Can I use this for historical backtesting?

Yes, where coverage and serving rights permit it. Save the query, source, known-at cutoff, schema version, and coverage result with the experiment.

Earnings dataset

Earnings calendar and history

Scheduled and reported earnings events with version history, session timing, confidence, source, and known-at cutoffs.

Start with the API Read the API docs

Primary source

Issuer disclosures, SEC filings, and publicly available calendars

API route

/v3/stocks/{symbol}/earnings

Coverage

Explicit by stream

Serving

Rights-aware

What this dataset means

An earnings event has at least two timelines: when the company is expected to report and when the report actually becomes public. Research-grade calendars preserve schedule changes, before- or after-market timing, confirmations, and observation timestamps instead of replacing history with the latest date.

Fields delivered

symbol and issuer identity

fiscal period

scheduled event time

before/after/unknown session

reported time

status and confidence

source and known-at timestamp

The endpoint is /v3/stocks/{symbol}/earnings. Responses retain deterministic pagination and the metadata needed to connect normalized records to their source run.

How to evaluate a provider

Evaluate earnings calendar and history on source authority, historical depth, identity, timestamps, correction behavior, coverage reporting, and serving rights. A polished response is not enough if the underlying evidence cannot be reconstructed.

A current earnings calendar is unsafe for backtesting unless it preserves schedule revisions and filters them by the simulated decision time. DataCedar deliberately exposes that boundary in the product rather than leaving customers to infer it from a missing endpoint or a legal footnote.

Failure modes to test before purchase

Source identity is missing.
Coverage is assumed rather than measured.
Later corrections overwrite the historical view.
Acquisition access is treated as proof of redistribution rights.
An empty array cannot distinguish no event from incomplete collection.

Source, freshness, and reproducibility

Source. Issuer disclosures, SEC filings, and publicly available calendars. DataCedar stores source identifiers and retrieval runs before normalization, then serves its own stable downstream schema.

Freshness. Upcoming schedules are polled proactively; every observed change creates a new version so historical as-of queries can reconstruct what users knew at the time.

Rights. Open issuer and SEC evidence remains primary. Third-party pages may help discovery but do not become the serving source unless their terms permit it.

Buyer-intent guides

Earnings calendar with schedule historyFind upcoming and past earnings dates while preserving confirmations, changes, and market-session timing.Upcoming earnings datesQuery future earnings events by date, company, and reporting session with source and confidence fields.Company earnings datesResolve earnings dates to stable company identity and fiscal periods instead of treating a ticker-date pair as sufficient.Historical earnings calendar dataReconstruct past earnings schedules and reported events as they were known at each point in time.Earnings calendar APIIntegrate upcoming and historical earnings events with status, session, confidence, source, and version timestamps.

Questions, answered.

The research schema includes symbol and issuer identity, fiscal period, scheduled event time, before/after/unknown session, reported time, status and confidence, source and known-at timestamp. Fields remain connected to source, retrieval, coverage, and rights metadata.