Skip to main content

Description

Returns the complete stock split history for a given ticker symbol. Stock splits occur when a company divides its existing shares into multiple shares, changing the share price proportionally while maintaining total market capitalization.

Parameters

ticker
string
required
Ticker symbol (e.g., “AAPL”, “TSLA”, “GOOGL”)
response_format
enum
default:"json"
Output format: "json" or "markdown"
preview_limit
integer
default:"25"
Number of rows to preview in markdown format (min: 1, max: 200)
save
object
Save results to a file

Data Structure

Returns a pandas Series with:
  • Index: Split effective dates (datetime)
  • Values: Split ratio (float)
The split ratio represents the number of new shares per old share:
  • 2.0 = 2-for-1 split (each share becomes 2 shares)
  • 0.5 = 1-for-2 reverse split (every 2 shares become 1 share)
  • 3.0 = 3-for-1 split
  • 7.0 = 7-for-1 split (e.g., Apple in 2014)

Example

{
  "ticker": "AAPL"
}

Understanding Split Ratios

Forward Splits (ratio > 1)

Increase the number of shares while decreasing the price per share:
  • 2:1 split (ratio = 2.0): 100 shares @ 100200shares@100 → 200 shares @ 50
  • 3:1 split (ratio = 3.0): 100 shares @ 150300shares@150 → 300 shares @ 50
  • 7:1 split (ratio = 7.0): 100 shares @ 700700shares@700 → 700 shares @ 100

Reverse Splits (ratio < 1)

Decrease the number of shares while increasing the price per share:
  • 1:2 reverse split (ratio = 0.5): 100 shares @ 550shares@5 → 50 shares @ 10
  • 1:10 reverse split (ratio = 0.1): 1000 shares @ 2100shares@2 → 100 shares @ 20

Use Cases

  • Adjust historical price data for split events
  • Analyze company stock split patterns
  • Calculate split-adjusted returns
  • Validate price discontinuities in historical data
  • Track companies that prefer stock splits vs. high share prices

Build docs developers (and LLMs) love