IEX API¶

IEX - Account Set Up¶

Install the Stock Analysis Engine
- If you want to use python pip: pip install stock-analysis-engine
- If you want to use a Kubernetes cloud service (EKS, AKS, or GCP) use the Helm guide to get started
- If you want to run on your own bare-metal servers you can use Metalnetes to run multiple Analysis Engines at the same time
- If you want to develop your own algorithms or integrate your applications using python, you can set up a Development Environment
Register for an Account
Set IEX_TOKEN the Environment Variable
```
export IEX_TOKEN=PUBLISHABLE_TOKEN
```

IEX - Fetch API Reference¶

Fetch API calls for pulling IEX Cloud Data from a valid IEX account

Warning

Running these API calls will impact your account’s monthly quota. Please be aware of your usage when calling these.

Please set the environment variable IEX_TOKEN to your account token before running these calls.

More steps can be found on the docs in the IEX API

Command Line Tool Fetching Examples

With the Analysis Engine stack running you can use the pip’s included fetch command line tool with the following arguments to pull data (and automate it).

Fetch Minute Data

fetch -t AAPL -g min

Fetch Daily Data

fetch -t AAPL -g day

Fetch Quote Data

fetch -t AAPL -g quote

Fetch Stats Data

fetch -t AAPL -g stats

Fetch Peers Data

fetch -t AAPL -g peers

Fetch News Data

fetch -t AAPL -g news

Fetch Financials Data

fetch -t AAPL -g fin

Fetch Earnings Data

fetch -t AAPL -g earn

Fetch Dividends Data

fetch -t AAPL -g div

Fetch Company Data

fetch -t AAPL -g comp

Command Line Fetch Debugging

Add the -d flag to the fetch command to enable verbose logging. Here is an example:

fetch -t AAPL -g news -d

analysis_engine.iex.fetch_api.fetch_daily(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX daily data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#historical-prices

import analysis_engine.iex.fetch_api as iex_fetch

daily_df = iex_fetch.fetch_daily(ticker='SPY')
print(daily_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_minute(ticker=None, backfill_date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX minute intraday data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#historical-prices

import analysis_engine.iex.fetch_api as iex_fetch

minute_df = iex_fetch.fetch_minute(ticker='SPY')
print(minute_df)

Parameters:	ticker – string ticker to fetch backfill_date – optional - date string formatted `YYYY-MM-DD` for filling in missing minute data work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_quote(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX quote data for a ticker and return as a pandas.DataFrame.

https://iexcloud.io/docs/api/#quote

import analysis_engine.iex.fetch_api as iex_fetch

quote_df = iex_fetch.fetch_quote(ticker='SPY')
print(quote_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_stats(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX statistics data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#key-stats

import analysis_engine.iex.fetch_api as iex_fetch

stats_df = iex_fetch.fetch_stats(ticker='SPY')
print(stats_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_stats(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]

Fetch the IEX statistics data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#key-stats

import analysis_engine.iex.fetch_api as iex_fetch

stats_df = iex_fetch.fetch_stats(ticker='SPY')
print(stats_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_news(ticker=None, num_news=5, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX news data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#news

import analysis_engine.iex.fetch_api as iex_fetch

news_df = iex_fetch.fetch_news(ticker='SPY')
print(news_df)

Parameters:	ticker – string ticker to fetch num_news – optional - int number of news articles to fetch (default is `5` articles) work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_financials(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX financial data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#financials

import analysis_engine.iex.fetch_api as iex_fetch

fin_df = iex_fetch.fetch_financials(ticker='SPY')
print(fin_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_earnings(ticker=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX earnings data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#earnings

import analysis_engine.iex.fetch_api as iex_fetch

earn_df = iex_fetch.fetch_earnings(ticker='SPY')
print(earn_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_dividends(ticker=None, timeframe='3m', work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Fetch the IEX dividends data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#dividends

import analysis_engine.iex.fetch_api as iex_fetch

div_df = iex_fetch.fetch_dividends(ticker='SPY')
print(div_df)

Parameters:	ticker – string ticker to fetch timeframe – optional - string for setting dividend lookback period used for (default is `3m` for three months) work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

analysis_engine.iex.fetch_api.fetch_company(ticker=None, work_dict=None, scrub_mode='NO_SORT', verbose=False)[source]¶

Fetch the IEX company data for a ticker and return it as a pandas.DataFrame.

https://iexcloud.io/docs/api/#company

import analysis_engine.iex.fetch_api as iex_fetch

comp_df = iex_fetch.fetch_company(ticker='SPY')
print(comp_df)

Parameters:	ticker – string ticker to fetch work_dict – dictionary of args used by the automation scrub_mode – optional - string type of scrubbing handler to run verbose – optional - bool to log for debugging

IEX - HTTP Fetch Functions¶

Functions for getting data from IEX using HTTP

Debugging

Please set the verbose argument to True to enable debug logging with these calls

analysis_engine.iex.helpers_for_iex_api.get_from_iex(url, token=None, version=None, verbose=False)[source]¶

Helper for getting data from an IEX publishable API endpoint using a token as a query param on the http url.

Parameters:	url – IEX resource url token – optional - string token for your user’s account version – optional - version string verbose – optional - bool turn on logging

analysis_engine.iex.helpers_for_iex_api.handle_get_from_iex(url, token=None, version=None, verbose=False)[source]¶

Implementation for getting data from the IEX v2 or v1 api depending on if the token argument is set:

Parameters:	url – IEX resource url token – optional - string token for your user’s account verbose – optional - bool turn on logging
Version:	optional - string version for the IEX Cloud (default is `beta`)

analysis_engine.iex.helpers_for_iex_api.get_from_iex_cloud(url, token=None, verbose=False)[source]¶

Get data from IEX Cloud API (v2) https://iexcloud.io

Parameters:	url – IEX resource url token – optional - string token for your user’s account verbose – optional - bool turn on logging

analysis_engine.iex.helpers_for_iex_api.get_from_iex_v1(url, verbose=False)[source]¶

Get data from the IEX Trading API (v1) https//api.iextrading.com/1.0/

Parameters:	url – IEX V1 Resource URL verbose – optional - bool turn on logging

analysis_engine.iex.helpers_for_iex_api.convert_datetime_columns(df, date_cols=None, second_cols=None, tcols=None, ecols=None)[source]¶

Convert the IEX date columns in the df to datetime objects

Parameters:

df – pandas.DataFrame to set columns to datetime objects
date_cols – list of columns to convert with a date string format formatted: YYYY-MM-DD
second_cols – list of columns to convert with a date string format formatted: YYYY-MM-DD HH:MM:SS
tcols – list of columns to convert with a time format (this is for millisecond epoch integers)
ecols – list of columns to convert with a time format (this is for nanosecond epoch integers)

IEX - Build Auth URL Using Publishable Token¶

Build an authenticated url for IEX Cloud

analysis_engine.iex.build_auth_url.build_auth_url(url, token=None)[source]¶

Helper for constructing authenticated IEX urls using an IEX Publishable Token with a valid IEX Cloud Beta Account

This will return a string with the token as a query parameter on the HTTP url

Parameters:	url – initial url to make authenticated token – optional - string `IEX Publishable Token` (defaults to `IEX_TOKEN` environment variable or `None`)

IEX - Extraction API Reference¶

Here is the extraction API for returning a pandas.DataFrame from cached or archived IEX datasets.

Extract an IEX dataset from Redis and return it as a pandas.DataFrame or None

Please refer to the Extraction API reference for additional support

analysis_engine.iex.extract_df_from_redis.extract_daily_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX daily data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
daily_status, daily_df = iex_extract.extract_daily_dataset(
    ticker='SPY')
print(daily_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_minute_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX minute intraday data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
minute_status, minute_df = iex_extract.extract_minute_dataset(
    ticker='SPY')
print(minute_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` scrub_mode – type of scrubbing handler to run work_dict – dictionary of args verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_quote_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX quote data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
quote_status, quote_df = iex_extract.extract_quote_dataset(
    ticker='SPY')
print(quote_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_stats_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX statistics data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
stats_status, stats_df = iex_extract.extract_stats_dataset(
    ticker='SPY')
print(stats_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_peers_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX peers data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
peers_status, peers_df = iex_extract.extract_peers_dataset(
    ticker='SPY')
print(peers_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_news_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX news data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
news_status, news_df = iex_extract.extract_news_dataset(
    ticker='SPY')
print(news_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_financials_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX financial data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
fin_status, fin_df = iex_extract.extract_financials_dataset(
    ticker='SPY')
print(fin_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_earnings_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX earnings data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
earn_status, earn_df = iex_extract.extract_earnings_dataset(
    ticker='SPY')
print(earn_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_dividends_dataset(ticker=None, date=None, work_dict=None, scrub_mode='sort-by-date', verbose=False)[source]¶

Extract the IEX dividends data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
div_status, div_df = iex_extract.extract_dividends_dataset(
    ticker='SPY')
print(div_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

analysis_engine.iex.extract_df_from_redis.extract_company_dataset(ticker=None, date=None, work_dict=None, scrub_mode='NO_SORT', verbose=False)[source]¶

Extract the IEX company data for a ticker from Redis and return it as a tuple (status, pandas.Dataframe)

import analysis_engine.iex.extract_df_from_redis as iex_extract

# extract by historical date is also supported as an arg
# date='2019-02-15'
comp_status, comp_df = iex_extract.extract_company_dataset(
    ticker='SPY')
print(comp_df)

Parameters:	ticker – string ticker to extract date – optional - string date to extract formatted `YYYY-MM-DD` work_dict – dictionary of args scrub_mode – type of scrubbing handler to run verbose – optional - boolean for turning on logging

IEX API Example - Fetch Minute Intraday Data using HTTP¶

Warning

This will fetch minute data using your IEX Cloud account and can cost money depending on your request usage.

import analysis_engine.iex.fetch_api as fetch
df = fetch.fetch_minute(ticker='SPY')
print(df)

IEX API Example - Extract Minute Intraday Data from Cache¶

import datetime
import analysis_engine.iex.extract_df_from_redis as extract
ticker = 'SPY'
today = datetime.datetime.now().strftime('%Y-%m-%d')
status, df = extract.extract_minute_dataset({
    'ticker': f'{ticker}',
    'redis_key': f'{ticker}_{today}_minute'})
print(df)

IEX API Example - Get Minute Data from IEX (calls fetch and cache)¶

Warning

This will fetch and cache minute data using your IEX Cloud account and can cost money depending on your request usage.

import analysis_engine.iex.get_data as get_data
df = get_data.get_data_from_iex({
    'ticker': 'SPY',
    'ft_type': 'minute'})
print(df)

IEX - Get Data¶

Use this function to pull data from IEX with a shared API for supported fetch routines over the IEX HTTP Rest API.

Common Fetch for any supported Get from IEX using HTTP

Supported environment variables:

# debug the fetch routines with:
export DEBUG_IEX_DATA=1

analysis_engine.iex.get_data.get_data_from_iex(work_dict)[source]¶

Get data from IEX - this requires an account

Parameters:	work_dict – request dictionary

Distributed Automation API¶

This is a helper for the parent method:

analysis_engine.iex.get_data.py

Fetch data from IEX with the factory method fetch_data

analysis_engine.iex.fetch_data.fetch_data(work_dict, fetch_type=None, verbose=False)[source]¶

Factory method for fetching data from IEX using an enum or string alias. Returns a pandas DataFrame and only supports one ticker at a time.

Supported enums from: analysis_engine.iex.consts

fetch_type = iex_consts.FETCH_DAILY
fetch_type = iex_consts.FETCH_MINUTE
fetch_type = iex_consts.FETCH_QUOTE
fetch_type = iex_consts.FETCH_STATS
fetch_type = iex_consts.FETCH_PEERS
fetch_type = iex_consts.FETCH_NEWS
fetch_type = iex_consts.FETCH_FINANCIALS
fetch_type = iex_consts.FETCH_EARNINGS
fetch_type = iex_consts.FETCH_DIVIDENDS
fetch_type = iex_consts.FETCH_COMPANY

Supported work_dict['ft_type'] string values:

work_dict['ft_type'] = 'daily'
work_dict['ft_type'] = 'minute'
work_dict['ft_type'] = 'quote'
work_dict['ft_type'] = 'stats'
work_dict['ft_type'] = 'peers'
work_dict['ft_type'] = 'news'
work_dict['ft_type'] = 'financials'
work_dict['ft_type'] = 'earnings'
work_dict['ft_type'] = 'dividends'
work_dict['ft_type'] = 'company'

Parameters:	work_dict – dictionary of args for the pEX call fetch_type – optional - name or enum of the fetcher to create can also be a lower case string in work_dict[‘ft_type’] verbose – optional - boolean enable debug logging

Default Fields¶

Get defaults fields for supported teyps of data

analysis_engine.iex.get_default_fields.get_default_fields(field)[source]¶

Parameters:	field – types of data to get