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yfinance Python Tutorial: Get Free Market Data
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Dec 11, 2024
Learn how to get free financial data from yahoo finance using #python and #yfinance. This includes - Price data at minute granularity - Fundamentals data - Options data - Institutional holdings - And more... 👍 Subscribe for more: https://bit.ly/3lLybeP 👉 Follow along: https://analyzingalpha.com/yfinance-python #python #yfinance #pandas 00:00 Introduction 01:28 Install yfinance Using Pip 01:48 Import yfinance and pandas 02:00 How to Download Historical Price Data Using finance 02:32 Download One Ticker Using yfinance
View Video Transcript
0:00
if you're interested in grabbing freely
0:01
available yahoo data for your algorithms
0:03
you've come to the right place today
0:05
we're going to cover the y finance
0:07
library and i'm going to show you how to
0:08
get all of the yahoo finance data which
0:10
is freely available but i must copy this
0:13
by saying if you i would not recommend
0:15
using yahoo finance in a live trading
0:18
algorithm it's just more for research
0:20
purposes or if you're not there to get a
0:22
paid service like polygon or quandl but
0:24
anyways i digress let's go ahead and see
0:26
what we're going to cover today we're
0:28
first going to install y finance if you
0:30
don't have it already then we're going
0:31
to import the y finance and panda
0:33
libraries then we'll
0:35
go ahead and download historical price
0:38
data using one ticker and then multiple
0:40
tickers and then after that we'll
0:42
download some fundamental data again
0:44
with one ticker and then multiple
0:46
tickers then we'll learn how to get
0:48
options data and institutional holdings
0:50
and then finally we'll wrap it up with a
0:52
clear example of why you probably
0:54
shouldn't use yahoo finance for live
0:56
training
0:57
the blog article is right here it's
0:59
already live and essentially covers uh
1:01
the same thing but i do detail
1:04
a lot of the methods are you know easier
1:07
to to read here so you may want to
1:10
browse over there if you have questions
1:12
regarding
1:13
uh you know the different signatures and
1:15
things like that so anyways let's go
1:17
ahead and get started before i digress
1:19
too much installing why finance is super
1:21
easy i'm going to use pip
1:23
type bang pip install why finance i'm
1:26
not going to hit enter here
1:28
but if you are in jupiter notebook
1:30
you'll have to put the bang so that way
1:33
a jupyter notebook knows to run this
1:34
into command if you're just at your
1:36
shell with your virtual environment
1:37
activated you just type pip install y
1:39
finance and that will give you
1:41
or install the library now the next
1:43
thing we'll do is import a wi-fi nance
1:46
and pandas so we'll type import
1:49
canvas rfpd import by finance as yf
1:54
simple enough hit enter so they're
1:55
available to us and now before we
1:58
um you know go on with downloading one
2:01
ticker you know that another way you can
2:03
find out what's available to you is just
2:05
type d-i-r-y-f
2:07
or go to
2:08
the documentation but we can see here
2:11
you know we're going to be
2:12
using
2:13
this stuff here but we're probably going
2:14
to be concerned with ticker
2:17
and also
2:19
the
2:20
download there so so even though it
2:22
might feel overwhelming it's it's pretty
2:25
easy for the most part so speaking of
2:27
pretty easy let's download the data for
2:29
one ticker
2:30
we'll just say let's see tdg for trans
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dime it's an airplane parts manufacturer
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ticker
2:37
we'll do tdg
2:39
so what we're doing is we're using the
2:42
wi-fi names ticker
2:44
to download the tdg symbol into tdg
2:48
and now just hit enter here and you can
2:50
see that it is indeed a y finance ticker
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object okay
2:55
so pretty straightforward there and if
2:58
you're curious on all of the
3:00
uh you know functionality available to
3:02
you
3:03
you can just
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check out
3:07
this right here so there's clearly a lot
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you can do with that ticker object
3:13
okay so now
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let's go ahead and get
3:17
the history
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for transdime so we'll type data equals
3:23
tbg history
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and then i'll just do data ahead not to
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take up the screen with all of the data
3:30
but you can see here that it gives us
3:33
daily data open high low close volume
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dividends and stock splits
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pretty straightforward
3:40
but it might be
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more interesting to
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select a certain date so we'll do data
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tdg history
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we'll do and change the interval right
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interval
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equals one minute so now with one minute
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date i believe you can only get one week
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of data
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so start
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2022 0 1 0 3
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and we'll do n 20 22 0 1 10
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data this should give us minute data
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and it indeed does you can see 9 30 it
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even has the time zone
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so it looks like it does skip a minute
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here which
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is common with minute data if no trades
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were transacted during that time frame
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it would just skip a bar and that's
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pretty much how it works so there's no
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reason to send data to us if nothing
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happened okay
4:33
perfect so you know how long did that
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take uh
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less than four minutes from start to
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finish to learn how to download minute
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data okay now let's go ahead and
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download multiple tickers let's
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go ahead we'll say data equals yf
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download so this time we're using
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download and not the ticker class
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and then we'll select two
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tickers we'll do goog
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add meta
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and the period will equal one month
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and then data.head
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and now we can see
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it has that progress bar now we have
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google's and meta's
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data now if you want to change
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you know how the headings are laid out
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so you know obviously the columns are up
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here and the tickers are down here
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you can do that quite easily
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data equals yf download
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pass it the same tickers
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meta
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and we'll also do a start and end again
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it's
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pretty sim similar to
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the single ticker right
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then n 20 21 12 30
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and then group by
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and then what we do here is
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essentially decide what to group by i
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believe columns the default so this time
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we want ticker
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and then we'll say data head
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and now you can see
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now we have it
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grouping by the ticker first and then
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the ol gels ohlcv
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values okay so pretty straightforward so
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now five minutes in let's see we can
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keep this moving all right so how to
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download fundamental data well when
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we're downloading fundamental data we're
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just going to use the ticker object so
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this time we will download danaher which
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is an industrial another industrial
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company
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ticker
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dhr you can type it lowercase or
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uppercase it doesn't matter
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me just verify that for you
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not to make a liar out of me there we go
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okay good so dhr
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we'll do info equals data here info this
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just gives us
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essentially a lot of the
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often requested information
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right so
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you know how many employees what sector
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it's in
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let's see dict object has no keys
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info
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get that let's see here
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okay there it is
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okay so here we have a
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dictionary object
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there's a description
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then we have the keys there so
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try it again
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because that was definitely a dictionary
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object huh see so this is one of the
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reasons why uh
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you know you're gonna be careful because
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sometimes you know you just have issues
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with yahoo finance so
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anyways here's all the dictionary keys
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so you can see
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zip sector full-time employees long
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business summary city etc so that's all
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of this stuff available to you if you
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want to grab that general information
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and then to access this it's pretty easy
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let's just say we want this sector
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that's in healthcare so pretty pretty
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simple
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and if we also want
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the earnings
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you know
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we can grab that so so pretty
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straightforward but let's go ahead and
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get some more
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uh
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interesting information we can do dhr
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get financials
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i'd rather said p l
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financie
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so that'll get us the profit loss i
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consider financials all of the
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financials not just the profit loss but
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that's okay
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you know it's
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comes from
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lots of accounting so anyways let's grab
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all of the financial statements we'll do
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dhr financials
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it's a pnl grab the balance sheet
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balance sheet
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and cash flow
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cash flow
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and we'll say financial statements
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equals pd call cat
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p l
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balance sheet cash flow and then we'll
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output those financial statements so you
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can see now we have aggregated all the
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financial statements together so you can
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come up with whatever metrics or analyze
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you know uh you know what percentage of
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capex to revenue or whatever you're
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interested in okay and now the next
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thing we want to do is
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go ahead and transpose this because it
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actually is a little bit easier to see
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if you do it this way right so now we
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just have our rows by date
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and then here's all of our columns up
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above so what's next well let's see how
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we can do this for multiple
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uh tickers right
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so this assumes that you're going to
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know that you know pandas i mean almost
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all of these
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uh libraries assume that
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so we'll have our tickers right here
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got them all and now what we want to do
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is create ticker objects for all of them
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we'll just replace tickers we'll just do
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some list comprehension
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ticker
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ticker
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for ticker and tickers so what are we
10:10
doing so for every ticker right in these
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tickers
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return it here and then
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pass it into y f ticker so that's list
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comprehension pretty common
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in python
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so now we have a list of
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ticker objects so now we can iterate
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through all of these
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ticker objects to get what we want so
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what we'll do is i'll first create a
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list of data frames
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and then for ticker and tickers
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and now let's say we'll get financial
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statements
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for each
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so do p and l
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again this is what we did above
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ticker financials
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we'll do balance sheet
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ticker balance sheet
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and then cash flow equals ticker
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cash flow so now we're just going to
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aggregate all of these together right so
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you can cat
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into
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a spell into one data frame so do
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financial statements equals pd concat
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pnl balance sheet cash flow
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okay and now we're just going to format
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this to make it a little nicer right so
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just to make data
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frame format i don't know nicer perfect
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so as i showed before uh we transposed
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that to make the
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rows
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you know on the left hand i'm sorry the
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dates on the left-hand side the columns
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for the actual field so let's uh get it
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in that format so do fs transpose
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swap dates and columns
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okay
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data equals theta dot reset index
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because what we want to do is we want to
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reset
12:01
index or date into the column right
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so right here that's the index we want
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that into a column and we can do that by
12:09
resetting that index but now we need to
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rename that so we'll do
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data
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columns equals
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and bear with me here
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data columns
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skip the first one
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so what we're doing is we're skipping
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we're keeping all the columns the same
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but we're renaming the first one
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okay
12:32
and
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we use this this operator here to kind
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of explode the co you know the list
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already so essentially it's date
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skipping the first column which is that
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index and then we pass in a list so
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we're just basically passing in a list
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of columns
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um
12:50
to uh essentially get the columns that
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we want in the order we want now let's
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add the ticker
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equals ticker dot ticker
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say add
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clicker to data frame
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okay notice
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rename
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old index to date
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and then we'll append this to our list
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dfs append
13:15
data so again all we're really doing is
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for each ticker we're taking the profit
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loss balance sheet cash flow smooshing
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it all together using king king cat
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concat then we're just transposing it a
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little bit just to make it into
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this format to make it a little easier
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to read
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we append it to a list and then we're
13:34
going to just
13:35
you know smoosh them all together
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uh once we so we have all you know again
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we have this and then we're going to put
13:43
four
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of them right next to each other because
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we have four tickers so you'll see what
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i mean here in fact
13:50
i'll
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hit press data here just to give you
13:54
an understanding so this might take a
13:56
second to run depending if yahoo finance
13:59
behaves what did i do here
14:02
expected access has 63 elements new
14:05
values have two elements
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14
14:12
okay data columns
14:14
equals date
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data dot columns oops
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that would be why i didn't mean to
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put the one here
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i wanted to start at the first element
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not end of the first element
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right so this is just slicing right so
14:31
this just means skip the first element
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and continue to the end
14:34
okay
14:36
so that's what i did there
14:39
the joys of
14:41
coding live on youtube okay well not
14:44
live but you know what i mean all right
14:46
so now this is what
14:48
our
14:49
uh data looks like right so this would
14:52
be for the last ticker
14:54
google because it looped through so this
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should be google
14:59
right and then we just have a list of
15:02
this data frame
15:04
in here all four of them so now let's
15:07
get them in a format
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where
15:11
you know we can
15:13
see all of them together
15:15
so this might seem a little bit strange
15:17
but bear with me here we're going to
15:18
create a new parser
15:20
parsers dot bass parser
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parser base
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use calls
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none
15:31
it's gonna be like leo what in the world
15:32
is this well we've got uh we need to
15:35
make sure that we don't have uh
15:36
duplicate columns whenever we're
15:38
concatenating so what we do is we've got
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four
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df ndfs so that's data frames we'll do
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df columns equals parser
15:50
maybe the dupe
15:52
names do you have columns so we're just
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going to dedupe the names okay
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uh simple as that and then we'll create
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a new data frame pd concat dfs
16:02
ignore index equal true
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and then we'll do
16:06
[Music]
16:07
df
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equal df set index
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ticker
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date
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and df
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now we can see we have our tickers on
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the left we have a multi-index right our
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level zero is the ticker our level one
16:24
is the date and now we have all of the
16:26
data neatly organized so you can
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manipulate that however you want to
16:32
perfect
16:33
so that's probably the hardest thing
16:35
that we're going to do today but maybe
16:37
the most useful
16:39
but now let's go ahead and look at how
16:41
we can get the institutional holders
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wait did i mix i missed something
16:48
oh yes i did miss something hold on
16:51
i'm not going to do institutional
16:52
holding holders yet we'll do
16:56
let me check here
16:59
options data
17:01
i'm going with something
17:03
how to get options data
17:08
okay so perfect so now what we'll do is
17:11
we can get options data pretty easily
17:12
let's just use apple yf ticker
17:15
and we use the ticker object for almost
17:17
everything
17:19
okay so we've got that apple object now
17:22
the options chain is super easy to grab
17:24
so we can do options equal
17:27
apple
17:28
options chain
17:31
options and that will give us
17:33
apple
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options
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which hits option chain yep
17:41
okay so
17:44
here are is the option chain you can
17:46
pass
17:49
an expiry if you want to so we can do
17:51
apple
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options
17:55
and then you can pass that date into the
17:57
options chain if you only want
17:59
that information but
18:01
that's okay so now you might say
18:04
leo make sure
18:05
how do i get the you know calls and puts
18:08
pretty easy so do
18:10
calls equal options calls
18:15
and then
18:17
you can do the same thing for puts puts
18:19
equal
18:20
puts equal options puts
18:27
okay and that's pretty much it from an
18:30
option perspective right it's super easy
18:33
to get
18:34
and they are their own type obviously
18:37
but let's go ahead now and get the
18:41
institutional holders which is again
18:43
super easy like i said like getting the
18:45
fundamentals multiple fundamentals and
18:47
pushing money that's the hardest thing
18:48
we're going to do today so we'll just do
18:50
apple
18:52
dot institutional
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folders i can spell that right
18:57
i n s t i t u t i o n a l holders
19:03
okay
19:06
and that's pretty simple you can see the
19:08
top 10 institutional holders and now
19:10
let's talk about why you shouldn't use
19:13
yahoo finance you did see above
19:15
there when i was trying to get
19:18
the dictionary information earlier i'm
19:20
not going to scroll up but it just came
19:22
up with an error same thing
19:24
for uh you'll just encounter other stuff
19:27
like this so ticker
19:29
fb
19:31
okay we'll do meta equal yf thicker meta
19:34
so we have face we know facebook change
19:37
its name to meta okay
19:39
uh these should be this we should get
19:41
the same data right so let's see what
19:43
happens from a cash flow perspective
19:45
oops
19:46
get
19:48
cash flow
19:50
right and it should give us some
19:52
information
19:55
and indeed it does
19:57
now what happens
19:59
if we say
20:00
meta
20:02
get
20:03
cash flow
20:09
and there's nothing so we can see here
20:11
even though these are the same same
20:13
stocks
20:15
you know they're giving us different
20:17
information so again yahoo finance is
20:20
probably the best free source online for
20:23
uh free stock data but again it's free
20:26
and i definitely wouldn't use it for
20:28
live trading simply because you get lots
20:30
of connection errors and also
20:33
data errors like this so hopefully you
20:36
enjoyed that video and now you're able
20:37
to download that freely available yahoo
20:40
data so you can follow along with all of
20:42
the other algo trading videos that i
20:44
create on this channel so hope you have
20:46
a wonderful day and i'll see you in the
20:47
next one bye
#Investing