How to Pull Earnings Dates and Beat/Miss Data for U.S. Stocks via API
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data. APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed exp...
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data.
APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed expectations.
Some earnings calendar APIs can also include release timing—such as before‑market or after‑market sessions—which is useful for trading signals tied to event timing.
Combining an earnings calendar API with a surprise‑data endpoint allows automated systems to label events as beat, miss, or inline once results are released.
Create a landscape editorial hero image for this Studio Global article: 如图所示，美股的财报每天都会有日历跟踪，假设我有个信号触发列表，我希望列表中新增一列，来获取当前的财报发布时间，已发布的给出 高于预期还是低于预期的判断，还没发布的给出时间 比如5 12 盘前或5 12 盘后， 主要是需要这个数据接口 来集成到我的信号触发程序中， 哪些开源接口可. Article summary: 可以优先看 OpenBB 的 equity.calendar.earnings，它是 Python 侧较容易集成到量化/信号程序里的接口，文档给出了 from openbb import obb 和 obb.equity.calendar.earnings() 示例，并明确支持获取历史和未来财报发布，且包含 EPS 与营收数据。[1] 如果你的核心需求是“已发布后判断高于/低于预期”，AInvest 和 Bigdata 的接口证据更直接. Topic tags: deepresearch, general web, llm, agents, ai. Reference image context from search candidates: Reference image 1: visual subject "`# Upload the required packages library(xlsx);library(zoo);library(quantmod);library(rowr);library(TTR); # Set the parameters here pc_up_level = 6 # Set the high percentage thresh" source context "Quantinsti-博客中文翻译-六- - 绝不原创的飞龙" Reference image 2: visual subject "`# Upload the required packages library(xlsx
openai.com
For automated trading systems or signal pipelines, you typically need three pieces of earnings data:
The earnings date (future or historical)
The release timing (before market open, after market close, or unspecified)
Actual vs. expected results to determine whether the company beat or missed estimates
Several developer‑friendly APIs provide this information and can be integrated directly into Python or REST‑based workflows.
1. OpenBB: Easiest Python Integration
For quant or research pipelines built in Python, OpenBB is one of the most straightforward entry points.
Example usage:
from openbb import obb
obb.equity.calendar.earnings()
The endpoint returns historical and upcoming earnings releases and includes metrics such as EPS and revenue data. You can also query a specific time window using parameters like start_date and end_date.
This makes it convenient for pipelines that already operate on Pandas DataFrames or scheduled batch jobs.
2. AInvest API: Direct Fields for Beat/Miss Logic
If your system needs to classify results as above or below expectations, the AInvest earnings calendar API returns fields specifically designed for that purpose.
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What is the short answer to "How to Pull Earnings Dates and Beat/Miss Data for U.S. Stocks via API"?
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data.
What are the key points to validate first?
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data. APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed expectations.
What should I do next in practice?
Some earnings calendar APIs can also include release timing—such as before‑market or after‑market sessions—which is useful for trading signals tied to event timing.
How to Pull Earnings Dates and Beat/Miss Data for U.S. Stocks via API
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data. APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed exp...
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data.
APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed expectations.
Some earnings calendar APIs can also include release timing—such as before‑market or after‑market sessions—which is useful for trading signals tied to event timing.
Combining an earnings calendar API with a surprise‑data endpoint allows automated systems to label events as beat, miss, or inline once results are released.
Create a landscape editorial hero image for this Studio Global article: 如图所示，美股的财报每天都会有日历跟踪，假设我有个信号触发列表，我希望列表中新增一列，来获取当前的财报发布时间，已发布的给出 高于预期还是低于预期的判断，还没发布的给出时间 比如5 12 盘前或5 12 盘后， 主要是需要这个数据接口 来集成到我的信号触发程序中， 哪些开源接口可. Article summary: 可以优先看 OpenBB 的 equity.calendar.earnings，它是 Python 侧较容易集成到量化/信号程序里的接口，文档给出了 from openbb import obb 和 obb.equity.calendar.earnings() 示例，并明确支持获取历史和未来财报发布，且包含 EPS 与营收数据。[1] 如果你的核心需求是“已发布后判断高于/低于预期”，AInvest 和 Bigdata 的接口证据更直接. Topic tags: deepresearch, general web, llm, agents, ai. Reference image context from search candidates: Reference image 1: visual subject "`# Upload the required packages library(xlsx);library(zoo);library(quantmod);library(rowr);library(TTR); # Set the parameters here pc_up_level = 6 # Set the high percentage thresh" source context "Quantinsti-博客中文翻译-六- - 绝不原创的飞龙" Reference image 2: visual subject "`# Upload the required packages library(xlsx
openai.com
For automated trading systems or signal pipelines, you typically need three pieces of earnings data:
The earnings date (future or historical)
The release timing (before market open, after market close, or unspecified)
Actual vs. expected results to determine whether the company beat or missed estimates
Several developer‑friendly APIs provide this information and can be integrated directly into Python or REST‑based workflows.
1. OpenBB: Easiest Python Integration
For quant or research pipelines built in Python, OpenBB is one of the most straightforward entry points.
Example usage:
from openbb import obb
obb.equity.calendar.earnings()
The endpoint returns historical and upcoming earnings releases and includes metrics such as EPS and revenue data. You can also query a specific time window using parameters like start_date and end_date.
This makes it convenient for pipelines that already operate on Pandas DataFrames or scheduled batch jobs.
2. AInvest API: Direct Fields for Beat/Miss Logic
If your system needs to classify results as above or below expectations, the AInvest earnings calendar API returns fields specifically designed for that purpose.
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
What is the short answer to "How to Pull Earnings Dates and Beat/Miss Data for U.S. Stocks via API"?
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data.
What are the key points to validate first?
OpenBB’s equity.calendar.earnings endpoint is a convenient Python‑friendly way to retrieve upcoming and historical earnings releases with EPS and revenue data. APIs such as AInvest and Bigdata return fields like eps actual, eps forecast or eps estimated, allowing you to determine whether a company beat or missed expectations.
What should I do next in practice?
Some earnings calendar APIs can also include release timing—such as before‑market or after‑market sessions—which is useful for trading signals tied to event timing.
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:
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:
Earnings calendar API → upcoming events and report timing
Earnings surprise API → actual vs. estimate after release
Example workflow:
Pull upcoming earnings for your ticker universe.
Join the data to your signal trigger list.
After the report date passes, query the surprise endpoint.
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.
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:
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:
Earnings calendar API → upcoming events and report timing
Earnings surprise API → actual vs. estimate after release
Example workflow:
Pull upcoming earnings for your ticker universe.
Join the data to your signal trigger list.
After the report date passes, query the surprise endpoint.
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.
