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Downloading Financial Data

The Package yfinance

We’ll use the yfinance package to download stock price data from Yahoo! Finance. This Python library provides a simple interface for retrieving historical market data for stocks, indices, and other financial instruments. Before using it, you’ll need to install it first.

Anaconda

If you are using Anaconda you need to install yfinance first before you use it for the first time. In an Anaconda command prompt type:

pip install yfinance --upgrade

or in a Jupyter cell you can type:

!pip install yfinance --upgrade --quiet

Google Colab

In Google Colab, yfinance is pre-installed. If you ever need to upgrade to the latest version run this in a code cell:

!pip install yfinance --upgrade --quiet

Importing the Libraries

You can import the libraries as follows.

import matplotlib.pyplot as plt
import seaborn as sns
import yfinance as yf

sns.set_theme()

Data

In order to estimate asset pricing models, we need to get data on stock returns, for both a particular security and the market. An easy way to do the is to download this data from the web. We start by defining the security that we want to analyze and from which date we want to start the analysis.

To get the data, we use the function download() in yfinance. For example, to get the data for Microsoft (ticker: MSFT) we type:

yf.download(tickers='MSFT', auto_adjust=False, progress=False, multi_level_index=False)
Adj Close Close High Low Open Volume
Date
2026-09-14 505.410004 505.410004 509.950012 495.339996 497.059998 23091900
2026-09-15 497.119995 497.119995 505.899994 495.540009 500.059998 17794600
2026-09-16 490.299988 490.299988 495.970001 487.230011 493.029999 16670300
2026-09-17 497.750000 497.750000 501.470001 493.170013 497.799988 17841600
2026-09-18 493.779999 493.779999 498.649994 491.100006 497.970001 39622300
2026-09-21 501.609985 501.609985 501.869995 491.329987 494.950012 27959900
2026-09-22 498.000000 498.000000 508.500000 493.649994 507.320007 21652800
2026-09-23 500.589996 500.589996 509.440002 495.880005 500.760010 19445600
2026-09-24 497.929993 497.929993 498.899994 491.220001 495.070007 16657600
2026-09-25 516.169983 516.169983 519.400024 497.250000 499.040009 38193100
2026-09-28 509.220001 509.220001 513.330017 502.220001 505.470001 19626900
2026-09-29 508.959991 508.959991 513.640015 502.399994 508.309998 21341800
2026-09-30 512.900024 512.900024 519.830017 510.309998 511.609985 27223300
2026-10-01 512.799988 512.799988 522.849976 512.169983 519.880005 19731900
2026-10-02 517.530029 517.530029 522.500000 513.659973 519.390015 17822300
2026-10-05 525.179993 525.179993 532.349976 521.469971 521.619995 26604800
2026-10-06 529.299988 529.299988 535.690002 528.780029 531.679993 21107500
2026-10-07 529.760010 529.760010 531.729980 524.690002 530.729980 16790200
2026-10-08 522.609985 522.609985 533.510010 518.789978 529.809998 20228700
2026-10-09 535.070007 535.070007 537.219971 526.239990 528.239990 18500600
2026-10-12 541.150024 541.150024 542.739990 535.700012 537.530029 18314900
2026-10-13 543.450012 543.450012 551.150024 543.059998 544.640015 23824500

The output of the function download() is a Pandas dataframe. Therefore, all operations available on Pandas work on the dataframe just imported.

The yfinance library was recently updated so that the Close column now shows split and dividend-adjusted prices by default. To get both the unadjusted price and the adjusted price separately, we need to set auto_adjust=False when downloading data.

I use the option progress=False to suppress the status bar when downloading the data. The code runs just as fine without it.

The option multi_level_index=False flattens the column names to a single level. Without it, yfinance returns columns with two levels (price type and ticker), so you would need to write df['Close']['MSFT'] instead of simply df['Close'].

Also, note that by default, the dataset only includes data for the last month. We can specify an arbitrary starting date by adding, for example, start='2012-01-01'.

yf.download(tickers='MSFT', start='2012-01-01', auto_adjust=False, progress=False, multi_level_index=False)
Adj Close Close High Low Open Volume
Date
2012-01-03 20.833267 26.770000 26.959999 26.389999 26.549999 64731500
2012-01-04 21.323555 27.400000 27.469999 26.780001 26.820000 80516100
2012-01-05 21.541464 27.680000 27.730000 27.290001 27.379999 56081400
2012-01-06 21.876099 28.110001 28.190001 27.530001 27.530001 99455500
2012-01-09 21.588152 27.740000 28.100000 27.719999 28.049999 59706800
... ... ... ... ... ... ...
2026-10-07 529.760010 529.760010 531.729980 524.690002 530.729980 16790200
2026-10-08 522.609985 522.609985 533.510010 518.789978 529.809998 20228700
2026-10-09 535.070007 535.070007 537.219971 526.239990 528.239990 18500600
2026-10-12 541.150024 541.150024 542.739990 535.700012 537.530029 18314900
2026-10-13 543.450012 543.450012 551.150024 543.059998 544.640015 23824500

3716 rows × 6 columns

Close v/s Adjusted Close Price

The data query produces six columns. When estimating asset pricing models we will be interested in using the adjusted close (Adj Close) column since the price series is adjusted for stock splits and dividends. Let’s compare how the Adj Close differs from the Close price.

For this, let’s extract these two series and store them in a dataframe called df. The method df.loc[:, [column_label_1, column_label_2, ..., column_label_n]] selects multiple columns from the dataframe. In our example we want to select loc[:, ['Close', 'Adj Close']].

df = yf.download(tickers='MSFT', start='2012-01-01', auto_adjust=False, progress=False, multi_level_index=False).loc[:, ['Close', 'Adj Close']]

Sometimes in Python lines might get really long. A suggested way to split a long line of code into several smaller lines is to use parentheses around the code that you want to split and split whenever there is a dot.

df = (yf
      .download(tickers='MSFT', start='2012-01-01', auto_adjust=False, progress=False, multi_level_index=False)
      .loc[:, ['Close', 'Adj Close']]
      )

The previous line of code does exactly the same as the one we wrote originally, but might be easier to read and understand.

We can now compute cumulative percentage changes (cum_pct) for both series by applying to each column in df a function that divides everything by the first price. The apply() method applies a function along an axis of a DataFrame or a Series. The function can be a built-in Python function or a custom function that you define.

First, we define a function that takes a series x as input and divides the series by its initial value x[0].1

1 In the original code, I wrote x / x[0]. However, that syntax is being deprecated and the suggestion is to access the row number of the pandas dataframe using iloc.

def normalize(x):
    return x / x.iloc[0]

Then, we apply this function to our dataframe and plot it.

cum_pct = df.apply(normalize)
cum_pct.plot()
plt.title('Close v/s Adjusted Close of Microsoft')
plt.ylabel('Cumulative Return')
plt.show()

According to the graph, the impact of dividends is significant. Another way to see this is by actually computing the dividends from the two series. We will do this later.

Practice Problems

Problem 1 Plot the Close price for Nvidia Corporation (Ticker: NVDA) from July 1, 2015 until May 1, 2023.

Solution 
df = yf.download(tickers='NVDA', start='2015-07-01', end='2023-05-01', auto_adjust=False, progress=False, multi_level_index=False).loc[:,['Close']]
df.plot()
plt.title('NVDA Stock Price')
plt.show()

Problem 2 Plot the S&P 500 (Ticker: ^GSPC) from January 1, 2018 until May 1, 2023. Since the S&P 500 is an index you can use either the Close or Adj Close, it does not make a difference.

Solution 
df = yf.download(tickers='^GSPC', start='2018-01-01', end='2023-05-01', auto_adjust=False, progress=False, multi_level_index=False).loc[:,['Close']]
df.plot()
plt.title('S&P 500')
plt.show()

Problem 3 Imagine that you invest $100 in NVDA and AMD in January 1, 2015. Plot the evolution of each investment until May 1, 2023.

Solution

To plot the evolution of each investment, all we need to do is normalize the Adj Close evolution to 100 in January 1, 2015 for both stocks. We can use the same function we built before and multiply the result by 100. We use Adj Close instead of Close to account for stock splits and dividends.

df = (yf
      .download(tickers=['NVDA', 'AMD'], start='2015-01-01', end='2023-05-01', auto_adjust=False, progress=False)
      .loc[:, 'Adj Close']
      .apply(normalize)
      )*100
df.plot()
plt.title('Evolution of a $100 Investment in NVDA and AMD')
plt.ylabel('Portfolio Value ($)')
plt.show()