | undefined,
        dataAsOf: data.dataAsOf as string | undefined,
        expiresAt,
      };
    }),
  );
  const complete = rows.every((r) => typeof r.ter === "number");
  const weightedTer = rows.reduce((s, r) => s + (r.ter ?? 0) * r.target, 0);
  return { rows, weightedTer, complete };
}
```

The weighted TER is a common disclosure on a model page; because `headlineMetrics.ter` is in percent, the result is also in percent. The `complete` flag matters: if any fund is missing a TER, show the model as incomplete rather than under-reporting cost.

For the client-facing fund page, render `investmentObjective`, `riskRating`, `headlineMetrics.ter`, `topHoldings` and `dataAsOf` directly. The [SRRI explainer](https://fundfactsapi.com/blog/srri-risk-indicator-explained) covers how to present the risk scale correctly.

## Design notes

**Rebalancing inputs.** Store the fund attributes you relied on when a rebalance was decided (risk rating, TER, benchmark) alongside the trade log. Regulators ask "why this fund on that date", and a snapshot answers it.

**Caching.** The API refreshes every 24 hours; `expiresAt` tells you when. A nightly job that re-loads expired ISINs keeps a universe of a few hundred share classes current within a Pro or Scale quota, depending on size. Client page requests should never hit the API directly.

**Quota and errors.** Watch `X-RateLimit-Remaining` in the nightly job and stop cleanly on `429`. A `404 fund_not_found` for a fund already in a model should raise an alert: it may have been merged or closed. Treat `502 upstream_error` as retry-tomorrow and keep yesterday's record.

**Share-class hygiene.** Models should reference a specific share class ISIN, not a fund name. Accumulating and distributing classes of the same fund have different ISINs and different tax treatment; see [accumulating vs distributing](https://fundfactsapi.com/glossary/accumulating-vs-distributing).

**Disclaimers.** Fund attributes are facts read from the documents the fund house publishes. Show `dataAsOf` next to them, state that past performance is not a guide to future returns, and keep the suitability logic, which is advice, separate from the data layer.

## FAQ

### Does the API provide daily NAVs for rebalancing?

No. The indexedPerformance series is rebased to 100 and follows the frequency the fund house publishes, which makes it suitable for charts and analysis but not for pricing trades. Rebalancing should use prices and positions from your custodian or execution venue. FundFacts API supplies the descriptive attributes that decide which share class belongs in which sleeve.

### Can I show the API's data to retail clients in place of the KID?

The structured data is drawn from the KID and factsheet and is suitable for display alongside them, but distributing the official KID document itself remains your regulatory obligation where it applies. Use the API to render consistent summaries, show dataAsOf, and link to or attach the fund house's current document in your onboarding flow.

### How do I compute a weighted cost for a model portfolio?

Multiply each sleeve's target weight by its headlineMetrics.ter and sum the results. Because TER is expressed in percent, the total is also in percent. If any fund in the model lacks a TER in its published documents the sum will understate cost, so flag the model as incomplete and resolve the missing figure before publishing it to clients.

### What happens when a fund in a model is closed or merged?

The API returns 404 fund_not_found once the share class no longer has published documents. Your nightly refresh should treat a 404 on a fund that resolved previously as an alert for the investment committee, keep the last known record for audit purposes and hold the sleeve's replacement decision in your own process rather than automating it.

## About FundFacts API

FundFacts turns any fund or ETF ISIN into one dated factsheet (fees and KID costs, the 1–7 risk indicator, holdings, sector and country exposure, returns) read from the fund house's own documents: as a client-ready PDF under your firm's name for advisers and wealth managers, as JSON through its API for developers, or inside ChatGPT and Claude through its MCP server.

FundFacts works inside ChatGPT and Claude as a connector (a remote MCP server): ask about any fund or ETF by ISIN, ticker or name and the assistant answers from FundFacts' dated figures, fees, risk, holdings, exposure and returns, with fund cards, side-by-side comparisons, holdings overlap and portfolio look-through.

- Free API key, no card: https://fundfactsapi.com/signup
- Documentation: https://fundfactsapi.com/docs (OpenAPI: https://fundfactsapi.com/openapi.json)
- MCP server: https://fundfactsapi.com/api/mcp (set-up for every client: https://fundfactsapi.com/mcp-server)
- Pricing: https://fundfactsapi.com/pricing
- Site index for AI assistants: https://fundfactsapi.com/llms.txt
- Informational data, not investment advice.