# How to backtest on a survivorship-free universe

Delisted stocks plus index membership by date, wired into Zipline, WealthLab or QuantConnect. What breaks is identity, not the engine.

*https://stockmarketstack.com/how-to/backtest-on-survivorship-free-data · next to Backtesting Frameworks & Algo Trading Libraries*

**Answer:** Buy the dead as well as the living, then let the engine ask who was eligible on each date. Norgate Data at Platinum or above feeds delisted US stocks and index membership into Zipline, AmiBroker or WealthLab; WealthLab bundles its own for a few indices; QuantConnect's cloud carries delisted names from 1998. What breaks is identity: a reused ticker, membership dated at the announcement, and a price filter run on adjusted prices nobody traded at.

## The tools that do this

*In the order this page recommends trying them. Paid placement does not affect this order.*

1. [Norgate Data](https://stockmarketstack.com/tools/norgate-data.md) — Delisted US stocks and index membership as a 1/0 series per date, from the $630 Platinum tier. Windows only; reaches Zipline, AmiBroker and WealthLab by plugin.
2. [WealthLab](https://stockmarketstack.com/tools/wealthlab.md) — Wealth-Data comes with the subscription — S&P 500, Nasdaq 100 and DAX members since about 1995, delisted names kept, positions exited when a stock leaves.
3. [Zipline-reloaded](https://stockmarketstack.com/tools/zipline-reloaded.md) — The free engine. Norgate's zipline-norgatedata builds a Current & Past bundle and an index-membership pipeline filter, on top of a Norgate subscription.
4. [QuantConnect](https://stockmarketstack.com/tools/quantconnect.md) — Cloud backtests over a security master of about 27,500 US stocks from 1998, delistings and ticker changes included. Membership proxy is ETF holdings from 2009.
5. [QuantRocket](https://stockmarketstack.com/tools/quantrocket.md) — US stocks and ETFs from 2007, delisted names kept, included with a paid licence. S&P 500 additions and removals come with Sharadar fundamentals, sold apart.
6. [FirstRate Data](https://stockmarketstack.com/tools/firstrate-data.md) — Minute bars you keep, roughly 7,000 delisted tickers suffixed -DELISTED. No index membership, and the vendor says the delisted set thins with age.

## The short way

Two datasets, not one. The first is price history for every stock that traded, including the ones
that no longer do. The second is who belonged to the universe on each date — usually an index,
because "the S&P 500 in 2008" is the universe most tests mean. A vendor that sells the first
without the second gives you a survivorship-free haystack and no way to know which straws were
eligible; the reverse gives you a list of names with no prices behind half of them. Why the second
dataset costs money at all is in the guide to
[historical index constituents](https://stockmarketstack.com/guides/historical-index-constituents).

The cheapest complete version on a Windows machine is [Norgate Data](https://stockmarketstack.com/tools/norgate-data) at
Platinum ($630 a year for US stocks), which is the first tier with delisted securities and
constituent history, feeding the free [Zipline](https://stockmarketstack.com/tools/zipline-reloaded) engine through Norgate's
own `zipline-norgatedata` package. The bundle is the whole universe, living and dead; membership is
a filter applied per date inside the pipeline, not a list fixed at the start:

```python
# C:\Users\<you>\.zipline\extension.py  (Windows, where the Norgate updater runs)
from zipline_norgatedata import register_norgatedata_equities_bundle

register_norgatedata_equities_bundle(
    bundlename='norgatedata-sp500',
    symbol_list=['$SPXTR'],                  # total-return index, for the benchmark
    watchlists=['S&P 500 Current & Past'],   # every member since 1990, dead ones included
    start_session='1990-01-01',
)
```

```python
# in the algorithm
from zipline.pipeline import Pipeline
from zipline_norgatedata.pipelines import NorgateDataIndexConstituent

def make_pipeline():
    member = NorgateDataIndexConstituent('S&P 500')
    return Pipeline(columns={'member': member}, screen=member)
```

`zipline ingest -b norgatedata-sp500` builds it. Norgate's own bundle example puts the S&P 500
current-and-past list at around 1,800 securities, against the roughly 500 you would get from today's list,
and every one of the extra 1,300 is a name a naive backtest could never have bought.

On a Mac, or without wanting to run a data pipeline at all, [QuantConnect](https://stockmarketstack.com/tools/quantconnect)'s
cloud does the equivalent with nothing to install: its US equity security master carries
delistings, mergers and ticker changes from January 1998, and LEAN maps each security to the same
identity whatever its ticker was that year.

## What the options are

**A data subscription feeding a desktop engine.** [Norgate Data](https://stockmarketstack.com/tools/norgate-data) has
plugins of its own for AmiBroker, WealthLab and Zipline, and RealTest reads it through that
product's own integration. Its constituent history is exposed as a 1-or-0 time series per security per
index, which is the right shape: the engine asks "was this a member today" on every bar instead of
consulting a list. Silver and Gold, the two cheaper tiers, carry currently listed securities only,
so the thing the product is bought for starts at Platinum. The updater is a Windows application
that must be running for any of the plugins to answer.

**An engine that ships its own universe.** [WealthLab](https://stockmarketstack.com/tools/wealthlab) includes Wealth-Data in
the subscription: end-of-day bars for the members of a handful of US and German indices from about
1995, delisted ones included, with dynamic DataSets that offer each stock as a candidate only while it was in the
index. It is the least assembly of anything here, and the narrowest — a few indices, daily bars,
and a membership rule you do not write yourself.

**A hosted platform with the data mounted.** [QuantConnect](https://stockmarketstack.com/tools/quantconnect) runs backtests
against a security master of roughly 27,500 US equities from 1998, with delistings, mergers and
ticker changes handled by the engine. On the final trading day a delisted stock raises a warning
event; afterwards LEAN liquidates whatever is still held. Historical index membership is not a
licensed constituent table here: the closest thing is its ETF constituents dataset, built from
issuers' published holdings from June 2009. [QuantRocket](https://stockmarketstack.com/tools/quantrocket) is the self-hosted
version of the same idea — US stocks and ETFs from 2007 with delisted names, included with a paid
licence — and sells S&P 500 additions and removals as part of the separately priced Sharadar
fundamentals bundle.

**Raw files you wire up yourself.** [FirstRate Data](https://stockmarketstack.com/tools/firstrate-data) sells minute bars
outright, with delisted tickers suffixed `-DELISTED` so a recycled symbol cannot collide with its
predecessor. There is no membership data, so the universe definition is yours to source, and any
engine will do — [Backtrader](https://stockmarketstack.com/tools/backtrader) and [vectorbt](https://stockmarketstack.com/tools/vectorbt) both take
whatever pandas data you hand them and ask no questions about where it came from, which is the problem as much as
the convenience.

## Where this breaks

**A ticker is not an identity.** Exchanges reassign symbols, so a table keyed on ticker splices an
old company and a new one into a single price series and the join never complains. Every product
above answers this differently, and the answer is worth knowing before you merge in anything else.
Norgate gives a delisted security a suffix of the year and month it last traded — Enron sits in its
database as `ENRNQ-200411` — and gives every security an unchanging numeric `assetid`; a ticker change moves
the whole history under the new symbol and the old one disappears from the database. WealthLab's
delisted symbols carry the last trading date and a letter for the company's fate, as in
`EYE.20050527.A`. QuantConnect keys everything to its own security identifier and fires a
symbol-changed event, cancelling any open order on the old ticker. The safe rule is to join your
own data — fundamentals, a signal file, anything — on the vendor's permanent identifier and never
on the ticker; the reasons identifiers exist at all are in
[security identifiers](https://stockmarketstack.com/guides/cusip-isin-figi-identifiers).

**Membership has two dates.** An index change is announced on one day and takes effect on another,
and a membership series stamped at the announcement lets the backtest buy a week early — the
[point-in-time](https://stockmarketstack.com/glossary/point-in-time) problem in its most literal form. A vendor's 1-or-0 series
should switch on the effective date; a list you assembled from press releases or a Wikipedia page
history probably does not. The same goes for exits. WealthLab closes a position on the day its
company leaves the index. Zipline does nothing of the kind: a pipeline screen only stops the name
appearing in tomorrow's output, and Norgate's own worked example has to sell anything no longer in
the index in a loop you write. Whether leaving the index is a sell signal is a decision about the
test, and it should be one you made.

**Adjusted prices are prices nobody traded at.** Norgate's Python package defaults to total-return
adjustment, and so does its Zipline bundle; QuantConnect's default mode adjusts for splits and
dividends using the entire split and dividend history. That is right for measuring returns and
wrong for any rule that reads a price level. "Skip stocks under $5" run on a series that has had
twenty years of dividends subtracted from it excludes names that traded well above $5 at the time,
and a per-share commission applied to an adjusted price charges for a share count nobody
traded. Keep an
unadjusted close beside the adjusted one — Norgate exposes one as a pipeline factor, QuantConnect
has a `Raw` mode — and use it for anything that means a price rather than a return. Why two
vendors' adjusted closes disagree in the first place is in
[why adjusted close differs](https://stockmarketstack.com/guides/why-adjusted-close-differs).

**The last print is not the last cash.** Delisted history ends at the last exchange trade, and an
engine that auto-closes at that price has booked a sale at a number the holder may never have seen
— a merger settled in cash or stock after the last trade, a bankruptcy where the shares went on trading
over the counter. Norgate is explicit that a move to OTC is not a delisting in its database: the
history carries on under the OTC symbol until the stock stops trading altogether, and a
Major Exchange Listed series marks which days were which. That is correct, and it means the
exchange history and the OTC history meet somewhere you should look at. For a strategy that holds distressed
names, the delisting exit is the single number most worth checking by hand.

**"Survivorship-free" is a range, not a checkbox.** FirstRate Data and [Kibot](https://stockmarketstack.com/tools/kibot) both include delisted
names and both say the set is incomplete — FirstRate adds that it thins the further back it goes; QuantConnect's master
starts in 1998 and excludes OTC stocks; ETF holdings as a membership proxy start in June 2009 and
were only monthly until January 2015, so a month of membership changes can pass unseen. None of
these is hidden. Each one caps how far back the test can honestly start, and
[survivorship bias](https://stockmarketstack.com/glossary/survivorship-bias) creeps back in wherever the data thins out.

## If you outgrow this

**When the fundamentals have to be point-in-time too.** Norgate's fundamentals are a current
snapshot with no history, so a test that ranks on earnings or book value is using this year's
figures for every year. That needs fundamentals dated by when each figure became public rather
than by the period it describes — ask a vendor which date its rows carry before buying — and the
same identifier discipline, because the fundamentals file and the price file must agree on who a
company is.

**When daily bars stop being enough.** The membership problem does not change at minute
resolution; the data bill does. [AlgoSeek](https://stockmarketstack.com/tools/algoseek) leases US equity minute and tick
history from 2007 with delisted names, point-in-time index membership and a security master that
follows a ticker through mergers, from $1,500 a month for a single equity dataset on a
twelve-month minimum. Storing it is a separate problem, covered in [storing minute bars locally](https://stockmarketstack.com/how-to/store-minute-bars-locally).

**When the test is right and the result is too good.** Survivorship is one leak; there are
others that survive a clean universe untouched — fills at prices the book could not have given
([slippage](https://stockmarketstack.com/glossary/slippage)) and parameters chosen with the whole history in view
([walk-forward](https://stockmarketstack.com/glossary/walk-forward) is the usual check). A clean universe is necessary and
does not make a test honest by itself.

The rest of the category is [backtesting frameworks](https://stockmarketstack.com/categories/backtesting-frameworks), and the
data side is in [market data APIs](https://stockmarketstack.com/categories/market-data-apis).

## FAQ

### Can I get a survivorship-free universe for free?

Not backwards. The delisted price histories and the dated membership table are both sold, and scraped lists carry no guarantee that a change is stamped with its effective date. Going forward it is cheap — archive an ETF issuer's daily holdings file every trading day and the series is yours — but a test that starts today has no history to run on.

### Why does a Norgate trial not show the delisted stocks?

The trial runs at Platinum level and is capped at two years of history, so delisted names appear but only those that stopped trading within that window. The paid Silver and Gold tiers carry currently listed securities only; delisted securities and constituent history start at Platinum.

### Should my backtest sell a stock when it leaves the index?

That is a choice about the test, not a property of the data. WealthLab's dynamic DataSets exit the position on the day of removal; in Zipline the pipeline simply stops listing the name and the position stays until your code sells it. Decide which one you mean and write it down, because the two produce different results on the same universe.

## Sources

1. [Norgate Data FAQ](https://norgatedata.com/data-package-faq.php) — Norgate Data, read 2026-09-27
2. [Data Content Tables](https://norgatedata.com/data-content-tables.php) — Norgate Data, read 2026-09-27
3. [norgatedata — Python package for Norgate Data](https://pypi.org/project/norgatedata/) — Norgate Data (PyPI), read 2026-09-27
4. [zipline-norgatedata 3.1.2](https://pypi.org/project/zipline-norgatedata/) — Norgate Data (PyPI), 2025-01-20
5. [Avoid Survivorship Bias with Dynamic DataSets](https://www.wealth-lab.com/blog/survivorship-bias) — WealthLab, 2022-11-11. WealthLab's data page still lists Wealth-Data dynamic DataSets as included and survivorship-free, checked 27 September 2026.
6. [US Equity Security Master — Documentation](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quantconnect/us-equity-security-master) — QuantConnect, read 2026-09-27
7. [US Equity corporate actions — Documentation](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity/corporate-actions) — QuantConnect, read 2026-09-27
8. [US Equity requesting data — Documentation](https://www.quantconnect.com/docs/v2/writing-algorithms/securities/asset-classes/us-equity/requesting-data) — QuantConnect, read 2026-09-27
9. [US ETF Constituents — Documentation](https://www.quantconnect.com/docs/v2/writing-algorithms/datasets/quantconnect/us-etf-constituents) — QuantConnect, read 2026-09-27
10. [Data Library](https://www.quantrocket.com/data/) — QuantRocket, read 2026-09-27

*Last updated 2026-09-27. A reference page, corrected in place — not a dated post.*