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History()

As explained in Statistics Every Programmer Needs

history() is a yfinance Ticker method that retrieves historical market data for a security over specified dates and intervals.

history() retrieves a security’s historical market data through a yfinance Ticker object. Supply start and end dates in ISO 8601 YYYY-MM-DD format and choose an interval such as daily, hourly, or weekly; the default is '1d'. section 7.2.1

Use the method within a reusable fetching function:

  • Create a Ticker for the requested symbol. section 7.2.1
  • Pass start, end, and interval parameters to history(). section 7.2.1
  • Reuse the function for different securities, periods, and sampling frequencies. section 7.2.1

Daily AAPL data

Fetch AAPL data from 2023-10-01 through 2024-04-30, then select the Close column for analysis. Resetting the index produces a simple sequential index. section 7.2.1

>>> def fetch_stock_data(ticker_symbol, start_date, end_date, 
>>>                      interval = '1d'):
>>>     ticker_data = yf.Ticker(ticker_symbol)
>>> stock_data = ticker_data.history(start = start_date, 
                                 end = end_date, 
                                 interval = interval)
>>> return stock_data

Shows the reusable Python function that constructs a yfinance Ticker and calls history() with configurable dates and interval. section 7.2.1

How Statistics Every Programmer Needs covers it

How deep it goes
Explains object creation, parameters, date formatting, reuse, and close-price preparation without detailing the underlying data service.
Examples
Uses AAPL data from 2023-10-01 to 2024-04-30 and extracts Close prices.
What it assumes
Teaches ISO 8601 date formatting while assuming familiarity with Python functions and ticker symbols.
What you can do after
Build reusable Python data-fetching functions with yfinance for configurable historical market datasets.
How it connects
Connects yfinance Ticker, Yahoo Finance, ISO 8601, and time-series models.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Statistics Every Programmer Needs. The section links point to the parts of the book it draws on.