How to Pull Earnings Dates and Beat/Miss Data for U.S. Stocks via API

Typical fields include:

• eps_actual
• eps_forecast
• eps_surprise
• revenue_actual
• revenue_forecast

Because the API exposes both actual and forecast EPS, you can easily determine the result category programmatically.

Example logic:

• eps_actual > eps_forecast → Beat expectations
• eps_actual < eps_forecast → Missed expectations
• eps_actual == eps_forecast → In line with expectations

This structure works well when adding a "earnings result" column to a signal list or trading dashboard.

3. Bigdata Earnings Surprise Endpoint

The Bigdata API provides a dedicated endpoint focused on earnings surprises. It returns fields such as:

• eps_actual
• eps_estimated
• eps_surprise_pct
• reporting_date

This endpoint is useful for post‑earnings analysis or verifying whether a company beat expectations after results are released.

4. APIs That Include Release Timing (Pre‑Market / After‑Hours)

For trading strategies that depend on session timing—like distinguishing before‑market open vs. after‑market close announcements—some APIs include explicit reporting‑time fields.

Examples:

• Certain earnings calendar APIs group results into pre‑market and after‑hours sections when returning daily schedules.
• Some stock earnings endpoints provide a reportTime field with values like "before market open" or similar descriptors.

This is useful if your signal table needs output such as:

• 05‑12 pre‑market
• 05‑12 after‑hours

5. Suggested Schema for a Signal Pipeline

When integrating earnings data into a trading signal list, a typical table structure might look like this:

ticker | signal_time | earnings_status | earnings_date | earnings_session | eps_actual | eps_estimate | result

Where:

• earnings_status: reported, upcoming, unknown
• earnings_session: pre-market, after-hours, unknown
• result:
  • beat
  • miss
  • inline
  • pending

Example classification logic:

if eps_actual is None:
    result = "pending"
elif eps_actual > eps_estimate:
    result = "beat"
elif eps_actual < eps_estimate:
    result = "miss"
else:
    result = "inline"

6. Practical Architecture for Automation

A robust setup often combines two sources:

1. Earnings calendar API → upcoming events and report timing
2. Earnings surprise API → actual vs. estimate after release

Example workflow:

1. Pull upcoming earnings for your ticker universe.
2. Join the data to your signal trigger list.
3. After the report date passes, query the surprise endpoint.
4. Update the signal table with beat/miss classification.

Bottom Line

If you want a practical stack for automated trading tools:

• OpenBB – easiest Python integration for earnings calendars.
• AInvest – convenient fields for EPS actual vs. forecast comparisons.
• Bigdata – dedicated surprise metrics after earnings releases.
• Other earnings calendar APIs – may include pre‑market/after‑hours timing useful for event‑driven strategies.

Combining a calendar endpoint with surprise data allows your signal program to automatically label each earnings event as pending, beat, miss, or inline once results are published.