Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

The data files contain seven low-dimensional financial research data (in .txt format) and four high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own efforts. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS).

AMEX, NYSE, and NASDAQ stocks histories

Update every Satur... Sun... I mean Friday... >_< sometime during the weekend. I lied, I've been too busy the past few months and haven't updated in forever until today (2020.6.14) - Last scrape 2020.06.12 Friday evening (p.s. Download shows 3GB unzipped, zipped file is ~600MB)

Full history of stock symbols:

• Unzip fh_< version_date >.zip
• Each stock symbol has a .csv file under full_history/
  • i.e. AMD.csv
• Columns in .csv
  • date - year-month-day, 2018-08-08
  • volume - int, volume of the day
  • open - float, opening price of the day
  • close - float, closing price of the day
  • high - float, highest price of the day
  • low - float, lowest price of the day
  • adjclose - float, adjusted closing price of the day

Other files:

• all_symbols.txt - All the stock symbols with history
• excluded_symbols.txt - All the ones that I couldn't retrieve data for
• NASDAQ.txt - NASDAQ listing
• NYSE.txt - NYSE listing
• AMEX.txt - AMEX listing

Disclaimer

This dataset contains almost all the stocks listed on these exchanges as of the date shown in the file name. Some of the symbols cannot be found on Yahoo Finance, which I plan on using CNN Money to scrape. There are other symbols that have different classes that require some modification before I can make them queryable... I have yet to decide on the best course of action. If you want to know what these excluded symbols are, see excluded_symbols.txt.

Note: there used to be some tickers missing because of poor connection, that's been solved now.

I've also been asked why I don't put everything into one table, and here's my rationale (copy/pasted from my email):

It is possible and I've debated this before, but I've decided to go with individual files for quite a number of reasons, and I highly recommend you consider these before combining them: 1) I don't need to load everything into memory or search for the right rows if I only want to work with particular sets, 2) easier and faster to manipulate (append, remove, or whatever) when all the data of a ticker is in the same place, 3) I don't need to repeat ticker names for each row just to know which row belongs to which ticker, 4) reduce risk, latency, and waits during parallel processing of different ticker data, 5) in case of any unforeseen bad writes or termination, this way reduces the chances of affecting the entire dataset and allows for restart anytime without the need to keep backup things up every 5 minutes. I get all these benefits only at the cost of slightly larger compressed file and a few more lines of code. To me it's worth it, but I can understand if you are frustrated, but it is possible to concatenate everything.

Github - for you to DIY:

https://github.com/qks1lver/redtide

Data source

Listing files (i.e. NYSE.txt) are from http://eoddata.com/symbols.aspx

Daily historical data compiled from Yahoo Finance

Need someone to talk to?

If you have questions, e-mail me: jiunyyen@gmail.com

Happy mining!

This dataset contains HDFC Bank'sThis dataset contains HDFC Bank daily opening and closing data starting from 1st January 1996.

We have 7 columns which comprises Date, Opening Price, Closing Price, High, Low, Volume, and Adj Close.

I would like to thanks Yahoo Finance for such a smooth hassle-free platform to download useful Data.

This is a small attempt to understand the theory behind the stock market and can we predict using historical data.

Context

Intel Corporation designs, manufactures, and sells essential technologies for the cloud, smart, and connected devices worldwide. The company operates through DCG, IOTG, Mobileye, NSG, PSG, CCG, and All Other segments. It offers platform products, such as central processing units and chipsets, and system-on-chip and multichip packages; and non-platform or adjacent products comprising accelerators, boards and systems, connectivity products, and memory and storage products. The company also provides Internet of things products, including high-performance compute solutions for targeted verticals and embedded applications; and computer vision and machine learning-based sensing, data analysis, localization, mapping, and driving policy technology. It serves original equipment manufacturers, original design manufacturers, and cloud service providers. The company has collaborations with UC San Francisco's Center for Digital Health Innovation, Fortanix, and Microsoft Azure to establish a computing platform with privacy-preserving analytics to accelerate the development and validation of clinical algorithms; and Inventec Corporation. Intel Corporation was founded in 1968 and is headquartered in Santa Clara, California.

Content

Here is a simple code used to download data.

import yfinance as yf  
df = yf.download(tickers="INTC")  
df.to_csv('INTC.csv') 

Acknowledgements

This data collected with one line code using yahoo finance and yfinance library.