Most of you have probably heard about IEX: The Investors Exchange. IEX is the exchange started by Brad Katsuyama who was the protagonist of Michael Lewis’s famous book Flash Boys (review). Just last year, IEX scored a major win when SEC approved its application to register as a national securities exchange. As time passes by, IEX continues to gain more and more market share.
Just like any other exchange, one of IEX’s most valuable asset is the market data generated by all the trading. However, unlike other exchanges, IEX makes its data available to public for free via web API. On February 22, 2017, IEX wrote a blog post announcing release of its web API. Since then, IEX has made quite a few enhancements and added support for newer datasets as well.
As of today, some of the data that IEX provides includes:
- pricing data (latest trade and quote data as well as summary data going back up to 5 years),
- reference data,
- new data,
- earnings data, and
- financial data.
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I started looking into their API docs and figured I would write a python wrapper which gets the data from IEX and returns it in a dataframe. It’s a simple wrapper and is in its early stages so it doesn’t do much besides what the API supports. Currently, it supports getting pricing, reference, news, earnings and financial data. It also leverages IEX’s reference data to first validate securities before requesting data for them.
You can find the code here.
Here are some examples:
Getting latest quote and trade data
The final dataframe contains some extra columns as well such as ‘sector’ and ‘securityType’. I decided to leave these columns in the final dataframe so that the users can decide whether them or not.
In [1]: import sys
In [2]: sys.path.append('/Users/himanshugupta/PycharmProjects/iex_data')
In [3]: import iex_data as IEX
In [4]: iex = IEX.API()
In [5]: iex.get_latest_quote_and_trade(['AAPL', 'IBM'])
Out[5]:
askPrice askSize bidPrice bidSize lastSalePrice lastSaleSize lastSaleTime lastUpdated marketPercent sector securityType volume
symbol
AAPL 154.37 3100 149.2 100 150.51 100 2017-07-20 18:48:40.872 2017-07-20 18:49:08.068 0.00975 Technology Hardware & Equipmen CommonStock 108396
IBM 147.60 100 0.0 0 147.66 100 2017-07-20 18:42:59.245 2017-07-20 18:49:13.563 0.01422 Software & Services CommonStock 46842
# If all of the securities are not valid, you will get a message back saying stocks are invalid. In [6]: iex.get_latest_quote_and_trade(['AAPLE', 'IBMC']) These stock(s) are invalid!
# If at least one security is valid, you will get results back. In [7]: iex.get_latest_quote_and_trade(['AAPL', 'IBMC'])[['askPrice', 'askSize', 'bidPrice', 'bidSize']] Out[7]: askPrice askSize bidPrice bidSize symbol AAPL 154.37 3100 149.2 100
Get latest trade data
If you only want latest trade data then use this method.
In [34]: iex.get_latest_trade(['AAPL', 'MSFT'])
Out[34]:
price size time
symbol
AAPL 150.585 100 2017-07-20 18:53:06.818
MSFT 73.800 100 2017-07-20 18:53:07.953
Get latest news
This is a great feature. IEX provides latest news stories via its API. Using this method, you can get latest news stories for securities.
In [36]: iex.get_latest_news(['SNAP']) Out[36]: symbol time headline summary source url related 0 SNAP 2017-07-20 16:51:00 Elon Musk must be 'from the future' because he's so awesome at everything, says Dick Costolo No summary available. CNBC via QuoteMedia http://app.quotemedia.com/quotetools/newsItem.htm?webmasterId=102699&storyId=8588191995690775 FB,SNAP,TSLA,TWTR
Get aggregated (bars) trade data
This method provides users with aggregated trade data. By default, it gets data for previous day. Users can get data for 1 day (1d), 1 month (1m), 6 months (6m), 1 year (1y), Year to date (ytd), 2 years (2y) and 5 years (5y).
In [57]: bars = iex.get_trade_bars_data(['APPLE', 'CSCO'], bucket='1m') # Print out available columns In [58]: bars.keys() Out[58]: Index(['change', 'changePercent', 'close', 'date', 'high', 'low', 'open', 'unadjustedClose', 'unadjustedVolume', 'volume', 'vwap', 'symbol'], dtype='object') In [62]: bars[['symbol', 'high', 'low', 'open', 'close', 'volume', 'vwap']].head() Out[62]: symbol high low open close volume vwap 0 CSCO 31.7236 31.2887 31.4759 31.6939 18459210 31.5967 1 CSCO 31.8623 31.5453 31.6444 31.5552 18190130 31.6525 2 CSCO 31.5750 31.2183 31.4363 31.5453 20386074 31.4327 3 CSCO 31.7038 31.4066 31.6146 31.5552 19436616 31.5690 4 CSCO 32.0010 31.4561 31.5552 31.7930 25792153 31.7880
Get financial data
Use this method to get financial data for securities such as cash flow, debt, assets, gross profit, net income etc.
In [47]: financials = iex.get_financials(['AMD', 'INTC']) # Print out available columns In [48]: financials.keys() Out[48]: Index(['cashChange', 'cashFlow', 'costOfRevenue', 'currentAssets', 'currentCash', 'currentDebt', 'grossProfit', 'netIncome', 'operatingExpense', 'operatingGainsLosses', 'operatingIncome', 'operatingRevenue', 'reportDate', 'researchAndDevelopment', 'shareholderEquity', 'totalAssets', 'totalCash', 'totalDebt', 'totalLiabilities', 'totalRevenue', 'symbol'], dtype='object') n [50]: financials[['symbol', 'cashChange', 'cashFlow', 'costOfRevenue', 'currentAssets', 'currentCash', 'currentDebt', 'grossProfit', 'netIncome', 'operatingExpense']] Out[50]: symbol cashChange cashFlow costOfRevenue currentAssets currentCash currentDebt grossProfit netIncome operatingExpense 0 AMD -542000000 -299000000 653000000 2498000000 722000000 n/a 331000000 -73000000 360000000 1 AMD 6000000 188000000 755000000 2530000000 1264000000 n/a 351000000 -51000000 354000000 2 AMD 301000000 29000000 1248000000 2824000000 1258000000 n/a 59000000 -406000000 352000000 3 AMD 241000000 -85000000 708000000 2506000000 957000000 226000000 319000000 69000000 327000000 4 INTC -626000000 3898000000 5649000000 36058000000 4934000000 5073000000 9147000000 2964000000 5548000000 5 INTC 808000000 8150000000 6269000000 35508000000 5560000000 4634000000 10105000000 3562000000 5579000000 6 INTC 867000000 5758000000 5795000000 36216000000 4752000000 3573000000 9983000000 3378000000 5521000000 7 INTC 824000000 3845000000 5560000000 31188000000 3885000000 4560000000 7973000000 1330000000 6655000000
Get earnings
Use this method to get earnings data for securities such as EPS report date, EPS, estimated EPS, fiscal end date etc.
In [51]: earnings = iex.get_earnings(['FB', 'VRX']) # Print out available columns In [52]: earnings.keys() Out[52]: Index(['EPSReportDate', 'EPSSurpriseDollar', 'actualEPS', 'announceTime', 'consensusEPS', 'estimatedEPS', 'fiscalEndDate', 'fiscalPeriod', 'numberOfEstimates', 'symbol'], dtype='object') In [53]: earnings[['symbol', 'EPSReportDate', 'EPSSurpriseDollar', 'actualEPS', 'announceTime', 'consensusEPS', 'estimatedEPS', 'fiscalEndDate']] Out[53]: symbol EPSReportDate EPSSurpriseDollar actualEPS announceTime consensusEPS estimatedEPS fiscalEndDate 0 FB 2017-07-26 N/A N/A AMC 1.13 1.13 2017-06-30 1 FB 2017-05-03 0.16 1.04 AMC 0.88 0.88 2017-03-31 2 FB 2017-02-01 0.13 1.24 AMC 1.11 1.11 2016-12-31 3 FB 2016-11-02 0.11 0.88 AMC 0.77 0.77 2016-09-30 4 VRX 2017-08-08 N/A N/A BTO 0.96 0.96 2017-06-30 5 VRX 2017-05-09 1.84 2.8 BTO 0.96 0.96 2017-03-31 6 VRX 2017-02-28 0.02 1.26 BTO 1.24 1.24 2016-12-31 7 VRX 2016-11-08 -0.23 1.55 BTO 1.78 1.78 2016-09-30
As mentioned earlier, my api provides users with an easy way to get data from IEX in a python dataframe. The IEX API is still very new so I expect IEX to be making lots of changes and adding new datasets. I will try my best to add support for the new datasets.
Code can be found on my github.
I am a beginner, I guess.
This code is unable to return the data I request:
QueryQuote:
With ActiveSheet.QueryTables.Add(Connection:=”URL;” & “https://api.iextrading.com/1.0/stock/aapl/lastPrice”, Destination:=DataSheet.Range(“yahoo”))
.BackgroundQuery = True
.TablesOnlyFromHTML = False
.Refresh BackgroundQuery:=False
.Name = “market_1”
.SaveData = True
It crashes on the Refresh line.
Anyone want to hold my hand?
Hi Michael,
Not sure how you are calling the library but try checking it out from git and then importing it into your python session. Once imported, you can call the API class and then use one of the methods.
Nice – just downloaded and tried it out worked without a hitch (minor – since today is a non-trading day get_latest_quote_and_trade errored – as expected)
Perfect!
Hi Himanshu
is there a way of getting adjusted close historical data out of IEX ?
Hi Spyros,
According to IEX’s documentation, their ‘chart’ functionality should give adjusted market data. So, when you use my method: get_trade_bars_data, it will return adjusted data.
Thank you for your swift response but using your method I do not get adjusted data; do you ?
Which symbol did you use to test? If you are not getting it, then I doubt I will get anything different. You can try contacting IEX and ask for adjusted data in future releases. Their API is still developing and they are adding new datasets frequently.