---
title: "Look-ahead bias"
description: "Look-ahead bias is using information in a historical test that was not yet available when the simulated decision was made, which flatters the results."
canonical_url: "https://fincanva.com/glossary/look-ahead-bias"
last_updated: "2026-10-06"
md_url: "https://fincanva.com/glossary/look-ahead-bias.md"
---

# Look-ahead bias

Look-ahead bias is the use of information in a historical test that was not yet available when the simulated decision was made — a company figure read before its publication date, or an order filled at a price not yet printed. The test effectively sees the future, so its results flatter and cannot be repeated with real money.

**Also seen as:** Lookahead bias, information leakage, peeking

Look-ahead bias is a *timing* error in the data feeding the decision, not a flaw in the [strategy](/glossary/strategy)'s logic: the same rules, given only what was knowable at the time, would have produced a different and usually far worse result.

## How does Fincanva handle it?

A Fincanva [backtest](/glossary/backtest) replays your strategy's rules in chronological order — a [walk-forward replay](/glossary/walk-forward-replay) — so each decision uses only the data available up to that point in history. It runs to the latest available market close and never into the future, so a decision dated in 2015 cannot draw on 2016 data.

That removes the *computation's* opportunity to look ahead. It cannot remove a rule that reads a figure the market did not yet have on that date, which is why the verb here is reduce, not eliminate.

## What causes look-ahead bias in a backtest?

Look-ahead bias appears whenever the date a value is *stamped with* differs from the date it *became public*, and the test uses the stamp. The recurring sources are:

- **Publication lag on reported fundamentals.** A quarterly figure describes a quarter that ended weeks or months before the filing appeared. Attaching that figure to the quarter-end date lets a decision act on it before anyone could have read it.
- **Restatements and revisions.** Many series are revised after first release. Using the final, revised value as though it had been known on the first-release date imports information that did not exist yet.
- **Same-moment execution.** Deciding on a day's closing price and then filling at that same day's open, or using a session's high or low before it had printed, gives the fill a price the decision could not have known. Which price a fill uses is set by the [execution time](/glossary/execution-time).
- **Today's membership applied to yesterday.** Running a test on the [constituents of an index](/glossary/index-lists-and-point-in-time-constituents) *as it stands now* over a period when the membership was different mixes look-ahead with [survivorship bias](/glossary/survivorship-bias).

## What happens when earnings are used before they were published?

A screen ranks companies on their latest reported earnings and rebalances on 1 February. Company A's fiscal quarter ended 31 December, but its results were only published on 20 February. A test that files the December figure under 31 December can see it on 1 February and buys on it; a test that files it under 20 February cannot see it yet and buys something else. Repeat that across every filing for ten years and the strategy has enjoyed a three-week head start on every earnings surprise — a systematic advantage no real account had. This is why a filter lag counted in **Reports Ago** rather than calendar months matters: filings, not the calendar, decide when a figure was knowable. See [lag, period, and multiplier](/glossary/lag-period-and-multiplier) for how those units work.

## How is look-ahead bias different from the other backtest biases?

Look-ahead bias is about *when* the information was available; its neighbours are about *what* you tested and *how you read the result*. [Survivorship bias](/glossary/survivorship-bias) is a sample missing the names that did not last. [Selection bias](/glossary/selection-bias) is a sample chosen with hindsight. [Data-snooping bias](/glossary/data-snooping-bias) is many tests with only the winner reported. [Overfitting](/glossary/overfitting) is a strategy shaped so tightly to past data that it captures noise. [Data-quality bias](/glossary/data-quality-bias) is the nearest neighbour of all: a conclusion driven by the stored data itself being wrong, which is where restatements and revisions land when the corrected value is the only one you have. A test can be entirely free of look-ahead bias and still suffer every one of the others.

Fincanva is for education and illustration only. It is not personalised financial advice, and past or simulated results do not predict future ones. [Read the Terms Addendum](https://fincanva.com/terms/addendum#section-3)