Client¶

class pyEX.Client(api_token=None, version='v1', api_limit=5)[source]

IEX Cloud Client

Client has access to all methods provided as standalone, but in an authenticated way

Parameters:	api_token (str) – api token (can pickup from IEX_TOKEN environment variable) version (str) – api version to use (defaults to v1) set version to ‘sandbox’ to run against the IEX sandbox api_limit (int) – cache calls in this interval

classmethod acos(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric ACos for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ad(symbol, range='6m', highcol='high', lowcol='low', closecol='close', volumecol='volume')

This will return a dataframe of Chaikin A/D Line for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate volumecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod add(symbol, range='6m', col1='open', col2='close')

This will return a dataframe of Vector Arithmetic Add for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col1 (string) – col2 (string) –
Returns:	result
Return type:	DataFrame

classmethod adosc(symbol, range='6m', highcol='high', lowcol='low', closecol='close', volumecol='volume', fastperiod=3, slowperiod=10)

This will return a dataframe of Chaikin A/D Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate volumecol (string) – column to use to calculate fastperiod (int) – fast period to calculate across slowperiod (int) – slow period to calculate across
Returns:	result
Return type:	DataFrame

advancedStats = functools.partial(<function Client.bind>, meth=<function advancedStats>)

advancedStatsDF = functools.partial(<function Client.bind>, meth=<function advancedStats>)

classmethod adx(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of average directional movement index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod adxr(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of average directional movement index rating for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

analystRecommendations = functools.partial(<function Client.bind>, meth=<function analystRecommendations>)

analystRecommendationsDF = functools.partial(<function Client.bind>, meth=<function analystRecommendations>)

classmethod apo(symbol, range='6m', col='close', fastperiod=12, slowperiod=26, matype=0)

This will return a dataframe of Absolute Price Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate fastperiod (int) – fast period to calculate across slowperiod (int) – slow period to calculate across matype (int) – moving average type (0-sma)
Returns:	result
Return type:	DataFrame

classmethod aroon(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of Aroon for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod aroonosc(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of Aroon Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod asin(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric ASin for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod atan(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric ATan for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod atr(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of average true range for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – time period to calculate over
Returns:	result
Return type:	DataFrame

classmethod avgprice(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of average price for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

balanceSheet = functools.partial(<function Client.bind>, meth=<function balanceSheet>)

balanceSheetDF = functools.partial(<function Client.bind>, meth=<function balanceSheet>)

batch = functools.partial(<function Client.bind>, meth=<function batch>)

batchDF = functools.partial(<function Client.bind>, meth=<function batchDF>)

classmethod beta(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of beta for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod bollinger(symbol, range='6m', col='close', period=2)

This will return a dataframe of bollinger bands for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

bonusIssue = functools.partial(<function Client.bind>, meth=<function bonusIssue>)

bonusIssueDF = functools.partial(<function Client.bind>, meth=<function bonusIssue>)

book = functools.partial(<function Client.bind>, meth=<function book>)

bookDF = functools.partial(<function Client.bind>, meth=<function book>)

classmethod bop(symbol, range='6m', highcol='high', lowcol='low', closecol='close', volumecol='volume')

This will return a dataframe of Balance of power for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate volumecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

brent(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

brentAsync = functools.partial(<function Client.bind>, meth=<function brent>)

brentDF = functools.partial(<function Client.bind>, meth=<function brent>)

calendar = functools.partial(<function Client.bind>, meth=<function calendar>)

calendarDF = functools.partial(<function Client.bind>, meth=<function calendar>)

cashFlow = functools.partial(<function Client.bind>, meth=<function cashFlow>)

cashFlowDF = functools.partial(<function Client.bind>, meth=<function cashFlow>)

classmethod cci(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Commodity Channel Index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

cdj = functools.partial(<function Client.bind>, meth=<function cdj>)

cdjDF = functools.partial(<function Client.bind>, meth=<function cdjDF>)

cdjValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

classmethod cdl2crows(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of Two crows for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3blackcrows(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 black crows for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3inside(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 inside up/down for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3linestrike(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 line strike for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3outside(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 outside for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3starsinsouth(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 stars in south for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdl3whitesoldiers(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of 3 white soldiers for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlabandonedbaby(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of abandoned baby for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdladvanceblock(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of advance block for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlbelthold(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of belt hold for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlbreakaway(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of breakaway for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlclosingmarubozu(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of closing maru bozu for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlconcealbabyswallow(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of conceal baby swallow for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlcounterattack(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of counterattack for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdldarkcloudcover(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of dark cloud cover for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdldoji(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of doji for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdldojistar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of doji star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdldragonflydoji(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of dragonfly doji for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlengulfing(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of engulfing for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdleveningdojistar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of evening doji star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdleveningstar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of evening star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdlgapsidesidewhite(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of up.down-gap side-by-side white lines for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlgravestonedoji(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of gravestone doji for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhammer(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of hammer for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhangingman(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of hanging man for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlharami(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of harami for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlharamicross(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of harami cross for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhighwave(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of high-wave candle for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhikkake(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of hikkake pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhikkakemod(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of modified hikkake pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlhomingpigeon(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of homing pigeon for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlidentical3crows(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of identical three crows for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlinneck(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of in-neck pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlinvertedhammer(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of inverted hammer for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlkicking(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of kicking for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlkickingbylength(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of kicking bull/bear determing by the longer marubozu for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlladderbottom(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of ladder bottom for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdllongleggeddoji(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of long legged doji for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdllongline(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of long line candle for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlmarubozu(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of marubozu for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlmatchinglow(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of matching low for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlmathold(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of mat hold for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdlmorningdojistar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of morning doji star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdlmorningstar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close', penetration=0)

This will return a dataframe of morning star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate penetration (int) – penetration
Returns:	result
Return type:	DataFrame

classmethod cdlonneck(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of on-neck pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlpiercing(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of piercing pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlrickshawman(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of rickshaw man for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlrisefall3methods(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of rising/falling three methods for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlseparatinglines(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of separating lines for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlshootingstar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of shooting star for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlshortline(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of short line candle for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlspinningtop(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of spinning top for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlstalledpattern(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of stalled pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlsticksandwich(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of stick sandwich for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdltakuri(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of takuri dragonfly doji with very long lower shadow for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdltasukigap(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of tasuki gap for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlthrusting(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of thrusting pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdltristar(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of tristar pattern for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlunique3river(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of unique 3 river for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod cdlxsidegap3methods(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of upside/downside gap three methods for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart opencol (string) – column to use to calculate highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

cdnj = functools.partial(<function Client.bind>, meth=<function cdnj>)

cdnjDF = functools.partial(<function Client.bind>, meth=<function cdnjDF>)

cdnjValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

classmethod ceil(symbol, range='6m', col='close')

This will return a dataframe of Vector Ceil for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

ceoCompensation = functools.partial(<function Client.bind>, meth=<function ceoCompensation>)

ceoCompensationDF = functools.partial(<function Client.bind>, meth=<function ceoCompensation>)

chart = functools.partial(<function Client.bind>, meth=<function chart>)

chartDF = functools.partial(<function Client.bind>, meth=<function chart>)

classmethod cmo(symbol, range='6m', col='close', period=14)

This will return a dataframe of Chande Momentum Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

collections = functools.partial(<function Client.bind>, meth=<function collections>)

collectionsDF = functools.partial(<function Client.bind>, meth=<function collections>)

company = functools.partial(<function Client.bind>, meth=<function company>)

companyDF = functools.partial(<function Client.bind>, meth=<function company>)

convertFX = functools.partial(<function Client.bind>, meth=<function convertFX>)

convertFXDF = functools.partial(<function Client.bind>, meth=<function convertFX>)

corporateActions = functools.partial(<function Client.bind>, meth=<function corporateActions>)

corporateActionsDF = functools.partial(<function Client.bind>, meth=<function corporateActions>)

classmethod correl(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of Pearson’s Correlation Coefficient(r) for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod cos(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric Cos for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod cosh(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric Cosh for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

cpi(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

cpiAsync = functools.partial(<function Client.bind>, meth=<function cpi>)

cpiDF = functools.partial(<function Client.bind>, meth=<function cpi>)

createRule = functools.partial(<function Client.bind>, meth=<function createRule>)

creditcard = functools.partial(<function Client.bind>, meth=<function creditcard>)

creditcardDF = functools.partial(<function Client.bind>, meth=<function creditcardDF>)

creditcardValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

cryptoBook = functools.partial(<function Client.bind>, meth=<function cryptoBook>)

cryptoBookAsync = functools.partial(<function Client.bind>, meth=<function cryptoBook>)

cryptoBookDF = functools.partial(<function Client.bind>, meth=<function cryptoBook>)

cryptoBookSSE = functools.partial(<function Client.bind>, meth=<function cryptoBookSSE>)

cryptoBookSSEAsync = functools.partial(<function Client.bind>, meth=<function cryptoBookSSEAsync>)

cryptoEventsSSE = functools.partial(<function Client.bind>, meth=<function cryptoEventsSSE>)

cryptoEventsSSEAsync = functools.partial(<function Client.bind>, meth=<function cryptoEventsSSEAsync>)

cryptoPrice = functools.partial(<function Client.bind>, meth=<function cryptoPrice>)

cryptoPriceAsync = functools.partial(<function Client.bind>, meth=<function cryptoPrice>)

cryptoPriceDF = functools.partial(<function Client.bind>, meth=<function cryptoPrice>)

cryptoQuote = functools.partial(<function Client.bind>, meth=<function cryptoQuote>)

cryptoQuoteAsync = functools.partial(<function Client.bind>, meth=<function cryptoQuote>)

cryptoQuoteDF = functools.partial(<function Client.bind>, meth=<function cryptoQuote>)

cryptoQuotesSSE = functools.partial(<function Client.bind>, meth=<function cryptoQuotesSSE>)

cryptoQuotesSSEAsync = functools.partial(<function Client.bind>, meth=<function cryptoQuotesSSEAsync>)

cryptoSymbols = functools.partial(<function Client.bind>, meth=<function cryptoSymbols>)

cryptoSymbolsDF = functools.partial(<function Client.bind>, meth=<function cryptoSymbols>)

cryptoSymbolsList = functools.partial(<function Client.bind>, meth=<function cryptoSymbols>)

daily = functools.partial(<function Client.bind>, meth=<function daily>)

dailyDF = functools.partial(<function Client.bind>, meth=<function daily>)

classmethod dailyReturns(symbol, range='6m')

Calculate returns of buying at open and selling at close daily

Parameters:	client (pyEX.Client) – symbol (string) – range (string) –
Returns:	result
Return type:	DataFrame

deepSSE = functools.partial(<function Client.bind>, meth=<function iexDeepSSE>)

deepSSEAsync = functools.partial(<function Client.bind>, meth=<function iexDeepSSEAsync>)

delayedQuote = functools.partial(<function Client.bind>, meth=<function delayedQuote>)

delayedQuoteDF = functools.partial(<function Client.bind>, meth=<function delayedQuote>)

deleteRule = functools.partial(<function Client.bind>, meth=<function deleteRule>)

classmethod dema(symbol, range='6m', col='close', periods=None)

This will return a dataframe of double exponential moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

diesel(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

dieselAsync = functools.partial(<function Client.bind>, meth=<function diesel>)

dieselDF = functools.partial(<function Client.bind>, meth=<function diesel>)

directory = functools.partial(<function Client.bind>, meth=<function directory>)

directoryDF = functools.partial(<function Client.bind>, meth=<function directory>)

distribution = functools.partial(<function Client.bind>, meth=<function distribution>)

distributionDF = functools.partial(<function Client.bind>, meth=<function distribution>)

classmethod div(symbol, range='6m', col1='open', col2='close')

This will return a dataframe of Vector Arithmetic Div for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col1 (string) – col2 (string) –
Returns:	result
Return type:	DataFrame

dividends = functools.partial(<function Client.bind>, meth=<function dividends>)

dividendsBasic = functools.partial(<function Client.bind>, meth=<function dividendsBasic>)

dividendsBasicDF = functools.partial(<function Client.bind>, meth=<function dividendsBasic>)

dividendsDF = functools.partial(<function Client.bind>, meth=<function dividends>)

dividendsForecast = functools.partial(<function Client.bind>, meth=<function dividendsForecast>)

dividendsForecastDF = functools.partial(<function Client.bind>, meth=<function dividendsForecast>)

download = functools.partial(<function Client.bind>, meth=<function download>)

classmethod dx(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Directional Movement Index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

earnings = functools.partial(<function Client.bind>, meth=<function earnings>)

earningsDF = functools.partial(<function Client.bind>, meth=<function earnings>)

earningsToday = functools.partial(<function Client.bind>, meth=<function earningsToday>)

earningsTodayDF = functools.partial(<function Client.bind>, meth=<function earningsToday>)

classmethod ema(symbol, range='6m', col='close', periods=None)

This will return a dataframe of exponential moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

estimates = functools.partial(<function Client.bind>, meth=<function estimates>)

estimatesDF = functools.partial(<function Client.bind>, meth=<function estimates>)

exchanges = functools.partial(<function Client.bind>, meth=<function exchanges>)

exchangesDF = functools.partial(<function Client.bind>, meth=<function exchanges>)

classmethod exp(symbol, range='6m', col='close')

This will return a dataframe of Vector Arithmetic Exp for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

fedfunds(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

fedfundsAsync = functools.partial(<function Client.bind>, meth=<function fedfunds>)

fedfundsDF = functools.partial(<function Client.bind>, meth=<function fedfunds>)

figi = functools.partial(<function Client.bind>, meth=<function figi>)

figiDF = functools.partial(<function Client.bind>, meth=<function figi>)

file = functools.partial(<function Client.bind>, meth=<function files>)

financials = functools.partial(<function Client.bind>, meth=<function financials>)

financialsDF = functools.partial(<function Client.bind>, meth=<function financials>)

fiveYear = functools.partial(<function Client.bind>, meth=<function fiveYear>)

fiveYearDF = functools.partial(<function Client.bind>, meth=<function fiveYearDF>)

fiveYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

classmethod floor(symbol, range='6m', col='close')

This will return a dataframe of Vector Floor for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

forex1MinuteSSE = functools.partial(<function Client.bind>, meth=<function fxSSE>)

forex1MinuteSSEAsync = functools.partial(<function Client.bind>, meth=<function fxSSEAsync>)

forex1SecondSSE = functools.partial(<function Client.bind>, meth=<function fxSSE>)

forex1SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function fxSSEAsync>)

forex5SecondSSE = functools.partial(<function Client.bind>, meth=<function fxSSE>)

forex5SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function fxSSEAsync>)

fortyF = functools.partial(<function Client.bind>, meth=<function timeSeries>)

fundOwnership = functools.partial(<function Client.bind>, meth=<function fundOwnership>)

fundOwnershipDF = functools.partial(<function Client.bind>, meth=<function fundOwnership>)

fundamentalValuations = functools.partial(<function Client.bind>, meth=<function fundamentalValuations>)

fundamentalValuationsDF = functools.partial(<function Client.bind>, meth=<function fundamentalValuations>)

fundamentals = functools.partial(<function Client.bind>, meth=<function fundamentals>)

fundamentalsDF = functools.partial(<function Client.bind>, meth=<function fundamentals>)

futures(token='', version='stable', filter='', format='json', **timeseries_kwargs)

Futures EOD prices :param contract: Specific dated future contract, e.g. NG0Z :type contract: str :param token: Access token :type token: str :param version: API version :type version: str :param filter: filters: https://iexcloud.io/docs/api/#filter-results :type filter: str :param format: return format, defaults to json :type format: str :param Supports all kwargs from pyEX.timeseries.timeSeries:

Returns:	result
Return type:	dict or DataFrame

futuresDF(token='', version='stable', filter='', format='json', **timeseries_kwargs)

Futures EOD prices :param contract: Specific dated future contract, e.g. NG0Z :type contract: str :param token: Access token :type token: str :param version: API version :type version: str :param filter: filters: https://iexcloud.io/docs/api/#filter-results :type filter: str :param format: return format, defaults to json :type format: str :param Supports all kwargs from pyEX.timeseries.timeSeries:

Returns:	result
Return type:	dict or DataFrame

futuresSymbols = functools.partial(<function Client.bind>, meth=<function futuresSymbols>)

futuresSymbolsDF = functools.partial(<function Client.bind>, meth=<function futuresSymbols>)

futuresSymbolsList = functools.partial(<function Client.bind>, meth=<function futuresSymbols>)

fxSSE = functools.partial(<function Client.bind>, meth=<function fxSSE>)

fxSSEAsync = functools.partial(<function Client.bind>, meth=<function fxSSEAsync>)

fxSymbols = functools.partial(<function Client.bind>, meth=<function fxSymbols>)

fxSymbolsDF = functools.partial(<function Client.bind>, meth=<function fxSymbols>)

fxSymbolsList = functools.partial(<function Client.bind>, meth=<function fxSymbols>)

gasmid(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

gasmidAsync = functools.partial(<function Client.bind>, meth=<function gasmid>)

gasmidDF = functools.partial(<function Client.bind>, meth=<function gasmid>)

gasprm(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

gasprmAsync = functools.partial(<function Client.bind>, meth=<function gasprm>)

gasprmDF = functools.partial(<function Client.bind>, meth=<function gasprm>)

gasreg(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

gasregAsync = functools.partial(<function Client.bind>, meth=<function gasreg>)

gasregDF = functools.partial(<function Client.bind>, meth=<function gasreg>)

gdp(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

gdpAsync = functools.partial(<function Client.bind>, meth=<function gdp>)

gdpDF = functools.partial(<function Client.bind>, meth=<function gdp>)

heatoil(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

heatoilAsync = functools.partial(<function Client.bind>, meth=<function heatoil>)

heatoilDF = functools.partial(<function Client.bind>, meth=<function heatoil>)

historicalFX = functools.partial(<function Client.bind>, meth=<function historicalFX>)

historicalFXDF = functools.partial(<function Client.bind>, meth=<function historicalFX>)

holidays = functools.partial(<function Client.bind>, meth=<function holidays>)

holidaysDF = functools.partial(<function Client.bind>, meth=<function holidays>)

housing(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

housingAsync = functools.partial(<function Client.bind>, meth=<function housing>)

housingDF = functools.partial(<function Client.bind>, meth=<function housing>)

classmethod ht_dcperiod(symbol, range='6m', col='close')

This will return a dataframe of Hilbert Transform - Dominant Cycle Period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ht_dcphase(symbol, range='6m', col='close')

This will return a dataframe of Hilbert Transform - Dominant Cycle Phase for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ht_phasor(symbol, range='6m', col='close')

This will return a dataframe of Hilbert Transform - Phasor Components for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ht_sine(symbol, range='6m', col='close')

This will return a dataframe of Hilbert Transform - SineWave for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ht_trendline(symbol, range='6m', col='close')

This will return a dataframe of hilbert trendline: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod ht_trendmode(symbol, range='6m', col='close')

This will return a dataframe of Hilbert Transform - Trend vs Cycle Mode for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

iexAuction = functools.partial(<function Client.bind>, meth=<function iexAuction>)

iexAuctionAsync = functools.partial(<function Client.bind>, meth=<function iexAuction>)

iexAuctionDF = functools.partial(<function Client.bind>, meth=<function iexAuction>)

iexBook = functools.partial(<function Client.bind>, meth=<function iexBook>)

iexBookAsync = functools.partial(<function Client.bind>, meth=<function iexBook>)

iexBookDF = functools.partial(<function Client.bind>, meth=<function iexBook>)

iexDeep = functools.partial(<function Client.bind>, meth=<function iexDeep>)

iexDeepAsync = functools.partial(<function Client.bind>, meth=<function iexDeep>)

iexDeepDF = functools.partial(<function Client.bind>, meth=<function iexDeep>)

iexHist = functools.partial(<function Client.bind>, meth=<function iexHist>)

iexHistAsync = functools.partial(<function Client.bind>, meth=<function iexHist>)

iexHistDF = functools.partial(<function Client.bind>, meth=<function iexHist>)

iexLast = functools.partial(<function Client.bind>, meth=<function iexLast>)

iexLastAsync = functools.partial(<function Client.bind>, meth=<function iexLast>)

iexLastDF = functools.partial(<function Client.bind>, meth=<function iexLast>)

iexOfficialPrice = functools.partial(<function Client.bind>, meth=<function iexOfficialPrice>)

iexOfficialPriceAsync = functools.partial(<function Client.bind>, meth=<function iexOfficialPrice>)

iexOfficialPriceDF = functools.partial(<function Client.bind>, meth=<function iexOfficialPrice>)

iexOpHaltStatus = functools.partial(<function Client.bind>, meth=<function iexOpHaltStatus>)

iexOpHaltStatusAsync = functools.partial(<function Client.bind>, meth=<function iexOpHaltStatus>)

iexOpHaltStatusDF = functools.partial(<function Client.bind>, meth=<function iexOpHaltStatus>)

iexSecurityEvent = functools.partial(<function Client.bind>, meth=<function iexSecurityEvent>)

iexSecurityEventAsync = functools.partial(<function Client.bind>, meth=<function iexSecurityEvent>)

iexSecurityEventDF = functools.partial(<function Client.bind>, meth=<function iexSecurityEvent>)

iexSsrStatus = functools.partial(<function Client.bind>, meth=<function iexSsrStatus>)

iexSsrStatusAsync = functools.partial(<function Client.bind>, meth=<function iexSsrStatus>)

iexSsrStatusDF = functools.partial(<function Client.bind>, meth=<function iexSsrStatus>)

iexSymbols = functools.partial(<function Client.bind>, meth=<function iexSymbols>)

iexSymbolsDF = functools.partial(<function Client.bind>, meth=<function iexSymbols>)

iexSymbolsList = functools.partial(<function Client.bind>, meth=<function iexSymbols>)

iexSystemEvent = functools.partial(<function Client.bind>, meth=<function iexSystemEvent>)

iexSystemEventAsync = functools.partial(<function Client.bind>, meth=<function iexSystemEvent>)

iexSystemEventDF = functools.partial(<function Client.bind>, meth=<function iexSystemEvent>)

iexThreshold = functools.partial(<function Client.bind>, meth=<function iexThreshold>)

iexThresholdDF = functools.partial(<function Client.bind>, meth=<function iexThreshold>)

iexTops = functools.partial(<function Client.bind>, meth=<function iexTops>)

iexTopsAsync = functools.partial(<function Client.bind>, meth=<function iexTops>)

iexTopsDF = functools.partial(<function Client.bind>, meth=<function iexTops>)

iexTradeBreak = functools.partial(<function Client.bind>, meth=<function iexTradeBreak>)

iexTradeBreakAsync = functools.partial(<function Client.bind>, meth=<function iexTradeBreak>)

iexTradeBreakDF = functools.partial(<function Client.bind>, meth=<function iexTradeBreak>)

iexTrades = functools.partial(<function Client.bind>, meth=<function iexTrades>)

iexTradesAsync = functools.partial(<function Client.bind>, meth=<function iexTrades>)

iexTradesDF = functools.partial(<function Client.bind>, meth=<function iexTrades>)

iexTradingStatus = functools.partial(<function Client.bind>, meth=<function iexTradingStatus>)

iexTradingStatusAsync = functools.partial(<function Client.bind>, meth=<function iexTradingStatus>)

iexTradingStatusDF = functools.partial(<function Client.bind>, meth=<function iexTradingStatus>)

incomeStatement = functools.partial(<function Client.bind>, meth=<function incomeStatement>)

incomeStatementDF = functools.partial(<function Client.bind>, meth=<function incomeStatement>)

indpro(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

indproAsync = functools.partial(<function Client.bind>, meth=<function indpro>)

indproDF = functools.partial(<function Client.bind>, meth=<function indpro>)

initialClaims(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

initialClaimsAsync = functools.partial(<function Client.bind>, meth=<function initialClaims>)

initialClaimsDF = functools.partial(<function Client.bind>, meth=<function initialClaims>)

insiderRoster = functools.partial(<function Client.bind>, meth=<function insiderRoster>)

insiderRosterDF = functools.partial(<function Client.bind>, meth=<function insiderRoster>)

insiderSummary = functools.partial(<function Client.bind>, meth=<function insiderSummary>)

insiderSummaryDF = functools.partial(<function Client.bind>, meth=<function insiderSummary>)

insiderTransactions = functools.partial(<function Client.bind>, meth=<function insiderTransactions>)

insiderTransactionsDF = functools.partial(<function Client.bind>, meth=<function insiderTransactions>)

institutionalMoney(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

institutionalMoneyAsync = functools.partial(<function Client.bind>, meth=<function institutionalMoney>)

institutionalMoneyDF = functools.partial(<function Client.bind>, meth=<function institutionalMoney>)

institutionalOwnership = functools.partial(<function Client.bind>, meth=<function institutionalOwnership>)

institutionalOwnershipDF = functools.partial(<function Client.bind>, meth=<function institutionalOwnership>)

internationalExchanges = functools.partial(<function Client.bind>, meth=<function internationalExchanges>)

internationalExchangesDF = functools.partial(<function Client.bind>, meth=<function internationalExchanges>)

internationalSymbols = functools.partial(<function Client.bind>, meth=<function internationalSymbols>)

internationalSymbolsDF = functools.partial(<function Client.bind>, meth=<function internationalSymbols>)

internationalSymbolsList = functools.partial(<function Client.bind>, meth=<function internationalSymbols>)

intraday = functools.partial(<function Client.bind>, meth=<function intraday>)

intradayDF = functools.partial(<function Client.bind>, meth=<function intraday>)

ipoToday = functools.partial(<function Client.bind>, meth=<function ipoToday>)

ipoTodayDF = functools.partial(<function Client.bind>, meth=<function ipoToday>)

ipoUpcoming = functools.partial(<function Client.bind>, meth=<function ipoUpcoming>)

ipoUpcomingDF = functools.partial(<function Client.bind>, meth=<function ipoUpcoming>)

isinLookup = functools.partial(<function Client.bind>, meth=<function isinLookup>)

isinLookupDF = functools.partial(<function Client.bind>, meth=<function isinLookup>)

jet(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

jetAsync = functools.partial(<function Client.bind>, meth=<function jet>)

jetDF = functools.partial(<function Client.bind>, meth=<function jet>)

classmethod kama(symbol, range='6m', col='close', period=30)

This will return a dataframe of kaufman adaptive moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

keyStats = functools.partial(<function Client.bind>, meth=<function keyStats>)

keyStatsDF = functools.partial(<function Client.bind>, meth=<function keyStats>)

largestTrades = functools.partial(<function Client.bind>, meth=<function largestTrades>)

largestTradesDF = functools.partial(<function Client.bind>, meth=<function largestTrades>)

lastSSE = functools.partial(<function Client.bind>, meth=<function iexLastSSE>)

lastSSEAsync = functools.partial(<function Client.bind>, meth=<function iexLastSSEAsync>)

latestFX = functools.partial(<function Client.bind>, meth=<function latestFX>)

latestFXDF = functools.partial(<function Client.bind>, meth=<function latestFX>)

classmethod linearreg(symbol, range='6m', closecol='close', period=14)

This will return a dataframe of linear regression for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod linearreg_angle(symbol, range='6m', closecol='close', period=14)

This will return a dataframe of linear regression angle for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod linearreg_intercept(symbol, range='6m', closecol='close', period=14)

This will return a dataframe of linear regression intercept for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

classmethod linearreg_slope(symbol, range='6m', closecol='close', period=14)

This will return a dataframe of linear regression slope for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

list = functools.partial(<function Client.bind>, meth=<function list>)

listDF = functools.partial(<function Client.bind>, meth=<function list>)

listRules = functools.partial(<function Client.bind>, meth=<function rules>)

classmethod ln(symbol, range='6m', col='close')

This will return a dataframe of Vector Log Natural for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod log10(symbol, range='6m', col='close')

This will return a dataframe of Vector Log10 for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

logo = functools.partial(<function Client.bind>, meth=<function logo>)

logoNotebook = functools.partial(<function Client.bind>, meth=<function logoNotebook>)

logoPNG = functools.partial(<function Client.bind>, meth=<function logoPNG>)

lookupRule = functools.partial(<function Client.bind>, meth=<function lookupRule>)

classmethod macd(symbol, range='6m', col='close', fastperiod=12, slowperiod=26, signalperiod=9)

This will return a dataframe of Moving Average Convergence/Divergence for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate fastperiod (int) – fast period to calculate across slowperiod (int) – slow period to calculate across signalperiod (int) – macd signal period
Returns:	result
Return type:	DataFrame

classmethod macdext(symbol, range='6m', col='close', fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)

This will return a dataframe of Moving Average Convergence/Divergence for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate fastperiod (int) – fast period to calculate across fastmatype (int) – moving average type (0-sma) slowperiod (int) – slow period to calculate across slowmatype (int) – moving average type (0-sma) signalperiod (int) – macd signal period signalmatype (int) – moving average type (0-sma)
Returns:	result
Return type:	DataFrame

classmethod mama(symbol, range='6m', col='close', fastlimit=0, slowlimit=0)

This will return a dataframe of mesa adaptive moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – fastlimit (int) – slowlimit (int) –
Returns:	result
Return type:	DataFrame

marketNews = functools.partial(<function Client.bind>, meth=<function marketNews>)

marketNewsDF = functools.partial(<function Client.bind>, meth=<function marketNews>)

marketOhlc = functools.partial(<function Client.bind>, meth=<function marketOhlc>)

marketOhlcDF = functools.partial(<function Client.bind>, meth=<function marketOhlc>)

marketPrevious = functools.partial(<function Client.bind>, meth=<function marketYesterday>)

marketPreviousDF = functools.partial(<function Client.bind>, meth=<function marketYesterday>)

marketShortInterest = functools.partial(<function Client.bind>, meth=<function marketShortInterest>)

marketShortInterestDF = functools.partial(<function Client.bind>, meth=<function marketShortInterest>)

marketVolume = functools.partial(<function Client.bind>, meth=<function marketVolume>)

marketVolumeDF = functools.partial(<function Client.bind>, meth=<function marketVolume>)

marketYesterday = functools.partial(<function Client.bind>, meth=<function marketYesterday>)

marketYesterdayDF = functools.partial(<function Client.bind>, meth=<function marketYesterday>)

markets = functools.partial(<function Client.bind>, meth=<function markets>)

marketsDF = functools.partial(<function Client.bind>, meth=<function marketsDF>)

classmethod mavp(symbol, range='6m', col='close', periods=None, minperiod=2, maxperiod=30, matype=0)

This will return a dataframe of moving average with variable period: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) – minperiod (int) – maxperiod (int) – matype (int) –
Returns:	result
Return type:	DataFrame

classmethod max(symbol, range='6m', col='close', period=30)

This will return a dataframe of Highest value over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod maxindex(symbol, range='6m', col='close', period=30)

This will return a dataframe of Highest value over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod medprice(symbol, range='6m', highcol='high', lowcol='low')

This will return a dataframe of median price for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

messageBudget = functools.partial(<function Client.bind>, meth=<function messageBudget>)

messageBudgetAsync = functools.partial(<function Client.bind>, meth=<function messageBudget>)

metadata = functools.partial(<function Client.bind>, meth=<function metadata>)

metadataAsync = functools.partial(<function Client.bind>, meth=<function metadata>)

metadataDF = functools.partial(<function Client.bind>, meth=<function metadata>)

classmethod mfi(symbol, range='6m', highcol='high', lowcol='low', closecol='close', volumecol='volume', period=14)

This will return a dataframe of Money Flow Index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod midpice(symbol, range='6m', col='close', period=14)

This will return a dataframe of midprice over period: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod midpoint(symbol, range='6m', col='close', period=14)

This will return a dataframe of midpoint over period: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod min(symbol, range='6m', col='close', period=30)

This will return a dataframe of Lowest value over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod minindex(symbol, range='6m', col='close', period=30)

This will return a dataframe of Lowest value over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod minmax(symbol, range='6m', col='close', period=30)

This will return a dataframe of Lowest and highest values over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod minmaxindex(symbol, range='6m', col='close', period=30)

This will return a dataframe of Indexes of lowest and highest values over a specified period for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

classmethod minus_di(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Minus Directional Indicator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod minus_dm(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of Minus Directional Movement for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod mom(symbol, range='6m', col='close', period=14)

This will return a dataframe of Momentum for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod mult(symbol, range='6m', col1='open', col2='close')

This will return a dataframe of Vector Arithmetic Add for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col1 (string) – col2 (string) –
Returns:	result
Return type:	DataFrame

mutualFundSymbols = functools.partial(<function Client.bind>, meth=<function mutualFundSymbols>)

mutualFundSymbolsDF = functools.partial(<function Client.bind>, meth=<function mutualFundSymbols>)

mutualFundSymbolsList = functools.partial(<function Client.bind>, meth=<function mutualFundSymbols>)

natgas(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

natgasAsync = functools.partial(<function Client.bind>, meth=<function natgas>)

natgasDF = functools.partial(<function Client.bind>, meth=<function natgas>)

classmethod natr(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of normalized average true range for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – time period to calculate over
Returns:	result
Return type:	DataFrame

news = functools.partial(<function Client.bind>, meth=<function news>)

newsDF = functools.partial(<function Client.bind>, meth=<function news>)

newsSSE = functools.partial(<function Client.bind>, meth=<function newsSSE>)

newsSSEAsync = functools.partial(<function Client.bind>, meth=<function newsSSEAsync>)

nextDayExtDate = functools.partial(<function Client.bind>, meth=<function nextDayExtDate>)

nextDayExtDateDF = functools.partial(<function Client.bind>, meth=<function nextDayExtDate>)

classmethod obv(symbol, range='6m', closecol='close', volumecol='volume')

This will return a dataframe of On Balance Volume for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate volumecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

ohlc = functools.partial(<function Client.bind>, meth=<function ohlc>)

ohlcDF = functools.partial(<function Client.bind>, meth=<function ohlc>)

oneMonth = functools.partial(<function Client.bind>, meth=<function oneMonth>)

oneMonthDF = functools.partial(<function Client.bind>, meth=<function oneMonthDF>)

oneMonthValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

oneYear = functools.partial(<function Client.bind>, meth=<function oneYear>)

oneYearDF = functools.partial(<function Client.bind>, meth=<function oneYearDF>)

oneYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

optionExpirations = functools.partial(<function Client.bind>, meth=<function optionExpirations>)

options(token='', version='stable', filter='', format='json', **timeseries_kwargs)

Options EOD prices :param contract: Specific dated option contract, e.g. SPY20210714C00475000 :type contract: str :param token: Access token :type token: str :param version: API version :type version: str :param filter: filters: https://iexcloud.io/docs/api/#filter-results :type filter: str :param format: return format, defaults to json :type format: str :param Supports all kwargs from pyEX.timeseries.timeSeries:

Returns:	result
Return type:	dict or DataFrame

optionsDF(token='', version='stable', filter='', format='json', **timeseries_kwargs)

Options EOD prices :param contract: Specific dated option contract, e.g. SPY20210714C00475000 :type contract: str :param token: Access token :type token: str :param version: API version :type version: str :param filter: filters: https://iexcloud.io/docs/api/#filter-results :type filter: str :param format: return format, defaults to json :type format: str :param Supports all kwargs from pyEX.timeseries.timeSeries:

Returns:	result
Return type:	dict or DataFrame

optionsSymbols = functools.partial(<function Client.bind>, meth=<function optionsSymbols>)

optionsSymbolsDF = functools.partial(<function Client.bind>, meth=<function optionsSymbols>)

optionsSymbolsList = functools.partial(<function Client.bind>, meth=<function optionsSymbols>)

otcSymbols = functools.partial(<function Client.bind>, meth=<function otcSymbols>)

otcSymbolsDF = functools.partial(<function Client.bind>, meth=<function otcSymbols>)

otcSymbolsList = functools.partial(<function Client.bind>, meth=<function otcSymbols>)

pauseRule = functools.partial(<function Client.bind>, meth=<function pauseRule>)

payAsYouGo = functools.partial(<function Client.bind>, meth=<function payAsYouGo>)

payAsYouGoAsync = functools.partial(<function Client.bind>, meth=<function payAsYouGo>)

payroll(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

payrollAsync = functools.partial(<function Client.bind>, meth=<function payroll>)

payrollDF = functools.partial(<function Client.bind>, meth=<function payroll>)

classmethod peerCorrelation(symbol, range='6m')

This will return a dataframe of peer correlations for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart
Returns:	result
Return type:	DataFrame

classmethod peerCorrelationPlot(symbol, range='6m')

This will plot a dataframe of peer correlations for the given symbol across the given range

Note: this function requires the use of seaborn.heatmap

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart
Returns:	result
Return type:	DataFrame

peers = functools.partial(<function Client.bind>, meth=<function peers>)

peersDF = functools.partial(<function Client.bind>, meth=<function peers>)

classmethod plus_di(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Plus Directional Movement for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod plus_dm(symbol, range='6m', highcol='high', lowcol='low', period=14)

This will return a dataframe of Plus Directional Movement for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

points = functools.partial(<function Client.bind>, meth=<function points>)

pointsDF = functools.partial(<function Client.bind>, meth=<function points>)

classmethod ppo(symbol, range='6m', col='close', fastperiod=12, slowperiod=26, matype=0)

This will return a dataframe of Percentage Price Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate fastperiod (int) – fast period to calculate across slowperiod (int) – slow period to calculate across matype (int) – moving average type (0-sma)
Returns:	result
Return type:	DataFrame

previous = functools.partial(<function Client.bind>, meth=<function yesterday>)

previousDF = functools.partial(<function Client.bind>, meth=<function yesterday>)

price = functools.partial(<function Client.bind>, meth=<function price>)

priceDF = functools.partial(<function Client.bind>, meth=<function price>)

priceTarget = functools.partial(<function Client.bind>, meth=<function priceTarget>)

priceTargetDF = functools.partial(<function Client.bind>, meth=<function priceTarget>)

propane(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

propaneAsync = functools.partial(<function Client.bind>, meth=<function propane>)

propaneDF = functools.partial(<function Client.bind>, meth=<function propane>)

queryMetadata = functools.partial(<function Client.bind>, meth=<function queryMetadata>)

queryMetadataDF = functools.partial(<function Client.bind>, meth=<function queryMetadata>)

quote = functools.partial(<function Client.bind>, meth=<function quote>)

quoteDF = functools.partial(<function Client.bind>, meth=<function quote>)

recent = functools.partial(<function Client.bind>, meth=<function recent>)

recentDF = functools.partial(<function Client.bind>, meth=<function recent>)

recessionProb(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

recessionProbAsync = functools.partial(<function Client.bind>, meth=<function recessionProb>)

recessionProbDF = functools.partial(<function Client.bind>, meth=<function recessionProb>)

records = functools.partial(<function Client.bind>, meth=<function records>)

recordsDF = functools.partial(<function Client.bind>, meth=<function records>)

refDividends = functools.partial(<function Client.bind>, meth=<function dividends>)

refDividendsDF = functools.partial(<function Client.bind>, meth=<function dividends>)

relevant = functools.partial(<function Client.bind>, meth=<function relevant>)

relevantDF = functools.partial(<function Client.bind>, meth=<function relevant>)

resumeRule = functools.partial(<function Client.bind>, meth=<function resumeRule>)

retailMoney(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

retailMoneyAsync = functools.partial(<function Client.bind>, meth=<function retailMoney>)

retailMoneyDF = functools.partial(<function Client.bind>, meth=<function retailMoney>)

returnOfCapital = functools.partial(<function Client.bind>, meth=<function returnOfCapital>)

returnOfCapitalDF = functools.partial(<function Client.bind>, meth=<function returnOfCapital>)

classmethod returns(symbol, range='6m')

Calculate returns using daily close price

Parameters:	client (pyEX.Client) – symbol (string) – range (string) –
Returns:	result
Return type:	DataFrame

ricLookup = functools.partial(<function Client.bind>, meth=<function ricLookup>)

ricLookupDF = functools.partial(<function Client.bind>, meth=<function ricLookup>)

rightToPurchase = functools.partial(<function Client.bind>, meth=<function rightToPurchase>)

rightToPurchaseDF = functools.partial(<function Client.bind>, meth=<function rightToPurchase>)

rightsIssue = functools.partial(<function Client.bind>, meth=<function rightsIssue>)

rightsIssueDF = functools.partial(<function Client.bind>, meth=<function rightsIssue>)

classmethod roc(symbol, range='6m', col='close', period=14)

This will return a dataframe of Rate of change: ((price/prevPrice)-1)*100 for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod rocp(symbol, range='6m', col='close', period=14)

This will return a dataframe of Rate of change Percentage: (price-prevPrice)/prevPrice for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod rocr(symbol, range='6m', col='close', period=14)

This will return a dataframe of Rate of change ratio: (price/prevPrice) for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod rocr100(symbol, range='6m', col='close', period=14)

This will return a dataframe of Rate of change ratio 100 scale: (price/prevPrice)*100 for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod rsi(symbol, range='6m', col='close', period=14)

This will return a dataframe of Relative Strength Index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

ruleInfo = functools.partial(<function Client.bind>, meth=<function ruleInfo>)

ruleOutput = functools.partial(<function Client.bind>, meth=<function ruleOutput>)

classmethod sar(symbol, range='6m', highcol='high', lowcol='low', acceleration=0, maximum=0)

This will return a dataframe of parabolic sar: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – highcol (string) – lowcol (string) – acceleration (int) – maximum (int) –
Returns:	result
Return type:	DataFrame

classmethod sarext(symbol, range='6m', highcol='high', lowcol='low', startvalue=0, offsetonreverse=0, accelerationinitlong=0, accelerationlong=0, accelerationmaxlong=0, accelerationinitshort=0, accelerationshort=0, accelerationmaxshort=0)

This will return a dataframe of parabolic sar extended: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – highcol (string) – lowcol (string) – startvalue (int) – offsetonreverse (int) – accelerationinitlong (int) – accelerationlong (int) – accelerationmaxlong (int) – accelerationinitshort (int) – accelerationshort (int) – accelerationmaxshort (int) –
Returns:	result
Return type:	DataFrame

schema = functools.partial(<function Client.bind>, meth=<function lookupRule>)

search = functools.partial(<function Client.bind>, meth=<function search>)

searchDF = functools.partial(<function Client.bind>, meth=<function search>)

sectorPerformance = functools.partial(<function Client.bind>, meth=<function sectorPerformance>)

sectorPerformanceDF = functools.partial(<function Client.bind>, meth=<function sectorPerformance>)

sectors = functools.partial(<function Client.bind>, meth=<function sectors>)

sectorsDF = functools.partial(<function Client.bind>, meth=<function sectors>)

securityReclassification = functools.partial(<function Client.bind>, meth=<function securityReclassification>)

securityReclassificationDF = functools.partial(<function Client.bind>, meth=<function securityReclassification>)

securitySwap = functools.partial(<function Client.bind>, meth=<function securitySwap>)

securitySwapDF = functools.partial(<function Client.bind>, meth=<function securitySwap>)

sentiment = functools.partial(<function Client.bind>, meth=<function sentiment>)

sentimentAsync = functools.partial(<function Client.bind>, meth=<function sentiment>)

sentimentDF = functools.partial(<function Client.bind>, meth=<function sentiment>)

sentimentSSE = functools.partial(<function Client.bind>, meth=<function sentimentSSE>)

sentimentSSEAsync = functools.partial(<function Client.bind>, meth=<function sentimentSSEAsync>)

sevenYear = functools.partial(<function Client.bind>, meth=<function sevenYear>)

sevenYearDF = functools.partial(<function Client.bind>, meth=<function sevenYearDF>)

sevenYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

shortInterest = functools.partial(<function Client.bind>, meth=<function shortInterest>)

shortInterestDF = functools.partial(<function Client.bind>, meth=<function shortInterest>)

classmethod sin(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric SIN for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod sinh(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric Sinh for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

sixMonth = functools.partial(<function Client.bind>, meth=<function sixMonth>)

sixMonthDF = functools.partial(<function Client.bind>, meth=<function sixMonthDF>)

sixMonthValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

classmethod sma(symbol, range='6m', col='close', periods=None)

This will return a dataframe of exponential moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

spinoff = functools.partial(<function Client.bind>, meth=<function spinoff>)

spinoffDF = functools.partial(<function Client.bind>, meth=<function spinoff>)

splits = functools.partial(<function Client.bind>, meth=<function splits>)

splitsBasic = functools.partial(<function Client.bind>, meth=<function splitsBasic>)

splitsBasicDF = functools.partial(<function Client.bind>, meth=<function splitsBasic>)

splitsDF = functools.partial(<function Client.bind>, meth=<function splits>)

spread = functools.partial(<function Client.bind>, meth=<function spread>)

spreadDF = functools.partial(<function Client.bind>, meth=<function spread>)

classmethod sqrt(symbol, range='6m', col='close')

This will return a dataframe of Vector Square Root for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

status = functools.partial(<function Client.bind>, meth=<function status>)

statusAsync = functools.partial(<function Client.bind>, meth=<function status>)

classmethod stddev(symbol, range='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of standard deviation for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across nbdev (int) –
Returns:	result
Return type:	DataFrame

classmethod stoch(symbol, range='6m', highcol='high', lowcol='low', closecol='close', fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)

This will return a dataframe of Stochastic for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate fastk_period (int) – fastk_period slowk_period (int) – slowk_period slowk_matype (int) – slowk_matype slowd_period (int) – slowd_period slowd_matype (int) – slowd_matype
Returns:	result
Return type:	DataFrame

classmethod stochf(symbol, range='6m', highcol='high', lowcol='low', closecol='close', fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)

This will return a dataframe of Stochastic Fast for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate fastk_period (int) – fastk_period slowk_period (int) – slowk_period slowk_matype (int) – slowk_matype slowd_period (int) – slowd_period slowd_matype (int) – slowd_matype
Returns:	result
Return type:	DataFrame

classmethod stochrsi(symbol, range='6m', closecol='close', period=14, fastk_period=5, fastd_period=3, fastd_matype=0)

This will return a dataframe of Stochastic Relative Strength Index for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate across fastk_period (int) – fastk_period fastd_period (int) – fastd_period fastd_matype (int) – moving average type (0-sma)
Returns:	result
Return type:	DataFrame

stockOptions = functools.partial(<function Client.bind>, meth=<function stockOptions>)

stockOptionsDF = functools.partial(<function Client.bind>, meth=<function stockOptions>)

stocksUS1MinuteSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUS1MinuteSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUS1SecondSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUS1SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUS5SecondSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUS5SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUSNoUTP1MinuteSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUSNoUTP1MinuteSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUSNoUTP1SecondSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUSNoUTP1SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUSNoUTP5SecondSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUSNoUTP5SecondSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUSNoUTPSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUSNoUTPSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

stocksUSSSE = functools.partial(<function Client.bind>, meth=<function _baseSSE>)

stocksUSSSEAsync = functools.partial(<function Client.bind>, meth=<function _baseSSEAsync>)

classmethod sub(symbol, range='6m', col1='open', col2='close')

This will return a dataframe of Vector Arithmetic Add for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col1 (string) – col2 (string) –
Returns:	result
Return type:	DataFrame

classmethod sum(symbol, range='6m', col='close', period=30)

This will return a dataframe of Summation for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – period (int) –
Returns:	result
Return type:	DataFrame

summary = functools.partial(<function Client.bind>, meth=<function summary>)

summaryDF = functools.partial(<function Client.bind>, meth=<function summary>)

symbols = functools.partial(<function Client.bind>, meth=<function symbols>)

symbolsDF = functools.partial(<function Client.bind>, meth=<function symbols>)

symbolsList = functools.partial(<function Client.bind>, meth=<function symbols>)

systemStats = functools.partial(<function Client.bind>, meth=<function stats>)

systemStatsDF = functools.partial(<function Client.bind>, meth=<function stats>)

classmethod t3(symbol, range='6m', col='close', periods=None, vfactor=0)

This will return a dataframe of tripple exponential moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) – vfactor (int) –
Returns:	result
Return type:	DataFrame

tags = functools.partial(<function Client.bind>, meth=<function tags>)

tagsDF = functools.partial(<function Client.bind>, meth=<function tags>)

classmethod tan(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric Tan for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

classmethod tanh(symbol, range='6m', col='close')

This will return a dataframe of Vector Trigonometric Tanh for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) –
Returns:	result
Return type:	DataFrame

technicals = functools.partial(<function Client.bind>, meth=<function technicals>)

technicalsDF = functools.partial(<function Client.bind>, meth=<function technicals>)

classmethod tema(symbol, range='6m', col='close', periods=None)

This will return a dataframe of triple exponential moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

tenK = functools.partial(<function Client.bind>, meth=<function timeSeries>)

tenQ = functools.partial(<function Client.bind>, meth=<function timeSeries>)

tenYear = functools.partial(<function Client.bind>, meth=<function tenYear>)

tenYearDF = functools.partial(<function Client.bind>, meth=<function tenYearDF>)

tenYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

thirtyYear = functools.partial(<function Client.bind>, meth=<function thirtyYear>)

thirtyYearDF = functools.partial(<function Client.bind>, meth=<function thirtyYearDF>)

thirtyYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

threeMonth = functools.partial(<function Client.bind>, meth=<function threeMonth>)

threeMonthDF = functools.partial(<function Client.bind>, meth=<function threeMonthDF>)

threeMonthValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

threeYear = functools.partial(<function Client.bind>, meth=<function threeYear>)

threeYearDF = functools.partial(<function Client.bind>, meth=<function threeYearDF>)

threeYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

timeSeries = functools.partial(<function Client.bind>, meth=<function timeSeries>)

timeSeriesAsync = functools.partial(<function Client.bind>, meth=<function timeSeries>)

timeSeriesDF = functools.partial(<function Client.bind>, meth=<function timeSeries>)

timeSeriesInventory = functools.partial(<function Client.bind>, meth=<function timeSeriesInventory>)

timeSeriesInventoryAsync = functools.partial(<function Client.bind>, meth=<function timeSeriesInventory>)

timeSeriesInventoryDF = functools.partial(<function Client.bind>, meth=<function timeSeriesInventory>)

topsSSE = functools.partial(<function Client.bind>, meth=<function iexTopsSSE>)

topsSSEAsync = functools.partial(<function Client.bind>, meth=<function iexTopsSSEAsync>)

tradesSSE = functools.partial(<function Client.bind>, meth=<function iexTradesSSE>)

tradesSSEAsync = functools.partial(<function Client.bind>, meth=<function iexTradesSSEAsync>)

classmethod trange(symbol, range='6m', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of true range for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod trima(symbol, range='6m', col='close', periods=None)

This will return a dataframe of triangular moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

classmethod trix(symbol, range='6m', col='close', period=14)

This will return a dataframe of 1-day Rate-Of-Change(ROC) of a Triple Smooth EMA for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart col (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod tsf(symbol, range='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of standard deviation for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across
Returns:	result
Return type:	DataFrame

twentyF = functools.partial(<function Client.bind>, meth=<function timeSeries>)

twentyYear = functools.partial(<function Client.bind>, meth=<function twentyYear>)

twentyYearDF = functools.partial(<function Client.bind>, meth=<function twentyYearDF>)

twentyYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

twoYear = functools.partial(<function Client.bind>, meth=<function twoYear>)

twoYearDF = functools.partial(<function Client.bind>, meth=<function twoYearDF>)

twoYearValue(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

classmethod typprice(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of typical price for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod ultosc(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period1=7, period2=14, period3=28)

This will return a dataframe of Ultimate Oscillator for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period1 (int) – period to calculate across period2 (int) – period to calculate across period3 (int) – period to calculate across
Returns:	result
Return type:	DataFrame

unemployment(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

unemploymentAsync = functools.partial(<function Client.bind>, meth=<function unemployment>)

unemploymentDF = functools.partial(<function Client.bind>, meth=<function unemployment>)

upcomingDividends = functools.partial(<function Client.bind>, meth=<function upcomingDividends>)

upcomingDividendsDF = functools.partial(<function Client.bind>, meth=<function upcomingDividends>)

upcomingEarnings = functools.partial(<function Client.bind>, meth=<function upcomingEarnings>)

upcomingEarningsDF = functools.partial(<function Client.bind>, meth=<function upcomingEarnings>)

upcomingEvents = functools.partial(<function Client.bind>, meth=<function upcomingEvents>)

upcomingEventsDF = functools.partial(<function Client.bind>, meth=<function upcomingEvents>)

upcomingIPOs = functools.partial(<function Client.bind>, meth=<function upcomingIPOs>)

upcomingIPOsDF = functools.partial(<function Client.bind>, meth=<function upcomingIPOs>)

upcomingSplits = functools.partial(<function Client.bind>, meth=<function upcomingSplits>)

upcomingSplitsDF = functools.partial(<function Client.bind>, meth=<function upcomingSplits>)

us15(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

us15DF = functools.partial(<function Client.bind>, meth=<function us15DF>)

us30(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

us30DF = functools.partial(<function Client.bind>, meth=<function us30DF>)

us5(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

us5DF = functools.partial(<function Client.bind>, meth=<function us5DF>)

usage = functools.partial(<function Client.bind>, meth=<function usage>)

usageAsync = functools.partial(<function Client.bind>, meth=<function usage>)

usageDF = functools.partial(<function Client.bind>, meth=<function usage>)

classmethod var(symbol, range='6m', closecol='close', period=14, nbdev=1)

This will return a dataframe of var for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart closecol (string) – column to use to calculate period (int) – period to calculate adx across nbdev (int) –
Returns:	result
Return type:	DataFrame

vehicles(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

vehiclesAsync = functools.partial(<function Client.bind>, meth=<function vehicles>)

vehiclesDF = functools.partial(<function Client.bind>, meth=<function vehicles>)

volumeByVenue = functools.partial(<function Client.bind>, meth=<function volumeByVenue>)

volumeByVenueDF = functools.partial(<function Client.bind>, meth=<function volumeByVenue>)

classmethod wclprice(symbol, range='6m', opencol='open', highcol='high', lowcol='low', closecol='close')

This will return a dataframe of weighted close price for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate
Returns:	result
Return type:	DataFrame

classmethod willr(symbol, range='6m', highcol='high', lowcol='low', closecol='close', period=14)

This will return a dataframe of Williams’ % R for the given symbol across the given range

Parameters:	client (pyEX.Client) – Client symbol (string) – Ticker range (string) – range to use, for pyEX.chart highcol (string) – column to use to calculate lowcol (string) – column to use to calculate closecol (string) – column to use to calculate period (int) – period to calculate across
Returns:	result
Return type:	DataFrame

classmethod wma(symbol, range='6m', col='close', periods=None)

This will return a dataframe of weighted moving average: for the given symbol across the given range

Parameters:	client (pyEX.Client) – symbol (string) – range (string) – col (string) – periods (int) –
Returns:	result
Return type:	DataFrame

wti(key='', token='', version='stable', filter='', format='json')

Data points are available per symbol and return individual plain text values. Retrieving individual data points is useful for Excel and Google Sheet users, and applications where a single, lightweight value is needed. We also provide update times for some endpoints which allow you to call an endpoint only once it has new data.

https://iexcloud.io/docs/api/#data-points

Parameters:	symbol (str) – Ticker or market to query key (str) – data point to fetch. If empty or none, will return available data points token (str) – Access token version (str) – API version filter (str) – filters: https://iexcloud.io/docs/api/#filter-results format (str) – return format, defaults to json
Returns:	result
Return type:	dict or DataFrame

wtiAsync = functools.partial(<function Client.bind>, meth=<function wti>)

wtiDF = functools.partial(<function Client.bind>, meth=<function wti>)

yesterday = functools.partial(<function Client.bind>, meth=<function yesterday>)

yesterdayDF = functools.partial(<function Client.bind>, meth=<function yesterday>)

classmethod yieldCurve(curves=None, from_=None, to_=None, wide_or_long='wide')

This will return a dataframe of a yield curve for all treasuries over the given range. Note that this may cost a large number of credits

Parameters:	client (pyEX.Client) – Client curves (list) – List of curve keys to request, must be in: DGS1MO DGS3MO DGS6MO DGS1 DGS2 DGS3 DGS5 DGS7 DGS10 DGS20 DGS30 from (str) – Starting date of curve to (to) – end date of curve wide_or_long (str) – Build dataframe wide or long (default: wide). If set to “long”, returned dataframe will look like: ` date \| key \| value 2020-01-01 \| DGS1 \| 0.05 2020-01-01 \| DGS2 \| 0.06 ` If set to “wide”, returned dataframe will look like: ` date \| DGS1 \| DGS2 \| ... 2020-01-01 \| 0.05 \| 0.06 `
Returns:	result
Return type:	DataFrame