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Retrieve historical price and volume data for multiple ticker symbols with flexible grouping options.

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Parameters

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tickers
string
required
Space-separated ticker symbols (e.g., ‘SPY AAPL MSFT’). Must contain at least one ticker.
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period
string
Time period to retrieve. Valid values include:
  • 1d, 5d, 1mo, 3mo, 6mo, 1y, 2y, 5y, 10y, ytd, max
Cannot be used with start and end parameters.
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interval
string
Data interval/granularity. Valid values include:
  • 1m, 2m, 5m, 15m, 30m, 60m, 90m, 1h, 1d, 5d, 1wk, 1mo, 3mo
Default is 1d (daily).
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start
string
Start date for the historical data (format: YYYY-MM-DD). Use with end instead of period.
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end
string
End date for the historical data (format: YYYY-MM-DD). Use with start instead of period.
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auto_adjust
boolean
Automatically adjust OHLC (Open, High, Low, Close) data for splits and dividends. Default is true.
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actions
boolean
Include dividend and split events in the data. Default is false.
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prepost
boolean
Include pre-market and post-market trading data. Default is false. Only available for intraday intervals.
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repair
boolean
Repair bad data (missing values, outliers) using yfinance’s repair logic. Default is false.
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keepna
boolean
Keep rows with NaN values instead of dropping them. Default is false.
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group_by
string
How to organize the returned data:
  • column: Group by data columns (Open, High, Low, Close, etc.) - each column contains data for all tickers
  • ticker: Group by ticker symbol - each ticker has its own set of columns
This parameter is critical for how you’ll access and analyze multi-ticker data.
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response_format
string
Format of the response:
  • json (default): Return structured JSON data
  • markdown: Return formatted markdown table
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preview_limit
number
Number of rows to include in markdown preview. Must be between 1 and 200. Default is 25.
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save
object
Save the result to a file:
  • format: Either csv or json
  • filename (optional): Custom filename (defaults to yf_tickers_history-{timestamp}.{format})

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Example Usage

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Get data for multiple tickers grouped by column

{
  "tickers": "AAPL MSFT GOOGL",
  "period": "1y",
  "interval": "1d",
  "group_by": "column"
}

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Get data grouped by ticker

{
  "tickers": "SPY QQQ IWM",
  "period": "6mo",
  "interval": "1d",
  "group_by": "ticker"
}

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Get data with custom date range

{
  "tickers": "TSLA NVDA AMD",
  "start": "2024-01-01",
  "end": "2024-12-31",
  "interval": "1wk"
}

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Get intraday data for multiple tickers

{
  "tickers": "AAPL MSFT",
  "period": "1d",
  "interval": "15m",
  "prepost": true,
  "group_by": "ticker"
}

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Compare tickers and save to CSV

{
  "tickers": "SPY QQQ DIA",
  "period": "1y",
  "interval": "1d",
  "group_by": "column",
  "save": {
    "format": "csv",
    "filename": "etf_comparison.csv"
  }
}

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Get historical data with actions

{
  "tickers": "JNJ PG KO",
  "period": "2y",
  "actions": true,
  "auto_adjust": false,
  "group_by": "ticker"
}

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Response Format

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JSON Response with group_by: "column" (default)

Data is organized by price/volume columns, with each ticker as a sub-column: 
{
  "data": {
    "__type__": "dataframe",
    "columns": [
      ["Close", "AAPL"],
      ["Close", "MSFT"],
      ["Volume", "AAPL"],
      ["Volume", "MSFT"]
    ],
    "index": ["2024-01-01", "2024-01-02"],
    "data": [
      [180.0, 370.0, 75000000, 25000000],
      [181.5, 372.5, 72000000, 24000000]
    ]
  },
  "saved_path": null
}

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JSON Response with group_by: "ticker"

Data is organized by ticker, with each ticker having its own set of OHLCV columns: 
{
  "data": {
    "__type__": "dataframe",
    "columns": [
      ["AAPL", "Open"],
      ["AAPL", "High"],
      ["AAPL", "Close"],
      ["MSFT", "Open"],
      ["MSFT", "High"],
      ["MSFT", "Close"]
    ],
    "index": ["2024-01-01", "2024-01-02"],
    "data": [
      [179.0, 182.0, 180.0, 369.0, 372.0, 370.0],
      [180.5, 183.0, 181.5, 371.0, 374.0, 372.5]
    ]
  },
  "saved_path": null
}

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Markdown Response

When response_format is set to markdown, returns a formatted table based on the group_by setting.

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Understanding group_by

The group_by parameter determines how multi-ticker data is structured:

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group_by: "column" (Wide format)

  • Each metric (Open, High, Low, Close, Volume) becomes a top-level grouping
  • Under each metric, you have columns for each ticker
  • Good for comparing the same metric across multiple tickers
  • Example structure: Close.AAPL, Close.MSFT, Volume.AAPL, Volume.MSFT

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group_by: "ticker" (Long format)

  • Each ticker becomes a top-level grouping
  • Under each ticker, you have all metrics (Open, High, Low, Close, Volume)
  • Good for analyzing all metrics for each ticker separately
  • Example structure: AAPL.Open, AAPL.Close, MSFT.Open, MSFT.Close

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Notes

  • Use period for relative time ranges or start/end for absolute dates, but not both
  • The group_by parameter is essential for organizing multi-ticker data effectively
  • Intraday intervals (1m, 5m, etc.) have limited historical availability
  • The prepost parameter only works with intraday intervals
  • Choose group_by: "column" for cross-ticker comparisons of specific metrics
  • Choose group_by: "ticker" for ticker-by-ticker analysis
  • All tickers use the same date range and interval settings

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