Documentation: Earnings Surprise History API

Blog Thimble Image

Overview

The FinIQ Earnings API provides a precise historical record of corporate earnings announcements. It goes beyond simple "Actual vs. Estimated" numbers by including Smart Consensus estimates, precise Time of Day timestamps (BMO/AMC), and post-earnings price reaction data.

Key Capabilities:
  • History: 20+ Years.
  • Coverage: 12,000+ Global Companies.
  • Speed: <200ms latency from press release.
  • Alpha Signals: Standardized Surprise %, 1-Day Price Reaction, Consensus Trend.

1. Access Methods

Method Use Case Format
REST API Event-driven trading, volatility modeling. JSON
Bulk CSV Backtesting PEAD (Post-Earnings Drift). CSV / Excel
Vector Feed New: Query earnings call transcripts. Embeddings

2. REST API: Earnings Endpoint

Endpoint: GET https://api.finiq.data/v1/earnings-surprises

Request Parameters:
Parameter Type Required Description
api_token string Yes Your API Key.
ticker string No Filter by symbol (e.g., NVDA).
date_from date No Start date (YYYY-MM-DD). Default: Last 4 Quarters.
min_surprise_p float No Filter for "Big Beats" only (e.g., 10.0 for >10% surprise).

Example Request (Python)
import requests

url = "https://api.finiq.data/v1/earnings-surprises"
params = {
    "api_token": "YOUR_KEY",
    "ticker": "NVDA",
    "date_from": "2023-01-01"
}

response = requests.get(url, params=params)
data = response.json()

Response Structure (JSON)
{
  "ticker": "NVDA",
  "fiscal_period": "2024-Q4",
  "report_date": "2024-02-21",
  "time_of_day": "After-Market",
  "EPS": {
    "actual": 5.16,
    "consensus": 4.64,
    "surprise": 0.52,
    "surprise_percent": 11.21
  },
  "Revenue": {
    "actual": 22100000000,
    "consensus": 20620000000,
    "surprise_percent": 7.17
  },
  "Stock_Price": {
    "price_before": 674.72,
    "price_after": 785.38,
    "reaction_1d": 16.4
  }
}

3. Data Dictionary (Key Metrics)

Field Type Description
report_date Date The official date of the press release.
time_of_day String Execution Signal. BMO (Before Market Open), AMC (After Market Close), or DMH (During Market Hours).
consensus Float The "Smart Mean" of analyst estimates. We filter out estimates older than 30 days to ensure accuracy.
surprise_percent Float Alpha Signal. The standardized percentage by which the Actual result beat (positive) or missed (negative) the Consensus.
reaction_1d Float The stock's percentage price move on the trading day immediately following the release.

4. Bulk Data Access (CSV)

Ideal for backtesting Post-Earnings Announcement Drift (PEAD) strategies.

File Naming Convention: {Exchange}_Earnings_History_{Date}.csv

CSV Columns:

Ticker, Period, ReportDate, TimeOfDay, EPS_Actual, EPS_Consensus, EPS_Surprise_P, Rev_Actual, Rev_Consensus, Rev_Surprise_P, Price_Reaction_1D

How to Download:

  1. Navigate to Data Export.
  2. Select Earnings Package.
  3. Choose Ticker Universe (e.g. "S&P 500").
  4. Click Download ZIP.

5. Vector Database Feed (RAG-Ready)

Exclusive to FinIQ.
This feed embeds the textual content of the Earnings Call Transcript and the Press Release.

Sample Vector Output (Decoded):

"In the Q4 2023 earnings call, NVIDIA CFO Colette Kress attributed the 11% EPS beat primarily to 'unprecedented demand for Data Center GPUs,' specifically citing H100 shipments."

Use Case:

Allows LLMs to answer qualitative questions:

  • "Which companies missed earnings due to 'Supply Chain' issues this quarter?"
  • "Summarize the CEO's tone regarding guidance for next year."

6. Error Handling & Limits

  • Rate Limits: Standard tiers allow 100 requests/second.
  • Error Codes:
    • 400 Bad Request: Invalid Ticker.
    • 401 Unauthorized: Invalid API Key.
    • 404 Not Found: No earnings history found for ticker.
    • 429 Too Many Requests: Rate limit exceeded.

7. Need Help?

  • Developer Support: email dev-support@finiq.data
  • Slack Community: Join our [Quant Developer Slack]
  • Postman Collection: [Download Here]

Author Name
Team Nextmark
Category
Dataset Documentation : Earnings Surprise History
Publish Date
February 2026

Qualitative Data Is the Edge. Start Using It.

Join the hedge funds and asset managers using our custom data feeds to find the alpha hidden in the details.
Cta Image