Where do I get fund and ETF data for quant research or backtesting?

What quant research or backtesting needs from a data source

• A return series per fund with its benchmark, in one format across issuers.
• Exposures and a classification to group and filter the universe.
• Fees to net returns and compare share classes.
• Bulk loading and a reproducible snapshot with dates.

Why FundFacts API fits

The series is monthly, computed from the fund house's NAV file where published and from the factsheet otherwise, with hasIndex telling you whether the benchmark leg exists. Volatility, Sharpe and max drawdown are pre-computed in headlineMetrics and metrics, and this guide shows how to recompute them from the points. profile gives comparable groupings (kind, category, region focus, valuation, credit quality). The pandas page has the loading code; GET /export on Scale streams the whole store as NDJSON.

What it does not do

• Monthly, not daily: intraday and daily backtests need a price feed.
• History goes back as far as the fund house publishes (often since inception for ETFs, sometimes five years for active funds).
• Survivorship: the store contains funds that exist today; dead funds are not in it.

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 pandas
import pandas as pd
from fundfacts import FundFacts
​
ff = FundFacts()
batch = ff.get_funds(["IE00B4L5Y983", "IE00B3RBWM25", "LU1681043599"])  # Starter+: one request per ISIN
series = {}
for r in batch["results"]:
    if r["status"] != "ok":
        continue
    pts = pd.json_normalize(r["data"]["indexedPerformance"]["points"])
    series[r["isin"]] = pts.set_index(pd.to_datetime(pts["date"]))["fund"]
panel = pd.DataFrame(series)            # monthly index levels, rebased to 100
returns = panel.pct_change().dropna()   # monthly returns
print(returns.corr().round(2))

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 one-off load of 1,000 ISINs is 1,000 requests; Pro ($49, 9,000 a month) covers a research universe refreshed weekly, Scale ($249) a daily refresh with bulk export. One request is counted per ISIN answered; search is free. Plans are on the pricing page.

Alternatives worth knowing

• Market-data APIs or exchange files for daily prices of listed ETFs (by ticker), combined with the API for exposures and fees.
• Academic databases (CRSP, Morningstar Direct) for long survivorship-bias-free histories when the institution has access.

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