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Adjusted close price

As explained in Build a Robo-Advisor with Python (From Scratch)

An adjusted closing price, or adjusted close, is a historical closing price adjusted for corporate actions such as dividends and stock splits.

An adjusted close is a historical closing price recalculated for dividends, stock splits, and other corporate actions. It provides a more consistent price series for estimating historical returns. section 3.1.1

From prices to returns

Download the Adj Close column for AAPL, FE, and WMT, then compute daily returns with pct_change(). Annualize the mean daily return by multiplying by approximately 252 trading days. The first return is NaN because no prior price exists. section 3.1.1

Adjusted prices are appropriate for historical-return analysis, but not every simulation. A dividend-receiving portfolio backtester should use Close prices and model dividends as separate cash income rather than using Adjusted Close. section 13.4.2

How Build a Robo-Advisor with Python (From Scratch) covers it

How deep it goes
Explains corporate-action adjustment and implements return calculation, while deferring adjustment mechanics and favoring separate dividend modeling in backtests.
Examples
Uses AAPL, First Energy, and Walmart price histories with pandas pct_change() and annualized mean returns.
What it assumes
Assumes basic Python and pandas familiarity while demonstrating price retrieval and return calculation directly.
What you can do after
Apply it via yfinance and pandas to download adjusted prices and calculate historical returns.
How it connects
Connects historical returns, dividends, stock splits, corporate actions, yfinance, and portfolio backtesting.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Build a Robo-Advisor with Python (From Scratch). The section links point to the parts of the book it draws on.