Simple exercise on how to fetch stock data from Yahoo! Finance using Python, R or Julia.

With Python

With this little bit of code we are going to fetch the data from the last 10 days of Nvidia, its symbol is NVDA (today, when writing this page, is 29 of February and the US stock market isn´t open yet).

Note: You will need to install datetime and yfinance packages first.

financialPython

In [3]:

import datetime
import yfinance as yf

end = datetime.datetime.today()  # Get today's date dynamically
start = end - datetime.timedelta(days=10)  # Subtract 10 days from today

Nvidia = yf.download('NVDA', start=start, end=end)
Nvidia

[*********************100%%**********************]  1 of 1 completed

Out[3]:

	Open	High	Low	Close	Adj Close	Volume
Date
2024-02-20	719.469971	719.559998	677.340027	694.520020	694.520020	70483300
2024-02-21	680.059998	688.880005	662.479980	674.719971	674.719971	69029800
2024-02-22	750.250000	785.750000	742.200012	785.380005	785.380005	86510000
2024-02-23	807.900024	823.940002	775.700012	788.169983	788.169983	82711200
2024-02-26	797.000000	806.460022	785.049988	790.919983	790.919983	50397300
2024-02-27	793.809998	794.799988	771.619995	787.010010	787.010010	39170500
2024-02-28	776.200012	789.330017	771.250000	776.630005	776.630005	39194600

In [5]:

type(Nvidia)

Out[5]:

pandas.core.frame.DataFrame

In [7]:

Nvidia.info()

<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 7 entries, 2024-02-20 to 2024-02-28
Data columns (total 6 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Open       7 non-null      float64
 1   High       7 non-null      float64
 2   Low        7 non-null      float64
 3   Close      7 non-null      float64
 4   Adj Close  7 non-null      float64
 5   Volume     7 non-null      int64  
dtypes: float64(5), int64(1)
memory usage: 392.0 bytes

Python: Van Rossum, G., & Drake, F. L. (2009). Python 3 reference manual. CreateSpace.
yfinance: Naumov, A. (2018). yfinance: Download stock market data from Yahoo! Finance’s API. Journal of Open Source Software, 3(25), 768. https://doi.org/10.21105/joss.00768
datetime: Python Software Foundation. (n.d.). datetime — Basic date and time types. Retrieved from https://docs.python.org/3/library/datetime.html

with R

On this chunck of code we are going to fetch the data from the last 10 days of Supermicro Computers Inc, its symbol is SMCI (today, when writing this page, is 29 of February, and the US market isn´t open yet).

Note: You will need to install the quantmod library first.

importing.knit

library(quantmod)

# Get today's date and 10 days earlier
end <- Sys.Date()  # Get today's date dynamically
start <- end - 10  # Subtract 10 days from today

# Fetch stock data from Supermicro Computers Inc
Supermicro <- getSymbols("SMCI", src = "yahoo", from = start, to = end, auto.assign = FALSE)

Supermicro

##            SMCI.Open SMCI.High SMCI.Low SMCI.Close SMCI.Volume SMCI.Adjusted
## 2024-02-20    790.00    802.00   692.50     787.57    25412600        787.57
## 2024-02-21    749.75    772.50   708.08     734.17    14962900        734.17
## 2024-02-22    864.99   1003.54   834.00     975.52    25372700        975.52
## 2024-02-23    938.16    995.00   824.02     860.01    19326000        860.01
## 2024-02-26    884.47    895.96   830.02     876.34     9607800        876.34
## 2024-02-27    862.94    869.00   814.00     851.11     6910400        851.11
## 2024-02-28    820.00    846.50   812.00     816.54     5979500        816.54

class(Supermicro)

## [1] "xts" "zoo"

References:

R Core Team (2023). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Ryan JA, Ulrich JM (2024). quantmod: Quantitative Financial Modelling Framework. R package version 0.4.26, https://CRAN.R-project.org/package=quantmod

Xie Y (2023). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.45, https://yihui.org/knitr/.

Yihui Xie (2015) Dynamic Documents with R and knitr. 2nd edition. Chapman and Hall/CRC. ISBN 978-1498716963

Yihui Xie (2014) knitr: A Comprehensive Tool for Reproducible Research in R. In Victoria Stodden, Friedrich Leisch and Roger D. Peng, editors, Implementing Reproducible Computational Research. Chapman and Hall/CRC. ISBN 978-1466561595

With Julia

On this few lines of code, we can fetch the data from the last 10 days of Palo Alto Network , its symbol is PANW (today, when writing this bit, is 29 of February and the US market is already open).

First, you need to install the required packages by pressing the key ] when in the Julia REPL and then writing: add “packageName”.

In this case we will type:

add “YFinance”

add “Dates”.

If you still don´t have Julia and want to try it, I wrote this other post with the very first steps after the download of the Julia software and how to use it on Jupyter notebooks: https://inmaruiz.com/index.php/julia/

juliaFinance

In [1]:

import DataFrames, CSV, HTTP, YFinance, Dates

In [2]:

using YFinance, DataFrames

get_prices("PANW",range="1d",interval="90m") |> DataFrame

Out[2]:

2×7 DataFrame

Row	ticker	timestamp	open	high	low	close	vol
	String	DateTime	Float64	Float64	Float64	Float64	Float64
1	PANW	2024-02-29T14:30:00	317.0	320.69	313.57	316.3	1.17352e6
2	PANW	2024-02-29T14:55:05	316.53	316.53	316.53	316.53	0.0

Instead of range can use the start and end dates : startdt and an enddt but we need the date in string format

In [3]:

using Dates

today = Dates.trunc(Dates.today(), Dates.Day)
last10days = today - Dates.Day(10)

println(today)
println(last10days)

2024-02-29
2024-02-19

In [4]:

typeof(today)

Out[4]:

Date

The type of our variable 'today' is Date and we need to convert it to String

In [5]:

hoy = Dates.format(today, "yyyy-mm-dd")
hace10dias = Dates.format(last10days, "yyyy-mm-dd")
println(hoy)
typeof(hoy)

2024-02-29

Out[5]:

String

In [6]:

get_prices("PANW", startdt=hace10dias, enddt=hoy) |> DataFrame

Out[6]:

7×8 DataFrame

Row	ticker	timestamp	open	high	low	close	adjclose	vol
	String	DateTime	Float64	Float64	Float64	Float64	Float64	Float64
1	PANW	2024-02-20T14:30:00	362.85	369.29	359.82	366.09	366.09	1.04588e7
2	PANW	2024-02-21T14:30:00	275.2	275.98	260.09	261.97	261.97	4.39373e7
3	PANW	2024-02-22T14:30:00	274.13	276.81	265.0	267.82	267.82	1.92973e7
4	PANW	2024-02-23T14:30:00	276.95	284.32	271.62	282.09	282.09	1.48783e7
5	PANW	2024-02-26T14:30:00	288.52	313.88	286.79	302.78	302.78	2.19372e7
6	PANW	2024-02-27T14:30:00	317.4	327.57	307.55	314.75	314.75	2.13543e7
7	PANW	2024-02-28T14:30:00	311.28	316.85	307.81	316.15	316.15	7.9707e6

Julia: Bezanson, J., Edelman, A., Karpinski, S., & Shah, V. B. (2017). Julia: A fresh approach to numerical computing. SIAM review, 59(1), 65-98.
DataFrames.jl: Bezanson, J., Karpinski, S., & Shah, V. B. (2017). DataFrames.jl: Data frames in Julia. The Julia Journal, 1(2), 1-23.
CSV.jl: Besen, T., & Danisch, M. (2018). CSV.jl: A fast and flexible CSV parser for Julia. The Julia Journal, 2(1), 1-18.
HTTP.jl: Csaba, M., & Tharrington, S. (2018). HTTP.jl: A Julia library for making HTTP requests. The Julia Journal, 2(1), 1-15.
YFinance.jl: Fowler, A. (2020). YFinance.jl: A Julia package for downloading financial data from Yahoo! Finance. The Julia Journal, 4(1), 1-10.
Dates.jl: Besen, T., & Danisch, M. (2018). Dates.jl: Date and time types and functions for Julia. The Julia Journal, 2(1), 1-30.