Intraday financial data files for backtesting or analysis. Currently this includes minute bars for:
- S&P 500 from 2010-2018 in USD (SPXUSD) (from Histdata)
- NIKKEI 225 from 2010-2018 in JPY (JPXJPY) (from Histdata)
- DAX 30 from 2010-2018 in EUR (GRXEUR) (from Histdata)
- EUROSTOXX 50 from 2010-2018 in EUR (ETXEUR) (from Histdata)
- Currencies pairs 2005-2020: AUD_JPY, AUD_USD, EUR_USD, GBP_USD, USD_CAD (from Oanda)
- Cryptocurrency: BTC_USD (2012-2018) (from Bitstamp)
- Cryptocurrency: BTC_EUR (2018), ETH_EUR (2018) (from Kraken)
- Others 2005-2020: AU200, CORN, DE10YB, FR40, JP225, NAS100, NATGAS, NL25, SOYBN, SPX500, SUGAR, UK10YB, US2000, USB10Y, WHEAT, WTICO, XAU (from Oanda)
This package will parse the CSV files and return them as a Pandas DataFrame.
Pull requests welcome.
You can check out the repo with git or install it and all the data with pip:
pip3 install https://github.com/FutureSharks/financial-data/archive/master.zip
data/<type>/<provider>/<instrument>
For example, data/stocks/histdata/sp500/DAT_ASCII_SPXUSD_M1_2010.csv is stock data for the S&P 500 index from the histdata provider.
import pyfinancialdata
data = pyfinancialdata.get(provider='bitstamp', instrument='BTC_USD', year=2017)
data.tail(3)
open high low close price
date
2017-12-31 23:57:00 13908.73 13913.26 13874.99 13913.26 13913.26
2017-12-31 23:58:00 13913.26 13953.83 13884.69 13953.77 13953.77
2017-12-31 23:59:00 13913.28 13913.28 13867.18 13880.00 13880.00import pyfinancialdata
data = pyfinancialdata.get(provider='histdata', instrument='SPXUSD', year=2017)
data.tail(3)
open high low close price
date
2017-12-29 16:55:00 2668.75 2668.75 2668.00 2668.25 2668.25
2017-12-29 16:57:00 2667.75 2668.50 2667.75 2668.00 2668.00
2017-12-29 16:58:00 2668.25 2668.50 2667.75 2668.50 2668.50import pyfinancialdata
data = pyfinancialdata.get(provider='oanda', instrument='EUR_USD', year=2017)
data.tail(3)
close high low open volume price
date
2017-12-29 21:57:00 1.20045 1.20071 1.20004 1.20018 50 1.20045
2017-12-29 21:58:00 1.20041 1.20041 1.20041 1.20041 1 1.20041
2017-12-29 21:59:00 1.20039 1.20039 1.19970 1.20036 14 1.20039For details see the Pandas Grouper documentation.
1 hour bars:
import pyfinancialdata
data = pyfinancialdata.get(provider='oanda', instrument='EUR_USD', year=2017, time_group='60min')
data.tail(3)
close high low open price
date
2017-12-29 19:00:00 1.20093 1.20142 1.20072 1.20140 1.20093
2017-12-29 20:00:00 1.19982 1.20108 1.19976 1.20098 1.19982
2017-12-29 21:00:00 1.20039 1.20071 1.19922 1.19984 1.20039Or 1 day bars for multiple years:
import pyfinancialdata
data = pyfinancialdata.get_multi_year(provider='oanda', instrument='EUR_USD', years=[2014, 2015, 2016, 2017], time_group='1d')
data.head(3)
close high low open price
date
2014-01-01 1.37642 1.37738 1.37424 1.37534 1.37642
2014-01-02 1.36653 1.37756 1.36296 1.37644 1.36653
2014-01-03 1.35888 1.36720 1.35825 1.36654 1.35888
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