Free Sources for Historical End-of-Day Stock Prices
Summary
The discussion looks for free historical end-of-day prices for US stocks, especially S&P 500 constituents, with a database that avoids survivorship bias. It mentions several paid databases and notes that Yahoo Finance access remained possible through an R package after changes to its interface. Other suggestions include Alpha Vantage for daily and intraday data and a platform for testing lower-frequency strategies.
The replies provide leads rather than a validated data-vendor comparison. One contributor describes a free dataset covering US company prices and financial information, while another offers an example of retrieving a foreign listing through an adjusted Yahoo interface. The discussion does not establish whether any source preserves delisted stocks, how prices are adjusted, or whether historical constituents are available. Those details matter for avoiding survivorship bias, so the proposed sources would need to be checked against the intended research requirements.
Key ideas
- Free and paid services differ in access to historical end-of-day equity data.
- A dataset intended to avoid survivorship bias needs coverage beyond current index constituents.
- Yahoo Finance access was reported to continue through an updated R package.
- Alpha Vantage was suggested for daily and intraday price data.
- The discussion does not verify delisted-stock coverage or adjustment methodology.
Tags
Full text
# Looking for free historical EOD prices of stocks from S&P 500
# Looking for free historical EOD prices of stocks from S&P 500
I'm trying to build a database of historical stock EOD price data with no survivorship bias, primarily from the S&P 500.
I have looked through numerous data sources from this website to create a database https://quantpedia.com/Links/HistoricalData including
- quandl
- CRSP
- Wharton
The issue is all of these services are premium, meaning you have to pay for them. I would have used yahoo finance's API but they recently discontinued it. Does someone know of a free alternative where I can get individual stock EOD historical price data to build my database?
## Answer by HLus (score 3)
https://quant.stackexchange.com/a/35527
In our startup SimFin, we are working on exactly such a solution, which is offered for free, since we couldn't afford the pricey premium solutions neither back when we were students. To this date, we have financial ratios, Financial statements (directly sourced from the SEC's XBRL data and up to 10y back) and stock prices for over 1000+ US companies, including the entire S&P 500. The fundamental financial data is freely available and you can instantly download it via excel.
Feel free to check it out under www.simfin.com and hopefully find what you are looking for.
If you need any specific data set, just write us there and we can compile it for you in exchange for some valuable feedback.
## Answer by Eldioo (score 0)
https://quant.stackexchange.com/a/34610
The Yahoo API still works, they just slightly altered it. I'd suggest you try the `quantmod` package (if you're using `R`) - the authors already adjusted it to the changes in the API. Here's a small example for the "Danone" stock from 2010 to 2017.
```
# install.packages("quantmod")
library(quantmod)
getSymbols("BN.PA",auto.assign=F,from="2010-01-01",to="2017-01-01")
# An ‘xts’ object on 2010-01-04/2016-12-30 containing:
# Data: num [1:1790, 1:6] 42.7 43.1 42.8 42.7 42.9 ...
# - attr(*, "dimnames")=List of 2
# ..$ : NULL
# ..$ : chr [1:6] "BN.PA.Open" "BN.PA.High" "BN.PA.Low" "BN.PA.Close" ...
# Indexed by objects of class: [Date] TZ: UTC
# xts Attributes:
# List of 2
# $ src : chr "yahoo"
# $ updated: POSIXct[1:1], format: "2017-06-08 21:45:57"
```
## Answer by Varun (score 0)
https://quant.stackexchange.com/a/44511
You can try alphavantage. They provide daily and intraday data up to 5-minute frequency.
If your algorithm uses lesser frequency data then you can try Quantra's Blueshift to test your algorithm for free.Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.